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MisaHubResNet_final.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/stonerrb/MisaHub/blob/main/MisaHubResNet_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4PkpgiAcFLLx"
},
"source": [
"# **Classifying Bleeding and Non-Bleeding Using Resnet**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r89LWZP2g9GJ"
},
"source": [
"## **Importing and Splitting**\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "VoJ-HvHoeo0Q",
"outputId": "418b8d2f-5c9b-4bd1-e4f1-0c6a2280381d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "f4wAqXaHepk7"
},
"outputs": [],
"source": [
"zip_path = '/content/drive/MyDrive/WCEBleedGen.zip'\n",
"!unzip $zip_path -d MISAHUB"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Wc8Sl_fUfP1y"
},
"outputs": [],
"source": [
"import os\n",
"import shutil\n",
"\n",
"images_folder = '/content/MISAHUB/WCEBleedGen/bleeding/Images'\n",
"annotations_folder = '/content/MISAHUB/WCEBleedGen/bleeding/Bounding boxes/YOLO_TXT'\n",
"output_folder = '/content/MISAHUB/train/'\n",
"\n",
"# Create the 'train' folder if it doesn't exist\n",
"if not os.path.exists(output_folder):\n",
" os.makedirs(output_folder)\n",
"\n",
"# Create 'images' and 'labels' folders inside 'train'\n",
"images_output_folder = os.path.join(output_folder, 'images')\n",
"labels_output_folder = os.path.join(output_folder, 'labels')\n",
"\n",
"os.makedirs(images_output_folder, exist_ok=True)\n",
"os.makedirs(labels_output_folder, exist_ok=True)\n",
"\n",
"# Get a list of files in both folders\n",
"image_files = os.listdir(images_folder)\n",
"annotation_files = os.listdir(annotations_folder)\n",
"\n",
"# Ensure only files with the same base name are considered\n",
"image_files = [file for file in image_files if file.endswith('.png')]\n",
"annotation_files = [file for file in annotation_files if file.endswith('.txt')]\n",
"\n",
"# Sort the files to ensure consistent numbering\n",
"image_files.sort()\n",
"annotation_files.sort()\n",
"\n",
"# Rename and move the files to the 'train' folder\n",
"for i, (image_file, annotation_file) in enumerate(zip(image_files, annotation_files), start=1):\n",
" # Define the new names for the image and annotation\n",
" new_image_name = f\"image_{i}.png\"\n",
" new_annotation_name = f\"image_{i}.txt\"\n",
"\n",
" # Rename and move the image file\n",
" old_image_path = os.path.join(images_folder, image_file)\n",
" new_image_path = os.path.join(images_output_folder, new_image_name)\n",
" os.rename(old_image_path, new_image_path)\n",
"\n",
" # Rename and move the annotation file\n",
" old_annotation_path = os.path.join(annotations_folder, annotation_file)\n",
" new_annotation_path = os.path.join(labels_output_folder, new_annotation_name)\n",
" os.rename(old_annotation_path, new_annotation_path)\n",
"\n",
" print(f\"Renamed and moved: {image_file} to {new_image_name}\")\n",
" print(f\"Renamed and moved: {annotation_file} to {new_annotation_name}\")\n",
"\n",
"print(\"All files have been renamed and moved to the 'train' folder.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ukgpuDXAVy3j"
},
"outputs": [],
"source": [
"import os\n",
"import shutil\n",
"\n",
"images_folder = '/content/MISAHUB/WCEBleedGen/non-bleeding/images'\n",
"output_folder = '/content/MISAHUB/train/'\n",
"\n",
"# Create the 'train' folder if it doesn't exist\n",
"if not os.path.exists(output_folder):\n",
" os.makedirs(output_folder)\n",
"\n",
"# Create 'images' and 'labels' folders inside 'train'\n",
"images_output_folder = os.path.join(output_folder, 'images')\n",
"labels_output_folder = os.path.join(output_folder, 'labels')\n",
"\n",
"os.makedirs(images_output_folder, exist_ok=True)\n",
"os.makedirs(labels_output_folder, exist_ok=True)\n",
"\n",
"# Get a list of files in the 'images' folder\n",
"image_files = os.listdir(images_folder)\n",
"image_files = [file for file in image_files if file.endswith('.png')]\n",
"image_files.sort()\n",
"\n",
"# Rename and move the files to the 'train' folder\n",
"for i, image_file in enumerate(image_files, start=1310):\n",
" # Define the new names for the image and annotation\n",
" new_image_name = f\"images_{i}.png\"\n",
" new_annotation_name = f\"images_{i}.txt\"\n",
"\n",
" # Rename and move the image file\n",
" old_image_path = os.path.join(images_folder, image_file)\n",
" new_image_path = os.path.join(images_output_folder, new_image_name)\n",
" os.rename(old_image_path, new_image_path)\n",
"\n",
" # Create an empty label file in the 'labels' folder\n",
" new_annotation_path = os.path.join(labels_output_folder, new_annotation_name)\n",
" with open(new_annotation_path, 'w') as empty_file:\n",
" pass\n",
"\n",
" print(f\"Renamed and moved: {image_file} to {new_image_name}\")\n",
" print(f\"Created empty label file: {new_annotation_name}\")\n",
"\n",
"print(\"All files have been renamed and moved to the 'train' folder.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mm2rQtsDWwP_",
"outputId": "19691375-52d5-4353-d124-0e7fc5a955ca"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset organized into 'bleeding' and 'non_bleeding' classes.\n"
]
}
],
"source": [
"import os\n",
"import shutil\n",
"\n",
"# Define the paths to the 'train' directory and its subdirectories\n",
"train_dir = 'MISAHUB/train' # Update this with the actual path to your 'train' directory\n",
"images_dir = os.path.join(train_dir, 'images')\n",
"labels_dir = os.path.join(train_dir, 'labels')\n",
"train_dir = 'MISAHUB/newTrain'\n",
"# Create output directories for the two classes\n",
"bleeding_dir = os.path.join(train_dir, 'bleeding')\n",
"non_bleeding_dir = os.path.join(train_dir, 'non_bleeding')\n",
"\n",
"# Create the 'bleeding' and 'non_bleeding' directories if they don't exist\n",
"os.makedirs(bleeding_dir, exist_ok=True)\n",
"os.makedirs(non_bleeding_dir, exist_ok=True)\n",
"\n",
"# Iterate through the files in the 'images' directory\n",
"for image_filename in os.listdir(images_dir):\n",
" # Form the corresponding label file path\n",
" label_filename = os.path.splitext(image_filename)[0] + '.txt'\n",
" label_filepath = os.path.join(labels_dir, label_filename)\n",
"\n",
" # Check if the label file is empty (indicating non-bleeding)\n",
" if os.path.exists(label_filepath) and os.path.getsize(label_filepath) == 0:\n",
" # Move the image to the 'non_bleeding' directory\n",
" shutil.move(os.path.join(images_dir, image_filename), os.path.join(non_bleeding_dir, image_filename))\n",
" else:\n",
" # Move the image to the 'bleeding' directory\n",
" shutil.move(os.path.join(images_dir, image_filename), os.path.join(bleeding_dir, image_filename))\n",
"\n",
"print(\"Dataset organized into 'bleeding' and 'non_bleeding' classes.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "UavS7_rSVe87",
"outputId": "14f1ebac-8021-4b87-fb93-9cc890b09e9e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1309\n"
]
}
],
"source": [
"! ls MISAHUB/newTrain/bleeding | wc -l"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rq7U71RIWwRI"
},
"outputs": [],
"source": [
"import os\n",
"import random\n",
"import shutil\n",
"\n",
"bleeding_dir = '/content/MISAHUB/newTrain/bleeding'\n",
"non_bleeding_dir = '/content/MISAHUB/newTrain/non_bleeding'\n",
"\n",
"\n",
"bleeding_files = os.listdir(bleeding_dir)\n",
"\n",
"non_bleeding_files = os.listdir(non_bleeding_dir)\n",
"\n",
"labels={}\n",
"for files in bleeding_files:\n",
" labels[files] = 0\n",
"for files in non_bleeding_files:\n",
" labels[files] = 1\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FWUMxzgFuSqE",
"outputId": "576e4a27-8d6e-450d-ee4c-696d505780be"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Folders '/content/MISAHUB/newTrain/bleeding' and '/content/MISAHUB/newTrain/non_bleeding' have been merged into '/content/classification/merged'.\n",
"The merged dataset has been split into 2094 training samples and 524 validation samples.\n"
]
}
],
"source": [
"#Merging bleeding and non_bleeding\n",
"import os\n",
"import random\n",
"import shutil\n",
"\n",
"# Define the source folders and the destination folders\n",
"bleeding_folder = '/content/MISAHUB/newTrain/bleeding'\n",
"non_bleeding_folder = '/content/MISAHUB/newTrain/non_bleeding'\n",
"merged_folder = '/content/classification/merged'\n",
"train_folder = '/content/classification/train'\n",
"val_folder = '/content/classification/val'\n",
"if not os.path.exists(merged_folder):\n",
" os.makedirs(merged_folder)\n",
"# Ensure the destination folders exist or create them if they don't\n",
"if not os.path.exists(train_folder):\n",
" os.makedirs(train_folder)\n",
"\n",
"if not os.path.exists(val_folder):\n",
" os.makedirs(val_folder)\n",
"\n",
"# Merge the contents of the \"bleeding\" folder into the merged folder\n",
"for item in os.listdir(bleeding_folder):\n",
" source_item = os.path.join(bleeding_folder, item)\n",
" destination_item = os.path.join(merged_folder, item)\n",
" if os.path.isdir(source_item):\n",
" shutil.copytree(source_item, destination_item)\n",
" else:\n",
" shutil.copy2(source_item, destination_item)\n",
"\n",
"# Merge the contents of the \"non-bleeding\" folder into the merged folder\n",
"for item in os.listdir(non_bleeding_folder):\n",
" source_item = os.path.join(non_bleeding_folder, item)\n",
" destination_item = os.path.join(merged_folder, item)\n",
" if os.path.isdir(source_item):\n",
" shutil.copytree(source_item, destination_item)\n",
" else:\n",
" shutil.copy2(source_item, destination_item)\n",
"\n",
"# Get a list of all files in the merged folder\n",
"all_files = os.listdir(merged_folder)\n",
"\n",
"# Calculate the number of files for the training set and validation set\n",
"num_files = len(all_files)\n",
"num_train = int(0.8 * num_files)\n",
"num_val = num_files - num_train\n",
"\n",
"# Randomly shuffle the list of files\n",
"random.shuffle(all_files)\n",
"\n",
"# Split the files into training and validation sets\n",
"train_files = all_files[:num_train]\n",
"val_files = all_files[num_train:]\n",
"\n",
"# Copy the training and validation files to their respective folders\n",
"for file in train_files:\n",
" source_file = os.path.join(merged_folder, file)\n",
" destination_file = os.path.join(train_folder, file)\n",
" shutil.copy2(source_file, destination_file)\n",
"\n",
"for file in val_files:\n",
" source_file = os.path.join(merged_folder, file)\n",
" destination_file = os.path.join(val_folder, file)\n",
" shutil.copy2(source_file, destination_file)\n",
"\n",
"print(f\"Folders '{bleeding_folder}' and '{non_bleeding_folder}' have been merged into '{merged_folder}'.\")\n",
"print(f\"The merged dataset has been split into {len(train_files)} training samples and {len(val_files)} validation samples.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0gSmVvwUfp2u"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"import PIL\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"from tensorflow.python.keras.layers import Dense, Flatten, Dropout\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.optimizers import Adam"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "M6N5_xdqqz3V"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import os\n",
"\n",
"# Define the path to the 'merged' directory\n",
"merged_dir = 'classification/merged'\n",
"\n",
"# Load the labels from your 'labels' dictionary\n",
"# Assuming you have a 'labels' dictionary defined\n",
"\n",
"# Get a list of all image file paths in the 'merged' directory\n",
"image_file_paths = [os.path.join(merged_dir, filename) for filename in os.listdir(merged_dir)]\n",
"\n",
"# Create empty lists to store file paths for training and validation\n",
"train_file_paths = []\n",
"val_file_paths = []\n",
"\n",
"# Define a function to load an image and its label\n",
"def load_image(file_path, label):\n",
" # Load the image\n",
" img = tf.io.read_file(file_path)\n",
" img = tf.image.decode_jpeg(img, channels=3) # Adjust channels as needed\n",
" img = tf.image.resize(img, (224, 224)) # Adjust image size as needed\n",
"\n",
" return img, label\n",
"\n",
"# Create a dataset from the image file paths and labels\n",
"dataset = tf.data.Dataset.from_tensor_slices((image_file_paths, [labels[os.path.basename(path)] for path in image_file_paths]))\n",
"\n",
"# Load the images and labels in parallel\n",
"AUTOTUNE = tf.data.AUTOTUNE\n",
"dataset = dataset.map(lambda x, y: (load_image(x, y)), num_parallel_calls=AUTOTUNE)\n",
"\n",
"# Shuffle the dataset\n",
"dataset = dataset.shuffle(buffer_size=len(image_file_paths))\n",
"\n",
"# Calculate the total number of samples in the dataset\n",
"total_samples = len(image_file_paths)\n",
"\n",
"# Calculate the number of samples for training and validation\n",
"train_size = int(0.8 * total_samples)\n",
"val_size = total_samples - train_size\n",
"\n",
"# Split the dataset into training and validation datasets and keep track of file paths\n",
"for i, (image, label) in enumerate(dataset):\n",
" if i < train_size:\n",
" train_file_paths.append(image_file_paths[i])\n",
" else:\n",
" val_file_paths.append(image_file_paths[i])\n",
"\n",
"train_dataset = dataset.take(train_size)\n",
"val_dataset = dataset.skip(train_size)\n",
"\n",
"# Batch the datasets\n",
"batch_size = 32 # Adjust the batch size as needed\n",
"train_dataset = train_dataset.batch(batch_size)\n",
"val_dataset = val_dataset.batch(batch_size)\n",
"\n",
"# Optionally, prefetch data for better performance\n",
"train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)\n",
"val_dataset = val_dataset.prefetch(buffer_size=AUTOTUNE)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AewhITJMgvWs"
},
"source": [
"## **Classification ResNet**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PiWWXwTa0hAK",
"outputId": "ef35f2dc-103a-4ba5-b99b-630eb17320db"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
"94765736/94765736 [==============================] - 3s 0us/step\n"
]
}
],
"source": [
"from tensorflow.keras.regularizers import l2\n",
"resnet_model = Sequential()\n",
"\n",
"pretrained_model= tf.keras.applications.ResNet50(include_top=False,\n",
" input_shape=(224,224,3),\n",
" pooling='max',classes=2,\n",
" weights='imagenet')\n",
"# for layer in pretrained_model.layers:\n",
"# layer.trainable=False\n",
"# Unfreeze specific layers\n",
"for layer in pretrained_model.layers:\n",
" if layer.name in ['block4_conv1', 'block4_conv2', 'block4_conv3']:\n",
" layer.trainable = True\n",
" else:\n",
" layer.trainable = False\n",
"\n",
"# Compile the model after unfreezing layers\n",
"# resnet_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
"\n",
"\n",
"resnet_model.add(pretrained_model)\n",
"resnet_model.add(Flatten())\n",
"resnet_model.add(Dense(512, activation='relu', kernel_regularizer=l2(0.02)))\n",
"resnet_model.add(Dropout(0.5))\n",
"resnet_model.add(Dense(1, activation='sigmoid',kernel_regularizer=l2(0.02)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ocxlltpa09LS"
},
"outputs": [],
"source": [
"resnet_model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EKycvVcE1A7M"
},
"outputs": [],
"source": [
"resnet_model.compile(optimizer=Adam(learning_rate=0.001),loss='binary_crossentropy',metrics=[tf.keras.metrics.Recall()])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qGpENO-fnTUt",
"outputId": "e9837d08-676a-47b3-b413-7a09f08d89a1"
},
"outputs": [
{
"data": {
"text/plain": [
"device(type='cuda')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import torch\n",
"import torchvision\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"device"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "usvAkA32oHAc",
"outputId": "432d6514-6088-4cb5-d78e-d89dff28b0af"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Num GPUs Available: 1\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"print(\"Num GPUs Available: \", len(tf.config.list_physical_devices('GPU')))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8ZU2ADPF1Oic",
"outputId": "7fc52145-428c-469a-8b9c-86a664eec10a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"66/66 [==============================] - 34s 220ms/step - loss: 2.7757 - recall: 0.7769 - val_loss: 0.1563 - val_recall: 0.9474\n",
"Epoch 2/5\n",
"66/66 [==============================] - 15s 179ms/step - loss: 0.1895 - recall: 0.9402 - val_loss: 0.0884 - val_recall: 0.9783\n",
"Epoch 3/5\n",
"66/66 [==============================] - 16s 178ms/step - loss: 0.1176 - recall: 0.9634 - val_loss: 0.1617 - val_recall: 1.0000\n",
"Epoch 4/5\n",
"66/66 [==============================] - 18s 195ms/step - loss: 0.1167 - recall: 0.9537 - val_loss: 0.0468 - val_recall: 0.9964\n",
"Epoch 5/5\n",
"66/66 [==============================] - 17s 204ms/step - loss: 0.0774 - recall: 0.9731 - val_loss: 0.0293 - val_recall: 0.9925\n"
]
}
],
"source": [
"epochs=5\n",
"batch_size=32\n",
"\n",
"\n",
"history = resnet_model.fit(\n",
" train_dataset,\n",
" validation_data=val_dataset,\n",
" epochs=epochs\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2oOoHdvTboRC",
"outputId": "eebc3124-fd6d-4e50-b764-2d95943a3200"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1/1 [==============================] - 2s 2s/step\n",
"bleeding\n"
]
}
],
"source": [
"img = tf.keras.preprocessing.image.load_img(\"/content/MISAHUB/newTrain/bleeding/image_84.png\", target_size=(224, 224))\n",
"img = tf.keras.preprocessing.image.img_to_array(img)\n",
"img = tf.expand_dims(img, axis=0)\n",
"\n",
"predictions = resnet_model.predict(img)\n",
"if(predictions[0][0])<0.5:\n",
" print(\"bleeding\")\n",
"else:\n",
" print(\"non_bleeding\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eSg6Sj0Y3RyR"
},
"source": [
"## **Evaluating Model**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 472
},
"id": "NwjguSD73RMv",
"outputId": "06e1d1d0-07ee-41ee-ba12-ccd8fcfdaa8b"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig1 = plt.gcf()\n",
"plt.plot(history.history['recall'])\n",
"plt.plot(history.history['val_recall'])\n",
"plt.axis(ymin=0.4,ymax=1)\n",
"plt.grid()\n",
"plt.title('Model Accuracy')\n",
"plt.ylabel('Accuracy')\n",
"plt.xlabel('Epochs')\n",
"plt.legend(['train', 'validation'])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "j_eKIzuu8mnr"
},
"outputs": [],
"source": [
"true_predictions = []\n",
"for x in val_file_paths:\n",
" true_predictions.append(labels[x.split('/')[2]])\n",
"true_predictions"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QF7Nlo96A4QO"
},
"source": [
"## Saving the classifying Model:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DHMdcNA1A6qQ"
},
"outputs": [],
"source": [
"resnet_model.save(\"/content/drive/My Drive/models/resnet_model_MISAHUB\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BD0uSi9Z08dI"
},
"source": [
"## Testing the classification model"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "FZfydG102v46",
"outputId": "5e8dcec5-55e7-472c-a3d2-3d4a1b759d6e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"1/1 [==============================] - 0s 31ms/step\n",
"1/1 [==============================] - 0s 29ms/step\n",
"Validation Accuracy: 0.99\n",
"Validation Precision: 1.00\n",
"Validation Recall: 0.99\n"
]
}
],
"source": [
"import numpy as np\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
"\n",
"binary_val_predictions=[]\n",
"for x in val_file_paths:\n",
" img = tf.keras.preprocessing.image.load_img(x, target_size=(224, 224))\n",
" img = tf.keras.preprocessing.image.img_to_array(img)\n",
" img = tf.expand_dims(img, axis=0)\n",
"\n",
" predictions = resnet_model.predict(img)\n",
" if(predictions[0][0])<0.5:\n",
" binary_val_predictions.append(0)\n",
" else:\n",
" binary_val_predictions.append(1)\n",
"\n",
"# Calculate accuracy\n",
"train_accuracy = accuracy_score(true_predictions, binary_val_predictions)\n",
"\n",
"# Calculate precision\n",
"train_precision = precision_score(true_predictions, binary_val_predictions)\n",
"\n",
"# Calculate recall\n",
"train_recall = recall_score(true_predictions, binary_val_predictions)\n",
"\n",
"# # Print the results\n",
"print(f'Validation Accuracy: {train_accuracy:.2f}')\n",
"print(f'Validation Precision: {train_precision:.2f}')\n",
"print(f'Validation Recall: {train_recall:.2f}')\n"
]
},
{
"cell_type": "code",
"source": [
"f1score = (2*train_precision*train_recall)/(train_precision+train_recall)\n",
"print(f'F1 Score: {f1score:.2f}')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ht2WIM-LUexI",
"outputId": "7ec8dfc5-05b8-4314-d6f5-e442f5180b16"
},
"execution_count": 121,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"F1 Score: 0.99\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-3TlR9tp07Ty"
},
"outputs": [],
"source": [
"zip_path = \"/content/drive/MyDrive/Auto-WCEBleedGen\\ Challenge\\ Test\\ Dataset.zip\"\n",
"!unzip $zip_path -d Test"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QBg1qV4QBwFt"
},
"source": [
"# Detection on Bleeding Frames"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Dj0jgg2nB1MG"
},
"outputs": [],
"source": [
"from tensorflow import keras\n",
"classifier = keras.models.load_model('/content/drive/My Drive/models/resnet_model_MISAHUB')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uuTCxNzKClSq"
},
"source": [
"Now we will train the model for detection. For this we will use only the bleeding images and their annotations from the **MISAHUB/WCEBleedGen/bleeding** directory.</br>\n",
"images: \"/content/MISAHUB/WCEBleedGen/bleeding/Images\"</br>\n",
"bounding_boxes = \"/content/MISAHUB/WCEBleedGen/bleeding/Bounding\\ boxes/XML\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NjyoutgkCFQV"
},
"outputs": [],
"source": [
"zip_path = '/content/drive/MyDrive/WCEBleedGen.zip'\n",
"!unzip $zip_path -d MISAHUB"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OPg_-ktuzVcp",
"outputId": "4088334d-ddfb-4a6f-b16a-59d78f52d47b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/content\n"
]
}
],
"source": [
"%cd /content"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lP4okvjoF-8N",
"outputId": "5b844a57-927e-4a8a-bf48-fd83cde639be"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset split into train and val (validation) sets with an 80-20 split.\n"
]
}
],
"source": [
"import os\n",
"import random\n",
"import shutil\n",
"\n",
"# Define the paths to the source directories\n",
"images_dir = '/content/MISAHUB/WCEBleedGen/bleeding/Images'\n",
"yolo_dir = '/content/MISAHUB/WCEBleedGen/bleeding/Bounding boxes/YOLO_TXT'\n",
"\n",
"# Define the paths for train and val (validation) directories\n",
"train_dir = 'train_data'\n",
"val_dir = 'val_data'\n",
"\n",
"# Create the train and val (validation) directories if they don't exist\n",
"os.makedirs(train_dir, exist_ok=True)\n",
"os.makedirs(val_dir, exist_ok=True)\n",
"\n",
"# Define subdirectories for \"images\" and \"labels\"\n",
"subdirs = ['images', 'labels']\n",
"\n",
"# Create subdirectories for the train and val (validation) datasets\n",
"for subdir in subdirs:\n",
" os.makedirs(os.path.join(train_dir, subdir), exist_ok=True)\n",
" os.makedirs(os.path.join(val_dir, subdir), exist_ok=True)\n",
"\n",
"# List all the image files\n",
"image_files = os.listdir(images_dir)\n",
"\n",
"# Shuffle the files randomly\n",
"random.shuffle(image_files)\n",
"\n",
"# Calculate the split sizes\n",
"total_samples = len(image_files)\n",
"train_size = int(0.8 * total_samples)\n",
"val_size = total_samples - train_size\n",
"\n",
"# Define a function to move image and XML files to the specified directory\n",
"def move_files(source_dir, destination_dir, subdir, file_list):\n",
" for file_name in file_list:\n",
" source_path = os.path.join(source_dir, file_name)\n",
" destination_path = os.path.join(destination_dir, subdir, file_name)\n",
" shutil.copy(source_path, destination_path)\n",
"\n",
"# Move files to train directory\n",
"move_files(images_dir, train_dir, 'images', image_files[:train_size])\n",
"move_files(yolo_dir, train_dir, 'labels', [file.replace('.png', '.txt') for file in image_files[:train_size]])\n",
"\n",
"# Move files to val (validation) directory\n",
"move_files(images_dir, val_dir, 'images', image_files[train_size:])\n",
"move_files(yolo_dir, val_dir, 'labels', [file.replace('.png', '.txt') for file in image_files[train_size:]])\n",
"\n",
"print(\"Dataset split into train and val (validation) sets with an 80-20 split.\")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wuyAyZuwMXyv"
},
"source": [
"## Yolov5\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dy3ExisXl9OX"
},
"outputs": [],
"source": [
"!git clone https://github.com/ultralytics/yolov5 # clone\n",
"%cd yolov5\n",
"!pip install -r requirements.txt # install"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NQhIEocqnZ_w",
"outputId": "413a0b2b-5a24-48c4-fe9f-4a5133500ed9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setup complete. Using torch 2.0.1+cu118 _CudaDeviceProperties(name='Tesla T4', major=7, minor=5, total_memory=15101MB, multi_processor_count=40)\n"
]
}
],
"source": [
"import torch\n",
"\n",
"from IPython.display import Image, clear_output # to display images\n",
"from utils.downloads import attempt_download # to download models/datasets\n",
"\n",
"# clear_output()\n",
"print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rZFZYdTpnmjV"
},
"outputs": [],
"source": [
"!touch data.yaml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9fiRYCb7rKYH",
"outputId": "94daf983-d7aa-4c17-a8cb-3e0ca20c3180"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/content\n"
]
}
],
"source": [
"%cd .."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pyFpG647BG9c",
"outputId": "90cb679c-cbfe-41dd-dbdb-af1da2766d91"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=yolov5/data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
"\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
"YOLOv5 🚀 v7.0-226-gdd9e338 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
"\n",
"\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
"\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
"\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/\n",
"Overriding model.yaml nc=80 with nc=1\n",
"\n",
" from n params module arguments \n",
" 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n",
" 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n",
" 2 -1 1 18816 models.common.C3 [64, 64, 1] \n",
" 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n",
" 4 -1 2 115712 models.common.C3 [128, 128, 2] \n",
" 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n",
" 6 -1 3 625152 models.common.C3 [256, 256, 3] \n",
" 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n",
" 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n",
" 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n",
" 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n",
" 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 12 [-1, 6] 1 0 models.common.Concat [1] \n",
" 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n",
" 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n",
" 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n",
" 16 [-1, 4] 1 0 models.common.Concat [1] \n",
" 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n",
" 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n",
" 19 [-1, 14] 1 0 models.common.Concat [1] \n",
" 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n",
" 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n",
" 22 [-1, 10] 1 0 models.common.Concat [1] \n",
" 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n",
" 24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
"Model summary: 214 layers, 7022326 parameters, 7022326 gradients, 15.9 GFLOPs\n",
"\n",
"Transferred 343/349 items from yolov5s.pt\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/yolov5/train_data/labels.cache... 1047 images, 0 backgrounds, 0 corrupt: 100% 1047/1047 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (1.2GB ram): 100% 1047/1047 [00:05<00:00, 188.21it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/yolov5/val_data/labels.cache... 262 images, 0 backgrounds, 0 corrupt: 100% 262/262 [00:00<?, ?it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.3GB ram): 100% 262/262 [00:01<00:00, 152.31it/s]\n",
"\n",
"\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.28 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
"Plotting labels to yolov5/runs/train/exp5/labels.jpg... \n",
"Image sizes 640 train, 640 val\n",
"Using 2 dataloader workers\n",
"Logging results to \u001b[1myolov5/runs/train/exp5\u001b[0m\n",
"Starting training for 50 epochs...\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 0/49 3.46G 0.09871 0.02604 0 11 640: 100% 66/66 [00:23<00:00, 2.78it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:21<00:00, 2.35s/it]\n",
" all 262 262 0.0999 0.115 0.041 0.0103\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 1/49 4.27G 0.0741 0.02347 0 11 640: 100% 66/66 [00:17<00:00, 3.88it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.58it/s]\n",
" all 262 262 0.286 0.126 0.116 0.0361\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 2/49 4.27G 0.06492 0.02232 0 15 640: 100% 66/66 [00:16<00:00, 4.10it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.89it/s]\n",
" all 262 262 0.212 0.233 0.106 0.0387\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 3/49 4.27G 0.06186 0.0223 0 15 640: 100% 66/66 [00:15<00:00, 4.17it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.72it/s]\n",
" all 262 262 0.359 0.256 0.196 0.0669\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 4/49 4.27G 0.05963 0.02117 0 12 640: 100% 66/66 [00:16<00:00, 4.12it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.77it/s]\n",
" all 262 262 0.233 0.271 0.13 0.0466\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 5/49 4.27G 0.05594 0.02165 0 14 640: 100% 66/66 [00:16<00:00, 4.06it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.61it/s]\n",
" all 262 262 0.31 0.263 0.185 0.084\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 6/49 4.27G 0.05483 0.02157 0 15 640: 100% 66/66 [00:16<00:00, 4.10it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.61it/s]\n",
" all 262 262 0.246 0.314 0.207 0.0844\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 7/49 4.27G 0.05177 0.02138 0 18 640: 100% 66/66 [00:15<00:00, 4.13it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.59it/s]\n",
" all 262 262 0.297 0.344 0.237 0.103\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 8/49 4.27G 0.05147 0.02034 0 14 640: 100% 66/66 [00:16<00:00, 4.03it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.62it/s]\n",
" all 262 262 0.342 0.37 0.258 0.109\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 9/49 4.27G 0.05002 0.02074 0 15 640: 100% 66/66 [00:15<00:00, 4.15it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.34it/s]\n",
" all 262 262 0.356 0.424 0.3 0.133\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 10/49 4.27G 0.04804 0.0207 0 15 640: 100% 66/66 [00:16<00:00, 4.03it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.72it/s]\n",
" all 262 262 0.448 0.481 0.377 0.172\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 11/49 4.27G 0.04793 0.01987 0 13 640: 100% 66/66 [00:15<00:00, 4.14it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.81it/s]\n",
" all 262 262 0.463 0.473 0.377 0.18\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 12/49 4.27G 0.04845 0.01979 0 15 640: 100% 66/66 [00:16<00:00, 4.04it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.70it/s]\n",
" all 262 262 0.388 0.443 0.321 0.152\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 13/49 4.27G 0.0444 0.02011 0 16 640: 100% 66/66 [00:16<00:00, 4.04it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.63it/s]\n",
" all 262 262 0.387 0.443 0.36 0.184\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 14/49 4.27G 0.04518 0.01996 0 14 640: 100% 66/66 [00:16<00:00, 4.12it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.55it/s]\n",
" all 262 262 0.406 0.416 0.346 0.172\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 15/49 4.27G 0.04497 0.01897 0 15 640: 100% 66/66 [00:15<00:00, 4.13it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.72it/s]\n",
" all 262 262 0.525 0.515 0.408 0.217\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 16/49 4.27G 0.04512 0.01998 0 14 640: 100% 66/66 [00:16<00:00, 4.02it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.66it/s]\n",
" all 262 262 0.369 0.447 0.33 0.171\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 17/49 4.27G 0.04413 0.01917 0 16 640: 100% 66/66 [00:16<00:00, 4.10it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.99it/s]\n",
" all 262 262 0.359 0.485 0.354 0.17\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 18/49 4.27G 0.04392 0.01994 0 13 640: 100% 66/66 [00:15<00:00, 4.13it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.73it/s]\n",
" all 262 262 0.555 0.538 0.466 0.243\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 19/49 4.27G 0.04241 0.01964 0 16 640: 100% 66/66 [00:15<00:00, 4.13it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.82it/s]\n",
" all 262 262 0.501 0.534 0.439 0.228\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 20/49 4.27G 0.04191 0.01887 0 19 640: 100% 66/66 [00:16<00:00, 3.99it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.68it/s]\n",
" all 262 262 0.495 0.5 0.425 0.216\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 21/49 4.27G 0.0409 0.0192 0 12 640: 100% 66/66 [00:16<00:00, 4.10it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.79it/s]\n",
" all 262 262 0.483 0.496 0.454 0.237\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 22/49 4.27G 0.041 0.01901 0 10 640: 100% 66/66 [00:16<00:00, 4.03it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.85it/s]\n",
" all 262 262 0.538 0.565 0.493 0.26\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 23/49 4.27G 0.04199 0.01874 0 12 640: 100% 66/66 [00:16<00:00, 3.97it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.14it/s]\n",
" all 262 262 0.502 0.5 0.433 0.222\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 24/49 4.27G 0.04113 0.01839 0 16 640: 100% 66/66 [00:15<00:00, 4.13it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.78it/s]\n",
" all 262 262 0.575 0.523 0.512 0.254\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 25/49 4.27G 0.04118 0.01869 0 17 640: 100% 66/66 [00:16<00:00, 4.05it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.62it/s]\n",
" all 262 262 0.543 0.546 0.494 0.268\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 26/49 4.27G 0.04033 0.01916 0 18 640: 100% 66/66 [00:16<00:00, 4.04it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.57it/s]\n",
" all 262 262 0.458 0.557 0.444 0.22\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 27/49 4.27G 0.0389 0.01891 0 13 640: 100% 66/66 [00:16<00:00, 4.08it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.58it/s]\n",
" all 262 262 0.552 0.437 0.445 0.212\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 28/49 4.27G 0.04005 0.0185 0 17 640: 100% 66/66 [00:16<00:00, 4.10it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.69it/s]\n",
" all 262 262 0.543 0.584 0.467 0.248\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 29/49 4.27G 0.03868 0.01848 0 18 640: 100% 66/66 [00:15<00:00, 4.15it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.85it/s]\n",
" all 262 262 0.558 0.584 0.514 0.276\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 30/49 4.27G 0.03843 0.01805 0 11 640: 100% 66/66 [00:15<00:00, 4.13it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.80it/s]\n",
" all 262 262 0.596 0.603 0.533 0.297\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 31/49 4.27G 0.0384 0.01741 0 13 640: 100% 66/66 [00:16<00:00, 4.12it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.77it/s]\n",
" all 262 262 0.572 0.55 0.507 0.262\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 32/49 4.27G 0.03633 0.01805 0 17 640: 100% 66/66 [00:16<00:00, 4.05it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.07it/s]\n",
" all 262 262 0.601 0.569 0.539 0.301\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 33/49 4.27G 0.03725 0.01813 0 11 640: 100% 66/66 [00:16<00:00, 4.08it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.69it/s]\n",
" all 262 262 0.593 0.511 0.517 0.27\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 34/49 4.27G 0.03604 0.0177 0 15 640: 100% 66/66 [00:16<00:00, 4.09it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.78it/s]\n",
" all 262 262 0.614 0.57 0.555 0.301\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 35/49 4.27G 0.03655 0.01758 0 13 640: 100% 66/66 [00:15<00:00, 4.13it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.65it/s]\n",
" all 262 262 0.565 0.58 0.523 0.282\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 36/49 4.27G 0.03581 0.01723 0 15 640: 100% 66/66 [00:16<00:00, 4.07it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.71it/s]\n",
" all 262 262 0.589 0.534 0.537 0.289\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 37/49 4.27G 0.0367 0.01745 0 9 640: 100% 66/66 [00:16<00:00, 4.08it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.74it/s]\n",
" all 262 262 0.556 0.584 0.551 0.308\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 38/49 4.27G 0.03646 0.01762 0 14 640: 100% 66/66 [00:16<00:00, 4.06it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.56it/s]\n",
" all 262 262 0.58 0.576 0.563 0.318\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 39/49 4.27G 0.03445 0.01685 0 19 640: 100% 66/66 [00:16<00:00, 4.07it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.76it/s]\n",
" all 262 262 0.57 0.603 0.554 0.3\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 40/49 4.27G 0.03471 0.01725 0 13 640: 100% 66/66 [00:16<00:00, 4.11it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.54it/s]\n",
" all 262 262 0.619 0.588 0.555 0.308\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 41/49 4.27G 0.03451 0.0172 0 12 640: 100% 66/66 [00:16<00:00, 4.04it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.76it/s]\n",
" all 262 262 0.636 0.595 0.566 0.312\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 42/49 4.27G 0.03456 0.0169 0 14 640: 100% 66/66 [00:16<00:00, 4.01it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.84it/s]\n",
" all 262 262 0.56 0.576 0.559 0.315\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 43/49 4.27G 0.03426 0.01734 0 16 640: 100% 66/66 [00:16<00:00, 4.12it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.62it/s]\n",
" all 262 262 0.576 0.531 0.544 0.31\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 44/49 4.27G 0.03353 0.01705 0 12 640: 100% 66/66 [00:16<00:00, 4.10it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.38it/s]\n",
" all 262 262 0.631 0.592 0.584 0.326\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 45/49 4.27G 0.03521 0.0169 0 16 640: 100% 66/66 [00:15<00:00, 4.20it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.93it/s]\n",
" all 262 262 0.6 0.599 0.586 0.328\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 46/49 4.27G 0.03303 0.01693 0 14 640: 100% 66/66 [00:15<00:00, 4.15it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.79it/s]\n",
" all 262 262 0.611 0.576 0.581 0.335\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 47/49 4.27G 0.03267 0.01692 0 21 640: 100% 66/66 [00:15<00:00, 4.16it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:02<00:00, 3.89it/s]\n",
" all 262 262 0.607 0.58 0.581 0.334\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 48/49 4.27G 0.03281 0.01675 0 12 640: 100% 66/66 [00:16<00:00, 4.09it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.50it/s]\n",
" all 262 262 0.593 0.622 0.587 0.341\n",
"\n",
" Epoch GPU_mem box_loss obj_loss cls_loss Instances Size\n",
" 49/49 4.27G 0.03201 0.01718 0 16 640: 100% 66/66 [00:16<00:00, 4.11it/s]\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:03<00:00, 2.43it/s]\n",
" all 262 262 0.575 0.608 0.569 0.329\n",
"\n",
"50 epochs completed in 0.341 hours.\n",
"Optimizer stripped from yolov5/runs/train/exp5/weights/last.pt, 14.4MB\n",
"Optimizer stripped from yolov5/runs/train/exp5/weights/best.pt, 14.4MB\n",
"\n",
"Validating yolov5/runs/train/exp5/weights/best.pt...\n",
"Fusing layers... \n",
"Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs\n",
" Class Images Instances P R mAP50 mAP50-95: 100% 9/9 [00:06<00:00, 1.46it/s]\n",
" all 262 262 0.594 0.626 0.587 0.341\n",
"Results saved to \u001b[1myolov5/runs/train/exp5\u001b[0m\n"
]
}
],
"source": [
"!python yolov5/train.py --epochs 50 --data yolov5/data.yaml --weights yolov5s.pt --cache"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "p5aQ2tnYG1Qu",
"outputId": "d83d1f3f-03ef-4d15-dd4b-eb85dce8c348"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[34m\u001b[1mdetect: \u001b[0mweights=['/content/yolov5/runs/train/exp5/weights/best.pt'], source=/content/yolov5/val_data/images, data=yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=True, save_csv=False, save_conf=True, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
"YOLOv5 🚀 v7.0-226-gdd9e338 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
"\n",
"Fusing layers... \n",
"Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs\n",
"image 1/262 /content/yolov5/val_data/images/img- (1).png: 640x640 (no detections), 11.5ms\n",
"image 2/262 /content/yolov5/val_data/images/img- (100).png: 640x640 1 bleeding, 11.5ms\n",
"image 3/262 /content/yolov5/val_data/images/img- (1004).png: 640x640 2 bleedings, 11.6ms\n",
"image 4/262 /content/yolov5/val_data/images/img- (1005).png: 640x640 1 bleeding, 11.5ms\n",
"image 5/262 /content/yolov5/val_data/images/img- (1007).png: 640x640 1 bleeding, 11.5ms\n",
"image 6/262 /content/yolov5/val_data/images/img- (1008).png: 640x640 1 bleeding, 11.5ms\n",
"image 7/262 /content/yolov5/val_data/images/img- (1010).png: 640x640 2 bleedings, 11.5ms\n",
"image 8/262 /content/yolov5/val_data/images/img- (1012).png: 640x640 (no detections), 11.5ms\n",
"image 9/262 /content/yolov5/val_data/images/img- (1013).png: 640x640 (no detections), 11.5ms\n",
"image 10/262 /content/yolov5/val_data/images/img- (1018).png: 640x640 2 bleedings, 11.5ms\n",
"image 11/262 /content/yolov5/val_data/images/img- (1019).png: 640x640 2 bleedings, 11.5ms\n",
"image 12/262 /content/yolov5/val_data/images/img- (1021).png: 640x640 2 bleedings, 11.5ms\n",
"image 13/262 /content/yolov5/val_data/images/img- (1038).png: 640x640 2 bleedings, 8.4ms\n",
"image 14/262 /content/yolov5/val_data/images/img- (104).png: 640x640 1 bleeding, 8.4ms\n",
"image 15/262 /content/yolov5/val_data/images/img- (1040).png: 640x640 1 bleeding, 8.5ms\n",
"image 16/262 /content/yolov5/val_data/images/img- (1043).png: 640x640 1 bleeding, 8.4ms\n",
"image 17/262 /content/yolov5/val_data/images/img- (1051).png: 640x640 1 bleeding, 8.4ms\n",
"image 18/262 /content/yolov5/val_data/images/img- (1061).png: 640x640 1 bleeding, 8.4ms\n",
"image 19/262 /content/yolov5/val_data/images/img- (1062).png: 640x640 1 bleeding, 8.4ms\n",
"image 20/262 /content/yolov5/val_data/images/img- (1065).png: 640x640 1 bleeding, 8.4ms\n",
"image 21/262 /content/yolov5/val_data/images/img- (107).png: 640x640 (no detections), 8.4ms\n",
"image 22/262 /content/yolov5/val_data/images/img- (1073).png: 640x640 1 bleeding, 8.4ms\n",
"image 23/262 /content/yolov5/val_data/images/img- (1078).png: 640x640 2 bleedings, 8.4ms\n",
"image 24/262 /content/yolov5/val_data/images/img- (1080).png: 640x640 1 bleeding, 7.3ms\n",
"image 25/262 /content/yolov5/val_data/images/img- (1086).png: 640x640 1 bleeding, 7.3ms\n",
"image 26/262 /content/yolov5/val_data/images/img- (1092).png: 640x640 1 bleeding, 7.3ms\n",
"image 27/262 /content/yolov5/val_data/images/img- (11).png: 640x640 1 bleeding, 7.3ms\n",
"image 28/262 /content/yolov5/val_data/images/img- (110).png: 640x640 (no detections), 7.3ms\n",
"image 29/262 /content/yolov5/val_data/images/img- (1101).png: 640x640 1 bleeding, 7.3ms\n",
"image 30/262 /content/yolov5/val_data/images/img- (1102).png: 640x640 1 bleeding, 7.3ms\n",
"image 31/262 /content/yolov5/val_data/images/img- (1103).png: 640x640 2 bleedings, 7.2ms\n",
"image 32/262 /content/yolov5/val_data/images/img- (1106).png: 640x640 2 bleedings, 7.1ms\n",
"image 33/262 /content/yolov5/val_data/images/img- (1108).png: 640x640 1 bleeding, 7.2ms\n",
"image 34/262 /content/yolov5/val_data/images/img- (111).png: 640x640 (no detections), 7.1ms\n",
"image 35/262 /content/yolov5/val_data/images/img- (1115).png: 640x640 1 bleeding, 7.1ms\n",
"image 36/262 /content/yolov5/val_data/images/img- (1116).png: 640x640 1 bleeding, 7.8ms\n",
"image 37/262 /content/yolov5/val_data/images/img- (1123).png: 640x640 1 bleeding, 6.6ms\n",
"image 38/262 /content/yolov5/val_data/images/img- (1124).png: 640x640 1 bleeding, 6.5ms\n",
"image 39/262 /content/yolov5/val_data/images/img- (1131).png: 640x640 1 bleeding, 6.5ms\n",
"image 40/262 /content/yolov5/val_data/images/img- (1132).png: 640x640 (no detections), 6.5ms\n",
"image 41/262 /content/yolov5/val_data/images/img- (1133).png: 640x640 1 bleeding, 6.5ms\n",
"image 42/262 /content/yolov5/val_data/images/img- (1136).png: 640x640 (no detections), 6.5ms\n",
"image 43/262 /content/yolov5/val_data/images/img- (114).png: 640x640 1 bleeding, 6.4ms\n",
"image 44/262 /content/yolov5/val_data/images/img- (1150).png: 640x640 1 bleeding, 6.7ms\n",
"image 45/262 /content/yolov5/val_data/images/img- (1153).png: 640x640 (no detections), 6.3ms\n",
"image 46/262 /content/yolov5/val_data/images/img- (1155).png: 640x640 1 bleeding, 6.3ms\n",
"image 47/262 /content/yolov5/val_data/images/img- (1157).png: 640x640 (no detections), 6.3ms\n",
"image 48/262 /content/yolov5/val_data/images/img- (1161).png: 640x640 1 bleeding, 6.3ms\n",
"image 49/262 /content/yolov5/val_data/images/img- (1162).png: 640x640 1 bleeding, 6.3ms\n",
"image 50/262 /content/yolov5/val_data/images/img- (1164).png: 640x640 1 bleeding, 6.1ms\n",
"image 51/262 /content/yolov5/val_data/images/img- (1165).png: 640x640 1 bleeding, 6.2ms\n",
"image 52/262 /content/yolov5/val_data/images/img- (1176).png: 640x640 1 bleeding, 6.2ms\n",
"image 53/262 /content/yolov5/val_data/images/img- (1178).png: 640x640 (no detections), 6.2ms\n",
"image 54/262 /content/yolov5/val_data/images/img- (1188).png: 640x640 (no detections), 6.2ms\n",
"image 55/262 /content/yolov5/val_data/images/img- (1198).png: 640x640 (no detections), 7.8ms\n",
"image 56/262 /content/yolov5/val_data/images/img- (1199).png: 640x640 1 bleeding, 6.2ms\n",
"image 57/262 /content/yolov5/val_data/images/img- (120).png: 640x640 1 bleeding, 6.1ms\n",
"image 58/262 /content/yolov5/val_data/images/img- (1203).png: 640x640 (no detections), 6.1ms\n",
"image 59/262 /content/yolov5/val_data/images/img- (1209).png: 640x640 1 bleeding, 6.1ms\n",
"image 60/262 /content/yolov5/val_data/images/img- (1210).png: 640x640 1 bleeding, 6.1ms\n",
"image 61/262 /content/yolov5/val_data/images/img- (1217).png: 640x640 (no detections), 6.1ms\n",
"image 62/262 /content/yolov5/val_data/images/img- (1219).png: 640x640 2 bleedings, 6.1ms\n",
"image 63/262 /content/yolov5/val_data/images/img- (1229).png: 640x640 (no detections), 5.7ms\n",
"image 64/262 /content/yolov5/val_data/images/img- (1234).png: 640x640 (no detections), 5.8ms\n",
"image 65/262 /content/yolov5/val_data/images/img- (1237).png: 640x640 (no detections), 5.7ms\n",
"image 66/262 /content/yolov5/val_data/images/img- (1243).png: 640x640 (no detections), 5.8ms\n",
"image 67/262 /content/yolov5/val_data/images/img- (1252).png: 640x640 (no detections), 5.7ms\n",
"image 68/262 /content/yolov5/val_data/images/img- (1262).png: 640x640 (no detections), 5.7ms\n",
"image 69/262 /content/yolov5/val_data/images/img- (1275).png: 640x640 (no detections), 5.7ms\n",
"image 70/262 /content/yolov5/val_data/images/img- (1278).png: 640x640 (no detections), 5.8ms\n",
"image 71/262 /content/yolov5/val_data/images/img- (1284).png: 640x640 1 bleeding, 5.7ms\n",
"image 72/262 /content/yolov5/val_data/images/img- (1286).png: 640x640 (no detections), 5.7ms\n",
"image 73/262 /content/yolov5/val_data/images/img- (1288).png: 640x640 (no detections), 5.7ms\n",
"image 74/262 /content/yolov5/val_data/images/img- (1293).png: 640x640 1 bleeding, 5.7ms\n",
"image 75/262 /content/yolov5/val_data/images/img- (1295).png: 640x640 (no detections), 5.7ms\n",
"image 76/262 /content/yolov5/val_data/images/img- (1299).png: 640x640 1 bleeding, 5.7ms\n",
"image 77/262 /content/yolov5/val_data/images/img- (13).png: 640x640 1 bleeding, 5.7ms\n",
"image 78/262 /content/yolov5/val_data/images/img- (1301).png: 640x640 (no detections), 5.5ms\n",
"image 79/262 /content/yolov5/val_data/images/img- (1303).png: 640x640 (no detections), 5.4ms\n",
"image 80/262 /content/yolov5/val_data/images/img- (1309).png: 640x640 1 bleeding, 5.5ms\n",
"image 81/262 /content/yolov5/val_data/images/img- (137).png: 640x640 (no detections), 5.4ms\n",
"image 82/262 /content/yolov5/val_data/images/img- (142).png: 640x640 1 bleeding, 5.4ms\n",
"image 83/262 /content/yolov5/val_data/images/img- (144).png: 640x640 1 bleeding, 5.5ms\n",
"image 84/262 /content/yolov5/val_data/images/img- (151).png: 640x640 1 bleeding, 5.6ms\n",
"image 85/262 /content/yolov5/val_data/images/img- (152).png: 640x640 1 bleeding, 5.4ms\n",
"image 86/262 /content/yolov5/val_data/images/img- (163).png: 640x640 (no detections), 5.4ms\n",
"image 87/262 /content/yolov5/val_data/images/img- (168).png: 640x640 (no detections), 5.4ms\n",
"image 88/262 /content/yolov5/val_data/images/img- (184).png: 640x640 (no detections), 5.4ms\n",
"image 89/262 /content/yolov5/val_data/images/img- (19).png: 640x640 (no detections), 5.5ms\n",
"image 90/262 /content/yolov5/val_data/images/img- (191).png: 640x640 (no detections), 5.5ms\n",
"image 91/262 /content/yolov5/val_data/images/img- (194).png: 640x640 (no detections), 5.5ms\n",
"image 92/262 /content/yolov5/val_data/images/img- (196).png: 640x640 1 bleeding, 8.4ms\n",
"image 93/262 /content/yolov5/val_data/images/img- (202).png: 640x640 (no detections), 5.4ms\n",
"image 94/262 /content/yolov5/val_data/images/img- (22).png: 640x640 1 bleeding, 5.4ms\n",
"image 95/262 /content/yolov5/val_data/images/img- (222).png: 640x640 1 bleeding, 5.5ms\n",
"image 96/262 /content/yolov5/val_data/images/img- (227).png: 640x640 2 bleedings, 5.5ms\n",
"image 97/262 /content/yolov5/val_data/images/img- (233).png: 640x640 (no detections), 5.4ms\n",
"image 98/262 /content/yolov5/val_data/images/img- (234).png: 640x640 1 bleeding, 5.4ms\n",
"image 99/262 /content/yolov5/val_data/images/img- (237).png: 640x640 1 bleeding, 5.7ms\n",
"image 100/262 /content/yolov5/val_data/images/img- (239).png: 640x640 1 bleeding, 5.4ms\n",
"image 101/262 /content/yolov5/val_data/images/img- (245).png: 640x640 (no detections), 5.4ms\n",
"image 102/262 /content/yolov5/val_data/images/img- (258).png: 640x640 (no detections), 5.4ms\n",
"image 103/262 /content/yolov5/val_data/images/img- (263).png: 640x640 (no detections), 5.4ms\n",
"image 104/262 /content/yolov5/val_data/images/img- (268).png: 640x640 (no detections), 5.4ms\n",
"image 105/262 /content/yolov5/val_data/images/img- (27).png: 640x640 2 bleedings, 5.4ms\n",
"image 106/262 /content/yolov5/val_data/images/img- (271).png: 640x640 (no detections), 5.5ms\n",
"image 107/262 /content/yolov5/val_data/images/img- (274).png: 640x640 1 bleeding, 5.4ms\n",
"image 108/262 /content/yolov5/val_data/images/img- (294).png: 640x640 (no detections), 8.6ms\n",
"image 109/262 /content/yolov5/val_data/images/img- (299).png: 640x640 (no detections), 7.7ms\n",
"image 110/262 /content/yolov5/val_data/images/img- (3).png: 640x640 1 bleeding, 5.5ms\n",
"image 111/262 /content/yolov5/val_data/images/img- (30).png: 640x640 1 bleeding, 5.4ms\n",
"image 112/262 /content/yolov5/val_data/images/img- (304).png: 640x640 (no detections), 5.4ms\n",
"image 113/262 /content/yolov5/val_data/images/img- (313).png: 640x640 1 bleeding, 8.1ms\n",
"image 114/262 /content/yolov5/val_data/images/img- (319).png: 640x640 1 bleeding, 5.4ms\n",
"image 115/262 /content/yolov5/val_data/images/img- (325).png: 640x640 1 bleeding, 5.4ms\n",
"image 116/262 /content/yolov5/val_data/images/img- (330).png: 640x640 1 bleeding, 5.4ms\n",
"image 117/262 /content/yolov5/val_data/images/img- (338).png: 640x640 (no detections), 5.4ms\n",
"image 118/262 /content/yolov5/val_data/images/img- (349).png: 640x640 1 bleeding, 5.4ms\n",
"image 119/262 /content/yolov5/val_data/images/img- (363).png: 640x640 (no detections), 5.5ms\n",
"image 120/262 /content/yolov5/val_data/images/img- (381).png: 640x640 1 bleeding, 6.3ms\n",
"image 121/262 /content/yolov5/val_data/images/img- (385).png: 640x640 1 bleeding, 5.4ms\n",
"image 122/262 /content/yolov5/val_data/images/img- (391).png: 640x640 1 bleeding, 5.4ms\n",
"image 123/262 /content/yolov5/val_data/images/img- (399).png: 640x640 1 bleeding, 5.4ms\n",
"image 124/262 /content/yolov5/val_data/images/img- (4).png: 640x640 1 bleeding, 5.4ms\n",
"image 125/262 /content/yolov5/val_data/images/img- (401).png: 640x640 1 bleeding, 5.4ms\n",
"image 126/262 /content/yolov5/val_data/images/img- (406).png: 640x640 1 bleeding, 5.4ms\n",
"image 127/262 /content/yolov5/val_data/images/img- (410).png: 640x640 1 bleeding, 5.4ms\n",
"image 128/262 /content/yolov5/val_data/images/img- (411).png: 640x640 1 bleeding, 5.4ms\n",
"image 129/262 /content/yolov5/val_data/images/img- (420).png: 640x640 1 bleeding, 5.5ms\n",
"image 130/262 /content/yolov5/val_data/images/img- (429).png: 640x640 1 bleeding, 5.4ms\n",
"image 131/262 /content/yolov5/val_data/images/img- (431).png: 640x640 (no detections), 5.4ms\n",
"image 132/262 /content/yolov5/val_data/images/img- (433).png: 640x640 1 bleeding, 5.9ms\n",
"image 133/262 /content/yolov5/val_data/images/img- (44).png: 640x640 1 bleeding, 5.4ms\n",
"image 134/262 /content/yolov5/val_data/images/img- (442).png: 640x640 1 bleeding, 7.9ms\n",
"image 135/262 /content/yolov5/val_data/images/img- (443).png: 640x640 1 bleeding, 5.4ms\n",
"image 136/262 /content/yolov5/val_data/images/img- (448).png: 640x640 2 bleedings, 5.4ms\n",
"image 137/262 /content/yolov5/val_data/images/img- (453).png: 640x640 2 bleedings, 5.4ms\n",
"image 138/262 /content/yolov5/val_data/images/img- (46).png: 640x640 2 bleedings, 5.3ms\n",
"image 139/262 /content/yolov5/val_data/images/img- (466).png: 640x640 1 bleeding, 5.4ms\n",
"image 140/262 /content/yolov5/val_data/images/img- (467).png: 640x640 1 bleeding, 5.4ms\n",
"image 141/262 /content/yolov5/val_data/images/img- (471).png: 640x640 1 bleeding, 5.4ms\n",
"image 142/262 /content/yolov5/val_data/images/img- (480).png: 640x640 1 bleeding, 5.4ms\n",
"image 143/262 /content/yolov5/val_data/images/img- (481).png: 640x640 1 bleeding, 5.4ms\n",
"image 144/262 /content/yolov5/val_data/images/img- (487).png: 640x640 1 bleeding, 6.8ms\n",
"image 145/262 /content/yolov5/val_data/images/img- (489).png: 640x640 1 bleeding, 5.4ms\n",
"image 146/262 /content/yolov5/val_data/images/img- (492).png: 640x640 1 bleeding, 5.4ms\n",
"image 147/262 /content/yolov5/val_data/images/img- (494).png: 640x640 1 bleeding, 5.4ms\n",
"image 148/262 /content/yolov5/val_data/images/img- (496).png: 640x640 1 bleeding, 5.5ms\n",
"image 149/262 /content/yolov5/val_data/images/img- (497).png: 640x640 1 bleeding, 5.4ms\n",
"image 150/262 /content/yolov5/val_data/images/img- (513).png: 640x640 1 bleeding, 5.4ms\n",
"image 151/262 /content/yolov5/val_data/images/img- (516).png: 640x640 (no detections), 5.4ms\n",
"image 152/262 /content/yolov5/val_data/images/img- (518).png: 640x640 1 bleeding, 5.4ms\n",
"image 153/262 /content/yolov5/val_data/images/img- (523).png: 640x640 1 bleeding, 5.4ms\n",
"image 154/262 /content/yolov5/val_data/images/img- (524).png: 640x640 1 bleeding, 5.5ms\n",
"image 155/262 /content/yolov5/val_data/images/img- (526).png: 640x640 (no detections), 5.4ms\n",
"image 156/262 /content/yolov5/val_data/images/img- (532).png: 640x640 1 bleeding, 5.4ms\n",
"image 157/262 /content/yolov5/val_data/images/img- (534).png: 640x640 1 bleeding, 5.4ms\n",
"image 158/262 /content/yolov5/val_data/images/img- (536).png: 640x640 1 bleeding, 5.4ms\n",
"image 159/262 /content/yolov5/val_data/images/img- (537).png: 640x640 1 bleeding, 5.4ms\n",
"image 160/262 /content/yolov5/val_data/images/img- (543).png: 640x640 1 bleeding, 5.4ms\n",
"image 161/262 /content/yolov5/val_data/images/img- (544).png: 640x640 1 bleeding, 5.4ms\n",
"image 162/262 /content/yolov5/val_data/images/img- (551).png: 640x640 1 bleeding, 5.4ms\n",
"image 163/262 /content/yolov5/val_data/images/img- (553).png: 640x640 1 bleeding, 5.4ms\n",
"image 164/262 /content/yolov5/val_data/images/img- (556).png: 640x640 1 bleeding, 5.4ms\n",
"image 165/262 /content/yolov5/val_data/images/img- (558).png: 640x640 1 bleeding, 5.4ms\n",
"image 166/262 /content/yolov5/val_data/images/img- (563).png: 640x640 (no detections), 5.4ms\n",
"image 167/262 /content/yolov5/val_data/images/img- (566).png: 640x640 1 bleeding, 5.4ms\n",
"image 168/262 /content/yolov5/val_data/images/img- (581).png: 640x640 1 bleeding, 5.5ms\n",
"image 169/262 /content/yolov5/val_data/images/img- (582).png: 640x640 1 bleeding, 5.5ms\n",
"image 170/262 /content/yolov5/val_data/images/img- (586).png: 640x640 1 bleeding, 5.4ms\n",
"image 171/262 /content/yolov5/val_data/images/img- (588).png: 640x640 1 bleeding, 5.5ms\n",
"image 172/262 /content/yolov5/val_data/images/img- (59).png: 640x640 1 bleeding, 5.4ms\n",
"image 173/262 /content/yolov5/val_data/images/img- (591).png: 640x640 1 bleeding, 5.4ms\n",
"image 174/262 /content/yolov5/val_data/images/img- (599).png: 640x640 1 bleeding, 5.4ms\n",
"image 175/262 /content/yolov5/val_data/images/img- (60).png: 640x640 1 bleeding, 5.4ms\n",
"image 176/262 /content/yolov5/val_data/images/img- (609).png: 640x640 1 bleeding, 6.4ms\n",
"image 177/262 /content/yolov5/val_data/images/img- (615).png: 640x640 2 bleedings, 5.4ms\n",
"image 178/262 /content/yolov5/val_data/images/img- (616).png: 640x640 1 bleeding, 5.4ms\n",
"image 179/262 /content/yolov5/val_data/images/img- (617).png: 640x640 1 bleeding, 9.2ms\n",
"image 180/262 /content/yolov5/val_data/images/img- (630).png: 640x640 1 bleeding, 5.4ms\n",
"image 181/262 /content/yolov5/val_data/images/img- (632).png: 640x640 2 bleedings, 6.6ms\n",
"image 182/262 /content/yolov5/val_data/images/img- (64).png: 640x640 1 bleeding, 5.4ms\n",
"image 183/262 /content/yolov5/val_data/images/img- (642).png: 640x640 1 bleeding, 5.4ms\n",
"image 184/262 /content/yolov5/val_data/images/img- (651).png: 640x640 1 bleeding, 5.4ms\n",
"image 185/262 /content/yolov5/val_data/images/img- (655).png: 640x640 1 bleeding, 5.4ms\n",
"image 186/262 /content/yolov5/val_data/images/img- (659).png: 640x640 1 bleeding, 5.4ms\n",
"image 187/262 /content/yolov5/val_data/images/img- (66).png: 640x640 1 bleeding, 5.4ms\n",
"image 188/262 /content/yolov5/val_data/images/img- (665).png: 640x640 (no detections), 5.4ms\n",
"image 189/262 /content/yolov5/val_data/images/img- (675).png: 640x640 1 bleeding, 6.7ms\n",
"image 190/262 /content/yolov5/val_data/images/img- (681).png: 640x640 1 bleeding, 5.4ms\n",
"image 191/262 /content/yolov5/val_data/images/img- (69).png: 640x640 3 bleedings, 5.4ms\n",
"image 192/262 /content/yolov5/val_data/images/img- (692).png: 640x640 (no detections), 5.4ms\n",
"image 193/262 /content/yolov5/val_data/images/img- (693).png: 640x640 1 bleeding, 5.4ms\n",
"image 194/262 /content/yolov5/val_data/images/img- (701).png: 640x640 1 bleeding, 5.6ms\n",
"image 195/262 /content/yolov5/val_data/images/img- (708).png: 640x640 1 bleeding, 5.5ms\n",
"image 196/262 /content/yolov5/val_data/images/img- (71).png: 640x640 (no detections), 8.2ms\n",
"image 197/262 /content/yolov5/val_data/images/img- (726).png: 640x640 1 bleeding, 5.4ms\n",
"image 198/262 /content/yolov5/val_data/images/img- (727).png: 640x640 1 bleeding, 5.4ms\n",
"image 199/262 /content/yolov5/val_data/images/img- (73).png: 640x640 1 bleeding, 5.4ms\n",
"image 200/262 /content/yolov5/val_data/images/img- (734).png: 640x640 2 bleedings, 5.4ms\n",
"image 201/262 /content/yolov5/val_data/images/img- (738).png: 640x640 1 bleeding, 5.5ms\n",
"image 202/262 /content/yolov5/val_data/images/img- (740).png: 640x640 2 bleedings, 5.5ms\n",
"image 203/262 /content/yolov5/val_data/images/img- (741).png: 640x640 2 bleedings, 6.1ms\n",
"image 204/262 /content/yolov5/val_data/images/img- (743).png: 640x640 1 bleeding, 5.5ms\n",
"image 205/262 /content/yolov5/val_data/images/img- (750).png: 640x640 1 bleeding, 5.5ms\n",
"image 206/262 /content/yolov5/val_data/images/img- (769).png: 640x640 1 bleeding, 5.4ms\n",
"image 207/262 /content/yolov5/val_data/images/img- (775).png: 640x640 1 bleeding, 5.5ms\n",
"image 208/262 /content/yolov5/val_data/images/img- (786).png: 640x640 1 bleeding, 5.4ms\n",
"image 209/262 /content/yolov5/val_data/images/img- (787).png: 640x640 1 bleeding, 5.4ms\n",
"image 210/262 /content/yolov5/val_data/images/img- (791).png: 640x640 (no detections), 5.6ms\n",
"image 211/262 /content/yolov5/val_data/images/img- (792).png: 640x640 1 bleeding, 5.6ms\n",
"image 212/262 /content/yolov5/val_data/images/img- (795).png: 640x640 (no detections), 5.5ms\n",
"image 213/262 /content/yolov5/val_data/images/img- (800).png: 640x640 1 bleeding, 5.5ms\n",
"image 214/262 /content/yolov5/val_data/images/img- (809).png: 640x640 1 bleeding, 5.5ms\n",
"image 215/262 /content/yolov5/val_data/images/img- (81).png: 640x640 2 bleedings, 5.4ms\n",
"image 216/262 /content/yolov5/val_data/images/img- (812).png: 640x640 1 bleeding, 7.5ms\n",
"image 217/262 /content/yolov5/val_data/images/img- (815).png: 640x640 1 bleeding, 5.4ms\n",
"image 218/262 /content/yolov5/val_data/images/img- (817).png: 640x640 1 bleeding, 5.9ms\n",
"image 219/262 /content/yolov5/val_data/images/img- (826).png: 640x640 1 bleeding, 5.4ms\n",
"image 220/262 /content/yolov5/val_data/images/img- (833).png: 640x640 1 bleeding, 5.7ms\n",
"image 221/262 /content/yolov5/val_data/images/img- (834).png: 640x640 (no detections), 5.5ms\n",
"image 222/262 /content/yolov5/val_data/images/img- (838).png: 640x640 (no detections), 5.4ms\n",
"image 223/262 /content/yolov5/val_data/images/img- (841).png: 640x640 (no detections), 5.4ms\n",
"image 224/262 /content/yolov5/val_data/images/img- (844).png: 640x640 1 bleeding, 5.5ms\n",
"image 225/262 /content/yolov5/val_data/images/img- (849).png: 640x640 1 bleeding, 5.6ms\n",
"image 226/262 /content/yolov5/val_data/images/img- (850).png: 640x640 1 bleeding, 5.5ms\n",
"image 227/262 /content/yolov5/val_data/images/img- (861).png: 640x640 1 bleeding, 5.4ms\n",
"image 228/262 /content/yolov5/val_data/images/img- (863).png: 640x640 1 bleeding, 5.4ms\n",
"image 229/262 /content/yolov5/val_data/images/img- (866).png: 640x640 1 bleeding, 5.4ms\n",
"image 230/262 /content/yolov5/val_data/images/img- (868).png: 640x640 (no detections), 5.5ms\n",
"image 231/262 /content/yolov5/val_data/images/img- (87).png: 640x640 1 bleeding, 5.4ms\n",
"image 232/262 /content/yolov5/val_data/images/img- (875).png: 640x640 1 bleeding, 9.5ms\n",
"image 233/262 /content/yolov5/val_data/images/img- (876).png: 640x640 1 bleeding, 5.4ms\n",
"image 234/262 /content/yolov5/val_data/images/img- (879).png: 640x640 1 bleeding, 5.4ms\n",
"image 235/262 /content/yolov5/val_data/images/img- (884).png: 640x640 1 bleeding, 5.4ms\n",
"image 236/262 /content/yolov5/val_data/images/img- (892).png: 640x640 1 bleeding, 5.4ms\n",
"image 237/262 /content/yolov5/val_data/images/img- (898).png: 640x640 1 bleeding, 9.6ms\n",
"image 238/262 /content/yolov5/val_data/images/img- (90).png: 640x640 (no detections), 6.2ms\n",
"image 239/262 /content/yolov5/val_data/images/img- (904).png: 640x640 1 bleeding, 5.5ms\n",
"image 240/262 /content/yolov5/val_data/images/img- (92).png: 640x640 (no detections), 5.4ms\n",
"image 241/262 /content/yolov5/val_data/images/img- (922).png: 640x640 1 bleeding, 5.4ms\n",
"image 242/262 /content/yolov5/val_data/images/img- (923).png: 640x640 1 bleeding, 5.5ms\n",
"image 243/262 /content/yolov5/val_data/images/img- (93).png: 640x640 (no detections), 5.4ms\n",
"image 244/262 /content/yolov5/val_data/images/img- (938).png: 640x640 1 bleeding, 5.4ms\n",
"image 245/262 /content/yolov5/val_data/images/img- (94).png: 640x640 (no detections), 5.5ms\n",
"image 246/262 /content/yolov5/val_data/images/img- (940).png: 640x640 1 bleeding, 5.5ms\n",
"image 247/262 /content/yolov5/val_data/images/img- (941).png: 640x640 1 bleeding, 5.7ms\n",
"image 248/262 /content/yolov5/val_data/images/img- (943).png: 640x640 1 bleeding, 5.4ms\n",
"image 249/262 /content/yolov5/val_data/images/img- (954).png: 640x640 (no detections), 5.4ms\n",
"image 250/262 /content/yolov5/val_data/images/img- (955).png: 640x640 (no detections), 7.8ms\n",
"image 251/262 /content/yolov5/val_data/images/img- (958).png: 640x640 (no detections), 6.2ms\n",
"image 252/262 /content/yolov5/val_data/images/img- (959).png: 640x640 (no detections), 5.4ms\n",
"image 253/262 /content/yolov5/val_data/images/img- (962).png: 640x640 (no detections), 5.4ms\n",
"image 254/262 /content/yolov5/val_data/images/img- (97).png: 640x640 2 bleedings, 5.4ms\n",
"image 255/262 /content/yolov5/val_data/images/img- (972).png: 640x640 (no detections), 5.5ms\n",
"image 256/262 /content/yolov5/val_data/images/img- (973).png: 640x640 (no detections), 5.5ms\n",
"image 257/262 /content/yolov5/val_data/images/img- (974).png: 640x640 (no detections), 5.4ms\n",
"image 258/262 /content/yolov5/val_data/images/img- (979).png: 640x640 1 bleeding, 5.5ms\n",
"image 259/262 /content/yolov5/val_data/images/img- (984).png: 640x640 (no detections), 5.4ms\n",
"image 260/262 /content/yolov5/val_data/images/img- (985).png: 640x640 1 bleeding, 5.4ms\n",
"image 261/262 /content/yolov5/val_data/images/img- (986).png: 640x640 1 bleeding, 5.5ms\n",
"image 262/262 /content/yolov5/val_data/images/img- (987).png: 640x640 (no detections), 5.4ms\n",
"Speed: 0.5ms pre-process, 6.2ms inference, 1.1ms NMS per image at shape (1, 3, 640, 640)\n",
"Results saved to \u001b[1myolov5/runs/detect/exp10\u001b[0m\n",
"188 labels saved to yolov5/runs/detect/exp10/labels\n"
]
}
],
"source": [
"!python yolov5/detect.py --weights /content/yolov5/runs/train/exp5/weights/best.pt --source /content/yolov5/val_data/images --save-txt --save-conf"
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"\n",
"def parse_yolo_label(label):\n",
" \"\"\"Parse YOLO label string into a list of values.\"\"\"\n",
" values = label.strip().split()\n",
" class_id = int(values[0])\n",
" confidence = float(values[-1])\n",
" box = [float(values[1]), float(values[2]), float(values[3]), float(values[4])]\n",
" return class_id, confidence, box\n",
"\n",
"def calculate_iou(box1, box2):\n",
" x1, y1, x2, y2 = box1\n",
" x1_, y1_, x2_, y2_ = box2\n",
"\n",
" x_intersection = max(0, min(x2, x2_) - max(x1, x1_))\n",
" y_intersection = max(0, min(y2, y2_) - max(y1, y1_))\n",
" intersection_area = x_intersection * y_intersection\n",
"\n",
" box1_area = (x2 - x1) * (y2 - y1)\n",
" box2_area = (x2_ - x1_) * (y2_ - y1_)\n",
" union_area = box1_area + box2_area - intersection_area\n",
"\n",
" IoU = intersection_area / union_area\n",
"\n",
" return IoU\n",
"\n",
"def calculate_average_precision(yolo_labels, ground_truth, class_id, confidence_threshold=0.5):\n",
" precision_list = []\n",
" recall_list = []\n",
"\n",
" for i in range(len(yolo_labels)):\n",
" yolo_label = yolo_labels[i]\n",
" target = ground_truth[i]\n",
"\n",
" if yolo_label[5] == class_id:\n",
" if yolo_label[4] >= confidence_threshold:\n",
" true_positives = 0\n",
" false_positives = 0\n",
" total_objects = 0\n",
"\n",
" for t in target:\n",
" if t[4] == class_id:\n",
" total_objects += 1\n",
" iou = calculate_iou(yolo_label[1:5], t[1:5])\n",
" if iou >= 0.5:\n",
" true_positives += 1\n",
"\n",
" false_positives = max(0, total_objects - true_positives)\n",
" precision = true_positives / (true_positives + false_positives)\n",
" recall = true_positives / total_objects\n",
"\n",
" precision_list.append(precision)\n",
" recall_list.append(recall)\n",
"\n",
" interpolated_precision = np.linspace(0, 1, num=101)\n",
" interpolated_recall = np.zeros_like(interpolated_precision)\n",
"\n",
" for i in range(len(precision_list)):\n",
" precision = precision_list[i]\n",
" recall = recall_list[i]\n",
"\n",
" for j in range(len(interpolated_precision)):\n",
" interpolated_recall[j] += np.max(recall[precision >= interpolated_precision[j]])\n",
"\n",
" interpolated_recall /= len(precision_list)\n",
" ap = np.trapz(interpolated_recall, interpolated_precision)\n",
"\n",
" return ap\n",
"\n",
"# Example usage\n",
"if __name__ == \"__main__\":\n",
" # Load YOLO labels and ground truth annotations\n",
" yolo_labels = [...] # List of YOLO labels as strings\n",
" ground_truth = [...] # List of ground truth annotations (list of tensors or labels)\n",
"\n",
" class_id = 0 # Specify the class ID for which AP is calculated\n",
"\n",
" # Parse YOLO labels and convert them to the required format\n",
" yolo_predictions = []\n",
" for label in yolo_labels:\n",
" class_id, confidence, box = parse_yolo_label(label)\n",
" yolo_predictions.append([class_id, box[0], box[1], box[2], box[3], confidence])\n",
"\n",
" # Calculate AP for the specified class\n",
" ap = calculate_average_precision(yolo_predictions, ground_truth, class_id)\n",
"\n",
" print(f\"Average Precision (AP) for class {class_id}: {ap:.4f}\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 390
},
"id": "pgBHlpeoZsfv",
"outputId": "f6010bd1-3f3c-40b4-e7ea-bfe39a7e79db"
},
"execution_count": 128,
"outputs": [
{
"output_type": "error",
"ename": "ValueError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-128-f3780b7fb01e>\u001b[0m in \u001b[0;36m<cell line: 97>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 96\u001b[0m \u001b[0;31m# Calculate mAP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m \u001b[0mmAP\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalculate_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mground_truth_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprediction_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Mean Average Precision (mAP): {mAP}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-128-f3780b7fb01e>\u001b[0m in \u001b[0;36mcalculate_map\u001b[0;34m(ground_truth_labels, prediction_labels, iou_thresholds)\u001b[0m\n\u001b[1;32m 78\u001b[0m \u001b[0map_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 79\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0miou_threshold\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miou_thresholds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m \u001b[0map\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalculate_ap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclass_predictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclass_names\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_ground_truth\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mclass_names\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miou_threshold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 81\u001b[0m \u001b[0map_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0mclass_ap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0map_values\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0map_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-128-f3780b7fb01e>\u001b[0m in \u001b[0;36mcalculate_ap\u001b[0;34m(predictions, ground_truth, iou_threshold)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0mdetected_ground_truth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnum_ground_truth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred_box\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfidence\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 42\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mgt_class_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgt_box\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mground_truth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mclass_id\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mgt_class_id\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 3)"
]
}
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 383
},
"id": "uFWCY_ofvm06",
"outputId": "6ae28580-3d6b-43ae-c36a-8431f9bec8a1"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"No detections in img- (959).txt\n"
]
},
{
"output_type": "error",
"ename": "ValueError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-126-20902da4d479>\u001b[0m in \u001b[0;36m<cell line: 41>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpredicted_line\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mground_truth_line\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredicted_lines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mground_truth_lines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mpredicted_box\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparse_yolo_box\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredicted_line\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0mground_truth_box\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparse_yolo_box\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mground_truth_line\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m \u001b[0;31m# Calculate IoU for the pair of boxes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-126-20902da4d479>\u001b[0m in \u001b[0;36mparse_yolo_box\u001b[0;34m(yolo_box_str)\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mclass_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mconfidence\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Confidence is the last element\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mx_center\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_center\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Exclude confidence\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mclass_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfidence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_center\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_center\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 4, got 3)"
]
}
],
"source": [
"import os\n",
"iou_values=[]\n",
"exp='exp10'\n",
"\n",
"# Function to parse YOLO bounding box format\n",
"def parse_yolo_box(yolo_box_str):\n",
" values = yolo_box_str.strip().split()\n",
" class_id = int(values[0])\n",
" x_center, y_center, width, height = map(float, values[1:])\n",
" return class_id, x_center, y_center, width, height\n",
"\n",
"# Function to calculate IoU\n",
"def calculate_iou(box1, box2):\n",
" x1, y1, w1, h1 = box1\n",
" x2, y2, w2, h2 = box2\n",
"\n",
" x_intersection = max(x1 - w1 / 2, x2 - w2 / 2)\n",
" y_intersection = max(y1 - h1 / 2, y2 - h2 / 2)\n",
" w_intersection = min(x1 + w1 / 2, x2 + w2 / 2) - x_intersection\n",
" h_intersection = min(y1 + h1 / 2, y2 + h2 / 2) - y_intersection\n",
"\n",
" if w_intersection <= 0 or h_intersection <= 0:\n",
" return 0.0\n",
"\n",
" area_intersection = w_intersection * h_intersection\n",
" area_box1 = w1 * h1\n",
" area_box2 = w2 * h2\n",
"\n",
" iou = area_intersection / (area_box1 + area_box2 - area_intersection)\n",
" return iou\n",
"\n",
"# Folder paths for predicted and ground truth label text files\n",
"predicted_folder = '/content/yolov5/runs/detect/'+exp+'/labels'\n",
"ground_truth_folder = '/content/yolov5/val_data/labels'\n",
"\n",
"# Iterate through files in the predicted folder\n",
"for filename in os.listdir(ground_truth_folder):\n",
" if filename.endswith('.txt'):\n",
" predicted_filepath = os.path.join(predicted_folder, filename)\n",
" ground_truth_filepath = os.path.join(ground_truth_folder, filename)\n",
"\n",
" # Check if the corresponding ground truth file exists\n",
" if not os.path.exists(predicted_filepath):\n",
" print(f\"No detections in {filename}\")\n",
" iou_values.append(0)\n",
" continue\n",
"\n",
" with open(predicted_filepath, 'r') as predicted_file, open(ground_truth_filepath, 'r') as ground_truth_file:\n",
" predicted_lines = predicted_file.readlines()\n",
" ground_truth_lines = ground_truth_file.readlines()\n",
"\n",
" # Ensure the same number of lines in both files\n",
" if len(predicted_lines) != len(ground_truth_lines):\n",
" print(f\"Error: Number of boxes in {filename} doesn't match.\")\n",
" continue\n",
"\n",
" # Parse and compare each pair of boxes\n",
" for predicted_line, ground_truth_line in zip(predicted_lines, ground_truth_lines):\n",
" predicted_box = parse_yolo_box(predicted_line)\n",
" ground_truth_box = parse_yolo_box(ground_truth_line)\n",
"\n",
" # Calculate IoU for the pair of boxes\n",
" iou = calculate_iou(predicted_box[1:], ground_truth_box[1:])\n",
" iou_values.append(iou)\n",
" print(f\"IoU for {filename}: {iou}\")\n",
"\n",
"# You can further process the IoU values as needed (e.g., calculate metrics)."
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import precision_recall_curve, average_precision_score\n",
"\n",
"# Assuming you have lists of IoU values and confidence scores\n",
"# iou_values = [...] # List of IoU values for all predictions\n",
"confidence_scores = [...] # List of confidence scores for all predictions\n",
"class_ids = [1] # List of class IDs corresponding to each prediction\n",
"\n",
"# Calculate AP and mAP for each class separately\n",
"ap_dict = {}\n",
"for class_id in set(class_ids):\n",
" # Filter predictions for the current class\n",
" class_iou_values = [iou for iou, cid in zip(iou_values, class_ids) if cid == class_id]\n",
" class_confidence_scores = [score for score, cid in zip(confidence_scores, class_ids) if cid == class_id]\n",
"\n",
" # Compute precision-recall curve\n",
" precision, recall, _ = precision_recall_curve(y_true=class_iou_values, probas_pred=class_confidence_scores)\n",
"\n",
" # Compute Average Precision (AP) for IoU threshold (e.g., 0.5 or 0.75)\n",
" ap = average_precision_score(y_true=class_iou_values, y_score=class_confidence_scores)\n",
"\n",
" ap_dict[class_id] = ap\n",
"\n",
"# Compute Mean Average Precision (mAP) as the average of AP values across all classes\n",
"mAP = sum(ap_dict.values()) / len(ap_dict)\n",
"\n",
"# Print AP values for each class and mAP\n",
"for class_id, ap in ap_dict.items():\n",
" print(f\"Class {class_id} AP: {ap}\")\n",
"print(f\"Mean Average Precision (mAP): {mAP}\")"
],
"metadata": {
"id": "HMQSKhd8Vqmd"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import os\n",
"iou_values=[]\n",
"exp='exp6'\n",
"# Function to parse YOLO bounding box format\n",
"def parse_yolo_box(yolo_box_str):\n",
" values = yolo_box_str.strip().split()\n",
" class_id = int(values[0])\n",
" x_center, y_center, width, height = map(float, values[1:])\n",
" return class_id, x_center, y_center, width, height\n",
"\n",
"# Function to calculate IoU\n",
"def calculate_iou(box1, box2):\n",
" x1, y1, w1, h1 = box1\n",
" x2, y2, w2, h2 = box2\n",
"\n",
" x_intersection = max(x1 - w1 / 2, x2 - w2 / 2)\n",
" y_intersection = max(y1 - h1 / 2, y2 - h2 / 2)\n",
" w_intersection = min(x1 + w1 / 2, x2 + w2 / 2) - x_intersection\n",
" h_intersection = min(y1 + h1 / 2, y2 + h2 / 2) - y_intersection\n",
"\n",
" if w_intersection <= 0 or h_intersection <= 0:\n",
" return 0.0\n",
"\n",
" area_intersection = w_intersection * h_intersection\n",
" area_box1 = w1 * h1\n",
" area_box2 = w2 * h2\n",
"\n",
" iou = area_intersection / (area_box1 + area_box2 - area_intersection)\n",
" return iou\n",
"\n",
"# Folder paths for predicted and ground truth label text files\n",
"predicted_folder = '/content/yolov5/runs/detect/'+exp+'/labels'\n",
"ground_truth_folder = '/content/yolov5/val_data/labels'\n",
"\n",
"# Iterate through files in the predicted folder\n",
"for filename in os.listdir(ground_truth_folder):\n",
" if filename.endswith('.txt'):\n",
" predicted_filepath = os.path.join(predicted_folder, filename)\n",
" ground_truth_filepath = os.path.join(ground_truth_folder, filename)\n",
"\n",
" # Check if the corresponding ground truth file exists\n",
" if not os.path.exists(predicted_filepath):\n",
" print(f\"No detections in {filename}\")\n",
" iou_values.append(0)\n",
" continue\n",
"\n",
" with open(predicted_filepath, 'r') as predicted_file, open(ground_truth_filepath, 'r') as ground_truth_file:\n",
" predicted_lines = predicted_file.readlines()\n",
" ground_truth_lines = ground_truth_file.readlines()\n",
"\n",
" # Ensure the same number of lines in both files\n",
" if len(predicted_lines) != len(ground_truth_lines):\n",
" print(f\"Error: Number of boxes in {filename} doesn't match.\")\n",
" continue\n",
"\n",
" # Parse and compare each pair of boxes\n",
" for predicted_line, ground_truth_line in zip(predicted_lines, ground_truth_lines):\n",
" predicted_box = parse_yolo_box(predicted_line)\n",
" ground_truth_box = parse_yolo_box(ground_truth_line)\n",
"\n",
" # Calculate IoU for the pair of boxes\n",
" iou = calculate_iou(predicted_box[1:], ground_truth_box[1:])\n",
" iou_values.append(iou)\n",
" print(f\"IoU for {filename}: {iou}\")\n",
"\n",
"# You can further process the IoU values as needed (e.g., calculate metrics)."
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Y3pAEEKjhcWg",
"outputId": "8a2cd1de-cca1-46db-cfbc-872661765524"
},
"execution_count": 131,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"No detections in img- (959).txt\n",
"IoU for img- (651).txt: 0.4887215639039998\n",
"No detections in img- (110).txt\n",
"IoU for img- (599).txt: 0.0\n",
"IoU for img- (411).txt: 0.7427149310087174\n",
"No detections in img- (868).txt\n",
"IoU for img- (898).txt: 0.022860280490041747\n",
"IoU for img- (1005).txt: 0.532960154589691\n",
"IoU for img- (591).txt: 0.38659865249670616\n",
"IoU for img- (556).txt: 0.8753892815533982\n",
"No detections in img- (245).txt\n",
"IoU for img- (534).txt: 0.5813753989378394\n",
"No detections in img- (363).txt\n",
"IoU for img- (410).txt: 0.9249389104002552\n",
"IoU for img- (442).txt: 0.9063953000689938\n",
"No detections in img- (338).txt\n",
"IoU for img- (617).txt: 0.9189194499310168\n",
"IoU for img- (1062).txt: 0.7878760697941888\n",
"No detections in img- (258).txt\n",
"IoU for img- (701).txt: 0.9844355583200286\n",
"IoU for img- (941).txt: 0.4879769183350919\n",
"No detections in img- (191).txt\n",
"No detections in img- (962).txt\n",
"No detections in img- (1178).txt\n",
"IoU for img- (812).txt: 0.9701381804891197\n",
"IoU for img- (609).txt: 0.886466286280414\n",
"No detections in img- (1288).txt\n",
"IoU for img- (1108).txt: 0.7631581029933702\n",
"Error: Number of boxes in img- (1106).txt doesn't match.\n",
"IoU for img- (196).txt: 0.19741018082225986\n",
"IoU for img- (532).txt: 0.9232217215233909\n",
"IoU for img- (1102).txt: 0.7793601029376204\n",
"No detections in img- (1234).txt\n",
"No detections in img- (1243).txt\n",
"IoU for img- (120).txt: 0.8804344725897786\n",
"No detections in img- (92).txt\n",
"IoU for img- (800).txt: 0.8675618397506215\n",
"IoU for img- (642).txt: 0.2858406305554167\n",
"IoU for img- (727).txt: 0.8952444666187303\n",
"IoU for img- (433).txt: 0.0\n",
"IoU for img- (708).txt: 0.9283313180929699\n",
"Error: Number of boxes in img- (740).txt doesn't match.\n",
"Error: Number of boxes in img- (1078).txt doesn't match.\n",
"IoU for img- (30).txt: 0.6415113368967482\n",
"IoU for img- (1164).txt: 0.9488718454753895\n",
"Error: Number of boxes in img- (1004).txt doesn't match.\n",
"IoU for img- (1293).txt: 0.35923162127519687\n",
"IoU for img- (985).txt: 0.6078452921203797\n",
"IoU for img- (399).txt: 0.6733887290322246\n",
"Error: Number of boxes in img- (227).txt doesn't match.\n",
"IoU for img- (234).txt: 0.32072358066455015\n",
"IoU for img- (875).txt: 0.07044743713791612\n",
"IoU for img- (792).txt: 0.20047718163354247\n",
"No detections in img- (665).txt\n",
"IoU for img- (817).txt: 0.9225734022247684\n",
"IoU for img- (518).txt: 0.019349842325720023\n",
"IoU for img- (588).txt: 0.7652155112267122\n",
"No detections in img- (71).txt\n",
"IoU for img- (481).txt: 0.9380330834942765\n",
"IoU for img- (274).txt: 0.8937502965905819\n",
"No detections in img- (184).txt\n",
"No detections in img- (1275).txt\n",
"No detections in img- (834).txt\n",
"IoU for img- (330).txt: 0.9195963041664001\n",
"IoU for img- (786).txt: 0.7295257840084274\n",
"IoU for img- (313).txt: 0.0\n",
"No detections in img- (841).txt\n",
"IoU for img- (1155).txt: 0.915237454636582\n",
"IoU for img- (1065).txt: 0.869646333629877\n",
"IoU for img- (892).txt: 0.93303571699382\n",
"IoU for img- (401).txt: 0.773198421556\n",
"No detections in img- (107).txt\n",
"No detections in img- (1237).txt\n",
"IoU for img- (655).txt: 0.6011739435992798\n",
"IoU for img- (222).txt: 0.8853777541566518\n",
"No detections in img- (263).txt\n",
"IoU for img- (144).txt: 0.8704949186424299\n",
"IoU for img- (22).txt: 0.40350714091039924\n",
"No detections in img- (1012).txt\n",
"Error: Number of boxes in img- (453).txt doesn't match.\n",
"No detections in img- (526).txt\n",
"IoU for img- (986).txt: 0.798547765254399\n",
"No detections in img- (1203).txt\n",
"IoU for img- (922).txt: 0.8613944385600705\n",
"IoU for img- (586).txt: 0.7605649443958555\n",
"IoU for img- (938).txt: 0.38735486170648104\n",
"No detections in img- (974).txt\n",
"No detections in img- (94).txt\n",
"IoU for img- (114).txt: 0.7164127521717407\n",
"IoU for img- (809).txt: 0.6666687078209459\n",
"No detections in img- (202).txt\n",
"IoU for img- (1092).txt: 0.24683040607266118\n",
"No detections in img- (1157).txt\n",
"IoU for img- (11).txt: 0.8245869149400411\n",
"IoU for img- (884).txt: 0.8943381112846707\n",
"IoU for img- (480).txt: 0.9377369707890651\n",
"IoU for img- (693).txt: 0.4011862001047048\n",
"IoU for img- (406).txt: 0.7317324716673533\n",
"IoU for img- (750).txt: 0.8254738145242491\n",
"No detections in img- (163).txt\n",
"IoU for img- (553).txt: 0.10089830227534327\n",
"No detections in img- (1286).txt\n",
"IoU for img- (743).txt: 0.30080780006996816\n",
"IoU for img- (849).txt: 0.8925100683626821\n",
"No detections in img- (431).txt\n",
"No detections in img- (973).txt\n",
"IoU for img- (3).txt: 0.6012946818704399\n",
"IoU for img- (1162).txt: 0.1679601997060513\n",
"IoU for img- (675).txt: 0.7016534566459758\n",
"No detections in img- (1153).txt\n",
"IoU for img- (1043).txt: 0.8921568627450978\n",
"Error: Number of boxes in img- (741).txt doesn't match.\n",
"IoU for img- (1131).txt: 0.7985990350887403\n",
"IoU for img- (60).txt: 0.8526320031782256\n",
"No detections in img- (1301).txt\n",
"Error: Number of boxes in img- (81).txt doesn't match.\n",
"IoU for img- (536).txt: 0.7555245747443287\n",
"IoU for img- (471).txt: 0.8620434497159568\n",
"IoU for img- (979).txt: 0.86311204174036\n",
"IoU for img- (73).txt: 0.6850658080837244\n",
"No detections in img- (795).txt\n",
"IoU for img- (904).txt: 0.9234910606122355\n",
"IoU for img- (923).txt: 0.8973286445397797\n",
"IoU for img- (1086).txt: 0.0\n",
"IoU for img- (381).txt: 0.14499551435100616\n",
"IoU for img- (1210).txt: 0.7445076915567166\n",
"IoU for img- (1161).txt: 0.24579775964345865\n",
"IoU for img- (815).txt: 0.9311786122013997\n",
"IoU for img- (1115).txt: 0.8219214086223952\n",
"No detections in img- (194).txt\n",
"IoU for img- (659).txt: 0.6136615143449439\n",
"No detections in img- (791).txt\n",
"IoU for img- (1007).txt: 0.8440911388077988\n",
"IoU for img- (1150).txt: 0.8823903617635906\n",
"No detections in img- (168).txt\n",
"Error: Number of boxes in img- (632).txt doesn't match.\n",
"No detections in img- (90).txt\n",
"IoU for img- (833).txt: 0.6640153446557613\n",
"No detections in img- (984).txt\n",
"IoU for img- (879).txt: 0.7782803981900454\n",
"IoU for img- (467).txt: 0.8647808979198763\n",
"IoU for img- (59).txt: 0.7620695167697085\n",
"IoU for img- (1051).txt: 0.6824635740480698\n",
"IoU for img- (726).txt: 0.9178326377622374\n",
"Error: Number of boxes in img- (46).txt doesn't match.\n",
"Error: Number of boxes in img- (1038).txt doesn't match.\n",
"IoU for img- (861).txt: 0.8861317720549305\n",
"No detections in img- (1132).txt\n",
"No detections in img- (93).txt\n",
"IoU for img- (1284).txt: 0.06149498979434559\n",
"No detections in img- (838).txt\n",
"No detections in img- (299).txt\n",
"IoU for img- (239).txt: 0.8723740634998924\n",
"No detections in img- (1136).txt\n",
"Error: Number of boxes in img- (1103).txt doesn't match.\n",
"Error: Number of boxes in img- (448).txt doesn't match.\n",
"No detections in img- (955).txt\n",
"No detections in img- (1198).txt\n",
"IoU for img- (237).txt: 0.8580355410171262\n",
"IoU for img- (551).txt: 0.6057192098866105\n",
"No detections in img- (516).txt\n",
"No detections in img- (987).txt\n",
"IoU for img- (142).txt: 0.38040983747912466\n",
"No detections in img- (692).txt\n",
"IoU for img- (616).txt: 0.8030957483380539\n",
"IoU for img- (850).txt: 0.6006355203414903\n",
"No detections in img- (1229).txt\n",
"IoU for img- (566).txt: 0.7910126756637312\n",
"No detections in img- (268).txt\n",
"Error: Number of boxes in img- (1021).txt doesn't match.\n",
"IoU for img- (64).txt: 0.6464577056222307\n",
"IoU for img- (775).txt: 0.7311948856695195\n",
"IoU for img- (1080).txt: 0.6330252759914331\n",
"IoU for img- (943).txt: 0.9116362287025321\n",
"IoU for img- (1123).txt: 0.8321454941173383\n",
"IoU for img- (1209).txt: 0.0\n",
"IoU for img- (1165).txt: 0.9570672731712022\n",
"IoU for img- (681).txt: 0.0\n",
"IoU for img- (1309).txt: 0.3863359435247688\n",
"No detections in img- (304).txt\n",
"IoU for img- (581).txt: 0.7718413628883722\n",
"Error: Number of boxes in img- (734).txt doesn't match.\n",
"No detections in img- (1252).txt\n",
"IoU for img- (466).txt: 0.8792042608799865\n",
"IoU for img- (1299).txt: 0.0\n",
"IoU for img- (513).txt: 0.0\n",
"IoU for img- (1116).txt: 0.8593789655188011\n",
"IoU for img- (152).txt: 0.749810823122644\n",
"No detections in img- (972).txt\n",
"No detections in img- (1217).txt\n",
"IoU for img- (1073).txt: 0.0\n",
"IoU for img- (66).txt: 0.4027590198486912\n",
"Error: Number of boxes in img- (27).txt doesn't match.\n",
"IoU for img- (391).txt: 0.0132177161866565\n",
"IoU for img- (630).txt: 0.8867929872419456\n",
"Error: Number of boxes in img- (615).txt doesn't match.\n",
"IoU for img- (787).txt: 0.10422408586429101\n",
"IoU for img- (537).txt: 0.8671619695242793\n",
"No detections in img- (1278).txt\n",
"No detections in img- (137).txt\n",
"No detections in img- (1295).txt\n",
"IoU for img- (319).txt: 0.8110078661932176\n",
"No detections in img- (19).txt\n",
"No detections in img- (111).txt\n",
"IoU for img- (738).txt: 0.8423484931906827\n",
"IoU for img- (494).txt: 0.9437356613172541\n",
"IoU for img- (429).txt: 0.0\n",
"Error: Number of boxes in img- (1010).txt doesn't match.\n",
"IoU for img- (1176).txt: 0.7484198866997687\n",
"No detections in img- (294).txt\n",
"IoU for img- (87).txt: 0.8615698499950805\n",
"Error: Number of boxes in img- (1019).txt doesn't match.\n",
"Error: Number of boxes in img- (1219).txt doesn't match.\n",
"Error: Number of boxes in img- (97).txt doesn't match.\n",
"IoU for img- (543).txt: 0.4425703661820512\n",
"IoU for img- (325).txt: 0.9025962572876264\n",
"IoU for img- (769).txt: 0.6525939251831904\n",
"IoU for img- (1008).txt: 0.8268523573445784\n",
"IoU for img- (443).txt: 0.0\n",
"IoU for img- (1040).txt: 0.0\n",
"IoU for img- (100).txt: 0.29019555843597344\n",
"IoU for img- (487).txt: 0.9284480344515154\n",
"IoU for img- (1101).txt: 0.8282192248138728\n",
"IoU for img- (385).txt: 0.8672731513867348\n",
"IoU for img- (866).txt: 0.5851171443323381\n",
"No detections in img- (1).txt\n",
"No detections in img- (1013).txt\n",
"IoU for img- (844).txt: 0.5879023479822021\n",
"IoU for img- (940).txt: 0.9467538077922909\n",
"IoU for img- (496).txt: 0.8578626207006466\n",
"IoU for img- (44).txt: 0.9261630234722715\n",
"No detections in img- (1188).txt\n",
"IoU for img- (544).txt: 0.017911579628233643\n",
"IoU for img- (497).txt: 0.9326019907751816\n",
"IoU for img- (524).txt: 0.7370956973230147\n",
"No detections in img- (233).txt\n",
"IoU for img- (349).txt: 0.9115930610183463\n",
"IoU for img- (151).txt: 0.6370490506031646\n",
"IoU for img- (582).txt: 0.794730666742385\n",
"No detections in img- (1303).txt\n",
"IoU for img- (1124).txt: 0.8767119481641875\n",
"No detections in img- (958).txt\n",
"Error: Number of boxes in img- (1018).txt doesn't match.\n",
"IoU for img- (13).txt: 0.5833311666692679\n",
"IoU for img- (1133).txt: 0.0\n",
"IoU for img- (1199).txt: 0.0\n",
"No detections in img- (563).txt\n",
"Error: Number of boxes in img- (69).txt doesn't match.\n",
"IoU for img- (863).txt: 0.7440035731990939\n",
"IoU for img- (1061).txt: 0.41509286962532244\n",
"IoU for img- (492).txt: 0.9019610352941176\n",
"IoU for img- (826).txt: 0.9663087688039815\n",
"No detections in img- (1262).txt\n",
"IoU for img- (104).txt: 0.6345645059867322\n",
"No detections in img- (271).txt\n",
"IoU for img- (420).txt: 0.967420758196925\n",
"No detections in img- (954).txt\n",
"IoU for img- (876).txt: 0.658888024367966\n",
"IoU for img- (489).txt: 0.9014047548465874\n",
"IoU for img- (4).txt: 0.7939672027848916\n",
"IoU for img- (523).txt: 0.7586145466958845\n",
"IoU for img- (558).txt: 0.8950130400343678\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"sum(iou_values)/len(iou_values)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CYDxw6QihsJC",
"outputId": "1b60cec4-e622-4152-c5d8-db5e0c73bb42"
},
"execution_count": 135,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.4434957849274505"
]
},
"metadata": {},
"execution_count": 135
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "EwfSQBLTp1WW"
},
"source": [
"## Testing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OhXWhoTrp4IV"
},
"outputs": [],
"source": [
"!unzip \"/content/drive/MyDrive/Auto-WCEBleedGen Challenge Test Dataset.zip\" -d testing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5TBuUKcBJnl3",
"outputId": "727fdf57-37a9-405d-ba83-6a7310604939"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1/1 [==============================] - 0s 34ms/step\n",
"0.9977297\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 35ms/step\n",
"0.8515207\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 31ms/step\n",
"0.92515326\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 41ms/step\n",
"0.9740984\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.9805233\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.8605354\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.9999732\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 28ms/step\n",
"0.999801\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 28ms/step\n",
"0.8055139\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 30ms/step\n",
"0.98124963\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.92984545\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.8526661\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 30ms/step\n",
"0.948134\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.9930453\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.9990656\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 21ms/step\n",
"0.99987066\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.997246\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.5706075\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.999315\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.99969625\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.9983777\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.90778303\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.99999833\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 29ms/step\n",
"0.9999958\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.69866323\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 28ms/step\n",
"0.96337986\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.63207865\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.99996173\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.7214794\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 29ms/step\n",
"0.8979874\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.30320033\n",
"Bleeding\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.9998222\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.99509096\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 32ms/step\n",
"0.8685293\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.999998\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.95601207\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.9914324\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 38ms/step\n",
"0.4853052\n",
"Bleeding\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.9146022\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.99375606\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.03939064\n",
"Bleeding\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.99996483\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.89076555\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.98884714\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.9999497\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.2438441\n",
"Bleeding\n",
"1/1 [==============================] - 0s 40ms/step\n",
"0.42638665\n",
"Bleeding\n",
"1/1 [==============================] - 0s 42ms/step\n",
"0.9649322\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 43ms/step\n",
"0.9766723\n",
"Non Bleeding image\n"
]
}
],
"source": [
"test_dataset1= \"/content/testing/Auto-WCEBleedGen Challenge Test Dataset/Test Dataset 1\"\n",
"bleeding_data1=[]\n",
"non_bleeding_data1=[]\n",
"for file_name in os.listdir(test_dataset1):\n",
" img = tf.keras.preprocessing.image.load_img(os.path.join(test_dataset1,file_name), target_size=(224, 224))\n",
" img = tf.keras.preprocessing.image.img_to_array(img)\n",
" img = tf.expand_dims(img, axis=0)\n",
" classification = resnet_model.predict(img)\n",
" print(classification[0][0])\n",
" if classification[0][0] < 0.5 :\n",
" print(\"Bleeding\")\n",
" bleeding_data1.append(file_name)\n",
" # !python ../yolov5/detect.py --source \"C:\\Programming\\MISAHUB\\Images\\\\b_img- (1008).png\" --weights best(1).pt --save-txt --project .\n",
" else:\n",
" non_bleeding_data1.append(file_name)\n",
" print(\"Non Bleeding image\")\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gk-R5gOoOBjo",
"outputId": "95181e0b-7b50-43bf-b51c-bf77bc8395ea"
},
"outputs": [
{
"data": {
"text/plain": [
"['A0047.png', 'A0038.png', 'A0001.png', 'A0003.png', 'A0040.png']"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bleeding_data1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "barBV9XAMYca",
"outputId": "03eb76aa-08d4-4390-da8e-6683717d4456"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Copied A0047.png to bleeding_data1\n",
"Copied A0038.png to bleeding_data1\n",
"Copied A0001.png to bleeding_data1\n",
"Copied A0003.png to bleeding_data1\n",
"Copied A0040.png to bleeding_data1\n",
"Copying completed.\n"
]
}
],
"source": [
"import os\n",
"import shutil\n",
"\n",
"# List of file names to copy\n",
"# bleeding_data1 = [\"file1.jpg\", \"file2.jpg\", \"file3.jpg\"] # Replace with your file names\n",
"\n",
"# Create the target directory if it doesn't exist\n",
"target_directory = \"bleeding_data1\"\n",
"os.makedirs(target_directory, exist_ok=True)\n",
"\n",
"# Source directory containing the files\n",
"source_directory = \"/content/testing/Auto-WCEBleedGen Challenge Test Dataset/Test Dataset 1\" # Replace with your source directory path\n",
"\n",
"# Iterate through the file names and copy them to the target directory\n",
"for file_name in bleeding_data1:\n",
" source_path = os.path.join(source_directory, file_name)\n",
" target_path = os.path.join(target_directory, file_name)\n",
"\n",
" try:\n",
" shutil.copy(source_path, target_path)\n",
" print(f\"Copied {file_name} to {target_directory}\")\n",
" except FileNotFoundError:\n",
" print(f\"File {file_name} not found in the source directory.\")\n",
"\n",
"print(\"Copying completed.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RQMq-YSEORV2",
"outputId": "d1488ecb-d28d-4039-e2d3-2585655bf139"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mdetect: \u001b[0mweights=['/content/yolov5/runs/train/exp5/weights/best.pt'], source=bleeding_data1, data=yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=True, save_csv=False, save_conf=True, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
"YOLOv5 🚀 v7.0-226-gdd9e338 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
"\n",
"Fusing layers... \n",
"Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs\n",
"image 1/5 /content/bleeding_data1/A0001.png: 640x640 1 bleeding, 11.5ms\n",
"image 2/5 /content/bleeding_data1/A0003.png: 640x640 1 bleeding, 11.6ms\n",
"image 3/5 /content/bleeding_data1/A0038.png: 640x640 (no detections), 13.2ms\n",
"image 4/5 /content/bleeding_data1/A0040.png: 640x640 1 bleeding, 11.5ms\n",
"image 5/5 /content/bleeding_data1/A0047.png: 640x640 2 bleedings, 11.5ms\n",
"Speed: 0.6ms pre-process, 11.9ms inference, 26.2ms NMS per image at shape (1, 3, 640, 640)\n",
"Results saved to \u001b[1myolov5/runs/detect/exp7\u001b[0m\n",
"4 labels saved to yolov5/runs/detect/exp7/labels\n"
]
}
],
"source": [
"!python yolov5/detect.py --weights /content/yolov5/runs/train/exp5/weights/best.pt --source bleeding_data1 --save-txt --save-conf"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gJgZcnXOPQ7_"
},
"source": [
"# Test 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "JdgClnBVPSst",
"outputId": "72fded5e-a9d1-4e98-b691-1f2bb194e887"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1/1 [==============================] - 0s 24ms/step\n",
"0.5605878\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 21ms/step\n",
"0.99996054\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.9961612\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.051810574\n",
"Bleeding\n",
"1/1 [==============================] - 0s 21ms/step\n",
"0.19632779\n",
"Bleeding\n",
"1/1 [==============================] - 0s 21ms/step\n",
"0.86236984\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.20356205\n",
"Bleeding\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.5268939\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.99114436\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.33491045\n",
"Bleeding\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.055617135\n",
"Bleeding\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.29319757\n",
"Bleeding\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.95742375\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.487038\n",
"Bleeding\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.64885217\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.06868495\n",
"Bleeding\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.077232465\n",
"Bleeding\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.9325214\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 31ms/step\n",
"0.3355851\n",
"Bleeding\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.89997154\n",
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"0.03563356\n",
"Bleeding\n",
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"0.94455063\n",
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"0.4726199\n",
"Bleeding\n",
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"0.12765132\n",
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"0.0081175035\n",
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"0.4279229\n",
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"0.9990884\n",
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"0.22291282\n",
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"0.29415318\n",
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"0.99594116\n",
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"0.14639784\n",
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"0.2645852\n",
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"0.6515931\n",
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"0.4621543\n",
"Bleeding\n",
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"0.9363225\n",
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"0.9992906\n",
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"0.59403723\n",
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"0.65177524\n",
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"0.8111264\n",
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"0.42861435\n",
"Bleeding\n",
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"0.025623787\n",
"Bleeding\n",
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"0.0021354132\n",
"Bleeding\n",
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"0.9447101\n",
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"0.040198427\n",
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"0.98983777\n",
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"0.41010383\n",
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"0.99720436\n",
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"0.42758915\n",
"Bleeding\n",
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"0.56057537\n",
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"0.90550005\n",
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"0.053540424\n",
"Bleeding\n",
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"0.993775\n",
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"0.7352701\n",
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"0.24123219\n",
"Bleeding\n",
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"Non Bleeding image\n",
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"0.018745493\n",
"Bleeding\n",
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"0.34146225\n",
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"Bleeding\n",
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"0.9846639\n",
"Non Bleeding image\n",
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"0.9855494\n",
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"0.9999865\n",
"Non Bleeding image\n",
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"0.07155137\n",
"Bleeding\n",
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"0.9925028\n",
"Non Bleeding image\n",
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"0.3055032\n",
"Bleeding\n",
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"0.97397566\n",
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"0.7733766\n",
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"0.20444413\n",
"Bleeding\n",
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"Bleeding\n",
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"0.6223611\n",
"Non Bleeding image\n",
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"0.014372686\n",
"Bleeding\n",
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"0.80387974\n",
"Non Bleeding image\n",
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"0.99945706\n",
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"0.060892094\n",
"Bleeding\n",
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"0.98514485\n",
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"0.5825722\n",
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"0.9554369\n",
"Non Bleeding image\n",
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"0.9664532\n",
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"0.39078936\n",
"Bleeding\n",
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"Bleeding\n",
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"0.98663133\n",
"Non Bleeding image\n",
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"0.044601314\n",
"Bleeding\n",
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"Non Bleeding image\n",
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"0.15662801\n",
"Bleeding\n",
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"Non Bleeding image\n",
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"0.36567822\n",
"Bleeding\n",
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"Bleeding\n",
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"Non Bleeding image\n",
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"0.023672018\n",
"Bleeding\n",
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"Bleeding\n",
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"Non Bleeding image\n",
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"0.07468692\n",
"Bleeding\n",
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"Non Bleeding image\n",
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"0.02200372\n",
"Bleeding\n",
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"0.09272686\n",
"Bleeding\n",
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"0.58073366\n",
"Non Bleeding image\n",
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"0.99798405\n",
"Non Bleeding image\n",
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"0.086925894\n",
"Bleeding\n",
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"Non Bleeding image\n",
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"0.09507973\n",
"Bleeding\n",
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"Non Bleeding image\n",
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"0.038412914\n",
"Bleeding\n",
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"Bleeding\n",
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"0.85727173\n",
"Non Bleeding image\n",
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"0.39593342\n",
"Bleeding\n",
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"0.11356054\n",
"Bleeding\n",
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"0.7705322\n",
"Non Bleeding image\n",
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"0.9936918\n",
"Non Bleeding image\n",
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"0.8623572\n",
"Non Bleeding image\n",
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"0.9674375\n",
"Non Bleeding image\n",
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"0.03608216\n",
"Bleeding\n",
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"Bleeding\n",
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"0.9890265\n",
"Non Bleeding image\n",
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"0.18403886\n",
"Bleeding\n",
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"0.98901445\n",
"Non Bleeding image\n",
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"0.53299445\n",
"Non Bleeding image\n",
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"0.46959907\n",
"Bleeding\n",
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"0.9901715\n",
"Non Bleeding image\n",
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"0.38378683\n",
"Bleeding\n",
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"Non Bleeding image\n",
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"0.98982304\n",
"Non Bleeding image\n",
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"Bleeding\n",
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"0.96990156\n",
"Non Bleeding image\n",
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"0.9973411\n",
"Non Bleeding image\n",
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"0.007133114\n",
"Bleeding\n",
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"Non Bleeding image\n",
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"0.016059335\n",
"Bleeding\n",
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"Bleeding\n",
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"Bleeding\n",
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"Bleeding\n",
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"0.99517006\n",
"Non Bleeding image\n",
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"0.083307974\n",
"Bleeding\n",
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"0.9126365\n",
"Non Bleeding image\n",
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"0.58374745\n",
"Non Bleeding image\n",
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"0.5279575\n",
"Non Bleeding image\n",
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"0.007845137\n",
"Bleeding\n",
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"Bleeding\n",
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"Bleeding\n",
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"0.77661175\n",
"Non Bleeding image\n",
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"0.30023697\n",
"Bleeding\n",
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"Bleeding\n",
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"Bleeding\n",
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"Bleeding\n",
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"Non Bleeding image\n",
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"0.5048378\n",
"Non Bleeding image\n",
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"Bleeding\n",
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"Bleeding\n",
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"Bleeding\n",
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"Non Bleeding image\n",
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"0.34586453\n",
"Bleeding\n",
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"0.38283733\n",
"Bleeding\n",
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"0.9144521\n",
"Non Bleeding image\n",
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"0.46346894\n",
"Bleeding\n",
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"Non Bleeding image\n",
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"0.49876982\n",
"Bleeding\n",
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"Bleeding\n",
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"Bleeding\n",
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"Bleeding\n",
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"0.99127454\n",
"Non Bleeding image\n",
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"0.7205927\n",
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"0.9280793\n",
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"0.47044963\n",
"Bleeding\n",
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"0.39687505\n",
"Bleeding\n",
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"0.99953175\n",
"Non Bleeding image\n",
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"0.56057537\n",
"Non Bleeding image\n",
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"0.9993673\n",
"Non Bleeding image\n",
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"0.34091312\n",
"Bleeding\n",
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"0.06852892\n",
"Bleeding\n",
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"0.9971097\n",
"Non Bleeding image\n",
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"0.059021514\n",
"Bleeding\n",
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"0.9307266\n",
"Non Bleeding image\n",
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"0.27508995\n",
"Bleeding\n",
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"0.9335143\n",
"Non Bleeding image\n",
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"0.004722359\n",
"Bleeding\n",
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"Bleeding\n",
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"0.09992947\n",
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"0.036139946\n",
"Bleeding\n",
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"0.95781904\n",
"Non Bleeding image\n",
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"0.77569\n",
"Non Bleeding image\n",
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"0.013102379\n",
"Bleeding\n",
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"0.5852222\n",
"Non Bleeding image\n",
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"0.00533471\n",
"Bleeding\n",
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"0.8834713\n",
"Non Bleeding image\n",
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"0.4424642\n",
"Bleeding\n",
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"Bleeding\n",
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"0.5828168\n",
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"0.8898383\n",
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"1/1 [==============================] - 0s 23ms/step\n",
"0.9999968\n",
"Non Bleeding image\n",
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"0.97021425\n",
"Non Bleeding image\n",
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"0.039899923\n",
"Bleeding\n",
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"0.29142547\n",
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"0.039329693\n",
"Bleeding\n",
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"0.65097094\n",
"Non Bleeding image\n",
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"0.37215835\n",
"Bleeding\n",
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"0.8486823\n",
"Non Bleeding image\n",
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"0.12999254\n",
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"0.9996904\n",
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"0.9921902\n",
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"0.041318446\n",
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"0.8057265\n",
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"0.055917\n",
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"0.01914925\n",
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"0.9997911\n",
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"0.96826655\n",
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"0.97755504\n",
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"0.95494825\n",
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"0.21939597\n",
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"0.00839033\n",
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"0.6508174\n",
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"0.2436571\n",
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"0.016311992\n",
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"0.9990158\n",
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"0.15050174\n",
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"0.01603603\n",
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"0.89657265\n",
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"0.999044\n",
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"0.3991239\n",
"Bleeding\n",
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"0.9999615\n",
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"0.14374761\n",
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"0.07565498\n",
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"0.42310858\n",
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"0.983959\n",
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"0.3847161\n",
"Bleeding\n",
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"0.62386197\n",
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"1/1 [==============================] - 0s 23ms/step\n",
"0.4327677\n",
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"0.8608432\n",
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"0.9955901\n",
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"0.8529132\n",
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"1/1 [==============================] - 0s 24ms/step\n",
"0.11016049\n",
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"0.11641989\n",
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"0.1138642\n",
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"0.116914816\n",
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"0.78616995\n",
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"0.8589056\n",
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"0.91740376\n",
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"0.50828505\n",
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"1/1 [==============================] - 0s 22ms/step\n",
"0.16638629\n",
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"0.028084472\n",
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"0.9949757\n",
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"1/1 [==============================] - 0s 23ms/step\n",
"0.99923456\n",
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"1/1 [==============================] - 0s 21ms/step\n",
"0.7580741\n",
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"1/1 [==============================] - 0s 25ms/step\n",
"0.46070054\n",
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"0.20574522\n",
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"0.99220324\n",
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"0.9921022\n",
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"1/1 [==============================] - 0s 25ms/step\n",
"0.87830925\n",
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"0.17369233\n",
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"0.023573246\n",
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"0.12952445\n",
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"0.8787401\n",
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"0.07522349\n",
"Bleeding\n",
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"0.86174816\n",
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"0.24825844\n",
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"0.04823582\n",
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"0.9987459\n",
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"1/1 [==============================] - 0s 27ms/step\n",
"0.91836697\n",
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"1/1 [==============================] - 0s 24ms/step\n",
"0.5881231\n",
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"0.064745106\n",
"Bleeding\n",
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"0.975933\n",
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"0.26588613\n",
"Bleeding\n",
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"0.99948716\n",
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"0.99965894\n",
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"0.03683928\n",
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"0.22040784\n",
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"0.48318312\n",
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"0.114446916\n",
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"0.06822713\n",
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"0.072613195\n",
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"0.8125024\n",
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"0.4937088\n",
"Bleeding\n",
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"0.9793649\n",
"Non Bleeding image\n",
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"0.004418173\n",
"Bleeding\n",
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"0.36167374\n",
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"0.8650524\n",
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"0.9900614\n",
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"0.24540344\n",
"Bleeding\n",
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"0.52646315\n",
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"0.95794886\n",
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"0.852711\n",
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"0.0712205\n",
"Bleeding\n",
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"0.09830097\n",
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"0.06294057\n",
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"0.9932114\n",
"Non Bleeding image\n",
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"0.9952571\n",
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"1/1 [==============================] - 0s 24ms/step\n",
"0.7363253\n",
"Non Bleeding image\n",
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"0.08280252\n",
"Bleeding\n",
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"0.28837234\n",
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"0.5775551\n",
"Non Bleeding image\n",
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"0.8223652\n",
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"0.0312268\n",
"Bleeding\n",
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"0.03284856\n",
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"0.06157658\n",
"Bleeding\n",
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"0.8561401\n",
"Non Bleeding image\n",
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"0.98450875\n",
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"0.48667428\n",
"Bleeding\n",
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"0.95838755\n",
"Non Bleeding image\n",
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"0.23700789\n",
"Bleeding\n",
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"0.8594345\n",
"Non Bleeding image\n",
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"0.09192464\n",
"Bleeding\n",
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"0.99256223\n",
"Non Bleeding image\n",
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"0.9709953\n",
"Non Bleeding image\n",
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"0.9699794\n",
"Non Bleeding image\n",
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"0.028367665\n",
"Bleeding\n",
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"0.25610572\n",
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"0.32399133\n",
"Bleeding\n",
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"0.86091477\n",
"Non Bleeding image\n",
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"0.09811134\n",
"Bleeding\n",
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"0.9527042\n",
"Non Bleeding image\n",
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"0.94290936\n",
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"1/1 [==============================] - 0s 24ms/step\n",
"0.98703617\n",
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"1/1 [==============================] - 0s 23ms/step\n",
"0.026228547\n",
"Bleeding\n",
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"0.0034220715\n",
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"0.034461606\n",
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"0.3691098\n",
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"0.07243831\n",
"Bleeding\n",
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"0.9968714\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 28ms/step\n",
"0.99080956\n",
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"1/1 [==============================] - 0s 25ms/step\n",
"0.6920432\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.9999968\n",
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"1/1 [==============================] - 0s 24ms/step\n",
"0.8179151\n",
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"1/1 [==============================] - 0s 24ms/step\n",
"0.8268867\n",
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"1/1 [==============================] - 0s 23ms/step\n",
"0.62101775\n",
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"0.04435727\n",
"Bleeding\n",
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"0.5826946\n",
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"0.99987864\n",
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"1/1 [==============================] - 0s 26ms/step\n",
"0.9955493\n",
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"0.06595851\n",
"Bleeding\n",
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"0.18064326\n",
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"0.0241994\n",
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"0.31707963\n",
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"0.05512962\n",
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"0.30069518\n",
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"0.040616\n",
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"0.15748405\n",
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"0.04904495\n",
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"0.13615116\n",
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"0.022539677\n",
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"0.0048993365\n",
"Bleeding\n",
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"0.56354666\n",
"Non Bleeding image\n",
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"0.9237803\n",
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"1/1 [==============================] - 0s 24ms/step\n",
"0.03901896\n",
"Bleeding\n",
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"0.68688136\n",
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"1/1 [==============================] - 0s 23ms/step\n",
"0.68754905\n",
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"1/1 [==============================] - 0s 27ms/step\n",
"0.9028716\n",
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"1/1 [==============================] - 0s 31ms/step\n",
"0.9984303\n",
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"1/1 [==============================] - 0s 29ms/step\n",
"0.66192263\n",
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"1/1 [==============================] - 0s 26ms/step\n",
"0.735519\n",
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"1/1 [==============================] - 0s 26ms/step\n",
"0.025862787\n",
"Bleeding\n",
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"0.21339308\n",
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"0.014863929\n",
"Bleeding\n",
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"0.88138366\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 39ms/step\n",
"0.3617797\n",
"Bleeding\n",
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"0.57920694\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 34ms/step\n",
"0.050192606\n",
"Bleeding\n",
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"0.008171503\n",
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"0.0222982\n",
"Bleeding\n",
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"0.6115917\n",
"Non Bleeding image\n",
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"0.26202446\n",
"Bleeding\n",
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"0.11289205\n",
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"0.1007013\n",
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"0.30790153\n",
"Bleeding\n",
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"0.8591328\n",
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"0.9070546\n",
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"0.03555674\n",
"Bleeding\n",
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"0.89814377\n",
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"1/1 [==============================] - 0s 52ms/step\n",
"0.94640017\n",
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"0.044773612\n",
"Bleeding\n",
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"0.26780084\n",
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"0.08125934\n",
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"0.22456944\n",
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"0.12668778\n",
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"0.06452438\n",
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"0.32984105\n",
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"0.32690945\n",
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"0.5767742\n",
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"1/1 [==============================] - 0s 38ms/step\n",
"0.6541582\n",
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"1/1 [==============================] - 0s 37ms/step\n",
"0.028459756\n",
"Bleeding\n",
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"0.98065394\n",
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"0.88673246\n",
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"0.93313664\n",
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"1/1 [==============================] - 0s 40ms/step\n",
"0.789146\n",
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"1/1 [==============================] - 0s 42ms/step\n",
"0.36955562\n",
"Bleeding\n",
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"0.071797736\n",
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"0.2333727\n",
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"0.35576797\n",
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"0.7533132\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.99999607\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.061472397\n",
"Bleeding\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.9854833\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.6375913\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.08915358\n",
"Bleeding\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.0039516278\n",
"Bleeding\n",
"1/1 [==============================] - 0s 21ms/step\n",
"0.8845501\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.68500894\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.18455432\n",
"Bleeding\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.38581923\n",
"Bleeding\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.5935552\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.7471582\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 21ms/step\n",
"0.9587662\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.49277\n",
"Bleeding\n",
"1/1 [==============================] - 0s 30ms/step\n",
"0.080288604\n",
"Bleeding\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.023573246\n",
"Bleeding\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.16818093\n",
"Bleeding\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.9999585\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.2610853\n",
"Bleeding\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.086549394\n",
"Bleeding\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.088664815\n",
"Bleeding\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.9987783\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.63011384\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 35ms/step\n",
"0.99960583\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.9923287\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.03059042\n",
"Bleeding\n",
"1/1 [==============================] - 0s 21ms/step\n",
"0.46679726\n",
"Bleeding\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.03630671\n",
"Bleeding\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.009788807\n",
"Bleeding\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.009441804\n",
"Bleeding\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.04920271\n",
"Bleeding\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.21291497\n",
"Bleeding\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.0077819014\n",
"Bleeding\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.0067618745\n",
"Bleeding\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.99642015\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.91305184\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.000718205\n",
"Bleeding\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.35417944\n",
"Bleeding\n",
"1/1 [==============================] - 0s 30ms/step\n",
"0.37702614\n",
"Bleeding\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.9945886\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.97188574\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 28ms/step\n",
"0.45433438\n",
"Bleeding\n",
"1/1 [==============================] - 0s 31ms/step\n",
"0.9880056\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 21ms/step\n",
"0.15861098\n",
"Bleeding\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.7767071\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.024805523\n",
"Bleeding\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.4955125\n",
"Bleeding\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.8463903\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.108366415\n",
"Bleeding\n",
"1/1 [==============================] - 0s 30ms/step\n",
"0.9852406\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.9299041\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.5709623\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.93336034\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.86739033\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.1906643\n",
"Bleeding\n",
"1/1 [==============================] - 0s 28ms/step\n",
"0.021353606\n",
"Bleeding\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.20444413\n",
"Bleeding\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.052156515\n",
"Bleeding\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.004258491\n",
"Bleeding\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.96622247\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.0041985298\n",
"Bleeding\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.025807979\n",
"Bleeding\n",
"1/1 [==============================] - 0s 32ms/step\n",
"0.5605708\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.4682995\n",
"Bleeding\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.97835255\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.9195409\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.98632413\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.030250832\n",
"Bleeding\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.8719402\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.4241814\n",
"Bleeding\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.6784961\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.87219703\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 26ms/step\n",
"0.0017808658\n",
"Bleeding\n",
"1/1 [==============================] - 0s 27ms/step\n",
"0.00084973895\n",
"Bleeding\n",
"1/1 [==============================] - 0s 31ms/step\n",
"0.5628342\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.02979672\n",
"Bleeding\n",
"1/1 [==============================] - 0s 28ms/step\n",
"0.08375641\n",
"Bleeding\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.3985425\n",
"Bleeding\n",
"1/1 [==============================] - 0s 24ms/step\n",
"0.9417921\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.98973525\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 30ms/step\n",
"0.06585294\n",
"Bleeding\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.6444959\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 25ms/step\n",
"0.9947003\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 22ms/step\n",
"0.1290291\n",
"Bleeding\n",
"1/1 [==============================] - 0s 28ms/step\n",
"0.8607533\n",
"Non Bleeding image\n",
"1/1 [==============================] - 0s 23ms/step\n",
"0.8575955\n",
"Non Bleeding image\n"
]
}
],
"source": [
"test_dataset2= \"/content/testing/Auto-WCEBleedGen Challenge Test Dataset/Test Dataset 2\"\n",
"bleeding_data2=[]\n",
"non_bleeding_data2=[]\n",
"for file_name in os.listdir(test_dataset2):\n",
" img = tf.keras.preprocessing.image.load_img(os.path.join(test_dataset2,file_name), target_size=(224, 224))\n",
" img = tf.keras.preprocessing.image.img_to_array(img)\n",
" img = tf.expand_dims(img, axis=0)\n",
" classification = resnet_model.predict(img)\n",
" print(classification[0][0])\n",
" if classification[0][0] < 0.5 :\n",
" print(\"Bleeding\")\n",
" bleeding_data2.append(file_name)\n",
" # !python ../yolov5/detect.py --source \"C:\\Programming\\MISAHUB\\Images\\\\b_img- (1008).png\" --weights best(1).pt --save-txt --project .\n",
" else:\n",
" non_bleeding_data2.append(file_name)\n",
" print(\"Non Bleeding image\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "j0YOe-dtPd_l",
"outputId": "db8354d2-b069-4249-c4f7-72f21a538fc8"
},
"outputs": [
{
"data": {
"text/plain": [
"283"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(bleeding_data2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hcztPFq2Ph3y",
"outputId": "0a214c79-3ea8-4101-a885-8a3bbd8e84d5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Copied A0157.png to bleeding_data2\n",
"Copied A0404.png to bleeding_data2\n",
"Copied A0177.png to bleeding_data2\n",
"Copied A0167.png to bleeding_data2\n",
"Copied A0335.png to bleeding_data2\n",
"Copied A0200.png to bleeding_data2\n",
"Copied A0103.png to bleeding_data2\n",
"Copied A0286.png to bleeding_data2\n",
"Copied A0189.png to bleeding_data2\n",
"Copied A0342.png to bleeding_data2\n",
"Copied A0257.png to bleeding_data2\n",
"Copied A0553.png to bleeding_data2\n",
"Copied A0497.png to bleeding_data2\n",
"Copied A0141.png to bleeding_data2\n",
"Copied A0379.png to bleeding_data2\n",
"Copied A0409.png to bleeding_data2\n",
"Copied A0468.png to bleeding_data2\n",
"Copied A0250.png to bleeding_data2\n",
"Copied A0130.png to bleeding_data2\n",
"Copied A0248.png to bleeding_data2\n",
"Copied A0268.png to bleeding_data2\n",
"Copied A0234.png to bleeding_data2\n",
"Copied A0161.png to bleeding_data2\n",
"Copied A0522.png to bleeding_data2\n",
"Copied A0185.png to bleeding_data2\n",
"Copied A0291.png to bleeding_data2\n",
"Copied A0134.png to bleeding_data2\n",
"Copied A0365.png to bleeding_data2\n",
"Copied A0355.png to bleeding_data2\n",
"Copied A0368.png to bleeding_data2\n",
"Copied A0191.png to bleeding_data2\n",
"Copied A0385.png to bleeding_data2\n",
"Copied A0362.png to bleeding_data2\n",
"Copied A0475.png to bleeding_data2\n",
"Copied A0453.png to bleeding_data2\n",
"Copied A0180.png to bleeding_data2\n",
"Copied A0082.png to bleeding_data2\n",
"Copied A0262.png to bleeding_data2\n",
"Copied A0231.png to bleeding_data2\n",
"Copied A0469.png to bleeding_data2\n",
"Copied A0327.png to bleeding_data2\n",
"Copied A0446.png to bleeding_data2\n",
"Copied A0099.png to bleeding_data2\n",
"Copied A0172.png to bleeding_data2\n",
"Copied A0275.png to bleeding_data2\n",
"Copied A0179.png to bleeding_data2\n",
"Copied A0267.png to bleeding_data2\n",
"Copied A0499.png to bleeding_data2\n",
"Copied A0171.png to bleeding_data2\n",
"Copied A0121.png to bleeding_data2\n",
"Copied A0162.png to bleeding_data2\n",
"Copied A0096.png to bleeding_data2\n",
"Copied A0353.png to bleeding_data2\n",
"Copied A0473.png to bleeding_data2\n",
"Copied A0158.png to bleeding_data2\n",
"Copied A0361.png to bleeding_data2\n",
"Copied A0520.png to bleeding_data2\n",
"Copied A0470.png to bleeding_data2\n",
"Copied A0406.png to bleeding_data2\n",
"Copying completed.\n"
]
}
],
"source": [
"import os\n",
"import shutil\n",
"\n",
"# List of file names to copy\n",
"# bleeding_data1 = [\"file1.jpg\", \"file2.jpg\", \"file3.jpg\"] # Replace with your file names\n",
"\n",
"# Create the target directory if it doesn't exist\n",
"target_directory = \"bleeding_data2\"\n",
"os.makedirs(target_directory, exist_ok=True)\n",
"\n",
"# Source directory containing the files\n",
"source_directory = \"/content/testing/Auto-WCEBleedGen Challenge Test Dataset/Test Dataset 2\" # Replace with your source directory path\n",
"\n",
"# Iterate through the file names and copy them to the target directory\n",
"for file_name in bleeding_data2:\n",
" source_path = os.path.join(source_directory, file_name)\n",
" target_path = os.path.join(target_directory, file_name)\n",
"\n",
" try:\n",
" shutil.copy(source_path, target_path)\n",
" print(f\"Copied {file_name} to {target_directory}\")\n",
" except FileNotFoundError:\n",
" print(f\"File {file_name} not found in the source directory.\")\n",
"\n",
"print(\"Copying completed.\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "hA5fVqTJPh2s",
"outputId": "ffd6ef59-40e0-4305-e356-163f97ff0ce3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34m\u001b[1mdetect: \u001b[0mweights=['/content/yolov5/runs/train/exp5/weights/best.pt'], source=bleeding_data2, data=yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=True, save_csv=False, save_conf=True, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n",
"YOLOv5 🚀 v7.0-226-gdd9e338 Python-3.10.12 torch-2.0.1+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
"\n",
"Fusing layers... \n",
"Model summary: 157 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs\n",
"image 1/283 /content/bleeding_data2/A0050.png: 640x640 (no detections), 11.5ms\n",
"image 2/283 /content/bleeding_data2/A0062.png: 640x640 (no detections), 11.5ms\n",
"image 3/283 /content/bleeding_data2/A0063.png: 640x640 (no detections), 11.5ms\n",
"image 4/283 /content/bleeding_data2/A0069.png: 640x640 (no detections), 11.5ms\n",
"image 5/283 /content/bleeding_data2/A0074.png: 640x640 (no detections), 11.5ms\n",
"image 6/283 /content/bleeding_data2/A0081.png: 640x640 1 bleeding, 11.5ms\n",
"image 7/283 /content/bleeding_data2/A0082.png: 640x640 (no detections), 11.6ms\n",
"image 8/283 /content/bleeding_data2/A0086.png: 640x640 (no detections), 11.5ms\n",
"image 9/283 /content/bleeding_data2/A0087.png: 640x640 (no detections), 11.5ms\n",
"image 10/283 /content/bleeding_data2/A0089.png: 640x640 (no detections), 11.5ms\n",
"image 11/283 /content/bleeding_data2/A0090.png: 640x640 (no detections), 11.5ms\n",
"image 12/283 /content/bleeding_data2/A0092.png: 640x640 (no detections), 11.5ms\n",
"image 13/283 /content/bleeding_data2/A0093.png: 640x640 (no detections), 11.5ms\n",
"image 14/283 /content/bleeding_data2/A0094.png: 640x640 (no detections), 11.5ms\n",
"image 15/283 /content/bleeding_data2/A0095.png: 640x640 (no detections), 11.5ms\n",
"image 16/283 /content/bleeding_data2/A0096.png: 640x640 (no detections), 11.5ms\n",
"image 17/283 /content/bleeding_data2/A0098.png: 640x640 (no detections), 10.8ms\n",
"image 18/283 /content/bleeding_data2/A0099.png: 640x640 (no detections), 8.2ms\n",
"image 19/283 /content/bleeding_data2/A0100.png: 640x640 (no detections), 8.2ms\n",
"image 20/283 /content/bleeding_data2/A0102.png: 640x640 (no detections), 8.2ms\n",
"image 21/283 /content/bleeding_data2/A0103.png: 640x640 (no detections), 8.2ms\n",
"image 22/283 /content/bleeding_data2/A0105.png: 640x640 (no detections), 8.2ms\n",
"image 23/283 /content/bleeding_data2/A0109.png: 640x640 (no detections), 8.2ms\n",
"image 24/283 /content/bleeding_data2/A0111.png: 640x640 (no detections), 8.2ms\n",
"image 25/283 /content/bleeding_data2/A0113.png: 640x640 (no detections), 8.1ms\n",
"image 26/283 /content/bleeding_data2/A0115.png: 640x640 (no detections), 8.1ms\n",
"image 27/283 /content/bleeding_data2/A0116.png: 640x640 (no detections), 8.1ms\n",
"image 28/283 /content/bleeding_data2/A0120.png: 640x640 (no detections), 8.2ms\n",
"image 29/283 /content/bleeding_data2/A0121.png: 640x640 (no detections), 8.2ms\n",
"image 30/283 /content/bleeding_data2/A0124.png: 640x640 (no detections), 8.2ms\n",
"image 31/283 /content/bleeding_data2/A0126.png: 640x640 1 bleeding, 6.5ms\n",
"image 32/283 /content/bleeding_data2/A0127.png: 640x640 (no detections), 6.5ms\n",
"image 33/283 /content/bleeding_data2/A0129.png: 640x640 1 bleeding, 6.5ms\n",
"image 34/283 /content/bleeding_data2/A0130.png: 640x640 (no detections), 6.6ms\n",
"image 35/283 /content/bleeding_data2/A0132.png: 640x640 (no detections), 6.5ms\n",
"image 36/283 /content/bleeding_data2/A0133.png: 640x640 (no detections), 6.5ms\n",
"image 37/283 /content/bleeding_data2/A0134.png: 640x640 (no detections), 6.5ms\n",
"image 38/283 /content/bleeding_data2/A0135.png: 640x640 (no detections), 6.3ms\n",
"image 39/283 /content/bleeding_data2/A0136.png: 640x640 (no detections), 6.3ms\n",
"image 40/283 /content/bleeding_data2/A0137.png: 640x640 (no detections), 9.0ms\n",
"image 41/283 /content/bleeding_data2/A0138.png: 640x640 (no detections), 7.8ms\n",
"image 42/283 /content/bleeding_data2/A0139.png: 640x640 (no detections), 6.3ms\n",
"image 43/283 /content/bleeding_data2/A0140.png: 640x640 (no detections), 6.3ms\n",
"image 44/283 /content/bleeding_data2/A0141.png: 640x640 (no detections), 6.3ms\n",
"image 45/283 /content/bleeding_data2/A0142.png: 640x640 (no detections), 6.3ms\n",
"image 46/283 /content/bleeding_data2/A0143.png: 640x640 (no detections), 6.3ms\n",
"image 47/283 /content/bleeding_data2/A0144.png: 640x640 1 bleeding, 6.4ms\n",
"image 48/283 /content/bleeding_data2/A0145.png: 640x640 1 bleeding, 6.3ms\n",
"image 49/283 /content/bleeding_data2/A0146.png: 640x640 (no detections), 6.5ms\n",
"image 50/283 /content/bleeding_data2/A0148.png: 640x640 1 bleeding, 5.8ms\n",
"image 51/283 /content/bleeding_data2/A0149.png: 640x640 (no detections), 5.8ms\n",
"image 52/283 /content/bleeding_data2/A0150.png: 640x640 (no detections), 5.8ms\n",
"image 53/283 /content/bleeding_data2/A0151.png: 640x640 (no detections), 6.3ms\n",
"image 54/283 /content/bleeding_data2/A0152.png: 640x640 (no detections), 5.8ms\n",
"image 55/283 /content/bleeding_data2/A0153.png: 640x640 (no detections), 5.8ms\n",
"image 56/283 /content/bleeding_data2/A0154.png: 640x640 (no detections), 5.8ms\n",
"image 57/283 /content/bleeding_data2/A0155.png: 640x640 (no detections), 5.8ms\n",
"image 58/283 /content/bleeding_data2/A0156.png: 640x640 (no detections), 5.8ms\n",
"image 59/283 /content/bleeding_data2/A0157.png: 640x640 (no detections), 5.8ms\n",
"image 60/283 /content/bleeding_data2/A0158.png: 640x640 (no detections), 5.7ms\n",
"image 61/283 /content/bleeding_data2/A0159.png: 640x640 (no detections), 5.8ms\n",
"image 62/283 /content/bleeding_data2/A0160.png: 640x640 1 bleeding, 5.7ms\n",
"image 63/283 /content/bleeding_data2/A0161.png: 640x640 (no detections), 7.7ms\n",
"image 64/283 /content/bleeding_data2/A0162.png: 640x640 (no detections), 5.7ms\n",
"image 65/283 /content/bleeding_data2/A0163.png: 640x640 (no detections), 5.7ms\n",
"image 66/283 /content/bleeding_data2/A0164.png: 640x640 (no detections), 5.8ms\n",
"image 67/283 /content/bleeding_data2/A0166.png: 640x640 (no detections), 5.9ms\n",
"image 68/283 /content/bleeding_data2/A0167.png: 640x640 (no detections), 5.7ms\n",
"image 69/283 /content/bleeding_data2/A0168.png: 640x640 (no detections), 5.7ms\n",
"image 70/283 /content/bleeding_data2/A0169.png: 640x640 (no detections), 5.7ms\n",
"image 71/283 /content/bleeding_data2/A0170.png: 640x640 (no detections), 7.2ms\n",
"image 72/283 /content/bleeding_data2/A0171.png: 640x640 (no detections), 5.7ms\n",
"image 73/283 /content/bleeding_data2/A0172.png: 640x640 (no detections), 5.7ms\n",
"image 74/283 /content/bleeding_data2/A0173.png: 640x640 (no detections), 5.7ms\n",
"image 75/283 /content/bleeding_data2/A0174.png: 640x640 (no detections), 5.4ms\n",
"image 76/283 /content/bleeding_data2/A0175.png: 640x640 (no detections), 5.4ms\n",
"image 77/283 /content/bleeding_data2/A0176.png: 640x640 (no detections), 5.4ms\n",
"image 78/283 /content/bleeding_data2/A0177.png: 640x640 (no detections), 5.4ms\n",
"image 79/283 /content/bleeding_data2/A0178.png: 640x640 (no detections), 5.4ms\n",
"image 80/283 /content/bleeding_data2/A0179.png: 640x640 (no detections), 5.5ms\n",
"image 81/283 /content/bleeding_data2/A0180.png: 640x640 (no detections), 5.5ms\n",
"image 82/283 /content/bleeding_data2/A0181.png: 640x640 (no detections), 5.5ms\n",
"image 83/283 /content/bleeding_data2/A0182.png: 640x640 (no detections), 5.5ms\n",
"image 84/283 /content/bleeding_data2/A0183.png: 640x640 (no detections), 5.4ms\n",
"image 85/283 /content/bleeding_data2/A0184.png: 640x640 (no detections), 5.6ms\n",
"image 86/283 /content/bleeding_data2/A0185.png: 640x640 (no detections), 5.5ms\n",
"image 87/283 /content/bleeding_data2/A0186.png: 640x640 (no detections), 6.9ms\n",
"image 88/283 /content/bleeding_data2/A0187.png: 640x640 (no detections), 5.4ms\n",
"image 89/283 /content/bleeding_data2/A0188.png: 640x640 (no detections), 5.4ms\n",
"image 90/283 /content/bleeding_data2/A0189.png: 640x640 (no detections), 5.4ms\n",
"image 91/283 /content/bleeding_data2/A0190.png: 640x640 (no detections), 5.4ms\n",
"image 92/283 /content/bleeding_data2/A0191.png: 640x640 (no detections), 5.4ms\n",
"image 93/283 /content/bleeding_data2/A0196.png: 640x640 (no detections), 5.6ms\n",
"image 94/283 /content/bleeding_data2/A0197.png: 640x640 (no detections), 6.2ms\n",
"image 95/283 /content/bleeding_data2/A0198.png: 640x640 (no detections), 5.5ms\n",
"image 96/283 /content/bleeding_data2/A0199.png: 640x640 (no detections), 5.4ms\n",
"image 97/283 /content/bleeding_data2/A0200.png: 640x640 (no detections), 5.4ms\n",
"image 98/283 /content/bleeding_data2/A0201.png: 640x640 (no detections), 5.4ms\n",
"image 99/283 /content/bleeding_data2/A0202.png: 640x640 (no detections), 5.4ms\n",
"image 100/283 /content/bleeding_data2/A0203.png: 640x640 (no detections), 5.4ms\n",
"image 101/283 /content/bleeding_data2/A0204.png: 640x640 (no detections), 5.4ms\n",
"image 102/283 /content/bleeding_data2/A0213.png: 640x640 (no detections), 5.4ms\n",
"image 103/283 /content/bleeding_data2/A0219.png: 640x640 (no detections), 5.4ms\n",
"image 104/283 /content/bleeding_data2/A0223.png: 640x640 (no detections), 5.4ms\n",
"image 105/283 /content/bleeding_data2/A0225.png: 640x640 (no detections), 5.4ms\n",
"image 106/283 /content/bleeding_data2/A0226.png: 640x640 (no detections), 5.5ms\n",
"image 107/283 /content/bleeding_data2/A0227.png: 640x640 (no detections), 5.5ms\n",
"image 108/283 /content/bleeding_data2/A0230.png: 640x640 1 bleeding, 5.4ms\n",
"image 109/283 /content/bleeding_data2/A0231.png: 640x640 (no detections), 5.6ms\n",
"image 110/283 /content/bleeding_data2/A0232.png: 640x640 (no detections), 5.5ms\n",
"image 111/283 /content/bleeding_data2/A0234.png: 640x640 1 bleeding, 8.5ms\n",
"image 112/283 /content/bleeding_data2/A0235.png: 640x640 1 bleeding, 5.4ms\n",
"image 113/283 /content/bleeding_data2/A0236.png: 640x640 (no detections), 5.4ms\n",
"image 114/283 /content/bleeding_data2/A0237.png: 640x640 (no detections), 5.4ms\n",
"image 115/283 /content/bleeding_data2/A0241.png: 640x640 (no detections), 5.4ms\n",
"image 116/283 /content/bleeding_data2/A0242.png: 640x640 1 bleeding, 5.4ms\n",
"image 117/283 /content/bleeding_data2/A0243.png: 640x640 (no detections), 9.8ms\n",
"image 118/283 /content/bleeding_data2/A0244.png: 640x640 (no detections), 5.8ms\n",
"image 119/283 /content/bleeding_data2/A0245.png: 640x640 (no detections), 5.6ms\n",
"image 120/283 /content/bleeding_data2/A0248.png: 640x640 (no detections), 5.6ms\n",
"image 121/283 /content/bleeding_data2/A0249.png: 640x640 (no detections), 6.1ms\n",
"image 122/283 /content/bleeding_data2/A0250.png: 640x640 (no detections), 5.8ms\n",
"image 123/283 /content/bleeding_data2/A0251.png: 640x640 (no detections), 5.9ms\n",
"image 124/283 /content/bleeding_data2/A0252.png: 640x640 (no detections), 6.3ms\n",
"image 125/283 /content/bleeding_data2/A0253.png: 640x640 (no detections), 5.6ms\n",
"image 126/283 /content/bleeding_data2/A0254.png: 640x640 (no detections), 5.9ms\n",
"image 127/283 /content/bleeding_data2/A0256.png: 640x640 (no detections), 5.8ms\n",
"image 128/283 /content/bleeding_data2/A0257.png: 640x640 (no detections), 5.7ms\n",
"image 129/283 /content/bleeding_data2/A0258.png: 640x640 (no detections), 7.3ms\n",
"image 130/283 /content/bleeding_data2/A0259.png: 640x640 (no detections), 5.6ms\n",
"image 131/283 /content/bleeding_data2/A0261.png: 640x640 (no detections), 5.6ms\n",
"image 132/283 /content/bleeding_data2/A0262.png: 640x640 (no detections), 5.7ms\n",
"image 133/283 /content/bleeding_data2/A0263.png: 640x640 (no detections), 5.6ms\n",
"image 134/283 /content/bleeding_data2/A0264.png: 640x640 (no detections), 5.8ms\n",
"image 135/283 /content/bleeding_data2/A0265.png: 640x640 (no detections), 5.8ms\n",
"image 136/283 /content/bleeding_data2/A0266.png: 640x640 (no detections), 5.7ms\n",
"image 137/283 /content/bleeding_data2/A0267.png: 640x640 1 bleeding, 5.8ms\n",
"image 138/283 /content/bleeding_data2/A0268.png: 640x640 1 bleeding, 5.4ms\n",
"image 139/283 /content/bleeding_data2/A0269.png: 640x640 (no detections), 5.6ms\n",
"image 140/283 /content/bleeding_data2/A0270.png: 640x640 (no detections), 5.5ms\n",
"image 141/283 /content/bleeding_data2/A0271.png: 640x640 (no detections), 5.5ms\n",
"image 142/283 /content/bleeding_data2/A0272.png: 640x640 (no detections), 6.2ms\n",
"image 143/283 /content/bleeding_data2/A0273.png: 640x640 (no detections), 5.6ms\n",
"image 144/283 /content/bleeding_data2/A0274.png: 640x640 (no detections), 5.5ms\n",
"image 145/283 /content/bleeding_data2/A0275.png: 640x640 (no detections), 5.4ms\n",
"image 146/283 /content/bleeding_data2/A0276.png: 640x640 (no detections), 5.4ms\n",
"image 147/283 /content/bleeding_data2/A0277.png: 640x640 (no detections), 5.4ms\n",
"image 148/283 /content/bleeding_data2/A0281.png: 640x640 (no detections), 5.4ms\n",
"image 149/283 /content/bleeding_data2/A0283.png: 640x640 (no detections), 7.0ms\n",
"image 150/283 /content/bleeding_data2/A0284.png: 640x640 (no detections), 5.4ms\n",
"image 151/283 /content/bleeding_data2/A0285.png: 640x640 (no detections), 5.4ms\n",
"image 152/283 /content/bleeding_data2/A0286.png: 640x640 (no detections), 5.4ms\n",
"image 153/283 /content/bleeding_data2/A0290.png: 640x640 (no detections), 5.4ms\n",
"image 154/283 /content/bleeding_data2/A0291.png: 640x640 (no detections), 5.4ms\n",
"image 155/283 /content/bleeding_data2/A0292.png: 640x640 (no detections), 5.4ms\n",
"image 156/283 /content/bleeding_data2/A0294.png: 640x640 (no detections), 5.4ms\n",
"image 157/283 /content/bleeding_data2/A0296.png: 640x640 (no detections), 5.5ms\n",
"image 158/283 /content/bleeding_data2/A0298.png: 640x640 (no detections), 5.5ms\n",
"image 159/283 /content/bleeding_data2/A0299.png: 640x640 1 bleeding, 5.4ms\n",
"image 160/283 /content/bleeding_data2/A0312.png: 640x640 1 bleeding, 5.5ms\n",
"image 161/283 /content/bleeding_data2/A0313.png: 640x640 (no detections), 5.8ms\n",
"image 162/283 /content/bleeding_data2/A0316.png: 640x640 (no detections), 5.4ms\n",
"image 163/283 /content/bleeding_data2/A0327.png: 640x640 (no detections), 5.5ms\n",
"image 164/283 /content/bleeding_data2/A0328.png: 640x640 1 bleeding, 5.5ms\n",
"image 165/283 /content/bleeding_data2/A0332.png: 640x640 (no detections), 5.4ms\n",
"image 166/283 /content/bleeding_data2/A0333.png: 640x640 1 bleeding, 5.4ms\n",
"image 167/283 /content/bleeding_data2/A0335.png: 640x640 (no detections), 5.8ms\n",
"image 168/283 /content/bleeding_data2/A0339.png: 640x640 (no detections), 5.4ms\n",
"image 169/283 /content/bleeding_data2/A0341.png: 640x640 (no detections), 5.5ms\n",
"image 170/283 /content/bleeding_data2/A0342.png: 640x640 (no detections), 5.5ms\n",
"image 171/283 /content/bleeding_data2/A0345.png: 640x640 (no detections), 5.5ms\n",
"image 172/283 /content/bleeding_data2/A0346.png: 640x640 1 bleeding, 5.5ms\n",
"image 173/283 /content/bleeding_data2/A0348.png: 640x640 (no detections), 5.5ms\n",
"image 174/283 /content/bleeding_data2/A0349.png: 640x640 (no detections), 5.7ms\n",
"image 175/283 /content/bleeding_data2/A0350.png: 640x640 (no detections), 5.4ms\n",
"image 176/283 /content/bleeding_data2/A0351.png: 640x640 (no detections), 6.8ms\n",
"image 177/283 /content/bleeding_data2/A0352.png: 640x640 (no detections), 5.4ms\n",
"image 178/283 /content/bleeding_data2/A0353.png: 640x640 (no detections), 5.4ms\n",
"image 179/283 /content/bleeding_data2/A0354.png: 640x640 (no detections), 5.4ms\n",
"image 180/283 /content/bleeding_data2/A0355.png: 640x640 (no detections), 5.4ms\n",
"image 181/283 /content/bleeding_data2/A0357.png: 640x640 (no detections), 5.4ms\n",
"image 182/283 /content/bleeding_data2/A0360.png: 640x640 2 bleedings, 5.4ms\n",
"image 183/283 /content/bleeding_data2/A0361.png: 640x640 1 bleeding, 5.5ms\n",
"image 184/283 /content/bleeding_data2/A0362.png: 640x640 (no detections), 5.5ms\n",
"image 185/283 /content/bleeding_data2/A0363.png: 640x640 (no detections), 5.4ms\n",
"image 186/283 /content/bleeding_data2/A0364.png: 640x640 (no detections), 5.4ms\n",
"image 187/283 /content/bleeding_data2/A0365.png: 640x640 (no detections), 5.4ms\n",
"image 188/283 /content/bleeding_data2/A0366.png: 640x640 (no detections), 5.4ms\n",
"image 189/283 /content/bleeding_data2/A0367.png: 640x640 (no detections), 5.4ms\n",
"image 190/283 /content/bleeding_data2/A0368.png: 640x640 (no detections), 5.4ms\n",
"image 191/283 /content/bleeding_data2/A0369.png: 640x640 1 bleeding, 5.5ms\n",
"image 192/283 /content/bleeding_data2/A0370.png: 640x640 (no detections), 6.4ms\n",
"image 193/283 /content/bleeding_data2/A0379.png: 640x640 (no detections), 9.3ms\n",
"image 194/283 /content/bleeding_data2/A0380.png: 640x640 (no detections), 5.4ms\n",
"image 195/283 /content/bleeding_data2/A0381.png: 640x640 (no detections), 5.4ms\n",
"image 196/283 /content/bleeding_data2/A0384.png: 640x640 (no detections), 5.4ms\n",
"image 197/283 /content/bleeding_data2/A0385.png: 640x640 (no detections), 5.4ms\n",
"image 198/283 /content/bleeding_data2/A0387.png: 640x640 (no detections), 5.6ms\n",
"image 199/283 /content/bleeding_data2/A0388.png: 640x640 (no detections), 9.0ms\n",
"image 200/283 /content/bleeding_data2/A0390.png: 640x640 (no detections), 5.6ms\n",
"image 201/283 /content/bleeding_data2/A0392.png: 640x640 (no detections), 5.4ms\n",
"image 202/283 /content/bleeding_data2/A0393.png: 640x640 (no detections), 5.5ms\n",
"image 203/283 /content/bleeding_data2/A0394.png: 640x640 (no detections), 5.4ms\n",
"image 204/283 /content/bleeding_data2/A0395.png: 640x640 (no detections), 7.4ms\n",
"image 205/283 /content/bleeding_data2/A0396.png: 640x640 (no detections), 5.4ms\n",
"image 206/283 /content/bleeding_data2/A0397.png: 640x640 (no detections), 5.5ms\n",
"image 207/283 /content/bleeding_data2/A0398.png: 640x640 (no detections), 5.5ms\n",
"image 208/283 /content/bleeding_data2/A0399.png: 640x640 (no detections), 5.4ms\n",
"image 209/283 /content/bleeding_data2/A0400.png: 640x640 1 bleeding, 5.4ms\n",
"image 210/283 /content/bleeding_data2/A0401.png: 640x640 (no detections), 5.4ms\n",
"image 211/283 /content/bleeding_data2/A0402.png: 640x640 (no detections), 5.4ms\n",
"image 212/283 /content/bleeding_data2/A0403.png: 640x640 1 bleeding, 5.4ms\n",
"image 213/283 /content/bleeding_data2/A0404.png: 640x640 1 bleeding, 5.4ms\n",
"image 214/283 /content/bleeding_data2/A0405.png: 640x640 (no detections), 7.0ms\n",
"image 215/283 /content/bleeding_data2/A0406.png: 640x640 (no detections), 5.4ms\n",
"image 216/283 /content/bleeding_data2/A0407.png: 640x640 1 bleeding, 5.9ms\n",
"image 217/283 /content/bleeding_data2/A0408.png: 640x640 (no detections), 5.4ms\n",
"image 218/283 /content/bleeding_data2/A0409.png: 640x640 (no detections), 5.4ms\n",
"image 219/283 /content/bleeding_data2/A0410.png: 640x640 (no detections), 5.9ms\n",
"image 220/283 /content/bleeding_data2/A0411.png: 640x640 1 bleeding, 5.5ms\n",
"image 221/283 /content/bleeding_data2/A0418.png: 640x640 (no detections), 5.6ms\n",
"image 222/283 /content/bleeding_data2/A0419.png: 640x640 (no detections), 5.5ms\n",
"image 223/283 /content/bleeding_data2/A0420.png: 640x640 (no detections), 5.5ms\n",
"image 224/283 /content/bleeding_data2/A0421.png: 640x640 1 bleeding, 5.4ms\n",
"image 225/283 /content/bleeding_data2/A0422.png: 640x640 1 bleeding, 5.4ms\n",
"image 226/283 /content/bleeding_data2/A0423.png: 640x640 (no detections), 5.4ms\n",
"image 227/283 /content/bleeding_data2/A0424.png: 640x640 1 bleeding, 5.4ms\n",
"image 228/283 /content/bleeding_data2/A0425.png: 640x640 (no detections), 9.0ms\n",
"image 229/283 /content/bleeding_data2/A0426.png: 640x640 (no detections), 9.0ms\n",
"image 230/283 /content/bleeding_data2/A0427.png: 640x640 (no detections), 8.5ms\n",
"image 231/283 /content/bleeding_data2/A0428.png: 640x640 (no detections), 5.4ms\n",
"image 232/283 /content/bleeding_data2/A0433.png: 640x640 (no detections), 5.4ms\n",
"image 233/283 /content/bleeding_data2/A0437.png: 640x640 2 bleedings, 5.5ms\n",
"image 234/283 /content/bleeding_data2/A0439.png: 640x640 1 bleeding, 5.6ms\n",
"image 235/283 /content/bleeding_data2/A0446.png: 640x640 (no detections), 8.6ms\n",
"image 236/283 /content/bleeding_data2/A0447.png: 640x640 (no detections), 5.4ms\n",
"image 237/283 /content/bleeding_data2/A0448.png: 640x640 (no detections), 5.5ms\n",
"image 238/283 /content/bleeding_data2/A0449.png: 640x640 (no detections), 5.5ms\n",
"image 239/283 /content/bleeding_data2/A0451.png: 640x640 (no detections), 5.4ms\n",
"image 240/283 /content/bleeding_data2/A0452.png: 640x640 (no detections), 5.4ms\n",
"image 241/283 /content/bleeding_data2/A0453.png: 640x640 (no detections), 6.1ms\n",
"image 242/283 /content/bleeding_data2/A0457.png: 640x640 (no detections), 5.9ms\n",
"image 243/283 /content/bleeding_data2/A0458.png: 640x640 (no detections), 5.5ms\n",
"image 244/283 /content/bleeding_data2/A0463.png: 640x640 (no detections), 5.4ms\n",
"image 245/283 /content/bleeding_data2/A0465.png: 640x640 (no detections), 5.4ms\n",
"image 246/283 /content/bleeding_data2/A0467.png: 640x640 (no detections), 5.4ms\n",
"image 247/283 /content/bleeding_data2/A0468.png: 640x640 (no detections), 5.4ms\n",
"image 248/283 /content/bleeding_data2/A0469.png: 640x640 (no detections), 5.6ms\n",
"image 249/283 /content/bleeding_data2/A0470.png: 640x640 (no detections), 5.5ms\n",
"image 250/283 /content/bleeding_data2/A0471.png: 640x640 (no detections), 5.5ms\n",
"image 251/283 /content/bleeding_data2/A0472.png: 640x640 (no detections), 5.4ms\n",
"image 252/283 /content/bleeding_data2/A0473.png: 640x640 (no detections), 5.4ms\n",
"image 253/283 /content/bleeding_data2/A0474.png: 640x640 (no detections), 5.4ms\n",
"image 254/283 /content/bleeding_data2/A0475.png: 640x640 (no detections), 5.4ms\n",
"image 255/283 /content/bleeding_data2/A0476.png: 640x640 (no detections), 8.0ms\n",
"image 256/283 /content/bleeding_data2/A0478.png: 640x640 (no detections), 6.7ms\n",
"image 257/283 /content/bleeding_data2/A0482.png: 640x640 (no detections), 7.2ms\n",
"image 258/283 /content/bleeding_data2/A0492.png: 640x640 (no detections), 5.5ms\n",
"image 259/283 /content/bleeding_data2/A0493.png: 640x640 (no detections), 5.6ms\n",
"image 260/283 /content/bleeding_data2/A0495.png: 640x640 (no detections), 5.8ms\n",
"image 261/283 /content/bleeding_data2/A0496.png: 640x640 (no detections), 5.4ms\n",
"image 262/283 /content/bleeding_data2/A0497.png: 640x640 (no detections), 5.6ms\n",
"image 263/283 /content/bleeding_data2/A0499.png: 640x640 (no detections), 5.4ms\n",
"image 264/283 /content/bleeding_data2/A0500.png: 640x640 1 bleeding, 5.5ms\n",
"image 265/283 /content/bleeding_data2/A0509.png: 640x640 (no detections), 5.4ms\n",
"image 266/283 /content/bleeding_data2/A0510.png: 640x640 (no detections), 5.4ms\n",
"image 267/283 /content/bleeding_data2/A0516.png: 640x640 (no detections), 8.9ms\n",
"image 268/283 /content/bleeding_data2/A0520.png: 640x640 (no detections), 5.6ms\n",
"image 269/283 /content/bleeding_data2/A0521.png: 640x640 (no detections), 5.4ms\n",
"image 270/283 /content/bleeding_data2/A0522.png: 640x640 (no detections), 5.4ms\n",
"image 271/283 /content/bleeding_data2/A0523.png: 640x640 (no detections), 5.4ms\n",
"image 272/283 /content/bleeding_data2/A0524.png: 640x640 (no detections), 5.7ms\n",
"image 273/283 /content/bleeding_data2/A0525.png: 640x640 (no detections), 8.5ms\n",
"image 274/283 /content/bleeding_data2/A0527.png: 640x640 (no detections), 5.7ms\n",
"image 275/283 /content/bleeding_data2/A0528.png: 640x640 (no detections), 5.4ms\n",
"image 276/283 /content/bleeding_data2/A0529.png: 640x640 (no detections), 5.4ms\n",
"image 277/283 /content/bleeding_data2/A0536.png: 640x640 (no detections), 7.0ms\n",
"image 278/283 /content/bleeding_data2/A0537.png: 640x640 1 bleeding, 5.4ms\n",
"image 279/283 /content/bleeding_data2/A0553.png: 640x640 1 bleeding, 5.5ms\n",
"image 280/283 /content/bleeding_data2/A0556.png: 640x640 (no detections), 5.4ms\n",
"image 281/283 /content/bleeding_data2/A0557.png: 640x640 (no detections), 5.4ms\n",
"image 282/283 /content/bleeding_data2/A0562.png: 640x640 (no detections), 5.5ms\n",
"image 283/283 /content/bleeding_data2/A0563.png: 640x640 (no detections), 5.4ms\n",
"Speed: 0.5ms pre-process, 6.3ms inference, 0.6ms NMS per image at shape (1, 3, 640, 640)\n",
"Results saved to \u001b[1myolov5/runs/detect/exp8\u001b[0m\n",
"34 labels saved to yolov5/runs/detect/exp8/labels\n"
]
}
],
"source": [
"!python yolov5/detect.py --weights /content/yolov5/runs/train/exp5/weights/best.pt --source bleeding_data2 --save-txt --save-conf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tPXJPJ-zSooF",
"outputId": "33bd7a66-4418-4826-d2b8-2d512f9b3b67"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top five sorted text files: ['A0333.txt', 'A0403.txt', 'A0360.txt', 'A0268.txt', 'A0145.txt']\n"
]
}
],
"source": [
"import os\n",
"\n",
"# Function to extract the last value from a line\n",
"def extract_last_value(line):\n",
" values = line.strip().split()\n",
" if values:\n",
" return float(values[-1])\n",
" return 0.0 # Return 0.0 if the line is empty or doesn't have a valid last value\n",
"\n",
"# Directory containing the text files\n",
"directory = '/content/yolov5/runs/detect/exp8/labels' # Replace with your directory path\n",
"\n",
"# List of text files in the directory\n",
"text_files = [f for f in os.listdir(directory) if f.endswith('.txt')]\n",
"\n",
"# Sort the text files based on the last value\n",
"sorted_text_files = sorted(text_files, key=lambda file_name: extract_last_value(open(os.path.join(directory, file_name)).readlines()[-1]), reverse=True)\n",
"\n",
"# Print only the top five sorted file names\n",
"top_five_sorted_text_files = sorted_text_files[:5]\n",
"print(\"Top five sorted text files:\", top_five_sorted_text_files)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"background_save": true
},
"id": "mFa5173HSuJ7"
},
"outputs": [],
"source": [
"from IPython.display import Image, display\n",
"import os\n",
"\n",
"# Directory containing the images\n",
"image_dir = '/content/yolov5/runs/detect/exp8'\n",
"\n",
"# List all image files in the directory\n",
"image_files = [f for f in os.listdir(image_dir) if f.endswith(('.jpg', '.png', '.jpeg'))]\n",
"\n",
"# Display each image using IPython.display\n",
"for image_file in image_files:\n",
" image_path = os.path.join(image_dir, image_file)\n",
" display(Image(filename=image_path))\n"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "NPLRTjMWTcxt",
"outputId": "e3af5e6b-7fcb-4db4-bf56-fa41192fbe2f"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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eaZoNw2Rs49jaMAzYEK1WQKWUHjmODUyQl5zm/AsDDs1mWCywNZ/jrrtw+eUoBSR6x333YRzxhGYzLBbYms+xXOIJDQMuvxylgETvuO8+jCO+rWHA5ZejFJDoHffdh3HEtzUMuPxylAISveO++/C934vFAlvzOZZLfDvEY5hZZuIvjiT+HYGuzBdfc+mPXH3cMroVdzhRyxmKUPZavLcefZxMJpDMrHjprZGcTialFh2wAwBIK6VI2XuQzOyRPTPdjaSR1T03FJmplLuTHMfR3WutQKaCMAESrLh7BdB7RygzjATYxhGEADMvpUoaxzaOo3tJYDJMoDT3b3zjG0OphHpvw2TwWiFhwyhgvWo9wooZHcB6HM0MQGvN6iAAkpn3lNRJXnwK8y8MODSbYbHA1vveh9e9DpdcgkNf+xpOnMDnPodMPM5shsUCW/M5lks8jhmuuQY33oiLL8ahkyfx3vfic59DJp6QGa65BjfeiIsvxqGTJ/He9+Jzn0MmnpAZrrkGN96Iiy/GoZMn8bGP4R3vwNZ8juUSh17xCpw4gXe/G5/5DB6DpRQciAhJ+E7xgA685IWXvugFxz2i+sAawrSaHCqlVCMBM67Xq8yokwrBzQiM65bKoZbq7uUMQBBoFOhezBgRACJ6qCsRET3adDKd1ipl75EZJN2d5DiOmTmdTt1sHFcRCSPEWquXQreIQGYbW2YOtZpZ2+hNwmQyMfPeY6sldqfT6N3d9/b2DCzFlWHFEzKau1vxHtFblloiN0RyHFspbmZ7+6sAMtPMvNSIVAbJ449i/sWKQ7MZFgt8aw89hOuuw8mTeKzZDIsFtuZzLJd4rOPHsVjgggvwhB56CNddh5Mn8TjHj2OxwAUX4Ak99BCuuw4nT+Jxjh/HYoELLsC3Np9jucTWK1+Jm2/G1s0341OfwlnEXwpBQSAAUgCkv3XNpS97wdPVRpvYMGCCYqxQRoQbh1qpBJSZ5vTiZkZAmdG6gIzuZqUUM5MEwGgwlrpRJEg5trH1sa1jo0efToejO0fcLTM2JNiBzGytkay1lMIMtDb23mk+DJOd3Z11X/exZY9MZUYtFdC6rUnWMpRSMnMcx0zAijKVWUox80cffngYhqNHd8dx7BlG8+JWvPUePd1LKAECAliHgeSp06f3x0bAzCSkYDQjLz6t679QcGg2w2KBb+vzn8eb3oTHms2wWGBrPsdyice67TZceSW+hb09vO51OHkSj3XbbbjySnwLe3t43etw8iQe67bbcOWV+LbmcyyX2LryStx2G7ZuuAF33omziL8EkiKZKmSgQOOLvv/il//w06Aw81KcJITidCBaB1FrVeQGiWkdhlq82EZmSglg/9QeBCvMTG0kMnKyU6fTaSnu5mP21pqkNmamWht7a0d2d3Z3p2bEGdyQZGYk1+v12HqtdWcy9Gjjet16eKlHzzkmZBtbtCZlayOkUqpg7lZrzYzMOH36tKBaam/NvdQ6iYgxo5RSDV5KRIxjq3UymQzjOLbeyjBIzMzeO83cS0C997F3CUpEz+ouMHpcvIcb7h1w6MUvxvveh0Ot4dd+DX/6p5DwrGfhZ38WtWLrkUfwD/8hTp7E1myGxQJb8zmWS2wdP46PfhTnnout1vCBD+Duu0Hiiivw8z+PUrD1W7+FD3wAW8eP46MfxbnnYqs1fOADuPtukLjiCvz8z6MUbP3Wb+EDH8DW8eP46Edx7rnYag0f+ADuvhskrrgCP/dzqBWH3vlO/NEfYWs2w2KBrfkcyyXOIv4SDAQFUCSUL7rq0h954cX0qEO1M4g8w81qdUVIKuYQeu9mNh2GUoo73R0Aichoq5YRoSjumbm/v9rfX+3uTjcADMMAMwDuHpG99/V6derUqeo2nU53d3eHoZoZyXEce++1VpKn9/Zaj6dccH5m762NrUscdnbMMXhRRGSM47q30bwCtjEMFVBmrMcRSJqQAlC8iGQpZtbHDrKWYbVe9R6lVN8wo3mPXK/X4ziCRjMvxYqv1qveEyISZ9DGcbzkNN7yZ1Mcuv56vPnN2PrSl/DGN2JvD4d2dnDrrbjiCmx95CNYLLA1m2GxwNZ8juUSW9ddh/kcW1/8Iq6/Hvv7OPSsZ+HWWzEM2Dh5Eq99LcYRG9ddh/kcW1/8Iq6/Hvv7OPSsZ+HWWzEM2Dh5Eq99LcYRG9ddh/kcW1/8Iq6/Hvv7OLS7i9tvx+WXY+tjH8MHP4it2QyLBbbmcyyXOIv4zhFwYySB1EuvOv6S/+QSeVSr1Y0EQUmAzDgUJyCBBIWIADGpE9LcUUoxM0lmTGFcr1er/VKKma3X61On9ia11uIp1VJYfBgmGxExjuuIeOSRR9t6Bejcc887duwcdyultNbGcbQDp/f2N55y4QWlWBvHHgmYD9Ush1IJ0NB7H1dr99JDUvpGMUBSttaIrLVmKCJpZHFzz5CAyWToPfb3V5k5DJPJMO29jy0kRYSE1juL11q7MiKViJ6rvX2vNVOXrexn7hlw6KqrcOut2HrXu/CHf4jHeeYz8c/+GYYBG3fcgRMnsDWbYbHA1nyO5RJbN92Ea6/FRmt4z3tw//14nJ/8SVx7Lbbe/3584hPYuOkmXHstNlrDe96D++/H4/zkT+Laa7H1/vfjE5/Axk034dprsTGOeOMbcc89eJyXvhS//MvYuuEG3HkntmYzLBbYms+xXOIs4jtFgPQkkPGyF1z6shdcohgbVM2mk8GMFDI6iFo2bIOkMgURVKpseCFhZpIycxgGEuv1OjPX6/Uw1Ezs7+8ZuJGZJM19sjM9srs7jmNElOqrvb1vPPTwo6dO7e4eveCC86aTuiGp915KMXI1jo8++qiR5517bqml9egRPbq7TScTAu6QMjMISgyFAJJDHXrEarVPYDqdKtHaGBkCaEai1Gr0PDCODWAtQ621RwKQ0CNX69V6HM1sMpm6l0z1Fnt7e3UyAeypj/T5FwsOzWZYLLA1n2O5xDe76SZcey02/vzP8Q/+AU6dwsZshsUCW/M5lktsHD2K3/s9HDuGJ+mGG3DnnTh6FL/3ezh2DE/SDTfgzjtx9Cj++T/HU56CjTvuwIkT+GazGRYLbM3nWC6xNZthscDWfI7lEmcR3xGSgACT8kUvuORvv/BSl7oI9MHt6O7uuq2QaQIIczfjZDK4We8doJOZ6e6lFDuQmREBwN3MPKLjwDg2SdFHggB6hLuXWifDIBKQlNVsHNsDD359PbbdnenR3UmtA0lJtdZSiqT9/f2HH374nHOOnX/+eS1iNa7X67GYT3YmboYMM9RaxnHM7F5Ky54dk8mkK9s4EhyGWrxE9tVqjyBIAKXUNvbWWi2lh0gaHSSNpPceKQla7a/HcRyGoZYhUz0U0a1UgBefyjfdTRyazbBYYGs+x3KJb/b61+Otb8XWr/wKfvd3sTGbYbHA1nyO5RIbb387XvtaPHknTuCOO/D2t+O1r8WTd+IE7rgDP/ETeNe7sPUbv4GPfhTfbDbDYoGt+RzLJbZmMywW2JrPsVziLOIviCQOCKD0khdc/NJrnmaRDkPtyUlRugmUGylkJkkgd3Z2invv3TbA3jsOuPtkMgHQe3f3UoqZZwYPrNdjZihDkrlLGtsIsA61lGIgqGpnnNrf+8Y3Ho7ezzlyZDKZ9N4BTCaTYRhIkNjb24+IyWRi1XuP/fV6qIO7tdYmw+TI7rT3aONa2etkEhmpLKWMbYwehNVaah1KsfW4zrNqHdrYN4ZhiFDv0TZ6DMMwmUwBgliP43q1lrCRCYAAe++R8FIu3cd/9acFh2YzLBbYms+xXOKbPf/5+NCHsPWWt+Df/BtszGZYLLA1n2O5xMbzn48PfQhP0q//On77t9E7nv98fOhDeJJ+/dfx27+N3nHVVbj1Vmz9o3+Ef/2v8c1mMywW2JrPsVxiazbDYoGt+RzLJc4i/oIIghAA6UXPO/7yay4vZLMcagxJ0AVlRim+szMxsLWWmVKW4kOtZhYRkDIyInRgOOAHzBwHMlJAZkSkmUiaWR3q/mp/vR7NrJTiZiSKYUPE/mq1v7ea1qGUMo5rSZPJdAMUCYLrcd17H6Zn7K32x3FtZqUUo00nk94zolEpkGY0kYjM3nu0KLVOhkmp3tq4oQO1VPcamdE3MiJ6z406DJPJVIKINrbWema21iIE0MwBhehenvpIzO8pODSbYbHA1nyO5RLf7PWvx1vfiq1f/VX8zu9gYzbDYoGt+RzLJTZe8xq8853Y+v3fxx/8AZ6QGR54AF/7GnrHxmteg3e+E1u///v4gz/AEzLDAw/ga19D79h4zWvwzndi68Mfxu2345vNZlgssDWfY7nE1myGxQJb8zmWS5xFPDkkBVBwWsCh8UVXX/LSay7O1sx9KDaphQKhQg7DAKCU4u6997GNmT0i3H06mUbvEWEHeu+tNUnT3Z3d3V03B6RM0CBlJpiAAHDDrJSSmavVqkUvXmupmeHOyVDdrbVxf39VipdSWmur1YpkrdUOuHvx0trYet/dPdJ731ut3L3WAqF4qXUAsrVxb2/P3evgW7339XoNoByICAARsVo3M5tMphAiFJHuBnhmttbMTAdqrWM/4/T+vpKEbUAKsZRy0SPx0/cUHJrNsFhgaz7HcolvdtNNuPZabDz4IF7/enzjG9iYzbBYYGs+x3KJjfPOw8c/jgsvxMYdd+DECTwZ552Hj38cF16IjTvuwIkTeDLOOw8f/zguvBAbd9yBEyfwzWYzLBbYms+xXGJrNsNiga35HMslziKeHANoTAE0Zbz0hZe/7IUXR65JQipm1c2A4j6UcvToEUmtNZIR0TdizAylppOpmWVm9UK3zBzHcT2Ow3RyZHe31mrG3numzIwkkJmhTG0AJM0sM/fHldGHOhiRGaXYdDLJjHEczazUYrTxAEmQtRQzK7UAOHXqNCD3IkAHJnUYao3M6XSaGXt7e5LM6W4RCcgPSALQWhuGQcLpvf3e+zBMSIPYe5IAHJCZtdZWq9Xu7u7Ozs5qXK9W673VSjLCemsErFQzv+Ch9c/cN8Gh2QyLBbbe+U780R/hcZ7xDHz0oxgGbNxxB06cwNZshsUCW/M5lkts3XQTrr0WG+OIN7wB996Lx7noIvzSL+G5z8U73oHPfhZbN92Ea6/FxjjiDW/AvfficS66CL/0S3juc/GOd+Czn8XWTTfh2muxMY54wxtw7714nBe/GO97H7bmcyyX2JrNsFhgaz7HcomziCfFCJKRJBMvvfqSl/7gZT3Wgw/ujozsQSWhWsrOdDKdTmutSqUyDqxX+4AARITDhqFu0A0giLGNAmqt0+l0GGrvvbUOgGa9j4QgYYMbZkaQY2+9hXup1SJCiqFWczCRmZIgRAYASSCHyWBmmVlKITmOI84QoGEYooeUJM2cMAggpGx9BEjCzEopESGptVZKqbWOLXrvEkgjLCJ7DwAS7EBETCaTUsrY23o1rnsfx957IpNQ0t3KUx/tP3PfBIeuvx5vfjO2vvQlvP71GEccGgbcfjuuuAJbt96K227D1myGxQJb8zmWS2xddx3mc2zdcw/e+EaMIw4NAz7yETz72di65RZ88pPYuO46zOfYuucevPGNGEccGgZ85CN49rOxdcst+OQnsXHddZjPsXXPPXjjGzGOODQM+M3fxOWXY+tjH8MHP4it2QyLBbbmcyyXOIt4UrgBUhl/6/nHf+yay8zY3AaiejGit4ZMII1mxmGou7tHSEASBGFcr9rYJI2rlaTJZHLkyFGfFNKGWiNztV5F7zu7u5PpIEChjR5tPe4biQ3JvXhxCLVWEfv7+xJqrYCU3c6gmyszlWaMyAPReq+1TiZTKTdqrWYGIDNIunvvPSJK8egRPUG6uzkzugRJGeHuAkj23iP6MJkOddJaG8cm0WiZGMd1pCaTyd7eXillOt0ZxzVpIgmm9PDDp3qPodaIDMHdL3q4vfXLuzj00pfil38Zh1rD+9+Pu+4Ciec8B3lfELYAACAASURBVG97G2rF1iOP4E1vwv33Y2s2w2KBrfkcyyW2LrsMiwWOHcNWa3j/+3HXXSDxnOfgbW9Drdj6ylfwlrfg5ElsXHYZFgscO4at1vD+9+Ouu0DiOc/B296GWrH1la/gLW/ByZPYuOwyLBY4dgxbreH978ddd4HEc56Dt70NteLQu96FP/xDbM1mWCywNZ9jucRZxJNCtxIZ5wz5X/y9511yQUV3uor7UCqJaA0bSigDGGrd3d2VkkiQBkbrfRxba2NrEeHu5513Xp1OE6q1llJWq9Xe6dN1GM495xwQAEmsx73eG4n1ei2hlBLRSTtyZBfg2BvBWis2FARSKqWYmRSSeu/YoDJVvGQmAJLuDoAHMpMHJGWmMpEyMxrNTJmt94wkYMXpbmRrbT2OpdShDJIiYr1upNdaAezt77XWa62llMw0s1qHsfUIZWq1v2q9Q5YpGAUcfyTe8uUdHJrNsFjg2/r85zGfo3ccms2wWGBrPsdyiUOl4MMfxmyGb+Hzn8d8jt5xqBR8+MOYzfAtfP7zmM/ROw6Vgg9/GLMZvq35HMsltmYzLBbYms+xXOIs4smyYwP/zsuf8ezveQoBYY/QTjnqbgZlJlJAQhLpbkMd3M0MEghkb5Jaa5Iyg7Tpzo7VYl7cvZQSEXt7eyTPO/cYDpBobe3FMuORRx4x81pr772UMgyDmaUUmQTc3QzKMyRNJhN39t4yZG6RvbdOWkQAqLWaGQ7UWklmpiSS4zgqs3pxUlJmCiA2mEpBMhpIs/U4ttYUmEyGTKzX4zBMMkWyZ1ut1sWLmUuqdTDz9dh67xIA299f9R6EJZGZl491fk/BoVe9Cu95D761hx7Cddfh5Ek81qtehfe8B1snTuCOO/BYx4/j9ttx/vl4Ql//Oq6/Hl/9Kh7n+HHcfjvOPx9P6Otfx/XX46tfxeMcP47bb8f55+NbO3ECd9yBrVe9Cu95D7ZOnMAdd+As4knhsaPlx1/ytKuecYHWlqaeq8LJZFKRaQajEYBCEs1ImJXiNII0EMhuZhFdQEZGdPeiWofJJHqYWyk1MqL3Sa3VvQ4DACFISQlgvR4jopQCQNJQq5fSI1pr7l6cmSkpItx9GCqg3sOcZowemQIwjiNJdwdgZrXWUgqA3jsApSLDzZQJUpkCIGwYEBlj75J2dnZab+PYkNxYrdZmvrt7dDzglQB6T8Lci3uJyNZ7RI5jI02CEuPYWoakZ8T0hnsrDpWCV78aN96If/pP8bWv4R//Yxw/jkMnT+K978Wdd2Ic8Til4NWvxo034pZb8OlPo3c8zjDg6qtx4404fhyHTp7ELbfgj/8Y44gnNAy4+mrceCOOH8ehkydxyy344z/GOOIJDQOuvho33ojjx3Ho5En84i/ioovwC7+AW27Bpz+N3rFVCl79atx4I265BZ/+NHrHWcQTcMCAbiYl6Z7R//Y1T3/ZDz+1rXrxWkJKwySnw6AemX0o1c0iGgWj8wBISCRBGDFMhp4BqGes9vcTubN71MzW4+juOzs7AHpryjCzc44dMxKEkZFZazl9em9/f28yGTITxHQylFoAtNYkOY0giMwkUWslrfcWkTS6bXCjt76/2tcBO6MMQ3UvktxdG5lORATOUO8RvUemiA2SZkZjb12AAhvj2CPCzM4559ze+97enrmPYydh5mYmIUICWuukkdZaX6/WY48e/YqcvvXLO3iMT5y3vpf7xac9xmFn94KnX0ErkYy2fuhL96z39zNTG0RISkjCAVrJ3lk8ewcoKTNJjwNubkYO06c+43uHOlGqrVdfufuu8fRpAHmARrIA2gCIs+ru0UuuuKJOp5D6uP7KF/503DslYYMwAClFpFIgQIAEORw559IrnlmnO6Fo4/jlL9y9On3K6hCJBx/Zf+R0BzmbDrdecQU2SkHvKOWGu+9e7u3hLOJxCBBIB5MbqJnrpz/lnB9/2RVPOV9DnTjhCWfNMjoKgYzu5mZGJAAFCLg7JJoRFBLEZDoRZG4gT++dWo/rnZ1dCRFBchgGM5MARO99d3d3GAZJpZSIBNR6z2ju3lpzN3OWUkhKKYE6w8xJSEnQ3CRFj8gws2EYIoJnoEdE34iM9OJDHUop5hYRkAoNgKQ44GZj72MbAZiZuwMgTVBvSdo49t5bZu7uHnEv63F0L6311lrvPVMkI3EGjWCmMhS9t8zI/J5W3/KlKR7jF489eHfpZoUwM/aMceyZAmBmEjMTgMhAZs8USEpQgoQkgAAzJSVgfSOjeDEyuiKC2kD2jN6VKSkzAZCMhJm5e2bigIBMAAIl4QwZCEgQaA4gInrPjABII4wCQIIEEUpBgEgKHvDT++v7Hzw9hn7o6NF/9bzn4TFe8id/8i8ffRRnEY9DgGBiQ14oPf0pk//0Zd97wXlVsgkxNaAMMDia2UBQiswkWaq5e7YEaGaSzAgwIqSY7kzpnEwmZRjW42p/vZdNmWlmksys1ioAytZGd9/Z2QHkXtyLlBtmiIhxHM2NBiNIuDuNCPXezYykzkiS7p5SRBAotQDgAUm99zY2pdy9HCDZWpOyWnF3SXmAZESs1+uIIGlmJAFICMG9jK2v9vfdfaiTYTJpvQEEbP+A0eowjC2UGZEpAIyeZwACLlvZz355B4/xvgse/Tz2ISteaOyKNvZMSAlAIgACMusZvYcEkhAkbEgCbENSZkrsPRIynKFE9MgICQZGj42MUKa5AYyEGUnDWRIiBQgUtmQAJKVEGIDIzMhMJc6gm5EiRWwIBAEEz7AEE/7wajz5wKNXT3b/t+//fjzGS/7kT/7lo4/iLOJxCNBMMGW4KfInfvSK511xpPc03x2gymQZYBgM7oXERmZKGCalFM/QGUBGuBvAyHBjGUpETCaT6e5OIlsb+9jH9VhrdffWWq0VQGb03gHs7OyaWUSvtZqx904iI1IJyN1wRrqXUhzCer02s1Lc3KSMSJI4ICki3J2kpIjITAPNjCQAkgAyE9BQ6lCHiDwQrXUAGdF7T2UpBUBEQATN3ASeOnW69zhy9JzJMBlbk5SJ1Wo1jiPN3D0Skbleje7F6ON6lJREa/1pK/u5rx7FY/zqRXv/t63X604ajCAyMlMA/h+y4Pfn1vQ8D/J5Xtd9389ae+/xzLjjX0lwGDstSWyHQESr1lVQkVoQQYAqQaIkRXwAkU9NWkpTkdAIEBClVeF/QMKpkfjIN8QXsAQiTSQ0DSF2gtq0jn/MeGb23u9az3Pf13my3nc86G04jqqyCSAjilhVa+mGJAyYhgHYiAiSVbZhS4ZKtiGQVKmqwlyr1poq2Y5MgDYIGAARDACSZeMmYBuATdyQtgHalmH5plQ3mS0zDRgWwBsQAcAkjJDsjD9858X3z/6/f+ELeOSLb731lefP8SHi/y9ARwcO60d+6KM//qPf83TkafQIBCMjVEtVrWXPFgzDAEi21jLDEEkAa83ICAbgaAHA8unJaZzHupkrEPt1b62dz+fL5SJpjFEPbGbGGGPOw15Pzme7qmTLtlS9dwZhBCMyAOz7bqP1jOCNHtiOCADHcbTWIgKApLUWyWRkZkTYrirZkLbRtu2kclVJ2vcjgsko1VqTRFVZIDMjsrfW+1z1znfej5bn8znZ1qrLftQqkKsmjAKrdMyZbATXvjJjWpfL9fvX+PmvP8Mjf+ejL/5vXmdZNoM3Nm4i8jiOKkcEySnJqJtVAD9gm6QkkkAAsA2wqtYUwIyAfezr2HdIMmx8KADY5gPjuyThhnTQhmUTBhK0DViiDVu2JVUVM4OMCBO+AUgieGMbpO2wnq/4+AX/y5/4QTzyxbfe+srz5/gQ8UcQAAmG/SM/+LF/8c98couj+9SSLRvJnqGqNWdmjjEIyrIEkMEHyMzeu1RxL21li1IRPD89R495zLWWS/OYvfdnz57NOa/X6xiD5FprzgWg9wZAXqNn8B7g0gIYETaCsW1bRFTVnHOtBSKTNkjiQVWRXGtlZkSQzMy11rHvwdi2LTNtV5Uk2C3Z+7CgB8cxI6JFlKpqAV5LNiwDOJ1P2RqY3377nf2YH3n11Z59znXdp6RgHHXAmKWSJcFhuWb11vc1n7948Vk8+YU/fAWP/Nd/7OXvxj6XZZm4kRzRIrjvhw2SVTVLiABQqwBGhO1SkWnYMhkwbNhYa1Y5GKO1Ndf1uq85Cdqw8aEAYJsPAMj3+EC2YJsATNzQ8A0M06Yt2/UgMhk3BGl8KAiAAEjZAhz5Axj/w8e/H4988a23vvL8OT5E/BEESQun4Z/51z73yWdnR7R+tADAlpFkMEhkJhm2+UAWDMMEbffeIyIzWmswkK57yhbjNADI0lz7dSf59OnTiFhrZSaAOedxLEmZjGQkKUew9x4RVWXLMAwb27Zlpm0Aa5VdDK618kNVFRFVZVsSgNaa7evluubctm2MAaCqJMEOCoiWHYCky/VKMBkMAB6jS75crvNYALbTBkYf24uXdy9evhzb1nOQcaxac8GYdcgWokr7fmS0YNQs2Nd5zFVv6vTzX3+GR/6bN+5+N/Z9FkhZvoeIBnitsmFb0pQiW5CrSuXMVNWSQVYVwGDY8INayybBFnHc7FOlICXYxncFANt8UJJtkhFBsqSybJBhgqRLAGwDtGnLtqSqikzegGgBwDYAEzcRAUDwMlzrnzs//W8/+iYe+eJbb33l+XN8iPgnkUByy/HnfvR7fuizH8ktn/SMUutumZIyovdOEjey5RaRmaLLH4BUEdy2rbVsrRll4aZUssZoOZptAvvlernuwdi27XTeMsPymnXsRy1HRiRIkAbQWo7RQbx88TIyeu+2+70mKYI2qsrGWst2z5aZALJFqfxgrSIREXOuFy9eknj65Nlp29ZaJGqueRy2t7GNbbN0vV4lkYwIw9sYWrpeL8cxSx6jR2vn8xmR773/4thXRm+tzVrz3lpVABxR8nE9IhqANRfAfZ9r1Wdx+stff4pH/s5HX/xO7LJhypYFkEzAVSrZEgCRYABYa1kgKank0r1gI2gDhizbFADaOvZj7ss2QIC4Z9yjH4AkIJukg0FClmxLIPggWMt4xLZuDKkAfiAzDRsGiSAA3wC2l0Xn58f2pY+9iUe++NZbX3n+HB8i/kkkbX/2e5/+xT//g5p3bdue9HODgBkEI7YxglEqAARRyojWGoJLKhXJtZbtJ0/OvbfeGwBJsHM0wNIqiMFEzDlfvrjMOXvvz155um0jQBv7fsxZrSXpG9IkMpPk2Pp7773XWjufz5JI9t5JkwTgBwDmnDRaazBAyCIYEZLmnCRba3eXy7Hv2xin00kSwVql0g3JbdtI2pY056xVhkfrrmLEcRzX/dhOW2S01qP1Fy8vl8tx2s5j217e3c05Vboeh0FGSpZMxpxrrYqIeax9P970+a9+8xke+duvvf/VsQBooVQmiDDgBzJtq2oZIG1XFUw8kFG6BwQNixHhBxAsVdVxHOsoG2TYAIwP2cYNCYAAyGURDMC2LJmMiAwy1iwANkgYhmFbRlUBJHETDwoyAIKkH4As2OYXxvlLb/zTeOSLb731lefP8SHijyA+8erpz/+pT33sjZ6O83aOHC1FzGBu28i4J9uSltackJ4+fdq37ai573treVNVmTnG6L0DsMt2Gz0Ca81CISKN4zjuXl6ratu2sfUx+tY7Gccxj2O21gBJFcHee0SsqkzOOQFs42R7zglgbD0zAQPIDABzzgAzc60iAd/L1gDs+277tG2w7l7e1Vp9DIK4R4D7cUjaxuhjXC6XNacB2LwxLPXeJe370Vpm630MRr64u1zujj762LaXdxcJtfT+ixdVNbYzI465YFYVTIC2JX3vHn/lG8/wyN967b3fPxlgLd2DiTCgB0bYlmqVTdqWRAcDtmUuac0JR4BAZKZtSZBrrptjTi0DJMOWjT+KBEASwKxlIhmyLRmBIIOBqPIHANiGDZJgSQBISiKZmbJMIAjAsuCIMJuxPt+2/+6Nz+CRL7711leeP8eHCJAwEKZIWPhX/vSnf+xPfOxuvzw5jy2zoGT0xtYyIgj03jNSqnXMeRwwxjba6IiQ5RspIjKz954tcWMBYNL3KnqC0KqMNueaxxGRgE6nrfemMsk5Z60lS3Kmx9gyc60pi2RVtWyZDcQNgciwnZmtNculapERsV93aZGwPHoDWbVst8yWue/73d1d3vC7SqxSVZEEsNayQTJbWqq5JGVmRKi8am2nUx9bZO77vFz2spZEZkReL/t33n0fjL5tNiUBkBzMkixn5ifv9PNff4pH/vbr73+1LwMqSDIMkAxJVZIhGbBMB2HXKoAGLBkseR6HzIwI5I1kWyqtuY5j1loQAAK0jQe2AZDEDYkPLclABG3IAgOAcY8O25JIArAdEQCrCgBJwA/ADBAmbmzLjgjmkI4fbv1Lb/wAHvni33/rK++/wD0DINATZUANtH/wU6/8uR/73nFuo/PJuRMIJoCeIpkRY4zMdIkkSjcGTIDO1nrvJuacyYhgaxktbUGIoKA5J8lxGtFjTVmK4DqmSgROp1NrrUqttbXmvh9rTRsBtR69DwAk/KBUIHrvrTXbVUWy905mzUlyjNGyzX0/1kQEapGIgKSWIBMOqeacAdjmDaibMoCq2o+j9yF5rdVaiwhJVUWytQZgrYpMMJlxHEslZOzHYYfkfd+v+wTCDJJVvqkqkgYg2vi+Pf7y15/gkV97/f2v5iHfUJJtgDZubBqQoBsAhG9kkzKtslDltZYImz0yIiwbqLXmUcdxrKnGhFVVJCNCDwBEBEnckAAkmWCEhBsTHyjfS6QfkLQNgPdCkh+Q+ICDAEzckLRdEpI2PtfPv/7Gm3jkz/79t77y/KUNwKAJ9EAhJPO1c/7UT3zu9VfacRwteN567420atGOiG2M8/lMcr9cVQogIgwwmb21lhG5VGseMGxncpy2CNaUbYQlkRznERl1owrQJUsBtt75oPcmeT6oKmkR6L2P0TMTAEm5LteLpMzsvZOUFBEAArzJzNZa2HOtsmFbN4vk1kcEq8pSBAmuuQjInrVUIoOgjYi4u7vb99laO5/PmXm9XiOCJICMKLtkmft+ZLbtyflyc51rlY1VqlIZZEquB5lJRi2tVZ+e7Re+8QyP/K2PPv9aOyTIsK17kA0YDkTIkCzrBgAZAA2uWusoyyWJkNAYGQlA9jyOWp5zrllhwt8VEQAkkcQHSAMZAaBgkjZuDBj3yvfC4QcRYRsASRu2AUgFIDNJirgxYRs35FqLpIHPb0++9MabeOTH33rrf33xXCZuaIIETIStP/ujn/ozP/JJxrI5cht9jB6MVTUTEcQYo/WeEfv1er1cE+y9Z2vZc2xbH932sfZj32ut4ziMevbKR549e+bCfhxGkYyIHMlgBNea+3VPxui9ZzN8HDMiWmsASNje92POwxLJzOy9xQOEr9eLpMxsLSNSki3bLZJBRrSInk3GPg/alvfj6K2NNgjTa1WNMQLc9z0zZa9almXYADzGNo91uew2em8ReRx7RACwDbAkm8yYS8Fo29j3/e5yAJR03eecS2BmV6kkAK21OddaCsb37flXvvkMj/za6+9/rU3bMgFY2lfhHteSADJI2oZhWLIBCbZqSYKkZUnokRFBsKz9clVB0jwKJdh4EBEkJZHEB0gArXfYRy0AZAIwYRtA+R5FAJIiAg9I2rCtBxFsrQFwEICJGz8oCQhbn9+efOljb+KRv/DV/+t/+va7BgwCYDAN23i68S/+y5/51KtbwaOPnnkaI1ARIEwwghkBY/Qu6cWLl+uYY4wnz5720bO1SN4gOOdxc7m8OI79yZMnr73+es8m6bpfV1Vmtq1nJgNVdex7ixitb30rrTknQDxo9/I45jyOtVbpXmtt20bvg9BxXCVla4BAtNaturFlOxg9W2vZe68qVQG8Xq8kWzapGHQpyNZarQpSkumbtYoM25kNoOVjHhbIAFhVmbn0AS8Vs1lxHLNkRgC4XPdjlkokwUSE5OOYNkbvxz6r1Fr7vj1+4RvP8Mivvfbu7+QBEKaBktdaJC2uWgLJIAIEAxDWqqUb24QhoaxVZSEjkpSxVq3jUAFGLXmVbZIASBq2xZuIIAEI6K3ZXirZ+JBMAoZlk7RhISJsA4wIIKRaa8qKCGYQQCRJE4JhA4Rt2cTnxulLb7yJR/7Nf/jV//EffbtAwQDvwXka/Jd+/Hve/Pgrrdg2jNF6RNIjo/euWpLyJiIjR2Zk3t3dPX//RWvtybOzwe20tZ699+yjaq5ad3fP5zxab6ebvvXRS7q7u6uq8/k8TptU+34F0CIhjzFsSwKgB+1BRK41r5cLAEkAtm0bY5A+jkMqkvJqmX0MqaRSqWqN1lum7dYaySBBXq/XqgWwqsYYGaFV8YDG9Xo1/QC998x8+fJi+3Ta9uOACXPOIjnGmKW5JsjjOMwAcs5ahdNpa629/fY7+zHJyEwzJUXvx/WYcwVzzWWb5Pde46+9/Soe+dVX3/lqL0a4IGNJtZZvRMkiARJkkIRKVSpDsso2ABZcVSpEkLbkNUtVFsJRpZrLNhn3SHnJ4ocACOitAShLvocHNgEClmSaCDtI+B5I2pRUNQsGEBmZEUyQIpbKdkZAvgHxuXH+0htv4pGf/uY/+J/ffu9b770s3CMJGv/Mp1/9C3/uUzFpt0j1li2yhU69nU4nqySRDEaQLTMjVLq7u5Acpz5nta2P0fvo0bshW9frS8O9d9vb2FpEZlbVi5cvM+K1V18L4u7upW1J+76PMXrvmUlyzrnWysyIaK0BuLx8GRHb6RRklUhGYGnB94TKiGzNtZIEMOfcxmitzTkBxIfWWvXgmEeQo/VtDD1omVWaa661MltmStr3aaO1Zqv3QeS7775LxhijjKoFcj8OI4w4jiXzlVdeyWzfeffd5y9eWIgIRKsqZO7XY83Vs0uWJfuf2vM/evs1PPKrH3nn98+GoeKSZlXNyQgi5qoyIiKjAS6VyiQFrlW1ZAOgCEkWfFM3qiUCcNCoJa0CDCAiyQCqXATxgCTIiAAg4kYSvosAAUg6VGQEQzZsg7YhypLLNiNaC5DBBCC44BvaKpEE+bnt/OtvvIlH/tK3f/9/269/8PX3LwsAyeCA/61/9Y9/8tUnjEMgEEGMlj3ZgufzabQGwDey5SQyMyJqVakiE2RkRDJvto1hGMe6Vq2IIDl6z8xg0FhrzmOOMbZtANaDuZaqIuJ8PmfmcRxzzsy0LVXPRnutIjm2AVsSCFNk2F51kBx9EG4Zto9977233udx2CYJIDNtA7B9vV5VFeR5O2WLtSoIMo457+4uLVu2jIg5a61FIiJP2ymiXS77ixcvAETrDBpeqxixCi9fXo9jvfb66x99/aPvfOc7f/CP/jHAiBDIyMh27MecC2YwC6XSp2f762+/jkd+7bV3v7apSloWvEo3JOGQvWQygiGralmITJtzrrUKIEABsmhKWnOuWZIIBhNlyS4DtA0gAoBl2ea9IAniu4IGJOEBGQAByFqyZJCSeC9gSLANSr7HQNwwGYFA+R5ty3jww9v51994E4/87De/9hva33l3fuvdKxAE8MN//OM/8ae/DxZ7pJZJWmPkNppdPfuT07ll2p7HrLUyomX23g0c+y55e7JFUDaANjaHQZVWrRk35mls2xjyPQD7vlfVGP102kjOOW3POQG01nrvJNdaflBVGeyRx3FUrT7atm16ACAibFdNkmMMEKYhz+PI1jICdlWRjAiSACRl5n69utRaIxERGTc45jGnjrkyIh9Uec4DkOTeh8WI/vLFy8t+ZcuIIAyTEVPe99qvs/fx8U9+4r333v+H/+APsncySoy8aVU1V93dXSICGSA/vccvfvt1PPKrr7371bZUfkDYZpRsicyCVf6AHkQ0yXMuSTAB+h5uJK2lm6rijVirLMAgw0bVigBA26DISDaEZZEEoBAe2LDAB0DYLsFAwasKZgZtB5sfyDVrEmytMSMyTUgCECAB2wB+qG9feuNNPPLT3/y935yXufiNt1++PIpPT+0nfvwHfvD7X5u1R7aal8w8bWMbLeiqapHb2FpmZKxjzeOA3Vsb25aZ+/W6Vo3TyJ4AbDMbEqsOQ7AAUDiNbWyD4FqLZFUdx2yNp9OJ5L7vmVlVvoEzcoyBG3tVzTkz2LMdxzHnbD1Op9PoXbaliFhrSYtkyybKEM21VkaQbJnHcdhurWUmgLUWSUtzP1proAMcYwBaqy7XazAyO+Drvp+2c2Ze98vl7tL6yOyjj+NY77777mGdt9PoXdI+1zFlsBYMn588uVyu737nPUQwAmySeS/mqut1ryqTkfEZnf7DP3yGR/6rV7/zuzlh2gAIwOBadUOmCAu2qwqAbYBraa2yDRMgQABSraUqkfSN4LLKEGzc6F6RIAkgEgCDCcq4R1IhADZg2PgAGTZkmhCw1rJBwDaReGBo1QKQmSCyN0ToQQAE8eCH+valN97EIz/zzd/7P64vs42337u+/f6FX/iBP/ZvfPHNiDlbNqbXlYEx+vm0tQgLQQTjA6pac0FqrZ3P5z6Gqq7XfV/Hdho3AKINpPf9riBCkkMYrffWGbwBYFsSoIjITEmttaqac5KMiPYgItZaxzFhRXDOWbUimJm995YZZESstapmPJg1y0rQUkRkZsusKj8gCYCk7AC17pHIyG0bVXOMbd+PYz8i0vKLly9fefaRNtp+HO+///6c89mzjzx98nSt9e5779/te8vx9HwyuB/r7rrLCDbdwHOu/XqAZCSzr1WSyCh5rVo3MsjP6vTXvvkMj/yXH3nnq23WsiQgSRi0YVmGCAu2qwoAySodx6wySZg2APpDJVvQsmGXVQ7Qhm1JVSsCAPOmESBMWQgnE4RCAGzDsCmJJBC+FwiIqBJA27WWxYggCXhpkQQQGdGaCT+ADN8D8EPb6csf+ywe+dlv/d5vXF8i+lT842+9FW7kEQAAIABJREFUx7/0r//w976WLRsQGWZb85g0xhjn02nrI0j4HknYKsEOxrZt0ZDZbF/3K1uOMVoGM0GvmghYOva91hoPLLfWIpuqAmRSKtvZggzYq9aalZktW+vtJsh5c+xBHscBODIA1Jqtt9O2RaRUS4oggDkP1ZIMm+QYIzNVRaK1XlW2e+/Hcai09b7WrCoCEUH6dDrbuF6vESnV3cvL6XQmo1z7vt/dXcYYz5595Hq57vt+yNfLJaP3MSL73WW/XK/BJulYS/I8Vh/DoEwDkmwY1JKso1TSZ7T94rdfxSP/xSvvfK2vNWutRbYIyiDDtgyTKleVbZI21qo5l2QLESm5qgBmNhhr1aq1VhF0WVWB5puSSdukI4JEBEECMAw4IkSBgGEESdmWAZC0sWSATFr+AAAJxj3b5SJuGBHZEmRZNwRIWrD0uXH+u5/4DB75mW/+/m8du0HR7z7f+Vf/nX+2h4N5biND0zsMCFadtptxHhuCtZZlAjYsR8Q2BkK999ZaWSUByJvOqooW2xjHWvvLl3eXu+zt6dOnAVaptR5kMBica8rVWhq2fKNSRDAiSAKZLTPWcdBcOixF3sScB8nWWm/NwLJ67wC0lmqtOUv3WmuZaYlE7x3gTWttP65znyO7LFu+kTLZe5+zJG3blpnXy161ZGYLSc+fvziO+cqzj1QJgBgvXjyXY4wNkS9eXO7u7ojI1g1InseK1iQvGaDuWTLMiDikY87P1PaLb7+KR371te/8ti+1LBmITIJpwzYQy17HWrUigowqzblsSF6zWmuSqwqI1poKa616ANuCSkRYtswIABGMCMCkQRo3BowgCN+AYJD0h0gaWGUAmWlbkm2SBiXZkiELD4KRLUEKlgSAZDgkf35sX/r4m3jkp7/5+7913QFV6LrIv/6zn4sM9PZsjIaY2sfWgrGOo2dmxJMn54g4jmPNRQMkES1yjG4qInrvjCjLNsk20jbCmXkch1WqMtFbz4jjmK313hoMBNc6DJPwA5KS1loRkZk0eu9jjKrSTc1jzojovZUmDJAZURLk0XtEkJDq/xMRrbW4IWyQzMwIrlpzP8IBggTsuWZvGRE2JW3blpmXu+vz959Ha+enJ9jPn7+8XK69b6MPkNfjZpKZrTPb9Xpcr7uWQZZvoDIi5lz7sQxkpo21ykKQi3E99s/W+BvvvI5HfvW17/yf87lFMmySiGgGJNk8as1jSSKDDIBraa2lwg1JqapEJiO0XCrLLsGQsFaRYVlSkBGRGTekSRoul0mAN+ANQOoBSdiCg2F7CTeZaRuAbqqWDIAkQEE3JAOI1kgWLAkAbwADn+unv/uxz+CRn/rG137regWqWI7BX/53P1dM9NwimjMazk9Gb22/XFwVEU/O5zHGmmvN6aoyMqJl670bst1aQ5ARkRkkExEslGUALUOqGwMBVlXvIyPXXCDtAi0rMwkakOZxHLYzcvSb0VoDYIP0se9zrcwwqvfRMkt17Adty32M1hPWWnUch+3M7PdaBOcxq9RaA0HAMmRJmUHjul/H6ABsVGmMPsa4XK7vv/d+a32chkr7caxZjMxIWfssSXAsyYgqy15TkmctyTXFzCXt+5TdWpdcEgTZAo81v/9ov/z+G3jkP3/67d/WS5sRSYZtmSBUWuVVZbskOEhKqNKaJbn3XlV6ENFgrCVLMF02bGOtBYdtSa1lb52EJFskEHQYoEHTgZaZEVi1bkgCsE3SRhk3mWkbgFRrlQ1GkDRQKkkAgszWQBZsyTZJACR/uJ2+/PHP4JGf/MZXf/N6IapgIflL//6PBTlaAkXgNHoQEaFasHvL3ltG0qwq23PWTe/96dOnYQtmsCQTYwyQpWUoAiB7a723qrXmkgRA0mnbbM85g62q9uNie9u2MbptEpmx77vt3vsYg6Sqema2WGsdxwHAXq31m4iYc1bVnDMzT6fNcFWtteac+YGIDKosuaqytdF7Rrh07AeAyLhermMbMCSttXrvH/nIK8cxX7x4se8HGZm51ppzRSQA2QWutQJZxpxLAhn7XJJnLclr1VoCuYQq2V5LERnZqiS4pO+54998/gYe+c+efuutegkw2EgCXIDhNWWbDElrFZEwqmpNVZWNiPADgABRUFlSlW1Isg3ANm6MzNYyrSoJBGgSJMgoQ1DvPXPUjSYAkrariqRsIwCQjAjbay3fgDcADMv3ZCEiM0nalmSDhAokPz/OX/7Em3jk3/7G1/7efkdSRZv8lZ/7ky2yJaW1jT561ppVCnL01lsyUKu8jAdzruOYrbVXnr3SW+AmWNZSBbONBsgWYJLtXkq11iIp6TiO8+mUmXPOll3yy7vnx3GMe721NkZvGVW11mJEyzTgqnYv1lqSAKx1RETvvbUmyfblciE5RmeADNvHcVQVyW30rSWQll88fyHrtddey5aXu4skkrbXWr33KmkVAJKvvfaapHfffff58+dAvPLKK8dxXC7X1npEAJjGsR8WmDlXEQlwnyV71prHJLNKpRJylY59t5mtA5S8pFJ9evZffvejeORXnnzrt30BAggyYAiWtFbZIGPNJaNlI2KtOY+6kQwgIoDwjeiSyrpnyZJsR4RtACQzWkbYusFNGABpgGUY7qNH9DmP0spMALYlkZRtBAA+sF1VtsEAIMkwSD9AMDJJ2paEByqQ/Pw4f/kTb+KRn/zG135jv4sIOErmf/pzf6plBiytrfcxGgOyXdUyg45AMLUslax5lGQSmbG1vp1O0WKp5lo2+ui9Z+8JQFWySZOQTbKq9n0fvZ9Op1Ile0SsOq7XKwASvfcnp5ERINdakgBUVUS03jOx5pJEUlqMyIjMBEByzrnWkkpQbz0iJB3HsVY9PY+n24hscFyue606PxmZ7dgPA7ANWBrbqKXrZY+Ifd9772OM4zgulyvA0+m07/v1urfWgyy7yLUKhVl1uewRvbW+SseaYKx7VbJhOWWsuWyUASOYh9ec67M6/cffeR2P/Cfbt38ndjKAIGmhtI45jRvCXKtgZiaBtdZ+nb4H25ndoEqSMSVD8o0e2I4IACTICLagP0DSFElAAMswzAwiq8qozLQNwDZJ3zAB2AaQmYDXXAIJSCrLuEfCZGSStF1VhglavPn8OH/5E2/ikZ/8+td+c94x04ol8Vd+7k8m2YL2yozzeUsyeKPee81p47ydYNSqNdcxp421SqrT1s9PzoxYdU9wZjLQWo4xMuM4dlm994hYa1mqKtuttYggGBnZslS2j/0AvI0RjG00kquWSirB6q313kpSLVnEvVmr9ZYte+uW1pr7cZTUe8cNsea6u1yenrZn51NGwz0CUK2qOp3Pku7u7jLT9ugdjHksG8c8LN9kZlXt+xxjVNUxZ2azMNdipGQwrtf9xYuX2bbe+7FqrhXZ1qo1tVaZlFFSybVk0CRAAVX1WZ3/xtuv4JFf2r75u01BAqGS7SpVlQSSANdaFgkYrlXHscgACJCAZZVLpWUbNvwhPLBNgoxgMmgVwJtI3NiKyIIlg7QBGBAAAwzCkO8xGgDJkTH6kL3PaRmAbVm2AZCUgWBEwC6V5RsAZHxhnL/8yc/gkZ/8w6/95txBVrls/s3/4F9owQgnQTI709i2cdpakDaC2bNBWqtqaq4J4DiW7e0UrfWS4oZctiyhWmt9jN6yagHITAJVFREA5pwR0VqzxWD2xohgzONYcxIEcBotgoBrlcpJRDAzSUrLNh9c9msE+2lr2SzBJXmWggTA4Fp1uVxGywi0yG1sJC0FoyQTluc8MhNga3mzlo9jL1UgIwLAWuty2SMY5JKMWMtrLhI3Zq5Vd3cXsEXEPmuuVTIRANaCARv7sZdgw+RcKosMAZ/R9svfeQ2P/NL2zf/nHDbWrLVWlVSAb2gDsGUgqsp2Va2l1jpAC7RVqlKVbFSJoG/gG5IAJBEAI5lMSLJNMgIfiMyCLJBhwDYsAAZIgizLNsAbg2RkxpKrCoBtAH4AIBhlGSQBELAeACD5hXH+7z/1WTzyU9/4vb+3X2WULZu/9O/986Nlb9FbGLbKa57Op1eePQHcMjM6ZK2bIkJSVUkgGSEG51rn8zkzj1q25QoyIlrPCJABoKpsk8zMqgJAMoIOzuPI3k6nU4BrTpWq1tbjxpaqLPTW4HuZIRUAkpl5PY7SEgzgvJ16C4D7PkvVbjJX1ZwziDUPgk/O54iwDLtqGSJDVssEkJkRreR9nyQkV6m3JunFizsSrbVVVQLMtSSV7FUmQ/JcUmkZx1zHXEQANDL+X6rgtufa9CwP874fx3lea93P4/GACZExNgqMDa1ajEsJCVF/g0Po69eqRbwYUom2iuomJRAKpVHLh1Tql/6ESKlCitt/UIIdaCulgWrstOkwBPHimXmee63rOo9j37vuNXZ0d9siVvX1uoMBslrX64EIBLv1PTr//AcfwzNf3P7wnz6ExbVWd0uAfCPZhqSIhFlV3S35ZoxNstqWa69Wk2mhqiMCsCHbJAHYBkBGMEDjm0gDIMDMUgMkw4BvJAAGSAIQYPgmIgwCIGmw7VbbgO/wJCJkySbACALyE4gIfv/28Hc//t145t/751/9yvXStgwb/Gv//mfPc57PMwjAhF0rMj760ZdzZCAiEkKvJXmODUBV2WRQOgC0dDqdIkI2ArZsd/cYuZ1mRHS3JFsAt22LiO5eaxnKMY7j2NdxmtvDw0MwrOqqOSICkgAQMSMBSOouqQ0EeTqdmLG6Hi+Pa62H83kkA6GGLQCZOedcaxkifEMwInAjV1fQOQYAkgAygxwybe/7vlYd+0FyjLlWdde2bQb2fdkhWWrZqxSR3Xq8HCQRY9WyKaFLYCLiuqpaOcaqXtVVnZndul6PT+PhF15/G575z8Yf/l5WRErqFsBeJcmGDZLBqNJay09wkzFa7iXJKkuGYVkGGYBA2eadbADBIAgaAEk8keGMRLC6gSBpwLIk3JC4E3FnADIkRQRzVHdLAHwnG0BEyLqJiMwEaMuGDZLfvz383Y9/N575d//g7a/sV9kyJPOL/8EPzBynbcAmMUaMgOXzaW6nbc5UdUSg0dU5MmNk5OPj41oLNzQjxsgxBoKgI0JSVdu9nWZE2CYhubsic9smSLWOdYwcpi+vHyWdttPpfJ4JwqQBEJDa9jYmwe6urrYl2T6dTnNOw5fr5fHxcdu2EbRNxsxpuapOpy0jgAbcVZIiksRg8M4A7LIic46h9lGKiFevXsHoVlUBtGErIsA4VluQ3F2yIwcY1+vxeD0iB5FrLSMkVHeVM3PBS83IWqpqmyAl7Pv+aZ9/4fW34Zkvjj/63VwR0XeSahUMMGDTAaOqu9S6MR0E1OgWSEb26pYMCIbDEGHwG/AMAyQiEoBUuAkGw7AFGzcGSuKdJAAkDUiKyLa7mzcxJMvCnSzjXwjLQc5ts11VkkCD+Ffn+e994i088+/8wde+cr10t2CA/C9+8ocojxFJRMQ2xxyREbDGzNNpU5WlOWZGrlWwt3kyUGutWobvlCO28xk3NmDJRgMkMeYYOY513DCYY8w5xxi6q6p9390iuW3bw3lmENBNRti6CTPuDLTV3VVF0naMyMzXr19bmiNtkzFzwqiu07aNDLsBEyAJwPaMxJ0BSw5GBIB11FEiebk8WiCju/d9H2MbIyVFjpZrqe4MGpRQ7b3KZjCPY61qIKrV7RyJkZd9Lzk4qmotgYwY1f2Wtp9//1vxzH++/fHvxgFwrVZLMsBtjG4d+3G97JYhtyzhLmCuoyKYYwrqsgHDBmwCAkCCIEjcEeATgLjhEwMURBKABBg3BgREhO4AkDQgKTLB0JMWkqRvYIKltg3SthoR3LZTRHRVdUtCEMBnt4e/94nvxjP/9rtf/cp+ack2QP78T/4Q5Uie5ozgyMiIOTJ4gzEjI60ewYeHh+raLzvMMQdAW+5eXa3OjJzTdMgkAfiJuptkbpMRXWUAxLZt5/MZwPXONg1JkTmoOUcmbY+IyGi1S8EYmcxEEERVw15rCZrb7Oqq0logxpiBsGBrjAzeKAJkELAtaTBsAwgGCETI3a1uGWljP3YIkqpq3/fMScL2mFPmOmqtvkHwONpm5FyttQoGyFUCoqpXKTIUsa99XwpmVXebEY7s7k/39jc/+Bie+eL8o3/Cw+axr26TMSIG83q9vvrg9XFUMCTDAOIGoMCuJhkZ5bZMhmAANgEDJolnCAZvAMIGiUyCaBs2YDtgfMgRNrrLNgCSAGRnJiJ1ZwRIE7YBVFXbESGp25l5Pp8lVZXvQML+7OnF//iJ78Yz/9a7b3/5epFtGCB//id/GNBIPpw2EklCiog5B+m5jRE5MglHxDYnjMfXF8hjDANy78dua4yxekXEHAOAJNuZAdCWiBwjAMGySW7bRnLfdxgRYSnIjJRWd0XytG0jEmStw+qZMzK6FRlzTlkgV63jWCQMbNs89uux7zO3zAwG4K4CMAZHBklJ3Y0bG8YNSRAGumSbEWTYWKsIrlWrllotd3dERqaEqu5Wd5NxParKOYYRRy0gMrLb3b4ex6pmhMBlVatWG5QtARGS39Lpb736Njzzxe2P/7GuXaqjwSRwXOt63Y/roeqIZKTbIHgTAdCEjO4GYBggGYZlAIQNmDeAAZAACAYRQdIGCASfCK4qEEAYgG0CoCXBBA0QMMC4SQPV7Sc00PANyZZsk7Rkxrir6qoCTEbryb/28PLvf+dbeObffPftL18fbZgmyF/4wo+0e6TOY0TEeY7BMFC9AM0xtjHmGCC7apvjxcNHunpd98ysbhBSrSrbVUeMHGOAUGvOmZmSjuNoaI6ZIw1Ul+055xhDEo0nBA0K85RrHcexb9t2mtsYgwF3qUWyqmyfti0yTR+1qkSgu3ME4WM/1B2MMcfMAaCq1JXJOSYI31WXWoYz8gbm4+NFrcgERCYZAK/X6zo6M9uqUmZKlNUt27XaoIBjdbfbsE0OAzC6tR+rDMFtGCFj3xdIkGstgRH552r80qs/g2e+ePqT38W6Pu5qn7eH6+PlT7/+qspWByIifQPTvEGQoAjAqxs2QCAYAcBPaAA2QROCQRIETXhmMkQDJBEEAa9aIBEU4CeyBDAyItJ2Sd1iRGZKqpZB3BDlJ5EBuyUYhiNyjEmyqiTZBmBb0ufOL379k5/BMz/27le/fH2Nb+IvfuFHEAhWAhHx8rw9bOccuWqtdcA6zZsBGsaIPJ8eYIQBcB2HYRKr6vFyMTRGjjFAjnkzbK/u63GtqjEyx4iIrpZ65JinGcH9crWxbVsG1dq2jIjr9UJyjpxzjpEBHvshyXZVkZxzbuet1F2KiKrqXlVLXUEDtJHknJut41hWjzEyE0BEdLckmDfbdoqIV69eH8cxxlzriMjMYfly2at6204O1KpaEhyZalvo6tXVpoSWq1USmNaNAQos6VirTDBtVHW3AMpercj4nj79rVcfwzN/Lf7w97LX3iqH4+tff/+DD16PsdEgCFKGYRokQRAEID8haRsIjgRgw6Zh2YaNuyAMW0FkBsMBEiADd91tEoQB2YZpMG4IhmRJLZnMHLKqWmCQAMsCHBGSbAPEDZkZjOgq2ZJwI5P87Hb+9U9+Bs/82Ltf/fL1NQDbAPhLf/XfAJ3R6Cb58nx6cTpHhCnfqCydti0GExFAxlirtjHmmGpULbltP75+LfTYtpHZrdPDaW5bS23Jrl7dHRkjB2RLAB2KiDoWgNNpI7muu+HTaUQEbuwIzjm3scFetWAcN+sYOd54440csVapZZuBy/Vxv163bYuI7lKJ5Bgjk12rquOburuqYQLYttO2nS6Xa9WyvdaKSNswq9omwHbb7OqSxhhqW1jV1S2jDYtlVavtetJkMLLsVVUKILvbtiQ4AF6OZeAtnn/59bfhmZ/rd/6Jj8B4fHX54P1HOoD0N5EhWMYNQYAkbiTDYgQSQJBhWAZMwwJgGzbAoGVJERiZpPghRAAGZBkwnggGMCLNsN3dNsAAWZbxoTBgGIBgAJalBr9BUt5VVXfjQ3JEfHY7//3v/DSe+bF3v/rl62sAvuMvfuEvjRlz0F2GX754eHE6qcrwNkd3H8cxZ562IXtmut2r4wacMUnK3epWW80gIqqbwfPpZMLkGMPWcRxVFcHMIOkbtCSSEQm4W1K7O8g5R5AAbI855phzDsrd1d3HsWCMMV6+fAHger0C2raN5H7shi11NUlbQY4xunutBWDOabu7JY0csixEZFVLrqrujgi1SiajqskMpqSqOlYZttCyAJhgytz3Q3BErNK+7zLbRgRImUfj2Fd3R2REwrS9H0cZf27lr66P45mfq3f/Tx3H5fjg/dfHsTJGIIEwUZJsPCFAALItkYwIABFJBkjbgA0IAAHDsAHbjLCt7iQy0zbJzCRpwDDuBD+BYRAJQHarDUQmiNUNECBIEJYBmLbc3YZJGiAgMjNJ1lqwM1OS7SA/e3r4B9/5GTzzY+9+9Sv7I4DuhsG/8eM/dD5v28wIAx4ZMxiR5/N5RO77Y7Wq1hsfeYiMERlgr86MOlYgImi7qiTLsuXA6XwyaCsjODLGIFxV3W2LxBgjIoyuWpIZrFXdPcbYxrBsN4AgAUcEyCBHEPCcM2OsY10u1xcvztu27fvVdkSMkYYZVPW+77ghaPDOtiSSEQGguwkaUiMibXb3vl9tdGsdq4WHFy/2/YAZkdW9jrruVwYzpuGSu21T5qqKTJLX63Gsisiyj+5gNFBNCd0tGabkm5LA+K4jfnV9HM/8x/XP/3Fd3/vT9/d9JQcMGUAIbsCADRhPbMm2+CRIkBkME/+CCNuwTcoCkJkgLQECGaQNkhGBOwIgBN/IT1qQTYARhnEn2yRAA74RBFsi8YQEYVuApIggqe6MIAOwpO7+3MPLL33q+/DMj737ta9cH21Ligj+zR//82PmGNxmjpmW6N62+eLFiznGWscYeblcznPLMYI8b7Or1nHIptEttWHZJtntQ/vctrlNABHBDNlrHbbHGPEEEYEnWrW6m6Qk3JADkRl2AyBujCeWlMGRPJ1Oc57qqOt1nzNJ7vtOYowEEBkx0qW1FsmRuVZZypEA6g4ASQCyYGQMMmzYWGvZUHsdtbrHnLVa8o1kta7HNTIzpq0lWWyh2qsqx4jI42Y1yJJWW5aM9iBDUlVXdZcyY5XJ+O6av1J/Fs/83P7uP3r9/uX1Vc1AApbRdoMmyPCHZMkfAkAyImAHg0GDJG5EPDFMtFrSGAOkbQIkAdgmGRF4QgZvBLftlizBBkgCMCDJMEmQQIAwoLYkGCCekAhK8l1E4MagzRvAgKTPnV/8T5/6DJ75sXe/9pXrBXZLvPmlL/wlogmPmafTJDzC25Nx2jbCc25rVVUTkHQ+zfM8Xa6XdawkDEOsagiZg+TleGz1OG0EImI7nWJEVbXadgQjiW8wwVbdRATJ7qq1CM6Rc04ChmF0r+PYT6ft4XyOCBojR0YYlrTvuy3eZaZgyGq1e2RIrirYmQNA3QGMCEk2CAJhOyIkdzfMlqp6VZOp1qqCAca+7yAyZnWZtNlyC6vKYER0q9ottSGwqhvoJsCIWEcdx1qrAUg24tM4/Wp/HM/83P4Hv/X+149rBUdX+4ZoQYAJ2d0GEKBtSbZJAshMywQYBBERAETaJoBgdavFCJC2IxhByQAiAiRgRvAmKN9AUktlRwQJ24ItgyBoEAYCliUDIGlbNoIRoRsbQJCGYcCGwbsxxvfP069/4nvwzF9592tf2S8wuls2f/FnfmQbtBRwjhgZM7idtwxtc3s4n4mQvNcBI8CMGJm9ynZAgIHYr4dKY8zMbFV3KQJQBMc25zYjsrtLBdiWocggk0b1sdYiGcHuZeMmwbzjDWD3WofgmePmNMY2Zma0WtJai3eSABBksG76IAliv+4qbdvGiK6GARJmd9sAiCe0TRJwdR+rYBLZ9r4f9UQjZ910z7EBaKKqZZjRLVmS15LBVR05HbFKLa1yMGC+9977x7EyB0mAAN/C6Vf743jmP60//M2vv/f4eg+mpHYzwoYAG929qo0YEQQk2Y4IkrijYRp0RAAQwjZvgrLVBoggABIkbYuICAAGSAIgaRiGLMkFRwRAArJsk5QJGHeSbAMgKRvBGwC6gSMCgCwaJC3f5N2/kvNLn/wMnvnRd7/25etrWZZv+Nd//AdfvjgTHhmkbZ0y5mkSPcf4ljffhLlWybY0IjKiq2Gftg3wqsONYz/UnnOTGoEbB0mQAAmCN4DhsQ0Gq1YkiawuuwHbiKDUNkgkiA/ZEZHJbZtHrfe//t4c42Pf8q00bOWIm7UWAJJrLVsgCMqtNsmqulwuMDIHAAs3mTnGyMzXry9VlZl64ogAsLprVcQgxmW/HseStFaNnLYlZQ7JTXbLpkBZILvcrVV93Y8YW8x5VK/VQADYr+vVq8eIyBwA/QRv4fRf6+N45j9Zf/i//umfPL4+iAAgNSJMSjZge1XLSEaQtgFEhG3c2DSRBkFShoybjERAttoAEcQNyaB8BwRpQgYBBgH4Rm4LGQwCsAzA8B1BkASgOxgm4s6AYUm2I4KGLILJkAQ7Mm19/zx/6bu+F8/8lXe/9luPr2yTkMy//uM/OEeMjPNpO5+3WivZOQahbY4XL16MmFUFQN3qthwk5NM2t9O8XC779YjIXh2RkcFASya2bWaGbMNu2UYgR44RshgAsrtB4Y4E4JuIGIz+UJXtCJxO2zxtaz9qrdPcTmOOOUBv22b7uOtuQ1UrIsYYFqvKdnfT7FZ38y5ijJFjxOWy7/uRmTYk4e5YJYFIyftaVQWgquAA0N1EVLdzSDJCRltEqF3ttbrUiCyzJZlE2rhcLvu+IlIyDBs23uL2t/0deObn9nd/80+/fhyyYBuwAAMyZFtqW2aAxDdkpm0AlmAwwSABGW3cZCQCstUGiKABMEjalmWYDJCCCYIgCViyYQdIApCEO0lmADBAwHZ322Zmwd1KAAAgAElEQVREjryxLakl28mgITiMIAH4Q9LnHl7+xnd9L5750Xfe/u3jSrK7LfFv/Ic/OBJzG+fTfPHyIWyobRkaGaftCQHIkrp17AUHCJoP5wnrul8ZOK7r5sWLj5y2PNZhYzttp9MmWBZBWRGwLSmCDJDITFk33U0gMkmMkQS723ZVHccBICLmNmKE27Rm5sP53FXbdhpzVB3rOKpabnkBcSOjq+HwDQHz2I+qGjnmPCUjwYav+y5pjFHdtSojDRyru0Vwde/7AWe12rIsWTaIzFEtIFZXtQxKltClBgR2ewkkgyTi8fF6vRxAdDcMM0m8hdPfxsfxzF99fOe3PnhPC9WSQVI3dsk3IG3IoAwbAMnMtA1AatARGUHZBiTLyBg5UnKpAUQESBk3Jm4EA2TQNO5IArAsC6QNwyAZoTsAJHFnW5JtZmQmye6WhLswAsT/n+5+8OUbv/Gpz+CZz7/z9m+vK4GqAslf+Ik/H+EIzBEvXj6c50bIkmGrIzDn3La55SSjqi+XHWaOAeE0Y5tj36+vL6+O6+p2RM6BiLQRmafzNuZA4CaewPZaiwRhU3OOqpZkm8CY0zZpMgBERFX1naRIzm1mJCS4YczxDTkIeN+P47hu21irq8oGwFqSFZnBrFXdbTljjByDUdK+ju6OyFYf+5ExXn7k5VH93tffhzm37XK9dBHkktwm81iH7Dm3bpk81qoygt2yWFVLzjFlHC2AAXbpej32vYjsFgARBj6t7b/JT+CZL7z+Z//o9SsdWiUTLQOwJVGAbYA2aMAGQBKAPwQTfBI0CcC2ZMbIDAClBkDSgEEAJp5EGDZAEoBtACS72wAISQQdAGnDFgB+EwBJvosIkpJs444IkPATRuDGbhXgf/3lR3/jOz+NZ/7y77/9W5fXACTx5r/8qb+YyQzPmafTHGMEsJ02EmvfuxcDmXmep9P5rNZxrIxBsqtHxDazaq06jr0eHy/X6z6S27bJtLVtcztvOZPkGDlGSOruzLBldGZWVXcDiDvAkiIiM0nqm/bjkCpHbttpBGeGZZLbnHkzYszs1vXyGvBxrO4GIiL361FdjAhERII49kPljKTRlmAAarVufFxrzDG303t/+p7BedrWKpiReV1rHT1yru61asQwsdrdBmngWAW72gJyjOreq4Cgo5Zt7vuxDtmy3YSBt7T9Wn4Cz/z0B//371wedbjkUkuIYFUZYbK7AUYkZBogbJME4DsANm4cvAEsGZHBJyZubMs2CMAEI0Aaxg3pJyIJsqtJmpAEQLCBzJSEb8pMkrojGREAJNnGNwQIy7KCNyGrakXED7/x5j/4ju/BMz/67lf/4eMr3/Hml37yh8dgJsfIMeJ0Os2IuU3A++UyxxxbHscxEKfzads2mC11l6Uw5kjA1WVZ7VW1bo461opgJsc25rZFMjPnDICSSEQQNAnbay3e2c5MwB+KCDwxwVbLXVWWXr548cZHXuz7XlXzQyMjQ/KxX/b9Kpmkzczcr0uSaZUBRkTfLMGgvbpt5EiQ3U3Efl3HOrbtvO+raoGxqgCOsV2Po1Zv27nl41gGJQskw4Dk/VjdNpkjj6pVJQIIF2o5YuzXdRwFQLZJUW95+zV+As/81Pv/9H+7vq7dNgV0y0DVAlNkV5ERmTQSAViyYdgG5BvYxk1kBH0HPokIZgCQ1JJBACYQNJ6QRNC2pMiwnoA0aJhGwwAiwrYkAHHnb8qRkSlb3bbxjO5457uI+MGHj/zGJ97CM59/5+3furwCQBIAf/mn/8KYjJvkcVxPc370Iy9PpxOAXjXHnNustSCfTtvcBoDjOABl5nG9wjhtW3dHMCPXKgmSLpdLZsglec4ZMwFHcIxhu3tlply2I4LknDMiqsoG4O6yDYAEaQJqjRGAj+Mg+eabbwTjer1m5jZnjoG77nW9PkYkwH0/AKrxoet171aAN0AECfnYjyVFpmGbc44qXS5XIGr1DcljVRkR2SXJc24Wrse+V2UOMuQb2lir92owIvO6HwaZw9Y6qo7O3B5f74+Pl2072TCB4Kdj+2/xHXjmp9772v+xX66XAwhwVLfgmzZbssSIyJFggJAl+QYwLFh+AiBikARgG6TtzERQtiUbAkCCEPGEBMEIS7JBwpaUIwV0NwCSAEgCqCoAYwyStgHYZgSDttVtgKTlyNAdbJAwQCQDwPfP0//yXd+HZz7/ztv/8PEDkhEBgL/yM39xzswMubvXy4eHF+fTnJNkHWuMcTptBGlkRs6AsdYCPEYe1/3VBx/kGNucD+ez7VevXpOxze04jm2bpKvbNkdIArRtG8m1jjGyVWsdmTnvAFRVREg6jp1kRJAAnKRaUo0RNo9jP5+3Nz76hrq6FRGSbI8xAF+vF4A3Vaoqi5lJ8nrd1yoaN2TOMVR93FS3BSAiM4fkfT+Ofa3VEQFgrSoEbkwybGSOal2O3QYYkgHa6NZestH26o6ctltSqYvkeP+9V5fLdc5NEkcy8nvz9GvxHXjmZx//3//98vp6OSS2UN2MkNVCSyRBMpJGGDRsAzBsQHZbtgFEDJIAbIO0nZmy5DvAEQQdMGCApIkb35GUFDeZpZbEO9u4k0QyInAnyXaQkWlbEkkAtiPStiQAtvENBvC584v/+VPfi2f+8u+//ZuvPwCQmbb5X/3Mj2ynjAi5ST+cTiM45wSg1hxzbnPmGBGGIpiZ3S1VRNi+Xq6Pj4/rOMYYDw8PI6Lb3a0Wie00b7qq6YioOiKYOUiMka26Xi+ZOcaoKkkkt22bc3ZXtzKDBKBkuNVdgOec3X2s64sXDy9fPnT3WtVdNiJireMG4E1EVrXFiMgMCVVNw0a3LPUqySW124bBkQPkOur1q8cu58i1lg3OU9+UIhJgt0Au6TgWwcgho1vdXi2T1V0yGDaq2w04rfj619+/XtcYQ22OiJGfju3vzE/imf/o8vu//fr9Yy8Jq7yqmGFbCEmwTTISchg0bJM0YFiw/ARAxCAJwDZI25lpQpJtASZNgjDuggBI2gZAsrsBkCwLQGYC8B0A2xFBsrsB2AYw5mCEulsiCcB2MG3Alp/gjhDJz51efOlTn8Ezn3/n7X/4+AHvbPNXfvYvbHPOEXNOqGHF4GnMHEGYRsY4n85zm+qWFJm+kSIpy/arDz5Y+yH1NrfTdppjWzd1tHpkcoSBTM65SS217TlHZlbrWEcEMvJYO8kxkuD5fAJ0HAeBIEHTMcfoXqDmHLCrS/K2naqqu8iIIECrrV6rMtNgVRNs6bpfP/rRj65j1erI7KUIqlUtIuxYa12vO8htO0m6XPfr9SoESTDAJFhVbQOoLht2XK47EWPbJMg+1lrVjFxVBsGUudaCwkjLr15dPnjv9RibwcgA+ZnY/rvzJ/HMz7x+53euH6xD3V5HAzSzumTY7i7eRMKGDYBkxMBddeHOxg1JAAYiAkBkgmhYUksCQDKCd4INxxhB2pBky7YkkAAiAoAk2yRN3JC0DYB3mSlb3bYZQcC2TOPOpvEh4skPnM5f+uSn8czn33n7y9dHPKEk/srP/vCLFw+nMTIiCFtMzsw5GIS6VZ5zOz+cCXZ3RJCURFJot9ZasGH36szYtrOsVUfdHXW09bBt5/N5jHE+n/b9sladThsiVxXpCO7HFcDD+YwbW25ItgETzpgPD+djXQDPOfDEVS1prWUbd3POjLBcVQDm2Oac+3697HvMmeT1sku2qeoxhqRjtcXg6NZayzYYAI619uNoMTKqDWTmsF3dqzuD3apySy3LAMjMqt6PIyKrJQcYpe4SHBItP1721x9cyOyWwTHH9+X575y/E8/8zOPv/8711SrVobUKoJlV7TtJAEjimwxEjCBbskzCtiTbEUFSEiNAAmamiVZ3yxG8ieCdYQExMiJ0B8C2JN4BMEDCN7KJG5K2SUZGMABI8h1JALbbxIdsGh8invzA6eFLn3wLz3z+na9++fqIO9v85S/80BtvvvHitHXVHHFje2TOwZGxjlVHkzzd4S4ibEsyFeSxH4SDUasszXnyDdtGd+9rv+47u7fTFpFvvvmG3Nfr9XQ6RWZLgLrrhsTLly9suGU0buQIAr7JkXbbTQJkBIMZEYaPm/2oWtt2GpnByMx938fMly9fbnP7oz/+kxgTwAfvvSJjrapjgTRgUXLEANhdXd2Nm5ZkyazWvi85byKipWOtzKzqahus6iqJyBwtVSkiZVRLwl4FgcxV7fZx9HH0Ouo4Vtljzn95PPz3L78Lz/z063/2O9dXVe6FatkwYnXbBkASgO9IArANR5At2Q7StiQAvJOECBJtM4Nk6wkyItMwgIgwYYA5CHSV8USS4cgk6TuSvgMQEbqLiDEGST8DkoDtNvEhAxLuiCefOz186VOfxjOff+ftL19f4842f+EnPvvixYtveeONDJImYHBkbDNH8vH1Y1ePMU/b6cWLFySOY9kmIAnBCNYqd4/MjJQMMJIMGLa8tGqtOnYCa9XLly/O500SQBDVFcHMsC3XnEONJzQNSbBIyDaUgQiCyA9FAli1JHW3bRjdvc1tzvH4eKleH/vYx779z377q1evf+//evtbv/VjI/KDDx7XsbraNySR3e72TVWto2SoW7ihGd06VtsZDAAC5Bu1LEc9aYEmAVa1bIA2W5C9VpEB5HGUJAtduF72lq7Vkv6lPP8P3/IWnvmJD/6f39lfdUFiyyoZUd22SUaEv0mSbTIA3liWxDtLthlBQBBzBNmWARO22yYZmbZBgGQQIDMBVBUAkrYlRQZISbjxDUCQDFKSjTEyMmHL3wDAAAHbMgHYeCLZBhAkgM+dHr70qU/jmc+/8/aXr494Yhv8pZ/83Mj86Bsf2ebMCDzReJK0upctG0C8eHgxx5DU3WFkBBgEZR/HEcE5J8DuziSJhkypCxa6Hx8fbT88nOccEewWibYiMk9DVb2OMZKRJNEGQdrqiLQbABkkxoht27pbLVlVlZm8s329XsYcJI/jkPTmm29+y5tvxhhf+e3fhvntf+bbX33wuNZKDttqMAhHlau01rpcrtc6LIOR8wRk/X/swd/Oret5HvTruu/necf45lxOHCkENX8gtkuqSI2DACWBUNhHxPQUUB1aCdE2hRbYoBQBAgRNGgRsQFLY4gSQ4mOgTtI9NpCc1o3dhKSV7TXn943xvs99XxfvN5aXmQXOoPn9qrodEWBUlYwxZrcNtnwcR3UjwqDkbpUoqSwYZMgw0AermqTkXt2ypPuq46ifiMuvff/n8YEvf/vv/u37sxpA9KlaDpC2ZfMEkATZ3ZJIAmEbgCUYmWlbUkQAMMAgggZkd7cIgAYyE6cgHhgUQNI2/lGWZQGwDCAySAIwnDkIyA9wZgLobgARUVUWTzjZsvEQJICfvjx95Ue/gA986Rtf++r9BYBtnv76L/1cZlznFoGIZAByZo6RGcxgd+37buPp+vR0vc65dRfsjLANgOB+7FW1zW3O2Xa/WgByBGBLdL9//x7AnIPEGCMzSYI03HCtRWubl7lljnSralUtktfLJTOk7pbUc+a2bbZJVpUsGMdxdPecIyJk+YEkgM985jNjzm/+3u89v3u+XK7HvtQeOSWtEgxGqtHl7l7VpT6OQyaYMshYq2UwwoZsCREpo1tV1ZIAGTaq28iIPGpJBgJAdd9vDTAiq0vljHGs4yhV+49j/I8f/Tg+8OVv/Z3fvr9TBxnd6up2gLQtiZ8ADAI+AZDgEwA5SAAkbZPEiTiJYIRkwyJsgK9wIkGQBCAiIvDQLcARYcCWbckASJAEvyuCVS01GafMlNTdACLCJ9E2PmCbAMkvXp6+8iOfxwe+9I2v/a3bs6R44K/8xZ+d25xzRDIjLXU1GXPmzNxmGu5uANucc8zr9VpVXaXutY4559u3b23v+56Zc87V2vfdNmxYEZGZ6uM49n0/MknicrnMOddaDDKjAbdoB8h0jkxGZq613+/3EXl9ukTQBgnSALp7m1urSQI49uN+v485nt48dVc/ALAcGYy4HcfaF4zjKIJ0rCqZXd1tMuCIGBGxr/39+/fVNqLFU5VWKzLJlFzVMgF0t+2WBEgwUFWNIOKoBVAyGTJu95Y8RhJR1VWqWtWo1j/D7X/66MfxgS9/++u//fKxRYM2YLRZtiQ/8MESQRLdJsMnADIB2/EgCYABw4gAIRlBEycjSNgWHA+GEUHStiTDMEiasC0JZJAguyoyL5cLyGPf9ZCZcwydug1IiojMtCjJD5lJUg+Z+cXt+hs//Dl84Evf+NpX7y8AbPP0q//+vzzixIy4XC6267gDHCNHxjZnRnS3pMycY162TVJX7fve6m1uT9drZnYXI2RLysxjra5KgBEAZvo41nEcatu9XbY5R6sBRAYjAIwxbMMNmOTlcsnk/XY7jgVqznmZg4DUq1vWGCMjWooIwGqBYITtVlUtyxEJIyJWt1a3sI7VssqrygiC3bJDwmlVv3v/AgYju9FGda2jWoxIPLS8VnW3CJKSzZBssiWLtlc3zG4xAohjdZcZBPI4lu21quVuf4Hz1z/6HD7wi9/53a8+f1syEQABGrG6q0WGJNtJWAAYQdsAbQOwDQMkARI2PmEaEbJLYgYJGyZtE0QSpCVGMMMniREk4RNk2caD7e4mObbtzZs31X273WCDhB0kSDxIzsxg9CqStiVFBMnuJpmZPzUvv/HDn8MH/o2//3e+env2AwD+6n/4p2aOsIOYc44x7JZEIIOXbRuZVWWbjDESdjAio1bt+y7p6XK5Xq+2I9OA0E/Xp/v9XscKcD/24zi+/zNPlo9jdUvqOceciaBtSXObkUnStnrZIjlGnLq7quyec2TQ3QBMgiZpq6ojYoxhGzBAAIYsVZdlIE5VBQOOtWpVr6Ory0iC3ZbRDdtr9bvnu2QwhLBR3VXq9gmgQbVkHVVtRSQYAiSAYbtbtrslwyDBKkmQWVXdboEMyVUt6fMYv/7R5/CBf+vd7/6td9+xYcM2TDBW9WoBgZMVOBFgRNgG4Ae84gmvjE8QImAYaAsZIAAClAQSpGEAEcEM2/gUSQC2JeHBtqTInNuWmfu+V1Vm2u7uzMR3sdsMEkQ3HkjiQVJEkPzivHzlR7+AD3zpm7/zm/sNdncD4K/85X/per2GTSIjL5eZmbIgZ3IEA5T/H12dkds2xxiSqiqAOSeAHIMMs+IT4P12X7WeLhf10dXdtiH1GJkjIknGcRyMiAyS3YLLboB4pcwRQdCwCY0IkCAFVVWQksYDHrqbIGnDllYVECT34yA4cq5Vx1Frr6NKAsAqSWi5q408yrfb3aBMMKpVfYKNk+yjKiLq1CbDoEADICVLLVmCZESoXd3ksHCsVWWANqvbLdlfwPj1z3wOH/jFd7/71XffASChWzQQ2d1H26btDIZtEGBE+AMAP4FXxidIEToRzDCAABFuySYpgg8GHHwFyAZAMiIkWTJAgBGWQWSmpLUWiTFGt2xnpk+SDMkASIZxIhkRtmHL5sMX5+UrP/oFfOAXvvG13zrumamTxb/xV37+6foUkLojYtu2McgICGPEIG13t6SIIGmpVpF4enratq1bqiIJIDINGM3gWseInDkYHDmO+7PasomQOoJjBDMiuO/HfuyG55w2CAE+dTfp6/USkXYHEMkRIbXJyKyq2+1l27br9QISBghXRwRhw5KqWm1GrFoSMkdVH0fte63jkCm7ShKruqVa3I9mhIFqM4fk/agqkQRZ0n4ckGUgUmC3yJAN0kZ3tWyjZYASJMPRcnV12wrB1XK17D+O+Te/73P4wJe/8/e++v7bQNio6gCZo+1qS5aUEUFIgBERtiUBsE0S4AmvjE+QCna3iMwUTUZkwLRkwACDUgswCYAkHoIE2d22SUYEI9RtICMAkuhuSQBIRkS3bEkGeIqIBAHIr0hGsLsJnn5qXr7yI5/HB/61b37tb699zimpqvjf/eU/RWJug7StMTKDmRkRc86IAEBA1REhdVep2uo3b54ul0tLYgCwPWeq+1jatqlex3Fs2/Z0vaiXxWAAgl2r5CYRwYgo9e12q6rL9drdQUaE3LAIj5FjZhgZGQlJtiM45wSxquwGYIAAw5IjKAEgJLW7LbXtloywcRx1rJZMxH4ct/thBZltv3t3O466Pr1Z7aOKDEZ2aR1lnLi6q0umT0GbBiRWFU4kEdUq+SQBDMkqtSxbDckmDXSpqv5EPv3a2x/DB37x42/87x9/G6CtbmUkItsGUNVVFZEGaMMAAoDUkm0QmHOS9CtFBElZjmirYWaAHBEOOgK2JD5UlW0yAEdkRNgGILu6bWe8AglbMiPHGLarSpJtACQlASAJgA+SIsIPEWFbEmWSP315+sqPfB4f+IVv/s5v7i8RCbsl/rf/7s/buly3bZtSAQ4iIuaDbZJBjkiSx363Tbl7zTm3bTOQ29WWuufM7j5WPz09ZWDf9+7OwBxjxCTQLgi1ltzHsUfw6ekpRlbVcRyrqrsvc0am3UEC6q45x4icY8wt/QDAFuzr9WnVvtYaY5CQSraAbkVkH0sSEJIArOoWTlWWYUPCWut2P+qQEWW/vOz3vQ20XGVEkGFQpe4W2Gr5hLbBIKPltbqqIhMgENVa1QDskNQyHQZs2KyqVcox1lpV9ZPz7a+9/TF84MvvvvGb79/5JLVEhhk2TpK6/T0AySBpu7vVtpSZIxM82QBJ24gw4CRIWRFhwqRtALYJgvArkMxMkgC6OyJsqxt8AAxIFpiZALrbNj5lGwBJ2yT9wAcAJG3DoF/99OXpKz/yeXzgS9/8nd+8v0SEH/jf/5V/ZYxkoFXHfp9zXi9jjDnHiHwVpKRkzDmDWGv1ql6H4cyQEXMbcxLIpLrlGBlSkbzf7y/P77c5rtsT4FaNGEFGsLWqKiNymxEhaVUBGJkk7Y4gLMBjZiKCZADwac4RwWpF5lp7V40xSBiKTJ+E7XK5Pz/fbveIoW6QMlo8jl0CmLbXUlXJqMO3+9G2OZ5v6/3zCxGMbLsbmSOAY622QQC8r6ruEcnIY1WVSAKQoYaMNsiwsVa17AZAGzL0Cggex7FW/eT25n/+zI/jA1/+zu/+1v3Fth5syLAZQclVsmUbPIEcfPBJ6CpJJMecpCWBgMEIEyIYNJCZBhS0VFURISki8IoRQdK2JIBjJMi1lqTMjAjbkmVEBABJfLANQJJtkpLwKdskM9MPPMkAfvry9Bs//Dl84Be+8bXfPG4RATBI/g//wb865lDXvt+P4/7R2zfX6yUi5rY9PT3hRMBAa45xuWz3+22/32odJEfOliPzcrmAJCA1yciAvW1TXd/59rfXWiMHZMNzzBw55zjt99PN9LZtYwwAJGwDyOQYqe4IZCZhS7Zsk9y2CaDVDMptdEaClB2kSRpv377d7/u3vvUdN2SRyDGIOPZ6edlLAKjW++fnEjK3qm65xaN93491lBEyZWYkgPu+247Mtqu11pKcOSUfVXwVklerBZ6QkleVZClkS5YMBoxVJanVfyKu/8v3fQ4f+M9e/vD/WDfLsFtyiwyQBGxUteUTgyBhkqFuAJkjI60+1ooIkLABG0DQgEkQtjlijBGRkgzD6C6QeMUg8WCYIMClBpARAEHAkFoCSQB8wINtSXiwDMCwuiMzIgBYNgyARpA/sV1+5Qd/GB/407//d3/ruK+qYJDgr/57Pz/nCALw/X57erpeL9PAtm2XywUAI0gGsI0J+H67Va1VRzIYA8A2J05kZvqkGg8ZhPt2u99uN9iWu5uk7Yh4+/YtCanlHg+2usu2JJKXyxyZQEsOmoAe5hxzTttVy25mzEucjmOt1hgpeOb4wR/8wdvt/gf/1x+qcKx1HMdlXi7b5eW2Pz/v+2rJsF9u930JTDDgaHnJtfp226sVeRHQ1bZhlwSwARDdWqsj0mZVMYKMVb2kqmYEHGqsVZLMAaBbkhFhed/3CEbkT8T1b779p/BHPvWnf+/rv73ux3GQjAj+8i/93JwjAnPkWsc25/U6AeYYEZGZkWl45tjmtH2/3dVl+X6/AbhcLmOM7s7My+USjLXuEZE5CAdFxlrL8jpe2e5uSWOMbZuZYWiMsW3bnENqSWst25kkQJonGGqQ+SoyU1LVsmU6Z8w51qr7fkQGAyPHP/lDP3Ss9Q/+8B+uvT/++P3zy/3N09NluxzVXdhXHUfX6rb3fd3uh8wcW8Roed+P55fbWsq5wTxWkYwYq9dqMQIggGodaxG0gQibVS2gWsG0qFeoapM2JLUAUFJV2Y6In8w3v/b2x/BHPvWlb/7OV+8vkiIiM/nLv/RzmUF4zjFGjJEjyTil4ZGDGd2djMtlm2OSvN1u9/sdLY7ITBJqMWKMYSmJOeYYedlm1+puSb1WVUdwjLFWdXdVjRFVC8S2bWOMjz56a4tkVXW33ZYiECfYFsnIIEASgNRSIzFmZobs+/3IzH3ttWqb25zb2tvyP/iH33p52bdtA3x9ejO2y3HU7X7cb93Vzy/3+77AYRBgq7vc8u1+tGjAAkeSlF0tkDZktVzVlhuwCRCmybXKsmQJEdmlJZ/0ygYBqNVq2/9EXv637/sC/sinfv7v/Z9fV/shIvjX//zPjBljZCbHGHPOTDKYGbYjwu4TEdt2mXOOTAAff/zxtm3IUPfMWFUEMmBpjG3EyOT1stU6VCVpdYHYckaE7SAllbXf7rZhr6rPfvazOTKT3WULcNciQUYEbNkGwMCcY4xpyxYAkpm0XVVz2z5+9/FxHDZhBPNyudxu92995+PkOI4F5pu3n1Hrfj/uh9axPn73UuUcl/1Y9313ZDDAfLndWu5umxwDsE0ZYETmfd9bIkOy5G4TPLVDLUtdkmBCUjVl62TIAOiTZDuAH43tv/7oR//p3PCPt6+t/d/8+1//Wh0m8cDTL/+Fn9m2MbdJOjPHGHMmgxEEQNJuAIxBQFYwLnMDsKpasjsBRmTmSBKIGABuLy8j47rNka02My0AACAASURBVKGWYLWIV91tads2EWrNMSDd9p0kiDlzjCQhddcicSIdESRtM5CZ8Qp+wCtFBICWqqtbapFx7IvgsQrIbv3+7/9Btd++/SgzjWhFrfr2d94fR5tZpf1YAmUbsaokr24gQFoyAmSMafvl5YWZZHZL7aomCYRAtdzqlmQZBlo0YFuGzZa6GzYAAy3869v3/7XP/DH84+0v/cE3/9d33yLgh4gAwF/5iz+7bWNuA3BmjpGZAYIEycwEZIM5IgjA9oi8Xq/v378nuc1Z64iIOedIqnu1MzOIEeyqDF7mdqxjv+8ExhiSunsbs2nIl20LRqvfPz8fVdfreLpeIwh4rd2SbdJzDhuZGUnbkjNpyzZJqQAQGNs81rItgeZalZlr9e1eBN+9e373/jkiyYiYq12r9qMl7kvHsWSUuapNWm6g25+QGggwcsxVdb/vOTYE3ajqqgKCZAvdbalLEkxIbtMGSZBGVLWk7oYtuE2DfyzmD8UUDRAgAAMwYMiSJQMGSCKSIVldkiNoU1JE2AYQmQAsgaBpyDBIAyRjpKS1CgwGghEZMGQFw3Z1kzCAYDBs0JQFGAg+APADwAjaJmlbkm3YIGGDxMk2QDyQ3U0yMy2B6BaAP6j6eh1hfE9EAODf+Ev/YmZkMpMjc8w8GSZBMjLtljpzRASAyLxuFxL3fSficrnQ6m6SSUQQ5Ns3H6lrHUfVioiZuVZ1VwYJSrIFQLKlzJxzZA7Dz8/P1QU7gk/XKyC5JdkifbpctsiQZCte8ZSZVWvf98y8XC/7cQCuaiIs5Ji3l/v797dtu9p89/x8u++1GuAqr1V2MOZauu2HhAKruuXuNiBjtbplCYzIjBzVVauNiJFErKOqGwgAa5XbALok2WS3lkASpA2ALUsqyZJsg2BIMMwMADJtVxVARtiGLUA2ELRpnizYBiBZ6ogACMAGySAtt9oAiIgwnHNEjO6uLmb4AYANEieTlhnMTEaou7oJ2sYrkhlBSd0iGREASNiwDViSbTyQBCAJAB9sr7XyFNFVmQOEZQCG3eKnMtM2/5tf+rkgkyJ8vWzzMjNzbjOTRxUA0hGRTJ/kjz76KEfuxwGfsNaxjZGZ3T2Cc455GuP9u/drrW3bRqa6SUrKEUmqtXqRcLm7JW3bPI2R+37s+36/3yPier3kCEAAI7Dv9zFi2zbQJKQGwIfM7G5JMFaX7W0mhG5JqPa755cuSRCo1vP9roZNCW2sQy0acd/Xvh/LaJ1wUkvG0V1tmASZZITa1V3yGCOYq7olgLZrtdonyZJtdqmAiLAhG6BBSW21ZFuCDDJwCsqWebItWzBkgIbbBoIybCIYGaTttZZtkgAZaYsgjaqSZJjBMRJkjnSEpJYE2rINgGTmAGDDNoCIINndVU3iUyQDgG1JJAHYBsCHiJDU3ZJs88E2yXgAIIlkAL1KdmbaBmAbAEkAtscYAPhf/Dv/wswciTGCQCY/+9nPtjoCY27VZXdm0iEpIsYYmWOMrKruPo6d9rZtETGSGSE5GJYBREQSlg11dyYJWF61pKbph23bxkhJESHpfr+vtUiTyBHbttl+8+YpM9baLcVIwIAlRxBgd825SX273SIwRwZS0rF6P2o/jv04agnMKr/cD8ndMJhjHkfvRwN5ux/vn1+WaQsgGG1DbGlVkwHQMBmWWmrwZGFVg4xItdYqCSS7ZcNCt8qICAOSjLAhqWHDFmTYkHziSNsyP1HdpabJoO22gaAMGCLAzARQVQBIqptgZhqQ1N3ySQDmnMxgBIKSqiUSgG2SEZEZAGFKwkN3A5BkOyIASPj/8gPJiCBpu6WuAsAH2yQjgiQeeJIlVXdG4MF2MBj0Q2YC4H/1F35m20bCDAdA4nK55MhtG2Nuhu0Ohhs2GLQ0TnP6laoWpMvlEpFBB9HtkWOOV1WtqiBXHVIDXuuAAEhWMiMCQAQlRcQYIyK6e62ju1at7nW9XjPzB37gB7pXVQEmAdhwd2cmgLXWmzdvpX55eR7bgLpWA7S5WsexjmM/9jaixdt9davaLYApsYV9r9t934861G3AiEgDEterzhwkJTHShqSyAHSpWow8STqWZMHsFowWJNmMCANqGZQpdVl+BYM2ZFiKkQZkngDoBFgGYLhtICgDtgBERviBJAB1q5WZJARIsg0YREQAZgQyALQpBvFKfhURJAMhCYCk7s7M7pY0xgDcbdkwIoKkH/BA0g+ZSbC6bJPEB0hKigicZNgkAdgGYDse/BARAPhf/vmfvV62YFs1R5I4Xa+X7bKByDFIA6ACBEkYkUESxsgk2d0RQYIMAklbzsxt22yvtSypF0nbtRZ8gtRBZoYBSd29bVuQDKobcNUCYEjqiNi27XK5RECS3QD0EBGS9n1/+/YtGce6jzH2fb/fd5jbdrG9qvfq+8tushvPt52OlvfVNoxxHHW7HxKq8bJ2gDZMEgHwOFZVR2bLsMcYpa5SdQNUG2DmkCxZxlrLRrUMSpZEJknZlsGQaauttgxakCzAAEmfECR9AoxXfpBthCS8YjBIGrBNvHILMj8lyTaJtgWDyBwRwQgDDgRTgtQ+CSdJeJDEB8kCMtO2JH8qxwDgbgMRgVPLQJ7A6raEoPH/RtIPfLCUDAOWTJAEaNgSAP6nf+6fu1y3bTDTybD6zds3+7ED/sz3fx+ACJwCSTIiJAHITAAj0rakOaftzCSAPlVmzjkByrJUxw74BDsQrbadQVuZeb1e11r7vmfmtk3A3XWKiFXHWsflciE5xsgMW4BISrKdmbar6nK52G51BGtVVdvoVrcNHKX9fggAx/1+rNXHqlWOmNV6//7WIhxHrXu3bThkyyDiqILBiO6OyNNetWqpRSRAMgBW9QmMrpLRskxJ3R2RZMiWDFCyAQHtlm0RRtktzTkkMMKGugXIBhARsEuSKYmEEQAJkARgG0AYNF4RwQBgnNwSIkhGBgADIEwCtCGJpAU9+IEPgGUYPNnGg20AkQlbEmyCJwAk1U3jEyZBnGwDsI1P8QEnOxm28eAHkpIA8D//t//5MTKhTM8xSFy2TbCtedkiGEEyRmTmyEzbEUGyu5NB0nZESPJJphQRY4yIAEgiInvdqtYpGCNy1eruOTIzMvNyuVatfT8AXC7zVHWcSNiS20Zm2iZNIDPGHLBXFcmIkBQRVQUiImBIXqtutx0nxrF0HEeLYLZwv++32y7EGNtaut2PKnTpqDrs6gZosltqV1UwGdkSySCX1G1JAMmwsVZZsN1ugJJaMCi5uyOSDNmSLbcM0kSp24YIQ6DgzJAMJoDuNiDAEl9BshCS8Io2TpkJoLsBBBmgbQAkMwKkbQAxBgIwWq8MCJIAkIQNmLZJAARAUq8aoE0AtkkCIBkMByRZhl8FGBEmai3ImUnSJAh/CoAfSEYESdtBBugHfEASAP4nf/anr9crXICetst2mQwwGERmRqbREEYMkjmGpe1y2rq6uwDkGO6GX6mbRkTYJjnnHGNIgpbdx7FIjsxatR8H6cvlMsaoKgBjjKoVjG2bkrrLdncbsj2SDHaXrTHGnJNkrWV7jOGHtVbm8AMj9/1QW8ZxVEv3/dj3AhPMVb0fC4jjkORu3o5lU8DtWKsqI7tdLZIttRwMAZIAygQgyQaZMNZaNiRXNwAbEkzYtGXzJLvbagkAaaJ1sk2QMkGD9Am08V1ktU4ACJq0DcDGiWBk2JZkm2SAkmxH5ImkLBg5BgKWJQEw2bZkALYlwwD4Pba7WxJfBQDbeIgIvrLlkyTbQQaj9IoGHwwwAoQeANgGwE/Z5skwTBB2kIyALck2/+Mv/9Sbt0+Gg9hyzstAiHYQ29wMSx1kMG1npKzrA4BVR3dv20Z7ZthWdzC621ZEAogIwO5FOnP4QVatWmtt28wcx7HDmHMCIHGyHZGSjuOwvW1j25IBaXV3RJIBmQRJALYB2mKkBFskq7rK+1HdvW3b8+3+rW+9s8kY5ugWjNv9uN2P275aYAaZ92NJJvNYZTsiDTRsy6aMVd2lzCBTNhQkqqpb3eoWAJsATdqQhAcJ1S3DpEEDtiUbFCAhgiZB+AEgHmS04ZZhkrYByAbAYDAMw7BNErYsyxEjIkDCNkAySAM+SQIR4QeYfsCrAEBSD/gUSQC2AZAEkBAAA5YMkARgQTIA2wAkZSYiVi0AEUESnyIJwA8AcgzKsCMTtvWKf/XLf/Lp6RqvwBaDOTzGIDFyXC8XQ10N8DTGAEAyM+OUlNVH2d7GDBDENtM2ye4+jiMzr9eLu9aqOaek7p5zwNiPO4lTVbl1ysztsgG2HBkk11rdHcHM3LYxRshSq1u2RjAyJVctkpnRDYMkqsrk/baME4/Vx177qvu+VtkOMEAeR717f3v/cs8xGdFSN0BKPo4FBgBGmuhuG0CsOnXmyEy1JJyqSrKkY7VxIkBGdFsSSQCyqyXDIEDBsiUbBCiDJAjZAGyTgQczbOjBNgCfiBPJyCDoB4IEJNkmEw8kQQIg6e8BzLANgAgAfgWAAEjaloQH2/gASQB0A/ADSYAkiGSE+hWA7iYZI6sbQGbaxv8f23kiLIGEDcMW/6M/8yefrpcYMUdqFeAxuY0BqKqfnp5IVNWcGz8VD3kacapV61gzByQZYxjGGMN2VW0PAdSqiKgHkt0t1ZgZEV09IvfjqK45IkeSBKEWSdvdGiPHzAByjKrq7iAiQDIzj2PZJlndYMp9HAcQRuz7XgUD9/tRpX3pdlsyI5MRz8/39y/3EnJuhrvbCoCGj6MARKQNEbbVlsEIyTZs2YZDUlWTBLCv1skGCCYMST4BbUmWaRBgW7JlAwFQckQg6Ae8Ih4MyvA/Cp8gIgIg/IoPfgAomSAyEIQNUGqcSAA2bQMgAoBtvAoAJAF0N0nbkvBAEgBJAAH5AQAfAATHyfZxHAD0CeJ7bOOBpG0AEUFSEsk5An4lKUDb/Kt/9ouXuWVw2yZtWBmec0rqOgAQGHNu24aHMUZm2s4cJGyPMSRlhOyqglYwtm2zXo0xAMycI4ft+q51HAvw5bLNOVoFuLtqVQRIArTdXdu2kbSUY5CsWoDHGHNOW/t+B/D09ATgfr9X1ZzTxrvnZziYgYiXl30/6li63/ZuGGGbDCOW+PG79+soR5DZggQyuluWhG7lHCBb6G51gwTCr9AtGwQlrbWCaWBfRUZ3y44YJHUCZKkt0EbLANqSLQEgGAAi0oAsS4ywDYBkC8Z32QbgT+EhIgDYZkRkSILdbRg2HMgxDKg7IgD4FYAgIck2ANsAg8N2REjCg21JtgFEBAA+hG2IAGiAeEXAmUkGYMlVpXbju2wDIGkbgCSQIyIYVRXkGAOEZUkkbfOv/bl/dmaSuF63MPR/EwZ3u5a123WQW2v9HXOuVVXb2IAScRJiFPPjRFwHECNFKAJBfgwIUARCOQgxEgdcHkLiIkIOOAjYib33rqo5x9t7a8w1vl3is8DieWzRt9utSgIez0f3vt/vVQXgdruttQDYTkKwVh3HkURVq6q79/P5dr/Xqt5dpFZ5RuBah19mAswMAM+UVEt7n85UMYlIJN3T00nWWqJIgFSJwHk+16q3t7ck379/s/3p06eqdZ7P89xrrb3316/fQRns9rrdPPm//uRffP/2rHXfHZKq6p7HOY/nCbBDOzNxIFaIGduxw1KAHr8kAJggl2kDlDTjmUboZLdV1d12QFUpQTtAHEwyExuJTQAERMqJE4ITOxaVBICkJBMAxA8kk9hOQOIlCX8iUsqLMxMASUCpKknPiKQEwDZCIHZwSYIPIpkEAEkASQAkAUASF5KwgQChQBAggO5WqaokejI91HI8/pCEl/ygqlWVHtuS+EMSAEn4P/83/+79OMDcVt3WkZhIqbRqiefzufcmoeLb/X7cbiQBVJUvb29vkrobwFoLSHerSlKRR60kHlcpF9vneSZe61DgcS15JvRx1OPxCHKsleQ8d5UAJGFIoEp1rPN8xr7d7ySnG8RaBz6ku8/neZ7bgI3vj/N57uN2v9/uCf/5P//lt++njtvz2ZTGOPe23eMJejJOAqkkObBjc4LdDSIJSYQJfMkF4MzYJmXHUPeesVQBX5JMPjgZw0ECv4AhEb4kGAfgORtEVSUBQDIvIEj8TF4MUrzMDElJSSTZTgIwQRJ8oD0JSAIgGcBBYoC4JMEHAgQCgCSAJACSkMQlCYCEgIGQAYgPtAdAlUh1TwKpZiZxLroAsJ1EVUsFfwAgQSJIJAGS8H/6L3//8+dPt7UQf35/Z9LTYFatonrO6RlbhdvtJtXLqgIw0ySrFkUEtgOsWiUFkUQATmzH9+MAcO7d3UhAVNWc7Zn3z+/xdG+V9t4k11qJu+d2O2Z6ZmKTWLXWsbrbnnUcq9ZMz5hAz0hysl/ODcrg89w9Afj506f3T5//5E/+7I//+E9DPc4GFNB2wt1j0MHuyQeoiqwZ7+1QMwPBDgJSSWwnAWh7xt2dxKFtULYBShVgZgAMMGPHiUAmdDyAHTsAHeQDBiMVAJIAkpAEGBKXJCRtJyT4Isk2AJK2JeUDSAHIBT/YnhmJqmMcOwBIAnkBSCIJSQAkAdgGkIRkghfb+MAkQCQkuIhEYpIAZkwKQHeTIIkLLzOTRFKpEAMg8ULxJXaAJPyjP/y33u63ko61fvvLZ1GP52MtHetAEmKmzz6LdRyLH3QcK4Bn1lqS1lqkuscOEEESJBLwjD0SBUq1z/Pc58wkIbnP53R/+cUv3u43e15EqURi3DOz1rLHHvwgyTECkrfbzfbz8YwtreM49kySSfbeZ/dM2vDgdr+/vd//9E9//cd//C9mYFQ+ENHYu2fdb+L6+vWrgd1dWrXWHnc3UIkhxZmJpPgl4Afbe/e5OxCAACJnPA7xwp4BadLOxDYJgmKp497bjrSMxJkZSJQ8w0sSAFQFv5GEpG2EAF/wM7YlAUiciCQ+hEQC20BmDAQskHECkEwChBQQO7wASALkBSCQhAlyIYkLSZAIgNiWhD/PNslcAJAEYJukSBBgCL7kBSA+5MJ/9Hf/TTElfXp//1d++7c9L11SlZBQdLy7S7VWkbQjEYBUx7FEOVmrJIGM7Z4kEkpCMtPJeHKs1ZeZeT6ftg+J4vv7+1qVOAlJ20nWWiTHG6RIACS6O4kkALaP49h7P78/CdQ6VJpxHbcAX799+/rtm0GixpCOAL/65ddvj9MRwAAIA3RPO2vd2vM8t+1z7wC1joQAAe5ukFXLRuIkdl4QOPZkd49BMkBJNnrPeAIEyAsLxIxngoC1WDLS3TMmC6QnzoAEmQSXJCRDAsQlCUnbCAH+JBdJSUgCsJ0wCcmqAjAzSQDYnhlSrMIPtpOQBEAyCQDbSUgmwW8IlyQAJOFCEpfu5gUASfxQVbZnBheS+IEEEFxEArCNSxL+47/3b99uh5hjrS9v7yJVEkm8BGTizig6joMkgL03AEmkJHb37X5U0c5LUbYlrKoljec8H24fxwHANoDH42H70/td1AuJmbYNwIaktUTStj4ABMCZsYekpL23JJJwbJ9nAyBVt+Nsn+f5/XySy8H56LOnp2OCa4LH4+l41a23n92k9ryYEqXnPvduaZGqtRKe52ngOG52xoMgiR1fAO7d45ACwMs+Z/eGKCqBw7EniZHAYBCIFD2OydKM80ICSAIgFwBUgcQlCYDEiQi+AMhFEi9JZgYQyST4wCSSknR3Yqoo2cYlF1wk2QZAMhf8PwQgCYAkVUVyZvDn5SIpCYAkx3EAmJkkvOCFQAAkMYAkxQ+45ML/8T//G/fbWtQiiay1aul23NaiM3YIAXBm1TqOW5LH49E9VUsiGE/XKkkzTfKoWzJLBHAcS+Q+T7dFOZYUZ+8NoopASEoCYE8SgJIQBK7SWgtALvYkARnHcZ+7qn7x5YvtX/7qV7ZBDnL2TLvNWrf97O+Px9lNiFSgmXx7PBNAGnCfM3nBxJJqrc48v58JtFaCmHvvAe/3e5zH47HWzS/jGGOPx0aMECSXBHJ3P56nHa0FwOCMMwYY0kCQ4IONJFVlxy+JqpLYTlK1Pn96/+Wvv4KUlMQ2LjEA/sQ2PwAQILtJ4YcEM0NSEsm+VJXEGb/wgxACAQigp/VC4eI4iSi8kABsh1xrAZgZO0D4AT9JIikXOnwRE/AD8uLwg+wOjSCACElJpIozE/7RH/71YxXju6pKay0ga9X97QDyUjqq1LO7R6y11t77PHutVcXEziSR6jiqSkIlVrD3qSUGHlfV9HQ3RQAEVYoHMS8S8UMCiS9AVCKQYKadrFr22FEpzvQcx7HWMTM9/atvX78/H+u4k/V8to2Ex+1I8Dz3r3/9bRywJkHQzjn2YBwbg0hUqT37uUuLx9p7PCHFqrXq+/fn4/vjuN0RJMhgPOO2ESOEXiDHKj2e5/PcdRwBxvEYDkCqLMyLDSDhC4AEIABCIjAzAG632+/8y7/zp3/6y2+Ph8hcSObFSMCLbZIAEiRIQgoISUlJZiwRQFUB7N4AktixDYBkIiS42OYFJIDYABwjkATCNqSqApj4JQlJXEgCIAnANmyAJAhKApDENgBSgZ2GoRJJiUmkijMT/tEf/vVjiU4RR9VxHImBvL/fWEQgrqoa796dsKpmZu+R6nardaxkns9nktttrXXAyDj2qlrH2nsTIXmeZ89IWqvWWghmxjY+hMRxHHmxQa61JO0+k5AEMGN7SM5MkttxSGt6Ho/voupYAR7nc7cT2jnPCWD7L/+lv/yv/qV/7X/5X/+3x+Px9v4phBNS45x7PEgwziCkJD6eTzuSwApetI5DVXufX79+T0BQKlExzr3bjTCGERA0xl7HcrB7DNjek9gMAFJlYjxOSAJKkEwCUgHAD0m6W9Lb29u5u2dI5oKLDQS8JCGZxE4CkrlIIgnkBaDtt7e32+0207v7nI7hcRKSgGzjYpuXJECAvNjmD44JkeIH2E6CD0wCgKSk/ACAPyQhKQDBi+NkElSVRCAvpBDMhP/47/3+Uboda5HES0hU6e3tUFU+UGIyCQnOdPfYkbRWrVVaOp/PvTeJ46Xuq6r3eTtuVfV4fCcJei4l1VqrVs+4G8haixQQe2as0lqFEIjEnqZE0mPHBHqm917rUNXe3btXlQMjPT6f5x7PZCYBz73f7u/398//9J/+H+s4bm9vYz+eJ8CqmklPzyChQQhExpNwetpex12U1qqqX//6V/vc67g5IUUKg73PtmPY4SWJAxUDnrvPbtuB7MCWBGoIEg6SAMqFpBMnpEgCmBkAeeGLANgGQBKAHYQ/SUIysQ1SLzOTC8kkJF5m+na73243e8aewOOXJKJAxfmATA8/KHEAIWMnkcQLgPHwIsmObZIAX3AhmcQ2AhIAP4hxAAggmcQeFSWSSgzADl/AmfAf/ie/d691v9/ebksgGRUTr1UsrbUAAWScBMDeZ4IXEiVUFYu2n89z7Kp6v71X1fN8KlzSeKQSB0j3IAgoKQlpksdxVNWMv39/JFjHOu5HnJmuKntskwCBgOTM7L2rSuJL9wCa2ObMPM/dPWN0e6KZ/PrrY2a+fPlC8XHuaZ89ACjNYGJAdgAZsS3GyYwdVN1AJBHYM04CgBgnDhwbM/Y4QdUi6XjsIAEfz207xIRIMq5VIHuGRWC5E+ElCaWBYQKQRDIJgPM8WSUqyIwBSAIwbYA/sc0PsAEQl5lJIokfRLK77SGVZK0lcWZykUQgQV6A6UkglWdAlorkTCeRRBLg6QFCEkA+QCIA/gAgyczQIQmAZFXFHhsAyVxqsaoS2I0PlJTEDv/7v/3Xlni/Hbe1butFFIJIWMcC1ePYi1qX7p6Zvbdn3t6Oz5+/GPHMuc/H8zk9Qr2/3fkC3tYau7tLSZzETgJRL44lrVW2cenuAGstqRLbQ2HG9khca0nae3c3SUnH7f54Pjz4/jhnTFFa5/M8e7ptKEG3qZLU9vPc3Z7EDoCEYwcvAtj+QIHUudsOqVprxgAkOelx4jgOMO4ZgAkzURXJPZ3YwTi7R7XsDAwgE4ovRgJkaEerANgOoVokPQaQBADJmQ5FynYSkkkAzDiGJJIAeJmJbZIAbJNcawFIDMDOzABIsNaqUncnAUCJQC4A7QEIcLqr1v1+O47j3PvxeAAgAHIC27iQTMILAJK42E5CEL+RqmWPbYGSSCSgIgmAbSAAX5LY4T/6z35/URIz+9PbO4lw1rFqVR3L9j47Yyaf3t9v9/vMdPfz+ezu2zre3t8orVW2f/X16/PxfWl9+fyZZEnHWt19Pp/HwZnxB5QKAMm9+zhux3HMtITEuzeLImstEra7NwlAcUBImhnbIsHSWt399ev3Hs84ye247z17OoGjcRIYeu49M2DZ7hk7oAievWciFcCx2w4CYLcTqKpUTijaeJmZOA6QOL33lBapfTYAUrs3JQcBZzzBeCBKyjjJuh3rOB7ns5+TcB0riWe4SlUkew9JX6oqSdsAX/IDxRgxfsKLJDszlpTENsnb7Uam54yT0EY+4O3t7XY7vn37dp5nXQDYxsV2grncbre3T+/HOp6XALGTOATAi20AvOBC0nYSXpIASCIpycwsqqokAQGD38gFAIHY4f/wn/6NY5Xdcb+/3UU8z/MXv/gi4Xmev/jFLxI/n8+x7/f7bR3TLcqe6SFlex2HSJB9ied2uwFIIokkgNlnVQGYHpVI9m47t/tt1QI8Ho+rBGam11q16jzP7j6OWwnn3jNzHKtq7e6epo4Yz/N8PB63+x3k83l2p7dRLyvB7Hl2nz0BASaY8dgAQY1n9zhAAKjjhABm2g5VJPFCUrJDwo4nNmY6jh1+0Ngzk8QJpYAJ2jN+AUsAZobk+/vbp8+f/uzPfvnt23eg1lqAJkO9EKAnay2SM5PELwkIQLaBACJICsDM2AYgqarszLiqZiZJVR3HQWZ82iHKTvcAeH9/v9/v3759+/79e1WJVBWAOEHGJtHjvc+1jjrWOs8m3AAAIABJREFU/bjbPs/TH2AbAC8AbAPgBReSSQCQTPASBwTJJLZF1pJIAERwkZjAtlSJX/gP/+Pfv60SDbiEYxUoxPYcx3G73cCANLKqlmp6VhWDJHbO87Td3Wut2+229348HsdxvL+/AZjxWquqHt++rVUAbK+1kvSMe9axJPED7AmAGOBxLKn23rbXWna/kFxrVdV2n7vXqkTP5xMUyD29ex7fz72tWqUap3v2TI8TgozhxAbJEOfuGQfEB7VjO45jEICSAKBElW2ACLsbgJ3ZkwulAHMJVbXGnhckQYIQSWwfx/ry5fOXL19+9atf/vrb925PG1T4E5CKsS4zc54ngDAJEgLIiyEWhOmxDUCXqrKz95B4SSJpraUiMC+I8oFIINWxPPP9+/cAklYVySSqAllSkK/fvnm8qt7f3hE+no/uBmgHAC8AkuAvlhhAEpKSANgmBYJMUSKBACCVxLYkALb53/5Hv/d2P44lZACvtSR5OsjtdpN03Naqde7N4LZW7yYhiJexPbO7kYD02B4Anz9/XmvtcwdZa81+xqEu5NgzE0cqvohVsp1Y4oukquoeIC/dbed2O6oqQWdeQP7Wb/0WVf/kn/zvx/39eW4nNvbukATHSTDj3TNBAoKsCuiJk31uJ1QhQNSIPeNxQgJhABAAidU9kgB2N0C/9CQhEDKA7e429AJw7LbH+UCTXKs+f/r06fN7VT3P5/O5H8/+/u3RE6pIAiEYQNJay/beG0CAIAlfbMeQCkB3JyEpqaok2dm7bVcVAF1qiZwZx0hAVpK998S1Vnd7hqSqRAJYa9VxVNXtdnt8//7rr19FHseB8Pl87r3JSsIfcsH/FxIJgABIQkISSQAJE5AuSSQQfCAA2wBIJuE/+Ft/7Xart9sBWAyF3v3ly+e16uvXb2vV/e1Owp3bOtaq8zzjCCypSgnseT6fPeMZkoFA3u93JD2D5Ha7EYOABEmQtqcboFRJJFWxZyStWolfJCU5jsP28/lIcL/fSPWMYzvteXt///rt2z/7Z//nL37rd2YCacbn3k5IOUDg4HnuHot6Ya0A+5yXnkkIESHAzouD9IzHlFSFIAGinhFfamaS7L09A1AkyJC2994Gq5aNnnHsMAhgFd7e3v6l3/7F/e3ePfuDu/vb1+f372coknaABCBQVX5JCBggAXDG+EBScV5IAkhCcq1lp3tsSwIgiaREyAgQJARpxx5PgNgBApIASAKU1iqqjuNw/O3bN38IghknJmVHEi92EuNCikSCn5AEQjJBEgAkJZFMTBIwgCKB4ANfZgZAEgD87/72vyO6iuuoQpDEOY7b7b5meu+npNvttqhjLZBiIdh7k1yl2CDP59NxHNuhqkqS7ZmRVFXHqir5AkDSXCTZJrnWSkJSBEmRSQAex2HPeT4dQ3RMUFoz/vr9keD5OLXW7fbu5DzP3d3bA0vHJE4CdE/vJnWsW4TH87nPAQmW454BBTL4CffuGYNULYB2pMo44yAAMu4XD0kjoBI47u5B1lqAuqfHCQBWcR369On2+cundawZP5/nPsfG+Zxv355nDymAIZIAkJQElyS4zEwSSSRj2AFwrOWku1etADNOQhIXvogs2taHNS82gRi+SCKZBEAuvKy1SJ7nOZ3xAJBkGxeyAJDEJR8gkRQQ27wkAQwgCQCSkvDBEhMgEYkfkkhKggv/wd/6veOowCW+3W8lwmNnrarF5/MpcR3HqiopzqoVZ+9NcK0FQFJ3285FIqmXJABsz8z9dpCYGVzWWgD23jMjCQDJ4zjyglAUmQ/gizi7Z0aLImsdMXrm+/eHIal2u2c87pmAY5+7bQSAqntsizQoCeTzee7d4Es5GZuqAAnGs3fbIVVrUZUgCVkZZ+w4iXtsB3AcAJLtcWx3XGvBdDJJDIBS7m+3L1/e7m83FvfZ53P3bocefv9+fn08S0UKou0k+JkkAHLhBUCCbgNZx4Ggu3lBmAt+UEmHPAZQddgeTyYJcpFEMj9jm2RVrbVmZu+xTVKSbQCS7OAHXmwDkJTEtiSSSewBQBKAJAAkAb+AFCn8RhIAVYULX/7r//DfOI4F5DjWfR2r6NlB1lHHsSR0bzvv7+9VtffOhGBVeYxAF9vdPTMA1qokJCWttUh++/ZtLcVOIglAlWY8M0go2SZ5HIdtihIBJiFBUlLvPvcGIXGtw86Mn2c/exCOPePuIVXrCPl8PmcS0s5jn0lW1RgfyL33TPihAowdMIAT/waqVq0VIqYThJnAHs+LxwRCJHEcciaOAXReiFBVTqbxQuV205cv78f9AHGeG+He+/E4PezB49yiAAV2AoBkEgAEAuRCsqoAeGYCgwAIBolDgSFDALZJJEASoY5CYLvqABlP93hCEgBJALmQBDAzSXRJMhMAtkna5g9JACThJQkvSWxLIplkZkhWFclc+CGxQ4oUfoNkEkm5VBX/qz/43eNYVTyOAzEJEiTut+N2LEo9O/bb+72q+nmOvWpVaT9PoZLYmZnEpF4ASiQlUarE3QM4McGqcux5sWNeEusShIBeqpDMTBKQmfTMeGxXrVoHpT2zd+8ZJ3Z6T89QR8CxAyaY8eM8AUqcHicAErwEIMuAk+lxQr2UPTOWVlXZSDJjAwg8nm6PRRIcmOAYg7zYIbmdGZOqdTjx+GUyEt8/Hff7AYIkgvM53749ZwxqnAQvjvHCDwlIOhEJYGYAVBXJmQEIAkEQBB8IpmIEsQMRL8F4AJCsKpBxHMd5IYlLEgBJAJAEYBtALmQBSGIbgCT8TC6ScKkqALkASIILSUm2k0gCE4QBAZIARErCxXYSvvwXf/Ov3m+HxGOVBJAi3m5HrYpdVYTHQ/J2HABnmiQcj+/Hfe/dPUmqVFVJZswf1lrdnYSkbZKSksy0ncQSAZBQCYhtsQiyhGDvbRviogD0uGdAqgpge3rmnOluhN1uz1p3QHsm4IztnLsBguzdtiUCxAtJlZOJ97nj1HFUqXvsCSQKEQAnIDAZZ2a6mxQCxwBtmHA8hqgJZgyEFCjbSdrTvatyf7vd7zcgCXtz7wECsp3YeElAhMQHgXRCgMDM2JZE0jbClyQAJOViE+ALwIkB+AJAFwBJbANIQhJAElxykUTSdhIAdkiRTGKbl1xIAkiCSxIAupC0DSAJSVyS8AIgNAAGL3wBREoimcQXSfzDv/lXb8cq8lh1HIvIKt6OQ8WM11qA7QlwrMPzwTO2RZVWVSXBJRcbSYCQXGvZ0QcmxiVB4hcAvAAhYTsxKZJrLYI9M91BJAHsmfYkdDDxjBFM0uNShdx7AAY8e08Ac8ZtizWe89wkJAH8yQRjjx3HtlZVrcRJug1AXAmAINk93ZNkZgKAIpDAAYi2x6CI4CXOJCABkLLTc85YxWMtslSFsLtJBJwgNklcQiQg5SSkACS+JJFkjwcAAZCUlN8gwJ+0DWBmbFeJFC5JHCNIwksSXHKRBCA/AEj4kgQASQBJbPMHAEkA2AagC4BcJAFIAIQXAIaBMHjhCyD+BoCZsS2Jf/8PfndJQm5rvb3dFwGaiYpVq6S4k6hWUbic51kkoO6RZDvIqlVVkpL4MjO8SAKw1rI9M5KAzDgZigSBBIkNQLVEHccRYO89M0ikCjEejyfwC2IHkZEekwJ47p6xg3PvtkuHEwdVq/ec+xRFApREkD0v7pmqQtg2hReSu4dgadkGiPh57t4NcuyAVeUPoRSgPTZAIaGIpMeg8ELYAZDEdsKqVVpgurdIqtofAJAEYAQgQCcAjuMgYHsuBB3HIkUSgG1cSAJ0EhsSgFwk4ZKL7SS4kMT/Sy4zk4QUwJckJCXlYpukJJIASALwRRIAkrmIBEEQZBIClJwJouCFJADxA4D8AIB//w9+d0mxj9L72xsZ1QCUSIpEVcXJzHHcCMYWQDLAONPT50ny06dPx1qSbAPYe5/nWZe9N4DjOOZSVSRnxvE6iqQvAKSSKAnJubeTdRxIeiwVgL139yQhYMpAgr0bVIK9GwjJ85zdHRYlO6qamR4HAUhRVQTjxD53A6Bq7w5gSmTwG7bFcnvvPTMBeqaqSNrOC+jEiEMERnCxAxA/IRMATGJbqiS2Sa61JIWci2OKdgAkAVB1rLWS2NPt3pOEpFRgSejuBEmQqMohkBeSSfAzSUgmSJwgwU8kAciFZC4Autt2VZHE/x+SknwhiZ8RjB9IApCUBIAkXpKQTGIbgKQkM8O/+x/864deeFQda5FRpaokzeU4DoKZIYnkdtxua8HZ3R1XlXfPzNvbW0m7mwBJ20kkJTnPUx+qu5NQXFUAAkgimQSAbQB8Ec/9oVat40jS3aVKMNMAE0zPgKjKZHcHtD3tvBB2xoAUsGdIuT0xSAQgIBLYZ0tM4GDs6UBi6YUkwJm2DXA/dnfbpjS2JJJJSI7heGKHCIwAIGEjwU9UZQcgiDgAbSehRFJVay2Su/vcZ2KAAJKQkopkEiCedPslCVWoIjE9SUgmIYXg53IBwEsSAPkAgPiZJLjkQtIXXvAXS0JSEgDbSaoqF1wE4wdeAJAEIIlkLiQBJAFAEoBt/p1//6/c1hJwLJWKBNC1lsjneRK43e8leffMVOnT++dbVZyztxEC6QFA0naSKpHCJYkvlADMjEovBCkK7DGJqkrQe4/HAMiq2ns7kUSpSElAEsTonuneSVAAZuJgxjOTpN2BAkAVYAwk6RlAJTsAQiDp3SDIMtDtGBIhUgJAcveOA3A/9tixVdUzuEiqqnF29+SFSYy8SAKQEBdVzRigJNsJ8gKAxAu5jvUS4Hw+Zxrg3htA1SI1MwAkAejtxEnIgorkdDuWZAcf+AIgCS5J8OclAUgKl/wAgCSAJCR9AUASfzGSSUhWFYCZwSUJLoLxAy+4kJQEIBf+ACAXAPw7/95fWauKPKokkgBHVPyStapUQAR6huTtdltVS2V7x9NdpKqez2dJRy0EQEDih9gBEvwcSYh5AUpKsvceT4CqAjFjfhAJgC/Ts7vjBGD47AlY64gxcXeP4cxjn6KkZSCgnTh2AqxVATwzNi/z4jghKkQCSABi/9+Mwd/P9ft6luXzuj9jvHOuAhVatP6gLSFKUDZIdMdEg4YAkR0MMQhE//8tlcVqu9Z83/G9r9PxfOd82rmCNRwHsNd2m+R6Xbs7M4/H49t19XYe5/F8Xtd++/ZahVEMbc85gEKCZk4ryXmc3VUIGK0iJPny5fk4j3W/fvvW7c0kMNa25/GYyfXq7iYBkpnk2uXWlgTyo1VbNQmgAiqQG4wKeEsCJAHUtiqgJuG3JVH5mbYz83g81Ou61NyAIEn4MEg+qEA+cUvCp7ZA/o//5Q+fj8dzTmYeJ2hOJrHOTN6q9pyDdhf48uXLd8/nt9eLx5zk2kVnxu0kaCt4znk+n+p1bU1boN0qMDNkAHEywHVdIPB4HJLremXOOaO+tpBtr9erFWi5riUkD2Dt1mu79rqumfN8frlazXXtW+HMnHOu61Lb5o0Yv70uzZljxkCmLTWk215X5bpeuzsz333/vfD169fr2vN8zpy99rWtJANUtjuZDIUQ8cxROzkzu0vY7TDdivkRZIZhd69rk9ntXp05SdqeM+c8Wl6vlwokk9DKB5U/l0SoH5K0BXZ3ZgAVkgyQpC0wM0naqsDuJgHUJPy2JCr/X5K0VXPjlgSSOAgkAVRgZpLwM2pubfN//rM/+u75PDOTeT6mrRjyNpO3w4AzQa/XpT7eZrabx7Sd5PF4+LY7mWtfkwFmzsy8XheZzHRXbasCMyOBzAQRd/e6LvX55fn2uq7iObPt61rI4zx299vXVxL1dV3bajIjrN2F0KokUwLstgok6e2cA6jXtdtKwvB2Hpmp3d2AhbXd3evr1xXnzO/8zu+Q/Po3v9nu5FltNWlJAvSWtxkhQXk+H9uunZkku732olgJM5Okt0wERdmryZkZ32jI4/GA+fr1qwp584aSD9740QygclN3d2aS7F6amZMEaAvMTJLrutQkahJA5T/AzAAq4A1IAqj5EUzklkQFcuNn1Nza5t/807/1fD6+PJ/fPb8Mrtu+bCOP5/P5eJyZwGSu63VdV5LzeIyory7w/XffJbmu6yRnznJNjrK719XdTuZMQmamtltwZjYmeZzHtdfuJvn69Vvlu+++S2bttknU3ZXMnOu62oYBrr22hUlyabs1ydhoYUwKvS4J4U3RAm0hrduFSebampxzmPjW9mpId6+3srvJfPn+S5Jv377VmlhaqtVweEvfgHOOkESdeVSL55yQb69vq9Ruk5xzgO0H3vI2EIwfAkKVx+PR8nq91CSQWjQJkERAVMAEUNvmtrvn9nq9gOSo3NoCSbh5mxmgLZ+SAN6ScEsCqEASb9ySACqQZMKZeJsZFciNWxIVSAKo+df/9I/Pmefj8f3zmRTrbr3Ome8ez8c5TE6O5dvrh92dmfM4kwB+IEm7SZ7P5ySBvZbkuq5WfIuKZObMACLiZCbJ7O6365VkZmD8wO62mxmgIKnsdakQi1RLpnKtHwgZS1s1j4e6b9fCPL88SV6vl29hd8NJsrslq9iZI0HsB6C73S3jLWfOOXtd1TWgpqJAgLrtJpk5c8afBHACXK9ut/TMAWxJHo8ve12v65qZc06SmcH54Ycf1CSA5kfXdUHmnPoTbkkAFVCTAL2pM5Pb4/FQd5vM7qqACiThg61Awpvyo7ZAEm4zA7TlpubWNgmg5sanhIQfRfMJUHMDkvAp/+qf/PGXL88zeZ6Z4eDutvvdd1+eZyaBeeu1r9dLJc7k8Xicc9pe18VtZh6PR5K26vPxvK69rvXWgmYmfJhzcjuP85tf/2a7tSTfff89zNev34DdBZKoxZm5rm2rQmwyCsqWba96w6ICzqh928I8v3zJ5PV6kcyZ3XZtu7tmhLzNKN2iwO72urYlJ0lbJuec3QWuokLqWyBA291Nch6HBFC5NSCtwrpjbKtnznl82b1e377lcZ7PZ97Irq/Xq+3MAC0/2l2Yc8Ybn5IIVUBNAqhtgdzUx+ORpBWyu20BFUjQekv4czqAym1mkqhJ2nJTc2ubBFBnJgk/k8gtfAjkbQYlCeQGqED+1T/5o+fzeU4eQHpISN0v3z2/ezyGVJOwXPtSzzlJns9DeL2utoEqMDNAyzmP7758uXava69r38BzjtqW5HHOzAlh8u3bt90tkjwfT8J1bZK2SdBrV0zS9rq2bQjMnKluXalc176q22gS3s7Z3bwRnJwRdzeJsSXm2g/MIeG21dYK7O6AWgZoSyLyISUqUNFAgN1tm0wmwjkDAdu+XCRMzlzXhXQXyJxzHu1erytnns9nQleNurtAW50EsDUZoAoE5EMSoAqofPI2M0Dbc87MQCC729YbkKAC3vgLaQucc5KogJqkbRJAza1tEkA95yRR+Ykgt5nxlplzTkBFBzIDqED+93/8h4/HOWceoHsyCW9fvjy++/JdQq8mE9CC55yZOWe+vb7tqzN5293WJG1lztvjYVvpfhBUIJPJvKkBFSh2y83JzASqhKp9sxZ5va5rW8VkJmc0u1vp9tt1nTmBfjCPA+SNaQhT901Te11LjtaamcwAwl5rfeuumgRS8S2gFSGhjvKmKBDfIAmwu4bn8wn4odtuqyS5uuc8ZubaRTOnu8BkSNAK8tYP8iHckkCq2wJq3maSqCigAkn8xK3tuSWj9KZyUxL8gArySQXOOUl2V83NG5+SAEm8nXNmRrkJvrVNmLdEBc45SVQUUPmUf/1P/ojwOPPd40EbKE7meR6P54NkaiZQcTIzmQlhr0WStKt5U96Sw5sQ3va6rt1iZtomeT6fwNdvX4/z1i7iGyZDwgS8ruu1O5NzjrW15dt1XdsShfD88iVwXZdN37aGWohSq5zHI+dc7e76VpJJ8nq9roqA3M45wG6r1tpAlaKIBhdIw21aIYBKEQx/LjMZdguo/SATtW3mPJ7P1+vVOpPdnZlwAH+mbTLJgK1vM5OkumKrAjOThN+WRG2r8ik3iPI2M239xM0bn5LwM22BmQHaqkn4mdzUJDMDw4cCarsJJz9Rk5wZ+UlbPuXf/NO/rX08zvdfnnQpOXPOoQqTPM+ZDAhkmMl1XTNJeEtmt7tVkWTmcdS2grrXxVtmJte1bWcmifbknJndbZsbYCBp+3q9ru7j8UiiYAo/fP327XUlQyaT5+NZ7W7rtiGvvRYnZyaVazfJnMe217UqMPN4e71e13WpQBJvEDKtWhVQEatSsEIaIIZWDG++8SHhR4Gk7e4mAdS2nBO4rovM4/lUr+vKGwg4GhXwQ61kEhQQSAaoCiqgckvCzyTxEzeVW5KWmXk8HrvbFlABb/yMys+oQG7eknBLogL5xIfhQ4Ek7SYOmZkkKjAzam5tueXtX/3jv53wOPnu+aQLzmOGdFc95/ziu+9CdgvMhOS6vp1zkhQy8W1pbTdvM7Wt+GHbxzkzZ3ev60qitp2ZL48vZ851XbtXknMOUJVce12vi8k5x7aYOcpvvn799loSnEwe5/RmqQUKDRATpYqQGNCte/WcOedxvV5tAW9JeDOK0pabH2j9AAqk4a3YipPgB/6cSkJoq8wEUKvMBK7rKjnnALsLnvNotVFUIEnVNomi5gZ0d/kLM9NW5WdyA7xxU9sC5xwYIIkK+AlQ+RmVmzc+zUwSlU8zk2S37Z4boOFDgSSgdpKTkHibGW5J2gJJZib/8h/98Zl5POZxxnYmkyqxyvP5/MX3X8DXpTUTb+dMSOYk6Q1Qk3jLbXev6zrnzMy3b18hM7OrFZhbt6/rNeecx7F+wN1eewW+fPmy7VXMXB8KCNvmzGSsuwKigAG2H5g4A7QVw0zGtzqZ3RXEtiGEgHpdAio3rab1LTOva5nJRGiLaERA0jYJEJCftOVWPiSQVHflVl0JmUTSys+oiCIieYP6F+bW1hufcvPGLQnQFjjnwKiACqiACqjcVCAJn1RAbZtkbm3VJOcMRG07N5WfScItNmFmkgAqt9yAtkD+5T/644THmccM8UyCgUCSc87zRChJxnbbmSCZhBG6K04mCaAC5xxgbyrwel0zCce3SqKIgDAzgjYz3d2ukuRxTutre5XrupKc8xBf3TAt3TVoREQBBUUrlFbQmXPOo+11XZODVtsmOeeo/aDy5g1oC3RlRnxtz+Mpbote19owJIHwIQkzpzegNrzF8KYyUVshIZIFWwgEUAEVUEOUN29AEhVQc1PbAkm4qUkAb8DMJFGBZCAqoAIqoAIqNxVIwk3lZ9Qk5xxABRITWt9mJokKw6ck/KibcM7JTKAtb0kgCdBWzb/8R3+sPTPPM4/HzMnAJJNwewzbLpkZdXdzmxmlLXDOSQKIfa16zlHbqm13xczkrUUFBCFBmTMk3SWRN2/MZD9A5nVt23OOeNXWrRBAUN60KrfdVYEktczjnHNdV9sw20XySUSEirb1R7UhLgaTypxz7Ycz02qpipBkzAAzo+6uChwCNLypmanFQADhWkUgCeAN8JYM8qa2VfMJUAE/JeGmJgG8AXNrm5CMBkgCtPVTEkAFVH5GBZIASdqq55yZ4Zbw5odCfqT8uSTctAmZCZDYArkBSdTdzf/2P/8ReCbPMzNMcs4kDHmbSbTdl8zMOXNd2/b5fMwc7W7POTPTD4ZghUmuvVwJaJsfKde1CIEMQV09c86cay8hE+RNVGxrIN/21W3mKJXX9a1yHg9FDFFWsYCye6kzh7DXZk4muyuY2WtPPkACIiCU2J8A1pDKKiGZ83x+/fr19XqdOY/HY69uS1ACzeFTWxU4M+q2M5NEbgmiqX3tQmYmiZ/acksGUYG2KjAzubUFvLVNAqj5MOCNt5lJooIwSfiktgVUIAngDfAGJAFyU9sCSeam5gaoCOEWPggkURMmEJCfS0ISSFLtbv7FP/zDxzkzTERPOOckJM5bJkEpcvM2M8/ns93rupI552j3cubBrJVAsc2tpTVht62AEJyZ4moMCSIR3eYcyPW6RKVQ2wpjCPl2vapnHkLbJIWahDd1bzMDtM0cQIU0sT1EUCGBWqGhre1WmgPIJrtl+PLly1/5q3/t69cf/uRP//R67TlHqznnqH1jkgB+AkTkLQm3vJ1RaaoXxcwcwE+7qyaBICq/LYk3IIm6uzMDtIU557RVgdz4S3jj5i2JN0AFVGBuQNskQJK5td3dmckEokXeksOHAgI24ZHhLbxNIj/JGx/Uav7Xf/i3vv/yHVavk5zHTN5IQJOcM28QtW0Ste05owIzJ8nudkmifQPmHDQJyV7bNklrMurunpM5Z9tvr6sy4XEe5Aitmbi+Xpd2A4lStRjQrUpmhMpbWz552131nJNEAuwuCXO6C8lkr1VBboXWXkUgEcLC7s6ZX/ziF3/j937v216/+ne/+vWf/rrtzADJJFzbmjdABdp6yw1QgZnJGcVSbcQkowIq0NafgHibGX7GW267OzNq23wYSFtuuam5ASqfvHFrqyYBvAEz4w3IjVsSbjPj23ZmABXIB3T4UCBBnMkk3JLMjNqWn5mZtvnn/9MfPjJnmPg2h5kBE0IeM3MCnBwgt929rgs4Z1TIzKi7KuiZI25XSAJ4C28hs9e2PeeRzGuvbZUkkyERus3Ecu2FbBSSCNeWW1dzSNS2wFbbBLXtOae3cw4gUXeXhDkqMDOv6+oukATY0q27mUeSEWFjW/XL99//3u//HsnXr1//7Fd/9ptf/5q8DeCtTBKUfOhNTeINSDJv54h7KRQViMpv213Lm8rPJPEGzAywu4/HY2/nnOS0Vbnlxl/CGzcVSAKogArMjDc+5QaoubHlFpCbZA4fyi1xzrxxSwJ446YCM9M2/+J//AMez2seX+LpN0nOCQnm5EweM4FASCZvkG/fvkLOOW1398yBactbcmbU3VWBzAiKShLTD+YGyTnV1+u1W8E3mETdduAihJmBbIWI315X5sxM271KsJh6NIZ7AAAgAElEQVQSugJt1eTMjK2gLubm7czjeu3Va3KS7HZ3vQEzkwRz6SSC9nf+2l/98v2XL8/n169f/+//6//Z7WQkEAjDJIJ65ohtr9dFSNgV8zaTmQP4k7T1Lcgnk8le+3pdM+ecURKUtgmJrW8zw0/Sok0yM34CcuOmtk1yzmnLTeXmLQmf2uYT0BaYmSTcvHFrm2RmrCI6Q0jCj5IAM8MtN5WfyFskpyX//H/4z3PS83jg8WXOWyA6jwAnnJkzad3d5/N5zrmuF2Rmdvf1upKxqOfxSLItMDPdVUlEREEUP8AZKyRn2r5el7amBcwMsLtJiJI3mFYS8du1wMx02zozakLt63VZuZ3zzEztTYfMBPzQyWOvbndmIHt1u97yqdKSRBRzzve/+P677768Xtcvf/nLrjPDh+Ft8gaoM5Okt+2GIC0q5Jx5E6xKW9+C/MQyc7Rfv35L5vF4JPHWCk1UVCA3yK5gEjU3b0mAtrl5O+eo3PzELTdABWZGBVRAnRlATaICKpAE0ZKf6OYG8iFv3PIJ8EYERMnkmX/23//x5JXHeU5jPOfLY5CJMyEeyBsESGYm0DYzcx7XdX379g1oUR+PR5K2CqEtSiJBBOUWlWS7LcbX62qbzJmHgCSp7S4JSaXWysycB7C7Km++BbAapddr1ZC3mQdJ7Ztvk8wEvEEsu5tEs7ttueUG9A1CKuLq4/k452H94YcfgCT8JAUVMpNzU9vuLrhr17cAj3MeD7Stpq03fpKQmUnyel2tSWbGT7sXmBuflFYgCbckgDduSQAVSAIkAbwBKpAbn2YG2F0/zQygzow34DEzGaCtOjNJls0bn/LBlmTygURFeYuARLmu5L/9u7//N//G8xy+ezyh5JzJhOBMZjKEcELeSFsxySTOtHtdRbYFk8l5TmZvKpBJiLfkACq4UOnu9kcmM+dA1CS9zWRLrVXIOTMDqK0gUm1tKxItZNAkMOInmLzxpgKi7F6Q1t1NwofwFhS0fGitZh5FK9h6zkBUEDBRgZnzeJw3YLc3r9e1K5CEyTknST/41iryyZrMOdOy23aTSQKo2wubTG5qWwUCzAw3b0n4GZVbEiAJoAJ+SgIkAdrmkzcgiTcgCbdDVCAJkJvsJLwlQN5APuTWFmg7MyBvSZs//dOv+YPf+8Xf/69+95n9/vFX9BtjfMwZNPFxHjN5O0jYvdrO5JyT0IxvxVtrMo/HF6A3oTVBA3LzAwkwatvd1kJECYkK7O7MJGyBqIgngNqtYnizvVrDydDWvPGjTrH4xm9T3tqqwO62nvMQkeIbEAJRtq2dx0Np/dHMJFEBJcc3YGYej8fM9ANtbfaWjDIzgcwg7Qq7K4EwvvVtO3OStFUhyQC+USi3MEl2V4FJyI3b7gLnnLb8//LGTQUCJG2BualAEkAF2s5MEnVEBZJwy9uYBFCBmckNSOInPvmWvC5++cs/yzP8/f/y9/+z/+QX9DrzIFc6SQgDX748ZwZEEnYLziQhmUystRCIH4DwowRoq7bmtrttk5xzdl21LSqZzEyX1jdgd4GZqRIUkuJbrSvE8Ka2dfI4B60ob4pFULmpfGpRgd0F1CQwKuAbP1HQAkEwwe6izk1NUJjsFpiZc+atVdtqefMDSpWQxFtILYaECLS9rsXMxNvMwAC+USg/Muec3W2FSciNm7q73GaGv5xY5aYihEkQFUiiArlxU3NTR9QkgJpkZkgzCfGWZG58UpMAbbmt/Mmf/PDrX18J+YPf/f6/+Xt//ZE+nt+Fb8cDJDmTM0NmQoaZua7X7s5kZs45QEtbIBmgdXeBmUlSTdLWDwGT7K6a5Fq3giSZfJjp2uVNva5LPecIhARIw3VdbSEhhg96tcVzjh/yBqhdhCDkbXf5yUAAdT90JpNp8UPLX1BUhnPm2hq7bQN5PM7MeGtLznVdwHzyjQW65NaqvHZLW4Ezk8S38iG+7a515syMN0gygG8Uyo/MzLRVlNwANbfdbZtkZvjLiVV+Jm9gBZIA3maGW5K2uamHqEASFcjbODNJ1LZJzjlJVEDllsSCkHx9vf7dr364LkN4wj/4u3/99/+jL87MycNDfJyJBDIJkMxJ3et1kTwec87BCLYwyQDedmsrzGTmobSXCpxzdrcfXAYQISq3CkKiXtcFzAwwM2R8g9f1urbJTCL0DXkLEECdGWCvQmYOZHfBJNe1SWaO0hb49u2bes5JBrwhKoa3OICQUF37lvB4PJJR3vq2NaPy2xIQ8pOrrVWS7FadGcDSD4JSFQOZCTclGW4qyE1JUNoCSVRuuQEqn5J4y03lk8pNzQ1om2Rm/ATMTBKgLZAECJCogSTVvNG8zQSqaGZ4U36UcNNaJs8/+dPf/NmvvwHJxPo3/9rzH/zX/6l+fT4Szwxn5uTDBLU6J+fM7qrnzBsE8K1oQCDJbt/UmTnnsVuwFUjSbjLAkqtFAf9CBAE/JJE3J1MCqK/r+va6ZuacQ7K7TCA31G6Fyew2mTfI7gJJrusCkijXtb0lmRugViFArTAcQD60Xc0wk5lRWyZT+ybDzRu3SXjLB2DtttxadjdvBAJjd7vEmbEoKshPhlsSPqlJ1LZAEpVbEhVIwn8AlZuahDcVksyMCnibmSRAW2BmVCAJoCZRk8zw5/IGVZSEWxJutWH24t/+2z/d8pYQ4iR/9F/87h//wfdfUuZxJomTnMwMyttM5qQtkJgEBvADmLZAEkBpe84k57q2CiTjbSZvl17Xtp0ZFVQkijduahJ0CZCw29dWPeckacsZIAnQcl2X9ZwHBFCBJGpbFdit8ra7+Rlv1WSA4ttwAEFt6+ScGUxSbJGIbW3aBjKThB8pb4m3YiGAXutbEgSSHOjuSpMgGvCNW3IAld+WRG0LJFG5JfGWBEiickvCX07lpgJJZoabt9yAtjOTRB1JArRNouZtSHhLMjNqWxWEAEm4mWnzJ7/69Q8/XPIhIUQ5J/vf/b2/+fu/+2gyAd3tSc6cDI9z5gx0dyGJIWQAlTfjDUhGQN4ku10NOY8DqBD06ra2BSapHzIHqVXAtsKZ2baSxDeybfWcE1IrkEwidL2uBWZOEkhbcOaor9drJsC3by/IOUcFVD4JKgQwfHB4C7ZAZvJGSYStEkHd9Q2YGTWED/IhfmhVZEbtCsyMgrRqRTCJHxKS8Gm4teVnkqhtgSQqn5IAKpAE8AbMTBKVv5y3JDPDTeWWBFBzU0dJAm2TAUmgSYBzzszsLtAqIoSEHyXPP/v111/96oeAfAgJMZ7o3/r97/7O3/krX84JUd6USc5kHpw5JF6LzkwSREwCqIAKAzbBtJiA9kJmkpld+ZDdKiq3turMVFTC2+6aD221yRSSWM2QaasCam5tr6vqOZOcZNqq55y2r9fr8Xi0fb1eapKZCVy71Ukyx1uC8qPNoGf40cmH9UYqlWSAV30DVCAJMPJJ7cw8Hs+vr29fv/4w8+Q2M8h2xRsz4dYFwn8Ab0na8ik3QAUejwfQdneBmeHfowJJuKlAEv49SVQ+DeXTmclE8ZZkZpKogBlFC8qboD5++ctfv16L/CgQkCTmy+nf/3v/8e/91S9owiTtZuZMJk0CGUBnRmkLnDPJgG98iGD+X97ghVkO6zzP7PN+uw9ISQQpKbLHlhg5qXKmpio1//9nhTdRAE7v95nuDTR9aNAZWpeslW5bTQLESrhJK6JWwINJar0hHiTqboGZ2VU7s3YlwMxaba/Xa5K11t67rTKT1ta1JlnJ9EjSluN6ve69895M9x2w1iVzqQZ6o4DgZBT69PQEDHfP+y6JWRUIsIkKqEASYOQHez9/+umnn3/++bt377766muIrUoG2baAd0COVuVnUpN48JCEo3WtuVwuSa7Xa1sgCT+mAkk41BwqP5aEF0J5mJkkgEcODyAzvBBu8t33z99++yagvBdYoSC5iL/6lP/53z//1S9/MclMoMBlzRpvCIuhJQHaAjkAFSECTehIaqvrslrxJopWJfHI0VYN2VpN0nZ3k8ysvatmpuZGnRl17z25u+7dFlhradTcTTJg601bbXeve1dn5nK5tFX33mBmkQtVVEAOJ2D01aunJO6tFtpKJDWAUKICKpAEGPnBxMy8fv360198+t13f/rT92+u16u2pXtLAI8kKnfhIyof8ZgZoC0/lkQF1gHsvVU+ogJJeMjhwQs5eEjkEJJMAuy91dzMACpKhIQA6sx68+btN9+80ygQEAwE5L0M9n98+Zs//OET3715dfksodmXWWsQgSiYBKLlg4igBqIIkwF2C0ggWsnMdG81CaACSdTeaCUZSLt3m4zE2ghBMlMKsd4k8d90JmShcpPJmpm2e+/e7O5WBXKAigcBw52CvBdmghNnVhIULahtDJlpbW3GA1CTAJMA3rHWtHtmff7Fa+Cbb77du0pr9wYUFYQBPOZQ23J4JGmrzkwSlQc1CdCaA8KduZsk7VYSkwAeM9MWSMKhAkn4KUl4WENAfkRFuckdCmhIJdwNWd98/d2bN5u7cCcQfmySVyv/8uUvfvfrV5+sTy7rSTaTSSbhzkAQ0pYjCSDeMUmAtkJAvW5zqEk8kgAq0JpEvW4zScajFaIjNNy0nbVEtFZN4gG0G5y5ACokTJI+KG1VIAkPHrwgynthVnDizAKiwLXtbk3WzJqW1k0AFVCTAEl8mBng3bvnTz65PD09vX37tiVE8YMqSTSAR5KZAVQOtW2SturMJOFQATUJ0JqsmdyoQBIOP+gMGpBjZlQePJIAeaEHkIRjxUBmAB8ul4ttFUjSdmZgoOW99d13b77//pmPhB9Lon7xq1f/8//+h09mX+ai1iuZm4QAmmRNSjlCRESESQK0VZOo1y1JuGubYyvgHUoSazWZJN7RFgIjNNyoJLVqQhIfgL2v0MwFSUISR71er2pbiMpPUQENhzwEZoKDsxbg3ur2Bk3WZCLTeq2AypGEn9J2cKBWB1DU3QpJIMqNB0cSHjyStAVmBlCBJEBbdWa4myQ8JAE8AC0I5GibZGZUDo8kHDOTBPBIgHDEDSRZayVpq/Jj3XtmSIBCMs/v+r+++l7DnbwQ/r3MXGD/828/+W9ffvaLy6XN836nPD09rTVod4FZM8NdCLnxBkJAYGbUtsA2k6i9c2YSru2uAc2N2ladWcmorVaMI9Bw0zaTepfDB2DvrU4WMGtubK53W6tCVCAJP+YNaMJNRN4LzARXmLWU7m23iQJjIs4s5bn+IAfQdmZytN17rxu4Xq8kYdTW2pvMJGmFAD4ASXhBBVRgZjyAJBxtZwYC8ZiZJBxtk3DnDTCTmdl7qzPDg0cOjhyAmoQPEne7k6y1ZkYFkvgDQElAiKTlm6+/f/csDAjyQvj3kmBM+X//9b/87vPLbq/4NJeZqICamAzRmmSOJEorGJwZrQyk7cwAe7eQRN17t50ZJRm1LaAmk4y2BVMEGjxmZiMtAYRoW8G9jbPWysPe+/n5KngAbQNZI3cqh4poZgawEm5yqDOz1urNrshyb2eWBDJzUa9W7aHmUHlQk8wMjcQPqgJaDhUG8OCnqIAKzIwHD0n4IBAVSMLhAcwMx8xwVw9+TE3CMTNJvKs6M/lgdLfNgwrMjC9wSCdr8sm33/35T396w38g/JQk6Oe/uvzrv/zul6/E/fT0pFyvz2tWJgkJrW2TrLWSAEkULXZm2pLJLNsk6rYajr13EqAtTBIP7gaoxdCUkjR4zEyxLQ+1yM31eSdzuVyAJOq7d+/aZsY7AiKQRAKo3HiA5MaqhcxMErwLzFptgSSOM0uZWevy1Pr27buNgNqWhyQqoLYFkkyWB4cKqBytEA6Vn6ICag5g782RRE1mJslAVMCDQwVmpi1wuVyA9gok4cfUJGoO7uqRZO4C8QBmBvAAkqi8kLvLmzfP33375rrLfyB8JFlRpu28/uzV//PH179cdVYm2kzWrJkktLTN0RaYmczQQmemrQQGvGkrlKBJ2oLJeBegLZAERquEdFvMTIMKzExx743h2N3qZPZ2LmvN2t22+64EgorkABQM4HuAVoszQ9I2kGPkJocKZGL85JNPWpTMul73u3fP22YmSVtABVQe1LbAmgV4g4ByJzfavQsDJFH5KSrgMTNqW2Bmkqg5IIACKnvvJDMDqElUYGYl6E7CR5KovJBEC6jrUMEbIElbQG2bZGaAJBzJvHu7v/76+13+N8LHknCTEN3/+scv/vE3v3C/efX0izB6Ze2ZBSvQdq0F7L2TANqZXNaq7r2VmaW2aiHykNl7J4FotDdrrSR7c927NIQg4Saoycykdm+70QKCGJCstTKx7r2v+9rdMEnaCoFMEAG58eAQtp3cteU9ucystTJjW50EyCWvPv207fNzW/eNtM0M4AGoiJBkEqEtN0JQUaDKjcOdvTOHykMSjySA2hZIAqhJZoajLXfRwTtABWZCuEnS7oRkkoAcKg9rrSQeHElUQAHXWjNLha0CSTi8A8wdSZQk8PTVV9+9efOcRDkCgrwQPhYguIKTvnrKv3z563/44olmnPKuXsmsy6unNerMAP6brrvL9XpVQeC6zSwsIB8kq3dbo6hrrZlRr1ev3Q3gLHSqk6gzk6Tde9NdFTA8ZK2VRN0PYZJpq85M601CEhVQeUFFq4HMAGutmVH33upkiPO0Xr161fru+XlvFfUq7+Voy41Rk8yM2pYjCdDDSkiWB0QLJFGBHIAHh0cSHnL4wF00qNypKGFmgCTtFZiZJG05kgBJ+LEkkIR2J1HBtS4z8eAhCR9MYhKpokzWn75//u67N1buAoHA5sfCTws3SVT49Cn/47/+5vVnT91/ngwkzFor48wAVjHH1plJUEnU7t6sdfEGUI5ktd17W8mstfbekEwqW4i1pmEsSYAkQK2b1pskRY4kMwvYR1vuEqatOjPa1mQgoPKCdudoC8wMCZCZ7q1yiFm5PL2yXK/72sIkKSYBkqi7Gwmj5uCYmSTAPtqqQLLaqkl4aAvMTBIODw4VSMKRo626987dKITAzFz37m4OIIluYGaSqDzkANqqwMwkAZLoTqICa60kWuUHOdoCSYAkLTPz/fdvv/nmLUcSlbuA/Fj4SEAgvBdG+0+//uUf//g6/nlKfJVZiVmuWWLbSYAk13qTyWSSqLXcRQvhB0bdeyuZdbP3rs6Mmaq4u6UxShIgiUeMhGNbIIlyo16vV6A1IQykrZpEASEQjyR8YCkiqECCgKy1CAgBlWxzQ6Z1azKTwJ4DuF6ve2/AcpODY2bWWsDeu+3eW+VuABVI4gNHEsAjCUcSlR9T2+6911oz05o7ZuZ63TcJMEm4KzAzSVQekswM0BYCAkkgCWSHiMDMJGmL4VBnJklblRAihGXz9dffvXtuEjWJyn8g/CxJ+P0//vLL331y4ebSmXJdca1L25kQPCZLXWsB1lo1CQ8qh+S6tzqzzCTxJtxogLZaLUSZGQ7fK8oHk/eA5+frDeDDZCUXoC3QCibhQeVhB1sgiQokEdAka60kHFusQkHJBAjOzOVyUd+9e6cC3SYBkgBtVWAdSZ6fn9sCM0vZe6scSTh8ANQkQA6grUce2u69Z2atC4QjyT6AJHxQYGaS8GMzk6RFmwgBEpNoVOhaK0nd6loXK6AmAdpmhHDj7OZP37158+bKzxN+nkym/v4fPv2n331+WZmV3Y2diZqbiRaYWUgSwIdkeFB5L7nura51KVGTEAQMyb67JkCUmfEDQIwmgSSTtVaS63U/Pz9fr1cOD5iwAG2OtiqEQ+UQtlWBmUmiciQBZiYJRw1YaK1kwo2dA7heryrQbQghiQ+AOjNJegBrXZS2KkcSQAVUDo8kQBLAhxwzA+y9gZkF4Uiy927LjxSYmSQqRxJgZpK0aBPeS0wCA7Z7Dqk6WWoSlUMFISHPu9999+7d251E5WcIP09umLj/9b//l19//rS6aTY7CTDDTYDc7b05EiBgclGTeHAThGqUTAlHESSXgNruxDbqzPge4s1YgcvlwuTmcrk8Pz+/ffuurZoEUFvjENomAbQQDYfKe6FQi6y1kqhtE5IBcnDU3FS3N2QC6A5RZwZo602BJCRpy7HWUq/Xa9uZUYFkID4kmRkOj7aAykMSPpJDBZKB8LD3VgGVDwrMTJK2HLmbmdxotInKTWLuFtheIWstqRoGmTVtOZK03CT59ts333//DEmWbpD/P+HnmuSiz59/dvlvX3726ZrlxbWVtUYF8h7x4C7gDWRmvONG682YDFi1IfGGm0wmE6CHoPKeAdoClZl5errsFpiZvXu9XtvuvdWZaWuTDKC2gtzlhsMDEEyAkBsC4pFEmJsEEK42pCpmJsQjBCThRgWkigJJ+CCK9iZJawIE8ABykBBQYLeAFQEDJDyoOdoCOYBkONS2gHflyAOw904CzEwSIAkE5APBJDMLbPfevVwuM9ktcjMzQNuZSeJN5t3b6zdfv9m7EAiUnyH85/3mV09//PKLT56qzIyIJJkjjto2mSTaGyCJyl203iyYgFYbPghMwo21Nol43bvtzIQL2FbY3esAkqitN/tQZ0btJlkq0JaHJBzeAVZlcnAjhBuVY2aSAMI14h2QBFDDAEkAlYe2HjMDqHsLrLUUbWvCTZIeSTiSEN6zAiIvyAfuJllrtVWTeAAzA6iACqgcOXiYGf4DiUASYGY42q61krQF2s4Mx8zsvUnePe9vvn6zr/KfFH6mcGcmT3r9zev545dfPE1sk6jJrDVrLet1b3WtBXhwqLkBuXMUuYtFTTKzIG2BturMEJ6v17ZrrbCSAOLz9QrMTBJA3buAuvcGZqY3GxgePPgpypYcKj+YcCThB0HBg7sEslR+zAegLZBk7w25XC7g3gWTQCDq3jsJoOZAbkQgieEHKod1gKRtEkBtq85MEkAF1CQcSdoCM7PWSgK05SeYkATIw9677cwkmZm2SWYmicebt/3m6+/3BgKC/GzhP2EgxKSW377+xR//+RevnmZvE5JJmMneVZPMGqQtmEwSFUiiAo61wMwKaU0ycwH23moPlURMSAYnyWQcnp+f1SSA2juTqG3VmenNJgy5A9S2ahJeSKJcaw4PbpJZw6HyMDMqAgpoZkoAlRfa8pCk7cy01ULaqsDMwCRpu/dOAuRGkR/kDhP5oBruVMDjcrmobVUgCQ8qkIQjyd5bnZkc/FgSDm1iMknUJIAHMEfbtRaw2zXz7t3zV1+9vV6bLA0UBPl5wl8mIL9+ffnjl79dacolT2Tr211n1qwJ4SbcdJOEI0nbJCxE7pJMyExaWvfeYr3be2fuIHvXNjNrLiyer1eOEXXvreBARSqMDYiYEGyTXBGNvJeED6LsNjNJ1CKQBw+OOdS2gJrEjAcwMx4cKg8zA7RV26pJADUZbSuEF5JwI4QkWHGtJ6BtEh3dWo4cKg9tgSQ8qEk4VCCJOjMqH0kEknCoeQFom5Bk72Zdrs/9+us/XZ/lLxX+Ol98dvmvf/jiMl1k8rSvLc8zWbMyUSHo3js3MzlUkMUHBjKzLpfL9bqv16vHtkDA3Cl7V527ZbzuDVhXAuw7MVLtkDaYG5VEBIRtA5GPBLJbkplRi0ASIInKQw6grQokYZbaFlhrtVWTAL6Qh5kBrterB+DB3fCRJCThxnYnM7M8kqXVJgHaJgFmJsk+gMvlksSHHB4zA6hJeEHlSASScKgzA6hAbsisUXe5Xv3m2++v78pfIfzlkoz2N589/eGfX18uezqwnN4kmZkkM9m72rzgAUK4y8wks9a6Xq/7er3uLWTynnizN+pMklnrsuvz9RlQL3N3vdsWUnWIxo72Zi4rM9Fr97UNDOEI4b1E2S0za0Yt8pCEF5Lw4JGEWT2AtZbadmYAH9om4bhcLkBbtS3QVk0Cw4MKqDMDJJlBbdXMhLsBdSs3HoCy1iTxWGsBKqAmATxygDPLgyMJRyKg8jAzSfbewFqXBMhkvXn37ptv/vz8rslo+UuFv0bAp9DXv8yXf/j8KV0snmbv61orCYQ7EyAck6i92QWTCcNEhdTdaiVkJkAQWhU1ydwt8e3zFQUuawWu1+veDbP3VTuZmdXSFiE3IMW2vGD4wCgqycwARSAcCXchBJB/J0mTHsBaq62ahBfaAlVwZpAkKtA7+SA8qDwkARJnRtMWmBlIq1brkXCjJMysmQHaJuFQkwBJgLZKkrUWoHIkJAFUqMpDkplJAsxM3TDP7/rVV3+6Xk2CkfKXCn+5QLgJ2F9/9vSHf/r10/Lq81prZpIAahLtWgNRIzdtU0PIELZCFFOBhg8kmkFu1CQcu911Zrhp1XZbYnbvZsYjmTC8pFXDew3vKRg+kptJEXNTyE1NArZVk0CctFVzAG1VXkiiVsgQBkLaJukBJOHBA0jCv3EmM6ul3blbvXNGwA82oLlZawEqkIQXZgZoq+RQgZnhzpnhriqgcqgzc7lc1L131jw/+9X/+tO+yt9C+MsFBjYBBvvrz179/p8+n1wJa62ZAXK0e60FdO8QFRiUQARvSDJSEAdoCxLJQHhQn5+fr3vPujw9Pe296QdWzA2g7r2BmRWGF3wvvNfwEAyHB8fMZFK03JS7ywzgAaiAiZqDoy0vqDl2gUmAoG2T6G4LzAwPalsgCQ+JuRtFbU0GUBOBJHtv3RANODM5+MjMAG05krQF1lpJVCB3gDeAyjEzkL2vM2t3vv7qu+fnQriTv074KwRkYBHDVfn8V09f/vPry4owydwtoF4TWm+GqEnAWoxACAMxDYQFtAWJEsh76t77+fl5t7MuT09PbWn33m0xSBJAbQsmKwxHW4KKMOFoeE+xfGxmkhS5y1YOdTIEFRVUYGaScLTlBTUJUG6ScLet5S5tgZnhQW0LJOEhAdSoSSAKCIGqHLohEBVYayUB1CQ8zIyiTYQAapKZSdIKJDMD+IMcQNuEd++u3353vT4XFndC+euEv4GA3ATk9Wevfv9Pn/q99KYAAAuBSURBVF/W82RWnhLkiqPp3pnMDAISq23VJDOTBAO0zkwSrVK3OHNRuUvb694BIckwe/dGBVQgiQegidyoIcXamUkCNDykYiW8FBEzk7sptPVYayG7G0gCqDOz1mqrAioveECSAdoCahIPYGZ48ACS8IIPM4EAe29grZVE3Xu3e2YlgSQkmRm1bRJeSALhIQeHFpgZEORoS4RYZz09P+9vvv7+epUPAvJXC38Hrz+7fPn731xG6prUwgBtAyQzE5Il0NZjZtZaNnvvtjOTRG2VLc5c2qozC2jrMTPdqG052qpJOBQl8oPiTQ6g4SFhfOCF2lkrGaCgDswMUHuDzIxJW2BmABVQ+YjmRt1751DbqjmAHB5AEpUjiUdbHpIASWYmyfV63XvPJJkECDAzHgkvBEjCT0kyM0B7VXlJzNr1m6+/vz6Xv7XwNzdD+8Vnr778/a9fPbXXulenAZVjXRaSSHivbZKZ6fZ63XMAKlK2mKy9t7rWgvRIAun2PY4eOThaIu8lKW0FZgZoeEgYQG3LvzNJRt2aZCAghwpJSHYLzAyHBw9qEu6SjLr35mHvreYA5mirAkn4MbUth5qDQ+UuUDAHDzl4UDmS4YOAfJAbQLfKQ5KZ9e7d/vqb76/PJqPlbyr8zWUg2Ne/vHzx+eWL178YVtlJRG6SEEhoQhJuBCRp7e4cao5aqdJ6MzOtbVUOG48kito2d0CAFuQmCdBoBZIAhh+UhGjVJIBKzYzcqcxM4t5WwswKiMnUyl0SEqAthwqoOSSQgNhdNUlbFUiy1mrL4TEzSXjwIQmQROXoMTOXywVsdxJeyMELKi8kmYlyowEStECStsk8X69v3lzfvd3XqxAY2PxNhb+XC+zgP/zuF7/77S/ZBYG1VlEJE8zkBiVRJxFsZyZJ2xy7rb3hLoDaVuWw8UhGA97wgkYQkgAGBFF5SEK4dq9ZSO3MAG1VXlhrAW3VmSg/UHMIQhK1rQqoQJKZ2XKTBBDU3FS0mgTYe+dQ287BkaTt3ludIwngASQBkoBaXkgCqBwzw+HBw+Vyac3d4BZFBAJzbb/79s9v35S7QLgrf1Ph7yQJAgO/++2v/stvfwW77rUGq3I3ySSIIcDMILZrLaCtGnnutQokgaj8WDceyWjA93hQDPKB3MWogJpkZtRru2ZI2ubokYMjCcneW50ZlYfJB9UqhwegAkm4mSEgN0nUJFYImqQHsNZqy0dUDnVmkgAeM7PW6p3QhPfaAjOTpC3HWosHlUMFcijtBpIoM+v56p/+9ObtmysfhDv5Wwt/ewMlvBeZ5PWvnl6//vRXn306aejeGwyZDAlK7taMR47+QEUgd9MWmBkeumnLXSAq0JYHb4KgAkIgiQooa63AbmtmxmNmgLZqkpnxSAJc1XZmVB4uM0CS6m6BHCqgAmoSJoAHoCbpNgRIArQF1lpA2yQ8eORQeSEJh0dCwntt1bXWzKgcSfiIuvdeawE9gLUuMN9//+7t2+fn5yaBaKAgfwfh7y9Ecln+8//1+evPntzvkGQBCZAbjrVWW5VjW7XtWgtoNwRQgZnhTkvLjQoooKK8pyjvJeEQBAUkyUTlUNQkPKg52qozA3gkAfbewKzFC/JBEsAXgGRUCEFtm8TKkWRmPGYG2HtfLhdA5VCTcCQBVB7aciQk/EBNstbiUIG2QA4Ode+dZGaUva/ArKc///n5u2/fwoLNXbiTv4/wf0Qy2qfF68+evvj800+eLui6LEJuoGoVAxgtYFCBmWmrJpkZDh9awwAq4AMMDxqOJBxC5UZlwuGRO5QfqEASXmgLJAHaollLfkISD0AF1GSFAGJbK0GdGaBtEiDH3jvJzCQB2uYA1LZ54CNKUo4k1+sVePXqFXC9Xmcmyd6bIwkPbblLMsCbN89v3j4/P9cCA+XvL/yfkgQRf/Xp5R//8fUnr5Zek0wC7NaWZBHAGxCBJMDeG7hcFoTDF3AAFfDgbvggEA6VQ6jcqJkh3HjXHCoPyo2aBEgCqEkEFFABEz7iAagzw6HhwQNQkwAqkARIoibczKwkbdU52u692wJrrSR8JJEjyfV6BZ6entTr9brWSrL35iNqEjVZ795ev/3/moO37SauLYqiY66SMbcQ8v9/eUgD2VbNkdK2lWNa8hzT+/8edoFA+a+E/0bAwCRqP7zfvnx5//nTaQIGaAudLRtbAKk1BJJUL5d9Zk6nk1YJ1EMBNWwqoBxUrsKLSMLBtoBEUA5qtkmiAglq2ywsyqJyyDIzQFs1ic8SFUjCTVtABbZtY2lRgSQu/CwJ0HZmmxmXJDNpbfdtm5ktQdn3S9tkIFyZBGRJeJYEaJvEJcnMtFV5RYVs2zw+Xs7nx/O5dmBEUpT/RHgz+ePr3dcvHwexGRKBYRLU1plJwtVc2sC2bV4B1h3EAVRA0eFG5cYkGHy2GyAZFXCCVwkz4wIkAZKoLMr/JSwBQQWEtkASXkmiAjMDqC03HlhUfhIgycwA+77PTJK2QGKSmShqkhZNggpy1SQz45LFmyTAzKhtgSReAZPkctm/ffvx9CRvJLyFJBzk7pSvv999+fwuqU1yAoOKdmaSqDPb3qvT6ZREbevCi/GAGAmLyrMsGGyrlKjJtAUMrzgzSdS2M8Mryt92ZcmisqhAEl6ZGRcWlRsXXsiNkoUlyb7vM5OkbRYgyb7vQK4GctAqzxISFZcsgAIeAJckBCTZYPvz2/fzw5NF3kx4I0lwxNP004e7L7+9u3835TJuySjaZBICwmXfA6e7u0na7i0IgQG84iCRFyqvBINtlRJgZuuSbYAkgJZXkgBJWJS/yYskgMohUfmHJCwubZOBcOWBf1BnJhlATWbfLzNbQtsZ/uaSZWZgXLgaMNkVtS2wbdvMqPu+q0BbyAyzBed8fno49/HhIhsgO28kvKEAQcAPd/PHHx/u77NlqKTKwQxs0n3ft8Nps7ZVSWjUJCIE0QZIwpIEqnvLlZMRFa8AgZkB5Kq6bdNWSaJmAZLwigqoSYAkgAmLmoSlbRJABVQgjOChEpK0TQIkAdQkykwOrW2zADMkth5mxgWYmSQKCGmrzuTQJQtX0WoBERJG5uHh8c9vj0o4RA7yRsLbm3DohPv326f32+eP77cNaG1NZlNCZtvAHhRNpju1kwhhUTQJkJtqvWLxhiUJSxISEhcgiZrEJcnMcKMCKpAESFL+ncrPJIKtvFBzgCQkgEsWFyBAEgyowMy4sCQBkoCth2Rm0lYF1H1vkm0bgm2yKT9+PJ4f9stT+WWEtzRcCSYcNMGvXz/89un+dBLqYTIOQuLSNgFn36sm4SYQFiWZhEQtVy6ACzAzSVQgCzP7vgNJuAqYROUqCc9UwCUJkKT8i5lpq2ZhqYosrWASliwqLpAFqeUgWiTLzHjDkkXlZmbaqoDadmZOpxNwubjv/vj+eH648CIQKG8tvKWBcgiHCGxy2N+d5vPnd58+3t2921RESWgLKAma7lWTqCyBSVRAyAGqgksSQGVJMjNJXAATliwsMzl0UZ4lAVyAJIAJ/5AEaAvMDIsKJAHaWjNoEpIAbRUVnENGbKsCbYEkM5MEUAEVmBm1LZCrSWirJlHB2U6TeXraz+enH9+flIM8G67KWwu/nOGqwPt3p48fT58/3meSaCvlYCQKyqKyZGmr8koSF15Rt21LwtJWJCSj5gZoBRJUbpQsgArMjLi3QBJulJkB2gJJ1EzCC6FtOISbtoqakHBI4gueZQKEQwjIIYvaliUZsO7JhABtSs7fHx8en/aLEK7kFxN+OeEqQTFwOs3vX+4/vL9LgF3rEjYYFhVIogJt1SQsWbzhlSwsHoBcqSxJgFZwZvhZQhJuZqbwdLlMrqo8k5lhUXkh4SYoiQqyWJ8l4RAOyjNtCIuayWSUZ0mAtkCSbRtiqyWZy2U/n/fzw6W7kIMCgvxi/gItBSkmB0QtMAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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trCj7w7EfhJ7rMsZGo77n0NBzp6MBY0xZawlbW12RZTkejjnlSFilpBsEp8+ebDTjm5tb69fWEWmj3nI9L0+yNCmfvnxTWvED3/Gm0Pf/9NEndjaHUlqnHv3iv/iVd337d0T1FgCMR+Nf+dV/fvmFy//LB//nU2srxFYf+vCf/Pmf/eF8O15eWu4Px2mal1IaDa7n+a63e9T/+GNPHc4KIAxBo7XGwt8AbAKMARCIBYMAFgiAgVcbJaiN7dT9C8eXu3WPMlJkpZWVtQYRbiGEGAOIJIzjWr2epllRFFEUA9ijo8OiLM6fv31xvntzcyvJ8vvuua9Vi5559rnRdOYyCtYgWEKp63qUUkKQc0drlSeJtdYYSykFQCmFtcZ1XXoLQ60tAFVKUUZdx02SBBBukVp0u916rWatKctqliZ5IQI/oIwhABJSFHmj0XQdZzqZxnE8HA4nkwmlzBjt+77j0OFwrK31Ar9Rr4M2RZ4tzXenk8l4PEFEQqlSOgwjwp240SKUdboL7U7n4gsvDPr9yHfHwx4QI6qcc6a1MdrW63UpJaPcdZy8FIZgZ35+ZWWRM3bp0rUrl9fr9fqZ0yfiuj8ZT9PZrFRy5djqN73ljZXARz/0iScff2avN50p2Vxove2bvukb3vTWbFZ8+MMf+uO/+PP7b7vr3//m7545dXxj49I/+1//p53Nq29/w8OnTp2+dn1jMp0hEilNkuVgsB66g9Ho0s2dq7v9QhpEYq2Brz5sAowBEIgFgwAWCICBrxTCyyzCLWgBAcx87L/2rpPzrVhJladZWeQAFuEWAwCEEGuJsTDXabfbHSHVcDhijBujijw96A8dh50/d8rxw/2DPmVsudOsykooqYTQSriuU1WVNpoyxwLeYq2VZUkpZYwgEgQilbTWAADjLI5DIXVWVJ7nNptNIcRkMtFaE0KajZgxJpXUWlPmaGO1Nh5nvu8TQoSUQspGvREEQX8w0NpIKXu9nus4WuvpdOp7PiBWUnqev7KyXOR5miS3nzk5HA6OekcApBIVobTV6hRCM+55QXj+zgunz5574onPPv/sc3ecOr5xc31/dBiEnstYlaZUW42EMtaIa0jorMgtACBttTsnjx8/ODg46h915+ebrYa2ajLLW/XaXKclLV2Y63QXOgf7mx999BOfePxKWikJgEgD31eiqpQMGHvgwvn3fe/3GKs/9NEPf+GZp+aatXvvuq3Vbo8mM8/1gLLBYDSdTLRUjLHAc6USm4eD5zZ6pQYACy+zCGDhqwWbAGMABGLBIIAFAmDgK4IACIAEDEGrLbqOc9tic22hVY8jgppTUuZZkedaa7AKwCICIRwJNxZPnzi2sLB4OBxfXd/QWkeBp2WVZmlelfUonp/vWq1FVfqe7zDmcp6kMy1FHNeUUVmWKG0o5YiUEKK0ZvQWNMYgAGUMAIQQFiCu1wihaZq4rru6ukYpGQ6HQkqrNYCppEqSBAipxbHreIwygtb1vLKU4+kkK0vHcRijjsOsNpwx8bJKlMJai0AJIYtLi53OnBBy/cb1JEnm57uizPMspYDamqjWWFxaksZqQ9Mk63aX3v6Ob3zm2Wc/8uEPP3T/3UBUr7/bCgOby62NnZ29o1FZHlvqzDXDXBuhKoIglXf65ImHH7pnOJke9A8txULKXFRhGF44f4fI5PMXr/oN9+HXXYDC/skfPfqZzz/nUEaVmqaSgOGMTJVdXZ1/w2suKJDXr29u7Ox7vteNI4dTPwrrtUatXi9KMZpOh8MRRXAoRW0JpYbxQZodzYr13SMLQBGsBWstAAKgBQOvKmwCjAEQiAWDABYIgIFXDgEJWI0AQNAa8Ki977ZjJ5a61kgwmiAwSrSQySxRShK0FG6xyBzKHM79KI7rzfZgMtvY3CIEl7pzVZHJqkBrirJoNRpxFDFKtNJZljFG8zwlAFEUe74nZJUVBSXMcTxA1Eo5DnddRwhRFgV3HM651ppQ4nge57yqpNY6imPXdafTqdHa81ywMs+LUgjOnVq9roRkjFWVKMoiz4ssyyilrVar2WzmlajKwmEcEZMkEUJ02m3Pc40x7XabErZ/sH/U75OXIWfM81yrpR+ErXaHux51Xals73DYnV968MGHPvvZJ27cuP6m1z64ON/Z3Nr6wue/uLPX2xlNtyc5o/DIA2ePL7V3hgNd6URXnhvec8dd3aX5wWCwf7A/yzLqsFZn7sLdd83NdT7/+aeffPLppeXuN3/jm3p7g8c//USepVab3t5BWUo/jCyCQVheWVhY6G4d7k2G42aj4QW+ygujDSE0CMMgjAjjxtjhcFSVOSUErZHaWEKDuBbUmxev7z5/5QYiGkBr4UssgIVXFbYARgAIxIJBAAsEwMArhQAIYAABCIAOKN5/2+rxhbbSFSKCMUopRikBkqe5NYqgAWsBrEVKqeN6obSoLVbGKGW6ndbyfHcy6h8c7EhROdypRbHjMIpECjFLEgCwRnHKENHzPcfjShkhlOu4SIioKu7wWi1WQgwGA6mU47pRGAZRqLRW2iChxhjOnVuGw+F4PK7XokbsAxJlDGM8jAItNSAZTZLRaEAJiLIKfO/uu+7qzi/c3DvYuLnhMicIguKWPG82mwBAOTPGHvYO86Kcm+s6LgctXc4C36OI3HGY42lrmOuJSmVpudBd9jz/maefWVtdSdP06ub2+u7h4dH42OqaQd2bjMdp+vrTC+984Paj0eTG/lEqiloQLC8ue0GghDg8OKyqqtlqrh0/fvLUiWk2e+r5i5s3DtcW5u6968zW5v5gPGvW2Kg/2tk7jHyHOF6mzKnVxbofre8ebRwenFjsPnD3+bIS2zc3ha6U0kgoc5y41oiiSAg1GU1c35kmU6WV54VREBNkWVVcvL59bW+oAQEQwBKwBl5l6AAIuAUBLLwMASy8UgjAEQQwsPbsUvNMt9asR9YqpTUAKKW0FPV63XWcLMmUEFYJShDAGmRSGT+IV9ZWC6mPRjPHj8+ePVOPvUFv59qVy9PpNAqCKAw4pdaYqixkJaw1WknHYcYYICSKwjCK8zwvKwlIlNaU0DAMlJSTybQSFSJ05+cbjbpSMi1KawknxOVsfr7r+f7G5mbv8LBRC8MgMACEEO8W3wfAwXA4myVVVYZhhACEscXFpUmS7e3t1uNat9PxPHc6nWZFkeYpIdR1PVGJZrNx7Pjxo96hNSoMgiRJjNFKqizLXc/ttrsa0CjlB/Hm9v54OGLU+dDTL0ylDQn5+z/1gfe//yfy0WR/c/03fvd3PvaRT7zjwVNK6ev7R41O4777T3OLIreirA73d9M0d91gbr67uLwg0VRCJZNcF6VDYDZNmMMoNcPxJC0qx2EGcWl17ezJkzKVT790+ebO9l1nzzx43z1ZVX7xmadnaeK5LuccwNbqcbvZUkoxxgnByWwmtXYclzEnmWXJbGrADmfl+sF4mJWIBEEbC68uhFcbIoKF21c7dyw3iJWWuoQwqyWAVVpZqeM48j0vyzJRldZaKSqX87jW2OsdxY3Wd33Pt3UWFi9f2600W5hfnk3G/YOd3c31fm8fEULP9TgliFmaCFFZo4zWhCKAtUCY4ywuLiLiLEmlslJJAIiiEIzN00wbba2No9D3fUQUSittCSVpkXqee+70WWrh0uUrQsl2q804K8pCKRUEUbc7F0VBv9+/fv16q92xxg7Go1q9XlWSUjLXbhOEVrNViGpvb8/3XdfxhajCIFqY7zoOT2YT3/cGg8HW1rY2WistpDx58kShy8P+ZLHTpY771NMvrix0Kqszy9/5jvcszK+96zu+y4sCAA1A/vUv/tM//cPfOrU699LFGywI3vquN7zpzQ9vvrR+uDXsHfR2djb6R8O8MCurS8dPH/Oj0CFsMhxvb22XWcEZDTyHIKZFrqwJ4qhWb0RxVAsbRaE2djZ7vd6ZE6cu3HVnb9S/eOlF3wujMBiPhw7n9Xrscg6AlFOtNCJSypHQWZqOp8l0lltZtBq1VNinr2710gqQgDXwqkJ4tRGA8yutO9e6xohSSc48zj1rpTbKKGO11koRJJw7DmeFqqbpzCG0XWvmlexn+Vve/oZ3vec9EpxSoM/j0dHouaefOdxZH/R28zzzHOIyyhkRoizLAqz5ryyARWIJm+u04yjSxlgL5hat5jrtei1Ga4q8SNPMWhBSiUpaYywYhTDJkqIsOs1OK66XeVFJ4Xp+EIVC6VmSCKk6nXZ3rq21Ptjft9ZGccQJBYDhZLKwMN9uNvu9I8f3Z0mS5fnCXDv0o/FkTIDU67Usz5vNGmj19LPPjUZjx3OllEWl5ha6R+OBLvT9d9xBfXd7a//h++4Zy2r57rt/6P0fBACjQVclWkNd/6/+9D+/8PSHGx5+4amLzcXm17/toTQTycG0W5t74vEnb95cT5Isz9XC0sKJU6tRFGlljnr9o6Ojsig4Z6HvI5hSCD8IFhYXO3Pd2XiijS2E3TrYZ4yvLS15Dts7OnQ8du89dxltX3j+hTzPwzCUQhqwWisEjKKIMxcAszzvD0fjaQpGN+vxwvz8Xn/0xMX1o6RCRGstvHoQvlIIYBFeZhHAwvml5h3HW4wSDY4pK0YMdbixthJCCYmASkhroTs3v7aw0JsOtvb3UOkQOQ/CvelEgXnr29/+4Gte50XxeDRNxrPrl66IPJlOBuPRkBLjMmq0NFogorVIKZFauV5AuDuZzeIwbDUaFlFLCQDW6mat1mjUXM8pyjLLcgQqpUlnSZql2ghKUFRVKcRcpxvVGpPZFI1yXZe7bqs9J43Z2z9YXV2t1+LnnnshL8vuXOfY2mrgent7ewdHh6dPnT514uSlK1f29w9G47HjOnHo16Ka53rGWMfloioJwf3Do6PDIyBkNMsOBtPFpfa3fuu33dw//OJjn33b6+7vLLWuvLTuo//Yi+tuvfZTP/szj7z3fdwJdA6EAPHx2nNP/vtf+9ViOp5faqyeWZVgb97YOrNyHCv48F9+xJjKaBDC1pq1kyfX/MDLsjLLsqoUQlRlVWqtlVa+H9Tr9bXVtdMnTyXT6ZWr1/cGI2HVvffc41L+0sWL0zxdPbZ4+viqMbbXO+r3B7NZUhYV48xaTQh1HReAICBlvCzy6WSYlpo5/lynRQg5Gs8uXt/eHWeICNZaQAALXzGErwACWKAAmgACoWDUuaXWHcfaBC2llACKskRAzri1RpSlEBJvAUCgrWZreWVRCLGxsSGFcD1OEAnBXpoBd+++8+5Ge26WJNk0qZKsHvlVkY3HI2OVw5nWUhvFkAAQP/C1tksry/Pz3avXr1VlSZFKY7Q2lFIpBKck8H3GmGWYVXkYRrWwrkp5uH80mo0JCgVKIu+EnZNrxz5/6Xnu0NVmjXEMvJoENk6T86dOPXTfhS88d+nJLzyzvNhdWehYyq5tbA0Hg2Nrq8vLy9evXjvY3ye3IBAE5vBudyGu1yyx1urhoN87HBohXNf9wpWDzurpn/mpH33vD/zoQW//R7//ffOBe+eZU1evvrRztHdte7Y1Kmq++3P/w8///X/wCxSpQcO5829+5Zd+69f+d88j3/zIG77+9Q8899x6Oc1OLq98/nNf3NzYqNdrZV5YaxaWFrvz81KqvCgYpYwxVVazWTItUr9eW1teCX3fZW6jVusdHHzx2RdGaTbfrX3j295aCfuFZ14cp9N6I6xHMefUajWbzvb29pIkC/3Q4QQArbXGAiIBoJ7vVVV5eHhEmeN6ruO5gCTJi6ev7w9nGUWigVowYDUCWHjlEL4CCP+NRQbWnFuqn1+qIyWO6xBAtFYpCQYpIUKJsizBolIKAAih1ljfc+v1mtEmL3KthONwxmkl9DjPDWM+87txk1iwlHg+16IsikIriQBKS8YoAhhjKHOVUksry6dPn9o93KuKssjyaZJqY1zXazbqRsnJZCJF1eo0Aa1Wut5oNOvtySTp9Q61LizCKFMn125/97u/9cWt9c986mM1YqOEsCIAACAASURBVFyHaGnGsxw4sULGjdaFO88PDnYRjDY2FaY/noGUlBIlJQFQUlhrfc9DtJVSgR822q24HgOglJXMyl6/X4ri+ev7P/bTH/zZf/iPjTJPfPJTH/h7P3zH2sKFO84dTfYblB5uD//osWeqyjTa7Y985GO33X8vADz6x3/yYz/x46Cm5493H7r//IP33PPCpU3UjJjy2vWrvuehsUVZhnG0sDDvB2FRldZYsFZJKaoKjKWUHT9xojs/n+XZjY0bh0dH+wf7eV4wQgLfv+vC3QBkPJ1JLfgtjBOCWoluZ25zc3Pj5pbv+aHvWAtVWRHKKGPWouO4SDBJs6KsKKWEEm2Bc7fIzfObWwezDCgBDQQsgDXwyiF8BRBuQYsI1ty2EJ1dbCJS13c8x2GUcMqkkrKUSkpRVUopC6C1JpQw5ihtCEXf96wFawyAAWu0lo7vnD57djxLNza2XOYGnhe5gVJCVCW5BVFraa1FRECkhFVClmXp+O7CwgLlzHO5EnIyS8qqCoLgoYceotY89fQzh72eS0ktDpQUSOnCymoYxlkyVUUGQNf3xw+/9q0/8bM/R1vRv/7lf/Lkxx9FqJa7i73+MCmTVlzbPRqfXFtanOvkpRxPE1mVrWZUST0cDNFCsx4rpfK88DxPW6WBgMV6q92dX6SEOpy7jL90+Qq3uUJ0F8/86A+/f/Py1X/2L/6lrfrf/e5vjGuNcTobDaaDG/ubu3v702SQlu/9vu/7hZ/74HPPPvf+D/y94Whw/5nGmVNLYD0trNC2U48YKupwRpgUUijl+Z7nB34YMM7A2slonGUZo9T3fcf1grhmtU7TWb/X6w/6RZ5xRtCaLLfA/VozXltb9F23LKs0K8CYZiN+zYMP3djYePJzn0dAlxGltNKWMmYtMMYpZZRSJGSapFmeO44DSBzHZZTNhHzxxu7uaGoBASx8ZRBeKQQgCBpdMOrMfHh+KbJagxMFDueUcIc7DldSikqUeSGFsMYaaxljYRR6rldVQmqptC6rstWotTv1LM+39w40Zz/5dz+w2On+wX/6T1ubmw4hPqFWQyUFIcR1OKPMcXmaZRSwVqsneZYkqVQqiqNmu+n5fiVEVVRFWUgpH37ta+fa7cc/+/h0lmghwBpE1NZ2Fpbm2k3UlZVCKrh083D15Pmf+umfPfbgvU9+5C9++9d+NZsO5ubmlNauQzrNxuZ+7/TqsXpc3z487A9GqkhPrS1P0mxrZ5cwFkeRVkqJIvBcoJxQB5Fw1+90u2EcG22opRevXFZqGjCbpDKI5i5f3do5OvrR977n9Onjg9E4S5KnXroYMvfUYmd7r/fMjV0A/M63fMsfP/7k+ubGhfnwxFqz051T1jno9YWo1pa7cRxHcT3wvWSW5mWppJJa1ZuNVrs1Go37vT5BaDTqjLJSVMPxWEnZbrXQ2GF/UJaFtVqhPuynQRg/cO+drZqfpWVeisFoBGhOnTp94cJdk8nsk5/8TDJLHWZlVbmOi4iUcYuIlDNCLWIl5Ww6swAWwAuCMAqIxbKyn7t0fXMwQkLAGmvhFUN45ZAQtMaeXqjftlgHoy2hvsNdRhGRUIKIoiwRwBqTZ6UxhhBCCYlrtSgMK1GlaSa0UVrPtWvduXohxAtXN4Ql3/+DP3xsdfVTH/v4zfWrgcM5QUp5UZR5njuOU6/FUegPRyMp5dLSUlmKjZs3GefzC/ONRi3JizTPGaWIUGR5HEaNRn00HDJGbplMJlJKzv1Gs9Vq1SlKq+QkSQ/HRdjoftt7HllbW330ox/a21yPfT6ajLXGxblWs1nbPezPNVorC4tZWVzbuDEZjbvtTlFk/dG4P5rU6jVEsLKKo9AS6nkBIplOZ7Vmc2F5CQmT0ly6fG1/fz+djcOAl9okEl537+2vuesuYcjgqD8bDSbZ+J577wyoc/nK9qWNHWYFIP3clb3FBj3eiWv15vETJygj6zc2lJTd9lyt3QrjiCJJ01RUwlibZhmlZGlpuX901O8PAz+Ym2uBhSSdplniul4QhEppRFKW5UGvJ4xaW1m+49w5l3uT8XQyHApZpWVhwLY6nblOhxDn2rUNJVSnEx8dHpZZWotrlDGhldKAgJQxA5BleVGWhNJ6s+UFoUOBWDvO5KeffnF3PENCrDVg4ZVBeMWQoDXn5mu3LdWtsYYwxqjLmcMpAhijjTFVWTDCGCVJUgAAIiitfd9vNhoAOEtnyiDjDrEy8Hkh1fbh6Pbz59/2ljdfvnKlyPLBUU+VZRz6nLE0zSaTCaM0jkLPc6WUeZ4z7hht0yzzfK/RqIdBMJ1NtbH1Rh0BqrLI8jxLU0JJFIWuw9NkJqXy/TCMIkBUSoCu8jwX6Ib1drfd8lxnMBiFLq4sL0xm2c5er9NszM+1+4NxUZaO6wae3+/1xrNpGMcup5RAMkuUVkprAyikAmuWl1e01ju7e8z1lldXms22LGw6PUpnvWkyyaVBGp48ff5bvuVds/7wpUsvDfo9KwonYp12zbHe5es769cvEagmWeH7fqcVNRudhYXlu++9p9frPf7YY4SQuXYnjCPH90VRam200llRSCldx2nEdSnlYDCijIWhL6Usqpw7PI7iJEmbzcby8sp0OuuPhlEc3HXbWUb55fWbSpMynSXTca4qBCCAjuM6jk+pM9fuNurRtfWrB4f7QeBrrYw2RhsL6HoOYTzL87wo/CAIo1gDMkqNENaYWak+f+nG7ihBRGstvCII/z8ggIX/l9sWG6cXGmgBCHeodtAQ7nKHU4pavQwBPIcj4nSaZ1nGONdKaa3jWi0MArBaWWTcNaoUZV4IPUzLh1/72u/+7u968aWXtjY3d7a3s2TWiGNKUFTVbDZDBN/zOKeNRqMScjgcVVXlul4QBHEc3WItRFFQq0WiEmVZpnmapkklKiEkpegyrrVmjBEkeaXSLCuysRTScndl7cRcu6GlkFKjLiniLCulpXPtzlyrWRbV4WhYVpVLqBZyv3/EQ+/U2ioouTDfKctqMBqNk7xSdrnbjqNoMpldu3692Wqfu+N2pQ1hbHGuVswmB/3p4Vje88DXP/zQ6+44f8/l557+5Kf/Mk0ORZEwz9PSuISUWVHIaZZNi0QQBvPtxVZ3sb3UjcLo8uVL29ubDuWB7yGhLne1UtZare10OpVK+a7XarQAYTKeaWMAbSlKA+B4LqXUD8Ljx45TyhzHWZzvNOr+1l7v2WdfYIinTpw67A32Dg4qmTJCpBBSagJsdfX4Qnex3ahP09kzF58bTwcMicu4VRoY8TwPCNHGCKkIZQBGyVIB05bLqoqiwDLnyeeu3DwaAwJY+GsIYOHLg/DlQkACVlNEAwjW3LbUvH2lqbWyFgghlBKCSAhxGXM4M/oW5XDCOUPAvFSTyUxrjYDWWtd1/SAwaCmlURhaY5JpMpvNpJBrx0/e/9BrlIXLly/3eocIuhYFHqMItixLIYTSWgjR7XabzXqWptNJypgDBAkh7XazVot93zfGJGmKiK7DhZBSVFJVSZK4jtdsNpWURV7kVTlNk+lkHPpBvVaPa424Fh8eHA1HQ89lRquiLLnjxHHD83wEG0dRFEf9o/5gMMzLKq7Vm/Wgqgo/CLjjZHmZFyKMouNrK2mS9I8mV66tnz63dvv5c4eHM4P0+Nri6OiotHz++Plv/74faXTbw+2D3/2NX7t59fl2zTGqopzVOy20MuQ89P2t7Z3Do34c15bmFgxhhkBRltPpRCmJRmulpBQe90QlyrJyuKO0niaJ6zrHVperSmZphki00bISGsEwwhg/tna8Xq9PJpOlpaVTp07s7+9++rEnN25uLy0snlhbMaqajIeFKBlFKVRZCiFMqzXXaHbOnj4T18LLVy9fu3GtylJCidEqjmPP94WQhBDucK2hKIskT6TSRgMAsZZ02h1t4aNffLE3nlEkGowFChYRFABY+P+G8OVBeJkFAMLA6DtWWueXG9ZoJMQaewulFAAQ0eWMEWKtkVJyhzNGwVqCVGtdlAWjLIxiz/W01qPZLM8z13WjIBSVSJPEaBM3Wu3uQlaUlRCAloAGq1xKGrWaMWY0Hk+n0zzPXdddXV0Og6AoRFmKPM+zPA0C13F9S6hSSmsNiN3OXL1eT5JplqVaqTAKa3EsldRaSyGU1mDtfKcDgEmSIaG9w0OwmhKwWjkOl1JZpJRxwpjveZxTSlmWZVJpzwsoJYRgKYRFqIRU0liE7nzbapxM0+F49MCDF+r1Wr8/6x2Ol+Y7QeiyoLl04vY3vfmdmze3Pv2xj/3Fn/3+7WeXYz8wAimD7lJcFWWWV6VSqhRQ6fmFBTcOhJBCCK01oViVlRCVqEpZVdZClqSBH3a73cFwOEuSOA5rcSSFUko7rgMAohIGARidm5tbXFiSUuVF4XDaG44ur18vkmkjCiPfp9ytNdtCVFUxA2sIoBS6LCoviI+fOH3yxEmkWFblQW//+vrVdDZ1XafT6XDOk1lqETqdttJ2b29/mibGGEoZAFHKBL5fq9WHs9HnLm7szwQQCkYRsABg4MuC8OUhcAsaRLTmzpXGbatd0IZS4JwbrY21lFKtNSJ4DBHQWFDKWAtSSkASBZ7rOMZasBAEIXecPMtmWZ7lWZokURjGUUgApZRCKmScOZ4ymnO6sjQ/GQ4m49Hi4pLrulmaJVmqvySKgnq9XlVqOp2JsjRWIRiplAEK1rqBh0gIQLvZskiG46kUotVuUsYqWYZhSAn6rgvWMiRBEIwnM6XUaDTO0onvOVorPwgCPyjKyhiUFuIoch0GRvtBMByNrbbtdstxeFlVszTRGgyQue5cvRXubA+kVEHEjx1bBUsZ41VaZkniuLwEVll2cu1EQO1nHnu819993ese6vcGYNB1Kaeid3RUVFIpZbUlliwvr3Tnu5WQ2mgLMJlMyqIw2hilhBBVWVVltbS40Gw2d/f2jLVBEBAkSgtjdBSFnHMphDRGarO4uHTixMk0yYajYZomw/GoKCvPdaMgtNooKV3XkVqXZWGNch0HLADSMKxFcbPTmYsbtf6gP5oMjg72yrLoznU4oWle5Fnh+d7C4kJVya3t7WmaSikJIYColHEcx/c8a824LF64vn84KS3Cyyx8mRC+PIgASMGYc8ute9caRkrNA5cCZ9RaCwCEEK01QaRgLYDRRiqVF2VRlJzxRr0WhqHWWlTC8z2tdZ7nhFFGWTKbKalqtVqjUUeESoiqEkJrqRRjtNNuVmWepRmjjHPuuK7v+4Hv50WRJDPOHQBI09xoxRhqo4zUYAEQKLuFi6oSVeU4PhBHoerMtZcXFjmjsyy/ubnZnWs3G/UiTT3PMxrGk1lVVYbYm9s7c61GKw6KUnph3Gq2dg8OXvOa1506eewvH31USkUQyqKYbza0kXlRjMbjleXVhYWlu+6+MBPFhx79ZBT6SyudLM21tItL857jbW1s+mEgDczSwnN4qxZd37zh+cHy0nJZ5HHopcnk6PCgP+wrIRlhFuzRaLy2duKOc7dPZ1Pk1OHu4eFhlRfGWi1UVRbGGGtt4Puu6xZlaa11OOeOY8EySjh3LFhjtNImL6owDJeXVyils+nUdd3bbjvbH/S3d/ctUCWUliVoKURZaUMRucMpZVKZeq0plB5Nps1aPL+wMM1mB4cHRqt6GAOYNE0RsRbXAElRirystJFFmYuyQiSIxAIgEksY5UYp+8Ur+/uzHAm1xgIY+DIgfHmQoLXWJ/j6C2cajgUjGeecMfxr1lr4EmLBWKukNMYIIYqiYIxGUex6rtG6EoIzbq2VSgae57t+kqSzJHUc1ul0ojjQxk6ns8FwRBhFQlzXtdaA1gjWGEMI6bRbrVbbWjOdpVIqz3OyLJVCAoCUFVgAawkhCMg4U0rlec4pd10/EVW90X7km999+223rd9c/70/+I9lVZw/d4ZTNNow6pSVrsXxQw8+8B//+E8HRwfHl9uD8UwAe+ih1zQ7C4tLy3ecPfGbv/07h/u7xxfa/eEYkAxGIymFELLVbDbq9XqjMZilB4eDleX55ZW5/tFICt1p17UyUsqV5ZUiy7TRRVXt7R94nttoNMLAC33PKHGwv1cW5S2j0aiohLFWCLm4vDI/v6CMjKIQLY5H4yovyrISlbRGG6MBgBBC/5rv+4gICK7voaVFWSqltNZKaSQkikKXc2NMo95oz81dvnYty9JWs6mk1FpxzvM8VbpyHd8YQEr+b8bgA1rT8y4Q+/9pb/96ud+tM3Pv9JFmRmVULUtRsYUNNnjBYBJKvEDg4BA2wGE5sIRdUnZJsruQUJyTQMCms9hg3CRAkmU1S5qRptfby9fL+35ve2rGw+EcbHIO+/sZwIHvx1nWG04Ktj1Tq+Yii7M4jzOPMsZIknPHLwAmkyjigjOLGQRZlvE0VcrYlm0QMmC0QRq04/g3dvtnb+4CIoCN0RoM/JMQ/GdBAMYlcM/BhValYGTOCLZuY0RrjRACAGMMACCEQGlxB76Dcw4AnucRQpRSnHOlFCHEsizbZgjwJAzjaYIwVCqVRqMGBk0m4WA0woT6ntdo1ofDQRSGju1wnsdxUi6VlpaWjNac80JQrNRKo+Fwr93O81xrabQBYzDBCNBtcIcxmlnMGCea8sNHTtx9+gQC8fbbX9ve3vRdy7VZuVzChHJhDi0f/OAz73/n0sXPf/lLYASAGsXZgSMn968co4x9+Nmnfu1X/8PGrWsP3HW4Oxz2w6TT64dhZAAYs0TOszQrFAvVanPf/vmZVnVtbXs0DC2HYC1dx6lVqwgQs6w4SXq9nlYKI+QFfp5zLqRWijIWBIV6o7G3t7O2tlarVur1utbSsmzbdiaTSOScZ1kURghRSlCWpUIIAEAI+b5fLBZtx9bGGKUpY47jSfF1WZ5lPDPaEErAAL1jNAkHw1G5XK5WqwRjgpFSKk1jBdq2LKUkAsQYcxxHaT0YRfE0MooXfc+ldDqdYkCgAFHbL5e5UhnnyihETMYVIcQidDSeaKURIQghbQAAUdsWBla3Opc3exJhMAbAwD8FwX8OhBzGTh9o7q/6WuQGEcqoazNCqTFG3wEAGGNCCNJGCME5V0ohhBhjrusyxuI45pxrrZMkMcY4jmNZVAiV59wYgzFyHLdaLVNC0zhN8sy2bWpZnuuANqPxCGOc53kYhr7r7VvaFydxNJmUSqXZudk8z3b2dtM0wRgRjLXSCCEA0FozxixmCS0wQSWvqBGJpYrSvBpUGtVyp7sTR2OteLEYlCrVLOeFYvXQ8n7HtlbXN3vtXR+bJFekUBeIHlhZ+dYPPPNTP/3TjOITR1aSJCkFtu974WQaRpFWWgoV+EFQKglhKpViqeLvtQftdl+pLPDcNM2iSei6LkKQ5RxhJPLEse04zcNoWmu2HMezPW/l4KH3vOex11/96sWL55cWZ0vFQhSFecbTNE+iOAwjRinBJE0ypYRScjqNLctyHCcIAt/zASOhpMwFISQICkZphFCaTaMkRAhjTBHGhFKtdBhNpTK2bQkhS4VCvV7Ls2yaTKUyGGMDWorcsS2lpefYROm13d0oTRvF6mQc7QzGUS6FVPPN0kqrUfJdafA4EVEubBvvW1rEgAaDYRwnXAhltFSGIGp7LsFaS3O1F757Y1spZYyBfwqCb4YRGIMMACBAYDBgMFqd2lc/vH9OplOGgCFCCLYoQ4wgjLVSSmtjDLmDIrhNCIkxVkomSWLbdqFQyrI8TRNjTJ7nQgiEkOu6xoAQnDGGMSKE+n6gser3p4abfXP12tzMjc0dkWSBa1mUZFk2DMeu48zUGlE8HYxHyECxEDBKpJBKK6Ulo1QrjTE2xjh3cM611gghy7Zc3/X8QhhOKWVLC/Ojfndvb1cIjhCu1htC6uFo6FjW7OysyPnu9nbR95IklsYYTPYdWC7UZj7/5eeeffIRTPD1rd3TR/Yv79+HDEyG4zSJlZDlcjnXsLW5ZQCo5xZdT+b51u42RmQ4HIXhxGIUA4ABx7EthxCMtrb2lEbVRqs5t68201peOeT73iuvvChENj/bsiwy6A+G/ZFIeRxFcZ42qtVD+5Z7g/6NjXWtjZaiVAgC30cABhCmRBujtTEahJScc2OQNgKQJJgiRCillmUbA1GSZJwrIUSWH1xZWdq3L4yibq8XTWOMTK1W6gwmaZowlC3Pt8KIv3D2SiWwWtXaxVvtYZxEmUokPlomzQJtzLXK1dogikfDaRD4Bw4sEtCUIq3NZDLNOc+1ZpT5nms0RGmOfP/F195eb4cYU2MkAgADBsAAgq8z8A8g+GYYAAFSYAAwAYPAyEZg3X9kPnAdUJKAIQhhhCihGGO4Q2ttwFBCMSEYfR3G2LJYnvPhcJjnebFYDILAGMM5l1IKIbTWlmUTQhgjvh9obfI8I4QaLTuDyThKyr7z7DNPIUxeeeNNhHTRdZIkHYYTx7br5cokCqdJQhG2LUYJNlIZjDQoMAghIIQIIVzX9X0/nk4NINuyACOtNbMsx3bK5XKSJOtrq0oqTJBfCCrVqgaSTMM8mRJCQes4njJKEDJZlmScY7s4mORzs3MfeP8TQcU/d/mKxWi1Uq6Uq91Ot727myWp63qgVdkvUtfd7XbnZ2ZsTM5fOi/T2CL0tizPCCaAoNFoKDDdXg8DjhNRKFWO3nXadoP5xfmr1y51Oju1WsVzLItZO9s7e3udwHYJxmEclUvF++4+tbO3e+HqFd/zlRIMY0KIEIIQCgiCQlAslpMk6fUGcZIqqRA2QcGlxJJSamMsy3JsJ8nyJM/yNCu43slTp5lFr1y9vtPrcQP7FxqPPHpPpz36ygtfZSInQK7vtSuu/eEnz5BiZXWnt9KaOX994/e/+KoRQCxSr1qnDh6q1WeXDx0ej4bt9pYUacG3pRBJkgEiiFLHdiyLSq1TISXgnU7vq2evJdIgAAMGbjPw/wvBN0MAgMEYAIMpGNUI7DNHlooe01JgMBgAIQQAlBBkQEnpOK7t2IILrRVjFmXMGC2EMMZIKZMk4VzYtl0qFS3L0lpLKfkdGGOEsOPYi4uLCKHt7e00yQI/0FL2xmF3Mrr7+MHHHzqzvt69sXqTGJVl+SSZBn4w15yZRGG726GYBp5LMJJSGmMIxYwx3/cZY5PxOEkTTAjBhDDmBwVkgOc5wpCnGSZkOp3yPK9WywAGYYQZK1UrtWpFC7m9tdVtd3zfVUIoJSxG0jQfZMb1vAfuuXtuYSGRMBoOLUaklKVCcWZmjgtx6eKlvU6bYnPi0JFJml1fW733rlONUvncpQvxsAdaYUqV1lzKSq1WrVbiOMqzvFap7e22AdPlg4e1wc251s7OlpQ8CBxGqOBqY3VzNB4utFq+6233OkbLVrkWZTFmxHPtyTjUSiqppdLUsvI88z2vVqvznPcGI0ywyIVQvFov27ab5bmUohAUS6XydDqNopBn3HM823XWt7amWTYzN9uaax07cvDBB08+/7cv/z//96eIzFUO7YlsNgsfevqe7/mO/0IaHXHiFFt/8oWvfO6zL15a32kU2Acfeei7v+cHHnzk8StXzr/y1RevXH53POqF4ciy7GKhxAijlAADZRSzbIs5jDkXVzdfO3clFlrDbQiMQWAMfDME/xhC2CAAbTAxWj1yfGm5UeI8ZYwRQEZrADBGE4xBG6O15/uu6/L86zDG1GIIYc6F1toYkySJEMK2Ldu2GWPkDnkHITTPc61VsVS2LWs0GqVJwpgdOA4htJ/EymT75+Ys5K5tbRqQjLBpkniuM9uc4UL0hgOLMYKR4JxRKqRgllUIfNdxAKPRcBROJnma+75Xq9YRQVzwNEkBtM0YxTiJY8JotVFTWuU8j6bTRqN57NhRi7HJeNzebQueD0d9MLoQBK5fJl7Qmqt5Flnf6rxzcXVhbv6xR+9b3d4UAP/1j/4YUub3f/fT3fZegYHLvHPvXhxH4fLyysNnHmzvbu3tbKW50NpIQI7nP/roQ4ViqdfZ6Xf28izb3txklrNy6EhvMPKLhWKhmCUxAtBa7+7sdTtdDXpxbk4K3hsPXdcuugFm1LasPI2jMMQEI4MAIQMoSRNEqO8FvudnOS+XypyL0WRYqgSUWlJIwGh2djYIiuvr64P+wHYs0GY4mY6j+MzDD37bRz9UaLhiND177tK/+5VPpu3uPYerAlmfO9eemwl+8RMficLocy9f/pYPffRHPvEJx3VvXbrw2tfe/J3/65Mmzn71f/uN2cXj8bR369bFN1772/W16/1ux3Ec1/NAgVSSa6FAuY5b8AuMsFTol986f3l3iDHTRoPRGMCAMfANEHwTBAiwAQRIgYaDrcpdB2ZsAhQMpQwBQgYQaABt7gAAyijBRAihlARAGCNCGACilBJCwjBSSts2E0IkSeLeoZSyLMtz3TRJpkkqpLCY5Tp2nqVhOLUdZ6bZZIQMRqMwTghjSTK1LFYKCnmWU0aLhaLR2rbtnPPpdJIlqeM41LKUMkHgua6dZvloNJZcGKFAqntOnaIO3tjaylVuM4aVsQhGxiBGbc9NeA4ICc4911tcXFBIY0CO7YBWw+HAC/w4yQ4ePsoYzbJca3ju+Rfa3f7x4yceOXN6td3bmET/8l//cqPe+L1f/7Wb58+dOLjU7452N7aMEdc3to8ev3tldkbLfG23a1ksV7B/5fAHnn5id69za/Xm7tbmeDyYjAYI42qjZXu+bdm+71HAtmVtb21vbm9atu06LmPUgBQi9zwvzW4ToHQcRsrIcrHg2K6SEsBM84wr41h2qzVLieV5nuSy3W1Ti1BKpRCW587OzhKKN9d3ut1BUHSYbW9udxKuf/Dj3//Bb/+W1dXV3/7tTz/3t6/o4fhYybF89yvrbXdm/n//H37aJfqzz31l8eiZf/7DP1apVhKpPEYA4Of+2+//9//np/7VD/3Yz/70z4Uqf+nFv1pbPd/Z2Rz2hoxZlkPjJMuyXGllDBBKKKUYX3pnRgAAIABJREFUY6Jhb5y+cW1rMI0RMsgAIKKNBjDwDyD4RxCAwRhrvTJbOX1wDmlJCaEYCKYIkFEaIwOg/w4AUEoBwBiDEFJKYUwopVobQgjGOMtySqnrOkmSjMdjQohlWUopy7KLRT/P0l5/JJV0bddxLKVEmgkwJvC9aqUilQrDUGszTRMNulIqE0I81/X9YDIaW7YzHI+i8QQho40pFctK60q5Wq/V9zrtLM/iJBFZtjA3f8/Jk65rXbp6ZRSNCUYizwyXnuNYtgUGsjS1bBsoIZT4pWIqxc7Wluu4R48cyXMeBP5gOFxa3I8xGo8GhNC33jqbpcILguMnDo1ziCT7hf/x39bnZ19/7jN//se/t7KwuNKauXLu7GA0Wt/bK9dmlhYPUIIuXblaKfppkpYarQfuu2e3O+j3enE4FDyVIk+SBDA9fOx4EARSyvnWbJ5lr77yipDi4KFDhaC4trZar1dty0rieGd7N5xMqUWVllrLSqViMYtnGYCRWmdCGm1qtbrjeARTzkUYTTBBt0kpbdetVCuMMSFlu9PRyri+PxyNas3mt37420fj8DOf+Xy3s9XyLR2N13eGFzq8PDvzqT/8zYcf/0B3fUshMrvvAAhQXOREOQ678vbrP/x9H3/ryvXFWuUnP/7dsZA3b14reFQLNeyPXd/1AyccTbiQxhgNgBAYA8YYAiAMHqXi3PWNwTQHBGCMgW+G4BshAIRAG2AYv//MSt1SylBNGQEgmCAAow0YDUYBgNbaGMMYo5RijKWUec4xRowxhAgASCkxIYwyQrAQgnNuwIABfQehRBsTJ2mSJEJI17EtRpTUCDRCUC4Wq5USIIIQnmZplCS+7fh/bzwcdQfDXq+vpKjXalmWU0YxIXOt+bnW3I2bN6IkTnlar9aeeebpouNNR+OL1y+3e11MUBpPkdKEEsxoo1JFSqdplkrOPGdmYYFn8p13zomcP3DmwUq5cuLE8eFo/PIrXy0V/WatisDs7ux12r3xJFo5eLDamosV/NCPf8JzrYvnz37ttZf3t5pPPfzgX3z288+9/Lo2fHlhLgjKhtivvv5aq+x6jjuM0pOnTi4dPDYY9Do7G0k0shhJkhgRurTvQKFcEkLUK5VbN25euHDRcZ1aveq6QXtvr1Qu1qtVwWW/O8hTXij4pVIxzVOeZwRjrXSaxo7va4SSOLFtl1CGMFbKcJ5jjG4DhCzHpow5toOxGY4GSpJCoegVnNZ8i0t46+3z7e3tQ7MNC/i7l29d2UxwrfHJT//mM+/7iJKcYAs0aK40EoAMZU487X3vx77rhS9/5SOPntju742jtFGrLszN+J5nY2cyiW2HMZeEo5DnQmkllVJaa6UAfR0juFCtXNrovnJhFSEEBgwY+EYIvhFCGDC2MJxcqu1rBoQwm1EKEgg1GoxWAEhKYaSilBKMtdYAwBjDhBhjsjRVStm27boupUwrI5UkmHAhsjRljFFGpZRKKi640IgQtNhqVEsBF7I7Gnf6I85zBCCF8By31ZphloUBKc25yG3LuU1KZdu2MTAYTfr9PtayUatwLqTBGtGZemO2OXNrfbs77DkBu+/+e07dfSoO4+5e+/w773RGvXKlTIQiCFcaZYxBG/A8bzKe5Dxvtmb27V/K0vSdc+cn0+y+Y6cX5pYeeOK9PEl/+Zd/eb238z3f8cGDcwuvvvXOpauXjYgffPjRJx5/6jN/9QVFWbVUNiIbx5NqJXj2qacuvHP5hZdeFoI7Djtx910zM/N/+Id/MlsKCp6/NR7s37/w8H0PdfrDdme3292hFAHCeap8v9SYaRDbDAf9m9dWLcAGqbWdriGWbVu27e2rFVzXS4QmRtWq5Vq1Mp5Met2BbduEoPFk7PkeYJznOSEME2oAjAEpJTJAKWUWo4wBgNLasi2jDSi1sDhvKFrb3J50w5xLrhNPqNWN9q1ePAXyq7/2Gx/70R+WXGGDtFQEEUCgtABiqEWnUfTJX/tfdbx9dKH4+b/8wu5etG9pyXZsRm1i2TwXWitttORyEoZZmhkwWmspJcYELNv1vErR5cq8emnzykYXQButDXwDBN8IIWSMWagG7zl5QPMUsGUR5BBjCDYajNbGGCmlkYpgQilBCOk7jDGlUolSGoahMcbzPMdxwaAkSQjGBiAKQ8pYsVhUSkkp8zwP4wSDaTaqy/uX6o1GkvEbt9ajaZRMp1EUBUHQbDQRQkpKLlNlBALi+/5wOFRKFQtlQmiaJCLPCEJSSmY7gInvegtz83vd4ebebnO29tQzjwd+aWNzK55Eq1evb+xuOBZbmpnT2jgFd2FxbjyeRNMYEdJs1BcW5hHB/X4vH0d5rjy/4PmlI0eO2Qa//PpXf+ezn3ng9Kkf+96PXr1x9eU33siT9PhdR06ePP38375wfXW1UizOt2Yc382FWlpYTKKks9vOhTxy4u7HHn88nEz+j1//9apLa4VgbzgIAmeuPic0KCNyniZJbFtuuVR1XL9Uq1BiXjv3RtSf1N3yq9evWH7t53/+F7uDzh996lOzLlTK5VQhhk25VKQYTcZhngvHsRE22W08Z5ZFCAWEbwNElDIi5wQjy7EZY4QQbQxCUK5UOecgZblSvrW5sbG9M1+fMzIHw0f99JV3b21L/f0f++7/+MlPUtdHEhODQRswtymNDGJgMGcsANBrb33hF37+l86dPX/3wny54huEGs2WUDpOEiEEYwRjIqUcj8dZlmGMAUAbwxzf8VzfYcy2hxz9xfOvxplACLSBfwjBP1L22Jkj842Co7XBAARhTAkhcBsyYO7QUoHWhBDGGABkWSaEaDabxWJxMBgkSeLe5nhK6iRNAMC2bc65EMK2becOrXV/2A+jiAvFLKtWq7u2NY0iQomSMs8y3w8q5TJCCIyRhifpdDIOHcdNs5RgihC2MC4GBWmg1x9IKaqV8ky9Op6Mgdhprrc6e7Vm+Vu+5X3N+uxbZ88qwQNEbq3e7Ozu1IrloFya5PHKwZWZ5szm1k6e88V9i5VKuT/oX7t+o14qHT6wfzAZxwk/vHTo+urGfY8+2O22X3j+bx576AEpp8P+IBom/XCUGx7HIWhZLBXmZ+c86q3vdm3bsi2GMMkFfvrZDz/zoX9248L5f/erv5KNtpcbNWQQJQjAykFq0IywcBwyyu67955ateoU/NXNjdfefq3muL3O9O3e5H/597/xzAe/XcfDj3/sO4OkPTfTnGQy8GwpYTKe2Lbj+16apoA0ApNmSaFYKJUrk0k4mUwJYVwoinEh8Czb1qDBAEIIELJsJqUsuIUkzS5fv57wtFmpNnxnGkbPvbOxNYhdP/i9P/vj9z37LI84RhTfBqCkJA4FC/5Ov3vrrVde+o+/+ptvvvb2gYJ/aKFam6l6QaFSqU2zrN/v53mOCUYICCZK6+y2NNVGM8ao7QSB57gesSxM6OUbGy+dvZFJZYyBfwABAAIwcBsCBGDM/YcXjsyVeJ4SZlkYEYQ1QgQDBvR3wBittFYKI8QYwxgrpTjnvu87jjOZTJRStm1TygSXnHOlFCHEGJNlGUKIEOL7fhAEaZYOxqMkzYXSFrMsSkWeuq5jM8sY8FwHENJal0slZqFOryO4ZIzxPKfMAgMyyzzHrbdmkyzfa+9ho+ZbzcFwPJnmjl/IpFzaP7d/eX97r/vOhfOuay1Wm0kUKp5hberNJkfGC4IDBw5kOR/0B5VK0ff98Ti8cPUa88iZk8eLnre9vjcaxRfXt/7L7/uBx+498bVXX1nb2eMqLzDCJNrq9NZ2VhmDxdZMUCoOR6EFdiYFsRiAEVxi7FVnlu6+5+HlA4svvPbiX3/hT99z8riFWJpk5WKtPe4keYyBRGEST6czMzOPvedhQsmLb77Z6+8tlMsXbrXv/+DH/sXP/xIA/NWf/fHP//c//oMfeHS+Xr10cy3Ns8Eosm1nef8B13HW1m4ZowkjhKJGtTq/uLC1tbu+tkmpzSyrXC4ySqSUgBCl1HGcOI7TPHNdx3cL/d6gMxxwniItC469ttF95VpnCPDEgw989nOfL/lVI8EwpIU0YFhg9brtP/yDTyNtDOdf+sJnXnrj7dlK6f7FimUyxbzmXKNer1nMTbO8PxhkaSqkyqX0fW/f4qJS8uat1f5gSJjFGKtVSk5QIMzCRikgf/3a+SubHYSx0QaQQQZuQwBAAABhBQiD3l8t3HO4RRkFrTEChgmjBIEBBOg2jAkgMEYppbVGAPjvKaUopQCQ5znGGCEEgMBghIBzLoS8TQjBGDPGKKUKhYLve5zz6XSapikmBBMipQx832JWmqaM0TzPsyxrNJpBwe33uhhhSmmeZ4RQijEYSLO8Ui7X6rXRZJKmqRAiTTIpFaKoUCgeOnSwWCq2223Oc2PM+voqBlTwfcl5q9kMSuVcG4RQq9UiGFEMtXI5mkYXr16zPDY7W2cK8kSEab43GD32niff//TT66s33zr/ll/0PMayKGrvbk8GIw7mntP3zczMPffSSxRwxaP9eDqOEz6JZkq1re54ItGP/sjHd9q7f/nZ//RdH/4Wy3I2NtpUSKW5Y6tRFI7DeNgfMK+0srLoOrQ7mKRJgo3pjvmjz370B378X2zduPmBD32Aysmv/Ouf8Sj+qy9+odsfGaHLlcrSXKvoF/Z2dsbRJOW5NrpaLjWbrSiMwii0LMsYYMwSQnDOHccp3yE4H08mQkrAeBJFYLTkmcjTKEy2Ngerw7gr4Y//30992/d+j0gFVsQYrZC2A2s8GPzUJz7xu3/0Rwq+bsEmTz9yz6GF+qC71x2NseUwn5XLlXqllqX5YDBM4kQpnSs112qeOnFMSf7uxctr220B1LVJ4HvMsqjFAGFC7eFg9DdvXu5NM0AUjMTGAAACBMgAAqpBlWz85H3HXaaNMYwSZAwCRDAGMAAaABBCFGMESClltIa/h+8AAGMMIQQAOOdaGcYsSmme51mWa620NlJKYzTnwvO8mWbN8/wkTeMkwZhwzrXWzUaDUdput5XWUqrpdOr5frHgZ2mitcYIgTYYYwCglGqtOeelcjkoBErrSRgqIdIkTbLEdZ1qrVItlXzXXdm/f9QfXLl2JRdSI0g5rzVqjNkO9XIhSuXiTKOOMPiuu7253uluLR1YdguF3b22zVitWDIGNxaXW42Zq1cut4ddRFClUKRGx+O+lOTmZv89jz313ife++W//WIa9osevnTtulYmm07yJEXUAxbUm81ypTSdTpv15kc+8h2Dbu/3P/3pLE2OHV4chJN+f+BaTpyr2VbNINPpDLCUtXJhdxKfu9H54LMfevOds8+98PJTD574l7/4U4v75n7nN36rv7XnMWccRY1GfX5+vt/v7+3tpUlqkLEdC2PsuW6hWNRaj8dj0ACAbgsCPwgKt5VLpSRNNza3kyxFGFNKKEbhZDQdT0wi3txoFxeWP/elL5VnFhVoojAgBZhcO3fxF37pF7/wl5+tUlKfq584vfwdTz7mcvHyV17rj0PiggHOBZ6dna/X61EUdzqd0STU2mBkmo36oZWDANAb9MdhlCSp1hoTDAgoszAmmDBmu1fWdv769fMSECAERiMABAjAYIQIGHFyX+PupYY2mlKCMSEYG2OQAYQAkL7NGIMMEIyV1hghRqlSyhhDCEF3YIwppYSQPM/iaWoMEEKUUkJIjLExJs+5lEJrjRCqVoq+7yMA23Fd10uzTGtdr1WkkLt7bSEloazX7wuhSgUPg5FcYIQJwdoYyYVlW8xmWZoppWbmZsvlMiGEc7G7txfHEcFIS87TdK7VOriyvLWzlWUZprfZlUpFaz2ZRAjo8spymmX9QY8QIAgPw1F/NDy0MH/82KEUVH8UDvuhEurw4WWbsk63qzB++513Zmr1Jx55T8Fir33t7OWt9iPvffqBB870h/2w31E63FhdJ4ALgXXp4oWgWL37rjNxNi24FrOC6xvtRx9+YHG2/qk/+KN33738xKP3pyLzPH+mWm8PJieOH3z3wjvb63uzpYJfYG/cuvWVc7eKlr/QrKcym5lr/Jv/8D/PVSuf/J/+bX+v3Wq0hqMxtpkTeO1OV6TctW0ApLSilHiuRwjJeZbnGaM2ZRallBCqtbIsa3auFU/T7d09pRShzPNdimBne1PwtBb4L1266bX2/+5v/07QmBuNJo1qK0ynf/4nf/7J3/qta9evNl386P2Hn3ns/rml1mgcf/mLL8Zh3KhUbBtjil2v4PsFjLGQKkrTje3teJoszs6UKxXALMkyLSVGJk9jzgWhFmbkNm2QUmAMSqX46tlLm8MYYWq0QmAQ3IYQGHAZfuLkgbqDEbXw1yEECCFACG5DCMAYJaXWmhKCERBCEMJKKWMMQghjjBCyLItShgAJKdIkk1IJITjnCDTGGAAQQlmecc4JIY5jWbYNBnw/qFSqec6llLVqBQFqd9tKKs/3x+FkMo6q1QqSKhyOmW355VIUTtJpEgSeVsKAjkTuuu5D99y/uLAYJumVa9dG434ynU7GI8HzSqlsOXYYxYTAzExraWlxcWHp1s2b7XYXGbw4P28xsru74zg2IVgDXN/YEEI8dubew/vndwf9V89e3dhqrywvLi8ube5tL87Ph5OxAn334SMuwi+9/rUx12ceeOSBMw96vkcJevutN65cvIhBUQz9fjcoFOdnF3Olw1Hf8UudXnT/yZNa53/x/POHlpbuPXHs3KUrs/v3yZw3KuVT99z12S98PhqMKr4TTsN+b2zb3nufeE+syLXra619M3c9dEqH4avPPRdPJnMLc4BhGmcI0zzLLWYxTKIolsrYtk0IjuIoyxLXcUulMkLIGCSkVErZtmPZbhRFaZoijC1mFculLE/3dvco6JJnffHNC5Gwfuy/+m6nXHrzzSs8loPp6NzVS6M4LWD8A9/++LNPnt7Z7Vy5uvbO5RuCZ0utBiDLcoJqISiUPKkURsSybal0p9tNkqRaqQRBMVcaITyNwjxPMRie55QywqjSIKSSUgsla9Xi1Y3uC+euG8SQkQgMgjtsSu4/sTxXYJYWmNkYYUIAAUYAgAwAYIwpwVpKpRTGmGAghGhttFbGGABACFNKbNsmhEippFS3McbiOBkNR8ZoyuhtAMB5rrU22tiOVSgEUhoA8N1CkibTaey6bqlc1loapRFGuRBCqply1fPc9e0tnmVHDx5WUl64clkZ7doWwiiReZqmdx85fuTwEaHN5vYmz9M8zzqdtpLSc1yjjTHacZ2VlQNz87O+H4xGo0sXLmENWinXsijB5XLJDzxA5Orq2urW5vJs6+DSvKZ4dbujFKpUSoUgSHgKSh4+eNAQM+z2wvFoOOoR6hhSfuDhZ06fOiWy8fl33r565RKjsLWxPjPTXD6wsrq6JgzkedLpDZszi9/2vveffetr51dvPf7oA8cP73/r8s0vfuWsx/QHn3qgVqp+5eVXii5TWu6NoiyD48fufuKDTz//wssVq2i53treTslmjaI7Gu4Zk0stjUFgCGiECVZKiVxiTAwYJVWSxkmaWJbVbDSMMZwLbYzWwIUEQJwLrZXj2Iyxcq3q+/5wNI76AxFO37h660InOr5v5n2nz3z57IU31jYWHfbgTP3KoFddaPzrn/nxMBy/9JVXu+1eJkSlULQobrTm5+YXJE8n0SROUiml7TjGGIyQVDrLMq2B2Xa1Xs953u92lZQWwfltnCuDpNFCKAOmUio6pdor71x758qqNhoAEMIItNnXKD544gCIjBGCEcUYCEEIMAIAZAAAY4wAtBQAYDsOQUZrLYRQShkDhODbKCGEUmMMz/Occ6W07/taQzQNtdIAgO9ACGmtOBeuaxcKBSGUFBIjKqQMw0gq6XpupVRyHEcIjhDmUtqYtGZbw8lkZ2urYDn7lvZlUqxubUTTqWUxj1mc526hsLC4xGxrEk3yNJ1rzWAE43HouR6jlCCwbFosFgC0bVv1RuPc2+e2N7YIxqA1wYhR2mjWKtX67l5na3ez7LkIiFMuYWQWmw2vUBRaBZ43DCcZ557rKmOmk8jIlBLU7ibjGB2/6y6eTZN4opXwfbffaRcCv1KpbW/tYEoI1hmXcaLnatVqtdidJqNo8vSTj7iFyh/82ZcqgX3fXQe6vcn62kaj5GVZenOvQ+3is89+KEHi7XNv/+BHvnd9Y/fdy1ceOn3as/C7F9+2bEXA9PtDbZBlO1zKOE4k5xZjjuMIIZTktwkhms2mZVlKa0LIdJpMJhNt0G0ABmMipazPNBuNep7ng24/HI3Wd9s3t/ouwSXXv94dGWNKLi3bJiiVvuuffdv73v/0p//TX1y8dDkg7OjBZdvxu73B8aOHq/XqzbXNnb2dyXhsjPZc1xhTr9cOHjyYpNnNG6tRktquhzFkaUowEID+YDCdTjG1iGUZA9poylijObPXH3/xxddHmQZACDC2kX7y3iNVFxmtCbEJxggBIQgjjAABaEAII2S0MlojhGzbthgxBoRQOc+N/jqpNCHYGNCSa6O1NkppTDAjjFKKMJJS5TwHAEaZVNJoXSgEruOkWc5zbgxgjDkXUolCocgoxggDGMosi9lCCiHkdDgWSiY8LxeKR5cPSq3OX7skBJ+vNZRUKdbFQqkYFAVIkWXFUjEoFCmmPMtLQbFRb/R6baW5lDzLMkBmPJwIIUGrLE3AaG10lqXLB1ZmmzPt9jZoMxhPkWvNN6v7W81UarcQlIvl8TS6ubrmOa7j+/E4kVIwYjybvXvuQoZpLCQAOrSyvG9hbm9nb2NrmzFKMWUIUWSo50VhYiP00KNnNoajzb3+Rz/84bLvrm2uWZR19nrr2ztpPGWgwmgS5fzBh95bLJRffvuNaZ7+5H/z45fefbc3Gtx/6vQbb3zt7DvvzrUqs83yYBxyqdwgkNJMotCzLS0lIxQhrLXkeW7ZVq1eS9NEKo0RHo7GxgCXkhEGoJXWjFLCmOM6pUJBGNMf9rIkDUfTabs/jhOEqFYZRyool0+fOn766GkhzfVb18JJlGl9z8njtuPHXILkYTje6Q7yKJJKFgoBpTSKIs/zDh850mg0w3A6Go+FVAgBgMnSJA7D8XiSpClmjDJLG2QQYEw8hxkEb11aP7e6hzBDAHCgVT9zaJ6gjBFKDAYM+Db097RBCFEMgJAxBgBhjAghlFJtjFYKISSFiNMszXOjAWmBMLpNgzFKY4xc23FsB2ESp6nRyLKo4Dmj1LJdAEiyVApukCEIaakMoFKxyCw2Hk8IIZTSWq3meu721k48mUZJkorcc9xWuTrfaiVZstPvAkDB82OVM0Rmao1SvcSQbg+Hca5nWwvNcqHf7YVJbowIfJsx0t7ZC8NJ0S/Ui2Vt5GQ6TNIEY5ok2cm7Tx6YX1i7sRrGcY4gzKKl+dm7jh4dDIfj8dhoNBwOEMIn7z4eZ/nq5m7RZuNRp14qDfqjtXZvOs28evnU8RMP3HXi6q2tF984WwyoMmoQxRZ2dnu9Q/OVhVLjyKED7ZTPHjh0z8lT586eoxbxPOfdc2+lUZSEYwwGY7qycvjUvWf+5iuvrG/vUgsdWVkOR6MwConjbKxvztVm6uWC69v9yWgyjRzXRYDq1Vq1Xtnc3MimGTFESmEwFIqe43mD4ZhSJqXc2dtbXNwXx9Pd3bZF7dZsy3Gd8WhYrVUXF+amcZJmouC7o35na2MdlLYoHYehHQQry/t9P7h8eW1nu7u4NMscGklx/PjxpaWlLE3Xbq3tbW8n05gC8gu+ASSUopYzieI4Tufnm0tLC4Hj5jzXWkstp3GcTePpNE6yLBdCKYMwMQCEWoFv+8WgG+XPvfLOYBIjh+EHj+6fK3saCYtZFhBABiFEMDbGAAA2AGAwMoQQ83VgjEYI27ZFKWaEMEKkklmWD0ajPBdGg0FAKUWYaCUBjG05gevarsO5zHKBMRCMysWSVjqcTrM8B4wQIK01z7k2ynUcSul4PCKEYowrlcrC/Fxnd69SrGLGzl44n8Rx0XKbzcaB/ftSyc9fuVwrVRKeaSFrtWprrlmrFK/euHn55vr87NKjZ+5t73Y2d9ulUuDYuFQKxqPh2upqOSg2iiVEIM5jKYVUZppkhw8dqgRBe3tPAVi+q5Fq1iv33n2y1x989ZVXldQIQcb5yvKBar22vbPD47g37PqOZ7RZ3d6eac61Fpccz7/vrmNpzp9/6ZXRqL/dHZ584qkf/on/7md/4mfeef3Vx07sPza/PyX6wceeUID+5C/+/OH77q96/vrGKrbptWs3A8c7eGBfrVxybWc8HiutCWaDcbjR3grTuFgsPXr/wwdaiwijnc7u+UvnkzybRuFca+bkXXfHWXr1xo10OmUIK6XcILBtJ815mvOZmRme8yRNTp8+LTi/eOnyaBI2Gg1KSa/fKZaKjXpdCum6PsUw6HbTNMYYKykBsO0GCKFut9fpdDgXCEGpUp5ZXlhZXi4XSpP+MBqOtjc2w1HolUv79i8rpSm1l1f2b29vvvG1N/Z6naBQaDWalUrFtu1JFAopjfw6TEguRJwkgJAxRhsolkquV0DMfuWt829fXkPLrfIDh+axEtpmGCMH8G0IIYIxABhjCMJGKwQKIWRuA4MAEMIAyLZs17FsRvM8i+9IMy6kAUx832O2zfPcaG1blm9bQaHkeF6Wi163jYyplUt+wQWM271enMSEUCEl58IoYIRxLjjnlBJKGbNYo1bRnBuFqq3WTqe7s7PjIEKwObZycPnQypXtDZAqCaP+aGjZVqNR2Tc/v7XXfvvC1Xp95okHH0rj9NyFq5WK32rVOE+T6aTX62GlHIwL5QKAxhatNVsbm7vDwaBWCrDGvu+7Bf/w8cNZNC4HwZVrNze3dx3bsRzHGB3nyUyz0SgXp5PphWtXPce3LZtrNVura0TBDc7ce8K28dUbt9a3925uD3/i5/7VQ+/76LuvPfe9H/uBKvBvfc+JvsgYK1qYttvt+++/DyO84/oBAAAgAElEQVRDkCEIzp2/WKs3ZhuNcDyuVKuYsCTLTh0/dePmzRdefWlmpvnAvfc/9shjW50O5+Jrr7++urmeZUkUjr/tA8+ePH78b156+dbGNkNKiwxhXG/O2pa32+lXG42jR470+z1G6L59S1mWdjqdja0t3/elkmE4rtVqxijbcpQx0XhitMIEpnGiwVSrtTQTPNMI4SQZT+OBzax6bYZajgEQUvI8E1xMo0hkgnr+8eN3txpz1XJlYa6xunr5tddfvnRzLU2Fa9v79u2r1Wr9wXCaJAQhTAilVBvNc66UAoQIQxpTAJsgMgonL755Hr3/zKGKjR3CFEEKjIMpwYhgDAhAGwQIA0IIACvQBowx2hgECCEDQDC1bRsjlCVJOAm1kgBIaKCUVWvVWq0aR/FoMEQYiIUCx2nNtGqlyo21jas31gbhdLZeOLE0n2je6Q+oa2GM0oQjxQBQLnIDxmiNEbiObTm26zqjcagBVSt1nvPJYJALZTP2wAOng7IfjcZ5mm9u7nT742q9dOrUUWpZG+udYlBdWtwfJ+lrb72NqL776KHOzqbGehpG8WhMFF9sNY3jeOXS+594vD8Y/t4f/RkyploqEZssLc4dO3r0+vWr03C6vdPGFANGvu+Xi0XQhud5sRIstVpvX7yy1RksLy7Uq9Wzl644TnDi5FEp9KDfv+vYvtb8/G//6XPf+f3//Knv+DhB4ic//oOv//Vf/ciH3nP2xi1C/Qfvf3AaT6XR0sh6rTrq965dvXH48KFpnKcpJwQPxiFgOL6yb2t7exxGjNrHj93Vas1ajtMbDc+9e1bkMaNQq1TuPnqCIuuts+d2B12NdJZOi46/sv9QKuROZ++B+88sL6/sdHaLgTfu9le39m5tbmMpF+bnMpEtzc/VapXheJClWbvbD8PIdRxmMc657TiMMdty9u/bXyqV9na3hqMeo0Rw3e1NhsMRYIowmUwnhkCx4NvMqlYalXK9WqocO7LS7++8/vpXr9za4ELWyqVjx44VC8Wt7d0kSYqlUs7FeDIJw4gQYlmMECyN5jw3EjPLRRit7u6hjz5+ghptYQuI0QgsoIQijDEYo6UiCGOEMMGYaKON0doorcAYAEBAEEYYSy54zpWUSkhtjEYYANVrtbnZWcFFv9+TXNgEpzLJiCjbXjzNJlF6ab3dm+rZcnG26FUZjHlc8gPbKI4wsTyhOLGoMcoo5dqOV/BKlarWptvtE0J91wvHE2UQpqTgW9WSn8TxcDiZRulef+KVi489cs+R5UXFjTaWxaztnb0ra1sa66P793kWs4teZ3f71tWr6XQ6U6mwUtEvl+4/dtT3/C+9+OKoPyQEWx579skn42R6fX3N1pin2TgOvYK/b3nZdp3rV2/ESWZb5KHTJxVhz3/1tZlS6cx99569emOm0fAtc+XarY2tzvL+2eOnTz3/+sUzj7z3+z7xszpNfui7v/Pm1bMf/9Ynr23uPPnEM6dPnrp09cr65roQnFIURpPNtY1qocAVznKxODez3e70eu1m1cfKEOyEXIVGz84vnlg+pHIeRmOlMse1pBRRlEyjlOc5V5lCYNtWs1QrFUprm1tJFh84cKBcbVRqpUceur+9tfsHf/q5F7762v5Wc262iRhdaDUJRdSigevttTvb29sYY8dxPNctlUpplufSrBxcsS22t7ebxrExejqdjgahVkCIZdmuV3AxxVwLpWSa5tNw6jruvfeeJthcvnxpfXNbcF4uFxfmZh3XjaeJMbhSrWite8Nhp9tlllUsFqQQ0zgTPDUKMHMoQWGaoo8+eohSahEXiMHYWAYDwbcZrY3SBGMEiBCMqTFKG6210korgwBjwig1AMl0mmcZRhhpo7SRgPKcB0HQbDZty0rSNJ5OLaViEf9/NMEHvGZXQSj6VXbf395fL+d8p/c6PZPMTDLpJIFICIQmRUDfBVFsVx9XsesV9adeK/ieClK80ruEkDopk5lk+pl2ypz69V52X3utdQO/9/7/mt2WCfN7niobTTd8pdzzATAAkACQAZgb0EXMXN8bzKQYhIIoiaoCIAScmxE9lUpqWsT2vPW1TcB5MpEAPEwnEwEhe7u73W4/5Kzd7dgBdwJw/7FDdx1eLDebZ65vJWPG5OBArdWrNdtzY1OzM1NIQbVqobB5q15reI4fienpTCoeiUGEut1WYNkdpz86MvLex5946cJrV1ZXc6pJiW/5biQWHZ2apJRvb+8VimUEyLHbDk3PzdcbHavXmhgf6VjBra3NpCaLAr66vnlrazeWSC4ePGKmsmLEHM1kPvt3f+e6/cWxUScM5xeXjEhEVbR6vWb3u5BTSRD2SsVOs67osVg8fnB54TtP/nB97eboQFyXFECFtkdCXZ+cmTMENW2aFIT+T3ira6tm1NAjaqPZVCQlZpjJdJJSurtX6LV6oihaAbEC//iRQz/7zsfb3f7f/N1nL168fGh5XpakEPJYxGCcGXFTV9S93b1ioSBJkihJmqYmk4l2p1eqtXVdN01DU5WIrhHi12o13/YRFCDA0zNzBw7sb/c6V65cbrZblmP5gcc5M82oKEphSAkhnudoiixLmFMQMk5CJoiCIIgUgE6vxxgzIhHOqON4lJIwpD5lkBEEBfjeOycRljFSkMAExEWGgYgQhIwxyAGCEDAOEYSYQw44Y5xyyhlCCIsYQUApdR3H8zzIuSRKnAM3YIxzwzBMw/R8z/d9DoCMOPPdaq3eaNotj1sh7BGmxqO3Hz4qqipznCsXLzWajYMZU5cDn4aqEZEVJZlOK5rW6dkYsEwyLsmKacaarc5eYU/TdFOXIaWEsEq50my3BUlyPNdyfS+Ex/cvLYzlXrh87ckrJYzAW0/sO354qWf5mhAZzg+53F5fvyazUICwXKxKCozGzEQ8a/W7nDicwkqve3B531sfeujS5vqNjU2FsJ7db3X7iURiaHhU0Yy77rjr5edeePLU9+YXZ/YtHXBsz7Lbg5n0zl71n7741cfuu/vBk7effv21tfXtmBmNJc1K16nVarMjw71uF0JoaAYDAOs6CcOZ6Wmr1+dhmIhFief2Q2q7tiCpoqyGvv3Cyy/v7G4Pp9Vc0hSVaKcXppK5ffsO+i5RTKVULxI3SMUTvueOjw5hEJ65eLHV6p04ciSZSV9dW+11e7ogU8KK3c5etbo0PfnoQw+UqpUf/PBZ33VnxoYVRQkhp0HY6/d8QhGAfcsOaQgAoDTUFE0QRc8PCKGSJGbSKcOIyLLU6/U7nTalAABsGrHp6blYLFYsl3Z3tmyrR4iHBI4wDEIWMWKjo+OSJGxurHuuzRiDP4EZ5QFlFEBRlmhIGaOaqnDA260O8T2GgEcIDANZkOB771rECAgYIQAwxghBjAFnDCPEOWeMQQ4QQhBChBDkIAxDxhiGSMCIQ8YY9TyfEAIAQAhBAL2AUgSzyZSp6vVu2yK+Iko4pKFtF6r1jbJbCPjUcP5d73v/8TvvvO/BB6AkAy946Zkf/9Gf/8+Lr5zZNyDJIgcYR81kLpNMpBOWjxAIM8koY9zqW6qqeZ7fs/qY42art7q9AziJ6UiGkIfQcXyHs8nRkUzcuLlTaXeDmh/WHPuj73jTvUdua/SciKZYvdrq+obnhzLkOiCEUTUeTSTMcqHk+ySi6cW9Wp+H47Pjxw8sc4/1HXd7Z6va6B/et39makJP56JmdPPy+e+f+pEiaSfuuLNrWV27n0vGuq3206+eM0V0++Ell3ieHWSz+b1yxSN+r+NiGEoipGFgRHSrR4SIDkWUz+cDL2jUaqIoybJ84MCiFQalZjuiRBrVFkPi6XNnr1y8dMd8ytDlZldMmunxyTEzmZJkeXd3G/BwZDivqnK/36+Uyze3CjT0H7r7diDq1UZ3OJNUEOhajh0Ge8USAzCVTjeqjcAPMqmEIguM8YCErVbb94MwDBhlGCKPEd/3Rwfy6XiqUKrW2w1NlRRFFQQRYayqKgnDbq9reb6imbIkQy5CACH3fccHmEJAGaOqqiZSyXQmK4hSuVx9A2MsJCGlTNM0QkjLdsxoPKKpgesl42YqEWs1apu7Bct2EAIBCTwSSpIKf/bkEkJARAhjhCAEgAsCBJwjCDnnAADIwRs44BhhBCBjjFKKABQQAoBRRn3PD8MQIsgoAwCSkCFRnBwdjxtmrdOqd9uaojDHrtZbu9XGXovsBuyTn/jEn//93wMAwpBznyIJYxH2q9U/+MPf/9qX/v3OmVTWlCyPh4IaMw1FFAQOc4ODQBYvX7saEhLTjaim6wJu9fvnN27VGq2kqkVlIQw9QJmuaYl0So2Y/VZnOCLkR0e//NJlzYy975F7+7btEN/rdwbTaQLAfz3/vOv6U7nodD6jRZPbuzUaBLOTYx7xrxUKO7XKiQMHlydmbu3tbBW2sYfuv/ueiBnp9t1oLHblxoXNnVvDA8NHjxyjGK5v3cqlEynTKOzsPfPSaw4IHnvswbFcfm+zHE9kMsn4c8+8RLjHAWm06kiEzaadzWXn52d7vX6hUGq32gihWDT2vve9bbvZW7lx68jizFal+5b3/sKNy+fe9/6PotA+NK5pkirr8emZubnRSZ/ScrMGOVUUsV6vua7Xt+2e7SqqLkvCQC6XTqbLxb1GrZaKxxOZhKyosq47ntdqtkQkRHSt1++RgHCOMMZBGDSa9cALYkbMZywkdHFqJhmLrW6sleslUeBWP+hbQTyeGh4ZBIB0+o2+E4iyDiEoFysRPZKI6ZRQwlgYhpKANV3LZDJYlBqNZq9n0ZBhjBjnkiSNDA8TEm4UigxgSki1XIkakVwuY1n9brvFWAgACIhPOcSCBN9/9zJEQMJvQBBCzijCHEMEwU/AN3DAfwoCACEEHDDOMIeCIEAEwjD0PI8EhHEGAUQQIoDj6fTUxETKiJaajbXdrbhhSJxfvbbWbJQ9gl6vB7/40Y/95Wf+ibmUIcwBkCgMERU1wWm07rnruBk03nJsudrtEy5wLyCUWF4AkSDJUrfXBQDIspSMx1VNFSCLKmqxXF1Z23Z8X0TEcUJZ06fGY/NT46WKt1cqPXB0kTHp8vbu0f3zKzd2nrxwDYHw2MTII3cevbx2/dunzhuasG84NZrLdpyg7/QeefBkJBq9sb7Wd4LARydOnlB0eOr5F6t7rbmx8XgqudfuhJ4PnF6IaSqRXV460A+8l187M5BLTQzla5Xaa1c2FvYtPvLQXUY0+9WvfHt+ciBhRJ/84QuJZARwX1FFCLnVC8yEcfTYbdVy7dRLLxcrNUlUotHYHScOX7mxXms43b4tJgf+/t++JAD4V3/yx3/5F381lcQHRrPxdGb+wFJUT7t+UKuXfc8OAq/ebDAOFFVNxqJewK5t7t5+aN9EPvv8Cy81W524qUsSnpyZXTp4ICBhtVLt9S3fdpxeXxIlUVJUXbH6/Vq9SmmomXEKBM6wLsiIkVanRhHx/aDd7nKGUum08hMiZWG7Z+XzYzNzU88/83y321mYm/Bdb317T5IkQ1NN04zHYhygZqsd+CGEUBRFDrhhREaGhznnhXIZIQyxcP3ajUajqagKhIBTIgoYIUgZhVhAWIQfuHcf4EAUsIgRQpDREAkQQ4QgYIwhhCAHjFIOfopxxhiEEAEgCiISJUpDx7GDIOCcI4QhhJIgpLK5gWwu8Pxaq84xpGFgtbt+20aBhSC/Ufd++zP/9tDj7+YugwAwBhCHIQgFXXj2e//1wff/rBa6dy6NmEndjERFQncrlZ4bCBAjwDFCkqqIougHxOdMFoSp/ODgSK7eal2/ubW2Va5aXtfxj+8f/vC73uwR/qVv/ahYqB+am41LNBaLf/fsjdVCfSKiiCAYycffemz/pe3iD15bTWny8bmBVNx87Vb52O0Hn7j/rpfOnOs4PJfNZMcHMplU0Kj94NTprZ29AwtT83PTW6tbVqvfDlw9Ytx+7NhepbRXLkSj2vDAQCaR9wI0MTRwfWd7//GT3/ja127dWLn94LLT7riuZTneXcduGxse2N2rNhwrloxOTYzdWt049dyLsigm4lEqyeu3NufGJm6UW3e+5R2PvvsjApasZvnnP/Ce15958fa5zPDESGZ4RJA0mVHb6ja7nVhMj0XNvd2iR9C+A/sn5mY3VrcFYtm91muXLsfMCOR0p1BWtchd996bGxrkjIGAtBvNbqvj2raqKIpuRHQ5CLyb23vVbn9oaFiUZK/e5sTvB7bnuSxg+ZHBTC7V7nQcj4iSWq01Qkoeuu/BuaW5r3/j65XdveXFub7tnL+8ImAsCkIkomczGcM0LcuxLVtRNU1V/YAwwCMRw3Fc2+rNz80Oj4yura2trFzttNuMMwQQhJBxRjmHgihKMvy5+w8ADkRBECAAgDMaYhFKoggYY5yjNwBIKeWcAw4YpYwzCCDiAEIoCCKAPAxDCAHnIAwpDSkFXNcNTYtYtu04diwZZSywul3fCaHjOZ53o+n+yu//yX/7lV8PAwahgEIOASSYiaqwcvb1D7/vfSu31u+eyRycGd6rNjngmDHEoGHolFLPdxBEGIuO7TqEMICdIExmE7Oj+YSml2utm6WKT4K7Di8vLEz3nO7OTrFQaGqiOjWaXisUv/nUBTEEQyaIx7VG1x4dGrhjee7Fla0fXNmajKEPnZj3CFptdt772CMj+ZGNvZIbuKIszS0vzC/OrV65/uzTz2xvbywvzlE/3CuUBUM7vH9+dnrq1Us3Ll+/tW9x9sDcBPO5T8WVm2sXb1z7/d/779/6/lNf+c9vPHbfXSh0SpWKHk3edeyOdMIs1ppSMqGKeHwgs3Fr679+9LQqiqPpRN/xi83mxMTEesv71d/64/z8fs92FV393Gf/+eMf//jxEf3A8vjw2BRCcuDY7V49m40vzM7aff/HL5zOz8x97JOfisayN8+cWT3/8ovP/8jx7bnxvG27m3t1VdXGJqfEiDo2OprUtGateWtnr7y9hbFgpgenx/O1avm5Cyszi4t3n7zz+rVr9c0tFSE3dAFjgMPZ2ZmBoYHN7W1CuaxGCsWS5Tn3nTzJIH/yR0/ywJ+fntF0Y+X6jXK5CgBXVSWZTKiqihASRUlWFEEQe7ZDwhALguO4nm0N5gYSicTu7m6jXg/DkDEGGOccMM5DzrEkq5oOP3DPMoRIliQBAgA4owQgrioqYJRxDt/AAf//sZACABBCIsKUUs5CSkNN0yK67vm+YzskDEPKAUSUAhKEEEEOmCKLqioUG22/bTuBd6ngptLpL37xS0cffihwCKYQQ8RkCASACP3KP3/mj/78f+4bT3z40Qe+88yLa4XioGlEIzrAgAS+73nUD2hIA5+EHABRcNwg8ImkKAPZxKHFaVkRkCRGNNMJiO0HELJ8Oi0hQQSwWWl8+QcvbpVr9yyODCe089s7Wy3nkaO33Xl4/5eePfv0xZUj4/H75sZLHUvSIydvvwOKsFSpQg5PPvzA9IHF+vrmzvWbr7562goc13VFES0tzeaH8rFoUovEz11YkQRpbnqSBO7Tz77y1IuvKhr+vd/62Hatv3ZjPYLA1evXZVleWpgXJXlkdBwhcLNUHMykj+3ff/rchS99/ZuOZS2OjiqCOJzPNxzyR5//3sc/9LG//Mz/YgIkAfmt3/jkP/+/n/nAnUsP3rl84MChre3KjfV1y2ubpmIases3dpCR/qXf/Z3x+dsYQ15p89//+X+98PR37ziwIHK2XSgJSmQkNxyQcLNYwLI0mh+kAalWW26v4/khQXIyYQgCsoDyoY//4tKBIz/+/ne/99X/nVQERG1BwYRwWVbjiaQoSgCBZDJuO+7mblFS5EQsRkmwsbFu6vrQQL5Yra6tb3DOotGYJMmMMyMSUXUdvAECz/MlSdY0jQPg2I4kiJTSQqEQklAQMGOMEsIAYIxzgPRYTJIU+K7jsxhhRZIUWcIY0iAIQagpCoIwDEPOGOQQQCAIAngD4+ynIOcIIcZDEgSarhqG4Tpuv9enjHHOBSwSwgGAuqaZRsQN3LbThyGTILN7vfVi50bVP7h48M8/85kjJ+8ABFCPUAShCERJuPH8U7/+a782lTPf//h933ry+bVbxZguS6ooCILvup5j85DSkPGQSZLAIItGNAlKu/XOdrs1NJi969hh04gwN6w1WoppiJqqyVK10dq5tXNgbKTR7L547uId+yZAEN7aLW81rdhg7tO/+cFoMvHssy9evnQtFtGmRkeaLo3EtKXxQUL5yurm8uL8xMzIzZtbqqTg0ALEsW3PC5Fs6BFFFDBuWW6pWl+cmxkbzK1u7L308lke2G0vSKYTMxNjEVW3bWuzUAx9kk3Fbu0U9x+5PSILz750emxqcv+B5UKx2ul6E5PT165ePPXCy//jwz83PDj8nv/7052u/a2nvr90/M56uXD3HYdysvC+R++dX5jJD09+9zvf81pNO/QrvZ7P+cDoxEd/41Nji0cZAG6neubJb3zly59TFLA4NdKqtpvtXiadSurxft+utNoeJ/FkHITE6VmMkEar07LI8PDg5PTk4tF773v8XaIWW1+59Osf+6hCegvjaQrDvuP1ulYkEh0dGVNUmfPAdZxavdX3/ImR4WwyeeXaioDx5Mhoo9dbuXoVAGAYJqVMURTDMCVJ9Dyv3elIohiNRhVZhgD0bVeUJABAtVp1HQdCGBAiQggx8oNQkGQzkUJIgO+4bUzAGAKgqpquqpwxALggYAgA54zSEHIAIQQIYow550EQhGGIEMIIYcgYZRACQZCCwA8CghBiIUcIYowRwtFYdHJigoRhubTneb4kyKHnlsq1Sxvlm/1wKJX/1O/+j7d98D2xeIpTDjHsNVt/8Uef/MxnPvexdzx496Hpbz/zYqfVysTjEAgAsFaz43meKIoQspAGmAsYo4Sp5qJm27YvbxUqbXd5eWppcixhJnerNRHwqakxJvPL19bPn1+dyeeXpsdfvba6srXVs30BSDTwT5xYfsfDd4eM79zaqG5vLI5M7D+43Oh3e7a9MDMchOTJ588Z0ejsxFCh1kRYGB9MxxRRRELH9vuez4l3bWNzu9S44/D+yeGhnmVvbGw06k1DlzAWHJcquizJkbHRfNKQr169gbBsuZ5kRI0Idlx/MDcVIr62d+uhh96z/Mi7T33nS7/48Y9/9NGHP/KWx/7+y9/60298++c//JF//Ny/nXrya3/43z/x6Inbc+kkk1U/ZC8998JkKiVGZBfziGo+8aFfmDh2HwBap7z7H//6t5dPv6KJcHZhyvXseq2WMOPZeMr3vHa7HRBixE0jHhMRdHq99c2deqMZi5rJdK7lhEdO3vv4B39eiWQwx1de+NHX//WvRZEiQfQ9r9+3FF3RtAjCQhAEnud7P+FjjBRFdRxbEISDBw4Ypnr2/OW+4xPHS0T0wfxQu2f3rT7C0LYtGpJ4PCpLMiEkJDQWi9GQFMtF1/UIIRgLAgSUMsIABdhIJPVoHL7jyChGiDEmIKhruiSKggAxFjjnAHDGGAAAY8wYEwQBQkgIcV0XYYwhxJCznwIAhCHljEEEGUMIYUmSGGMQIUVRjIgJBeQ4HqQhDH0S+D5HDZu8dGOr2nHvOnbnO978lsff/gghwZ/82af/49s/0Bj9vQ++ZX5i4OtPv+j73kg65XqhT8JWq+M4riyLgoAYp4ABASEM2dL8LBaks5euFGtNSRKOHzk4MjFWrNf8rheLmCMDmc2dves7pYWpyeFUnItyo93c2t0uNbpj+eTbHr7v7Mrmd3/8fLXVdQk7MTn04Xc+oOnKc6fPAUgjqlAo1xRVnp2aiJgxy3PNiF4pFgxNm5icsizXI0Gz1amWa1PjY5KIddNo1ms727sxU3MshwMxlooLopaMxXL5+I9ffNm3g5HcsBOy1FCMcsA8PJDOdnvWbXc+PHLXXU9+4wtPfu3Ly9NT44ns+sbGV55/xZKjf/snn/rqf/x7vVxemhpWJMFIpmzH9yx7KJ3WogaURV015g8cGb/jRHm39vl/+vuLr55K6frkSD49mO7afUCZFjEcz2s3GoHrCgBNT06aphGLx+zAW93YKpcqkoBULbJdbc4sH/7lT/6ukhgEAPidwhf+5tNXz76aTqWgCERJDENKCIEIaZrmeV670wsCH0KEEKzVagDC228/ury0sLJ6a69QBZQe2r8kivLLZ15vNhtz8zMCxtVq2TQiAADOuB8Qznm/18EYMUpbzSaEkDNAGXe8QJC08alpJArwiSOjAHIMIYYAISSJgiSJb0AIQYg5AJwxLAiA/4QoioqiBEHgOA6lIWch55xxTil7A38DYwgJuqarqhYEhBAahiEHIBQECQQRAQUhsHw2NTFx77HDt7YLX/jOD26sVuocLE5NiAisrW3GZTg3GLtzec7jYKtUUkRkqGoQcihKgU+63a7neRxQCDmAKKobIKTJXCoWi1V2i4VysWJ5hw4ePH54qVCuvHzhxspWaXZgYP/IYMO2hoaHDi/Ox814OqYUy5s/fun09MT44aWlv/3Ct54/vzqpiz6nWxa7++DMWw5Pru7uAUYNKSScJFIZhERDV7L5/GapdvnazfHh/G0Hl11KBKiYgtyq16qt1tTM1Fg+c3VtY3trTxY58QJKsRZRO31fwOLSvqkLq2u3NndFKMbjqYWFcVlWfI+SgGfjwycff6LWq/31p/9gaig+Nzd17fqtVr2Zi0evXtsUoCRpmkccJPrRqJHPj0aNOIJcVuSuZXs+UZWIEU2mcgPf+f73V9duAhKMpJIjQ4ORRFSORBAWHcvuttuhHwSO63vO0NCQJMuyqrQ6PdsNGKWMOMlUxiZ8tVA9cd8Dj7zlLZXy3lPf/+6N8+emctmIpgSYCRgbekRRFUWREULFQrFUrVLKOOeqoriuS8JwYGBgemrSCXih2ozHom+67+5mu/f0Cy8PZpLH77jthRdONerViKFzxhRFvb6+uVssptsCmGgAACAASURBVGJGOpUMA79SKnPGOEA9x+Uc7tt3yDDMnb1d+PbDIwhwDLkgYAgYgkhVZUmSEcYIC5wDxjiCP4ExhhAakYgsS/1e33GcgPiB7wPOGOVvABACCCCEsiyrisYZpIwFQcAoFQALfI9zgAUJQkHQtEgyOjM1xAN/d2t3Zb2wsVfnfpBSxJnB6OzkqMvRpbVbWBJNTaEB5xCrukYpdRyn2WySwEMIMgQTsYSGJUXVVEm0rX65Vqy7ZHQk/+DxQ7blnnr54m6jZfvheMocTSUmFuYOHzlSb9ovnrty+uqVYrMdV7Q3H5ytdLpPX95cSigjcVTqMYbgvYt5BsRUPCkrqGR3Yom4joR2t3PniWOKrq9tbAS+JyqqHfhmLBv0+sOJuE+4ahpLi1PX1tfPn7+IIVNkDSOBA963vWKlMj81FknGbmztIkH2PH95YsxUI4Kk2f1AjiTv/8DPbl698r0vf+H44SP5oahsKE+/cLZeqRPPHc2Ph4Rs7m2FwJNEmB+dmhqfvLWxUWl3KpWaEYlEdB1yaFuW57kcQd91RYhNIzI8Ma7HTNt1nV4f+qFlWY1mIyAkO5hjnHu+7/mUMo4AYDQwDBNLyqUba9u1+vjIYGD1dQDzyUTC1DkGLoSaqkV0QxAwCQIaBm/o9q16o0Ep1fSIKEq+53HGk4mEqETsIIwYxv3334tFodZozk9NWb3ej370o3q9Jgg4m81OTEy88tq51bW1sdFRTlm326aB79lWEDIsirncYCaT7XT7zVYLvv3QGAIMQYARwIgDwCOaLMsqBxALAkSYU8A44xBiATPKdE0zDKPf6zuOy1hAgoCHlFHKOVdUVZJlSsMwpAhhAYlYwLIs2Y7ds3syFkUOAj+ACCFFsRx/am4mP5IdHEw3qrXrF1YC27L6LQmLkzNzTBL2ShXLclnAMEMcQYgFwJjj2L1ulzEKAaCcRSKGImuyKEPGLKdvW12HwVjcOLo41en7l6/eRIQggDsBoYIwOjz06P0naUj++ovf36y0VAEwDm8bTw7lIuVOb9CMDZrKTr3uhHw4mWx1veHBxL7ZsfzY+O5uwXHcrVp9cWFicmKktLNlyKoeH1jf3kZY6jabqiTEMtme4wwNZnRdv3nzZhgSABDkkIYhZazW6sSMSH5scHph3/Do1AsvvVDaumVZge+7J48c2yxXyz07a8Y1QF1CzGhkdGxoZXW1WiqMDwwcu/2ujZ2tcxfOcOp3HG9qbmn//NS1te2Xz1xWZXFwMAtY6HsOAjAMAk1RU8mUH5BEIj0yNmq7TqNddV0PUtZotTqdTiQSyWazASG241qOyzmnjPmeL4hCRNdjMbNQ3KnVmxIWI6IiQKHV71uBBzGPRmMilj3PJ4EvCNAw9EQsHoZhpV7vdHuU8YiqRzRNVmRRUggHtutOTk9puhaLmvnBgVqleubM2UKhqCrq5OTkyXvuvnxl5bVz5xkDrWZTFFDM0BnxfRJGdEMQBUEQPUIDQuDbD40jwBHkGAEMOQdUkbCq6pxBWZaxKDAGwpASTgEAnAEAkKpqJPwJ4rs0JJIAOWUccFlRVE0HjAYBAW/gUFLEqGm6rltt1lUsChD0XLtrO7IkRRUjkcgmcwOT8zNjUyOrN1ZOP/dso1gKGYJYyA8NaZrebrWtXl8SBAGLhIQQQkIC13UCEhASAA4juokk2Sck9AMBAsSIR6iuytOTQ6VWf2OnEJWECEIhC5sBL7bY0lT+gTtmzt5Yf+XSpibD/fPTA5nU0vSQqCif/Y//atiUkf577j+6b378+TPnaRDsnxifHpusVSr1vt0LnPmlSYLw1ZVri2NjEdlcuXo9JIEkiUBAi8sLQEYR04wZ0Wa9WatW282W7wWAc12LYEFmWJBUzYiZE5PjgIJXXz0rSTJxrdtvPxRw+PkvfxVgODM9dXNjy9CN25aXHLt7bX3Vtt18IiVLou32RBzuNO07jh7NpdOvvH51anzytsOHW63G9Wsrlt0PAr/dbht6JJcZsG0nFkukUknX8xrdehAQCIHnegCAWCyGEMrn84Igbu3udrtdWZJlRXFdN5lMAghWV6/3210J4vANnImiPDYxhiRYKlX7Pcdx/JDSkBJVkQeyqXQqTRhrNN/QlkXJiJipVFpV1Xq32e33CGO9npVOZd7x6CPlYuG5F170A9q37Fg8+cQ73yWqWq3RPHf+3Csvnsok4hPDg5ostvv9TrdHKYsnEliU/SCAbz80igAXMBQwQAhwxjCGsqRAhBRFRRgDgCAAlFHKGKPA9Ug8lsgNDiEIa9VCsbiLERAxegOHEAtYQgLCCEEUhqEgCJqueJ7rdpyAUyjikBDuE1VWMBKQJCqqmU0Pzs3PbG2vnX3tdccmUUlWJVGURCxiKGKHeE7gqlgGlDPOMUYcMMd1m82m5frpdHYsN+TZbqPRCChBIieMqyKeHR0q1LrlelMXmQRDxgHn0GW42nPn8unF2clzN7er/fZ7Hn/TeH60a3tmVL907urTFy4PpGO//K6fycSNb/7w6VKxsDw3VahUy9W644fLC1MP339ir96pNHvLM+Ou5z9/6ky/UeWcS7L6gfe83Q+sG5s7im6kE4l+zyK+DxkAlKmKTIPwzLUbrusP53OmqR9cPnDp5lq73yYWERT1vnvvIH7v6VdebbQIELWxscFDs+Mvv3L2y997sdxxJQaW8kAyEqmkKUnCvsXFC+v1ZrP7Z7/5S7vFSqlU8Hy7Z3UpY34QOI4riTKCOKJFFFUNKAlCD2MkSaIkiK7jQgQX5heiUXN1daPbdykNEcJm1FRkxTCM3VJpp1wWQipx2HV6lJN8Lrd/aWEgn2w1u2fPni+WqpzDaCxqmBHLsjnn0URClGSIMCOhY7uxaAKLsNwoOb7rB2GxVMsPDB8/fKC4VyiUK812b2NrV1H1tz/xxMkHHsyPDF2+cPEvP/1nsgBHB9OaiDxCG42WKEqmaTKIOIDwrYeGBIQlDEQEAOAYIQEhLIiCJGAkQIQAgggiCACCiAHuev7w0Mhth28TBWFnd/vqyuVapSIIAhbEkHMAQDYejRgRy/V6/T7gXBZEDKFPSK/flyRVFATXcSlloiTGDJ1DFHDWtxyr142oalTTs8l4xIhU641Ot2eYhizLVr+PEQBI6Ds+RkCVRNcPNgulStfOxRP3LC9rIl7d2SnVGwhSP2RxXV+YHN8oltqdtigEkiBAiCkNIREIxV3fmp8cncgPvL66JiTSmVx6KJceH8iko2a5UdEj2uTAiG25e5XSxddPeyG5uVcu13sIoPc8evLI/mXbD3PZ9GA263j+sy+eOn32ioTA/sWZdz/+5qDb+Y+v/ZcDhDc9eIJAFNeMXqNVL1dF4BfKjavrt5IRyQfKk+evH1weOzA7tb65t1ntvXxp98NvP/HRdz589srG9bXCof1zh4/uf23lyg+fegmDqKjJgRHHGL/wzDONWv2D985nUulvPHf5PY8/eudtB/7pc59XBDw+NBzwEDHGCGn3e7Ikx6JRznlIGcJQ13VJlhRNcWy7b9mZXE4Q5csr1z3HmZ+ZkhW1UqkpqhaPRROJhCyKrmcBAMMw3CuVdvaKvk8GBwfzg+lCYa9eqWEEI6o6NT6WzqRWd4q7hQIWUDqdHhjIk4A0ag0sygHxmu2aKCJD0wcyA5qsX7p5o9N2IAttp7dXbnU8tnTowP/18Y/OLS8T1/rHv/mHV0+9PByXjh6ev7axG3ihJMleSBDEqqTAxw6PIABExDHkEHCMkCyIqq4pisoA8H2fAY4gErEgiALjoNvrqYp2/I5jqVRqff3W7vZ2o1aVZQlh7Aa+IAjTI0OZbHavWNze2+OUGZquaxqSRZ8QShmEMCQh4xwhaCgKxkK726vV6gghXZZEhIYHs6YZaXWtVrsNAMxlM7qmu57b7PZsn4S+h1jIGN+rtXTdiBlR1/ZFyDHz+r22S5gd8qFUYml6anVvt1wuGToSBRESjDBCAqN+2PGIj+CBqenhTELNpOV4MpeJSghe3ypJkuJ0K1MjQwbWnMALub9d2l3f2JEYHh1L33vnUQBB2/Ipw5IsI4BC16qWCklDV/QIZ3AwavY77VOXrsaTcVHB2WjilYvXTr++CgDHnsMwrDvUJ3xwMH54/9xwKrFyY+3MzWK55e2fSN9z+7wWM4GIbMeFFFmWtbZbnd938B0f/ujIwgkAwM2zz/7LX/1paf06AEyN5n7tF34+4OFzr7xg9/uyIEIMeUgxgpGIqWl6t9dtNpuiJI2MjERj0U63ZztuuVISJSmfHypXa1eu3ZibnlmamwIIl8tVz/MRhOlUMmqYjud2up0gCACEQUACnzDOAWaO5dSqVUkQzUhE19U31DudkIYD+YF4PA4A7LS7Tt8hlHZ7Xdd3konE1PjE0sL8yuXLp145m4gnI5oKINguVgqNbnJw4O5H7n/3Rz6eTWb/4lOffPqbX3nzyTuQpDx/5kwumcRYbPV7AhYlQYTvuH0Cco4BhSxECELORSzokYiqawBCShkDHAIAAcIYcQAs2/Vcb3hoOJvO7BZLtWoFcR41dQShHxJZUUZyuVg8fmtza3tnD0GUy6RVWcaiFDENDqmkyKlkslytlctlyIEiKr12p9fpYwRDFgoiHkynFEXpOY7teoSQqGGmU0mfkEK1gkVFgIzYfVEQO7Y/Njiayw+/euXKrY31mCrrAiS+Vet6IwODBxdmtqu1Rq2GoCdAiLisqgqjDmJE5ELdCop95+G7Tz725nscke/Va06nl09lK63++s7m0vRopdi8uXnrxO2Hbzuyz/c86BNBRWEYqqrUsoL//M6PMonYsX3zWECAu7VGKxVP8wB0u/2RTKxdr7187hrm3m0LEy9eulrYrSd1s9m31hp+QtfefHQxM2De+aZ72337mVOvP3duzQv8xcn84cMHOaRbu9vXNiu3TU1sbBWfu1l774d/4Zd+8zdFSZclvH3l5X/5yz/oVYvAo6n00JvuO9Z1ndVbt8qVEoMgl0wzBBjjU+OTjMNyuQQhjCUTmq6RgFi2HQRht9PGCCqKQihzfRI1DCOiup5PQtrp9jqtthHRJUnuWY7r2kZESyYThq5jLCAINU3yg3Bl5Xqr2cYCRhABCAgL8sND8/PzhIb1aq3ZaCOAbN91fMIYUhQtmYhLGGzdWrMtL5E0FVFESNaT0QCjWsceX9z/id/6A+yHf/rbv37h1WenRwbOr9ySdXkyP8AoLzXqqqrJggzffWIGcQ5YCCgBkAPGOOWCKBqmgQUBYxELiHMOAeCMcQggRJRQQRBURXNdr91usTCIRg1REAGGiqxICHshKTfaQeAJgEuSDjhPxoxUMmU7brPbUTQ1HjUhYMT3AUeVYrXX68uKGIShpukjg4MhDav1uuf5oiiJomBouqoJbcvqWh7iVJewIsm2zxyf3nXy2Mjo6Cuvnj/zymkdcUSduuNPj0/ODucvrt4ixIPE0SWJcSgDJEHmc1dn3HbYSjvsiPLvfOCx4Yn0Z771w2ubtQcOzJw4MCdrZiIZff70a1fXNo4fOvjYg/f6gdvtthKJ2A9PnfVDujw3XtzaGs8PT06O11v1nZ2trXJzNDewMDFpWX6vXd3YK3zx2y/HxfDAcMwHoalIGUPbrfT7tvvQnfvrNit7PJmMHjxwSJKVla1tDFgmGUumB92+df7cxVanARDusuiHf/sPl44cAz915bUXf+VjH9u4duN99y7EIqjUDSaGJ2hIPMdtNJsA85mpGTsIeradzWYUVVEkJZPJWpa1tr6uaFo2kxYQajRbjuu1Op0wDPMDOVGSGOeu61DKur1er9MVRTlqGpIkSKJkGhFFUTilvu81202PBKNDY4og7GxvtVo9IxpzfMdy/eHR/NBI3nbcYrHU79u6phrRRDyeAUC0bLewt93vNUQREY8IgIqyCCVlaHzMYTTEynve/eGFpQOvn3ru3/6ff9ja3ogZuu/TRCpmamq30wUiTiXSIaHw3cdnJQwR4JwSykJKQs4A51yWZUmRdV2XZZmQAEEOAGCMwf8PwkgEIbUdOyAeQlgQhGjUVBTFcZ1qrWb5fGZiVEb86q29vuPNT+cTRrTbtrq2W6nX4oa2ND0d0aS246xu7DpWfzCdELDY6bumroWA+n6Q0CKUMaRI2XgynUnanrO9u9vv2YQyyBCgbKVQmJ2d/p2P/lyxUP7Xz3/Vs9syojst9547j+6bnvj6U887Vj8ty4oICXMAYZwKHIWIgVYz6AXSVdd74NDE207MfOXZszeqLkXsFz/wlgeOHeq73oXzN3cL5fvuOT47NPDq2bNXNzdHBnNjI0ORmDE3N+1UK6YaqfetSyuXIoaxU6hFFHliaCiVTA/EI88998pnv/yDGCSzWT2ejEMBCii0feIF6Lb9S6VOf2p5+dSrr2mx3N0nj7W73ahp+D65cXN9fCDT6/bWV9f2qu0WjHzoNz6VHpnECBX2Nn/3t3+7dWvt4QODo6PDAcau50oA0ABKohz4DoQgmkjsFEsdx83l0nccPZRIZ8uV6trahmPb0xOToigVyyXXsTOZbDaXq9TrnW4/mUyFgWfbPVkSg8Dv9yzIkSIrooIFKHiO5weBaRqCiCuNpuV7U6NDpqRWimXCCYFop1jRVHVufkLTlGqt3Wj0Xd9PpZPjY1NjI1OaZvhBcPnyhXqjFLKg2mxgymNxk2KEuKBIkaN33/Mzb/vZfqf5xX/9u+tXzqmygASRQ4wwDsNAluTB/FAsFi+Wa/Bdx6dVSZIw4pSEbyCEcU4pFTCWJFlRZFmSIYIQ8jcAwH8KIogxFjAAruv4gQchVFXVNA2EccfuN+t112UPnDhxx9FDPz5z7vrmpoioIcnc9VVN69q9Rq25PD+fjBnbtdr1jc1kxDi+b4nScL1QlLDgBQHCaHxwuN3pNLrtoexAKpfr9bvMdyhHpVqnWq5GBECw8PzVraNLs3MDxsvnrjHHRqLYcel7f+buWML48fOvhj0rZeqM+QBxXVBLNetiw2lDSVOUXrcXQTCrC48dm+rYTjoTV6LRO07eDRh/5eKlwwtzk2NjEMKd9U3b6Z9fu3nxytqvf+TxIwcXOJYdxy2WKs22FdMkiIQwDOOKsFso39wsLE8MmaL46rmVixcu5EwpbigkDFVZ6nikZYUL48NMlgbHRtuOb4Xi0GCuWG3kBzIhZZVGK2lo9Vr97Lmrm5X2papjh0iSNBFyGrrDMfHwoDY7lMwMjiDZAIx1u616r69GDF1Wup1up9vfLZZ8CieHB9/5tkeBqrx09qxtWZl4ImNE9yq1jWJRQfzowX3HTxy/dO3GXqWRSqUDt9fptDFGiqIEfhgEIUIiEsVSYbfVrKqyIomSJCmKqiMJ97o937MZ4I12z/EpkrAiwtHhgVQs2qz1+v0AinIincgNDSWTucAjnU7PcvqCyOuNMnHdxekZLOHrG+u7O8WlpQM//4lfHZo9+NQ3vv6/P/ePKUOIRJQ+8T0AeMAVQVYVZXR8HCHh8so1+L6751VRwhCEQUBpyBgFAHDOBUGQBFHAGAsC+gnIAQMAMMYoBQgiURAVSeSAe57NOZBlGQs48H3XdR3XbTvB7NTEx37+QzulpkvIpZtXC+sbKg0lETMAQgYyiUyr32p37XanHzO0/QvTgNFOuy8IosdD13Wjiu4FXrXZSJjRZCrR6HXS6ezk6DgPgrUbN8qlKuHs/GZptdxPxmXXC22bugAcmxj65fe9ab24+frZlSiFLPQlFRsyl6H849XGNlX/6g//ePbg/Cd//09eOf1qGsIH58aeeOhYLbA8BN7xzke31qvf/OGT73rrg+1efa9Sy8ZzCVm+cu36hZsbb3/zibmF2dcvrqgy71tWEEhHFpe2C8WkqU/lYj945qWnTl9cHM7ee/CArKjfeerZamF3OKGGFGLAQx66BOVzmdVG38jl5qdH9s9Nr6zunrm8Cjhp97tTcws/9/53FQulT//D52qtXsI0290+D4OoJnE/vH1hFIlQMvTxwQFRUO0QdZw+FnjUjMQ0Y+vWZnGv4gaUcAw5uf3IAUmSu91eOpXGAnYde3N7q2vZjHHTNLLZLCFEVSRBFPuuxwE3zAjnoNOzIBCGh0aHB3Pra9duXL9EfMey3FxmaHh43LKd3VLBDlyXhLJuLh84HE+mX335tILC4VyqW297PomkorKhCljzXFKrN2VFG5scVXUsSSgqaYYkl8ql1y5d2N0pzs4ufuTjn9AM+bP/8A+9Zmcwm2u3m45nWU6PMaTpkWw6o2pau9MrVxvwQ/cty5IAAA98H3CGAESAI/wGASOEMUYYUkoBABghCCGllDEGGcAYy5IiytiybRKGGGPAAfF8Rhyf457Put3u2x5988LC4sTU7Pb2+qsvncKMhZ7reb5uGIqmVVv1ZDShCMrm7k7XsUMviKlaKh0LQtLt9gQkYIwDQjBCRiQqKmJ2OJPJZJkfNkvlVq3IKbERbpEgnxns1t3vv3ZFQei33v/Wh+479sxLp//969+NAaoJoueHOuYtmz61259aOvDi6dOKoV44c/Zn3vJmq9WaSZn/+Kn/lhlM3dgrDw2kivU2of7M0NgXvv2dYrX4xCP3mkb0/MWbhiI9cvJQvdP8ly9/PwzJQ/fdfu/dJ/sW3d4qCMCx/M6rZy6agrYwPg7CMJ/PXTy/8szps4NxCQeh68GqHXYZmB3O1mzn0P65seGhgcHchWubF6+uWo4FJGl+duHXfueTLgk/90//Nj0+PDsyWKtXXnjmycDu+CFX1fjhQ4ciaXN1a4P5nAEMQ3j7HYd3ywXb9iu1eqVYGsnnZidHrq1tCqqajJmFUl1TtVzS7Pfs9e0dn3gQcMDCoXw+m8lSEja7/ZbtGJHIwMCA6/m268mqZkRjHPG97Vu72xuYUhkK48PjEcXs2nbXa1VavenFAx/71U/osWS52PvRD74dujW31WyUm7IuGQkVSUJlp1ppNHs955577l84uP/axk0zoucS6c3VtVqlyGjYqDf6ffvIbUc55mtr68lEWlO0drttO30aEsvxKecTExMICa1Oh4QMfui+JVkSOWcsJBBwBKGAEBZEhDDnHACAMWScAQ4Q/AnGWBAEGEDhJ0QOmOXYCCEBY0YYCQiHnNIgZKxj+xQpT7ztbQeXF86ee71aLqKQEc+zrL5PiB6JxFOxTCoti3K92bIdp1Is9zvtmGkosuT7AQcQIQQAME0jm0lrutLotHdLFdv2GWWKIi5MDGfzAy0vOHzk+OS+w1/5l38JO6Xlyclao5lISk+duvC9s9cOT5qsR/oubHf8rV4wfnDf9556Jp5Or5w9/7Y3P+R3OgGnn/zAI29/7J5XXr/Yb9ezg0MH9u2TOf78t75OIX/T/XfP7t/X6VuV7Q0pDDdvrr5y+kqj2nrs0bufeP+7uhSEAXRb9a985T8D27rv6AEzEtmr9BQMgOd974UX6/1eWtcMRcVSJJ5JjmcT1XYznUph1eCiHIZBu9Vx/TAIUd8OTj708PLhI5cvX+EhGM4O97uNb3/z31utct/hXhg+fP+9CwsLFy9cABT4CGxs7d52aH+n3cxkc+X/wxR8gMl1FobC/sqpc6bXnZmd7VUradVlNVuSbck2xsZgXDEYEjCm3FBCYtIgBEhCSIAESCj3Eoox18Y27kWWLMnqvWzvOzs7vZ85/Zzv+xWe/3+e/32zhZV0erC3e+PomqXFFZZhfH73yYtXFV3rTEaKldpypqCpLQSp1yUMDQ0Fg6F6vSkrmk0pixmW5SzbdgiUPN5ANMpKLoHD+Ux6YWZSayoC5hjEefxegkhFViKJ9lgi6UAmm8n5RNAW86WXVuW65ucxgka5VlvKFa9larW69cDBfXt3bnzpnSOyTtf1JpFjUEJ0VWnKTcN2AsGIqikMw3jdXo5lbcs2baveqKmG3WjKLMf19PbpuqlpOnzs5kGGwYA4CAIWYwAAZjDLcBAiQggAAGN4AwCAUgooJYRYlsVhBmNMKbVt27RNnmMRgJZh2ZZNAbQdgzJOtD0FoOARfZ3J1JlLFwxVA7bVqtcEniMQA4SGB3t84WCxXGYh7oq3MwiOT06USyWWYSkAju0AAFiO7e7qTKaipmHn85VSqWRTUzG1VssIBoLr169RTBrrWfuhJz995rXfH33hGaNl/PL1sztHeh/cu+W/Xn2jWa0OR30SFeYK+nul5rabdrx96mh2fvmD9z9gLU8OeN3Hl8sfvm30voM7zl2d7OiJuz1uv+DtSMbS+SWEmZF127zdPYtT15dm58qF/OL1sWNn5+azrQ/sW/v4x+6JDvYGE/Hy4sr3/+nfXSzT1R4rt9RUMmY2mjGPt1Iun5mY6k919iRj7Qlfeyq1mK9VFL1aUTXL1FWNUMPn9XBIbDS0XL2KXa7HnniyVKv84qdPM0Boj/gc0jpz5aKhG7G28MG9e/s6++ZnFiTeRVzs8fOnbd3iWK6vu6tZqxm64QBgOg4x7a0bRjgeHz97WZRca/o7lzPZ+eXVfD6PIO1MJbo6OwCFqqpjhmUYptVqyXKLUIgYPhSOdPUNxpJdnR1ty4szv3/2ads0lWZL5IVUdycF7Go+p+p6Nlt0bCsc8W3bNBqPRfLVeqFQiHC8blh/eO/i9Kq89aatO7ds4U0lHOAzqwu51dVQICDyPIaoUi3LssJyIitKDAYuQcCI4QVB0/RKraxoKgCMadkAIY/H5xDHdih8dGcvggACyjCIxQxEkEEMy7EQYUIIAABjBG8AwCGEEgL+CENo245jWQRQxCAWI+oAUzeobTMYGQCuVMpDQ32b1q8rFhr5QsOyVExJbjVj6bpHkgCADMd1dLaLHlepUpFEV8Tr93o9TaVVLZU1RbVtWxAFhJAgioODA5zHNTszt7KYwcDxe7lYLGTZdiZX9gV8smaUFOYb//Kv2A1+9I2vNVZyv3z3UsIf/OGTqL0yqQAAIABJREFUH5+eH/vVm4c5yO3oSl1fqfw+XYoEAt/++teee/HFd44ee2pbD6Xms1dXtw7Fn3zkrppNLEA9YX/I65OQI/BwaWllKattv2mbrtRDwahhNE8eO/Kvz5yeLKkHh6Pf/OwDhPPmKsWQi5m5Pp6Mt3E8pphDtvLuqeuJaORDB3ZPrSxlc1XIcZTlPS7RIXTnlk3ppeypyxdt0+YFlw2Am+XCPu+ZueUj5y9/7WtfXr9uzS9+8pt6sd4WDKqaslpI79+/bce2LbbGXLg8eeXqNdt2sMhs3bZpZOOWl158qV4sQUC9Ljdw6GounykXkp2Job4+0wIiz/tEXlU01iUphmJoKoSAxUyj0QQUQoQsxzZ03TZtBrOsIEKAJW+go2/QE/CcOnHUUuoBt5QrlLDAeTxuBruC4ZDAs1cvXyO2FQ4HAoGQWxKmFmcsyxjs7nnzvSuvX5j84hc++/kvfCkajV96/bXnf/dLTE3gqIqucSzDsmylUoeAFVwey3EEgRN4DiPM8nylWsnlsoZlQoBE0YUwJhTaDqEUwEe2dyMEMEIMghBBAAALEeZYxGAGMxhjiBAhBND/FwAAY0wptSzLMS2EEcMxCEHgAEs3HMuihMiOU2i2Dtxx55qBwauXr1RrFT/PQEomJiYppQGvzzZMlyjyLpEAwLFMd08Px3P5fM40TREzkFKIQDgStgmt1ZrxZLItEXeIvTA3t5JeIbadSrQZunp+bCLRmexKxrMFvWvtZk4Ecn454feOTy+HorHeRATqytLy6vOvHekN+QDlezZvtRn4i2eeT8vWnmHvE/tH37y68KsTqx/a0ffUZx7VKWEh6w17C4XSkSMnecZeya5cmS13xNr+5st/0tPZWymWLl4+/b1fvnVyrvzURw9+5M5dv37pzSvXZ+7cuSHscZkOHO7ri0eC7546/ouXjuzasvbJR+6bnFmYmF4QBYEwwCbAMOzNIxsoQu+eP+loWiwYcVjEACBg+OKR8+km+ebffPrm/bue+80LuYUMw3AuFnYlgh398enl1fGpfCaTa5QLFHC8x33wwP5bD77v3MnT6anJQjlr65pbFFfz+dVapXdgeM/u3fFYXJaVSrViOrbbJfAcYzt2vdZwCNE0DRDakJVKrc4g6BZdPMsCCpSWQiGUgqHVSkVtNe++ebdcKS8Xysjt6e7rGxle39Pfn1mafeH3T5u6ySFecrtYXljJpAWBgRx/4vLMnoP3/vU/fIsQjBA+dei1T37isQ29beu7I7lSRRBFlmXzuVowkEwmU7ajG7apKIrtOB6PW1FaudyqaZqiS6IUEAo03aAUAoThw9u6EAIYIYwgQhAAgCHCGCMG38CyLMKYEAIoBX8EIQQAUEoJIY5pQQQRiyGFiAJIALEdQzcLSi3a1f53X/uWUtde+v1zNbnIA6DILVluKnJL4PloKMwiplSpUAB8Xm+sLQYAWFpaMkyDYxlBFGKxiNfrrVQqK6u5cLRtdKi3LRxqyK25uaVSpeJyuyEAc8uZzZvWbF/b02xZxy9OFGrlNb3dAmajbmnNUP9bl64Wy7WRWFtmaeX5Y6cxAz/74D09ne1f/f7P8nn5of29D96/85m3L796KvPpRw+0t8XKJR3bumHrrVrjwrUJ2dbz+XxfIjFbbj38wJ23bL2pKWu6XFxOL5dV/ROf/URmdvbnP/3d9cn5W28a3bx2pFipOaZpW2a+mG225Ifff4Dh+LdPXAKmFfaIUBQ5Viw2Gn5/MBaOFKvlWqnsINuB1MN5GnW5qbRu2rYt3tvvikTeePm1ZqUaDkhBl1ApV89Pzx29vIgoHkqIbV7R44oEU8mlUsFxuHUDQ6yjNPJLxLEEr//ixHTIF7hl1y5/NKzajmabECMEUb1a4TCORaOmZSmK4ti2y+WybWd5JVOtlCWeh5S2ZIVSQglVNE2znKDf39nelsvlECe2dfaNbtoaj6dcHvfS4vjpE4fr9RpwgEfwQcxUaxXLUFey+e377/qzv/4WFXwAAMfUHn/8kRPvvv6Zh+4STW0xk2d4VmkpDBa3bNoVDIartWImv5rJrHIcZzuW49gAEAAgw3KWZdkO0QyDUkwhgA9v60IIsAyDEYAQAgAwBYhhGI7FDIMRAhBSSiEA8P9DCKF/BGxAgA0QApQgAHnEQgpNU6/qhs2yH/rQ/UM9Pe8dfbdQLDIMqFYquq4bmqYrWnsi4ff6soWiKIoYo0ajIbdkCCHPczzLQEDD4aDX622pWkvXRVHyuFyQUIyQpiq6rnIMCgX9Aa9flFyFRn0xne1OJYcGuyeuz1ydWGIZOjw8LPCucrW+vDS/fbhrZrX49NsnO3yc3yOWZT3iERDLHdi3uam2wm2dQ/0DlWLtnVOXnn7juI/lP3HvLklwXjt1gbOpn5H6164d3dzfP9TfaOjRgE9ryfHOTscv6PnM/Nji9GJm82DnQFfHfDo3MzVZq1Y0xxoaGlzb13nizIW5xZxfkkzLlrxST7KjSZyqogRcHp9LsiyjUCnNp3M3j25oKvJ0pbJxdD0D4UqlwTL8UH83hi3Rtt48dOYnLxzPGCggutpYvSMiDXR0joz0/eHIyUA4unHj8PTkFGdTpaVaDGPq9q6tmzx+z+zyUrZcDoZDQ30DHk6oVKsmsUVRtG1LVRQKaCDgBwCml9OtluyVXMSyWi2ZUkiJY2iqbQGGZXmJb2gqZKRgKLZxdIgQptFs5Qurht7CADXqMsWs5PPpmsIxKL2as1nP937+dLyjm9rKd77+9W//43c/csfum7YMzi8ulstVxzFty+zuGhzoXWcaZjaXvjY51haP9/b2Tk5N1Go1t9vFcZyqa6qqsgzvAOo4lAAIH72pB2PIYIbBgN5AKIYQswzH8xhjCCEFgDgOBAAihDEGAEAICSGUEAwQoBBASCmg1MYIAEBs03Yg19RMyDBdne31akkSBZGBjuMwHO/3BxcXF1VN97g9HkmQRAlCury0lM0W2pNtkuTWNJ0QhzLA5/G2R6KMwOuWzmBmeXm1Vm3yPCsImBKbEuj3eOLtbbpF0qvZjmSiq6N9Zmr2pXfPX5rPP3LXzr1bN5y6PK5r8iN37WIZ4dT5y5lsvlAueX2+eCKmWWZXe9Ln9q4UC6lEfE1Pfzqz+rM/vAMg/ObnHqtWC3949U03J5br+l0Hb422efsG+3Orhcnp5Vy+uG3rxrG5uV1bRmLhUGZ5Jeh1L6YX5hbnfKIr4HI5FLs83kZLnpmdp4RapmlbdioZCYaiK1UZYaazLWbomtvtzuXrs/OLe0bXaJT519++sH7NwFNf/uxLh1/v7R/Z/b4PrJx/t7W8+LNfv/5fh69/+5tfu/vgrW89/X/OnjrtOPLm0a6jF2Y+fPf7Ax7+xddeiwXDhy9MxDp6v/qXXzLl2pmTJyuVkm7IhmpuWL8uHm2zbWoSq1gsyUpLFAW/3+f1euqN1vjUXD6XTbZFgh4JA4fH0HHsekNxHIoZzu3z8oLoEAowDgSDgiDKzeZKZoXlOJ7jqzVZ9Plv3ruH5/HU1OSly2Onzlz586f+bscdt/3u6f/zb9/9yf17Nt+9e8O56bl8qeQVWMMwJMnrknwM5gXeVa1VJ+cmN27cMDjYn0mvrK6s2I6NEKjJLct0RIF3CCUU2ATCj+zoZTDCGGEMAf0fGECEMctzCCEIIYAQUIoxhhBSSgkhEEJCKaSUQwzGDCGAOIQCB0AHAAdDhhckiFgCUblSNA2N57DAcm5J8gUCoVC4pSgQIcchxDYtw0aIFHP5YqHa05MSeVelWnN73A1dIZa596YdAKOp+emA15PNFpYWVr1+byAgiS7RNgnDcpJH4ljWdmxKAQRQV7SJ2SVZdR68a3dU4t4+d2Xr1q1tMV+hWB3o7q6WchOz04pp3HvffWq9dfrMOS7gF1kkQevy+HIq1XH/Ix9UNL28nCWQKq3WxQtjVbm1ZqB7eGRgeWn5zaMn3jo/Va0qf//EXVs3bOwcGJ6Ynjxz5nxXW+Dq+CRmmLtv3cWxEoa00WheHxsHEDEMLpZKjm37gwGel3Ll8s4tm4b7e89cuWoRpl5qGLoe83szlebpycU9O7feccf+c1PjbbG29WvXHXr5ufTk4v9+/niL9757+kTvwHB+7NTfP/UUduoj/bEjxy/u2L7LMcwLly57fcGVeu0LX/jiHX/y50q99Mvv/72cWcC2fnUy7Y9Fu7raeMxZusVwLCcItVqdZXEg4Bc4DiNcLldWs7lGo+71eNwu0TB0x3FUzaAEhCPRYCjUaNYJAV6vzzQNhKBDbdumgiAoqunxBPfftrdWKxw9eohhuXK5ml7JzeVb1UbjfXs33X7T1qWVxSvXrvskTywWy+YrckOXPL62tnBPbwJC68TZa44NU6kEtZ1mvaGqim2bum1TAAEhFECHAMuh8LGdfRhDBG+gAFBAAQMgZlleFBBChBAAIUKIZRgAgGVZtm07joMQYhjMYw4C5NgEAECobdu6QyyIuIDX53VLlmXl8oWGLJu2HfQH/H6/aZqEOsn29mQyAQDN5fOlYo3nsK4orYYquTiRd3Es7wv4KkrD0NRt6zdawL46cV3iWcu08/mqz+dNxELt7YmO9q6x6ZlitaLrBgI0HAzzLAeJE/F7HdMuV+tLC+lsvbZh4/rN29cRwHokV6WaefoPrzim89X/9WS1qR4+cXq4p0PimTfeOn726qLNu/7mb79w287RyZOnLWolIsH3Tl7VNO39B2+2BNdPf/2c3qyYDn395Pidu9d++YnHMctemJiwbLR72/rx81fVpuoNibliJRryaq1WfjUPCbQBqdSr4WjE5fLkVou5amV0zcDB/bccP3e+JmvQsB0bBIKBdLZY1+wNm9fZgNRkjYdQwPTi5fNy3Xz+2NQTf/EXf/uNbyCM333l/371C5+/a+9mw9RWFoqbR0dXSzlVLrZFwg3Dvm3v7rW7drn88d//8lekng97XVPzubqqxWI+DKFtk77+AVXTz5475/V4uru6gn5vNBhw+wJLy5lr16+1FNXjdgsCb1tWsykzDBeOxDierzUaLlEM+P2mpauqUigVCoWSIIiiyy0I0tDgQEuuX7583udzx+MxCmk6V3F53Jv7u/PZ5uHTl3wiHOxMORis5ouOjTo7OoNBXzgiAWieuzTTaOpuSUAUqq2WorYIcRwIWJaFABqGZZg2AQg+tqMXQgAhAIBAACAECCCe50XJxTCMbduO40CEEISEEIQQAMC2bAABpIBjGI7lHYeapmnZpuOYtmNbFIocH/R6eI6pVKrlWo0iBlAqSRLHMuFgIBFvo4AQx5F1S262/B6XpuqNumxbukdwJRNJluMqck3TtGQk5g36cqUCJTaLGVOzlJbMCYwoil5PoFRvGrYtsKxtmQiCge6uSDB08vz5yZnFZq21tFpz8YSw+POf+0g4GkuEoo1S4Se//r+i29Obio6sGUKcmIwFsytL3/7hb5ENbUsfGu2998Cuhbn85fHpoAiadT1TbX3l04/5Y8Hj5871JWJGrXTm+mysPdXZ2zs9P7t2aGD7uvXZeiU7vRjAOCeXEcPEk23lYpV1CKTQ7fNcmZqqG+Z9d97ZaOo/+t+/6EglHv7A3UvphXKl5GJFx0Km4xDMVmSF9/CY4cNubzWbVzTV7/MsLa2kdc/3f/t7ly/AIPjdb339red+/eSjdxcKhVquumGoe6VQMC0rFvaXVGs5V2xpjZDX1xlPDPR3Oo6xmilCx8IQy5pVk+uCS8pkstVabfv2m/p6exVFMUzLMExDVy1dZxnEYQQBLNcaiGUxL4aDUZ7js7nVSrXc1hYLhcKVcjGTXSk3Wm7JnWxrayhN3bB0RVNbcjgSMi1d082gL+YNeG1Tm56YbCpKyB+MeAOUoyzPen0Bt9tnGpqjG4VicXxuQXK5PG4XpQADSBzbtG3LoYhhMMbZXJ44wOV2w8d29BLiAEAhpAzGHMcihDHDcCyLMaaU2rYN/4hSijHmWZY4xDRNx3EEgRcEwbJsWVGbcgsjhuN4m9iUEJZlfR4PAqDRrHMMozsOpQ4DYTQUdAlCo9GwLYcRXV6vx+dzS5K7XK5kM2mBwT5fQHK7NVVTVVUQxWAwSBwHYxQO+ASOyWVX63ILcoKs6RDAjkRbLByrtdSW1gh7XYsLq//+zFuqTVM8Y2vQzSGPh7tt/4ZAPNTd1cVw/GqlqqjKxEx614Z1Az0xWVWIYVwdWzw3MX3bvpsCHoGz6FsnLr597FLYzcTD7vNL9YcObNuzfaMJMQfZ3770+rYNAzyPo6kux7AdpXVtcv6VM1cEam/oTdQM9a69+27Zsfnd0xcTfk/A62mLhV44dPTY+NKPf/m0Pxz+x7/84ti5U/fu3QYc0Gipbo/HcmzbJpLXTzGqN5oMwyZjMZbScqns9bjTq8XzK5Uvfevf127aDizzyvE3jr/+nJfHlmXkyhWBY1vNWqo92RFPyJXG+Mz8UrkaDkr9qYQJ6Gq+pLWc9cMDHq80v5QuFwuUAse2/X5fd19fMtlmU1irNwu5VbnZEFgc8HpYjDXVbLU0A9sGoRFfm18KmI5+5vJZl8tz5769tWp9pVTk3SKDGEwgz6C6ZdZrjcFUIhTzv/PeKWKj9YPDrZZSqVUzKyu2ZVBiI4jauzo7UglZM3NlWcBAYsji8spCrpQI+4eHBhqKockNS1UNB4kuV0szkIA1Ra2XZQYD+Imbhx3HdhyLEMKxmOd4hmUQQgzDQAgppeB/QAgBhJC5AWHLNCmlDINvgBA1m01/MCRJ7lwubxhme3uCZzlVUxmEeZazLFO/wXYc2xJFzrGseq2mqirLMJJLgJwo+QOpWERAjmFo2UJNlhvxWJQCgBASeN4wTEqpW+ACAR/D8vV6vSHLvMAT4Lhc3HB3f75cb+haT6p9PrP6xltHK0U5X21igakYyDTt3Slfm8+lcvjW3WtTXYNr99wuuf3TV69rxazPQw6fPnNlbGn/9u1r1/V2jo428pX5y5dOnj2TXlxiEIYu19pN60a6UhrgU+29s5Mrz7z07Gc/fPDyzPI9H7zTauo/+eXvdFUta8b2kb7VYv2nr1+87+a1//LVT716+LiuqJuGBwUWFarVPOHu/vTfBMPtE2df+OenvnL39iEJShDw7qhnOrO8sLzk5sQ13QMUAMU0ay1FFESPP2Doyspi+vT4bMfwlj//6t9OXb04cfVcdmkm4HMjjBrNljcQUGS1sz2xed265cX5Zr3C8WxmNXd9er7akltNs9Rg6o7zvju2d8ZCM9PTyDEDbsnrFjwed7Kjw+X2NGW1VMgXi3ni2D6vn2NZy3QQwI6tCBwq5MqGjbfs3EUgMG3b5/NevXKVUMqLrkKp0t3dtWvbdoPaIstWcvkzF89PzMzcctPuzWs25PKruqMWKvnJ6RnTJF1d/dFoKBkLzM4vziwsB7xuU5WXsgVZM9evX3vHgdub9dr1a9dX0quCKPX0dq2s5lRDA7ZTztdYkYFP3LoBAEKIY5o6pIDnOcxCluEYhqGUEkLgHyGEKKUQQgZh4jgsx2GEbNu6QZZbwVDY4/VVq3UE0UBfp1tyZ1dztmVhhHTV0HXDojbHMcGAr16rrGZWWY6FENkApVLtqfbEajbbaDQ7kglECaAEI2hZNqEEAkAopYQgDAkA9aZaa7Yc6sTCvnVrBl2SVGpo166OxfzSmrX9iBVfefElo6WNbtqy667bro8trEzPLUwtvXZ5WqfOVx++bcPaYU9nn0uSohJPNPnUlQtHT1+ZnJk+sHPH5z75ybGVNLRINb/y1huHdFUf6k3uO3jX2lv3nT92rKVo+3Zsv3Jh7MiRw4rSIBh86AN3Pf/q8edfO3LXrvVbt27afevtzZb5D//03WJh+d++/RWR8R9+491gkDeR3deemJwvVPjI5//iry8defWZn/9oS3dM5IRwpC3a3lauVoqFUr5YdPs8brenWCxm8zWPL9LWmbJMNeZxz6/kZuczG9evB4jMTE82m43+gb5oPCZJnvUbRqevj2PA8qJ4bWLMsvSQx7WaXqG2psnVc5OFCyvGzoOb/+pLX14/vH18cuzwKy/UMvM8cgJBPxZdCCFom7Isa5qKEOY4nmV4ACEglsjhfKl8aXwGIO6BD9470t87t5Qem51rNOqd8WSjrs4sLW/atuWe993FsUBi8PFjp09cuMyL3L23315t1iemphziNOWWpii9PV29vb3FWt0rMGPXrixlsgLP6oaBXX5eFHzBaH9vl8TAw0ePZVZLo+tG1m4YOXb89Ep6UUAsdFjGzcIn9o9iBiEELcughHAch9H/AAACCG5AEAFIAaC2bUMIMcIQAAiAbduWZUDMUEoN0zIMnWF5URBZlmF5Qdd0TBA1LV3XORcX9vIeX4hgtlErVQpZACgnSJzAt7Ult2za6PG6zp09Xy1Xu1MRt9tdqtSaLUUzLAiAS+Q5BrskwTDN5dVCUzMsy+5Mtu27ZY+AyHvnrk2trK4f7Bod7ICs8KOf/IoxjQ/d97B7zZZLY7O37Lgpmer/zx9+/wc//8Vj+9Y+du9+i5csXanUakP9g28cOTY9M7VtpLe3syvZv+7C+JRA7YCbnZqa0aoW7/Luvn0vK6CXXnkrk6985rF7das1NT3VaLWG+nvD4dh7Zy4tLSxTjKRw20ef+BxmmWd/+oPFmalb9u7evXn7tfMXllbmCnKVAQgjsanS9p4OR1VdmJpak2Fx0OtHgkBsFI0ELMdaylY1w6w2ao4J46mULxwClr1uoKtULR09dam/s8slMK+/827Q7+3u6XSH2zZvWu9m0fMvvTaQSpXK9WuLaRcDw153XWnYul1rtn53ZHzXbTf/6pmfev2DmtYktcLSlUsXz59slPIeyVWqVgxd5TAljsMihtwAAMDIpmAmvZrJlgc7OoIR/2w60xaKjAz2BdxiOBLULceyHOo4uUL+yviUQ5nt2zbraiubybq93lAgsHvHTRMLM8++9KpjgfZgqL+/M9IWqdfk5ZUcMZrZYj5XrAf93o8/9nD3wODVqelGrTrY014pFt84dNyizC17bukZ6HvhldfOnjrjcwkIUCkQgp/cu57jMMIIAEIoxRAxGFNKwR9hjBFCDnEIdRzbBgCwCPM8TwhRWoplmxBhCqhl2xzHUQpt20EMFlmOAdAhJJ1eadTriWg0GvT5QmGP31+rlJr1siAILc2QVZXBQjyZCrf5tWbLzQkIObJS13RTUTVRFH1+HwKAZZDPFyDEUZVWIh6X3N6mrOTK1VQyYulgZjnj83Ejg92HTp46c/JqyOO5lq30rN315JOfu/Pee5Dogrb+0Qcfmzr/+rM//FuHwrdOXv7Vy8e8HHnwjl0dydhgqsu0TJWQpqrJ1YapaBwLexOpsfnMSrnKEWd6br7QKD3x+Iei7UnNsN2c2zEpw3Gvv3PIVOrtkWCpbjhC4J1zY9gs37ptOBGNB/3BlqJmi4XZhRkGkN5UyuMSFMPweoOJaHJ+acFwzM5UYmk5PzGR7h/ueeih+9LZwutvHFq7aYOq2ZeuXmoLhyXEDvWm5Eb17Plr7nAknc/XanIs6E/EY5R19Xa1F0rln/32hYcO7IgFvKuVRsjjDgb8VaWpa1oy2T5VbHz0c1/sXbNBbdT++2c/zE3Pre3qhhyV6xVL0wxVszQDQoIQEnjBti1N1xVNq8mNQk3Zf+f9N++/zefhTr737ovPPtMVi7Ql2wEEcrPl8XlV3Ww0moVCsVarB0NBSghGoLOzIxaNcRzf1OTVQjkZaw94JI3qq6sZvSzfkK3V8s1WSzP3bdt6/z13rVQr77x3MhXzSQycGJuRVdI3uG7zxq0j6/rHpiZe/sMrSrPKICB4AvCTe9exLIYIYgwpoIAAQCm6ASIKKMYYQkgcGwAKAKCEYIQi4QiltFgsaoYGKIAIIYwhgo5NEcYeyc0hQCzTsPS5hUVV0QXM2wR3dSY6klFVabZ0EzOsphvlltwRT1IAL8/MxkPB9X29sq7Mp5dcPMszcN2aIZcolqt1m1CXeINAbSsUCsYT7dVma2xyprsj7ue5Wq3V35+ggPznL38jUWQ5fHL7wY985s8T3d2OZlg6Yf3Cv3zjO7/79++88dt/YkXue9/7oQi09s7ITXt2+P1tpkJKxSLLOYn2eDZbrhSbPIM8PvfVqfH5pZzSMFvN2i17N45uHEn0DFLTOvTOmWK22tke+83Lr68USrduHemMJ6fmVw6duz7cE92/fW04HBZYgeelptqamp2MBn1DnR0QOS5RKlea+UrDLbkBpJlCRq4p6aUyFt1f/KvP8R7uP/71p/fcd//w5q2/f/bZxbEryDF6OrosRfe6hf6u1JtHTiq6Fg37GobZv3YkFg2+dvjEiTPXnnzofTwCq9mKILAMy0IEi82aDZgdt++989E/IYSpLs585fOftxV1x4ZRMSipSktEjKUZelNBCGm26QBaq9WJ47AQWRSWVfrRT39h/4MfA4BOnnvzW1/9cpDj3BKfLxQRZkPhCACIQowggI7ZbDYc4mi6xrKc2+2LxdrCsTDB1OWSBF6ampsrrC7vGFljEufnz73Vu37D459+cmRow+Ujbz/1zb+fXiz5eWawI4Chw3Dc8MjI8EBvNBqdX85cvng55POIHCcbFvzkLesQBhBBhMANEEAIALwBQQgghAAASClhEMQYU0JZhgkGg6Zp1mo1yzQsxwEAchxn2xYhQBQlgREJtCnUdblRKdUz+XpJtk0LjnRHNw112MQu1BXMsAwGJrF3b93qC4RPXLka8ri6YuGmpp27dh06dnd7dKi3u1arFcpVB2KRZ+LxNoYXLQJ4XiCOzbMMJHBybNwhzo7t623Tfu7lN3vagm53YNPdD2774BPg/+fkS8//xec/8e2vP5mIRf/5n37cUlSV0A1r+m7bsw1jWC1X5JbCcVy5XN68YUPY51tIZ/OtNtdgAAAgAElEQVTlnKbrpXwtGgrcedvNLdOyWMbLmpWamk4XOUqeeePkS2dn+6Li7g29nTF/rtQADnAo8EV8N2/bHPO5KSD5akVrGZZpuTxuYNs9/d2KqWfTmVatWVda0YA0Ob54dS77+Kce3XPrru98+2cIez7ypx8/feJUZm482eaTVWN8fDoRkiiglyeXeQZ2JiP5qrxz146Dt+87dvRwo6H4PK6WbjRlW+RZrdUIuaXr6fyxy+O37LvpS3/7Vx29W4+++sw//OVf79s8OtzXN5tdVtRWT3snsqlSbzSaTdlQ3X6PorYc08YUWRbMNw1POPbwJ5/cfddt5bmLv/nB9xbmFl0YOI6DEAKANWwSisQgJZLIy41GsVwyiY0wKyt6PBbvbE/pjEV51NHW0aq16tVSVzxSqjdefufij3/xi+5NewAASrZw4OAt58am++LBVFCgto4wSiaTI2uGbeJMz80363I44EMAVJsq/JObRyACGCOEwA0IQgggxggiACEEN1BEKUGAIoQghBhjnuf1P4KUWpZNHIIZ1uNxCwKn64aiOxARFjosROWyfGFioe5AFrPrOkP7tgwbtjM2lyaE+EQmGAom2jtkXYnEIgODA6ah1cql02fOWYYVCwV9Hg8hjkMdizqOTd0uKR6PIgRVVW01FZbhlldy1VIlEQsNDHabDn3n3ePb+1OaQn59fnrdre8TvBFCbOgYfsRLxPzpf//skTt2PfD+3T99+vXnT093d/U/ce+em7f2C9i+dvnyc68c4lkUD4fWrt+4cdPaQrGIiOkQRGw7FvADxNUNPV3Mt4f9iDLFRiPk85w+cX5idr7Rsn1ebtPosCB62zpTgOEWM4W+tqgAbbfHxSF2fnahpin9fZ2VcinV1Y1Z9tChdxRFTSXbPawzdn3u0nR6/+27Hrz/vlcPX3jn+Fm3111uNNf19t5zy573Ll384W+e83Eiy0LdVDtjwVgkVq0326KR23Zt97jEQqV5dXpy05a1Lo5fWc5V5CqDUWeqe3BkTUvVK6o6OLrxhWefLy3M7r1pA7LB+NS0gyHmsGOaPp/fckC1VA56PS4R15t1RSGG4QQjgXB7t2zY3QPduaX51bkZrdHAADGQNDSt3DLrDa0zmRAYSEw94vcRYqu2STmeONAxKC8KPr9foXq8LeZ1ifVGnbHp4nL29NjUZ77wVw8/+YXVfPafv/nNuZnJhx99eKArdebIm8cPvd5sVEXJHYy2MRAxCGiqYugGQNi0AfzTW9ZCBCAEGCMAKaQQI4QZhDGilAIAKIEAAEodCABGCGPM87xlmoZuEOKYpgUARAhLksvv92MMy7VytdnEDJ9qS8U8wZWFpWwhy4hMMhro7WyvtrTZdFa3SCjgi0R8FrFrjXp3T097e7uhtghxrl0ZK+QLXo8bIxyOBN0ul6ZrECCEkc/nqZRL9AYClJYGKfHyPETQ5fNILnF+fs5NnfMzpSPjBQ0AC4D+UGB04/rzM9O8UYx5PWv6hv7uqU9Mzy9Rf+fam/dJIcFYnmjNzZ0/c+rZP7zW4Q963YLC8vv370eANpuNdK4SlFwBt+u9c5dGN61JJJJXLk02GirHgaE1vYVsvp4tnZ1Nn57Kf/yunT4/U2Hc9916K0bs9asT18euipz1gdtuEQX+/OSUo5uKLEseL+Y4x6G1Wl1TNUh1aNrXZ5ZjPR1f+9uvHjt++ennXuJEHPF79mza1NHe8daJU6++e2YpW+2PiImAKEmejq6ulqyVyhW/z+cPhlayeb9fevyjH+Qw8/sX3qzqxvBwv6mqCHE+X7DYrG/esc1Wtezi3NVLF4mmeySXBYlBKaR4y9ZNPX29Z0+cn5qY9nlEhqWmYxu609XRG4oHx6ZnZmeXkAM74+FQwLWwvIxZiQ9GGxbZsWOHG5DiyvK1q1cdXU8lY6zANVSNxZzcUDTHSKW6lvMFpaX2ppIQmjwi1Wrr2IUxg3F/8skvn790bWpq4sc//8+t23cAANLXz/7j3z1VLmQVTYEsl4xGfR6pXinLrSbGHGR4+Mlb1kFEIUYci23bdhybwxxmEGYRpRRQQBxgWRYBjiAIHMNQhwgcDyiwLUszdNM0eV6gFBDH9gd8As8XatVSvd7Z2b1+YCQRDFVL5WvjYzZRIUIUQIBQOBKRJBcEgEUUQIAwu5LJUOJ4JXcqkVB1c2xy0nAsCmk4FAj5fI5hOjZhRS7o95bKRbfLzbHc8nIGAuKCQLcpldw9yZijNzIrxVpZWVyYJQgaNrt+ZMuBO++xRfz0r79/01Cqrat94+bNsgW7+rqrgJerulYtcGZtfnLi1XdOuhi0pjtRV82RjRv7OhLlhvL0C2+ERTYS8L13eWbnlrW37Bx999RlhhHdAk2mUpeuzy3ML4Z8vitzC4+9f3+rpf7w+aMfuOPmndtG/S7P9NTU3OJc1OcdHRqoN8pe9w0ezLGKqhmmJcutYqm8uJoNsSBfrGgu9wMPPrg0v3r42EnM0zv37EyE214/cTpbKNiqyghSWyzSkmVZNUUWMZZOHVvyhTDDVeqNlqnt27fDK3mWF1c7+nq2bN387onTzz3/Mk/5jv7uDz3+4a5o4I3nXz5x/IwX0c72tr7hwZbtrBZrG0Y3Dvb3T41Pzc1OEkeFkJoOYiAHKZUVWdFVAJz2eJzHbKulNQx9995t/QODobZeKdUNmpWzR9/+6c9+o9TrWzeuF90igLSvq396au7awuL+W281iD12/ZqAsFyvOYqKGTSbzo2vVnfuONDfPrBx9033fewBAIDRavzqx/92+vAbPZ0JVW0hlsMMm8lkDLXFsgxErEUg/NO9IxACACjDYEopIQRDwHIshBAACAC0TNuyLEnkPV4vIY5p6AxmyA2OQwmxbYdlBUIBYrBpmqqua5adTERu3r6FZ4WZucX5xbRDbLeIiUMV2wyF/WsGhiXJ06iVK+UqAKhaq6XTK7zAA0ghsfv7B9ojERaDSqNGAQ76ggAQRVMkSRJd4uLSIiVEEqVyuaJpuqYoktsV7+xsj8ca5ZIu6+OzS78+dlngWFFgcw3tiXs/+MjD7/vJf/2HZigfuuvW4aHufF0r5QsL03NT16bbuzq6uuLRoNsf9i4uL6h1PSAFRtYPCl5hNVf6/SvvvG/ntqhLevnE+Wh7/K6DuxYXs6VyLeD1pzp7soVVw9Tkulyq5pORUFDyXxkbDyZSJgCbRoZNw/jDq4cQtTatHSlVygQ4EFJCGUEUQ0G/qVv1arnWrFuyUqw2dAZt2bwpGowtZgoWsjYM9F2bWvzBM695WDzal7z9zjtMk4yPTZiatro8D6je0xH3ujyRcKBUa12fX9mxZ+uagb7MUkHye7ft3K7I8pE3Dy8uLLn8PoBoQJIgdFZXVqhGfGH3jj07MHZfG5+rqUokGPNLfKte0lo1iIADGI5lOQ5Wq7IsK5QSt+TFSASAbt86EomGT188v/Pg7f0j67RC8+U3Dn31W/+WSvo/cNueerG2Wmq+79bd1KGTq+WP/cnHIt2d186evnrqxLWrVwdSCVNrPv3aibyMPnr/Q8NDa944+d4Djz60fvu2b/393x1/7ZX1vdG+zkQ4HGR4vlyp57MFCChmGNN2TIvAP927BvwPAiFkGJbBEACKMKaUUAdAiACgPC8EvD4EUaNZd4jNcaxlW47tEMvBmOVFiVCq6rrjEMyggN/b152SRNf0wvLYxAywzaCbJxRYBiU8jrfHfJ6gA5hQQEK2NjW9mFkpsSyMtflL5WKlKg/0dW8eGfS63ZPLq5rh7N661SPi5dU0y3II41KpxDCMx+NZXJjXFENtyQODvZs2byoUK6V8Ke73nbt05dmjY0O90c89cvuLh483ge8vP/+n3/iHf7Rt6/OPP+wLBSZnZp579S1EQGcwhDkcCLhHR9fGw9HMStaBltctMYTleMHQ9OfeOhSO+Nwsmlle2blp3dCaQeowDmFFKXRlYooX4fYNw9VaczFfLGZXuuPRlq6bhr28mgt4PeFQ8MKlsb7eRGeq89DR98rlougWo6FER0fH0GBfrVjJLC8YjhWMxOotWVVqg+1Jj+CSIWooRkcicX5i6rv//XJ/W9uu0cGB3hFGFGuNmiXLC4szCyuLGDoigsFwlGA2Hk+MDI24XOzs8szl6blkqvvegwdr+dzc1ARkACvwHGI5ninVm4oMPaKrqzNOKcgXsqu5vGISBuNKqQiIiYDt80mhSFRy+4GhFkvlQl13IL9l0+jIQM/y8sqh906/d2UsEo1v2Lj5g488unPPrYeef+6FZ36+Z+NajuN+8PSzYS/34L33VmRjeMOWOx/6qN3Iv/rsL158/oVkNCbXrcl06e733/PgB+/97e+f+8f/+GHI7xMFUZWrOzcPdiVD9WrVsDABAgNtjkWEUFXTTZsSCuAn941QCiAkCEGMGYbBCAIIoeM4lFKMEYDQJYgCK8qtlmnqoiggjAxN001T0W2EGJZhA8EAz7EQUJdLdEsu29QLhVK1qdZlhSWWiB1e9HjcISCwGzaPSILn4tWxaNjb2x5+6533ZhfzO7dt3rV13fXr19957wzL4fcf2BOORJ995Wit0bznwG43z+q6Lop8NpfXdC0YCHZ2dhiaUa41Al5x/85txUrzt384JPD8nTvWEVsvluuCy8Eu/5ELk5Nz2Sc//OEXD729YbRr18Y15aZJbOtXv3ux3qgd2DFiWI6iw80btiTCoUajpNlavl6dXVx1I2lNV6pUqZTkBsNQDGmqLV5syB5/iGNc1XL99PXrnlDk8fvvikaCr7x9bG5hPhYK8AwbD0rVZguxLhaDUqEcb4/Lis4iiBlabbbCvsCu3Te3FGX22rVCqXhpcuoDBw4E3eLVmcnZdC69UhY9/p6OxPbRUYHHL739btAXcDh0YWJ27/atbaGI3FSnlpbeOPZeUzMxRD0J/3137uyMJxtVc345e236eqnZMm3zEx979P233Z5NLx868qahqwwAwWCE8QZYxo11kIpHNVIvF3KWrKUScZ7j05lMOleQDcsdCNqUQGC7Waob+qXp9Ice/fjt+24+duiNV157rZrLUAtdX1UOPvTRb37nnyPRMFTKP/r2132CcN+dB378y18WVlc/fNf7MtXydKZy8/4DhtV69umf5ZaXedE/snPv/R/7VHuq2+8Pff2pP/vNT384nApKIuf1+iTRxWDGME0HQMwyGCEIoWGYlmXbBNoOgZ/cNwL+B8EYI4QgABBSjBlAKYAUAAAB4HgeU9xsyAgBr99LiNNsNiv1ugM5ySXxLNfX0xsOB4ulvEMcithKbtXDY8smimkrLcWx9HAwGApEWZ+wYfO6RqV+4dJVn1vwC9LU0opBwX133jPc0TM5PXn49LF8sfTxh+5ZM9jz1uFzhWIhFJQqVdnjliQXq2pqvV73er1Dg4OhYEg1W7li0TTR5UvXy4Xilo3relJtwVAAQ3ZpdWVhLj00MiR5Q5G2yLXL57q7ox3dPTwWeQTmZubeOvquTe2oPxJwSX5/sNJo5sulm7ZsZhk6vbi4tFrggLVz/Qavy2sR3TC1K1OLl6aXBIFt84ealZIrGro4ldm1dcuTj97z7tETxUoBM/y5KzO379lcKBaXs5Vtm9dhhzR0bSVfDfvcO7ZvyZSqyDQV1Th26tSt2zZxgvi9X/9u27qhjz5w99Ezp8+cv5ZK9RQaraG+3u3r1lWKacO2gAM0yFYbWk9Xp+UQREhTbpy+cC4QCnEsl0rEU9FYLpNezqYvTcwVS3IqGsyWc4n2zv/1qU+lUslXXn/j7SNHEMICAz98z4FYKPLSa+841GGBtbxcWKmooXhstL8r7EICtju6exYrrXSu6GFYudWaX86MbNz0pb/46ulzZ3/wve9Rudru51pyTWHC3/3vF7vWbQUApK+/9+Wv/BmizD998bMixqfOXrJNUzdaNdPyBLxzC4sn3jsRcPOBSOzxJ7609/5PAAAWZ6/+6F++df7kyaHhwYjXVchmLNuEkHAMK3A8hNByoG5YtmWzHOtQaFoO/PTtax3bAQDgGxACgFICEQIII0iJbdkcx4qiCAE2DYMCIgi8bpmVWt0wjZHhodv33daotmbnForVUstSPT4J2rBcKhHLtE2TEIIo0HUNcpzL4+Yl4QboEGg6PGaararDCJu37rz9lr3nz185d/GSh4OUGvtv2QUpmFnMpNoT4aD70vXxcqUWDQYgcGxLDwT8hmkLkivUFrk2PnX50jgwzbU9HV0dyVK9uX50TbmhvvLOyf544OBtN1u8P72cLuYXDGLce+B2FsBrUzOQwlpD1Q21XK92xSMcI5w4P1avt26/ZZtPxI5NNF27PDbVkYhvXTNgWOTc2Ey5Uo6HXAAx+WJ9Jp2xEEyvVMVA6FP33ZatVdPZfEsmL52+9JWP3r9usPvK9cupRJxhEMRcqdKsN5T169balmOoajqzcOrK1U88cI/Act/92dO9/V1Pff5jZy9ffvvQkSce+oBKhdPXF0cGklOz6fG5heFUMuB2c5IPYs5EJJGMqXJjfmrx1l03jYz0Ty6uFFYqWrN2beai6ThBd3JwcFi17QsXr3W1Rz/w/tt11Thx+sT18clavfn5TzyyODP3k1//odpUVpqmqQEXAFkANAhEBFNe5rZta9f3dhcq9c5UUhC4k2Oz9z/04MhQ33f+7XtGqzXSHr5y7drpyaXpIjl454GHHn7YJfJHX33m+LFjIsvu3rp5oKt77PqErhmhkDsY8ASjgUa9funSFeAgw3BYb9v6bVvbkrG5qWmRxR6vt62jnRLl9Inj+UxG5DiB4wmlhmnrFrEcwgDAcbxNHN2w4GcObHAcGxACEYUQohsgopRCCCihAFBRdGGELRsgBBgMKSCqptabMsewD9z7gV03733jyLuHjx41Da09Fulsb88WC0vptKFpLl6AEFq6ziAUj8XC0ZhuO4bjcAiLDFMrl/4f8uA7ys6zMBD+8zxvb7f3mbnTq/qoy6pWsQEbN8BgiilGa7JOyGYhWQeSrLEhhBo6C1immGKDjbvkKstWsbpGI42mz9y5c+/cXt9ennfFp3M4Obtkw37n+wPO9/stlJccRMQT7d1dPRNXJubnpuPRYDwSWrN8IFcojc+l49HI6mX9imZOz0wHPDwAOOj3QAjHJyclr29F/7JSrjA9N8OxhFdkCsVyw3DfdsNenuF/+9wrXj+/btv6to7eyuzihXOXfvCbJ//m4+/ySOLUTLohq8GgePs791VKNccl/f6ArsrjY5dmpucatSbPsKFgsNZoFKvVTcPLooHwz554RZb1vRsGZnK5C+PThu3U1FpfW8xwYWtrtyRKz716kmPFnr7Q5lWDawZ7ZENfKtQAJFwHag2FQmTDtlgCQsdCwHBYVpLEK2OTb5680N3Xt214MJPLfffxlwKh8NbhZf3dfd3diX/8xiMXL85+cOfagY7402cu5RpKezTcFguEAqGxuSy0tWW9bWvWDAHbKhTLpVq1LdK2omd168Ag62Enz5+5PDa6Ynm/beFcYen0mVM86922aeOzT7349OFzacdaNTxEOlS2WF2+Yvn++/6Cp/gXnn7hBz/6zkCUSPokyR9et3YlZr233HZrs5z90pe+zBHOlvUrnzt85vJMYbGsZZuqhwU9UaFFkjw0kDgKspKOUb1aDvvEUDjqQiSI3FXZbKZSroiioFlWVVVdSIb90ZXLVvsD4Vhbm+ABbx07cvLYcZogWZa1bduyMSJIiBDAGLvAdlxNN+DHdy6HEEMAIHQJBAmE3N8BwHUxBIFgkGU5XbdkxQAu5lgKIVCtVZqKTEBi3ephmhPOjIzU6vVwwLtqoC8eDV+Zm5+ZmSERwZCUbduOaYoi1xoPC6JvbCqNCSoQCPo9vOsYFU2xTQM7aCqVdQy1IxawMQ76fRuGlzdUfXR2oVat9LUk4vE2XVdpwuFomqFQU26ml5Y8Pv9QX8/87FxqMSeyjNaskTQpBKKxSCgaDJ0+Oyqrzc3bNvb196oN7fHfPv/asVMfe+8tlqY0FL1Rr5m29aG73uW6kIYUgG6+mp9PL4xdmNQamotwPBgL+sSRhdSWjWuWtSe//j9+RSG8dvmKAy++SQNtc2/LdL7MS56AIPiD4bXLl2fLFUu3fAFxYmrGcnBPdwfHkvF4bGpmZimTYxmOF4TOWHhhqRwKe12aslxQXsyWS4VYolXkhGKlcezy7FJNa4357/3I+3o6Ova9/z6k6Ku7EqzAmcAaz+YLRaXVI+6+bn3T0mYW5lOFajQofezOdxQL5XS2bBp2eyJGMBzHs0uLmWyh0DvYp6h2rVIQRTbA8xGRefzg8d+cmt574/YXXnjSsJBhQ5pgWIYDV7lg5MLJYy8+c+noG8t7Wnt7O2cLzTvec6ferD74hS8cO34KUsJtt9788Q9/MJ3Jf/Xb358YPbMiGYwF/aphhUJhnqUNTVVNGyMSA+jx+SXJa1qmYcj1ehkBx8tztCDKpuOYrlzTZN1q6epsaY9NToxVC8WA1ysJomnZmm6omkKTJITIBQAD1Gyq8J7tgwhBCAEAmCYJiqQcx3bBVRAgIp5IUBStqjrDcDzPqkqzVMrXajUIgQsgQrQLoG3ZEGKSRD3dHYIgzKbShWKeZzmWZkzDABjTJOmRpFK9mSlW+vqG+nq6NL3e3dtBEaQkioMrV81MpV5/8aBfoIvlcms82t3dUW3K+Vq9XCyJNNvZ1uGXpFwxv7iUc2wdY4MASLdsfygoeTyVUq1WLDME6Gxv8fi88ZDXK7KPPf/y6QvjG4eHB5d3y5ZVKdfbk8k1K1ddPn92IZt3TC0SDgNEnzh5rjUcbW0NT8xcMS3sF72kCzKlksjz7fHIhZn5W+68Bev2977xyK7hvraWtp8cPEIQkAfQJmyfx+uQnOQR921bx/HMkRPnJmcXbcvxcBxJ01PzqaG+5EBnVNb0Qq2p1eWA5C019Eg8xkm85BUjfk8xk/Z5A/F4m66qAsdIPt/CUiHdqAd9wTdfP8GR9MRcutyU79v/gcEVHd/57oHxyXyx1li9qvOD77ohm8196ds//fgH33fd6uGfP/70xNREICg2FX3ZQE9PZ0dDMYKRsKwokldIRONH3jrW29aGa+jer32P8nof/s7X97ztVuhCkuVM07J0gxUFAsHMpZM///oXu5MBkmZfOH7mtve+f92Kld/8+r8ePXkScfzDP3m0fflmAMCV48//5F8fIl1sOa4kesLBAACuKiuqpgGIBNEXjUTbkkmMnUujF3RNISlYrTVVw3CAa9uWrpmNpkKxnBjwqqpq6FYkFBYlCbgucEG1VsKOjQiKohlZ1WXVgPdsH0QIIgQBxAREJEEgiBBFIESSJEWznCIrjabs9XgTiZhlG5VySVUVXdMJiABAJEkA6GLs+HzeYDCkG7rt4HAopOt6rVotFvKWaVJXMUxFVkSfvyPZkQhHDENLtLdyLE1TdCQcyi8tjY6OUBCRALS3RmmGzuYLNMd0tnfNTE7XK1WEien0IiIJSaAoGiGMUtliKBLdvX07TaHF1LxH5DiacB1zcmauVK8LFOYQnaqpc4XyLddft2Z5X1U2Eq1t2DJT6RyJ3PbWyPHzIwdfeT0gBtYM9WFdS80vsRIverhMZonzUAzF2lD8wKf+avLUmR9+7bsRL+OYrmO7DuEUavXWeJRCbLZa6+ru3rtt7cilK6++dSEgiT6R41hmYTF/Yiw12Bm9+7Y9OjaL1WYgGNINS2nILfEWn9eDEBYE/vSF0emFHEmxiWi4u6O9Vm/IpuOVvOlsqlIu0IhtmA5g0L7tW7cPr3nxlcPf/8UzU+nclrW9937o9rnZuUce/+1993z01hvf8dzzL09OXGpqciqzFA2F1q1d7fX5G005lVoMRWPxlvilscttfT08Ej7/0LfPpjJhr9i3rJ8g0Ac/fM977/ogyXKOWv7aV7/99OM/TnjcsOjBGC2Wip19ffu2bjcb1bfOnrySK777Ax9/94fukRvNf/3ig7MXT/QnoxTBMhTFcwwCwDAM07Y03Whojs/nW758WcDnfeP11y3TiCcSuqFXqhXbNhrNhqZrLobLl61Yv3HD5PTszPwCxwmu62qKDLBt2pbtODZ2XUhW63WaE+A92wcRAgghkkIEgC4EjgsxxhAimuYwxqqmWZZFkITXI3kkkUSo2WxqikqRFHZNx3YQgURRjEYiPr/ftEzTtBmGsS2zWq0uLqYdx5E8EsGS7R0dW6/bdvnKlfHxqYA3GIsk5nNZCrgiRahqwxfyCbw0PzUHsNne2dZUFUVV/T5fJV9wTLPSNBpNeXh4RVNp1utyrlBpSbRet2mDJPAkgcbHLoX8Ho/Ez89OPvHSkaYObtyyZuPyHk23Xz5xjmOIfbu22C42dNOFJHChosiSKLokPn/5EjDwhrWrQ/5gOlVYXMoXC2ng2hXbXiiW33vzHTfecdOpV499/P4vigTulrhkNEgLxHylITFc0BssKc1oOHjdxnVnLo0vZHIDHQmI3csT02en0mGR3bC8I9rezvFSIu7bsGZ5Ple4PDbjAIpk+EZDa8iNTGYuly/KmjnQ3d3eGpubT1G8tGnV8JnxK2evXOmKtjqEmyrl+lqS73nHba+dHHn06edXrRhY0dtSLuXHp+dkRbnvQ+/raWtL5SsImQhiRdE01QAEoijSsJxKWVM0c8OmtRPTk6+eOD28dtvmjbue+NVj3//N4wiANgAwSe667ZZQR8vcxUvH3nyjvyO2prPNGw5BEtm2xTM0x9Itibgqq6NT8/Op9Ps/8KHOjvbnnn5SlyvYMiie5xgauq5jmQxNUxRVrTdmsiWGYSPhEEkQwMUEAqLA25ZVKOQgdAF0642mpho9PX3rN2zIZJdqDVnXzUqlhKAb8vssxymVKk1V0XTLdJxYvBXes30QIUBcRSISQgyAZmNFUREkEvGEIPDYAYoi2+FMeh4AACAASURBVFgjIKQpkkAECQiv5IEIlcoFy7IYlqVp2rIsAhGiKBkuKJfLjuNABDVVRQjygoAIsjUR37RueDY9nc5l9+3a42OkF15+I5tfogk75OVXDg1hQDz21DMUIocGB1mBL1eLqdkZnyQGPJ6Z+fmuzi7G7zdJYdeuGx//xc/WdLf5Rb6tu4uhyEOHnqdIODTYk19a/OUzh7ANb9u320ZEKp3BjWap0Yh3tOzdtr5Yqj198LUVg/00zVQbzc3DKyOBUKle5zy0Zam2illAVKvl+Xy1aRltbbFNK1f5OPHrjzz1xUcPbQt7AhxZxdrKjngTm7yf5xBTqmvt7Yn2jo5fPHvYK7LXD/fPp7NX5pegY0a8rDcQSLTE2xLBUlMnKHZsbJKgqJbW1kgsVG/UCsWCrepyUyvXGn5JvH7rZgxBrlRrSwQwJE6dn8gsLFEIpXKFsubsvGH3Le++i2E9Y+eOqI38+PjsuRMjrgs+8K53dLcnR6bnbKzwDMUgKpmMVmqVckVhWIEkqPmFtIOhauKZYu3LP/xxe2uX0Wjcf//9jz388CqSypvmOcsM8OCOnat2LRsiXVioV9t7+0zC4RgmnyucuniJl7w97d2NanV8bGz5YP/N77y5Wi2fPHlqIb0gej1eySPX66oiswxtmbaq6yQvDQ4NQeBm0mmOY0mScGwb20DXdew6pmU1m02W43hetAFgOV7VzVw+DyFwsUURQOJFXdVkVa/WmzTLJds74cd2DhHodyBwEQCQRCRJYexGo/HB/kGO46rVytTUZK1RBcC1LBs7jt/j9YiSbVt1pYkggRBhWZama5Zp0zRLUqRl2+FwOBQKuNjRDc22bdNyXAKRBCk3Gjawd27ZvKF32Xxq4fTF85quGqYeicVUVR2fmFwx0Nvd3c17fUvl0uTEpNJs1KsVL89v27bl9UtT7/rwf1m36x0nDv3qwqsvWLXazTftLVZyx46dEASqv6eFJ+knn391Yj7z7hv3trbEXnzzJNYMn8RAgrn55utrlvvIo89ct37QKwi5Yr0n2dGZiKYXF0uVYr1R1xRVFPlYNOb1BpIdrRRPnjs9QkHmmz9/8eSl1CBDFV07FqRXJMOdXS2YBHPpar7SvGnH2u6ezi/8j8d3bF6zd+3gtx59qqkobR6x6ZiKaawc7JZ8YiVXT+crhy/N3XH99jWDSZ2GZy5PDcQjIknygjg+PVuuN99z69v8knD60qTP7+tOhIvl2uiVqfnp1MT8YlUnbrzjtk/90+dpwf+lh/5Ozc0OJdtnZyYXs6UP3H7Hpj2707nCc088Xi7nU9nM6qHBG3dsKhRK9YaRzVdrjaYg0KVK89TI9D99/evDW68HALz82yfv/sD7gaq3h7hIgLn1ph03vPPmY28cT8+nLc1SbcwHOI8kzE8uNDSjs70t6PWm8rnCUhFAJAYCDrBIjD00SRGEVxJtbMuyXK4309kczXG33XTzxk0bi+XyiRMnUqmUJHkYhrFsh6YYjHG1UrVsmyRIiBAjcoiiFEVvNGXbtJuNmq7Ifd0dHlGoN+RsrkgxXKI1CT+xd5XruuB3MHYcmiYpmuF5YdmyFQInLi0tlcqlWrUKIdZNQ9d14AKWZRmSgsAlSHSVpukYuwQiXNc1TcsBDkGgYCgQDocDAT/HsQBARVEr9ZLWrAc9Pt0B4zOpoDfsYWjHUAGJ6lozFAr5BU+1URUFMZls8Yd9JIUcB8xMLUxPppb1JYv1aqoJ/ss/fy8UihaWFh68727ObOzasr5aq6iammxNKM1qen5+MVtwKPqG3ds2r1hx9NTIyZFzXYkodMC6javKsnr87Ng9H7mrmC8vZjLIxrphzM/N27ZFkaRtWwC4LfF4KBDoaIs3LON7v3h+Q39/Olf87q+PtAtI4Kn1K9ociNckh2YLxaNX5nwB3wfftt7nE4+dGVnW05mIxJ577Xg+nxN5MRSJKIZSUVXXNPpi4fOpigWpf7jvo4ffPPrcG6fPjy9sWt69uifm8XiWSlVZUTatXekRxTdPnS/UGn7JM9jdsmXjmsPHLn77l08HfYGf//bp5PKN59868rMffrOnvWXlwCAy6oVKVZLCkPGzNJOeGBUF9+UTJ948MfKZ++7pjMdIklqqyxMzM+2hQL3SODUyFu/r++gn/xqo9s9/+uPLly+WF2f9rN3eGud5LybZbKmCHcNy7FytHgsJyVDcy/oi4Ugmm67LzbppNhpqXVYhQbS0xAOSYChN7Jgcy0TCoaYsz6cz2WJxzfD6O267GUF47sKF8+fP26YlCmIwECJo3nZMRa1i16IoLr9UqddUy7HC0eCylYMkhc6dPV8r1klAdbXGWZFJl4qL+aLASZFgBP7FvtWu62LXBa4DMKYYkqIoCFEwGHFdlC8UdMMQOF5gSFlVNF1HJMFSDAQuTVChsB9CmMvlbNtBCDm2oxumiR0IIcPQkiR2dnZEomHs4Fy+fOLcaRLgG7ZuS7Ymj5x8a2p2xiP6+1rbFV2ZLabbIrGNK9eo2MyV6wxJhvxSJOgPBf0YuKnM4txs+ujpi+GW7vu/8hVBDHzzoQdnR47u2bysXq64Fg6HfP29/YvZwvGTx3TD4ETfri3r+juTo1MLp86caY0GS7VqR2crRfLzxawo+ubmC/1dibVDgzTLHT92tFQq0SSBHUySRKneNAG5b/N1yfaWK+nJ7mi8Wdc+/ZWfzOVKa3taexKBkqqs7Bow1HpNrm3avLq3K3lpcu6JV06aqrx5sCuTyc0V6n3dHbfecnuwJXrx7Jszl8cPHhtDXPD+v/jYXCH7g1/8dlWEC0vkSCa/aUU/QbOL+TJPgraWhGaj2dk5H0c2FJyuNvbt2nL9prWzpeqzr52MRxLB1raXXjzUHwts3bie8vrnZ+Ya1eLolYnxmcVEItGX7Lp+Q1++sPjZbz8ZjQf/5i8+/M5b3taUtSOHjxx56SVbbwb9XptkaMF7w5Ydvcmk5BXOnz725ok3q5otCFxXaxRY9olzY+PpfGdn6/XbVkHINOumxFFyo6YZ7qqVw5giKk2VhISladMzk/MLsyJHBQN+7DiIQLKiVhvyth07h4Z60+mFqalpXdMpgmrW6izNdPb0utAxTE1u1jXdIggms7hUa9QDoeCKVSsCQf/01PT01LShGLFoEJHQxK6sGhTJ+D1+eO+elQgR+CrHRsglSIRIEgLkYsgwnK4bstwUJQEBLCsyxpgXeJEXCIR4jouGwqZpzc3NGYbhui7G2HGw7QBeECKRkGXbBEIsy4QjUVlWT42eE3l+z6btksSdOHuiXq0lEslt6zeb2Pz1waf8orht/aaSXAeU65Uktaktzi9iC7d3tBMEOn76HEFxHM0G41FI0JfPnLpubf/gQPKV195IhGNd7S1dXV2VmvL4E0+WarUt6zbuXL++aeiXZ+bnZyY5hlQds7e7g+clE9kTk7OqhlcOdrfHY4Fg4M0jb2TSaZ/kCQf9Qa9nJr14+NyVd73jln03XF+uV068eTQRFinTvjA+728JNZtNvxjzCxJF2YajAddySYqm0HOHT6Wz5RXJRF21R+dSydbEfR+9t601NnLq5SsTqe8fOv3h/ff9xV/95YGf/Pie//xfh0LiUMLTnojsum750Qvjo1dmNq1Z1tXR1qg3z5654ONYiqCfOTmRM9x37Vz7d/feZdLeJ549+Ngrr47OVG5c03Lrvg2BQGhhoVBYKsxl87ve+e6u/qGvfOFfWkT35g2rzkzknnjz9PV7dvzzf7/f6xXPnT7z6COPyMUMCS3JH3EJioDUug3rbOjMpBYsCwd9QiwcIjE+c+5iPle+8x13RKORaKv41MuHf/iLZwbbI0GfwEjBndu3Da3Z6PHFFyZna5lMOOCbTc8dO31U0art7YmwP0gSfLmh8F5fICwUC7l8Nuv3+nRVm52d1VS9s7ezs6PDBcR8asHRVa9XmlvIprKFQCjW09+XyWRt06SQbTRKBMt6fP6gz6dq1lKuzHu88OO7lhEEBVzoYJMkXAABJAiaYkiCikRiBIS5XEbVVFnTXIx93qs8iEAUSRKIwBjIslKv1RCEruMyLEMShGNj5neoWr2u6abk8YqiRxJYxGCWpHvaeouN8usn3qwUG8u6u9935+2maT/30vOiyLckEqValSAhQ9KqrpVKFbmhCqwQD0f6hvo6uzsXZlKvvfyy65g8TwkiF45F0ouLHMXFo1GOZ6+MT506fcXB9m1v2zXY2XPkrTMLuazPR4uCyPP8yqEBDF1OEBBJxGJRx3auXLrMMfTl8alMrsgzbFci2hUPVer1x18/8/Y9O/bsWFcuyYvZnEvCanapWCxt3bUeE0yhqCxm0g62atVmvSlbptGfjMdCQU3TvR6R4sT5xUXLhoO9A47SOD869usj58TO5b949iBJCxg7D3//Ky898u2euLRl7QrNJb72k4PIMO7ct55h6XK5pjeaXpaRJM+xy3PHpguKaW1f3rZz3eC+Hbsohnvh8OF6oxYLBwJez9T4+OmxOexv/eHjz/gjLWdeP/jzb31FcPTu9qRLstliraM9SXMgW1yYnZqpFwokcGORuCB4S7WaPxbt7u+vNRUCwJZ4uFmqZmZnnzp6MhzrePQHPxKWDwDXOP368+dPnY76QocOvX70rbPv2rvt3e/7MHap1w+93Orx7d26y7Hg2PTE6ORpIUxBhipVmiRBJxIJG9qp2almveaVJNMyNd0wbWxhHImEiqV6NpdvC0kMiTJlhZK8d33knuUbtk6Oj4yeOJybm5iZngoF4wIvmZadLeSr9UZrRwf8yPZ+ElEQIkQAmoIAuAQBESIJRPsDIQRBpVpSFEXVdAiBJHkRgrKsiKLg8/k1TW00GgRBAAhcB7MM6wKgqTqC0AUugMi2HQgIUZSi8VgwKFYKRdNwImF/U65NTs9LgtDb29VsyhA44UhIURTTMEgCWZZJkCTNsM2mjDGQJLG3p98XCI+cO6PVixLpGpYNWG+yK8azsFJqSJLPcfCbJ04fO3aRQHDjqu540FesNQxgSwKNABkOxYZXDGEEZV13ESQIwsUuNtTWWHhyZuH4qfP5QjGZiA11JRuyWmmqy1d0Hx6dyRXqf/P+O6rF/Dd/9pvlfa17tqyVIpGnX3mzdahn07ZNx5995eRbI3pdifu9N92w3eMla4qm6RbQNMOwab/Hw7OXLk//6PlTBV06+MKvBtZvB7b77I++PX/m1WYzG4xFsYWffPVMT9y3oSd6ejI3Olfoivt7E3E/S3Unw/P58lg65zraYrkmE/7d64avW9YqiezsYvnAc6+dvjzftMC267b96rHHPNEQQRDPfPerzz75KIQ4HvJFgv6R8VkCoutWDTiOdXrkkqpooaCf5xmelzp7+ts6um0Hj49POY5TqeTSC/MnJxcvLspbVq9+257rb33vnQPD6wBAAIBTLzz+zS9+ebCj42P77x6duPzLX/0K2sTOTbveffv72HhkaXbs0POPzy+m6roaDHoSkbCtu8V8nkCuR/KUa5WGLIej8daO7pb2jvGxy089/UzAww10JK6kipt23/DJf/oiQAIAYHH05e9/5cHDxy4mYqHejk4AmezSoqI3+oaG4Ed3DEIXAgBZluZ5CkIAIUIEZdsYuNAwDVlt2rZFAkRSFMfxXp/PdSHH8xzHVivFZqMBADQNAwBAU5TrurppeTwSQVIsw87OpTTNuPcT9/UuHzr64vNjly7JmrasryvZEs+X64VSQbN1kePjgQDD0dVGQ601GIq0sO0iGI3FItFYU5aXlpYsC9imWcwvBkN+r9frmJZAkx1trYqsLixkgiE/K9LVhnruwpV8rswj7GEB6xFtiH2SwNFiNJb0+f3ppcWmqljYQSRhGIaXFztaEiQEmcV0qVzJ5EqqiU3bufPW3SSATx08sWPHlnXDK48fO/PLgy+vXdY2PNjRcPH07NK267dvfdvu8ddOXHjrwmI6V5W1det627sjFy7NzE0tMBRUbCccCK1Z1uOYxqHDp45cSK3dvPYfHvrnzPTC5z/3uZiHTSb8/V1tyUTL6NQUS7nNuvLKyQlN095/+566bv3syUNbV/VtWTO4sJB21GpFNl84uzBZdW5c0/XAJ+6aylbu/+bPFdMuycqG4TW/fvKpRHvyuV//5vP/9Om1LbRjaZD1hANewzE12RroHEi2BidmJlILeeCaPEMahrlixRrJE8EkoVrm4uzsXDr10unJQFvys5/575VK+V/++bNeIfSp//TxZSuXyapWmp/Kzc1n84WNm4ZjbS3f+tEjo2PT29ZvuP3tb+/q6Yak/eJLz01NTHoFDyLdhlInLJJEBEFAAN1sPleoVlYNr9uxc8/WG27WqoVPffrT2fTCLXs2v3h8BIuhf7z/s4nkgK+trTB38qt//8lDr5wmGeGu974rFPa/8cax8cmZcCwGP7a93wUuQRAMw9A07TgOdlyWYQAALgSO41iOadmWoZvAxh2J5L69+wiayRbyC5nMzMyki22aRoaqIYBIisEYqaYVDvhCAdEGzuj4hMD7/vZv/yHQknz2Fz/Jp6YBhBgRgZCPowlDNwzTYmna5xE4nq1WKoV8nmYpmqEZjo/HE62trcVSeX5h3rGwIjcatWowFGAZ2jZ0mkSOZmSy5UpTDkcjVUVNJIJ37L4+NZ56+cTxqiaTEAY8vlgiwrJ0wOtLZwunRq+U5Oqu7cPbNm44+MZbZknxsrzf7+nvbaUokJpfKlWVSMR7w67Njz790mtHznzynrv7Vw5llkrp9IKu1AGyGs2GbaPZTLYlFuuNtRqy6bK0vy1GkmB8Ymx05KIf0X6vRwe4v6/HFwiStnn23PlcsZQt6i7LaKZDQ3LvxuHW9taQQJGsePzSRYYk6uXm8WMn164Y+vDH7xwdn/ncl3+gNNWdq1cIFNNQapZVqlZq4xVSCoT+8ZMfyVerV2azd95+x6tvvq4T3k9+7qtqPnf3Bz/4m1deubEvtHe4v6NnEDFU72DbzNzCzNTiYF+frisVpXZhdLxargZYXmSZaFtSDMcjsRBt1t86e+aJ1y/945e/dcddHwEATF88cuedd4+Np1Z1+7virREfJ0AgmzbHidt3bLWxPj4x5vMFRc5TrdQRiTiRK+SXeJ7F2FhaWiQwI3IeAG3VbGZylWpD3/f2G2677Q7LJl9/8fkXXz8ci8Y+cMc7f3PwhSMnR67ftnP79Xs3b995+NCTP/vRt5aqNUSL73n3nUG/Z3Zy/OhbF6bm0/Aj23qp/4fruizLOhgrsuo4tsBxiURC9EiVaqlWb9SbitqUQ/7QyhWrKIbN5gskTanNaqNeRghrikISVMAfcmxoYZdhWFnXC/Vavlxoj8du2LFLEIPTM1OVak5Rm41G0y95gqJIkASAgCAImiFZhjVN/SqSIEgCUSzDMKzP69dNQ1VV2zRIBCzTIglEIOgRBdfFl8euOBjUG9We9mRra8fY3NTyvsFkMDqTXSjW6l6CXdHXn6uXJuanVw10VKuN545PrXzbTTffelPMz/zwX78x+dapvmQMuHjbjo2haLjRtGPhUK1STM8uZKqFnK4Tur577cZIOEpI9Ew606hVIpLvmedfms3mOZpsbY16BXHv3j1tW9c7pvbUwz+RMJJIZjKToWlu8/q1Dcuq10tTEzOkZXtZdGVuUtGJ9vZlm9YOA7/vwuWLuirXZSUuComA9JuDr2aqyvXbN/AUce7cqKEaPo7jaIqgkaxVKQIu5pXOFUOf/Zd/HLk4dfLVt7bs2L6QKZy5MO4NBBdSqR//9lkBwXdt7L1118ZLC8WxdGawu11VzEy5eMcNeyMBvyUQ2NIPPv9CtdjEthuPhVevXwV4duTi+MT47FQqc+M7bv3I33zaEwimLr31s+9+a/bSaHci5vP6FnI5x4UMzZumQ9NkT2+rZWmOAyUpuJQvAgAHBvoIBJrNmqLVi4UCclmKZF3X1Ay5WJURLd71wbtYXvrFzx8fPXfK5/PcsHfP++64+dDLL/3o10/uffvt73/v+/NL+c8+8GAmNfvuneu27r5eJiTLAa5We+nQwUuTc/BjO/opikIIQQhFUbBtXKnWLcvwez093T2BQCBXWKrVqpZh1ZsN08Y0w4UjEYpkSIQIAlQrxXq9gi2TZfmQP2JaDiOKyEX5QkV38XxmPtkS2bJumCF508acyEFH15u1xcWsotmSQNE0oigGYxe7gKJoiqIQcAkEXQQxdkmScgFwIQCWQRHIdhyAXZIkBJ4rFAozU9M8Tzd0Y/PaTbs3bT42evHi2JXhvh5/yCdbFtTttliL7lpz6dmhzs7zoxfrLvOfPvMFEOiYO3/0B//yQIeXjAd805nShu2bl6/oX5hJhcOxXz/zwuj41Gf/6oNtydjFixPH3xwZm1sIBflVg92DXd3tA4OKQ85emTx78mhb2KeaZt3UN6xf59DUyOlzG3qGao3amZFRlhN8Hl63ncHeznpdKSxm5Vq5oZRKDZxrwP0feE+it+PFI0cCHBfz8ZalXx6fnZldypTzFrb9HM9gzBEE55FIliCAY5g2z/P5it69evivH7z/ytjlH3zpa6mF0lSqVGlohg0EHogc3RX3333Lrkg08NgLbyCW74gFJq/MZyuVtSsHN65bV1fllrawrStjFy5VizVZNjp7uiKdHRdGZ1LzmUqxCC0LSt72jmQ5P0dYeldYpBHFCKyFCRNRpombDUUUOcnLeDw8dqCmOobtaJpG01RbexuEoFLJy7LsOpTAexxHq9QKjaYZiXd86u/+6/zc5Be/9q80dkK+UDAW37J5+PLo2InT53bccMM9n/jEpZFL/+1T9xeyud3bNt/4zlu88aQoeZ797WMPH3gEMjy8Z+cAAABjTNM0z/OWbSuKCoHL87wkSgBCGzsBn9e1tKVCwcGu1x/o7u6hKXphPu1g1zT1arlkmSZN0RTFmJbN8BxLUkP9/YnW+OiVyxa2uzs7XQcZjutgx0MTnfFovpQ7d2nMtXWWIiBBRqIxCIh8qQQR4hkKOxgDCBBEAAEIAIDAsV0Xu47jutjn87Ice/bsOUdttEQCmaZJM4E9a9dVNW1s6srGFZ0OBV9561x/Z/f1WzYs5XMNRVm9avXRY6feOnFmzYYNyO89fPCg3Wi0hcK8xBZrjZ37duiA+OGPHrttz3Uxn4A56qa926cupx76zi+fOTUREMBHtw6SPOzu6QvFk+Gufko3KaVaqZQOHz1e0WQvK+zdvStdKhTLDQo5wFQAARqqZhjEluE1AFjlQm58plBTa7ToyRQad992U0tbbCG71B4LlTPzNR0/ffAo6TgYqCVdl1Xc7fdIFGqYFsMzGFgGhhHGH4nGYwMd+96+b+TihRd+/QSN6FKlSjF0QzcZVgh7/e1d7fGu1nyxcuXi5Dv37KVo++ChQ4qiNXVneHhdS1tsYmFOblQEFyjVZlk2CI5fu2HN4OCAYRjPPftcanJebmo0CcMRH8WQDOkCSCEIKUhhRHKiyHMCQK6NTYhcRdNV2QwHwwzHarqm6hpEwDQ1Q9c5RoiGY7qhlko5TbcdQO3Zcz12lNeOvGHbTtAbARRLspRSl9VGsz3ZMrB8+YkzZw+/9qrjAD7Q+on7/vodt94Bbe273/jKV7/7A5ei4T07+wEApmkRBPJ4vCRFAuBatuMCiDFQNY0g6YDXQ9NY1/TcUo5h2YHBIY/HWy5XkAtNw9Q1zdJNF0FZUXRTBxDwHNvZ2dHR3t5s1q9iaMrCFCIYWa0l21o5mgZmvVEt5ZeKNMf4ff5ILG7Zdr5QsmyLpShsY8txAEGyNANdF7jAMnXHMUiSpAmUTCYJmnrp5Ve8AtndGjMoieKDy9o6p6ZnFvOLrVHvWxcuVBR10/BQd0t0YanS1MDyZUNLCwupmRlI4cnZdKNRD3m8qzv7unvaDl+6cuu79tGI/Oo3Hn3PbTu8JKPoRmd77PFnX3nj5Hihbsd9/M6V7TrWgsEIRTCTM5kNK4dWdrWfH5tcLC9ZAMfD4V3b188uZl89/BZDOuGAhyaJXFP1+2Orlw/kc3O0Ax2CZThu+dBAvVZdXMrpltNUNEmkgelSiB+5NJYpF4PhgIdnC4tLnG1GQpHFYqOpaj4fa2NNtqGJ4b533HDT7bedPHHsycd+7RUkjqFoihEFL897PIGAquvlanluseja7kfvvLVUqx589ZBtKPWasXrVmk0b18wuLly5Mp5LZxmKtoErBLzXbdvV072sWqmfPfcWwFphKVsrlrwejmFY27IRQbqAsBw3EAwJLJtobak1arlCXjetRlOGCEUDIYqmaI4lSKJareYLeew4HS1tgWCg2mjUqjXsOrIiG4apGqYkiKYL2pPtQclXqdfqlQrjOmVNuzibXsiUSEgS0O3qTj74wOeDbT2n3zr65GM/O3tpjCAJuP/6ZQAA27Zd1xUEgeN5y7EVVTMMAwLg9/mj0RhNU7oml8vler0GIYpFo36/T9MM1wUkQi52AXYbcrNWr1uObbuAQNBx7HAoKPCs62DTMmuq6WHEdasGu3o7ZxfmETDr+UqukGc4xu/3S6JH1wxdN1zXJUmEsavpBkHRDE27DgYusG2VQK7HI9EkwbAsdt3TZ85alhaNhN7xzlt7Nu9484lDx08d7++ILi7l0un0js3rLMedmMvRrNDeEqNcJ5teBMBwsTozNUsRJMtLHfGW/u6uokV5I5LPL+g2Znny5UNvELbTEhTfPHlu24Z1Kwa6KmU5tZQ3TRWbpiiK2VJ9WU9PWyR09vK4S8HFXH6wr7OnozVbqM6m0tViwXVIDMmGo7xt79bWkH9+MaeotqwoyWgs6PXNLaYuTkw0FU1v6gzLdSSiAsdokJzO5oOipycSeur1E+Pp9PKWoEiTFU0LSD6CdBXDrMva9h273vfRj42fv/DqoUMsAARDYoBYQQrHEr5AYHxsbCmTXsrVEHJ3lan4VwAAIABJREFU797UUM2ZqQUPyxZLBZeEG9auJgi0mF7MLGYQgrZp+cKhodUbg/5ulhUVNSd58NzM+MWzo6ZuCIJA/Q7tALB85ar2ju7DR44sLS3xAsvQtIsdw7AYhvV4PY7jGJbFsIxpmoqiIIg4huM41tB1VVVJkhQEzsb2fGrRsbGBcUdHZ2ssUaiUKsWiSFGGbdbkZldHF3TR1Oy8AeHm665bzJXHJ65cGp9WdMvHQ/jxXcsAAI6DEYKSKLI8X6vVqtWq49giL2zevCUSCS8uZgrlsiLLDrZNQ6cIRNGUrusQEKIgQAAphEzHNm2r0Ww6DuY5VuR5keeqlYpHEiORyLkrM4uZ3No1vb0drWFfGCPn9IVLwLU4hmRolqUYbGMAIMewALqGaQmi6AuGVEUp5PIkQSDkIOTSFEkiiAjouCCVWmgoMsfy/f1dgUjrE08doZD97pu2Ly0tabq6ordTx5RigYGenvlUWlEVXVHmU3O1Qpa0oUfiNFejJY8khrq7B1zCJTlu9fDK/GJ65OyFiMCmFpfOjF3+23s/lhzonTk3NpWaqdZlvdnwSryGSYpANIHKqiUJTLUuhwNcb0eyVFWbmkxBhDgSmxgit62tZWoyXZbVjtaYphkQwdpV1RpF0JlCaTaV9nmFjtZWnmFbElGGYyGiquXGjw8elQ3cIrl+Qpd4iWBFP0tR0JFNk+A8A8tX2xjnlxawYyAMCJJ1Kbq1q9My7SujF/Vmw3VRU1MwSVYVkyfJbcOrM7nCyNRkX2/nYFdPJrOYzmUNyxAoprujKxRLaAb2eP2IAPOzU+VCniYpB9skSZAkYZim47rbd+5q7+o5O3Lx2aefFkXeK3kkgQPYpQg6GAsyLDeXStVqNUEQWJoFAGiq4Ti2wLIOtoHrxmNRr9+n6Obc9NxMeqGjo6utJTG/uIBcN+jxVOtVCO1tGzf6PP5TF0bnckXRJ5E0M5tKn7gwSTPccH8L/OiOAQAABIAgSIoiIYSqqiiK6mCXJMlwNGLb2LasgM9DUaTcbBia6mDHMC0AXZKgXey6LvBKnmAgwDOsLDcMx+B5XuQF0zQXFtIcy8bjiWypMjk15fGwmzaui0XCpXI5tZgWOYahKYagCEQ42IUuYBjWBa5tO6FIJBSONuq18sy8x3YZGmm6AbDNsQxBkABgx7aqjYauW6qiFetNVVWvW7tm546NxVLl3LkxRVFWLOuOxOKzqdLFK5PrVw8M9XadPXchlVl0TIsgoMAxLCcFvb7urkSl3nAh5Qn6a3LT0S0PTZ48fX42u7R22UDI559dzDWbDckrKVrDyzDLevsyubwGXAwJkaA8Ht60dOCCelPTDLm/t7Ojq01uaplsGUBydnaOpGA0FqzJVm9XRz6XTy1k+jvbW2KRdGZpZHwyFgz6vF5ZN7wiHwj4y3UFW+pAe1uhUDt68pTAsBwvyZpiWqbjGNFwKBFv1RyL4qmFTEGTVYaiWY4HiM4tLRHAoUiCY2ndMJbyJV7y8RwnsJwoSjPz8y3tLb3dnfml/FI2Y9gOQ7HYBS5wgWOyDOtCqOk6tnWPxDOs6GBHN0zTsiOxeF9/P4EI4OLzoxdURY4FAgyiGopS11SK5UVRKpaK9Vrd5/MiSFAUeZWh6QxFmaap6RpJ05LHK4pCrVrL5nI8L/E8azmOJAq2badSC67rcpxAkqTt2oFgiKJoyzEr1Wqh1IhEw+3tcfiRHb3gKuySJMkwjOu6sqKYhuU42LJtgiSwA7xeTzTstU3LMHQXu5qh2w4mScp1oW5YAACG5jiW8nqkgNcLKYAdzHJcsVSuVqvYdRFEELiiwAsC29vbLUn85OS4IiuiKLjYhRgQFIUgggARiIAEJAhS8noZhiMa6ttfvQz+fyscBl/6EhgYABMT4NOfBsUi+PMliiAWAyQJHAcsLQFZBn8c+OHtPQghCCBFkgzDYNdVNc0yLNtyHOxACAlEQYBjET/HsqqqNpuyapiqZgii5BEkTdV1y0QEusrrE8OhIENTpmlQNG2admYpWyqVPR5PLBgIBQMkCUkCdXS0T09PNpuyxysCAFzbhRABF5IESUASki6EiKApgqCkcuO20QK4Zvdu8NBD4IEHwKFD4P/K7t3goYfAAw+AQ4fAn6zdu8FDD4EHHgCHDoFrwmHws58BrxdcUy6Dj3wEFIvgzwtNg2QSvOc9YNs24POBa0olcOIEePxxsLAATBP8H8G7t/aRJAEhQghehTGGCBq6YegGhNB1HJ4X/V7JKwoESRqmWSpXZFmDkOjt6R3q680V8hdGL5IU0dXT6fN5bct0bEtWFNu2KZq2bNsFgOc4mqYcy4bAtW1DksR6tQYARBBTFEUShItd14UQEiRB2q5JECQkSAhQsKHddqkIrtqzBzz4ILjmwQfBCy+AP9KePeDBB8E1Dz4IXngB/Anaswc8+CC45sEHwQsvgKv+/u/BzTeDf+t73wM//Sn4M4IQ+OUvQTIJ/j3pNHjvewHG4N8H797aRxAESZLwKuQSBAEAUmVFN3SGZqCLIYQiz1IEQzCMbplNWUYA+kRPW6J1aGhwKbd0+MhhXuSS7W08x7nYQSRZLBXrjQbP87FoNBIK2pZVrlaaskJAaJm6izFJIJqmEXQJCBFB0DQNEOE4DknRGNgIkhAgjEFENm+/XAJXDQ2Bhx8G19x7LxgZAX+koSHw8MPgmnvvBSMj4E/Q0BB4+GFwzb33gpERcFU8Dh55BHi94JpyGXzsYyCfB39G7rsPvP/94P/sF78A3/oW+PfBu7f2IYQYhoEA6KZGUbRH9JAkqapqvV7H2KEpEmPHdYHX5+M53nFwsynTNO31+WVZyS5lXRdLksDzDE1RNE2pql4slRjud4IBf1tLArggm03rus6zrGOb2ME0iXieB8DFGAMABF4gadq0bAARSUPLsiEkCYLyVZp3jtfAVYOD4MABcM3+/WB0FPyRBgfBgQPgmv37wego+BM0OAgOHADX7N8PRkfBNS0t4AtfAH19YGoK3H8/yGTAn5HubvDoo+Aa2waHD4ODBwHGgCDA3r1g715AEOCaD3wAzMyAfwe8e2sfQSCKojHGstIALggHI5FohCCIfD5frpQQhC7ADMtEghEvLyIIi+VytdmwsGtbDoSAZRkEMIGAxPEkSViO01SaLM/TFM3SdGd7kiDQzNysZTsCx7nYwY5NQEDRNEkQLgAIIZqiEUG5ENiOAwgMCYJAlKYanZi85cISuGpwEBw4AK7Zvx+MjoI/0uAgOHAAXLN/PxgdBX+CBgfBgQPgmv37wego+D2SBLYNSBLYNvgzQtPgt78FgQC45t57wcgI+LdWrQLf/z64plYDt9wCTBP8IfBD1/WRJIKQcBzbdkzLsglEtrS0BAIBuSnn80uGoWPH5jg2FotJgmjpZqVSNUyTZujW1gTLspVKtZDPu9jxej0EgolEpFKtL2RyEBEURXW2t7AsWSzWDN1kGIogIEEg7DgQApIkGYahKRq7GGOACMIFAAOHuApSpm6v8oa2vX4JXDU4CA4cANfs3w9GR8EfaXAQHDgArtm/H4yOgj9Bg4PgwAFwzf79YHQU/Lm76y7wl38JrnnySfDlL4P/3ac+Be64A1zzne+ARx8Ffwj80HW9CCEAgHsVwBg7toW9Xq8gCJqmGboKAGBZ2uPx0DRdr9cVWcXYBS4gSUIQRb/f57qoUqkahu44NsNQ0YC3WmvUmrJlOaLIt7e1kgRUZcW2bIgAQSCSQIiADEMLoohd17Ztx8GIIAmSdF0XXgWAYzt+nz+i2ntOzICrBgfBgQPgmv37wcQESCYBSQIIgW2DVAqYJviDBgfBgQPgmv37wego+INoGiSTgCQBhMC2QSoFTBP8h2gaJJOAJAGEwLZBKgVME/yHaBokk4AkAYTAtkEqBbq7wYED4Jr9+8HoKPi/RdMgmQQkCSAEtg1SKWCa4D9E0yCZBCQJIAS2DVIpYJrg/xPvfS/45CfBVaYJ7roLZDLgfxePg1/9CtA0uOob3wC/+hX4Q+CHrusDLnYBgFchF0JIIApC6LoudhwIAcNQoshTFG0aRqVabzSbhm65AFiWQ/Oc1+ONRGLLhgZlRZmamuBYhoFurVGVVcU0jGRLaywUsXRT1VWSIgB0r+J5lqYpjmN4QarXG7V6jSBIlucRIlzsIkBA4NIUHBzsxbPZ6w5PgKsGB8GBA+Car34V3HUXiMfB7xUK4KGHwNmzAGPwvxgcBAcOgGv27wejo+B/gRAYHgaf+QyIxcDv5fPg858HZ88CjMEfhBAYHgaf+QyIxcDv5fPg858HZ88CjMEfhBAYHgaf+QyIxcDv5fPgpz8Fn/40uGb/fjA6Cn5v927w0EPggQfAoUPg39q9Gzz0EHjgAfDSS2DtWnD//SAeB79XKICHHgJnzwKMwR+EEBgeBp/5DIjFwO/l8+Dznwc+H/jc58ADD4BDh8D/a//wD+DtbwdX/U/q4DzczrqwF/33N7zTmtfaw9o7O9k7OyMrgYRBBpE65wrECVFUpEqUux4EBzw9Ulqw51xIua0em/aIE8OiKLaKtdpqkLZXWgQFkSlZQMi8s7PnYa29pnf8DXeR9548eRJAH3r/OZ/PSy/h05+G7+NUto2vfx0bNqDrwQdx2214JeTjb1onpdBam6bpJGyD8yCIPM9jjBmcA8qyLMPggNJKux13sVaPAglC8vl837JBJ5ncdPrms885Z3Jy6pe//A+304EI5udnvKAz0N83PLTCbwdhILjDIxF13FZPoeA4loZyHJsTY6G2GAShZVqmZWtoQigFtUwjYRuFnky498h7di2gq1RCpYLXVq9j2zbMzuJEpRIqFcTKZVSrOFGxiEoFhQJeUb2ObdswO4uTFIuoVFAo4BXV69i2DbOzOEmxiEoFhQJeW7mMahWxd74Tt92G2G234cEHEXvnO3HbbYi120il8IoOHsTVV0MInKRYRKWCQgGv7bbb8OCDeB1sG1//OjZsQNeDD+K22/BqvvQlXHopul58EddfD9/HKchVF60VQnBCLctyHIcQ4vl+6PuWZRmGIYTglFHGKIdtmYHnLc7NUY2kk1w2NJTu7V1qtvv6B9aetnFqcmrXs8/JKKAEYdDO51IrR5drhfnFhmE6pmVMTU+Gnr9yeLlpcVD4gR8FkZaaUWYZlmFYGlooqaENgxGtgsBbHuLyPQ10lUqoVPA77dmDT34SJyqVUKkgVi6jWsWJ7rkHGzbgNbgurrwSs7M40T33YMMGvAbXxZVXYnYWJ7rnHmzYgN+pXEa1itiGDbjnHsSuvRa7diG2YQPuuQe/jy9/GT/+MU5yzz3YsAG/07XXYtcuvA59fbjvPuTz6NqxAw88gFdzxRX4whfQtbSEj38c8/M4Bfnw+SsBGIwzShljIEREEQDLNAkhYRgSjS4zYQwUBygwPzfXbrSlUKlkykgmlxpN007m8z3tZiMK/Hw24zhMyMC27VTS8YPAtJ1MNttoNOfn51OJRC6TTiadju/OzM5wwlPJJGccGo6TBNEd19NKE6JEFEDLs/PFLU9OoOvCC/HVr+K4KMLXvoaDB6E11q7FZz4Dw0Cs2cTHP47ZWcRKJVQqiJXLqFYRKxZx333IZhGLItxxB/bvByFYtQqf/zw4R+x738MddyBWLOK++5DNIhZFuOMO7N8PQrBqFT7/eXCO2Pe+hzvuQKxYxH33IZtFLIpwxx3Yvx+EYNUqfO5zMAwcd+ONePRRxEolVCqIlcuoVhErlVCp4ERRhK99DQcPQmuMjOCP/gico2vnTmzfjuOKRdx3H7JZxKIId9yB/ftBCFatwuc+B8PAcTfeiEcfxevwvvfhppsQu+MOfO97eDUf/jBuuAGxHTvwwAM4BXn/mYOmZZqMSyG7DMPgnLMuSoUUvudrqaSSmXymv1iEUo16s9Vq+25ICLjDKDNBWBgEBtBXyObS6VCGzUZTSO04CaGEmTCHVgwqiWajkbCcYl+vH3hHJyY0gWNZjmUrqZTStuUQQoIoZKCaKMjISRglO/P2x4+i65pr8KlPIXb0KK6+Gq6L4xwHd9+NVasQu+suVCqIlUqoVBArl1GtIrZtG8plxA4dwjXXwPNw3Nq1uPtumCa6ZmdxxRUIQ3Rt24ZyGbFDh3DNNfA8HLd2Le6+G6aJrtlZXHEFwhBd27ahXEbs0CFccw08D8clErj3XgwPI/ad7+Cb30SsVEKlgli5jGoVsQsvxFe/iuP27cOnPw3XxXF/9me45BJ0LSzgox9Fu43Ytm0olxE7dAjXXAPPw3GJBO69F8PDiH3nO/jmN/E6bNyIu+9G7Prr8cwzeDWnn4677kLs2muxaxdOQT5w9nLbsikhSiqttWEYnFMCMEK11q7r+oFPGS0O9CWTyUazHXih1oQRahhMQ9qObVpGGAS+5yYdRynZarsyFFrDth3Kqet2ikODfYVCvV7v6+3NpDMTkxOe51mWxRgzGAcBIZQQAg2hJIGmWpmcpJL2amq//ckpdG3ciLvvRuymm/DIIzjJ6Cj+9m9hmujauRPbtyNWKqFSQaxcRrWK2C23YOtWdEURbr0Vk5M4yeWXY+tWxHbswAMPoOuWW7B1K7qiCLfeislJnOTyy7F1K2I7duCBB9B1yy3YuhVdYYirr8bhwzjJRRfhK19B7NprsWsXYqUSKhXEymVUq4hdcw0+9SnEjhzBRz6Ck1x1Fa6/HrG/+iv88IeI3XILtm5FVxji6qtx+DBOctFF+MpXELv2WuzahdehVEKlgti112LXLryajRtx992IlcuoVnEK8pELVjPOoEC0ppQyRgENrSl5mRSy47nJZGLVqpEoUrPz8yKSUKAEIBBRxDm3bUMpAQLTsnzPb7dajFDOGGdMKiWk7Onty+cyjLF8Pr+0tNRutymlSinTNAFQSjnnAJRSQghCFINmRJmcrWaJdz9fR1ephEoFsXIZ1SpOdcst2LoVXQsL+OhH0W6jq1RCpYJYuYxqFV2pFH70I2Qy+D1dey127UIqhR/9CJkMfk/XXotdu5BK4e//Hr296Nq5E9u341SlEioVxMplVKuIlUqoVBArl1GtIrZxI+6+G7GbbsIjj+Akp5+Ou+5C7Lrr8Oyz6Eql8Pd/j95edO3cie3bcapSCZUKYuUyqlW8DpddhhtvROwb38B3v4tX85GP4POfR2zHDjzwAE5BPnrhWkoIAK01IYQSwjiF0lpKwzAcx1lqNKD1wEC/H4Sdjmc7CSgVBX4kIiE0ZdQyOKU6lUqIULRb7UiGJueWYVJKlVKpdCaZTlJKTdNUSjWbTUqp1ppzbhiGUooQYhgGNJRWUkrLoEnHDDzXdTvDyv7DCYGuUgmVCmLlMqpVnOqqq3D99Yj91V/hhz9EV6mESgWxchnVKrq+8AVccQV+f9u3Y+dOfOELuOIK/P62b8fOnXjf+3DTTYh961u47z6cqlRCpYJYuYxqFbFSCZUKYuUyqlXESiVUKoiVy6hWcZJSCZUKYuUyqlV0ve99uOkmxL71Ldx3H05VKqFSQaxcRrWK12FwEJUKcjl0/c3f4Pvfx6v5yEfw+c+ja2kJV1+N2Vmcgnz0wrVKSgCEENM0LcvU0KHvU5CE41DG2u22EMLgTEhpGlYylYJWURBGUUgYSyQc02AG5wZn01PTgecbpsG5wQgYpZZtZ3M5TeB5HqXUMAwhhJRSa+04Dudca00I4ZwDiF4WOiZLJ61ms1GrLW1IFD4xqdBVKqFSQaxcRrWKU23ahG9/G7HrrsOzz6KrVEKlgli5jGoVXZs24dvfxu/p61/H978PIbBpE779bfyevv51fP/7EAIbN+LuuxH77Gfx1FM4VamESgWxchnVKmKlEioVxMplVKuIlUqoVBArl1Gt4iSlEioVxMplVKvo2rgRd9+N2Gc/i6eewqlKJVQqiJXLqFbxOtg2vvENlEroevBB3HYbXs2XvoRLL0XXiy/i+uvh+zgF+fD5q3AMpZQQmkjYhEIEESWEMiaiiBJCCYHWgCaEMkalVAAoJQnbdBzHMEzDMNrt9tTkpNKaGaaGZhQaSKdT2WxWSul3XMdxstlsFEVhGEopDcOgxzDGOGMgyvf8Lgqk0pbSUgn6hp7+LU9OoqtUQqWCWLmMahWnuuoqXH89Yn/91/jBD9BVKqFSQaxcRrWKrssuw403IvbP/4yf/QyviFLMz2NuDkKg67LLcOONiP3zP+NnP8MrohTz85ibgxDouuwy3HgjYnfeiXvvxalKJVQqiJXLqFYRK5VQqSBWLqNaRaxUQqWCWLmMahUnKZVQqSBWLqNaRddll+HGGxG7807cey9OVSqhUkGsXEa1itfnS1/CpZei68UXcf318H2cyrbxjW+gVELXgw/ittvwSsjl5wwzxiilhBBKqWkalBIoTQmRUoZhSCllhHDGOWeu60VRZBicEEoAxzIZY5xz0zR93/e6fM+PgnQ6lc/nOeeMMQBBEFAgn8slk0mtdRAEYRgCIIQwxrTWhBBGIKIoCkNNSSqVAIFpGKeZyS2/OYquUgmVCmLlMqpVnOqWW7B1K7oWF3HVVVhaQlephEoFsXIZ1Sq6cjncfz96etC1cye2b8fvI5fD/fejpwddO3di+3b8PnI53H8/enrQtXMntm/HqUolVCqIlcuoVhErlVCpIFYuo1pFrFRCpYJYuYxqFScplVCpIFYuo1pFVy6H++9HTw+6du7E9u04VamESgWxchnVKl6fK67AF76ArjDEFVdgdhanKhbxgx/AstC1YwceeACvhHzo3JWEEEopO0Z3QZncgFJaa865iCIppcEMSqnv+1EUUcqkFISQdMIxDEMpZVmWbdtBEDRbzVTKXrFiRSKRjKLI7biNRiOKZCabSmfSlBDTNF3X9X0fADU4AwnDkFOacmxGaafdaYc+YWYkBONyRPBPTQh0lUqoVBC78UY8+ihOsnIl7rsPpomunTuxfTtipRIqFcTKZVSriN1yC7ZuRVcY4hOfwNgYTtLXhy9/Gaedhi9+EY89htgtt2DrVnSFIT7xCYyN4SR9ffjyl3HaafjiF/HYY4jdcgu2bkVXGOITn8DYGE5y4YX46lcRK5dRrSJWKqFSQaxcRrWKWKmESgWxchnVKk5SKqFSQaxcRrWK2C23YOtWdIUhPvEJjI3hJBdeiK9+FbFyGdUqXp8rr8RnP4vYj3+ML38Zp/qv/xWXX47Y17+O++/HKyFXXLCaEQpoCkATpQShMA1Ta80YSyQSQggpBCNUiMj3Q3WMVMq27FwmzThzXTeRcLKZrJARpSSfzTiOHQRhrV53Pc/teLZjF4s9CcchlDHKFhcXoyhijJmWrbUK/MAy7Uwm5bruxMREKCPLTlgmNxgGXXldzUDXNdfgU59C7OhRXHUVwhDHmSbuvRerViF299245x7ESiVUKoiVy6hWEdu2DeUyYocP4+qrEYY4zjRx111Ytw6x22/HT3+Krm3bUC4jdvgwrr4aYYjjTBN33YV16xC7/Xb89Kfo2rYN5TJihw/j6qsRhjjONPHd72J4GLHvfAff/CZipRIqFcTKZVSriJVKqFQQK5dRreIkpRIqFcTKZVSriG3bhnIZscOHcfXVCEMcZ5r47ncxPIzYd76Db34Tr08igX/8R2SziJXLqFZxojPOwJ13Ira0hMsvh+viuL4+3HEHhocxOUk+dMEaDqK0hJREE0LAOGGME0IMw7BtW0pJCFFCBEHg+76UCi8jhmGk0knbtluthm3bA8UBxomUUistpfB8P4wiKSUAy7ZSjuVYjmGYYRi2223GGKXMNi2ldSQiwzAymUwkxcTE5GJtMZ1OFnJZEYSjmn9yWqHroovwla/guCjCjh3YuxeEYP163HADDAOxZhOf/CQmJxErlVCpIFYuo1pFbGgIlQoyGcSiCDt2YO9eEIL163HDDTAMxKamcN11mJ1F19AQKhVkMohFEXbswN69IATr1+OGG2AYiE1N4brrMDuLrqEhVCrIZBCLIuzYgb17QQjWr8cNN8AwcNxNN+GRRxArlVCpIFYuo1pFrFRCpYJYuYxqFScplVCpIFYuo1pFbGgIlQoyGcSiCDt2YO9eEIL163HDDTAMHHfTTXjkEbxu69bhvvsQkxIPPYSHHoLvw3HwrnfhXe8C54h9/OPYvx/H9fXhu99FNotjyAfPW8VAlJJQioFSRplBKKGMMcMwGGMAKKVSSKlk4Iee62l0EcNgjm1TCt/3li9fns1mg9APw8D1AkKJVlJrTSlVSlEKThgBOOMa0FpblsUZJ4xJJaGV0jqVTAz0F5WW40cnWs2WZXFG6XrufPhAG12lEioV/E579qBchhA4rlRCpYJYuYxqFcdxjjvvRKmE17BnD8plCIHjOMedd6JUwmvYswflMoTAcZzjzjtRKuF3KpdRrSJWKqFSQaxcRrWKWKmESgWxchnVKk5SKqFSQaxcRrWK4zjHnXeiVMLvVC6jWsV/xmc+g499DK/t7/4OX/saTvSHf4jrrsP/Qi4/d5RqgGhGCCeMEFBGutgxlFJCiFIKIIwavu91Oh3OOaWMUmpwIkRECUZWjoRh5PmuwbkCFWGkteScQmtKCOcMIAAIoQAICKHMNExJqYbmnBGg3W46tp3NZkQYuh1XK8EZvbAm31qX6NqyBbfeitdWr2PbNszO4kRbtuDWWxHbvh07d+JExSLuvRf5PF5RrYZrrsH0NE5SLOLee5HP4xXVarjmGkxP4yTFIu69F/k8Xtv27di5E7EtW3DrrYht346dOxHbsgW33orY9u3YuRMn2bIFt96K2Pbt2LkTJyoWce+9yOfx2splVKv4zzBN3H8/VqzAqzl6FFddhTDEiQYHcddd6OnBMeQD54xAacqIyThjjACEaHYMIUQfI6Vk3LQtG1q32x1KSCSFhjSgtFbpVNpOOs122+Q8nclorT3XjYKQc04pNQ2DU6YpsSyLECKMOS7QAAAgAElEQVSkDCNJKbVtGxSUUsYYpWyp1awt1CIhe3uyvfkcpDRb7sdmol5B0MU5Lr4YN9+Mv/gLzM3hj/8YxSKOm53Fn/85du1CGOIknOPii3Hzzbj9dvz85xACJzFNbN6Mm29GsYjjZmdx++147jmEIV6RaWLzZtx8M4pFHDc7i9tvx3PPIQzxikwTmzfj5ptRLOK42Vn85V+irw9/8ie4/Xb8/OcQAjHOcfHFuPlm3H47fv5zCIEY57j4Ytx8M26/HT//OYTASTjHxRfj5ptx++34+c8hBE5imti8GTffjGIRx83O4v778Ud/hFi5jGoV/0mJBJYtw5VX4txz0duL2OIinnwSf/d3mJqC6+JUQ0O4/XasW4f9+8n7z1pBCTUMZlBGKSUE0IodQwhRSoVhKKWkjOeyeQpo6DAMlxoNrSVVUTKZdGzHC3zCaCKZ5JSCkCgMoyA0uGHZlohEFIaE0XQm49i2EDKIhGEYtuMQRpRUWmsAjPMoChfmF6UIhwYGCmDX7m/gRJxDCHAOIZBIYGQEpglKEQQ4fBieh1fDOYQA5xACr8ZxMDICx4FSiCKMjcF18Ts5DkZG4DhQClGEsTG4Ln4nx8HICBwHSiGKMDYG1wXnEAKcQwiciHMIAc4hBE7EOYQA5xACr4hzCAHOIQRejeNgZASOA6UQRRgbwwc+gOuvR+yv/xo/+AH+/5LJYNkyJJPwPExNYWkJr41zCAHOyeVvWMkIZYxy+jIABIodQwgRQnieJ4SgjCUTiUwm09Pb02q2jhwZD0I/afGeQk8URZ7vgb7MsqxEIqG1EmHEWRfXUvm+L5TMZrPJREJI6YeCUmrbNjMMrbVSUmsNpRNJx3PddrORsJ1+X1w3j//dzb/rvAUb+/btc9tNx7E5pUpKgzPP7YR+oCCCwCUgSTvrOFkrYQsd1Zqdputf/Ja3SMKiMETY/PfHHpueqwde0FcoZJJppbWVSgUiMg06PDQkI73U6czU6oZh9mR7MrZVa9Sn5qf7c/mE5cwuLrqeB60Nw9SEJJN2IZOP/HCuttBqtaAloSwUkW3bPb097XZnyxf/r83v/RCAdm3h2x+7pNNsnb357P7ewqGj4/VGvdZopfK9Z2zavGqof+7ooeerL07P1RbmFkymGIQfIlRGX29hxdAyxzRFFC3UFyIplFaANAyDEgZASiWl5NygBFFXIAglhJEwiEzTMixjqd1ptF2Ds1w6RRgnHzxvlBLKOWOEEIBAU0IYY+QYIYTrulJK0zQSjlMo9ObzuVqtNjExJaUo5NPpVLrRaLTbbcoZAclk0ulUWkkZhQJdWnNOAUilbcfhBpdKCSEBGKbBuUkoBbRWKvA9EA2tiVYyEoO++lzTxP/mHtw8MJ0kSkm33RJhmLQt2+BRGLRbbUIooSwQvhKRxRxBDDNplzasen7fkRf27P/Qu9/t+mL/3n2OqXY9/3y7XuvJZAuFXs5My0mOrF7VCYKxw0eymazSKpVK7d63Z2JmtieVO3PlSE9P5u9/8Ss3Ih+59B2evzRZW8ylMynDptQ8eHh8oV6nlBAls9m836iFUkRSAmRkZGS0dMY1f3mXYVoAfvXgj777P/6k1mxTxi9+60WFpP0vj/7mN/vHD0+1zz1j5VXv2ZLgdN/BAzPzS1NTs83FOYvIhOmEEh2luGXls9mB3l4oJSB8KSAigxtaa6W01oiiiHMO6DCMKKCVBoVSkEqbtuWHorbUjIRIpRKpVJp88IJVlFGDMgICSKYJJS+jlAIQQgRBQClNWBZljDPOOA+CQEpJQJ2EZRhcCOn7XhAElNJCvuCYpucFQkhKKaAJVYRAKcI4J4wCIIRogBuccZMAWmtorWQkZEQpoZrIKBpVvLygcYJnNo/MWqTZbHien8tlk07Si4QSUdoxoXBkaqbRaGVSTiSV63u5bG7F4LJO4B4+ejTB7WWFguu2j0zNzy02vJbLGaEGvEgalvXWCzfP15q79x3JZQsvHjxyxsriaSuGXjo4Pl1blAJM6VzGGurtz+bSgqqllltvtNeuXF5vNhq1pWV9mblamzByydsvgsbErpfe3+Q4wU9KhWaPwzmLpPTdjt9pJy3Ddz3fj2wnobVshx2hRMJICUVhsPVrV/lhODY2vn7deqLI/r17p6Ym5xcWHKZ78z2GmeB2glBjcHCQAOOTR30lgiAYyPVOT88+f+ig1DhjzZqLNqyerbfuf/iJ0uqRt1+4sSWFwROdZoNTPnF04uj4pBRy84Vvv/nef1yYHP/Bjtvmjo5L6E1vvOiDn/1TyjiO+ad7dvzTvV/T0K2ONzhY3Lh6uLZQG1+oTS90khZfNljgRkpJaTIqgmBmerK2sEhBDIP7WkkpE04im8tZhsE51UqFQgIghBJQpaRhctuy3DDwXdfkBgHCKAIlSkMpKBDfC9wgUCCmZZEPXrCaUsIpo+hSTIMQAoAxBiCKIikl76I8DAMpJefcOiZ6WWDbdqFQYIzV63WlVDKZhFSu62kNzrnWijEQSrQiIIQw2kUIAQHjnDFOCNVaKykNTjPZdBSF83PzIoxGJPlC28EJHr5o9WGTLrW9IAiG+vsSBp+enTo6MxtJkrATCdPKJJL5TKbhe/uOjDGihoeWZRMJ5QdRGI5PzT6/9/BS06XQaVPbRFncDCR9aab55jefMzpS2Dc5O7sUtGcW3nLm+oOTswcOTHFImxDbpL4OPc2WD/V/4MI/2H90al9j8fJLtixMTz+7e3exP7d3bDqRdN7zljfJSB761dM3dByc4Cfrc0u9DgUZXDYoomj/3j0MOgpFKJTtJKVW7Va9p7cQRogC1dffq0lomgY0SdlJStmB/Qf279+voS3OHSdJmDEwMLRYr0dRmEzYbbfZjrwwiAbTvdITeycml4Io9KOtF75hqDf1s8efGZ9rXvCG0qpVK/xIzczMuK124HmB659x/lu/eNcP8ercZvO/ffLS6bHDnHPfj8IoXL5ssJBy2l5rcrZ29pmbB5cPPvHsS16nk0omLcMIPXdxbjbwPMYYpURDE0JBqW1blm0r6CiMpFQGNyllSknbti3HDMKg1WhyzmzTCsJQQwulw1By0yRgS62WHwSEMXL5+aOUUk4ZRZeiGoy+jBCitVZKCSG01oxwKQWgLcsyDC6VlOJl+Xy+UCh4nue6LiGEEiJfpijjhBCtFWMgXYowxqjBCSFaa0IpZZRSTgjRWispLZNnc+nFWu3o+ATjbD2zb/RSOMG/vmn9hEM4hRRqanbxmd0vzkzNKGrWPSFldOb6kQs2b+jv7xmfnJyfX6gt1VL5whvPOrc2PvPErt0HJ6cC1084xDK5YZiItKkUtJpoi3e89U1nnbHKjUQY+IaiU7Nzv3qmmnOsjE0bjbaUSquw44cNX3/s4nf19xZrRF144RsmJ8cP7t3Ptd/ypOZ01VB/oadv8fkDV+5ZxAl+tqG32ZcIwyCfLwSue2TskFLKdhKgLBRKKp1LOulMarHZ4jBLa9ccPLp/dmGBU4NT1lvoI6C7q9V6o2FSkstkWh0/39cXhYEUgVbScz0N+F6nL5fTjI7Pzgz3Ddjp3HQ7VGFgquDAgaOpbP7cszalbLPZas3NzQWhr7QWkJ/+v+88b8t78Upa9YU/u/p9S9NHZRRRSk3DDIUwTAtKNdx2w4v+4IJz164b3lU9UG920qlUNp0SYTA9OblUWyRKJxO20trz/SCMzIRj2rZUwqSMM0MprZRmjCWTjhCR0CL0AyEix7JBIKSSSkVCcdMyTXuxVncDn3GDfOj8VZQQRikDIQSUEkoIjqGUcs6llEEQSCG00pRQx7IIZ67bEVI6ptnX28s5r9frnHPTNIUQUoMyyhiVUmoNDUkIKOGccUIJpRQApZQQAkIJiCZQSmnopGNDq9m5uWajeZrp3CzyOMG/XLBuMsmI8KdmF/7h35954cjipoF8LpmZWVpkPFKKnT668pzNq9udxv4jU2OTs5tOWzM0OPjLXz07NbPoiyjt2H1pJ5KexbkhoWUw1Qipk3jX2y90bEYYa7X90YG+x57a9fiul7ZccPpgIbv7hX1LncAiypRBrR2RVP6D737rqpERQZLztfnJ2TEOZavIse1sb2HNmtXB3iN/8Mh+nODfNg/o0aLneYcmZ9r1eSGEIrQ3n7UM1vGF5dDNp23+7bMvNtvt3kJ6ZHR4YmJiqd7ywjDU4rTRNRvWnvb087v27H5+bGKutHowZTt7J2YH8kkt/EUvYDBtphqu55iJ3lTiyNRsui//yU9dYxQH/+FHP/GOjE9Oj7c8sXbFyFB/zvVFo9HQWoEQ1+0QxkY3n/9/3vo3vYPL8b8szk3/6z1f2f3E4xMT40orRpnv+wY1TM4Nk2tCaq2WH8m1a1YPDhShtZCamkYhk6VC7D2wf2Jq0mEGM5jSOvB9pXVPocdOOO1Wqx2EjFLhh7lsLpPJuJ7baLWYQaFk4HuWYVLOlEYUiUBIxg3LTtQbrWa7wzknHzpvlFLKKGWEEGgAlBB6DCGEMaa1jo4RYQStM8k0t4x6o+F6bsI0s5kMYyyKIifhmKYlIiEBxqjWKooiaIBqxihnBkAAMMYIITiGEApCCSXQCEI/l80MLVs2tzC/e1f1NCvxZzKPEzz0htGXVNBq1Rdr9cefOWwo443r1y76gU/lst7ss3sPzTUX3/PW8wayhV1jh/Mppz+R/dffPjV2dCFJqSfVm9/0BwmD79r9dMIkRiSEkpPNYHBocN3q4Sj0e/v7f/PM7vUriuNTc7PzS1vffMHoyhXP7t57YGLS9Rp21GbMOFgP148MffS9l8653mKrmbdooz730qHpKBRnbhh9x9svau2ZeNuj+3CC+5cx5/TVlJJndr84umKAABPTs7lUgmrRDsSK4eVJK/no409HUvTl06lCrtPs9KYSoRQdQdatKXHOntv9lOW51YOTz820Pvn2c8ygcWhqoem3PaZ7HcuxnHrEvUCO9mcX60uPHZi9/IMf/tJX/tsvfvKTByr3q6j10oEjy3p6lw/0ykj6vo9jXM9TUmmAWs7ImrXEtNxIBYGX5bqQSXiRanVajWZLCbVUq0dhyAjjpm0ZJJOyEslEIpP1IjQ7fsI0EkknDEKLG0JFS0tLfqdDtGSUCiG01sPDw8VisdFsHjw62W51ssnU8qHllJCpmZmO51IDSkYijPgxUqkwEkJpoaDBmh234/mcG+RD543SLkI4pdBKa00JMQyDUqqUIscA0ForIaGRz2SEVrPz80EYmJxZlkUp1VonEgknkaCESKmVUowRKWUURZSBdVGuNbo45wAIIejSBKyLEkKkFI5t57KZucXFl/bt35RM/zeRxwl+Uur/dbuuAl95wcGjSxY3cglrsu4WevK5bOI/dr1Qb7U+9b535NPZuXqtL+3s3rP/t3v29toJ14vMXPZ97/5A243+/ZF/o95immKhHS5Jten0tTbnvh+Zhr37pX1vOGPtocmpWtPb+sZzN595Vk9u+b898sund/1qRYZr5Y3N+zVXX3bpW87cvD4QqlVbOnTwpaXFpfGZGrWsD1+2pdj0t/x2Cie4pydSowOE0AOHj2wqrZVCHJmczjiWwXTDjRS0RflCo2VafGiwOF9vtupeIe8YDEoZjpP2lNizf89y0zk40/j+C9PvPG10fb81ceRwb9bgCSPlJDkztJGYb7bSSSNjsZ/++hBP9337b/57Mp36yl/8dWdhpt6saYGhYr9WMvB9eQxlDEAUCSGladBQyE6kFUhpzcjw8gHFeRCESilCaGOpEXo+FAmDyLLMkZXLQUnbc/0wml+sU2oUMrlmq7lQXyxkM5l0puV1RBBAgTEaRpFpGOlMhnO+uLQUBcGKZUMmM2bnZjueKwGtpdaKEiRth1Da9txISEKokKrjBrVGU2pi2Tb50PmrCMAo5YwSQEqhtbYt2+BcKQVCKAGljABRGCkpHccBIc1m0/VczqhhGAC01plMJuE4GlBKE0I4Z0qpMAyEFIxRSrhSihDCOQdAKQVAQF/Gu5iG1kpSSrwgnJmdWw3+31UeJ7h/OLXL9xdr9YnJybmWV8hltZbNpu8w5lM9XmutLPZe+qY3RlE4Oz/thf740SkjDFJJc95VhYHRd7z5ndS0Hv3lw/Nje5MsXOiEdU0++N53UkGf2/X85EJNav3G00ef3T95aKH5jjNLhUL/e/+PD81Nz+586IdM1EzDVYG5d6a98cw177/4zUrRZ5/fvTg9tXYg/+Se8efGalde+tZLli8799H9OMFP1qXnM2aj0ZhbbGRTiUTCbnfctGOLKAoioaQIQyEpXblyeW82e+Dw0cnp+lytls+lRof6MpmUF4W/efaFzqxXneksX3/aZ6791NNPPv7DH/9z3sH5KxJW0s6me7O2Nd2ot0NvVX/hmX1zz0y4Xyx/7JK3/8HjTz/18L/9gogoCKJcJt1oNmZmZkCoaZqMEgqEQkmtbUbaoehIOTDQX0g6hVxOUbQ9TwGEUhUJixkJy9ZCMNNK5wuhFK12g0ApIYRi2VTGsK3DR8aW6vWeQj6ZzUDRpaUlxpiUstlsMcbS6bRlc6201qRRXwp8v6cnJ6Kw6bqMcts0DZNrpYMgCkVIQKSUrbbX7LgahHGDXH7+Kgpwzg1GAAghQIhlGJwwDU26AK010ZBSKqUoY5wxQojo0sL3vCiKbNsuFAqMM600BaGUcs5BIEQQBCHnnIBKqbTWlFLGGDmGaso5J4xxgykoIQWh1OK802kPBuqGGsMJvlVgv2nVD40f7bR8avKEbbdcnyrhENYJdaCiNauW9fb0vzg2ljBogrJas5UxecLhE7V2/8Dq97/7fT2FwsP//vCTT/xahK2m0Ol89mPvf6dSxvMv7VtsNoZ6c4PZ5D/9YpdpGuecufrxFw9/+JIPrioW//aB70TRnEkFh+0rc/OGde+48NyZ+fmn9uyempm/aMPa8Zn5/3hu77ml1R8/79wLHzuIE/zD6tRCzpmdnau3WqZhrF21kgAzMzONpYbSmhGmlUok7Wwul85k251AaXTa3vMHD/f3JDesHy0krIcf2/3jxw8GlN3x1f/x8c/f0FqYefihf7j5llvlYu38NaliJp1KZhuuPz4/e0ZpdSrb860fP7p53cY//sMP+aTz9HPPNWsNCWEY5sJ87cChMT8ME07CYJRqHUZCg4LIRhjle3rWDg+JMFxq+qEIqcEU0Gy1Op122kn25gumyaTSpmUnkgkNaC05ZW7HA7By1WgURROTk4lEIpVMqkguNRpKKyGU0jAMTijljAqpWp7vdvylxYUVy4rL+nsXWo226xFoSqhUSkaRFEIqRJHyfd/1gkhJykxy+fmrGCGcMUahlNJac84YpVAKhDDGKIgQAgpd6hjOeTKZdByn5bUWFha01plMJpVKEUIA6EhqrTnnjFGlpRCCEAJNpFRaa0opY4z+fxhjnHUZXGkthGQGd0wW+N6w5J8a7+AEf8E7D08enV5YyqeSxGAi9KUvKYVitOaDQr3tvDNWDA6Mz8w4tnV0cvrgxOxgJmMydWi2BmqdtWljwnaee+nFZqMBwmfawZZzNm5aO+RRku/pQSCyhjE1P1v58cNvLJ125Ycu+ddf/faJ6t6EbQzkrDdfcE6n3v7FL3+tTWPt6PDb3nD25MzUz5969uh087qtbznztJUP/eY3k3Nz7z/t9PdUazjBN7O+Gh3K9xSkVlHgpxIJ1+tMjE/4QZDO5JaabZNom2O+3kj2FIvFZaU1oz2FzGNPPD07s9CfzxYLueqevV9/6BnwxA9/cP9bt76bUJ+C//Du//m5z92yMiHW91vJTDZSVq3THF2/fNPq1Y89vfeZ58feftZZG9ePerLDqap1liJF/EZ4eHx8br5GCU1YBiVahJFQpC0C27Y3rlmXSaXrXmdqboZqkk6nHduJwnBqelpIkUgkCKdBKBg3MulMOpVilEIrL/Q9z102uIwxNjc311MopNJpt9n2Oy4zDCEVZcy2bNOypubmAt8t5PN9vb3TU1NLS0vpdFpT2XE7SkhCiNJKRgIaUhHPD1zXC4TUhGhQcvn5qxghBmOEaKWU1ooxyhiDUoQQzjijVCutpBZCANBaG4ZhmqZt2x23LaRwbMc0TaUVNLTWURgKIQzDYIyCaK1BKdGKKKUA0GNIjDLODcoo54YGiaQglHJKoiAYUezT0z5O8Kfu/ONLizMLjYxjgkGGgUWpoGTBla0IKwf6L3nTWZaFtu9yhbl6+5mXxmgQOVw2grAd0N5sVhNRay+lTWOmFfXnMpdceFY7DOxUct36de1mszY7f3hi8qePv7hmqOdd522amaz/4re7IqXedsGGd77tjUfGZh979AmpPUn1B7a+iwnx2O791ZcOfPySN52xfvT/+dVTtXpw8cqRD4+3cYL7B5l5+mrOuWFyr9Op1+taK4BSxvr6ivV6fXxiQhOiVWRw1tM3mC0U+3vy9YXpmclJQpkXRINJ4we/eOaJI7V7K3/zniuvDfwON1Pt6X2Xv+c9u3cffNtociiXamqW6cuNjAwrEdYWFvcenmo2/Xw60zuQ7cmlpS9brp/JZPxQjB+dqC/VDcIsg4HoQAOKbVw96iTsubYXRFHoNrOZTDqZCtodznjH99wwEESFMhJCJ9KZhJXo6+m1LWt6eroTeb4fMEoTjuN7/uBAMZPJ1BZrnU5HSBmGkRDCsKzR0VXcsuenJ/t7cj25zFK9MTU7N79YB9GA0qpLsy5KoREK6Xpex/UjKTWhIIx88NwVhBjUtBkUU0JpgIJRqrWiIIZhUEIJARSiKFJKmcdIpcIgUErYtm0YRhhGUspEwiFUR6FUSlHKCKEamnNGCVFK6WMIpQqaAoRSRhlllDHGDUtrSKlAiFYKwKAnbqhJnOCP3dln2/VWqwUFEYFQHkI1wsh2Eivy2c3r13Kb7x8/qpVeu7xIbeOx374wMbmY44wxWEbCThma+m1P1TuSMFywYXSgmG0LlestplMJv9kWvtv2goce21XveGmqTVCTUE+rYn/23PXrHn/x0OTi/Okre/oyif7egWK+jyeNXz75eII72VSi2W5aduYMO/W5FsMJvrPCSG5YPT03E0nVaDTr9VpPPj/QX6RAvlCYayyNHz7iGJZlmb6ItGEAfM2Kwfm5hcmZyZRj1hdqI8WclHrXgfHCyGmf/bM/X3v2mwE88/CPPvuZzx/aO3N6j3nmmmU+JU5vfmRo2ezkzNiRCT+MpJRBEBgGT6ZTnPP6UnPF8uF1K1csLi7tOTg+V29QIhjT3LQKqdzywSEhoqNT023ftzkbHhrM5LITk1P1pSXLthmjtm1KFQZC9vYU+3OF9WvWDgwUn3j6qf1jY57nCSkGBoq2ZeXzOSHFkaOTkRdEShrMEGHUCNzTN2w87+yz9+7dMzM1k0qlDM7brdbc7KzUkYIOAl8IyQ3DMkwAvh+1XN8NpZZKyRDUIB94w0pAE25xKAatCOGMMMa0UloqzhilFAAFUcckk0nbsvwgaDYblGnbcoIgajablmXl8zlCoDUAAhAASmnGoLWmlBJKoAECDWitCSGUUsYopYwxQ2lAE0IhhFRKjUh6Q03iBP/FnXt4ctqhMGTYCWRTs04kl+dz60eHM1kjV0gv1JuPPLPHsRLvfeM5rdbSE7v27l9omISYwGBPfqCQcoNg/3wtiMS7zty45HU6VJ62cnhg2XJGVXNxSQuZSad++cTuJ6uHe1KGZVDXD5en80mbH/Eb061gpDez5cyNw/39u/aOZfvyywezz7546MEnq29Y3b8snRaUv2f96steqOEE38wGQX8eDNQ2pmdmags1y3LyuaxWKBYHPBU1ajWLsEa73Q5DbllMIZdL7zkwOTk1uW6oEARqvu2964JNnu/+4JEXe5aP3PanX8ilMt/4xjd+8eiToSuHs/yis9bk8un5ZkuAttvh3My8DH1KSRQGiUQyncsooj0/TJj26pUrg0A0216t3fL8DmWEUgqpbdMmIEuNZiAFZ2zVisGeYnF+YXFyesoPAsug6UyW25bwQuELYvC+geL61asX5+Zr7QZl1PO8dDptGEYymaCc7dt/yGu0NCXFvv5sOjuxOJfLZDauX9tqt/ftO9hutwhIFAVKCkIUoYikEkIAlBLKKG11vHqrozWjRGkpBbPI+89bzbQkhDIoEEYYNzkxDAaloyAEwBgjgFaagGgo0zQs2wTQabcZp6ZpuR2v0WgxZti2xTlzHJsQqpQGoLUGlNaaMUYpJV2UgBClFABKKWOMUkoIk0oTwrRWURRJKU8zE5+dj3CC/9KZ+ccDR7TUaYtQgpqr1w4Prh1eMd9cTGftDWtGteYPPvqb/oFlZyzrrc3NHJqqP3twMpdOgEhDYagvVw9cN1Knr1y5fuXQvzzxVCuIztqwevPppZ588oUXDiwu1keW9e3dN/b8/qNvPv+c/mzm+X0Hj0zNcB3lMs7QQNHzZe9A7+jKofG5+XxPz/JMcm6h+dATz5y/aa1DMDEz977S+oufHMcJ7sqLfUyGvr921XLP96an54JQMMMCaE+hp6evp7awKKKwHbpTc/OFbH7N0Egggv94/NkDk/WR/sQZo8MvjE/lEtamkfzj47WHnl9Yns+M5MxM5EduqChNZZ10xlozujwM1cTMEoMSodduutyg0AqEEoJ0Nk0Z63Q8RU0IOTI02D/Q14kizVin3Z6amGzWG4wamhA3CAnU6NBgoa9vYXGx3miEIsrlMgkn4fl+FEpKWavTabmdVDrFCenryQ4tH/aDUIhIKBlGUaHQ26gvLc7MBTLMZ/LDy4cnFmeVEGedvpFydujQ2PjRo0opzighiISvtSbccP2AgFLKlZBLnXar41FCDArKWDMi5Py1A8N5GyCEUBBCKLU45QbXUokwglacc9IFaK0BTQgoo4RoJRWlhHOuNQkD4XleGEapVCqbzSglw1AQQiilgM9ps8QAACAASURBVNZaUUo1ASXUNE3KaBiEUgqAGAYHISCUEAoQISS05pytYfZ1Mz5O8Mfu7LOBGyoyPTdvWcbynt5zN20IlJxZamaTicFiIZPLTh6ZaXn+9NyE12wnrHQYwHGcibmpMPJXr1zR7DSJab7zjRc5SesXj/56bGpm1cqhC87cWMimDxya7rguleGevfuHVg5fcPZZHddrLbUf/c1vfBG+9dzNYdh5+NcvgPP3vOOCgUJ2yRci0FHkdRqtC95wjmTqqRdeWAvn4/sWcIL/mfQOWXJmYb43lc6m0h3Xc93O6MioYdqNjr98+bJ2syGVXDYy9Nyu3Vqq09dt0ML/j8eefGT30fWjxbeUhmqN5gtHFtYODYyOLnvwseeePFwzGU7v5ymTWIwNDC6rB0E2l7PNZH1xiQnXMXltqS2iEBrMYJEQlm2CUtcP/EB5oervy5x15kZqJ6hhtZfq+/fsr88tUEKYwUKlo1BkUykraS82Wo7jFP9fxuAzVtP0PAzzfT/P89av1/OdXmbmzOy07eQud9lVwJhyoMAlgIHAMALYMGBDMpJIUBwJsmUHMIPYlu0oSIAgkCM7yY84TqyIjm2qmKSW5NbZnd5OP+fr5e1PuzOz9o9DKoB4Xc16EHhbWzsM6XvvvptkWeCFAEAM51GEAJd2NtZWVuI0OxmM+qOx77mB64KyKHjg+mEYnowH1pjL29uI2B8NkySh5ywiWmsIQBrtOG6t1shyuZjPZ0n6DKIRiIbE4ULhUi1461IXESz3ObOMrMsdLri1RFoCEeecITLGEIGAyJK1xlrDOWOIjHHP9R3HyZ4rSqVSEARaaymltZaIEJExhhytNZzzwA8E51mWSSmJyHEcQkBEwV0CsmQF40KI5YJ+bmLhnH/QFGcVH7V5vLeXarWytFwOfc1QWTafT6u+WFnuoRu8f+/+Ox/d04n6/MtXr+5e2htOHzx+tNwsteuV48EsTeOf+NLb25trH370yeODA8/l22trZB1uqVnxbt1/8u7dB1/43Iu1SvVstLh6Yef0+GQWJzde2H3n3Q/u7x8zxq5eXP3y6y+/+/H9RwdDxtFA/tLOzss3rpLHqoP5z7x7AOd8A2ezTlCuV4b9BWk+j6eeC1996808KX5w91GlXl3pdeJ4cfXaC3v7B08Pjqq1+nqrYS1853vvI5hK4JbK5VjaSqO82Wr0j4e3Hh1whE7ZiXQmPHd3+wJxN8lktIjn07FAkLlM4hQAjDXCdRhnUkmljMd5UeizRFfq/ssvvVBv9cIwTKL5nQ/uzAYjDgqZReEqwwyQAZ0oXS3XXrm03WrWq+1uKQjv3rlzdHSMnFlEJVWWFYlS1ZK3s77ieS4xcTIcn54NA8/1HbcclsMg4EIMp6NZNG83WmBtlCw4RyQyhhjj+AxgXhS9Xm91bW0RRUdHx6NZkqQLA4UgZ5Gyh7MMBcCrl5Y3WmVDxDhjpDnjnHF6xhoGwOA5RHQcgUhEYIyxVnPOGaIx1nO9MCwhYppmAOi6ruM4ACClzPPcGCOEQI5ElnPuex5nXEqptSYiQAQEAGSMM/4p5HGSrCn6L4sAzvlfLrS+E42bQZkbeLC/Z4iyLAHOlIEoSY21S+3G7s7mo6cH9w+GSPjTX3jj8qXdB08P+oNBNeRpnkS56Q8H1y7tvPLSi4/3D+/evnNhYxWc8M6Dx/WAvXb1wnu3Hz08GX/1cy8xS+N58sKlnTzLCFm31fzmt/5AEfM8R3C7vtT79vffG49jwYQUpC2+fePyn/7pz7Pj4z95awjn/Lds9sAXNy7tIDglv9ofjN69/d6NFy6avDg662dS3bhxHcCWy6UoSc4Gw9F4UgtKX3rrjemof/vR42Gce0FQL3sXNzdNpgZnx8PZCAEqfiXOpQLb6/Ya9YbrufPZ/OGDR8gEEKGFsFKKk3i+iFAIY229FPY6jQf7p+Moe/nGpUazYbnXbFZlIe/curP3ZM93weEgmGuBSatcx282GlmWlSqlra0N13EWUZIkKWfPoNZ6NptnWQpgFQrhuM1K8OqL1wfj+Q8+/Nj3HCDgyEtB6HpuIvMojUI/REtJGnOOZJ8hx3ERhTXGkK3V66VKJU2z6XQap3lWpFoWiP7eND9NJALgUr38ud0uQyAmHJCEjCFDAkRCALREAAyQc+QcAdBaS2TwGXgGHeGWSiXHcebzRRTFnPNms1mpVIhosVhkWUZEgMQ4AwDOmOu4xmgiICLOuSFjLTAuOGPCdRmxKEl6mfpVW4Fzfr0tvjs821nfGs/m737yiQdCK5XrArmIJMwStdwuf/7lKw7iJJVZnm/1Oo1aaRoVoesolZ/OF5a7WV5sLHcrQXDrzv0slS9eujSYDA6Hozief+Xt17WB09F8d3PVFMX+Sd9jZLTura52W40P79zjHJv1ugiCh/cfPd4/dJgAWRTaHCe00qr/ua+/WZlM/tIpwjm/tSR+b3LGwbt27frN3RdPBuP//n/9n72AOqUQLbbr9Wo5DAJfaWmAFnESJ/k8TbY21yqBn6Z5Jo2S2ue4udKbTqNHh/u5zawhH30PISdLXISuuLC1WS5XP75zfziLhLGN0O+urORFcXhyIrVWRFtLnQvbG7/38b3pbP5Tb7xSaIAwDDyWztPJZHHn7m2iLHS5xzwgpsA2Ku1XXrga5fGtJ0+9wOvWqvM4SZK402lXSmWH8ziJ54t5IlUhpet6rh+uLq+iVSdHB5osIpJBz3MBsTASOQv9EAmSNJIyZ4CAzOEOY461lhANQZwmFgARZZHnOmcKE8nvTONcEwIwB+0bV9aWKi4AccGNtQyQITAEfIYArGWM4XP0jLUWgDjDZ8Qz3PM813FEFCXT6UxK2Wq1qtWq7/vW2iRJ4ihmDhPCMcZwzhAZkSUC3xOO6xaF1FpzzoVwhHCQC0As9ye/ostwzn8dFk9Qlbzyu/eeHJwOGi4HyzzP79bqcV48nswTbd5+6cLrNy6mubn/cL8/OLWWcour7ebVrfVplmeaOo16WHIf7e/tP+1vdFeWa0G2mMaA33u496e+/qXrFzcf7p/E00V/0H+0f+gwnsisUa2/trvb6dYWSVFYE9bK7390JwTBdXZ6dmI0E37Q7Na3L6w0Z+kvRiGc85vL3nf6R5kRX3rrM5XmelLY09O9P/je9/ujszduXviJz79x78Gjw4MTS0jagLWNRo07fDifS4u9dmuj09ZSjiZzY2E8GkfRohx6WZFJqYXnCgFaU5LbSqO2s7aqk+zhwfF4Mm5Uyr2lnjb69OzMEAGicHm5UZcFq1Uq2xvtg9NBWCm9dGHr6WF/NF+MptOj/X2fo8cQrNHgOF71pauXizx9ejoAhErI52mU50W1WisHJc91wNI8jkdp4gix1O0y7iipORIpGUWR4I7reY1GI0mTKF5Ua5VSGAJAlmWLaA4WGGMIyIXgjpekWZIVUZy4n0rzuCgyAOdsYR5NU2AMkXGwplv237i2KazkjBFDIAuWGIJ4hjEyBgAYY4hkraXnrBCcM8YZR+SICABam8UiyvPc8zzGWL1Rr9fqxpgoigqlEIEx5rpuURRE5Pt+GLqIqJQhIs45IAOCZxhnq4X9uQmc9/eaNG2Ew+Hk2+/ftdZ4aF30quVuq75UCr2ng9M7e3tXLqx96fUX00T+4Sf3Pjna0wBxBlv18E+8+apXci0KR4j9g6fD6UQVxDnXRnrgjuJknOZ//k9//ebF7Q/u3P/uD947HQ4crUskYmtShLLv/8mf+oLvl48HpxXfn04jLdXHTx8djuOKEFfW2zcvb7aa9cZc/4WjBM75jSb77b0nS63qpReuML/1tZ/42nqr8j/949+8d/DoleubnnBPT0dpnE0ns9Fg6Pse57jVW+rP5k+Gw16r9erurkB8eHS0dzaYz+ahy7ZWV61Ss+kMgDMBRDSLZar0crf96tVro+ns3Y8/NtZWSiUjdRRHznNiIc0sTV6+uP3FL3wus/bffPeDg7PhT7x24/LG0nA0zZX56PadwWAQuh5YC4wz4S1120WW51JyzgOPE6oiV8Jxa7U6WRstIu4KiVAul5eWulYbq43WigzFcRJFcV7krXabC26t8X1PCI6IZGxe5EpKa63neo7nGWtHk1kUZ7lUgnHf9/MiVdZKcG7vDSMDz+AzAMARd9c6l3pVFzUwDgRIFgGEEByZNYaBZZwjEgBYa11XIEOwxBgD4kQWETkXs9k8yVIAUEpVq9XNzU1jTJpmWisiAgDOuZTSWhOGoesKzpBxx35Ka2MtIVkm+A76f3Vk4JxvlPS45j3cO7j18KgZ+K5AQhF6tZXu+vra8uFo8M5H72/2mpcvryV5cXYyNUaFfnj/4GhnbblTCeudTr1au/vw8enpwBeOpsIKOxhOFzFOM31jd+lrX/6yL7z3P7n1zvu3ygQVl6WZ8oW7QP5gkf1Hn3/lrVeuz5IFaauz5PS0/8Hh6XE/6lT4lz5zY6Pb0pm66JX/zN0zOOcf1M2/Ojt79YUdBZhK+uoXvxy44u69u17ocbTvfXQHLF3Z2SmSeG9/r9C6Px6/eHFbCOeTh0+NMVd3d2qV6vHZ8NHh0XQ+DRy+srTkcnF6OlDKMId36tVkEc2KwgvLL12+2qyHtx7cPz4bB57HCWWRkyUEyhScJdmli+tvvfGK73nH+0f/5++/m2v62mdf2Gh3Qt97cnr08d27jbDqcdciCQRCxyK261VjdC6l52Icx1pbv1RSRnuev7m95XgiTZMiy4GgUi4zZEWeL5J0Nl+EYTnPcuGKRrNhrMlkAUCqkDIv0BJjzHVdYGgB5ot4HqV5XiBDx3FJ6ZTwYJqdzRMLAASI8AwSMoHm7asby2VHWssY48gACAEYAREgWs4ZZ0gEiOg4AhGM1gDIkCMi44whT5LEAmmt0zQtl8udTtsYg4gAYK1FRACQUj3jCAc5eZ7rCC9JE1lIYywXjuAcEXad0s9NLJzzS2o6LLtRUTw8GAhtA4+PktQQrLfqlzY27/Wn9w73vnhtp1EW/UksU9vptsJy5cNHj7e3ts6ODtqtVrta++4HtwaTeL1ZW1uuNdrl48PBrSfjjOBP/YnPX9vduf9o/4NPPtnfP13x3Hbo5UCn8yQqcKTs19668TNfePNwdPbgYI+kWq41G62lOFocnx6srnaR+bdu3//q1vbP9SWc8+t19UE8393ZDIRzOhoWwGdprrV5+foNMvbju/fKpWB3a13l6cHe/nA2T6XeWeu2ytWDp8dn01F7dalWqUWjRZqlaZJqbYJywDnOFpEytlap7qws52k0jtNMkZZqc6XDXHFw2jdSCWRaKqsNA8oMP43zF2/uXLi0LQ12g2AyGvw/335vketra73rl7ZjGT/Zf1JmXqNUM2CsLnJFwJ1Xb16rVsp37j8cjAfWkjYUJYkX+ts7W8ury1leZGkaL6Ikjpv1hu/5WZomMtfGhn6pWq/NF7E1WrhOYRRjmMZJtFgAkcs4EGiyrudJbWZRnKQZQ4YEjPHTefFkllnGgCwSIAAgICAn0Dud8isbS5aIC2SIAAQADBgiEBiGyJEBAGfM4QIAlNHWEiJwzoUQxhgAKJfL1hqlCgCM46hUCuv1epqm1towLBlDSZIqZRBQuOi6rtY2iiKjDeOcM8EFF0JcQO+/iBmc89eSvlxutjtL9x8f3H/8xBVCFTLJpe9x5nrHi+LK1sruSmPv8Lg/jayFdqO21KkbbVIJR/3R1trSWqc5ieVHj55k6eL6em9neflwONw7m61tbvwHP/XFRs3/znd/8P2P7p+e9Lml1WbprSuX7p/O3j84cH3vz339i9z1Hj5+cjo82z+af+EzL716eWuyiKTRg8lAcP/kZFia5/+4vQHn/L1KfqdYMGSdRn2RFof9UZKpZrO+u70RuGwWxY1Wu1avjEbDvaf7w/44DCu+ywGMUsoa2+2tOo57eHQcJxE3hiN33KAwZprNl8rlmzsXMjK5tjLTg/FkuJg5wt1YWrJWzmazLC8QkAGCpUjqWMsvff6znV7n8f5Rs9a80O384P0Pf/+je81qaWdztdtuDfv9UX9UK5cdAZosIVhrlzrdVrUxmU6H0UIV0vNcYzQyrNaqUutpnLRbbc5wNh6HfkiW8ixjrhCuK4RTrVWZ4PNFjIjVcqBkEaVJvIissQKRMw4WpdGFkYZAKS0zZS3liE+HyUwSIQdrEAgBAQkAGIENOLzxwnar7BsjGQMOhIgMOCIRGIbIABGAP4OMgADBWgIAxhhn3JINgiAMA60NY5im6cnpSRCE9UY9S5PAD4LAT5JMKQ0AjLFSKbDWzmaLPM8RUQjH9wIUjCHbNPyXchfO+c+zUbHSvHHzxZPh/Hf+33+VJZEvII51jv7c6E49/OqrLwLYO08OnhwPZoWshv5mrdwqB9MsiZOiu7y0ttnZ7a0/3j/533//u0rCxV57qRW0g8rW5sruzV3wnVsf3n909+lH95+cZfLljcYXdrfmKX/34f0rF1e+9Oab3/3kjkryXqf2b77/weVrF29uXRgP5o2Gc+/oOJtEvUp9/Ojkn6xswzm/ase35cJzRMl1lDK5UsLxOp1WtVQSiInWjXarVqvG0eLo6Pjk6FQpi2C0VoEfrKyt9pbXZrP5wcFhHmWLPAEOr126Fnju737yPtfw2pUX+vORtiwQ7nSxSGSuC9WqlMMgyLJ8MptZSwBkjU21Ni67enl3udvNsswRjlJqOJztHZxaQTvbGxfX1hbj6e0HD6M8A9C+5/mexwAF50EYGGO1BteFra3VarW8f3A8nSXGAnLodFquI4CoVqku5tHh4XEqpeM429vbaZ6hYJubW4vFIk0TxtjR0VGapg7jDMBzXM55HCdpmjqebxGjJDcE/bg4GKcGGREgWABAYAAEjJCAE5qWx1+/tOYHHpHmjASg4AwAjZGcMQ6IDB3hgCVLxDgTQiCiecZaICqXy67LjSHGRF7kg9EoTlPOeL1SajYa1to0TRljQnDHcUthoKQajiZZljFEIZxSuaKMLor8Zrn5C4mAc34+GYiL673l3t5xf//4dDoazOexJhsXZmu1+8qVbeYFBB4YefvBo8dHw0VShA50GqXQD5v1eqlRkai2u614Ufz++7czqbtVd7leC1yXe25vc73RanBr+0cn73zwcVgq764uDYbD1d5yntmt7SW/Hn7zW+80q43rl1aieeKWw1azNZssmDB3Hx9+89u3dldbn2+3viGacM43wvQp00tL3TTJotnMGM2AAt+v1uqMi9PBwFrT7bZ931NSTcbz4XDiuhwYaqV6vd7K2kacpPt7B3kstbW5lS9sbl5aXv7+nTufPD3cWOoWMtNANb+U57lmIABcwmcskSKbZFme547nVQKvVq8sjA2rleV2UwCcHAwns1gwKIVep9PyQt8QDPvDs/FoPJ/VgqAWlDzGuMPBYYREQL7rL3W65UplNp+fDYZC8OWVXhD488WMjBVcxHF8cnKWSTLWbmys+UHgBf71G9fzPB8MhkKI9957b76Yu8IxUvueG3iezIsiK4y1BTErxCROHpzFBTAABmQ4EAEgIAABAhAwRHrm6nr3+mrFFrl1Ao5MMAsEBIQEjDHBmRCCMzTGEJHrukAkpbLWMGSu5zquC58igDRNZ/O54ziNatVzXUP2GcaQC26sdbjQSk+n07woBBcAyBiT1lqAV2udX0odOOdXRZ6sdeM4ms7nq8sraRTdu/cAyHIO21tbK2vLi6wgIWSeHe4dMPCjeTIYDeu1cKXXubC9pQEPzk7a9XoSpf3xaKXX5tYeHQ8mUVRYWN/c6jZrG2tL0/7p4GzU29xQeTGfzre3NpSyS72llOS/+J3fDV23VQtrtYbruhsrnVaz4Tjcd5y//uu/NZ9Pf+Wn3v6LpwWc8z9shKeh06hUsjQeT6ZJmmVZzoDWV5Zczu4/3pvO5vV6DQE84dQrZc45c9wiK4bjSQHU7fR6rfbh8eEnjw4rYfmli6u1UjBL7SJODo4OuWB+4BV5wQCllETW9zxrbFpIQlzpNlzHmS6SeqViTZEbrUBwz6uWK5XQK7Ksf3aKIHqdXsl3U6ljK8u+26k0BqOzs+EAkYWO5whHgrVorFGO43IuyEIQhhYtMlxfXXEc58mTvUUUF7nUShttQJt6u+OVgkWWeWHw5mc/2+10T/pnR8fHt29/Mh1PBOPMAkfkgjmMKUNZJq01VjgPR8nZIgdAeI4QgAAQfgSiz/DF1epGt6aBM+YItACWISMiZMg544iO4IhgLSGANUZKBUSMMc45MGSM8U8BgDHG930gI2XBheCcE5ElyvKMMw4EeZ4TAWO8KAoiQof7QXgRnb+ehXDOP+x6d0FLJYlst9XKk+zw4DCKE2tNGJbq7Va73bJIg+FsNpnXqqEvWJZmROA6fqNeRQ7D6awcVoiIORg4/HD/6HQ0WyRpqVza2tl2OLSbjdOjI9fzq60GaFOr1u89eToZj774xhu1Vv0P37vlC766vPTJ/UfVavnLb7ziciTmrix3/o/f/tbDh4/+08+++mfv9OGc/6Zh9gStLfWQ9OHRkVTG9wOrdb1SShbzO4/2lDG+F6RZ4nFer9U219fCkjcajGdxkkrle8Hl7R0C8/7dh4cnw1cub13Z2b735NARTErVH48810VLRmttjDXGFW4u89RYIZzru1u9TvtkOBNkidR+/0yIYGl5XTguknRdnE2Gg/68XKpurq4s4vxocray1Hrp0tUoWdy6e3cWzSt+SQCmShJjAEZwzhlXxjiuyxxHOE633eCcnw2GWZYrqYzSDJnD2cbGZuAH4+l8MJ955XD3yuVGo7a/v/f0ydNRf4CWHC6YJQPAOSNCbbHQ6mAwP4qNgR+F8MMQgQhaJfet69uuVYQcnyHNGCMixpAxRCDGmCMEGUvWEoBRyhKxZ5ARkBDCcRzGudGaMeb7fl7ki2iBiEEQIqKWSkrpOAKRAZD7nMiyXCnFOLqeu5KbX9FNOOc3ev4tnSLnWmmOLF4shv1RnhdaK0T0gyCslCv1Wq1a19JkWez7LkM464+msyTw3WopnEdJKgvhiAsbG9Vy+Hj/8OhkyJnpdlqNerXVrJfK5eOTfrfbabcraODxYf93/uAPe53G17/4uXqt8mD/dHmp88brr337B+/lMl/tNKIobrS79Uo46g9u37m/WdAvRAjn/JqfPKRitbvEOY8XczC6Wq1qbeIkOT0bxqmslYP+dJGkacUXs0Re3N64sLk0nE4XUVIOKzJXQeBfWF876g/+7Xu3XM7Xljpk7IWNZS6c+08PtNForFJKCEFEjCAt8lRb7rgvvrDTW+oenA6T+SL02TxJ8sJu7ey22u3B2RFHkxXJgydHYPH1mzctibP5oF4KuqX6NI2O+ieyKLZX1itBeHx2GucZIHsGCAhAaS2NBoaNWq1aryGCtRaM0UoabQCY7wVSSmvAcjZLosKaRr1arVYWi8VsPCWlXSEYQWHJAjIynDuDHG7t9xWRtQQ/DOGPQM4Y0VardGOjLTi3IJAUAgEAInLOGAIAcc6RAAEQUSlFRJwx/BTnnH2qKApEdF23MDZNUmttJSwxzoq8yLOMc4YMhHDKpTAIA2NMludK5gCwodnfwDac8/eb/KDsAGCSpoP+YDaZkiGtbCFzx3Edz82ldDxve3t9baWXpbrfH01ms0W04MwqbdByBmyaJmku17qdl1+6nsri3v3HnoBeu14OPC8seaVSGqfL3e7mekvlxbe++8EP7j9649UX337xahwnkziv1ypra6uzNLdkJ6P+eJa8fO3KSrN8cnL2r9/5qHwy/O9KS3DO3/LiuyqthmXf98Ba0iqazwgwLtRJf9Rrd7/0xusn4+jW7ds6m0eSVpc6S51qoqUsdNktzebzTGavXb0BHP7ld95JUxUILHnu5e01QP70+JSABONZlnHOGUNrrFQqU8QdsbO50mzW++NZvFh4Driez7nX7q20Wu2z48Ph4GQwHU+iNHS8m1evtZtdSZJZPTwZHA/OLELoe5e2t2ul8tHR4WAyYYIFri+lLJSygJaIgITj1Wq1ZrPOEJBI5mme5UUqc6VTWaRSCuGUS2VVyFkaVasVzoWSEiy5XICl1Ggi4mByDXf6yTA1jJE1Bn4Ywo9iDBHIEMDbV9d6FVcbYAyAEIGILOdMCAFgGT7HGENLSiki4uw5ZAyfgeesMQTAOA/CMCyFRiljDQLEUTydzhCY5/mMMT/wgzDU2iRpYpQEgB0QfwObcM7f9vNiYwURBsPRg4ePC2k5AlhbqIJxDpznUse5XF9Zeu3m1cU8uXX7zixLe93udqd7cnw2jCIONo6zVFnhOS/euHx1e+PsdDCcR81q4HA2mMfVallnGQpvealWC8J+khyd9ndWVy/u7kRxfnx8Wq4Grh/6gd/pNk/O+uPJ9NqF3VY5fHKw/0//xb/uzbN/vn0dzvlGKXvMNCK4AFGaR3ExG48ClysLs7TwQv9nf/pr7Ubrd37/W8fHJ62g1K43yWW6yAWh1CoqUuC4u7VTbzQ+vnPnyd4h4w5nvFYqhZ6TpKlFZIhFURAZzrm1VlvLhXBdr1arMcbSNEFkru8JhwvAaqtVrdXSRbx/eLB31g89b7nRKpWrYSnwGfND/+HR/tHRqSeE5zjNet3zvPlspqRs1ivlUilJ0yhOiMAAAaK05Hpeu9lwHaGkLPJcaZMmmTaac2atTZPM8/xKuZqrTEoJjDPGLQAg05aQNBJZxp+O0sejGIAhEAHBD0P4YYhIhIjEiFoV7+bF9bLDBBguHPOcFpx5rmOtFZw5jkOWtJTWWkTk7DkCgmfoOWstILqu2243G80GY4yMTpJ0MpkOh0NjwPcCKaUQolQpZ8/kWTksA9GWZb9CVTjnv7KzYnMZAQ4PjsaTGTLXGuU4AhnESZokKTGmLLSqlQtbG0f9weHJKRfO9RdeWGm3P/749jxO6BLUXQAAIABJREFUGSmpTWEhl8WFzZUvvf25yXD8dH9PK8kZq9QqtWr1ydP9aZQi2KWlzsbWehQllUp5Y2MDED/88FarU/NcX0p586WrStrBZEbGMiPjKP7ddz5Yz+0/6V2Ac/7HZfe07KZxHM2nZ6PpeDIru069FERJotCJdNHudpaXlo4Pj7MoqfmlWlgpbL5II487VqsgDOr1uud5gePmSt99fLBICgTwGNVDlxhLcqm1BgAiQ58CQD8ISqWSEI6UUmvlCLdcLeWqSKKo1Wx1u90szw1ZZFgJKybXJ8O+63uNUtmofDCfTRcJEjEgRoTIXNdTSoXloFFvKK2iKMqz3BjDHSfTqtls+K6bpanW2hoSXCgtpZKIaD5lLTiO67iutTYrpOt6yJghIEAEA8Lpz7PHJ9NYWwKE5wh+GMIfhQyIOJIhaJSD1y4slV3GuANEZA1jIAQnIoczIRwkIGO01gAgkDPOgJG1luy/RwCu64aVUhAEvd7SpYsXnz59+vD+g8lkpqTWSgNBrVEvV8pplqVJ0qjXAWglN79sKnDOzyen03Y19IPhcJTEmTHoBY7nOGBMVhRSKaUpV7pZLi91O49Oj6dRXHKCXqtFpEfTuWCOLhLkXAFOp7NXXry8ubXx4MGTNJqO5zEBfPXzn5NS/tt3fqCUzQrLffGF1292W41U2VJYcgPn/ffeD3weBmUpi6985S1P+KPRfDSflgPBLXxyb29V0t/UAZzzv13pjGrBbDp5/OjJSX+cxOlGr7PabkbxYjheZLnK0FiHV91QF4VFbDabUmaD6VgbU/H81VbHdYRBKAyQsZ7rWK3mUTyZRwSs5PtgtVQKgIgsIgohOBee43qer6SMk8R13VKpBIxFRZrJYrXVqVeqJ6N+uVJe7a3IrIjidFJki3hx8+KlLI6Pzk6MJt91rVFWa98P/DCI4jhX2vc8QAQy2mjQVmqtyNx88UXXcZ48flxkhTWWIzNGA8NKtZpk6SKKtLJe4NeqVc5FkmUEiMiBcUuWbDFK1CeH01QjgAUgQACCH4HwRyEiEQKzDMGaV7a7m90GqRyQASJjHBE4QzAGEFwhOGNGG7IGkXFEJhgRAJF9hgx8KtM6LJWuXLmye+nSBx98+OjhQ0s2dPwsjZXW3W6nUqksomg+W3iezzlbyc3fEi045z9Lz/r1sNPuMMJ7j59Mp4ulep07fLyYNcq1aqk8jRbj2WxtqV2qVD588NRqUwk8payxdnN11fO8g8NDk8uZzNB3Xr/2wmg82T/p1xyUUmnuXL2wrqV8dNAvsoxx16K+sLpy4/oVK1iSyqxIHz5+Esfzzd5SteSvrK8X0qbzWLhic3Nlealzcjbkh2d/5UzDOb+125k2wkF/8P0PPxnPZqD1drf7wta247m37tyNp3MSQoShJ5ysKNzAL4WhNPp0MkiTdKVZq1eqBiBO0llUCGSdZqlW8hBhlsj+ZG6M8TgnY4EsEHmeVyqHnHEiBIAoibMsKwVhpVxWWkkrS36p3WznWh31T13urK6sHvQHLopXXr75nY8+0EW+3mrHWWaKnKxhAETgOE4Yhrks4ixjDAFIcC64MMrESVJrNi5fuRLH8d7BfprLOM2JGFnyPb69saaKbDQaJ0kelirNRsVwxwKnQmorNVmryQh+Z79/OMsBOZFFIAQgAIIfgvBHISAhAQe0iFTieG2j16v7lggRBRcMgDGwxiJax3GQwBiNRAyQc46MAcJzZI01DBEZSwsJgCurK4EfDEfDNEl836+VylIWeZF6rut5fpqm81kETGjSq7n5+5V1OOeXzfSw5LTb7bIXfnDnztlwvNJoSKvOptNmUPGEGMynlvD67hZx8c6tBwHnvgBFvFFvbq6tRPFicDKYzufTQr508/JSvbF3fLh/fNYKgpIrZoWslN1urTKL5WA8QcJWszxdpBcuXXz5pauLeTSejPcOjpJo8eK1S6SKwWg+nMaLpGhUgwsXNq5d2V1e6oYnk6+/+xTO+dteZjZXR/3+92/ddhgrC76y1G112pro0cPHusitsdxxPT/MpRKc+65rOUwWMzKyV28QZ6k1RZyfjOZhOSwHTsnjnhBhEJLFWRSleeFw7jBujXGFEL7DgBljtNGZLKRSvnCDILDWFEVRDUthuTJN4iTLG9VaKQhHcbS+uv7WKy8+HQ4/vPVBRYgkK5TMSFuylnPOOA89X1uTSglkAYA9x2WuirzY3t1pNVuHh0fH/X4sVau3dHH3clGkj+7el2kWuq5R0pAJQr/il2JlDAoPwOosziUT/uEs2R/OFYEBDoBAhgERAMEPQfgRCM8gIQAQIiCAtQHHFy+sNmuBsNrjjDM0wAiZYIRA1liyBgE4Ms444wz+PbLWAADjTCurtA4CPwxC13WFEMYYrTWRQcQ8T5TSWhlLIIRIsqyXqX9U34Zz/k6QP+FEAM1q/bDfPzw5q/g+IBSFTOM0UypWxufOGy9fm2fZu7cflV1HgFYGOp+ajMbD4XhW5J1G48tvfiZK5ujgk72js5NRo+xPkzQIvdevXyJlTsYLAPId5w/vPA4r5T/79a/6Hg5Ho3gWx4vI8/hsMR31J7O5nOVQCnm7WV7udj7z6mubEn/21lM45xfTk2mjEnjeD2594hB0q9VWp2k4xHkWzWPQWhtbaOMIt5CyUirXK9XCyFm8cIXoVGppkY/iyGrSmrq9lnCcOImMVA5jgetyzoTrhr5HlrI0k0oC41Yba7XSRpPlnDuM+56vrB1OpwF3wlI4ixaEdPXS5UoYTqJ4fXPz8uUdo+Hp40enZ0dHJ32wtlqpGKmlkowxwRgg0wTGam204MJoo5SpVKvbFzbSJL9792GcFn619Jm3X3/z7TcHg9G//O1v7j18GrgeRxIuRw4IXCuwgEKAVYpYcLzIHvanFoEI4TkEIACCPwLhRyA8wwgYgAEghgicrN7q1K9dWOUy8Rgh4xoYIEMwZA1HzhkiAgIwRGQMEIEAEYiMtRYA0CIQICIAlMvlaqWSF0WURFxwx3HG40GaZpwJ4biuEAC0bcWviTac8xstfkenURI7KMB1+oORLCQhMWAyUwuZzQq50V35zI2rjw7237v3xAMKBBjLfN+vVyvTRdRfRI165fLy6tbFrf74bGd5aTiZfef7H9dDf56mlrOvfuZm1fcPB7Pdna08SX7zm39QWPqZz7+0utqURnMFRweHh/3jTBVUQLu+skjN6eDUD3mtEr7wwuVdUf4rZwmc8wvp8SB01lZW//Dj28PRpFZ2VxoNx9IiSSSgMQqIlDZEWCpVup2u57hxlvanQ991W6XKNIpGcdSot372S19p1GoP9g8+unN3PB0TaMfhoSfKQVCvN4y149E4SRLX840xAJYxro0xZB3GwyAAxxsvIm5svVabxwsp8/XllZLnK2KS7MbWasUrqzw7PDs87Q85QRiERimtNcFznHHGkHFmyWprsjzn3N3e3gpKwdHB6cHBqQEmAmd5s9db6RWJ3Xv0KI9nRuZA4Ho+ocitZsQQQYMWwAwv3TocTtICEQEIiACA4P8fwo8DGUO4sly/vFIXABrwOTBIQESMMQRgDP8dIkJEAmKAHJn9FBIgIgBYa13XrVSrruOkMpVSCiHiOJnOpkTkex5H5jjOFbf0y7oC5/zdsryjc6WUMUYIhywlWWYRfd+fzebT2cL3g+3t7Y3VtcPjw3c/uZ2khWOxEoTM4ZlRmSyWO+2d9fUsS5S1hBzIRrPJZDr3fd8Yg8jr9bo0Norzn/zsa0ol/+z3vseZ+7mXdprtCiCLktnh/mEc5UUhHca++NkvrHW33vnk/e89vMON/szVizcqnZ+fGDjnryVDtVLvteuHg8H9R3uTKK64ouHwOC+AucAsIpEhztjK8mq71bIARZGdjgdEGHJ3kcQKaWN5/a3XXnddfzRcPHpycHh8NI9Gjgte4CqpyqWS53txFBdZ5jquwzkytABaKW0MAfm+X65UhOt4QtQq1TiOT8/6BiBJcsf3EIAhdlqNZqMB1ownk7xQSIQADBkwNNYywQQX8AxRVuRpnlYqld3d3TQvHtx7mKQZINMWUDgAWHJdZChlnmUZAHieQwDS6tAJgQtlVKb041E6ihQAWLDwx0H4MSFyoitrzQtrXWYVAgEARyYYAyBjDAIgImP478AzlhCes9bSpxCRf0oI0Ww2Pc+dz2ec86Io+v1+URSe5yFy13WvOOEvmzKc80t5/2OZMMaDIFBKCeEEnlsKQyGcJE2iODHGOq5Xb9Q10aOne5PpwmjDAH3fsdZ2ljrXrl6pVcr7B/sHB4etZvvjh4fHk3i9XW74rFHy67XG3uFJlOVS6UazYaw5nae1avXLr13pNirHZ+MP7j01eVL3eG5wGhdvvvryf/jlLywW8W/88//7zv7JT7784lfWlv/C/gTO+TWR1m/sCE5ZkR2fDD96ctQfzj3k2uQVzyvIFkpXAq/kuu1GrVmruK4DwIaLeRwnjkVgWJBBjSsrPaUpihLfL+V5Np1NjJGeJ4oiF0L4vp9lGePMEU5eSEKWZxkXz0kpgzD0PafVrDWqtcDzF4vFk6d7SZ5LqcPAdxxHG+06ju8HnU7LaDNfLIxUDJEhIufyGWssMM65tdbzPSWl67q9Xi9JksPDI7KEgMZaYAwQBTJkyIUARG2M0koZbcEGbuB5pVjpe0eDUaIRmAUiIPjjIPw4EAAZAAhr37y+1Sk7ZCQB48ifAQBrNALhpwRniEjPWGIEiEhA1hIwZAwRkJ4BqlVrlXJJKVkURZokcZLoTxFhEATXw+ov6zKc81cnT49LXqvVmoxnz3ie125UPc8jAM8PJvPZdB4pbRCoWqnITBfaSLJK5qHD27XaUq9LnMBhTDij/rDilx/snd56dLjSrgprSp5YX10bjsZG61kap6SW6qV2t8sd3i5Xltq9vb2Djz5+VCiz3Cy9cu2Fjx4d3O+PPvfyjeVm+TvvfzSZ5j/51htvrTT+44+P4Jx/2OTJckupXIMte+4sih4+OZyMFkpnl9aWC6P7w3G9Wg0833GcQkolle+7iSpms6gZlLnnjOOo5PgOF8oaslZrKZCBJW0sE0xrpY3xfd/1PEvWaDOezUqVKiKGQeB53mQ89hyXyNQa1VqprKXKsmw8nWVKecKplwNtnrHaUiFlq91tNBp5EsuikIUkYwkhK3JljQEI/Od2tresNf3Ts0KpKJOLxUIg8xwHiAiJ4TPcWghLJRQ8zbIoSQmQcQRrfcd/PE6eTBNkDIksEfwYEH48iAjAgUy3Grx2adnnpKxlwDjnCKC1ZkCIwBAdRyAiY2i0QQBk+AwgAiICIKKxRmtdCkv1alUWcjKdZFlG1iJDa6zR1hh7Nax9w+/COT8/23dfuLS81Pvg41uT8aRQWhFWQr/TqsfSnvQHjUrJE+LgtC+Qtyu1Ur0clkNt7Xw695AVVs2Sue95ly7sWEKOzGTF7cd7Z9OFtkYw7jieAKx4IsmT7nLrha213lI3Qzo4OC655YODw6P90ziVvaXuX/7zf+Zg0P87v/nPyr7bLblg5KW1jStXLq0J/E9u9+Gcv8njd+YzZJZ7zstXdtfajUG/f3h0pJW+tLXJBB6fngByApHlcjCeJklWLgXayChK2o1mWC4dnJ4gsSBwHMEZYZqkhdIAnBGrhKEmM0nmtXp9pbsUx/F0sXAdcfP6C8J1A8cr0nz/6DCRUmrZqFZCPyiyrMgLpZQl8F3HcbhUSiqTF7JQutFotlpNo5WSRZHlRJQkSaFUrVqpVUuVSgWRh6WS0nowHCRpZoBFiyjLc89xhONwxsgSAQrB3SCQSidpmhYF41wgILJ5au71FwlxAGBWEwDBHw/hx8YACIAAVuvhje0VV1hrSAgOZK2xSMSQOGNCCM4ZQ0ZI1igicBxHcGGMscYA4DMA4Lpu4AWFLBaLSMqCwPie73meYGwyj3u5/Uf1dTjnLw+fxr3W+srq2XgAhMPx5GQ0aVXD9V7reDQ/6Y83llrrvc7txwfTebLZ62xsrSLCw73DOEqsAkDtCp4qs9SsLi/1hOu5HEaL5OBkeO3yxXq1+s4Ht5I0bldKgrPeUnupXm92WrYURFGso+Rb3/7Bg6NZwxPbS7Wf+skvWgb/9P/6pszkxlKn3ap2G9UwCMvz5BcjAef8xeHBuBl0GsGdR0/Xlno3d3ejaDqejo3WnXrbL/nz+XwWRVlWxEmWJCkgC/1AIBR5HpSeKU/mi0UcF0CcC49xY2maxK7rOACkjRC8GpaWOh1iOIujeZzUKuHbn33d467R5ujktD8eF1oBUbXsO8LRhTTGyKIAIs/zpNHWkAXI8sLz/VKpLITgAos8M1JxxvI8L4ytlEqtZt31vEKqebSYTGdpIcMgXG63siwbT+dKKyEEZ5wsERATggshjc5yqa3hjHGESaLu9+NYEQISEAIR/FgQflyIQPAMMiK7XA9vbC8HAoksAhltEIAhMI4OF5xxRHQcprUhY1zHYYxrbaw1RCSEIwQnIgZYFIXW2vM8QOs4ThAEjiNOBsOlzP5dfwnO+Uuj/Xsm31hbrVerUZIwzpL0mSTw3CiOTgbTICxdWusNJ9P/rzo4+dU1u/KE/Ftr7b3f7vtOf24fjR0R6XRTdjozJRCIAQP+CAZIzChRKkQNagCiJCRqiIREDUoMQaIZICGmSEgMqEwERlVKZ5ZdttOOiHvjtqf9mrfZe6+1OPdWIVyzxA470s/z9NXF733w/vtPHv706We/+Oy5OBPou9/4+qrt//FPfzL06b37j8Dh8LiH8Djqv/Vv/OtD1/6v//uf7Jd93u+ub6euT6fDcHp+ql2j5j2l/+1Pf/Dnn76O7N95cvjw/GS/+POXL4KEjz98f3U07G5vd7f7+8X+y9UT/JJ/5+lP+m9++K0PH/wvf/p/7bf7j997XDXvlkmrNaFZrTozu7i8GMdRJJZaCdymtk3JzVhkWK2arr+6ub3djB9/+Mnxeri6vnj26ouj48MPHj78/OXLV2/efHT/8fnp8evNzWcvn0+5tCl99MH7j87vb8bpZ59/FlOKRoGwWjVaNJesqjlnACnGXCocFMK8zA8ePQwhXl1dDUO/ub0h89OTk1rr7W6fcw5Ny8K7/Viq5pynZRq6/uHpCRGP8zJOo7sTCCBzC3eaVGpdci2qEsPVvv7ki6t9xR2CO+BgwPBXQPgrYIAAZYIHcidWM390tPr+R/ebKKVmN8Cc2ZkhLELMxCkGuMOdic3MzUF3ICzMbG7VdLffxRBPTk/gJkEItCzjZj99HIa/X1f4Jf/B9Wc/nDfHJ0f3Do5u95vz83uq+Pzl6yBclunFm5tZ+WyVyG2X67c//jpU/+xnPyFKkUNg/sNv/V5l/Pznnx8fDirBHB89fpCG/tX19v33HjZJLq83ifD8s8+evr5aD+29w4PQpJf7zWa7++Nvftdr/tmnT//RD//yg/uHD9bNq+tJmuH73/xaZLy8fT1u99eXm99vDv7h6Yf4JX/r5unrVj54cP75xeXt9fW6aYrm2aorkVOK3rTp9nZDRGenJ7XU7W6XQpNiI0zm1rTd6vBQYhL2b33yyUE/3FzffPrFF8s0f/DoMSA/+ulPs5Wz8zMr9fOnT8clN02UIHE9XN3cJAp/45vf3u/G3e62j6KqpdZSirsTkbvXandIuGnbh48e7vb7y8vLruuur65SkCePHtdaX19e7XZ7A4UQqlYzc3cQxRiSCBwSUy55HEc4ggRikiAcoxrmJavX3ZT//NU0LhrI1cnAgDPM8FdC+P+HAAdARO7+4Gj4w68/SmIZTkoRRlSJAhEHeQtQNycQMdwdICHATESalMa8bLab1cE6pcaNhqGbpvn6+rpp2o85/md6gF/yD8bXP553xTXGlEQOVut5WqYlHx8f51w+++J5qRbaRku9d7R+eHb07PXl9XY6HJoAN0PbD+thaJpgpq+vrqvhw/feizG8vLoB/GBojg5W85wvrq8ZHtT2S80lT1qM+I//xrcfnJ5dX1//4M9+SCQiPOf64PzsO598/OrNqx//9MfFtInNN9r1327O8Ev+o/nVP1luD7q+iXFZZlWFWa1VVU2tWE0pLnlJMZ6fn5VS9vtRiGOM9BYrzN1Xq1WMIaYYYwKQl7zZ7Ji4Sc3m9rbUOqyGru3Gccw5m9fitpl13o9//O1vff+73/3Lz54/f/E8z5smRmbSt8xBIlJLMXciOjo9uXfv3ufPni3LTETjOIWYzk9OaymvLy5rLinEEAKAXHJ1a1PjIDVz9xCCmk3zpLVKCBxFGMSxqGrN17P/6PntNhv+PwQ4/soIvzIiuD867L739fttAMydU3XAijAxMTOIcIeAIAyHuzOB4G1qhr5XwrIsBi+lEKTrOgIuLi9zLp9w/M+bB/gd9/fymx/bHEWayERY5oWZaqmqWkpZShEREIKEvu/8HWJ2dwJYuKpO0xRjSjHUqgBqrTHGEMM8zSUXJiYidx9WQ7gjIZelmjLHZZ6/8cknX/vwaz/+8c++eP5Ma26aFGMAYGYkkdxrrf0wMLOadUN/eXUJoiYlVZ/mWVjMbLfbC0uSQExEbO5gEpFaq5kTkd1xM6DkAhil1jhGm9mWi4V/+MV2u1QiuONXQ/g1MIuZPjrq/ujrD4dGxuJmzFSIcIcAFhJmAES4Q+ZuRuR92x0eHISUxmm83W5CCHBummY1DJdX1y9fvvxE0j9Yv4/fcf/x9OrnVBA4Cbq2zcucl2ymtWpKiZmISVjMjFncnYkgUkzvwAzmqgqAQMJipjlnESEiNYUbgQCUUiSEYRhWw1DVCb7u26Xq7T4bsbgGpnGetZYgfAcgCZGALjVHx0dLKZeXlyCSGFLTiEgI8fr65urmWkRqsZRSmxpz03fMnZkBiAQAtVaD3wFQa1USInLYJvuPnt9sFgUIcPyqCL8qAghwFjd7dNj90TeeJHIzBzkRubmbihCLwAFyIkJVcyNGE9N6WDVtMy/LtMxt2y5zgfvjx48B/OLTT3F1/d8efoTfcf/+/PyllnbomkAxyjTNyzzHEK3W1TCExAARyNzgyKUysaRYSlWtJWe4E1CrukMoEEG1mpmaAggSmEBwM1PVFFPX9epkhiRo+u5mO17d3vYpHKyHaZ6XeRYRYjJzIhne6pu2vby8ur6+iSmu1wf90APedt12s3v5+nUIAU4hhCYld5/neckZgLsDiDGGEGquRdXJHTB1dQ1CN0X+6YvdLiuROwyOXxnh10IMgMjcHh323/vocZsc5nfMjODMdAcAEZgZakQgBhwpvhVCICEzLHNmovfee+9wvfrFZ58+e/rsvtPf6x494YjfQVdt+O/fW/309mo3Tlbrg3unIvzZ50/3213TNG2MKQRpEgBzc3dmNnV3A5hZTGupmYhKzsuyWDUiZhYATOTuy5IBb9sWrlaLmTEJS6yqDjKzGEPTpHHcT9MYQhBhd8QQnGlZFpHQ90Pbdjnny6srIj44ODRVNe37thuGaZxvNrfExAjmzvSWmdVa/f8lwkxs5mqmpgqGI5C/zv6z17tdVoI4zMnh+JURfi1MMAJAYq6Pjlff++hxF11rIWK4Ak6gd0AAOWIMJChLgVlMqWkaEp7nuRYLIZydnh4fHly+ufzi1auSy78ZV3+nOcXvoP/hCH9xPqxXazO7ub4+Pz16/Pjxi5cv/+RP/tTUDterrutiatTNqrpDJACkqrWW09MTZtlsNqaWl2W/28/LIlG6pokc+65vYrO5vb3eXKcmMbuWUmt1B1hM1cwAT6lh5pxzrUqE1CQiMEsIUqu6m7CwSK21lLparQ8Pj64uL69vbo6Pjw4OD8dpGaf9vCxMgUXcrNRKRGaGd1TV3ISFiN09a3VikbCd7J++utlUAzGbATAwYPhVEX5VBDCgxIDAQVTd/cnxwR98dN4kVnMmmCoAYQJA7uQIQViolmJqIsIsJGRmWiHCfdethxWIXl9cXF7dsMg98JG6BCFAq66GoRv6/X5fciE3AKVqUSPiWhZ3JREJYb1aHayGoR9CkN1+NCt91+3Hcbsf3XFwcHh+ejbOc1Ub+hDEx92OSHKlJjVtk16+eX2zuWUJ7tSk1DUdMaAWmM10P87F/P6jh4d9mpacmkhuXRNX6/V2nF68evNqWW4SrYfh/Ow8Br64uIiMhw8e5qo//OGfjeMUhLuuTU1n7nAnCDGZGkBV8+NHj4d++PzzzzebLYG06lymEKVr2kjxaHVw//z+tBs/e/l0P+/gSu611lKqG9SVyZumZZZlWWopDk4pOUCMEAK5izARiYiqqSmBiDjGtN/vx3FcHayPDo9r1XGaqqk7YogKLPOcczYzvGNmuRY4WAIRqpmEsF3qX7ye97kyA07u7HCwwQHHr4bwayDAcYfwlhORuz847v/wG+8NhFpUiQhVqDoJgcmN74DcFHAikRAAqKmb8zttm5rU5pyZZb0+2O12r1+9XvIswkHk9PS06/urq6vb29smJletJR8fHp2cnDx78fzm9rYbVvtpbpr0/gfvl3qnzHMOgtPDXu2Oh9ik1LVtr3an7vbbcdx6NYDnJR8dn8xl2W42gemOSDCluuT1qm/bZObzPBczB7Vtd3AwGHypRbXeOz09OTq6ePPm9cVVutOkEKM7zWUZ91PXpPVqqHmpte52u1K067ogQswEsAgc7hZCVK2np2erYf306ecXby66LvV9R4A7zFCWIiz37t1jojcXb7a7TV0mIjDBzbRa1kpCKbRaq1ZTs6LWdU0K3Pcds1xfX5VS2rY5OT5x0+1uV6oC5I6qXtWqWkyJhYW5H/ompWkal1rykpc5m5m/hTuquRZzCRw4cNgU+tGrm11xAIS3HAQ4fj2ELxcR3B8d93/w0aMOqnBIqO6ohQnMJMSB2d2YnDiKSK21lEJEIuIOgjZNGobV6enZ+dlZrfXzp09fvHjRtAkOESGi6R12uBmtGFpLAAAVNklEQVQT7p2c3rm4vr64umravm3bR0+eqNVffPapmo/TRG5D1zLzsFqdnp5N07Isue97Intz8Wa/35tWAj948IBY3lxdBmF2h3nbtNO8wDwEjjGo+bIsYAkhPnr0uOkaJ9xuN8++eHZ8dHR8cLBMk5sTMzHHJm5348XllTsOVt3p0VGK0rWdmb14/qKqNakRESJiJiJ2dyIysxBkGNbjuN9uNuv1qm2TqtWqAM1T3m63zJRSJJCqLtN+mUe4iYibF1NijpKEJcXGgZvNFuyPH9yPKeVlubm9ncbRHUfHhyJ0e3OrajGlGJuSy7zkXNXc7hDRer0+Ojra73e5LFqtVr1jZu5Qs6xeYdEtMt8Y/7M34y47kbvjS0T4sjGzmT046r73yePD5G5cnLVMBGciIWJighOBJQBk7wBgYQKZmYjEGNu2OTs7a5p0eXW522yHfsilLPNctWq1eZnnaRawCEWRruvUfZoXB9br9b379811u9/nUjfbbZNS1zbX1zchyoMH90vV7W4HQ5tiiNJ1bc1LCM39+/f34/yXv/i5aWljbFIgkmWezTyIuBuLEPF2P8XUfv973z86Wlez69ubl69fMZOWUnNOElgYLDGFeSlvLi7mJQ9de3Z6GpiOjw7Ozs6fP3/5+vUbIgQRIhKRGGOpb5nVpm1WwwEcyzyWks1riskdy1KmaR6nWUQAixKbkFTzftxO06iqzGJuRJSk6buubweO4eZms5vHs9MTgHLOZla1TuNUtcYU3d3UhKXr2iXnsmRhzqrTvFDgGEKKjQgD5uZucPdaipnnUmdVEQfxZsHPLsZddRDBHV8qwpeNiRzsbudH/ftnw4fnR8G8uBoI7kwgN4YJOTi601vM7ubmzKzuIMBBhK7r+r4vpUzTtBoGYq4lM7OrXV5dzvNiajAX5hBCqYWYQ0ruUK33Hjw4ODrMyzJOU5NSELm8uixa7t07Y+btdnN5cWVqDx88ODs/czPmICJwv7683O22cA9M7m4OdyeQiBCzuZei/bD62te+frhaj8s057wsc855GkeYxhThDmYWBuj2drPd7SSEw/XaHQQcHR13bbfZbvfjhgARbpqGmadpBpCSDP2QUmtat9vb3biv1bTUtu1yXsghzES8m0d2biU4u1mdp3maJ2IwiZvDkWKKsYmpUbPtbs/CeMtDCABtt9vdbh9TbNvGzYmYmUopQUQYc85LLquDgxjjOM1RWMjdYU4OKqWaWc5ztVqVvtjkl4vP2ZgAuDm+XITfAAIB4lCCf/vJ2e8/PmfPYHZwNTWr5BoYLMkBImJih7s5ETG5mTFTCEHNiLhr22lZSKRJiYEmBTfbbnfzOO+2O3cXkRBEtZgqhaAGhz968t7h0VGeJiYqNddiVWs/dEfHR9O4m8Zxu90tOR8cHJyenhKRqsUUu6YRsuurm8vL61qruzOJmZtZEMm1EPHh0eHp6emwWi2lvHzxMoSwXq201lwWADFGe4ffMTN3T4ndKRfbj5Mwn56cmPnV1Zucl65rh6EHaFmyO/qh6Zq+LDrNu/203Y7zfvbjgxOti9ny8PQkmtWqt8ucSyV1d3VzIXJ38xpDZAq1lly0qEqId8ZpKqXGICnFEETVt7tdrRqCMLO7xxiZOecMIIaQS8k5d33HIZRaADBAICdyh9ZaygJgV/2nr/avxgoQwwE4yOH4UhG+dAQ4CAwG4Gz++4/Pv/n+aWRXVTOrptWJWQKD4CBiegtwd8BVmEIIILh5KaVpGnOqVVMIQSQI1VqjhHGaX715HWJwVbgzExGImTgMq3XTtMziZifHRzc3t5vtth/6pm3avinzMgw9Abv9hkCr9dpMTW0YhoODAzO9urq5uListbp7zpWA87Oz3Tjudrvz8/Ojk+O+781smqbr6yuRsBqGWqqqAmBmuFdVd2fmEMIw9Aerrqju9/O0LARiEMGvbm52+13XNP3QM7M73NF2STheXFzPyxhSfH15a0h/89/7m59++pf/5//xj06GLplGkdt53E+zm7tVrcrMTUrE7uoskQjjtEzzTGARqWq1FmFumhRjMEOpCoAI7m5m8Z1lWWqtKSV3z6WoaVENMYCYRBjOrjDNVUE8V/zoxfbldnEOgMMUIAcDii8V4UtHIAcBRgATjCL4/nH/5N7Rw9MV15lgTqQOctwhgJmJyN3VjLzGwMzi7kQAKMYA51oNgJvBtU0NEe3mcc45BBFirQVmIYSU0sHhYWq6l69ebzf7g/X661//2vX17c8/e0qB+75NMS55Pj05WQ+91jmGKCLjOLp723VD15VlnuYl5wJHCCHnev/e+be+8/uvX7++vLrq+75WrbWYuwHubmZ5yfvdmFLDzGYGwMwAmBkRD0N3cnRAhIurW1ULIdWcm5jmumy3G3eTd4iEmZuGa7bLyxsAoW1evLo6PHn4X/zD/+qzp5/+/f/0P+F5f9Z3XZNux904jU1qYpRa6zItIUhMMs9LXgoRsQQ1A0SY3T3nYlpTil3XMoeqWkoBwMyqKiJEZGZEFEIAU611LrmqMhExk0gkNy0Kmjx+cbF/s52uxwIODoIXdtwxMGD4UhF+owgAwQngnu0PP7736HSlpgUwIjYnBxGYiJnhMFeCAU7EQsRMzJxSAjjnbGbuaqZB4rIUd1ut+nXfrVZDXubLi0szj03qV4chxmfPXux3++Pjo/efvHd1ffXZ06dt033/+39U3X74F3+e3e6dnpytD7quyXleprGqgu8QmTOzmxMQYxSR9+48ebifpoury+1ud7O5hYMIEsKwGsb9/ObNxbLkw4Pjvu+1ZDcvWkMIAKoqC/dtIyK73c4dIlGEmXhZllIr4MwsQmbmd8znpSx5iewi/Pmr2+H8a//1//g///gvfvi3/t1/+zDqvXXfd+04L+M8hxhTSqY6z6OpClFVnZbFzGNIMUWRQOCqWlVNlZhTiCEErZrLAqYQgpmJSBBxBwhgcsDMtNaiSu4kSSVGWpKrcvvj19ufvtg43iGCA3ACHL8RhN8KYTLzNoXHx8PX7h8etKnW6kzExAQm4reIiapWswoHgUIQYRYRAletgBGBiFR93E8xxqZJbZsi0zhN+/2eSDgIs3Rdd3Oznef5cL3u225ZltvN9vz+g3/lX/3XKMQf/N8/+PzZs64J7z+4P/TdPO+WZQopVdUlF1djYVOFI8VIQNO2xwfrtusXzReXl5vNJqbUd13fd8Mw7Lb7168vzPzw8Pjo6Bha9+O4n8au65h5WuZcCrs176iamYvINE95yfRO33dd107TvN1ssrqaM7nAiOX5xRbt0d/+D//uP/vRn/9P/91/czLIkGToWjefS5lzrrUScwhiplbU3NRN1dwQgqSUCKJm5m+ZmVYF4O4ggN5ydxHp+56JSi3VzUwBcjNVdfcgwmwpNK925dOb8c3NrE5GDnP85hF+K5jIQQ6B17Mhff/jRwetqBsIjLeYOQQR5qJVVQEwSEQAEEiYCeYwYZIQVN3MhaVqZiIzLVWneXFQFB7aNqRmtxvneW5TI0QE7PajhfC1jz/56JNvvPjii5//7C+X3eb0aHV2dlLKUmo+OD5St2Updak5L+4uIkykVUF+dHgYUjT4drvd7XYxhPWwapvW3cxARKVUJlmtVmp2c3tbtKaUzKxqNTi7931/dHS42+1vb7ellKZp7t07m+f56upqGIauW93cXN/c3EBSCnK4anM14/Ti5RsG7t1/8PrNm93m9uRwHZnIdD10EsJ2P95ubqtqSi0TuTuRE5mqm7m7EXEMjYgYUPI7taga3xEBExxmRkQhBGIWYTd1Mwfc3dzdPAqaJr7a4Z98djkp6A7c4HDHbx7ht4IgBCeCEbvVoyF9cP/wg/PDFKSW4gABUViYFOYOItAdkKoSUQoRbndEmIVUvWs6dwM0hEAE5jBO036chWnV9xLi1fX1PC3CDLNairsrczMMj997ry7L7mazvb5m0vfee69tk7qFGIp513VtjNvtdhpHVyNmrRWARBmXmZiZSFWFeegGgNw8paZpkjvGca9qzKymDmLhWpWYAJjperUOMTx//mKel6ZJw7B6/OiBub14/oJZSqn7/c4dpfpB333vO99YFD/8yS+ur67uH644yLOXr8yQYiR3djs+WrepWUrZ7LbTOIHkDhOHwICaETGbKgA3ELOEoKr7cVxyBoiFidmdzBR36C0WDsyAO0FrJTMCJMiU7cVOP78Z50oCMlQA5DD8NhB+64jgjjufPDz6+uPzNgWy6rUQQyQA5u4AiAC4qjJTCI2ZkYOZiRxAm5K7A2BmesfMVNXUhDnGOM/zuN+rmYjUWmMIZrYsc0qRCHeWZW5TfPDwUdP2+3ne7sfV+uDRwweuS15mAiJLrXXcT6XWKZecl/VqbWb7/T7E0Pe9OzkQRAAQ0ZKzmw1dIiKtVlTVNIRGVavp2enZNI0///kvhqFfrdbzPIcUj46OxnFk4PrmRmtNKe2n0sbwB9/9VlH60x/841r0o/cfMdOz589rLUzMRAzrmkaimFutuszZ1JkFRCJsbzkRMTMAM6+1EhGIcimqzszV1MzhrFpAxiGyMAsJiMFG1bQGkHvYGf/s9e2bfcVXhPBVIMJbji7xN56cffjgiKyW6hQb8epaHCACM6kqEYUQARDAxO4qzMIMwN2JqNbq7iJCRG6mVUWkaZpa6zzPzKw1C7mqTfPk7jGIAw4/Pj0ZhlVMcRznN1fXD955+exp0zRnJyd9247jeH11s+Q8Trlru8ePH+3348uXL2JMIYhCu64HMM8zEQEeQuzbVGvNuVZVELVNV2pl5pOTY1V99uyZqrr7PM/Ocn7vvohM03R9dWWqTBRCUrOqxoH3+xHqbduuunazuXW3mFLJOQVJMVRThwMwg6mZQ6syA+5muENE7k7E7m5mauYAc3Agl6xqBFarRAgxsjAIzMRuDOcQMuQXL3fPNktRJqru+EoQvgpEhDsU3Goj/uB49eHD09ODzmomd7xDRCCoVmYWEQDkuONWRYQJ7iAidzczAETEzHCYKoBhGERkHEcza9t2t9uWUkzNvIYQyOno5ODs7GjJBSA1u7q6Xq0PYozXV1fr1XBydByYp2ka9xMROWi1Wp2fn282m3mez87Obq6vb7ebg4ODrutqraZWSnZ3YqpVAXJgyVk4snCKIswSwm63q7WaW4pJAZbQNGlzu9nvtjGmnDObK4Wb/cTkyauqF5P10LnmrOqOZZn7rj06WFfXnLOq1mpmXlVzLuwuTAADcDczBwiAv1PNRAJA6qZqbnAoMzu9FSI7QAymdLEtL3bz681EYAFVV3xFCF8hIoBxx/Woi9/58N7pKqYQcjUJQcigJVf10AYmdiNmM4ObiLiZqgZmIgKRsJipmQkL3B3etg0zz/PUD/37T9774tkXlxdXMabUNl3fllKIFGxBgjuiyLIsfdvVWrNq2zQl581my8wxNG3T9ENLxCkmrdq07YP7528uLp6/fNU3zdnJMZg3037cT17VyVPTMPO8LPtxBND1fZA4T8s8T6XkRw8fHh4cmOnNdr/b74Z+GMfRDE3T7Pd7V01Nc32zKSULwd3NiZmEAZCauVtK6WC9ZuZccl7ysixVvb7j5kQEEAH+DgAH3B2AA8QCQFWrAs6E6uRGQYI0EUK2r3S1qz9/uV1AYCFXcjd8ZQhfHQYccDAxu5nAzg+7h6eHj04PumBwLWpqLoAEAYiZ3R3uIuLuZsYEZgaImFTVzAILzEAeYmBmuLddsx6GaZznaVb1tu9Xqz7nfLO5mZccRAAcrNZaCxxE4Egpxt12u9/th75PqYFDgizz4o6mabquT1Gmearm98/OIvObq8unr17kpa66IUQR4bbtur6rqiDq265qncZ5mkYiOjw8YKLN7e1cTc1jDEzsTqo6TRMRMdFutzMzFqm1igiTAwghiIi7xxibpqm1LstCoFyyVq9a75g5HP8SIgLcHf8ci7ubmVY3BQiRPSXmGLeVXm/m15t5t1QncWJYBZwAx1eG8NUhkMPxFoMZ7nBj+Dc+uP/BvYM2wt7SAIMTwMwEdwKYGUQAmAmAA26mZoS32EECZhIJRORW3T1IZOZaTVi6rhGh/TgtSy25dF03rIbtZjuOY4icGu77TsDkLhKsVtWac5mXDFDTtgTkPBs8pvTB4yeRwxcvnz999VIr1v2qadJ+GpumefTokZrNy8wsScjNiKVtu+12e3V9xcQUQ62qWmJsmtQuyzJN07JkVQXATO6mpjGFKIGJmCWEYGbMJBIALMvi7qpqBn3HzNzwFoFADofjX8JCRHZHjYwpiAiDfbPo55f7l7sMCBE5HHC4Ed5yfGUIXyESuANGABPuOAhwAKsuPTpfPznuj4Zo6m5wMxYxc4czMTPBHeQA3J0AInJ3VRXmIEJMd8yUmcIdCUys6qpK8HgnsLAsy1Krtm1Ta11ynqapeu37/mAYYoymWuaZmGsupVSAHTbNc63F3MB0cnjUxDjOyz4vWtUrmn5lcDM7Pj4BMM5TrbUL4ehg3fQ9QL/49NPtZnv//n2JNI3jNM8pxvPzB+6+3Ww2+3GcJzNLKQIuwoAH4sCBCKpWawEovCWqVaveAdjcVM3U3PCWOzHjHQfMzQEiYgkEuBsDMYQKutyXl/v6+nZUVRDIccfxlgEgwBkwfEUIf80Q4PgXjvrmyWn/wb3DLjJAZu7E7gY4OQxw/AuBQcSmWmsV5hAjADd195TSaugCUylFVavWO0TcpDh0bVXdbjZN+xYR7bbbpeYYAtzdHISSS5SQQjR3Myu55LwAdGfJyiIxRsBjTACWeT4+XJ2dn005F1UWYWI3z2oxRgnBza6uruZ5TimBg5kvy+LAvfN7IcT9fg8tblXVmialFGvO4zguOZs7iGotqgqAiUkCEZlBq4LJATOYVri5w+FMjH/O74CYSYSJWYhMi9m++BdX+9e7sqgDDDjBHX+9EP6aYcABB4jY3QhYden3npzeP+ibyLAiuGPuZCAjAkDugBORm7kqiQQRuDu8VmVG13UxpJyzqoqImbo7sxDLfr9r2zaE4O4xJmZiMlXdbDZ5nlNMBidHk6KbmbubmTu/k5ellBJCIKa+67u+L3lZdcPZvfPdOG53IzE1qUmp2U3jnBdVNbPwTi01hNA0nbuXUmJMWm1Z5tSEtm1ynvOShdlcp3Ecp6WUauaAu4OZ/I4Z3QGrOzERyAF7B+4OEBHeIYMAEgKLUIgKvt2Nz6/3L7dlUQNAIIcRQIDhr5f/B0PLyAencCmvAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {}
}
],
"source": [
"from IPython.display import Image, display\n",
"import os\n",
"\n",
"# Directory containing the images\n",
"image_dir = '/content/yolov5/runs/detect/exp8'\n",
"\n",
"# List of file names to display with .txt extension\n",
"# top_five_sorted_text_files = [\"A0333.txt\", \"file2.txt\", \"file3.txt\", \"file4.txt\", \"file5.txt\"] # Replace with your top five file names\n",
"\n",
"# Display images for the specified file names with .png extension\n",
"for text_file in top_five_sorted_text_files:\n",
" # Change the file extension from .txt to .png\n",
" image_file = os.path.splitext(text_file)[0] + \".png\"\n",
" image_path = os.path.join(image_dir, image_file)\n",
"\n",
" if os.path.exists(image_path):\n",
" display(Image(filename=image_path))\n",
" else:\n",
" print(f\"Image not found: {image_path}\")\n"
]
},
{
"cell_type": "markdown",
"source": [
"# Evaluation Metriss"
],
"metadata": {
"id": "zGKhyUa8T8ZI"
}
},
{
"cell_type": "markdown",
"source": [
"## Classification"
],
"metadata": {
"id": "3ueE25EcUAyg"
}
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "5d5_6X9NT0gJ"
},
"execution_count": null,
"outputs": []
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"provenance": [],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}