{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "authorship_tag": "ABX9TyOK/tfXqNBMTnLqQ6UmtznZ", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "6-HEO5ZZjkJl" }, "outputs": [], "source": [ "import zipfile" ] }, { "cell_type": "code", "source": [ "# Path to the training dataset zip file\n", "train_dataset_path = '/content/sample_data/WCEBleedGen.zip'\n", "\n", "# Path to the testing dataset zip file\n", "test_dataset_path = '/content/sample_data/Auto-WCEBleedGen Challenge Test Dataset.zip'\n", "\n", "# Extract the training dataset\n", "with zipfile.ZipFile(train_dataset_path, 'r') as zip_ref:\n", " zip_ref.extractall('/content/sample_data')\n", "\n", "# Extract the testing dataset\n", "with zipfile.ZipFile(test_dataset_path, 'r') as zip_ref:\n", " zip_ref.extractall('/content/sample_data')" ], "metadata": { "id": "m5dvbzIJjnFn" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "# Organize the data directories\n", "train_data_dir = '/content/sample_data/train_dataset'\n", "test_data_dir = '/content/sample_data/test_dataset'\n" ], "metadata": { "id": "zqm-6MH9jnH7" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ "import os\n", "\n", "# Set the path to the extracted training dataset directory\n", "extracted_data_dir = '/content/sample_data/WCEBleedGen'\n", "\n", "# Define the path to your training and testing data within the extracted directory\n", "train_data_dir = os.path.join(extracted_data_dir, '/content/sample_data/WCEBleedGen')\n", "test_data_dir = os.path.join(extracted_data_dir, '/content/sample_data/Auto-WCEBleedGen Challenge Test Dataset')\n" ], "metadata": { "id": "LQ7GMenHjnM4" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "import tensorflow as tf\n", "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", "\n", "\n", "\n", "# Create data generators for training and testing\n", "train_datagen = ImageDataGenerator(rescale=1./255, validation_split=0.2)\n", "train_generator = train_datagen.flow_from_directory(\n", " train_data_dir,\n", " target_size=(150, 150),\n", " batch_size=32,\n", " class_mode='binary',\n", " subset='training' # For training data\n", ")\n", "\n", "validation_generator = train_datagen.flow_from_directory(\n", " train_data_dir,\n", " target_size=(150, 150),\n", " batch_size=32,\n", " class_mode='binary',\n", " subset='validation' # For validation data\n", ")\n", "\n", "\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "l6dPYUqAjnPO", "outputId": "8465f9e5-ce7f-4ed2-a637-9052bf110b2e" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Found 4190 images belonging to 2 classes.\n", "Found 1046 images belonging to 2 classes.\n" ] } ] }, { "cell_type": "code", "source": [ "import tensorflow as tf\n", "from tensorflow.keras.models import Sequential\n", "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n", "from tensorflow.keras.optimizers import Adam\n", "\n", "# Define and compile your model\n", "model = Sequential([\n", " Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)),\n", " MaxPooling2D(2, 2),\n", " Conv2D(64, (3, 3), activation='relu'),\n", " MaxPooling2D(2, 2),\n", " Flatten(),\n", " Dense(128, activation='relu'),\n", " Dense(1, activation='sigmoid')\n", "])\n", "\n", "model.compile(loss='binary_crossentropy', optimizer=Adam(lr=0.001), metrics=['accuracy'])\n", "\n", "# Train your model\n", "history = model.fit(\n", " train_generator,\n", " steps_per_epoch=len(train_generator),\n", " epochs=10,\n", " validation_data=validation_generator,\n", " validation_steps=len(validation_generator)\n", ")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "b5DsWJjI1cao", "outputId": "955fc3b8-8216-4220-f95d-5c5da2fbed28" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/10\n", "131/131 [==============================] - 181s 1s/step - loss: 0.3123 - accuracy: 0.8814 - val_loss: 0.0428 - val_accuracy: 0.9895\n", "Epoch 2/10\n", "131/131 [==============================] - 160s 1s/step - loss: 0.0743 - accuracy: 0.9737 - val_loss: 0.0466 - val_accuracy: 0.9847\n", "Epoch 3/10\n", "131/131 [==============================] - 171s 1s/step - loss: 0.0316 - accuracy: 0.9883 - val_loss: 0.1191 - val_accuracy: 0.9713\n", "Epoch 4/10\n", "131/131 [==============================] - 158s 1s/step - loss: 0.0283 - accuracy: 0.9928 - val_loss: 0.1047 - val_accuracy: 0.9818\n", "Epoch 5/10\n", "131/131 [==============================] - 159s 1s/step - loss: 0.0123 - accuracy: 0.9976 - val_loss: 0.0974 - val_accuracy: 0.9837\n", "Epoch 6/10\n", "131/131 [==============================] - 161s 1s/step - loss: 0.0023 - accuracy: 0.9995 - val_loss: 0.1689 - val_accuracy: 0.9771\n", "Epoch 7/10\n", "131/131 [==============================] - 161s 1s/step - loss: 0.0018 - accuracy: 0.9998 - val_loss: 0.0591 - val_accuracy: 0.9895\n", "Epoch 8/10\n", "131/131 [==============================] - 159s 1s/step - loss: 0.0171 - accuracy: 0.9947 - val_loss: 0.0362 - val_accuracy: 0.9914\n", "Epoch 9/10\n", "131/131 [==============================] - 158s 1s/step - loss: 0.0045 - accuracy: 0.9986 - val_loss: 0.0446 - val_accuracy: 0.9914\n", "Epoch 10/10\n", "131/131 [==============================] - 169s 1s/step - loss: 0.0492 - accuracy: 0.9862 - val_loss: 0.0800 - val_accuracy: 0.9895\n" ] } ] }, { "cell_type": "code", "source": [ "test_data_dir = '/content/sample_data/Auto-WCEBleedGen Challenge Test Dataset' # Update with the path to your testing dataset\n", "test_datagen = ImageDataGenerator(rescale=1./255)\n", "test_generator = test_datagen.flow_from_directory(\n", " test_data_dir,\n", " target_size=(150, 150),\n", " batch_size=32,\n", " class_mode='binary'\n", ")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-olZuu6R1cgV", "outputId": "2fc323c3-5dbc-4864-d751-ed28aab4c1ce" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Found 564 images belonging to 2 classes.\n" ] } ] }, { "cell_type": "code", "source": [ "predictions = model.predict(test_generator)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RkVc-2jA1ci8", "outputId": "071bbfdd-1ea3-4b5f-d590-f30b62b40f85" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "18/18 [==============================] - 7s 385ms/step\n" ] } ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "import smtplib\n", "\n", "\n", "# Step 1: Evaluation Metrics\n", "# Calculate and gather evaluation metrics here\n", "# Replace these with your actual evaluation metrics\n", "accuracy = 0.95\n", "recall = 0.90\n", "f1_score = 0.92\n", "iou = 0.80\n", "ap = 0.85\n", "map_score = 0.82\n", "\n", "# Step 2: Excel Sheet Preparation\n", "# Create a pandas DataFrame with your testing results\n", "# Replace these with your actual image IDs and predicted labels\n", "image_ids = ['Image1', 'Image2', 'Image3', 'Image4', 'Image5']\n", "predicted_labels = [1, 0, 1, 0, 1]\n", "\n", "results = {\n", " 'Image name': image_ids,\n", " 'Predicted class label': predicted_labels,\n", " 'Predicted coordinates of bounding box': ['x1, y1, x2, y2'] * len(image_ids),\n", " 'Confidence level': [0.95] * len(image_ids)\n", "}\n", "\n", "df = pd.DataFrame(results)\n", "\n", "# Save the DataFrame to an Excel file\n", "df.to_excel('auto_wce_bleedgen_results.xlsx', index=False)" ], "metadata": { "id": "28eA4pqs1clB" }, "execution_count": 30, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "z3Pgl-AX1cpK" }, "execution_count": null, "outputs": [] } ] }