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--- a +++ b/test.ipynb @@ -0,0 +1,2129 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from keras.utils.layer_utils import count_params\n", + "model = tf.keras.models.load_model('./models/{}/{}.h5'.format('R2UNet', 'R2UNet'), compile=False)\n", + "trainable_count = count_params(model.trainable_weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10050241" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainable_count" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "<PIL.Image.Image image mode=L size=256x256>" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "from tensorflow.keras.utils import load_img, img_to_array, array_to_img\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import cv2\n", + "\n", + "\n", + "def overlapMask(model_name):\n", + " # Get Array Image =\n", + " image_to_predict = img_to_array(load_img('./sample_images/image_to_predict.bmp', color_mode='grayscale', target_size=(256, 256))).astype('float32')/255.0\n", + " gt_image = np.squeeze(img_to_array(load_img('./sample_images/ground_truth.bmp', color_mode='grayscale', target_size=(256, 256))).astype('float32'))/255.0\n", + " # Reshape to model.predict\n", + " image_arr_reshape = image_to_predict[np.newaxis, ...]\n", + " # Load Model\n", + " model = tf.keras.models.load_model('./models/{}/{}.h5'.format(model_name, model_name), compile=False)\n", + " # Get Segmentation (Predicted Images)\n", + " mask_arr = model.predict(image_arr_reshape, verbose=0)\n", + " # To Binary\n", + " gt_image = (gt_image > 0.5).astype('float32')\n", + " mask_arr = (mask_arr > 0.5).astype('float32')\n", + " # Reduce The Dimension\n", + " mask_arr = np.squeeze(mask_arr)\n", + " # Overlap\n", + " gt_image[gt_image != mask_arr] = 0.5\n", + " return array_to_img(np.expand_dims(gt_image, axis=2))\n", + "\n", + "overlapMask('UNet')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [ + 11.837, + 10050241 + ], + [ + 6.563, + 8202053 + ], + [ + 7.73, + 7851969 + ], + [ + 11.973, + 9162753 + ] + ], + "hovertemplate": "Model=%{x}<br>IoU=%{y}<br>Time=%{customdata[0]}<br>Param=%{marker.color}<extra></extra>", + "legendgroup": "", + "marker": { + "color": [ + 10050241, + 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plotly.express as px\n", + "import pandas as pd\n", + "\n", + "# Data\n", + "data = {\n", + " 'No': [1, 2, 3, 4],\n", + " 'Model': ['UNet', 'AttUNet', 'UNet++', 'R2UNet'],\n", + " 'IoU': [0.936, 0.928, 0.936, 0.915],\n", + " 'Time': [7.73, 6.563, 11.973, 11.837],\n", + " 'Param': [7851969, 8202053, 9162753, 10050241]\n", + "}\n", + "\n", + "df = pd.DataFrame(data)\n", + "\n", + "# Plotting\n", + "df_sorted = df.sort_values(by='IoU')\n", + "fig = px.scatter(df_sorted, x='Model', y='IoU', size='Time', color='Param',\n", + " hover_data=['Time', 'Param'], title='Model Evaluation')\n", + "\n", + "# Customize the plot\n", + "fig.update_layout(\n", + " xaxis=dict(title='Model'),\n", + " yaxis=dict(title='IoU'),\n", + " coloraxis=dict(colorbar=dict(title='Param')),\n", + ")\n", + "\n", + "# Show the plot\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + 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