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| a | b/test.ipynb | ||
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| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "code", |
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| 5 | "execution_count": 7, |
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| 6 | "metadata": {}, |
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| 7 | "outputs": [], |
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| 8 | "source": [ |
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| 9 | "import tensorflow as tf\n", |
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| 10 | "from keras.utils.layer_utils import count_params\n", |
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| 11 | "model = tf.keras.models.load_model('./models/{}/{}.h5'.format('R2UNet', 'R2UNet'), compile=False)\n", |
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| 12 | "trainable_count = count_params(model.trainable_weights)" |
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| 13 | ] |
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| 14 | }, |
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| 15 | { |
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| 16 | "cell_type": "code", |
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| 17 | "execution_count": 8, |
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| 18 | "metadata": {}, |
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| 19 | "outputs": [ |
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| 20 | { |
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| 21 | "data": { |
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| 22 | "text/plain": [ |
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| 23 | "10050241" |
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| 24 | ] |
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| 25 | }, |
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| 26 | "execution_count": 8, |
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| 27 | "metadata": {}, |
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| 28 | "output_type": "execute_result" |
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| 29 | } |
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| 30 | ], |
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| 31 | "source": [ |
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| 32 | "trainable_count" |
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| 33 | ] |
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| 34 | }, |
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| 35 | { |
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| 36 | "cell_type": "code", |
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| 37 | "execution_count": 3, |
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| 38 | "metadata": {}, |
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| 39 | "outputs": [ |
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| 40 | { |
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| 41 | "data": { |
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| 42 | "image/png": "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", |
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| 43 | "text/plain": [ |
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| 44 | "<PIL.Image.Image image mode=L size=256x256>" |
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| 45 | ] |
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| 46 | }, |
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| 47 | "execution_count": 3, |
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| 48 | "metadata": {}, |
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| 49 | "output_type": "execute_result" |
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| 50 | } |
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| 51 | ], |
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| 52 | "source": [ |
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| 53 | "import numpy as np\n", |
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| 54 | "from tensorflow.keras.utils import load_img, img_to_array, array_to_img\n", |
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| 55 | "import matplotlib.pyplot as plt\n", |
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| 56 | "import tensorflow as tf\n", |
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| 57 | "import cv2\n", |
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| 58 | "\n", |
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| 59 | "\n", |
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| 60 | "def overlapMask(model_name):\n", |
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| 61 | " # Get Array Image =\n", |
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| 62 | " 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", |
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| 63 | " 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", |
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| 64 | " # Reshape to model.predict\n", |
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| 65 | " image_arr_reshape = image_to_predict[np.newaxis, ...]\n", |
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| 66 | " # Load Model\n", |
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| 67 | " model = tf.keras.models.load_model('./models/{}/{}.h5'.format(model_name, model_name), compile=False)\n", |
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| 68 | " # Get Segmentation (Predicted Images)\n", |
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| 69 | " mask_arr = model.predict(image_arr_reshape, verbose=0)\n", |
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| 70 | " # To Binary\n", |
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| 71 | " gt_image = (gt_image > 0.5).astype('float32')\n", |
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| 72 | " mask_arr = (mask_arr > 0.5).astype('float32')\n", |
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| 73 | " # Reduce The Dimension\n", |
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| 74 | " mask_arr = np.squeeze(mask_arr)\n", |
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| 75 | " # Overlap\n", |
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| 76 | " gt_image[gt_image != mask_arr] = 0.5\n", |
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| 77 | " return array_to_img(np.expand_dims(gt_image, axis=2))\n", |
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| 78 | "\n", |
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| 79 | "overlapMask('UNet')\n" |
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| 80 | ] |
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| 81 | }, |
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| 82 | { |
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| 83 | "cell_type": "code", |
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| 84 | "execution_count": 5, |
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| 85 | "metadata": {}, |
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| 86 | "outputs": [ |
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| 87 | { |
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| 88 | "data": { |
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| 90 | "config": { |
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| 91 | "plotlyServerURL": "https://plot.ly" |
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| 1025 | "text": "Model Evaluation" |
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| 1047 | } |
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| 1048 | } |
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| 1050 | "metadata": {}, |
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| 1052 | } |
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| 1054 | "source": [ |
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| 1055 | "import plotly.express as px\n", |
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| 1056 | "import pandas as pd\n", |
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| 1057 | "\n", |
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| 1058 | "# Data\n", |
||
| 1059 | "data = {\n", |
||
| 1060 | " 'No': [1, 2, 3, 4],\n", |
||
| 1061 | " 'Model': ['UNet', 'AttUNet', 'UNet++', 'R2UNet'],\n", |
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| 1062 | " 'IoU': [0.936, 0.928, 0.936, 0.915],\n", |
||
| 1063 | " 'Time': [7.73, 6.563, 11.973, 11.837],\n", |
||
| 1064 | " 'Param': [7851969, 8202053, 9162753, 10050241]\n", |
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| 1065 | "}\n", |
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| 1066 | "\n", |
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| 1067 | "df = pd.DataFrame(data)\n", |
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| 1068 | "\n", |
||
| 1069 | "# Plotting\n", |
||
| 1070 | "df_sorted = df.sort_values(by='IoU')\n", |
||
| 1071 | "fig = px.scatter(df_sorted, x='Model', y='IoU', size='Time', color='Param',\n", |
||
| 1072 | " hover_data=['Time', 'Param'], title='Model Evaluation')\n", |
||
| 1073 | "\n", |
||
| 1074 | "# Customize the plot\n", |
||
| 1075 | "fig.update_layout(\n", |
||
| 1076 | " xaxis=dict(title='Model'),\n", |
||
| 1077 | " yaxis=dict(title='IoU'),\n", |
||
| 1078 | " coloraxis=dict(colorbar=dict(title='Param')),\n", |
||
| 1079 | ")\n", |
||
| 1080 | "\n", |
||
| 1081 | "# Show the plot\n", |
||
| 1082 | "fig.show()\n" |
||
| 1083 | ] |
||
| 1084 | }, |
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| 1085 | { |
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| 1086 | "cell_type": "code", |
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| 1087 | "execution_count": 9, |
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| 1088 | "metadata": {}, |
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| 1089 | "outputs": [ |
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| 1090 | { |
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| 1091 | "data": { |
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| 1092 | "application/vnd.plotly.v1+json": { |
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| 1093 | "config": { |
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| 1094 | "plotlyServerURL": "https://plot.ly" |
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| 1095 | }, |
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| 1096 | "data": [ |
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| 1097 | { |
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| 1140 | "type": "scatter", |
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| 1141 | "x": [ |
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| 1142 | "UNet", |
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| 1143 | "AttUNet", |
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| 2076 | "title": { |
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| 2078 | } |
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| 2079 | } |
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| 2080 | } |
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| 2081 | } |
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| 2082 | }, |
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| 2083 | "metadata": {}, |
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| 2084 | "output_type": "display_data" |
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| 2085 | } |
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| 2086 | ], |
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| 2087 | "source": [ |
||
| 2088 | "import plotly.express as px\n", |
||
| 2089 | "import pandas as pd\n", |
||
| 2090 | "\n", |
||
| 2091 | "# Data\n", |
||
| 2092 | "data = {\n", |
||
| 2093 | " 'No': [1, 2, 3, 4],\n", |
||
| 2094 | " 'Model': ['UNet', 'AttUNet', 'UNet++', 'R2UNet'],\n", |
||
| 2095 | " 'IoU': [0.936, 0.928, 0.936, 0.915],\n", |
||
| 2096 | " 'Time': [7.73, 6.563, 11.973, 11.837],\n", |
||
| 2097 | " 'Param': [7851969, 8202053, 9162753, 10050241]\n", |
||
| 2098 | "}\n", |
||
| 2099 | "\n", |
||
| 2100 | "df = pd.DataFrame(data)\n", |
||
| 2101 | "\n", |
||
| 2102 | "\n", |
||
| 2103 | "\n" |
||
| 2104 | ] |
||
| 2105 | } |
||
| 2106 | ], |
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| 2107 | "metadata": { |
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| 2108 | "kernelspec": { |
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| 2109 | "display_name": "kuliah", |
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| 2110 | "language": "python", |
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| 2111 | "name": "python3" |
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| 2112 | }, |
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| 2113 | "language_info": { |
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| 2114 | "codemirror_mode": { |
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| 2115 | "name": "ipython", |
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| 2116 | "version": 3 |
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| 2117 | }, |
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| 2119 | "mimetype": "text/x-python", |
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| 2120 | "name": "python", |
||
| 2121 | "nbconvert_exporter": "python", |
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| 2122 | "pygments_lexer": "ipython3", |
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| 2123 | "version": "3.10.6" |
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| 2124 | }, |
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| 2125 | "orig_nbformat": 4 |
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| 2127 | "nbformat": 4, |
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| 2129 | } |