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b/test.ipynb |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import tensorflow as tf\n", |
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"from keras.utils.layer_utils import count_params\n", |
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"model = tf.keras.models.load_model('./models/{}/{}.h5'.format('R2UNet', 'R2UNet'), compile=False)\n", |
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"trainable_count = count_params(model.trainable_weights)" |
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] |
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}, |
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"execution_count": 8, |
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"10050241" |
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], |
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"source": [ |
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"trainable_count" |
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] |
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}, |
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"execution_count": 3, |
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"outputs": [ |
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{ |
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"data": { |
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"image/png": "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", |
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"text/plain": [ |
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"<PIL.Image.Image image mode=L size=256x256>" |
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] |
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}, |
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"execution_count": 3, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"import numpy as np\n", |
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"from tensorflow.keras.utils import load_img, img_to_array, array_to_img\n", |
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"import matplotlib.pyplot as plt\n", |
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"import tensorflow as tf\n", |
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"import cv2\n", |
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"\n", |
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"\n", |
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"def overlapMask(model_name):\n", |
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" # Get Array Image =\n", |
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" 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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" 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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" # Reshape to model.predict\n", |
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" image_arr_reshape = image_to_predict[np.newaxis, ...]\n", |
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" # Load Model\n", |
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" model = tf.keras.models.load_model('./models/{}/{}.h5'.format(model_name, model_name), compile=False)\n", |
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" # Get Segmentation (Predicted Images)\n", |
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" mask_arr = model.predict(image_arr_reshape, verbose=0)\n", |
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" # To Binary\n", |
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" gt_image = (gt_image > 0.5).astype('float32')\n", |
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" mask_arr = (mask_arr > 0.5).astype('float32')\n", |
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" # Reduce The Dimension\n", |
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" mask_arr = np.squeeze(mask_arr)\n", |
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" # Overlap\n", |
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" gt_image[gt_image != mask_arr] = 0.5\n", |
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" return array_to_img(np.expand_dims(gt_image, axis=2))\n", |
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"\n", |
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"overlapMask('UNet')\n" |
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] |
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"source": [ |
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"import plotly.express as px\n", |
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"import pandas as pd\n", |
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"\n", |
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"# Data\n", |
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"data = {\n", |
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" 'No': [1, 2, 3, 4],\n", |
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" 'Model': ['UNet', 'AttUNet', 'UNet++', 'R2UNet'],\n", |
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" 'IoU': [0.936, 0.928, 0.936, 0.915],\n", |
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" 'Time': [7.73, 6.563, 11.973, 11.837],\n", |
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" 'Param': [7851969, 8202053, 9162753, 10050241]\n", |
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"}\n", |
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"\n", |
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"df = pd.DataFrame(data)\n", |
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"\n", |
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"# Plotting\n", |
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"df_sorted = df.sort_values(by='IoU')\n", |
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"fig = px.scatter(df_sorted, x='Model', y='IoU', size='Time', color='Param',\n", |
|
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" hover_data=['Time', 'Param'], title='Model Evaluation')\n", |
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"\n", |
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"# Customize the plot\n", |
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"fig.update_layout(\n", |
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" xaxis=dict(title='Model'),\n", |
|
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" yaxis=dict(title='IoU'),\n", |
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" coloraxis=dict(colorbar=dict(title='Param')),\n", |
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")\n", |
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"\n", |
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"# Show the plot\n", |
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"fig.show()\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 9, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"application/vnd.plotly.v1+json": { |
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"config": { |
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"plotlyServerURL": "https://plot.ly" |
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}, |
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"data": [ |
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{ |
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"customdata": [ |
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[ |
|
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1100 |
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], |
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[ |
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1104 |
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], |
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[ |
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"legendgroup": "", |
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"symbol": "circle" |
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"name": "", |
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"orientation": "v", |
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"type": "scatter", |
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"x": [ |
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2055 |
} |
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2056 |
}, |
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|
2057 |
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2058 |
"text": "Model Evaluation" |
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2059 |
}, |
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"xaxis": { |
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"anchor": "y", |
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0, |
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1 |
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], |
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|
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"title": { |
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|
2067 |
"text": "Model" |
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|
2068 |
} |
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2069 |
}, |
|
|
2070 |
"yaxis": { |
|
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2071 |
"anchor": "x", |
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2072 |
"domain": [ |
|
|
2073 |
0, |
|
|
2074 |
1 |
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2075 |
], |
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2076 |
"title": { |
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|
2077 |
"text": "IoU" |
|
|
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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"metadata": {}, |
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"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 |
], |
|
|
2107 |
"metadata": { |
|
|
2108 |
"kernelspec": { |
|
|
2109 |
"display_name": "kuliah", |
|
|
2110 |
"language": "python", |
|
|
2111 |
"name": "python3" |
|
|
2112 |
}, |
|
|
2113 |
"language_info": { |
|
|
2114 |
"codemirror_mode": { |
|
|
2115 |
"name": "ipython", |
|
|
2116 |
"version": 3 |
|
|
2117 |
}, |
|
|
2118 |
"file_extension": ".py", |
|
|
2119 |
"mimetype": "text/x-python", |
|
|
2120 |
"name": "python", |
|
|
2121 |
"nbconvert_exporter": "python", |
|
|
2122 |
"pygments_lexer": "ipython3", |
|
|
2123 |
"version": "3.10.6" |
|
|
2124 |
}, |
|
|
2125 |
"orig_nbformat": 4 |
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2126 |
}, |
|
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2127 |
"nbformat": 4, |
|
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2128 |
"nbformat_minor": 2 |
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|
2129 |
} |