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test.ipynb
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{
"cells": [
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"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": {},
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],
"source": [
"trainable_count"
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},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
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"data": {
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},
"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,
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