Created
August 10, 2017 08:40
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Gets a batch of random patches from images in memory
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| def get_random_patch(input_image, target_image, patch_size): | |
| start_x = np.random.randint(input_image.shape[0] - patch_size) | |
| start_y = np.random.randint(input_image.shape[1] - patch_size) | |
| end_x = start_x + patch_size | |
| end_y = start_y + patch_size | |
| input_patch = input_image[start_x:end_x, start_y:end_y] | |
| target_patch = target_image[start_x:end_x, start_y:end_y] | |
| return [input_patch, target_patch] | |
| def get_random_batch(batch_size): | |
| index_list = np.random.randint(num_images, size=batch_size) | |
| input_batch = np.empty( | |
| [batch_size, train_patch_size, train_patch_size], dtype=np.int32) | |
| target_batch = np.empty( | |
| [batch_size, train_patch_size, train_patch_size], dtype=np.int32) | |
| for i, index in enumerate(index_list): | |
| [input_patch, target_patch] = get_random_patch( | |
| input_images[index], target_images[index], train_patch_size) | |
| input_batch[i, :, :] = input_patch | |
| target_batch[i, :, :] = target_patch | |
| input_batch = np.reshape(input_batch, (batch_size, -1)) | |
| target_batch = np.reshape(target_batch, (batch_size, -1)) | |
| return [input_batch, target_batch] |
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