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import numpy as np |
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import cv2 |
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import cv2 as cv |
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from PIL import Image |
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import random |
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from torchvision.transforms import transforms |
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def compute(patch): |
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patch = np.array(patch).astype(np.int64) |
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diff_horizontal = np.sum(np.abs(patch[:, :-1, :] - patch[:, 1:, :])) |
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diff_vertical = np.sum(np.abs(patch[:-1, :, :] - patch[1:, :, :])) |
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diff_diagonal = np.sum(np.abs(patch[:-1, :-1, :] - patch[1:, 1:, :])) |
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diff_diagonal += np.sum(np.abs(patch[1:, :-1, :] - patch[:-1, 1:, :])) |
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res = diff_horizontal + diff_vertical + diff_diagonal |
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return res.sum() |
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def bit_patch(img, img_height, bit_mode, patch_size, patch_mode): |
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img_np = np.array(img) |
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if bit_mode == "scaling": |
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mask_low = 0x07 |
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red_low3 = ((img_np[:, :, 0] & mask_low) * (255 // 7)).astype(np.uint8) |
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green_low3 = ((img_np[:, :, 1] & mask_low) * (255 // 7)).astype(np.uint8) |
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blue_low3 = ((img_np[:, :, 2] & mask_low) * (255 // 7)).astype(np.uint8) |
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combined_image = cv.merge((red_low3, green_low3, blue_low3)) |
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elif bit_mode == "thresholding": |
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combined_image=img_np |
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else: |
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raise ValueError(f"Unsupported bit_mode: {bit_mode}") |
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h, w, _ = combined_image.shape |
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combined_image = Image.fromarray(combined_image) |
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min_len = min(h, w) |
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rz = transforms.Resize((img_height, img_height)) |
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if min_len < patch_size: |
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combined_image = rz(combined_image) |
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num_patch = (img_height // patch_size) ** 2 |
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patch_list = [] |
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rp = transforms.RandomCrop(patch_size) |
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for _ in range(num_patch): |
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patch_list.append(rp(combined_image)) |
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if patch_mode == "max": |
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patch_list.sort(key=lambda x: compute(x), reverse=True) |
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selected_patch = patch_list[0] |
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elif patch_mode == "min": |
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patch_list.sort(key=lambda x: compute(x), reverse=False) |
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selected_patch = patch_list[0] |
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else: |
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selected_patch = random.choice(patch_list) |
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patch_np = np.array(selected_patch) |
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return cv2.resize(patch_np, (img_height, img_height)) |
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