import numpy as np import cv2 import cv2 as cv from PIL import Image import random from torchvision.transforms import transforms def compute(patch): patch = np.array(patch).astype(np.int64) diff_horizontal = np.sum(np.abs(patch[:, :-1, :] - patch[:, 1:, :])) diff_vertical = np.sum(np.abs(patch[:-1, :, :] - patch[1:, :, :])) diff_diagonal = np.sum(np.abs(patch[:-1, :-1, :] - patch[1:, 1:, :])) diff_diagonal += np.sum(np.abs(patch[1:, :-1, :] - patch[:-1, 1:, :])) res = diff_horizontal + diff_vertical + diff_diagonal return res.sum() def bit_patch(img, img_height, bit_mode, patch_size, patch_mode): img_np = np.array(img) #combined_image = None if bit_mode == "scaling": mask_low = 0x07 red_low3 = ((img_np[:, :, 0] & mask_low) * (255 // 7)).astype(np.uint8) green_low3 = ((img_np[:, :, 1] & mask_low) * (255 // 7)).astype(np.uint8) blue_low3 = ((img_np[:, :, 2] & mask_low) * (255 // 7)).astype(np.uint8) combined_image = cv.merge((red_low3, green_low3, blue_low3)) elif bit_mode == "thresholding": combined_image=img_np # To be modified else: raise ValueError(f"Unsupported bit_mode: {bit_mode}") h, w, _ = combined_image.shape combined_image = Image.fromarray(combined_image) min_len = min(h, w) rz = transforms.Resize((img_height, img_height)) if min_len < patch_size: combined_image = rz(combined_image) num_patch = (img_height // patch_size) ** 2 patch_list = [] rp = transforms.RandomCrop(patch_size) for _ in range(num_patch): patch_list.append(rp(combined_image)) if patch_mode == "max": patch_list.sort(key=lambda x: compute(x), reverse=True) selected_patch = patch_list[0] elif patch_mode == "min": patch_list.sort(key=lambda x: compute(x), reverse=False) selected_patch = patch_list[0] else: selected_patch = random.choice(patch_list) patch_np = np.array(selected_patch) return cv2.resize(patch_np, (img_height, img_height))