| import os | |
| import cv2 | |
| import numpy as np | |
| from tqdm import tqdm | |
| def extract_masked_region(img: np.ndarray, | |
| msk: np.ndarray, | |
| threshold: int = 127, | |
| crop: bool = True) -> np.ndarray: | |
| # Binarize mask | |
| _, bin_mask = cv2.threshold(msk, threshold, 255, cv2.THRESH_BINARY) | |
| # Apply mask | |
| result = cv2.bitwise_and(img, img, mask=bin_mask) | |
| if crop: | |
| ys, xs = np.where(bin_mask == 255) | |
| if ys.size and xs.size: | |
| y1, y2 = ys.min(), ys.max() | |
| x1, x2 = xs.min(), xs.max() | |
| result = result[y1:y2+1, x1:x2+1] | |
| return result | |
| def batch_process(collage_dir: str, | |
| mask_dir: str, | |
| out_dir: str): | |
| os.makedirs(out_dir, exist_ok=True) | |
| files = [f for f in os.listdir(collage_dir) | |
| if f.lower().endswith(('.png','.jpg','.jpeg','.bmp','tif','tiff'))] | |
| for fname in tqdm(files, desc="Processing images"): | |
| img_path = os.path.join(collage_dir, fname) | |
| mask_path = os.path.join(mask_dir, fname) | |
| out_path = os.path.join(out_dir, fname) | |
| if not os.path.isfile(mask_path): | |
| tqdm.write(f"⚠️ mask not found for {fname}, skipping") | |
| continue | |
| img = cv2.imread(img_path, cv2.IMREAD_COLOR) | |
| msk = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) | |
| if img is None or msk is None: | |
| tqdm.write(f"⚠️ failed to load {fname} or its mask, skipping") | |
| continue | |
| cropped = extract_masked_region(img, msk, threshold=127, crop=True) | |
| cv2.imwrite(out_path, cropped) | |
| tqdm.write("✅ All done!") | |
| if __name__ == "__main__": | |
| # adjust these paths as needed: | |
| collage_folder = "/home/darth/#/WaterMeters/images" | |
| mask_folder = "/home/darth/#/WaterMeters/masks" | |
| output_folder = "/home/darth/#/WaterMeters/cropped" | |
| batch_process(collage_folder, mask_folder, output_folder) | |
| # import cv2 | |
| # import numpy as np | |
| # def extract_masked_region(image_path: str, | |
| # mask_path: str, | |
| # threshold: int = 127, | |
| # crop: bool = True): | |
| # """ | |
| # Loads an image and its mask, applies the mask, and returns the resulting image. | |
| # If crop=True, it also crops to the bounding box of the white region in the mask. | |
| # :param image_path: Path to the original BGR image. | |
| # :param mask_path: Path to the grayscale mask (white=keep, black=discard). | |
| # :param threshold: Grayscale threshold to binarize the mask (0–255). | |
| # :param crop: If True, crop to the mask's bounding box. | |
| # :return: A BGR image with only the masked region (and optionally cropped). | |
| # """ | |
| # # 1. Load images | |
| # img = cv2.imread(image_path, cv2.IMREAD_COLOR) | |
| # msk = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) | |
| # if img is None or msk is None: | |
| # raise FileNotFoundError("Could not load image or mask. Check your paths.") | |
| # # 2. Binarize mask | |
| # _, bin_mask = cv2.threshold(msk, threshold, 255, cv2.THRESH_BINARY) | |
| # # 3. Apply mask | |
| # result = cv2.bitwise_and(img, img, mask=bin_mask) | |
| # if crop: | |
| # # 4. Find bounding box of white region | |
| # ys, xs = np.where(bin_mask == 255) | |
| # if ys.size and xs.size: | |
| # y1, y2 = ys.min(), ys.max() | |
| # x1, x2 = xs.min(), xs.max() | |
| # result = result[y1:y2+1, x1:x2+1] | |
| # return result | |
| # if __name__ == "__main__": | |
| # out = extract_masked_region("/home/darth/#/WaterMeters/collage/id_1_value_13_116.jpg", "/home/darth/#/WaterMeters/masks/id_1_value_13_116.jpg") | |
| # cv2.imwrite("meter_extracted.png", out) | |
| # print("Saved extracted region to meter_extracted.png") | |