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| import cv2 | |
| import os | |
| def preprocess(img): | |
| """Applies Otsu's thresholding to the input image.""" | |
| # Ensure input is grayscale if it's not already | |
| gray_img = img | |
| if len(img.shape) == 3 and img.shape[2] == 3: | |
| gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) | |
| _, thresh_otsu = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) | |
| return thresh_otsu | |
| # def preprocess(img): | |
| # """Applies Otsu's thresholding to the input image.""" | |
| # import numpy as np | |
| # # Ensure input is grayscale if it's not already | |
| # # Apply adaptive Gaussian thresholding | |
| # ## invert image | |
| # block_size = 11 # Size of pixel neighborhood used for threshold calculation | |
| # C = 10 # Constant subtracted from mean | |
| # thresh_adaptive = cv2.adaptiveThreshold(img, | |
| # maxValue=255, | |
| # adaptiveMethod=cv2.ADAPTIVE_THRESH_GAUSSIAN_C, | |
| # thresholdType=cv2.THRESH_BINARY, | |
| # blockSize=block_size, | |
| # C=C) | |
| # kernel = np.ones((3,3), np.uint8) | |
| # thresh_cleaned = cv2.morphologyEx(thresh_adaptive, cv2.MORPH_OPEN, kernel) | |
| # # or | |
| # thresh_cleaned = cv2.medianBlur(thresh_adaptive, 3) | |
| # gray_img = cv2.bitwise_not(thresh_cleaned) | |
| # return gray_img | |
| def load_and_preprocess_image(image_path: str, config: dict): | |
| if not os.path.exists(image_path): | |
| raise FileNotFoundError(f"Image file not found at: {image_path}") | |
| img_color = cv2.imread(image_path) | |
| if img_color is None: | |
| raise ValueError(f"Could not read image file: {image_path}") | |
| img_gray = cv2.cvtColor(img_color, cv2.COLOR_BGR2GRAY) | |
| # --- Upscaling Heuristic --- | |
| cfg_proc = config.get('image_processing', {}) | |
| min_dimension_threshold = cfg_proc.get('min_dimension_threshold', 800) | |
| upscale_factor = cfg_proc.get('upscale_factor', 2) | |
| h, w = img_gray.shape | |
| img_color_resized = img_color | |
| img_gray_resized = img_gray | |
| if h < min_dimension_threshold or w < min_dimension_threshold: | |
| print(f"Image dimensions ({w}x{h}) below threshold ({min_dimension_threshold}px). Upscaling by {upscale_factor}x.") | |
| new_w, new_h = w * upscale_factor, h * upscale_factor | |
| img_gray_resized = cv2.resize(img_gray, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4) | |
| img_color_resized = cv2.resize(img_color, (new_w, new_h), interpolation=cv2.INTER_LANCZOS4) | |
| # --- Initial Preprocessing (Inversion + Thresholding) --- | |
| img_inverted = cv2.bitwise_not(img_gray_resized) | |
| preprocessed_img = preprocess(img_inverted) | |
| return preprocessed_img, img_color_resized, img_gray_resized |