def clahe_equalization(image, clip_limit=2.0, grid_size=(8,8)): clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=grid_size) return clahe.apply(image)
def auto_canny(image, sigma=0.33): median = np.median(image) lower = int(max(0, (1.0 - sigma) * median)) upper = int(min(255, (1.0 + sigma) * median)) return cv2.Canny(image, lower, upper)
def clahe_equalization(image, clip_limit=2.0, grid_size=(8,8)): clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=grid_size) return clahe.apply(image)
def auto_canny(image, sigma=0.33): median = np.median(image) lower = int(max(0, (1.0 - sigma) * median)) upper = int(min(255, (1.0 + sigma) * median)) return cv2.Canny(image, lower, upper)