|
|
| import cv2 |
| import numpy as np |
|
|
| def crop_circle(img): |
| h, w = img.shape[:2] |
| center = (w // 2, h // 2) |
| radius = min(center[0], center[1]) |
| Y, X = np.ogrid[:h, :w] |
| dist = np.sqrt((X - center[0]) ** 2 + (Y - center[1]) ** 2) |
| mask = dist <= radius |
| if img.ndim == 3: |
| mask = np.stack([mask] * 3, axis=-1) |
| img[~mask] = 0 |
| return img |
|
|
| def apply_clahe(img): |
| lab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB) |
| l, a, b = cv2.split(lab) |
| clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) |
| cl = clahe.apply(l) |
| merged = cv2.merge((cl, a, b)) |
| return cv2.cvtColor(merged, cv2.COLOR_LAB2RGB) |
|
|
| def sharpen_image(img, sigma=10): |
| blur = cv2.GaussianBlur(img, (0, 0), sigma) |
| return cv2.addWeighted(img, 4, blur, -4, 128) |
|
|
| def resize_normalize(img, size=(224, 224)): |
| img = cv2.resize(img, size) |
| img = img / 255.0 |
| return img |
|
|
| def preprocess_image(image): |
| img = np.array(image) |
| img = crop_circle(img) |
| img = apply_clahe(img) |
| img = sharpen_image(img) |
| img = resize_normalize(img) |
| return img.astype(np.float32) |
|
|
