| import cv2 |
| import numpy as np |
|
|
| def segmentar_lesion(rgb, size=(224, 224)): |
|
|
| |
| rgb = cv2.resize(rgb, size) |
| hsv = cv2.cvtColor(rgb, cv2.COLOR_RGB2HSV) |
| S = hsv[:, :, 1] |
| S_blur = cv2.GaussianBlur(S, (5, 5), 0) |
|
|
| |
| _, mask = cv2.threshold(S_blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) |
|
|
| |
| num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(mask) |
| h, w = mask.shape |
| center = np.array([w / 2, h / 2]) |
|
|
| valid_regions = [] |
| for i in range(1, num_labels): |
| area = stats[i, cv2.CC_STAT_AREA] |
| cX, cY = centroids[i] |
| dist = np.linalg.norm(np.array([cX, cY]) - center) |
| if dist < min(w, h) / 3: |
| valid_regions.append((i, area, dist)) |
|
|
| if len(valid_regions) == 0: |
| largest_label = 1 + np.argmax(stats[1:, cv2.CC_STAT_AREA]) |
| else: |
| largest_label = max(valid_regions, key=lambda x: x[1])[0] |
|
|
| largest_mask = np.zeros_like(mask) |
| largest_mask[labels == largest_label] = 255 |
|
|
| |
| inv = cv2.bitwise_not(largest_mask) |
| num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(inv) |
| for i in range(1, num_labels): |
| x, y, w_box, h_box, area = stats[i] |
| if x > 0 and y > 0 and (x + w_box) < w and (y + h_box) < h: |
| inv[labels == i] = 0 |
| filled_mask = cv2.bitwise_not(inv) |
|
|
| |
| lesion_rgb = cv2.bitwise_and(rgb, rgb, mask=filled_mask) |
|
|
| return filled_mask, lesion_rgb |
|
|
