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import cv2
import numpy as np
def segmentar_lesion(rgb, size=(224, 224)):
# Redimensionar y convertir a HSV
rgb = cv2.resize(rgb, size)
hsv = cv2.cvtColor(rgb, cv2.COLOR_RGB2HSV)
S = hsv[:, :, 1]
S_blur = cv2.GaussianBlur(S, (5, 5), 0)
# Umbralizaci贸n con Otsu
_, mask = cv2.threshold(S_blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# Selecci贸n de la regi贸n m谩s grande y centrada
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
# Rellenar huecos internos
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)
# Aplicar la m谩scara al RGB limpio
lesion_rgb = cv2.bitwise_and(rgb, rgb, mask=filled_mask)
return filled_mask, lesion_rgb
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