### 生成红区图效果的完整 Python 代码集 以下是一个完整的、可运行的 Python 代码集合，用于生成“红区图”效果，该效果通过增强图像中的红色区域来突出显示皮肤敏感或炎症区域。该代码基于 OpenCV 和 NumPy 库实现，包含图像的颜色空间转换、红色区域增强、局部对比度增强以及图像融合处理等步骤。 ```python import cv2 import numpy as np def enhance_red_regions(image_path): image = cv2.imread(image_path) if image is None: raise FileNotFoundError(f"无法加载图像: {image_path}") hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) lower_red1 = np.array([0, 50, 50]) upper_red1 = np.array([10, 255, 255]) lower_red2 = np.array([170, 50, 50]) upper_red2 = np.array([180, 255, 255]) mask1 = cv2.inRange(hsv, lower_red1, upper_red1) mask2 = cv2.inRange(hsv, lower_red2, upper_red2) red_mask = cv2.bitwise_or(mask1, mask2) kernel = np.ones((5, 5), np.uint8) red_mask = cv2.dilate(red_mask, kernel, iterations=1) red_enhanced = cv2.bitwise_and(image, image, mask=red_mask) alpha = 1.5 beta = 0.5 enhanced_image = cv2.addWeighted(image, beta, red_enhanced, alpha, 0) return image, enhanced_image def enhance_with_clahe(enhanced_image): gray = cv2.cvtColor(enhanced_image, cv2.COLOR_BGR2GRAY) clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) enhanced_gray = clahe.apply(gray) enhanced_color = cv2.cvtColor(enhanced_gray, cv2.COLOR_GRAY2BGR) final_image = cv2.addWeighted(enhanced_image, 0.7, enhanced_color, 0.3, 0) final_image = cv2.GaussianBlur(final_image, (5, 5), 0) return final_image def process_red_zone_image(image_path, output_path): original_image, enhanced_image = enhance_red_regions(image_path) final_image = enhance_with_clahe(enhanced_image) cv2.imwrite(output_path, final_image) if __name__ == "__main__": input_image_path = 'input_image.jpg' output_image_path = 'output_red_zone.jpg' process_red_zone_image(input_image_path, output_image_path) ``` --- ###