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| """ | |
| @author: cuny | |
| @file: GrindSkin.py | |
| @time: 2022/7/2 14:44 | |
| @description: | |
| 磨皮算法 | |
| """ | |
| import cv2 | |
| import numpy as np | |
| def grindSkin(src, grindDegree: int = 3, detailDegree: int = 1, strength: int = 9): | |
| """ | |
| Dest =(Src * (100 - Opacity) + (Src + 2 * GaussBlur(EPFFilter(Src) - Src)) * Opacity) /100 | |
| 人像磨皮方案，后续会考虑使用一些皮肤区域检测算法来实现仅皮肤区域磨皮，增加算法的精细程度——或者使用人脸关键点 | |
| https://www.cnblogs.com/Imageshop/p/4709710.html | |
| Args: | |
| src: 原图 | |
| grindDegree: 磨皮程度调节参数 | |
| detailDegree: 细节程度调节参数 | |
| strength: 融合程度，作为磨皮强度（0 - 10） | |
| Returns: | |
| 磨皮后的图像 | |
| """ | |
| if strength <= 0: | |
| return src | |
| dst = src.copy() | |
| opacity = min(10., strength) / 10. | |
| dx = grindDegree * 5 # 双边滤波参数之一 | |
| fc = grindDegree * 12.5 # 双边滤波参数之一 | |
| temp1 = cv2.bilateralFilter(src[:, :, :3], dx, fc, fc) | |
| temp2 = cv2.subtract(temp1, src[:, :, :3]) | |
| temp3 = cv2.GaussianBlur(temp2, (2 * detailDegree - 1, 2 * detailDegree - 1), 0) | |
| temp4 = cv2.add(cv2.add(temp3, temp3), src[:, :, :3]) | |
| dst[:, :, :3] = cv2.addWeighted(temp4, opacity, src[:, :, :3], 1 - opacity, 0.0) | |
| return dst | |
| if __name__ == "__main__": | |
| input_image = cv2.imread("test_image/7.jpg") | |
| output_image = grindSkin(src=input_image) | |
| cv2.imwrite("grindSkinCompare.png", np.hstack((input_image, output_image))) | |