Python实现特定场景去除高光算法详解

算法思路

1、求取源图i的平均灰度，并记录rows和cols；

2、按照一定大小，分为n*m个方块，求出每块的平均值，得到子块的亮度矩阵d；

3、用矩阵d的每个元素减去源图的平均灰度，得到子块的亮度差值矩阵e；

4、通过插值算法，将矩阵e差值成与源图一样大小的亮度分布矩阵r；

5、得到矫正后的图像result=i-r；

应用场景

光照不均匀的整体色泽一样的物体，比如工业零件，ocr场景。

代码实现

import cv2
import numpy as np
 
def unevenlightcompensate(gray, blocksize):
    #gray = cv2.cvtcolor(img, cv2.color_bgr2gray)
    average = np.mean(gray)
    rows_new = int(np.ceil(gray.shape[0] / blocksize))
    cols_new = int(np.ceil(gray.shape[1] / blocksize))
    blockimage = np.zeros((rows_new, cols_new), dtype=np.float32)
    for r in range(rows_new):
        for c in range(cols_new):
            rowmin = r * blocksize
            rowmax = (r + 1) * blocksize
            if (rowmax > gray.shape[0]):
                rowmax = gray.shape[0]
            colmin = c * blocksize
            colmax = (c + 1) * blocksize
            if (colmax > gray.shape[1]):
                colmax = gray.shape[1]
            imageroi = gray[rowmin:rowmax, colmin:colmax]
            temaver = np.mean(imageroi)
 
            blockimage[r, c] = temaver
 
 
    
    blockimage = blockimage - average
    blockimage2 = cv2.resize(blockimage, (gray.shape[1], gray.shape[0]), interpolation=cv2.inter_cubic)
    gray2 = gray.astype(np.float32)
    dst = gray2 - blockimage2
    dst[dst>255]=255
    dst[dst<0]=0
    dst = dst.astype(np.uint8)
    dst = cv2.gaussianblur(dst, (3, 3), 0)
    #dst = cv2.cvtcolor(dst, cv2.color_gray2bgr)
    return dst
 
if __name__ == '__main__':
    file = 'www.png'
    blocksize = 8
    img = cv2.imread(file)
    b,g,r = cv2.split(img)
    dstb = unevenlightcompensate(b, blocksize)
    dstg = unevenlightcompensate(g, blocksize)
    dstr = unevenlightcompensate(r, blocksize)
    dst = cv2.merge([dstb, dstg, dstr])
    result = np.concatenate([img, dst], axis=1)
cv2.imwrite('result.jpg', result)

实验效果

补充

opencv实现光照去除效果

1.方法一(rgb归一化)

int main(int argc, char *argv[])
{
	//double temp = 255 / log(256);
	//cout << "doubledouble temp ="<< temp<<endl;
	
	mat  image = imread("d://vvoo//sun_face.jpg", 1);
	if (!image.data)
	{
		cout << "image loading error" <<endl;
		return -1;
	}
	imshow("原图", image);
	mat src(image.size(), cv_32fc3);
	for (int i = 0; i < image.rows; i++)
	{
		for (int j = 0; j < image.cols; j++)
		{
			src.at<vec3f>(i, j)[0] = 255 * (float)image.at<vec3b>(i, j)[0] / ((float)image.at<vec3b>(i, j)[0] + (float)image.at<vec3b>(i, j)[2] + (float)image.at<vec3b>(i, j)[1]+0.01);
			src.at<vec3f>(i, j)[1] = 255 * (float)image.at<vec3b>(i, j)[1] / ((float)image.at<vec3b>(i, j)[0] + (float)image.at<vec3b>(i, j)[2] + (float)image.at<vec3b>(i, j)[1]+0.01);
			src.at<vec3f>(i, j)[2] = 255 * (float)image.at<vec3b>(i, j)[2] / ((float)image.at<vec3b>(i, j)[0] + (float)image.at<vec3b>(i, j)[2] + (float)image.at<vec3b>(i, j)[1]+0.01);
		}
	}
	
	normalize(src, src, 0, 255, cv_minmax);
      
	convertscaleabs(src,src);
	imshow("rgb", src);
	imwrite("c://users//topsun//desktop//123.jpg", src);
	waitkey(0);
	return 0;
}

实现效果

2.方法二

void unevenlightcompensate(mat &image, int blocksize)
{
	if (image.channels() == 3) cvtcolor(image, image, 7);
	double average = mean(image)[0];
	int rows_new = ceil(double(image.rows) / double(blocksize));
	int cols_new = ceil(double(image.cols) / double(blocksize));
	mat blockimage;
	blockimage = mat::zeros(rows_new, cols_new, cv_32fc1);
	for (int i = 0; i < rows_new; i++)
	{
		for (int j = 0; j < cols_new; j++)
		{
			int rowmin = i*blocksize;
			int rowmax = (i + 1)*blocksize;
			if (rowmax > image.rows) rowmax = image.rows;
			int colmin = j*blocksize;
			int colmax = (j + 1)*blocksize;
			if (colmax > image.cols) colmax = image.cols;
			mat imageroi = image(range(rowmin, rowmax), range(colmin, colmax));
			double temaver = mean(imageroi)[0];
			blockimage.at<float>(i, j) = temaver;
		}
	}
	blockimage = blockimage - average;
	mat blockimage2;
	resize(blockimage, blockimage2, image.size(), (0, 0), (0, 0), inter_cubic);
	mat image2;
	image.convertto(image2, cv_32fc1);
	mat dst = image2 - blockimage2;
	dst.convertto(image, cv_8uc1);
}
int main(int argc, char *argv[])
{
	//double temp = 255 / log(256);
	//cout << "doubledouble temp ="<< temp<<endl;
	
	mat  image = imread("c://users//topsun//desktop//2.jpg", 1);
	if (!image.data)
	{
		cout << "image loading error" <<endl;
		return -1;
	}
	imshow("原图", image);
	unevenlightcompensate(image, 12);
	imshow("rgb", image);
	imwrite("c://users//topsun//desktop//123.jpg", image);
	waitkey(0);
	return 0;
}

实现效果

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