这篇教程Python实现特定场景去除高光算法详解写得很实用，希望能帮到您。
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 cv2import 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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