Python如何实现特定场景去除高光算法

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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<(i, j)[0] = 255 * (float)image.at(i, j)[0] / ((float)image.at(i, j)[0] + (float)image.at(i, j)[2] + (float)image.at(i, j)[1]+0.01);                        src.at(i, j)[1] = 255 * (float)image.at(i, j)[1] / ((float)image.at(i, j)[0] + (float)image.at(i, j)[2] + (float)image.at(i, j)[1]+0.01);                        src.at(i, j)[2] = 255 * (float)image.at(i, j)[2] / ((float)image.at(i, j)[0] + (float)image.at(i, j)[2] + (float)image.at(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(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<
实现效果
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