opencv之光照补偿和去除光照

 

本博客借用了不少其他博客，相当于知识整理

一、光照补偿

1.直方图均衡化

 

1. #include "stdafx.h"

2. #include<opencv2/opencv.hpp>

3. #include<iostream>

4. using namespace std;

5. using namespace cv;

6.  

7. int main(int argc, char *argv[])

8. {

9. Mat image = imread("D://vvoo//123.jpg", 1);

10. if (!image.data)

11. {

12. cout << "image loading error" <<endl;

13. return -1;

14. }

15. Mat imageRGB[3];

16. split(image, imageRGB);

17. for (int i = 0; i < 3; i++)

18. {

19. equalizeHist(imageRGB[i], imageRGB[i]);

20. }

21. merge(imageRGB, 3, image);

22. imshow("equalizeHist", image);

23. waitKey();

24. return 0;

25. }

 

 

 

 

2.gamma corection:

 

http://www.cambridgeincolour.com/tutorials/gamma-correction.htm

人眼是按照gamma < 1的曲线对输入图像进行处理的。

原图gamma=1.2ga=1.8ga=2.2ga=3.2

 

 

1. #include<opencv2/opencv.hpp>

2. #include<iostream>

3. using namespace std;

4. using namespace cv;

5. // Normalizes a given image into a value range between 0 and 255.

6. Mat norm(const Mat& src) {

7. // Create and return normalized image:

8. Mat dst;

9. switch (src.channels()) {

10. case 1:

11. cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC1);

12. break;

13. case 3:

14. cv::normalize(src, dst, 0, 255, NORM_MINMAX, CV_8UC3);

15. break;

16. default:

17. src.copyTo(dst);

18. break;

19. }

20. return dst;

21. }

22.  

23. int main()

24. {

25. Mat image,X,I;

26.  

27. VideoCapture cap(0);

28. while (1)

29. {

30. cap >> image;

31. image.convertTo(X, CV_32FC1); //转换格式

32. float gamma = 4;

33. pow(X, gamma, I);

34.  

35. imshow("Original Image", image);

36. imshow("Gamma correction image", norm(I));

37. char key = waitKey(30);

38. if (key=='q' )

39. break;

40. }

41. return 0;

42. }

 

3.拉普拉斯算子增强

 

 

1. int main(int argc, char *argv[])

2. {

3. Mat image = imread("D://vvoo//123.jpg", 1);

4. if (!image.data)

5. {

6. cout << "image loading error" <<endl;

7. return -1;

8. }

9. imshow("原图", image);

10. Mat imageEnhance;

11. Mat kernel = (Mat_<float>(3, 3) << 0, -1, 0, 0, 7, 0, 0, -1, 0);

12. filter2D(image, imageEnhance, CV_8UC3, kernel);

13. imshow("拉普拉斯算子图像增强效果", imageEnhance);

14. imwrite("C://Users//TOPSUN//Desktop//123.jpg",imageEnhance);

15. waitKey();

16. return 0;

17. }

效果不好

 

 

4.对数变换

对数图像增强是图像增强的一种常见方法，其公式为： S = c log(r+1)，其中c是常数（以下算法c=255/(log(256)），这样可以实现整个画面的亮度增大此时默认v=e，即 S = c ln(r+1)。

如下图，对数使亮度比较低的像素转换成亮度比较高的，而亮度较高的像素则几乎没有变化，这样就使图片整体变亮。

 

 

1. int main(int argc, char *argv[])

2. {

3. double temp = 255 / log(256);

4. cout << "doubledouble temp ="<< temp<<endl;

5.  

6. Mat image = imread("D://vvoo//123.jpg", 1);

7. if (!image.data)

8. {

9. cout << "image loading error" <<endl;

10. return -1;

11. }

12. imshow("原图", image);

13. Mat imageLog(image.size(), CV_32FC3);

14. for (int i = 0; i < image.rows; i++)

15. {

16. for (int j = 0; j < image.cols; j++)

17. {

18. imageLog.at<Vec3f>(i, j)[0] = temp* log(1 + image.at<Vec3b>(i, j)[0]);

19. imageLog.at<Vec3f>(i, j)[1] = temp*log(1 + image.at<Vec3b>(i, j)[1]);

20. imageLog.at<Vec3f>(i, j)[2] = temp*log(1 + image.at<Vec3b>(i, j)[2]);

21. }

22. }

23. //归一化到0~255

24. normalize(imageLog, imageLog, 0, 255, CV_MINMAX);

25. //转换成8bit图像显示

26. convertScaleAbs(imageLog, imageLog);

27. int channel = image.channels();

28. cout << channel << endl;

29. imshow("Soure", image);

30. imshow("after", imageLog);

31. imwrite("C://Users//TOPSUN//Desktop//123.jpg", imageLog);

32. waitKey();

33. return 0;

34. }

二、去除光照

5.RGB归一化

据说能消除光照，自己实现出来好垃圾啊

 

1. int main(int argc, char *argv[])

2. {

3. //double temp = 255 / log(256);

4. //cout << "doubledouble temp ="<< temp<<endl;

5.  

6. Mat image = imread("D://vvoo//sun_face.jpg", 1);

7. if (!image.data)

8. {

9. cout << "image loading error" <<endl;

10. return -1;

11. }

12. imshow("原图", image);

13. Mat src(image.size(), CV_32FC3);

14. for (int i = 0; i < image.rows; i++)

15. {

16. for (int j = 0; j < image.cols; j++)

17. {

18. 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);

19. 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);

20. 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);

21. }

22. }

23.  

24. normalize(src, src, 0, 255, CV_MINMAX);

25.  

26. convertScaleAbs(src,src);

27. imshow("rgb", src);

28. imwrite("C://Users//TOPSUN//Desktop//123.jpg", src);

29. waitKey(0);

30. return 0;

31. }

 

 

6.另一种去除光照的方法

 

 

1. void unevenLightCompensate(Mat &image, int blockSize)

2. {

3. if (image.channels() == 3) cvtColor(image, image, 7);

4. double average = mean(image)[0];

5. int rows_new = ceil(double(image.rows) / double(blockSize));

6. int cols_new = ceil(double(image.cols) / double(blockSize));

7. Mat blockImage;

8. blockImage = Mat::zeros(rows_new, cols_new, CV_32FC1);

9. for (int i = 0; i < rows_new; i++)

10. {

11. for (int j = 0; j < cols_new; j++)

12. {

13. int rowmin = i*blockSize;

14. int rowmax = (i + 1)*blockSize;

15. if (rowmax > image.rows) rowmax = image.rows;

16. int colmin = j*blockSize;

17. int colmax = (j + 1)*blockSize;

18. if (colmax > image.cols) colmax = image.cols;

19. Mat imageROI = image(Range(rowmin, rowmax), Range(colmin, colmax));

20. double temaver = mean(imageROI)[0];

21. blockImage.at<float>(i, j) = temaver;

22. }

23. }

24. blockImage = blockImage - average;

25. Mat blockImage2;

26. resize(blockImage, blockImage2, image.size(), (0, 0), (0, 0), INTER_CUBIC);

27. Mat image2;

28. image.convertTo(image2, CV_32FC1);

29. Mat dst = image2 - blockImage2;

30. dst.convertTo(image, CV_8UC1);

31. }

32. int main(int argc, char *argv[])

33. {

34. //double temp = 255 / log(256);

35. //cout << "doubledouble temp ="<< temp<<endl;

36.  

37. Mat image = imread("C://Users//TOPSUN//Desktop//2.jpg", 1);

38. if (!image.data)

39. {

40. cout << "image loading error" <<endl;

41. return -1;

42. }

43. imshow("原图", image);

44. unevenLightCompensate(image, 12);

45. imshow("rgb", image);

46. imwrite("C://Users//TOPSUN//Desktop//123.jpg", image);

47. waitKey(0);

48. return 0;

49. }

7.又找到一个

 

 

1. int highlight_remove_Chi(IplImage* src, IplImage* dst)

2. {

3. int height = src->height;

4. int width = src->width;

5. int step = src->widthStep;

6. int i = 0, j = 0;

7. unsigned char R, G, B, MaxC;

8. double alpha, beta, alpha_r, alpha_g, alpha_b, beta_r, beta_g, beta_b, temp = 0, realbeta = 0, minalpha = 0;

9. double gama, gama_r, gama_g, gama_b;

10. unsigned char* srcData;

11. unsigned char* dstData;

12. for (i = 0; i<height; i++)

13. {

14. srcData = (unsigned char*)src->imageData + i*step;

15. dstData = (unsigned char*)dst->imageData + i*step;

16. for (j = 0; j<width; j++)

17. {

18. R = srcData[j * 3];

19. G = srcData[j * 3 + 1];

20. B = srcData[j * 3 + 2];

21.  

22. alpha_r = (double)R / (double)(R + G + B);

23. alpha_g = (double)G / (double)(R + G + B);

24. alpha_b = (double)B / (double)(R + G + B);

25. alpha = max(max(alpha_r, alpha_g), alpha_b);

26. MaxC = max(max(R, G), B);// compute the maximum of the rgb channels

27. minalpha = min(min(alpha_r, alpha_g), alpha_b); beta_r = 1 - (alpha - alpha_r) / (3 * alpha - 1);

28. beta_g = 1 - (alpha - alpha_g) / (3 * alpha - 1);

29. beta_b = 1 - (alpha - alpha_b) / (3 * alpha - 1);

30. beta = max(max(beta_r, beta_g), beta_b);//将beta当做漫反射系数，则有 // gama is used to approximiate the beta

31. gama_r = (alpha_r - minalpha) / (1 - 3 * minalpha);

32. gama_g = (alpha_g - minalpha) / (1 - 3 * minalpha);

33. gama_b = (alpha_b - minalpha) / (1 - 3 * minalpha);

34. gama = max(max(gama_r, gama_g), gama_b);

35.  

36. temp = (gama*(R + G + B) - MaxC) / (3 * gama - 1);

37. //beta=(alpha-minalpha)/(1-3*minalpha)+0.08;

38. //temp=(gama*(R+G+B)-MaxC)/(3*gama-1);

39. dstData[j * 3] = R - (unsigned char)(temp + 0.5);

40. dstData[j * 3 + 1] = G - (unsigned char)(temp + 0.5);

41. dstData[j * 3 + 2] = B - (unsigned char)(temp + 0.5);

42. }

43. }

44. cvShowImage("src", src);

45. cvShowImage("dst", dst);

46.  

47. return 1;

48. }

49.  

50. void main()

51. {

52. IplImage *src = cvLoadImage("C://Users//TOPSUN//Desktop//2.jpg");

53. IplImage *dst = cvCreateImage(cvSize(src->width, src->height), src->depth, 3);

54. if (!src)

55. {

56. printf("请确保图像输入正确；");

57. return;

58. }

59. highlight_remove_Chi(src, dst);

60. cvSaveImage("C://Users//TOPSUN//Desktop//123.jpg", dst);

61. cvWaitKey(0);

62. }

 

 

未完待续。。。