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Add AutomaticWhiteBalanceMethod.cpp
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‎AutomaticWhiteBalanceMethod.cpp

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const float YCbCrYRF = 0.299F; // RGB转YCbCr的系数(浮点类型）
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const float YCbCrYGF = 0.587F;
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const float YCbCrYBF = 0.114F;
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const float YCbCrCbRF = -0.168736F;
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const float YCbCrCbGF = -0.331264F;
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const float YCbCrCbBF = 0.500000F;
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const float YCbCrCrRF = 0.500000F;
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const float YCbCrCrGF = -0.418688F;
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const float YCbCrCrBF = -0.081312F;
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const float RGBRYF = 1.00000F; // YCbCr转RGB的系数(浮点类型）
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const float RGBRCbF = 0.0000F;
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const float RGBRCrF = 1.40200F;
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const float RGBGYF = 1.00000F;
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const float RGBGCbF = -0.34414F;
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const float RGBGCrF = -0.71414F;
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const float RGBBYF = 1.00000F;
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const float RGBBCbF = 1.77200F;
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const float RGBBCrF = 0.00000F;
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const int Shift = 20;
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const int HalfShiftValue = 1 << (Shift - 1);
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const int YCbCrYRI = (int)(YCbCrYRF * (1 << Shift) + 0.5); // RGB转YCbCr的系数(整数类型）
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const int YCbCrYGI = (int)(YCbCrYGF * (1 << Shift) + 0.5);
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const int YCbCrYBI = (int)(YCbCrYBF * (1 << Shift) + 0.5);
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const int YCbCrCbRI = (int)(YCbCrCbRF * (1 << Shift) + 0.5);
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const int YCbCrCbGI = (int)(YCbCrCbGF * (1 << Shift) + 0.5);
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const int YCbCrCbBI = (int)(YCbCrCbBF * (1 << Shift) + 0.5);
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const int YCbCrCrRI = (int)(YCbCrCrRF * (1 << Shift) + 0.5);
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const int YCbCrCrGI = (int)(YCbCrCrGF * (1 << Shift) + 0.5);
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const int YCbCrCrBI = (int)(YCbCrCrBF * (1 << Shift) + 0.5);
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const int RGBRYI = (int)(RGBRYF * (1 << Shift) + 0.5); // YCbCr转RGB的系数(整数类型）
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const int RGBRCbI = (int)(RGBRCbF * (1 << Shift) + 0.5);
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const int RGBRCrI = (int)(RGBRCrF * (1 << Shift) + 0.5);
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const int RGBGYI = (int)(RGBGYF * (1 << Shift) + 0.5);
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const int RGBGCbI = (int)(RGBGCbF * (1 << Shift) + 0.5);
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const int RGBGCrI = (int)(RGBGCrF * (1 << Shift) + 0.5);
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const int RGBBYI = (int)(RGBBYF * (1 << Shift) + 0.5);
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const int RGBBCbI = (int)(RGBBCbF * (1 << Shift) + 0.5);
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const int RGBBCrI = (int)(RGBBCrF * (1 << Shift) + 0.5);
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Mat RGB2YCbCr(Mat src) {
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int row = src.rows;
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int col = src.cols;
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Mat dst(row, col, CV_8UC3);
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for (int i = 0; i < row; i++) {
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for (int j = 0; j < col; j++) {
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int Blue = src.at<Vec3b>(i, j)[0];
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int Green = src.at<Vec3b>(i, j)[1];
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int Red = src.at<Vec3b>(i, j)[2];
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dst.at<Vec3b>(i, j)[0] = (int)((YCbCrYRI * Red + YCbCrYGI * Green + YCbCrYBI * Blue + HalfShiftValue) >> Shift);
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dst.at<Vec3b>(i, j)[1] = (int)(128 + ((YCbCrCbRI * Red + YCbCrCbGI * Green + YCbCrCbBI * Blue + HalfShiftValue) >> Shift));
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dst.at<Vec3b>(i, j)[2] = (int)(128 + ((YCbCrCrRI * Red + YCbCrCrGI * Green + YCbCrCrBI * Blue + HalfShiftValue) >> Shift));
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}
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}
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return dst;
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}
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Mat YCbCr2RGB(Mat src) {
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int row = src.rows;
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int col = src.cols;
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Mat dst(row, col, CV_8UC3);
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for (int i = 0; i < row; i++) {
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for (int j = 0; j < col; j++) {
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int Y = src.at<Vec3b>(i, j)[0];
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int Cb = src.at<Vec3b>(i, j)[1] - 128;
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int Cr = src.at<Vec3b>(i, j)[2] - 128;
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int Red = Y + ((RGBRCrI * Cr + HalfShiftValue) >> Shift);
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int Green = Y + ((RGBGCbI * Cb + RGBGCrI * Cr + HalfShiftValue) >> Shift);
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int Blue = Y + ((RGBBCbI * Cb + HalfShiftValue) >> Shift);
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if (Red > 255) Red = 255; else if (Red < 0) Red = 0;
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if (Green > 255) Green = 255; else if (Green < 0) Green = 0; // 编译后应该比三目运算符的效率高
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if (Blue > 255) Blue = 255; else if (Blue < 0) Blue = 0;
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dst.at<Vec3b>(i, j)[0] = Blue;
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dst.at<Vec3b>(i, j)[1] = Green;
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dst.at<Vec3b>(i, j)[2] = Red;
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}
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}
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return dst;
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}
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Mat AutomaticWhiteBalanceMethod(Mat src) {
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int row = src.rows;
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int col = src.cols;
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if (src.channels() == 4) {
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cvtColor(src, src, CV_BGRA2BGR);
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}
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Mat input = RGB2YCbCr(src);
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Mat mark(row, col, CV_8UC1);
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int sum = 0;
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for (int i = 0; i < row; i+=100) {
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for (int j = 0; j < col; j+=100) {
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if (i + 100 < row && j + 100 < col) {
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Rect rect(j, i, 100, 100);
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Mat temp = input(rect);
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Scalar global_mean = mean(temp);
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double dr = 0, db = 0;
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for (int x = 0; x < 100; x++) {
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uchar *ptr = temp.ptr<uchar>(x) + 1;
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for (int y = 0; y < 100; y++) {
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dr += pow(abs(*ptr - global_mean[1]), 2);
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ptr++;
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db += pow(abs(*ptr - global_mean[2]), 2);
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ptr++;
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ptr++;
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}
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}
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dr /= 10000;
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db /= 10000;
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double cr_left_criteria = 1.5 * global_mean[1] + dr * global_mean[1];
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double cr_right_criteria = 1.5 * dr;
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double cb_left_criteria = global_mean[2] + db * global_mean[2];
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double cb_right_criteria = 1.5 * db;
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for (int x = 0; x < 100; x++) {
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uchar *ptr = temp.ptr<uchar>(x) + 1;
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for (int y = 0; y < 100; y++) {
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uchar cr = *ptr;
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ptr++;
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uchar cb = *ptr;
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ptr++;
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ptr++;
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if ((cr - cb_left_criteria) < cb_right_criteria && (cb - cr_left_criteria) < cr_right_criteria) {
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sum++;
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mark.at<uchar>(i + x, j + y) = 1;
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}
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else {
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mark.at<uchar>(i + x, j + y) = 0;
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}
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}
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}
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}
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}
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}
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int Threshold = 0;
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int Ymax = 0;
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int Light[256] = { 0 };
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for (int i = 0; i < row; i++) {
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for (int j = 0; j < col; j++) {
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if (mark.at<uchar>(i, j) == 1) {
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Light[(int)(input.at<Vec3b>(i, j)[0])]++;
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}
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Ymax = max(Ymax, (int)(input.at<Vec3b>(i, j)[0]));
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}
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}
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printf("maxY: %d\n", Ymax);
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int sum2 = 0;
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for (int i = 255; i >= 0; i--) {
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sum2 += Light[i];
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if (sum2 >= sum * 0.1) {
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Threshold = i;
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break;
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}
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}
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printf("Threshold: %d\n", Threshold);
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printf("Sum: %d Sum2: %d\n", sum, sum2);
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double Blue = 0;
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double Green = 0;
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double Red = 0;
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int cnt2 = 0;
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for (int i = 0; i < row; i++) {
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for (int j = 0; j < col; j++) {
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if (mark.at<uchar>(i, j) == 1 && (int)(input.at<Vec3b>(i, j)[0]) >= Threshold) {
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Blue += 1.0 * src.at<Vec3b>(i, j)[0];
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Green += 1.0 * src.at<Vec3b>(i, j)[1];
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Red += 1.0 * src.at<Vec3b>(i, j)[2];
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cnt2++;
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}
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}
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}
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Blue /= cnt2;
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Green /= cnt2;
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Red /= cnt2;
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printf("%.5f %.5f %.5f\n", Blue, Green, Red);
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Mat dst(row, col, CV_8UC3);
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double maxY = Ymax;
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for (int i = 0; i < row; i++) {
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for (int j = 0; j < col; j++) {
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int B = (int)(maxY * src.at<Vec3b>(i, j)[0] / Blue);
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int G = (int)(maxY * src.at<Vec3b>(i, j)[1] / Green);
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int R = (int)(maxY * src.at<Vec3b>(i, j)[2] / Red);
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if (B > 255) B = 255; else if (B < 0) B = 0;
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if (G > 255) G = 255; else if (G < 0) G = 0;
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if (R > 255) R = 255; else if (R < 0) R = 0;
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dst.at<Vec3b>(i, j)[0] = B;
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dst.at<Vec3b>(i, j)[1] = G;
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dst.at<Vec3b>(i, j)[2] = R;
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}
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}
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return dst;
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}

‎README.md

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- unevenLightCompensate.cpp C++复现了《一种基于亮度均衡的图像阈值分割技术》这篇论文的光照补偿部分，可以对光照不均匀，曝光，逆光图像做亮度均衡，效果不错。原理请看：https://blog.csdn.net/just_sort/article/details/88551771
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- Adaptive correction algorithm for illumination inhomogeneity image based on two-dimensional gamma function.cpp C++复现了《基于二维伽马函数的光照不均匀图像自适应校正算法》这篇论文，对光照不均匀的图像有较好的校正效果，且不会像Retiex那样出现光晕。原理请看：https://blog.csdn.net/just_sort/article/details/88569129
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- Real-time adaptive contrast enhancement for imaging sensors.cpp C++复现了《Real-time adaptive contrast enhancement for imaging sensors》这篇论文，实时自适应局部对比度增强算法。原理请看：https://blog.csdn.net/just_sort/article/details/85208124
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- AutomaticWhiteBalanceMethod.cpp C++复现了《A Novel Automatic White Balance Method For Digital Still Cameras》这篇论文，实现了效果比完美反射更好得白平衡效果。原理请看：https://blog.csdn.net/just_sort/article/details/89183909
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