OpenCV图像处理专栏十二 |《基于二维伽马函数的光照不均匀图像自适应校正算法》

Mat RGB2HSV(Mat src) {
	int row = src.rows;
	int col = src.cols;
	Mat dst(row, col, CV_32FC3);
	for (int i = 0; i < row; i++) {
		for (int j = 0; j < col; j++) {
			float b = src.at<Vec3b>(i, j)[0] / 255.0;
			float g = src.at<Vec3b>(i, j)[1] / 255.0;
			float r = src.at<Vec3b>(i, j)[2] / 255.0;
			float minn = min(r, min(g, b));
			float maxx = max(r, max(g, b));
			dst.at<Vec3f>(i, j)[2] = maxx; //V
			float delta = maxx - minn;
			float h, s;
			if (maxx != 0) {
				s = delta / maxx;
			}
			else {
				s = 0;
			}
			if (r == maxx) {
				h = (g - b) / delta;
			}
			else if (g == maxx) {
				h = 2 + (b - r) / delta;
			}
			else {
				h = 4 + (r - g) / delta;
			}
			h *= 60;
			if (h < 0)
				h += 360;
			dst.at<Vec3f>(i, j)[0] = h;
			dst.at<Vec3f>(i, j)[1] = s;
		}
	}
	return dst;
}

Mat HSV2RGB(Mat src) {
	int row = src.rows;
	int col = src.cols;
	Mat dst(row, col, CV_8UC3);
	float r, g, b, h, s, v;
	for (int i = 0; i < row; i++) {
		for (int j = 0; j < col; j++) {
			h = src.at<Vec3f>(i, j)[0];
			s = src.at<Vec3f>(i, j)[1];
			v = src.at<Vec3f>(i, j)[2];
			if (s == 0) {
				r = g = b = v;
			}
			else {
				h /= 60;
				int offset = floor(h);
				float f = h - offset;
				float p = v * (1 - s);
				float q = v * (1 - s * f);
				float t = v * (1 - s * (1 - f));
				switch (offset)
				{
				case 0: r = v; g = t; b = p; break;
				case 1: r = q; g = v; b = p; break;
				case 2: r = p; g = v; b = t; break;
				case 3: r = p; g = q; b = v; break;
				case 4: r = t; g = p; b = v; break;
				case 5: r = v; g = p; b = q; break;
				default:
					break;
				}
			}
			dst.at<Vec3b>(i, j)[0] = int(b * 255);
			dst.at<Vec3b>(i, j)[1] = int(g * 255);
			dst.at<Vec3b>(i, j)[2] = int(r * 255);
		}
	}
	return dst;
}

Mat work(Mat src) {
	int row = src.rows;
	int col = src.cols;
	Mat now = RGB2HSV(src);
	Mat H(row, col, CV_32FC1);
	Mat S(row, col, CV_32FC1);
	Mat V(row, col, CV_32FC1);
	for (int i = 0; i < row; i++) {
		for (int j = 0; j < col; j++) {
			H.at<float>(i, j) = now.at<Vec3f>(i, j)[0];
			S.at<float>(i, j) = now.at<Vec3f>(i, j)[1];
			V.at<float>(i, j) = now.at<Vec3f>(i, j)[2];
		}
	}
	int kernel_size = min(row, col);
	if (kernel_size % 2 == 0) {
		kernel_size -= 1;
	}
	float SIGMA1 = 15;
	float SIGMA2 = 80;
	float SIGMA3 = 250;
	float q = sqrt(2.0);
	Mat F(row, col, CV_32FC1);
	Mat F1, F2, F3;
	GaussianBlur(V, F1, Size(kernel_size, kernel_size), SIGMA1 / q);
	GaussianBlur(V, F2, Size(kernel_size, kernel_size), SIGMA2 / q);
	GaussianBlur(V, F3, Size(kernel_size, kernel_size), SIGMA3 / q);
	for (int i = 0; i < row; i++) {
		for (int j = 0; j < col; j++) {
			F.at <float>(i, j) = (F1.at<float>(i, j) + F2.at<float>(i, j) + F3.at<float>(i, j)) / 3.0;
		}
	}
	float average = mean(F)[0];
	Mat out(row, col, CV_32FC1);
	for (int i = 0; i < row; i++) {
		for (int j = 0; j < col; j++) {
			float gamma = powf(0.5, (average - F.at<float>(i, j)) / average);
			out.at<float>(i, j) = powf(V.at<float>(i, j), gamma);
		}
	}
	vector <Mat> v;
	v.push_back(H);
	v.push_back(S);
	v.push_back(out);
	Mat merge_;
	merge(v, merge_);
	Mat dst = HSV2RGB(merge_);
	return dst;
}