直方图是我们在照片中使用来查看图像中每个值有多少像素，照片中的每个像素的值都从0(黑色)到255(白色)，图的左侧代表音阶的暗色调，右侧代表较亮的色调。在彩色摄影中，每个像素对于每种颜色都有其自己的值(0-255)。图片中的直方图显示了每种颜色(红色，蓝色和绿色)的像素值分布.
图像直方图，也叫灰度直方图，反映了图像像素分布的统计特征，是图像处理中简单有效的工具，图像直方图广泛地应用于图像处理的各个领域，如：特征提取，图像匹配，灰度图像的阈值分割，基于颜色的图像检索以及图像分类。
图像的直方图的形态很大程度上可直观的反映图像的质量情况，比如根据下图所示，会很快发现一张图片是否过量还是过暗。
图像直方图的优点是，计算量较小，且具有图像平移，旋转，缩放不变性等。
数学原理图像灰度直方图计算：
其中，W，H分别表示图像的宽和高，表示图像中像素值为r的数量,表示图像中像素值为r的比率，L是指图像灰度级数，比如，8BIT的灰度图像L=2^8=256.其存在256个色阶，即0-255。
OPENCV获取直方图：//获得直方图 #include#include #include #include using namespace cv; using namespace std; int main(int argc, char** argv) { //----------------------example 1-------------------------------// Mat src, dst; /// Load image src = imread("/home/caozilong/165823915.jpg"); if (!src.data) { cout << "load image failed" << endl; return -1; } /// Separate the image in 3 places ( R, G and B ) vector rgb_planes; Mat hsv; cvtColor(src, hsv, COLOR_BGR2HSV); split(hsv, rgb_planes); split(src, rgb_planes); /// Establish the number of bins int histSize = 256; /// Set the ranges ( for R,G,B) ) float range[] = { 0, 255 }; const float* histRange = { range }; bool uniform = true; bool accumulate = false; Mat r_hist, g_hist, b_hist; /// Compute the histograms: calcHist(&rgb_planes[2], 1, 0, Mat(), r_hist, 1, &histSize, &histRange, uniform, accumulate); calcHist(&rgb_planes[1], 1, 0, Mat(), g_hist, 1, &histSize, &histRange, uniform, accumulate); calcHist(&rgb_planes[0], 1, 0, Mat(), b_hist, 1, &histSize, &histRange, uniform, accumulate); // Draw the histograms for R, G and B int hist_w = 600; int hist_h = 400; int bin_w = cvRound((double)hist_w / histSize); Mat rgb_hist[3]; for (int i = 0; i < 3; ++i) { rgb_hist[i] = Mat(hist_h, hist_w, CV_8UC3, Scalar::all(0)); } Mat histImage(hist_h, hist_w, CV_8UC3, Scalar(0, 0, 0)); /// Normalize the result to [ 0, histImage.rows-10] normalize(r_hist, r_hist, 0, histImage.rows - 10, NORM_MINMAX); normalize(g_hist, g_hist, 0, histImage.rows - 10, NORM_MINMAX); normalize(b_hist, b_hist, 0, histImage.rows - 10, NORM_MINMAX); /// Draw for each channel for (int i = 1; i < histSize; i++) { line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(r_hist.at (i - 1))), Point(bin_w * (i), hist_h - cvRound(r_hist.at (i))), Scalar(0, 0, 255), 1); line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(g_hist.at (i - 1))), Point(bin_w * (i), hist_h - cvRound(g_hist.at (i))), Scalar(0, 255, 0), 1); line(histImage, Point(bin_w * (i - 1), hist_h - cvRound(b_hist.at (i - 1))), Point(bin_w * (i), hist_h - cvRound(b_hist.at (i))), Scalar(255, 0, 0), 1); } for (int j = 0; j < histSize; ++j) { int val = saturate_cast (r_hist.at (j)); rectangle(rgb_hist[0], Point(j * 2 + 10, rgb_hist[0].rows), Point((j + 1) * 2 + 10, rgb_hist[0].rows - val), Scalar(0, 0, 255), 1, 8); val = saturate_cast (g_hist.at (j)); rectangle(rgb_hist[1], Point(j * 2 + 10, rgb_hist[1].rows), Point((j + 1) * 2 + 10, rgb_hist[1].rows - val), Scalar(0, 255, 0), 1, 8); val = saturate_cast (b_hist.at (j)); rectangle(rgb_hist[2], Point(j * 2 + 10, rgb_hist[2].rows), Point((j + 1) * 2 + 10, rgb_hist[2].rows - val), Scalar(255, 0, 0), 1, 8); } /// Display namedWindow("calcHist Demo", CV_WINDOW_AUTOSIZE); namedWindow("wnd"); imshow("calcHist Demo", histImage); imshow("wnd", src); imshow("R", rgb_hist[0]); imshow("G", rgb_hist[1]); imshow("B", rgb_hist[2]); waitKey(); }
g++ new.cpp `pkg-config --cflags --libs opencv`
直方图均值化
直方图均衡化是通过拉伸像素强度的分布范围，使得在0~255灰阶上的分布更加均衡，提高了图像的对比度，达到改善图像主观视觉效果的目的。对比度较低的图像适合使用直方图均衡化方法来增强图像细节。
//彩色图像直方图均衡化 #include#include #include using namespace cv; using namespace std; const char* output = "histogram iamge"; int main(int argc, char** argv) { Mat src, dst, dst1; src = imread("/home/caozilong/165823915.jpg"); if (!src.data) { printf("could not load image...n"); return -1; } char input[] = "input image"; namedWindow(input, 0); namedWindow(output, 0); resizeWindow(input, 600, 400); imshow(input, src); //分割通道 vector channels; split(src, channels); Mat blue, green, red; blue = channels.at(0); green = channels.at(1); red = channels.at(2); //分别对BGR通道做直方图均衡化 equalizeHist(blue, blue); equalizeHist(green, green); equalizeHist(red, red); //合并通道 merge(channels, dst); resizeWindow(output, 600, 400); imshow(output, dst); waitKey(0); return 0; }
g++ main.cpp `pkg-config --cflags --libs opencv`
可以看到对比度确实增加了
但是对于那些曝光良好，直方图表现均匀的图像，均值化的效果并不明显：
然后，进行均值化后的效果，可以发现，对比度的增加效果并不明显：
另一个例子，原始图片：
图像直方图：
进行直方图均值化后的图像：
对应的直方图为：
结束！
