OpenCV-绘制图像的像素直方图-Histogram
2012-04-10 11:48
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随手翻了翻之前推荐的那本OpenCV2的教程,突然发现了之前看图像处理中的像素直方图的实现教程,原本就打算找时间自己实现的,没想到opencv是有这个功能的
而且这个功能可以辅助很多的图像处理和识别的算法过程,是值得看一看的
OpenCV对于像素直方图的统计有这么一个函数:
下面贴出官方的参数解释,我觉得很好明白的
Parameters
arrays – Source arrays. They all should have the same depth, CV_8U or CV_32F , and the
same size. Each of them can have an arbitrary number of channels.
narrays – Number of source arrays.
channels – List of the dims channels used to compute the histogram. The first ar-
ray channels are numerated from 0 to arrays[0].channels()-1 , the second ar-
ray channels are counted from arrays[0].channels() to arrays[0].channels() +
arrays[1].channels()-1, and so on.
mask – Optional mask. If the matrix is not empty, it must be an 8-bit array of the same
size as arrays[i] . The non-zero mask elements mark the array elements counted in the
histogram.
hist – Output histogram, which is a dense or sparse dims -dimensional array.
dims – Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS
(equal to 32 in the current OpenCV version).
histSize – Array of histogram sizes in each dimension.
ranges – Array of the dims arrays of the histogram bin boundaries in each dimension.
When the histogram is uniform ( uniform =true), then for each dimension i it is enough to
specify the lower (inclusive) boundary L0 of the 0-th histogram bin and the upper (exclusive)
boundary UhistSize[i]−1 for the last histogram bin histSize[i]-1 . That is, in case of a
uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not
uniform ( uniform=false ), then each of ranges[i] contains histSize[i]+1 elements:
L0 , U0 = L1 , U1 = L2 , ..., UhistSize[i]−2 = LhistSize[i]−1 , UhistSize[i]−1 . The array
elements, that are not between L0 and UhistSize[i]−1 , are not counted in the histogram.
uniform – Flag indicatinfg whether the histogram is uniform or not (see above).
accumulate – Accumulation flag. If it is set, the histogram is not cleared in the beginning
when it is allocated. This feature enables you to compute a single histogram from several
sets of arrays, or to update the histogram in time.
代码:
需要注意的是imread这个函数后面的那个参数,不清楚的可以查阅官方的手册~
截图:
上面的代码只是读取的灰度图像反馈的1D的直方图,那么如果我是3通道的RGB/BGR图像呢?只需要做相应的修改然后函数便会返回3*256的一个Mat值
但是在3通道的直方图计算过程中,有可能会觉得计算量过大了,同样可以使用稀疏矩阵(sparse matrix),calcHist同样支持
接下来一段时间,就要回归水鱼的比赛了,OpenCV就要放一放
打算晚上写一下北大提供的PoseToPose函数的分析指导,由于自己的学识有限,很难分析全面,真的希望有人可以来指正我的错误~
张巍骞
2012-4-10
而且这个功能可以辅助很多的图像处理和识别的算法过程,是值得看一看的
OpenCV对于像素直方图的统计有这么一个函数:
void calcHist(const Mat* arrays,
int narrays,
const int* channels,
InputArray mask,
OutputArray hist,
int dims,
const int* histSize,
const float** ranges,
bool uniform=true,
bool accumulate=false )
下面贴出官方的参数解释,我觉得很好明白的
Parameters
arrays – Source arrays. They all should have the same depth, CV_8U or CV_32F , and the
same size. Each of them can have an arbitrary number of channels.
narrays – Number of source arrays.
channels – List of the dims channels used to compute the histogram. The first ar-
ray channels are numerated from 0 to arrays[0].channels()-1 , the second ar-
ray channels are counted from arrays[0].channels() to arrays[0].channels() +
arrays[1].channels()-1, and so on.
mask – Optional mask. If the matrix is not empty, it must be an 8-bit array of the same
size as arrays[i] . The non-zero mask elements mark the array elements counted in the
histogram.
hist – Output histogram, which is a dense or sparse dims -dimensional array.
dims – Histogram dimensionality that must be positive and not greater than CV_MAX_DIMS
(equal to 32 in the current OpenCV version).
histSize – Array of histogram sizes in each dimension.
ranges – Array of the dims arrays of the histogram bin boundaries in each dimension.
When the histogram is uniform ( uniform =true), then for each dimension i it is enough to
specify the lower (inclusive) boundary L0 of the 0-th histogram bin and the upper (exclusive)
boundary UhistSize[i]−1 for the last histogram bin histSize[i]-1 . That is, in case of a
uniform histogram each of ranges[i] is an array of 2 elements. When the histogram is not
uniform ( uniform=false ), then each of ranges[i] contains histSize[i]+1 elements:
L0 , U0 = L1 , U1 = L2 , ..., UhistSize[i]−2 = LhistSize[i]−1 , UhistSize[i]−1 . The array
elements, that are not between L0 and UhistSize[i]−1 , are not counted in the histogram.
uniform – Flag indicatinfg whether the histogram is uniform or not (see above).
accumulate – Accumulation flag. If it is set, the histogram is not cleared in the beginning
when it is allocated. This feature enables you to compute a single histogram from several
sets of arrays, or to update the histogram in time.
代码:
#include <opencv2/opencv.hpp> #include <iostream> using namespace std; class Histogram { private: int histSize[1];//number of bins float hranges[2];//min and max pixel value const float* ranges[1]; int channels[1];//only 1 channel used here public: Histogram() { //Prepare arguments for 1D histogram histSize[0] = 256; hranges[0] = 0.0; hranges[1] = 255.0; ranges[0] = hranges; channels[0] = 0;//by default , we look at channel 0 } cv::MatND getHistogram(const cv::Mat &image) { cv::MatND hist; //Compute histogram cv::calcHist(&image, 1, channels, cv::Mat(), hist, 1, histSize, ranges ); return hist; } //Compute the 1D histogram and returns an image of it cv::Mat getHistogramImage(const cv::Mat &image) { //compute histogram first cv::MatND hist = getHistogram(image); //Get min and max bin values double maxVal = 0; double minVal = 0; cv::minMaxLoc(hist,&minVal,&maxVal); //Image on which to display histogram cv::Mat histImg(histSize[0],histSize[0],CV_8U,cv::Scalar(255)); //set highest point at 90% of nbins int hpt = static_cast<int>(0.9*histSize[0]); //Draw a vertical line for each bin for( int h = 0;h<histSize[0];h++) { float binVal = hist.at<float>(h); int intensity = static_cast< int >(binVal * hpt / maxVal); //This function draw a line between 2 points cv::line(histImg,cv::Point(h,histSize[0]), cv::Point(h,histSize[0] - intensity), cv::Scalar::all(0)); } return histImg; } }; int main() { //Read input image cv::Mat image = cv::imread("1.png",0);//open in b&w //the histogram object Histogram h; //Compute the histogram /*cv::MatND histo = h.getHistogram(image); for(int i = 0;i<256;i++) { cout<<"Value "<<i<<"="<<histo.at<float>(i) <<endl; }*/ cv::namedWindow("Histogram"); cv::imshow("Histogram",h.getHistogramImage(image)); cv::waitKey(0); return 0; }
需要注意的是imread这个函数后面的那个参数,不清楚的可以查阅官方的手册~
截图:
上面的代码只是读取的灰度图像反馈的1D的直方图,那么如果我是3通道的RGB/BGR图像呢?只需要做相应的修改然后函数便会返回3*256的一个Mat值
private: int histSize[3];//number of bins float hranges[2];//min and max pixel value const float* ranges[3]; int channels[3];//only 1 channel used here public: Histogram() { //Prepare arguments for 1D histogram histSize[0] = 256;histSize[1] = 256;histSize[2] = 256; hranges[0] = 0.0; hranges[1] = 255.0; ranges[0] = hranges;ranges[1] =hranges; ranges[2] =hranges; channels[0] = 0;channels[1] = 1;channels[2] = 2; } cv::MatND getHistogram(const cv::Mat &image) { cv::MatND hist; //Compute histogram cv::calcHist(&image, 1, channels, cv::Mat(), hist, 3, histSize, ranges ); return hist; }
但是在3通道的直方图计算过程中,有可能会觉得计算量过大了,同样可以使用稀疏矩阵(sparse matrix),calcHist同样支持
cv::SparseMat getSparseHistogram(const cv::Mat &image) { cv::SparseMat hist(3,histSize,CV_32F); //Compute histogram cv::calcHist(&image, 1, channels, cv::Mat(), hist, 3, histSize, ranges ); return hist; }
接下来一段时间,就要回归水鱼的比赛了,OpenCV就要放一放
打算晚上写一下北大提供的PoseToPose函数的分析指导,由于自己的学识有限,很难分析全面,真的希望有人可以来指正我的错误~
张巍骞
2012-4-10
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