opencv如何计算图像中物体的像素值

Posted 2023-04-05

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参考技术A OpenCV中获取图像某一像素值
This is a basic example for the OpenCV.

First we must know the structure of IplImage:
IPL image:
IplImage
|-- int nChannels; // Number of color channels (1,2,3,4)
|-- int depth; // Pixel depth in bits:
| // IPL_DEPTH_8U, IPL_DEPTH_8S,
| // IPL_DEPTH_16U,IPL_DEPTH_16S,
| // IPL_DEPTH_32S,IPL_DEPTH_32F,
| // IPL_DEPTH_64F
|-- int width; // image width in pixels
|-- int height; // image height in pixels
|-- char* imageData; // pointer to aligned image data
| // Note that color images are stored in BGR order
|-- int dataOrder; // 0 - interleaved color channels,
| // 1 - separate color channels
| // cvCreateImage can only create interleaved images
|-- int origin; // 0 - top-left origin,
| // 1 - bottom-left origin (Windows bitmaps style)
|-- int widthStep; // size of aligned image row in bytes
|-- int imageSize; // image data size in bytes = height*widthStep
|-- struct _IplROI *roi;// image ROI. when not NULL specifies image
| // region to be processed.
|-- char *imageDataOrigin; // pointer to the unaligned origin of image data
| // (needed for correct image deallocation)
|
|-- int align; // Alignment of image rows: 4 or 8 byte alignment
| // OpenCV ignores this and uses widthStep instead
|-- char colorModel[4]; // Color model - ignored by OpenCV
//------------------------------------------------------------------------------int main(int argc, char* argv[])
...
IplImage *img=cvLoadImage("c://fruitfs.bmp",1);
CvScalar s;
for(int i=0;i<img->height;i++)...
for(int j=0;j<img->width;j++)...
s=cvGet2D(img,i,j); // get the (i,j) pixel value
printf("B=%f, G=%f, R=%f ",s.val[0],s.val[1],s.val[2]);
s.val[0]=111;
s.val[1]=111;
s.val[2]=111;
cvSet2D(img,i,j,s);//set the (i,j) pixel value

cvNamedWindow("Image",1);
cvShowImage("Image",img);
cvWaitKey(0); //等待按键
cvDestroyWindow( "Image" );//销毁窗口
cvReleaseImage( &img ); //释放图像
return 0;

其实还有更好的方法，例如将其封装成类，调用起来更加方便，效率也很高。本回答被提问者和网友采纳

OpenCV3入门图像的阈值

1、图像阈值与二值化

阈值是一种简单的图像分割方法，一幅图像包括目标物体（前景）、背景还有噪声，要想从数字图像中直接提取出目标物体，可以设定一个像素值即阈值，然后用图像的每一个像素点和阈值做比较，给出判定结果。

二值化是特殊的阈值分割方法，把图像分为两部分，以阈值T为分割线，大于T的像素群和小于T的像素群，这样图像就变为黑白二色图像。通过设定一个标准如果大于这个标准就设为白，如果小于这个标准就设为黑，而这个标准就是阈值。

2、OpenCV阈值threshold

double threshold(InputArray src, OutputArray dst, double thresh, double maxval, int type);

（1）第一个参数，InputArray 类型的 src，源图像。单通道，8 或 32位浮点数类型的深度。

（2）第二个参数，OutputArray 类型的 dst，输出图像。

（3）第三个参数，double 类型的 thresh，选取的阈值。

（4）第四个参数，double 类型的 maxval。

（5）第五个参数，int 类型的 type。阈值类型。如下所示：

type类型如下：

enum  cv::ThresholdTypes { 
  cv::THRESH_BINARY = 0, 
  cv::THRESH_BINARY_INV = 1, 
  cv::THRESH_TRUNC = 2, 
  cv::THRESH_TOZERO = 3, 
  cv::THRESH_TOZERO_INV = 4, 
  cv::THRESH_MASK = 7, 
  cv::THRESH_OTSU = 8, 
  cv::THRESH_TRIANGLE = 16 
}

不同的阈值方法生成关系如下图。

技术图片

Mat img = Mat::zeros(6, 6, CV_8UC1);
randu(img, 0, 255);

int th = 100;
Mat threshold1, threshold2, threshold3, threshold4, threshold5, threshold6, threshold7, threshold8;
threshold(img, threshold1, th, 200, THRESH_BINARY);
threshold(img, threshold2, th, 200, THRESH_BINARY_INV);
threshold(img, threshold3, th, 200, THRESH_TRUNC);

cout << "raw=
"<<img << "
" << endl;
cout << "THRESH_BINARY=
" << threshold1 << "
" << endl;
cout << "THRESH_BINARY_INV=
" << threshold2 << "
" << endl;
cout << "THRESH_TRUNC=
" << threshold3 << "
" << endl;

上面代码中randu(img, 0, 255)作用是产出随机数填充img矩阵。输出结果如下。

raw=
[ 91,   2,  79, 179,  52, 205;
 236,   8, 181, 239,  26, 248;
 207, 218,  45, 183, 158, 101;
 102,  18, 118,  68, 210, 139;
 198, 207, 211, 181, 162, 197;
 191, 196,  40,   7, 243, 230]

THRESH_BINARY=
[  0,   0,   0, 200,   0, 200;
 200,   0, 200, 200,   0, 200;
 200, 200,   0, 200, 200, 200;
 200,   0, 200,   0, 200, 200;
 200, 200, 200, 200, 200, 200;
 200, 200,   0,   0, 200, 200]

THRESH_BINARY_INV=
[200, 200, 200,   0, 200,   0;
   0, 200,   0,   0, 200,   0;
   0,   0, 200,   0,   0,   0;
   0, 200,   0, 200,   0,   0;
   0,   0,   0,   0,   0,   0;
   0,   0, 200, 200,   0,   0]

THRESH_TRUNC=
[ 91,   2,  79, 100,  52, 100;
 100,   8, 100, 100,  26, 100;
 100, 100,  45, 100, 100, 100;
 100,  18, 100,  68, 100, 100;
 100, 100, 100, 100, 100, 100;
 100, 100,  40,   7, 100, 100]

THRESH_BINARY，thresh=100，maxval=200，大于阈值限定为200，小于阈值清零。

THRESH_BINARY_INV的作用和THRESH_BINARY 相反，小于阈值置200，大于阈值清。

THRESH_TRUNC的作用是对大于阈值的数据进行截断，其余值保留原值不变。

图像阈值例子如下。

Mat img = imread("D:/WORK/5.OpenCV/LeanOpenCV/pic_src/pic6.bmp", IMREAD_GRAYSCALE);
int th = 100;
Mat threshold1, threshold2, threshold3, threshold4, threshold5, threshold6, threshold7, threshold8;
threshold(img, threshold1, th, 200, THRESH_BINARY);
threshold(img, threshold2, th, 200, THRESH_BINARY_INV);
threshold(img, threshold3, th, 200, THRESH_TRUNC);

imshow("raw pic",img);
imshow("THRESH_BINARY", threshold1);
imshow("THRESH_BINARY_INV", threshold2);
imshow("THRESH_TRUNC", threshold3);

技术图片

3、自动阈值—大津法OTSU

最大类间方差是由日本学者大津(Nobuyuki Otsu)于1979年提出，是一种自适应的阈值确定方法。算法假设图像像素能够根据阈值，被分成背景[background]和目标[objects]两部分。然后，计算该最佳阈值来区分这两类像素，使得两类像素区分度最大。

算法原理为：

设图像Img长宽尺寸为M*N, T为二值化的阈值；

N0为灰度小于T的像素的个数，N0的平均灰度为μ0。

N1 为灰度大于T的像素的个数，N1的平均灰度为μ1。

ω0=N0/ M×N                   (1)   //落在N0的概率

ω1=N1/ M×N                   (2) 　//落在N1的概率

N0+N1=M×N                    (3) 　

ω0+ω1=1 (4) 　　　　　　

μ=ω0*μ0+ω1*μ1              (5) 　//平均灰度乘以概率再相加

g=ω0(μ0-μ)^2+ω1(μ1-μ)^2     (6)   //类间方差

将式(5)代入式(6),得到等价公式: g=ω0ω1(μ0-μ1)^2    (7)

OpenCV自带了OSTU算法。

Mat img = imread("D:/WORK/5.OpenCV/LeanOpenCV/pic_src/pic2.bmp", IMREAD_GRAYSCALE);
int th = 100;
Mat threshold1, threshold2, threshold3;
threshold(img, threshold1, th, 255, THRESH_BINARY);
threshold(img, threshold2, th, 255, THRESH_TRUNC);
threshold(img, threshold3, th, 255, THRESH_OTSU); // 阈值随意设置即可

imshow("raw pic",img);
imshow("THRESH_BINARY", threshold1);
imshow("THRESH_TRUNC",  threshold2);
imshow("THRESH_OTSU",   threshold3);