概括

从概念上讲，一个字节能表示0到255的整数。目前，对于所有的实时图像应用而言， 虽然有其他的表示形式，但一个像素通常由每个通道的一个字节表示。
一个OpenCV图像是.array类型的二维或三维数组。8位的灰度图像是一个含有字节值 的二维数组。一个24位的BGR图像是一个三维数组，它也包含了字节值。可使用表达式 访问这些值，例如image[0,0]或image[0, 0, 0]。第一个值代表像素的y坐标或行，0表示 顶部；第二个值是像素的x坐标或列，0表示最左边；第三个值(如果可用的话)表示颜色 通道。

C++接口：

1。处理图片
效果：（原图就不发了
application_trace.cpp

#include #include 
#include 
#include 
#include 
#include using namespace cv;
using namespace std;static void process_frame(const cv::UMat& frame)
{CV_TRACE_FUNCTION(); // OpenCV Trace macro for functionimshow("Live", frame);UMat gray, processed;cv::cvtColor(frame, gray, COLOR_BGR2GRAY);Canny(gray, processed, 32, 64, 3);imshow("Processed", processed);
}
int main(int argc, char** argv)
{CV_TRACE_FUNCTION();cv::CommandLineParser parser(argc, argv,"{help h ? |     | help message}""{n        | 100 | number of frames to process }""{@video   | 0   | video filename or cameraID }");if (parser.has("help")){parser.printMessage();return 0;}VideoCapture capture;std::string video = parser.get<string>("@video");if (video.size() == 1 && isdigit(video[0]))capture.open(parser.get<int>("@video"));elsecapture.open(samples::findFileOrKeep(video));  // keep GStreamer pipelinesint nframes = 0;if (capture.isOpened()){nframes = (int)capture.get(CAP_PROP_FRAME_COUNT);cout << "Video " << video <<": width=" << capture.get(CAP_PROP_FRAME_WIDTH) <<", height=" << capture.get(CAP_PROP_FRAME_HEIGHT) <<", nframes=" << nframes << endl;}else{cout << "Could not initialize video capturing...\n";return -1;}int N = parser.get<int>("n");if (nframes > 0 && N > nframes)N = nframes;cout << "Start processing..." << endl<< "Press ESC key to terminate" << endl;UMat frame;for (int i = 0; N > 0 ? (i < N) : true; i++){CV_TRACE_REGION("FRAME"); // OpenCV Trace macro for named "scope" region{CV_TRACE_REGION("read");capture.read(frame);if (frame.empty()){cerr << "Can't capture frame: " << i << std::endl;break;}// OpenCV Trace macro for NEXT named region in the same C++ scope// Previous "read" region will be marked complete on this line.// Use this to eliminate unnecessary curly braces.CV_TRACE_REGION_NEXT("process");process_frame(frame);CV_TRACE_REGION_NEXT("delay");if (waitKey(1) == 27/*ESC*/)break;}}return 0;}

background segmentation（来自官方示例

#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/video.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"
#include using namespace std;
using namespace cv;int main(int argc, const char** argv)
{const String keys = "{c camera     | 0 | use video stream from camera (device index starting from 0) }""{fn file_name |   | use video file as input }""{m method | mog2 | method: background subtraction algorithm ('knn', 'mog2')}""{h help | | show help message}";CommandLineParser parser(argc, argv, keys);parser.about("This sample demonstrates background segmentation.");if (parser.has("help")){parser.printMessage();return 0;}int camera = parser.get<int>("camera");String file = parser.get<String>("file_name");String method = parser.get<String>("method");if (!parser.check()){parser.printErrors();return 1;}VideoCapture cap;if (file.empty())cap.open(camera);else{file = samples::findFileOrKeep(file);  // ignore gstreamer pipelinescap.open(file.c_str());}if (!cap.isOpened()){cout << "Can not open video stream: '" << (file.empty() ? "" : file) << "'" << endl;return 2;}Ptr<BackgroundSubtractor> model;if (method == "knn")model = createBackgroundSubtractorKNN();else if (method == "mog2")model = createBackgroundSubtractorMOG2();if (!model){cout << "Can not create background model using provided method: '" << method << "'" << endl;return 3;}cout << "Press  to toggle background model update" << endl;cout << "Press 's' to toggle foreground mask smoothing" << endl;cout << "Press ESC or 'q' to exit" << endl;bool doUpdateModel = true;bool doSmoothMask = false;Mat inputFrame, frame, foregroundMask, foreground, background;for (;;){// prepare input framecap >> inputFrame;if (inputFrame.empty()){cout << "Finished reading: empty frame" << endl;break;}const Size scaledSize(640, 640 * inputFrame.rows / inputFrame.cols);resize(inputFrame, frame, scaledSize, 0, 0, INTER_LINEAR);// pass the frame to background modelmodel->apply(frame, foregroundMask, doUpdateModel ? -1 : 0);// show processed frameimshow("image", frame);// show foreground image and mask (with optional smoothing)if (doSmoothMask){GaussianBlur(foregroundMask, foregroundMask, Size(11, 11), 3.5, 3.5);threshold(foregroundMask, foregroundMask, 10, 255, THRESH_BINARY);}if (foreground.empty())foreground.create(scaledSize, frame.type());foreground = Scalar::all(0);frame.copyTo(foreground, foregroundMask);imshow("foreground mask", foregroundMask);imshow("foreground image", foreground);// show background imagemodel->getBackgroundImage(background);if (!background.empty())imshow("mean background image", background );// interact with userconst char key = (char)waitKey(30);if (key == 27 || key == 'q') // ESC{cout << "Exit requested" << endl;break;}else if (key == ' '){doUpdateModel = !doUpdateModel;cout << "Toggle background update: " << (doUpdateModel ? "ON" : "OFF") << endl;}else if (key == 's'){doSmoothMask = !doSmoothMask;cout << "Toggle foreground mask smoothing: " << (doSmoothMask ? "ON" : "OFF") << endl;}}return 0;}

效果：

3。腐蚀

#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/video.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"
#include"opencv2/imgproc/imgproc.hpp"
#include using namespace std;
using namespace cv;int main()
{Mat img = imread("/home/heziyi/图片/7.jpg");imshow("old",img);Mat element = getStructuringElement(MORPH_RECT,Size(15,15));Mat dimg;erode(img,dimg,element);imshow("new",dimg);waitKey(0);
}

4。模糊：

int main()
{Mat img = imread("/home/heziyi/图片/6.jpg");imshow("old",img);Mat element = getStructuringElement(MORPH_RECT,Size(15,15));Mat dimg;blur(img,dimg,Size(1,1));imshow("new",dimg);waitKey(0);
}

5。边缘检测：

#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/video.hpp"
#include "opencv2/videoio.hpp"
#include "opencv2/highgui.hpp"
#include"opencv2/imgproc/imgproc.hpp"
#include using namespace std;
using namespace cv;int main()
{Mat img = imread("/home/heziyi/图片/6.jpg");imshow("old",img);Mat element = getStructuringElement(MORPH_RECT,Size(15,15));Mat dimg,edge,grayimg;dimg.create(img.size(),img.type());cvtColor(img,grayimg,COLOR_BGRA2GRAY);blur(grayimg,edge,Size(3,3));Canny(edge,edge,3,9,3);imshow("new",edge);waitKey(0);
}

VideoCapture 提供了摄像机或视频文件捕获视频的C++接口，作用是从视频文件或摄像头捕获视频并显示出来。

//循环显示每一帧
while(1)
{
Mat frame;//用于存储每一帧图像
capture>>frame;
imshow("picture",frame)
waitKey(30);//延时30毫秒
}

python接口：

使用numpy.array访问图像数据

将BGR图像在（0,0）处的像素转化为白 像素。

img[0,0] = [255, 255, 255]

通过三元数组的索引将像素的颜色值设为0
下面的代码可将图像所有的G (绿色)值设为0：

    img[:,:,1]=0cv2.imshow('ppp',img)cv2.waitKey(30000)

并将第一个区域的值分配给第二个区域(将图 像的一部分拷贝到该图像的另一个位置)：

    img[y,220]=[z,210,56]my_roi = img[0:200 , 0:200]img[300:500, 300:500] = my_roicv2.imshow('ppp',img)cv2.waitKey(30000)

结果：

视频文件的读写

OpenCV提供了 VideoCapture类和VideoWriter类来支持各种格式的视频文件。支持 的格式类型会因系统的不同而变化，但应该都支持AVI格式。在到达视频文件末尾之前， VideoCapture类可通过read()函数来获取新的帧，每帧是一幅基于BGR格式的图像。
可将一幅图像传递给VideoWriter类的write()函数，该函数会将这幅图像加到 VideoWriter类所指向的文件中。下面给出了一个示例，该示例读取AVI文件的帧，并采用 YUV颜色编码将其写入另一个帧中：

import cv2
videoCapture = cv2.VideoCapture(1 MyInputVid.avi1)
fps = videoCapture.get(cv2.CAP_PROP_FPS)
size = (int(videoCapture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(videoCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
videoWriter = cv2.VideoWriter(
'MyOutputVid.avi', cv2.VideoWriter_fourcc('I','4','2','0'),fps,size)
success, frame = videoCapture.read()
while success: # Loop until there are no more frames.
videoWriter.write(frame)
success, frame = videoCapture.read()

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