opencv object detection

Pure reading, please visit OPENCV using the Harris Algorithm for corner detectionSourceKqwopencvfeaturesdemoA corner point is an intersection of two edges or a point that has several significant edge orientations in a local neighborhood. Harris Corner Point Detection is one of the most common techniques in corner detection.The Harris corner detector uses a sliding window on the image to calculate the bright

small, ignore it PrintCv2.contourarea (c)ifCv2.contourarea (c) "Min_area"]: Continue #compute the bounding box for the contour, draw it on the frame, #and update the text #calculates the bounding box of the outline, drawing the box in the current frame(x, Y, W, h) =Cv2.boundingrect (c) Cv2.rectangle (frame, (x, y), (x+ W, y + h), (0, 255, 0), 2) Text="occupied" #Draw the text and timestamp on the frame #write text and timestamp on the current frameCv

Note that the axis of the OPENCV, the x-axis to the right, and the width corresponding to the Y axis downward, and the height of the corresponding; 1. MOG2 and KNN Mog:mixture of Gaussian Import cv2 cap = Cv2. Videocapture ('./data/video/768x576.avi ') knn_sub = CV2.CREATEBACKGROUNDSUBTRACTORKNN () mog2_sub = CV2.CREATEBACKGROUNDSUBTRACTORMOG2 () while True: ret, frame = Cap.read () if not ret: break Mog_ Sub_mask = mog2_sub.app

=59244hilit=tbb_debug.dll#p59244 , follow the URL given in the forum, download a TBB package, unzip Tbb_debug.dll to D:\Program FILES\OPENCV\BUILD\COMMON\TBB\IA32\VC10 (this is my installation directory). Error 3.Haha, the end of the code more than a}, delete it is good.Error 4.The compilation has passed smoothly, and now a debugging will findVery simply, the routines are estimated to run main () with CMD, and the parameters are tapped into the comma

Use the opencv functions surffeaturedetector and detect to detect interest points; Use the opencv function drawkeypoints to draw the detection key points. /** * @file SURF_detector * @brief SURF keypoint detection + keypoint drawing with OpenCV functions * @author A. Huaman

Today's goal is to use OPENCV to achieve the detection of moving objects, here the use of three frame difference method. The code is as follows: #include The figure below is a binary image of the detected moving object and a rectangular frame effect graph superimposed on the actual image.

Adjust the object recognition parameters of the AdaBoost Cascade classifier based on the Haar feature 1. Object recognition problem of AdaBoost Cascade classifier based on Haar featurePaul A. Viola and Michael J. Jones published in 2001 the article "Fast object detection using simple features to improve cascade detecto

Opencv provides the pedestrain detection class. CV: definitions of parameters of the hogdescriptor class constructor: Cv_wrap hogdescriptor (): winsize (64,128), // detect window blocksize (16, 16), // block size blockstride (8, 8), // Slide Step cellsize (8, 8) of overlap block ), // cell size nbins (9), // Number of bin in the histogram derivaperture (1), // winsigma (-1 ), // perform Gaussian weighted

These days, we 've been stuck in the fast, surf, shift, brief, and ORB feature detection algorithms !!! All labs are in the previous blog. Opencv has already implemented them, so we will use opencv to test the effects of various methods, My original intention was match. The feature points detected by fast could not be extracted from descriptor !!! However, it is

Original address: opencv for iOS Study Notes (10)-mark detection Summary If you keep following our tutorial, you can run the program as follows: Even if we do not use a three-dimensional rendering engine for visualization, we have obtained all the necessary data. Let's sum up what we get: 1. One frame from the bgra format of the camera 2. Correctly used as the pivot projection matrix for Ar Scene Re

Detection code is as follows:#encoding: Utf-8##Sobel边缘检测#Import NumPy as Npimport cv2image = Cv2. Imread("H:\\img\\lena.jpg") image = Cv2. Cvtcolor(Image,cv2. COLOR_bgr2gray)#将图像转化为灰度图像Cv2. Imshow("Original", image) Cv2. Waitkey()#Sobel边缘检测Sobelx = Cv2. Sobel(Image,cv2. CV_64f,1,0)#x方向的梯度sobely = Cv2. Sobel(Image,cv2. CV_64f,0,1)#y方向的梯度SOBELX = NP. UINT8 (NP. Absolute(SOBELX))#x方向梯度的绝对值sobely = NP. UINT8 (NP. Absolute(sobely))#y方向梯度的绝对值sobelcombined