opencv face detection

implemented 1> use the OPENCV library package of the Hough Line detection function, in the original map corresponding area with the red line to trace the lane line 2> write a line detection, in the header file, traverse the ROI region for a specific angle range of line detection. Both methods can be reflected in the v

//sort function to sort data in ascending order -Sort (Matches.begin (), Matches.end ());//Filter matching points, according to match inside the distance from the characteristics of the order from small to large thevectorgood_matches; + intPtspairs = Std::min ( -, (int) (Matches.size () *0.15)); Acout Endl; the for(inti =0; i ) + { -Good_matches.push_back (Matches[i]);//50 of the minimum distance to press into the new Dmatch $ } $Ma

Recently, I started to learn opencv and made a small application using the self-developed opencv algorithm. I suddenly felt the wonders of computer vision.The procedure is as follows:1. Open the camera 2. Video Edge Detection 3. output the detected edge The program provides two slide bars. You can change the threshold value to observe the effect based on the actu

Original address: opencv for iOS Study Notes (6)-mark detection 3 Precise marking position // Adjust the posture of the tag Based on the camera rotation. // marker: the captured tag STD: Rotate (marker. points. begin (), marker. points. begin () + 4-nrotations, marker. points. end ()); After the tags are captured and filtered Based on the tag encoding, we should redefine their corners. This step helps est

, as well as the function Cvsettrackbarpos () to reset the display position of the trackbar. function Function: The callback function used by the Cvcreatetrackbar () functionfunction Definition:typedef void (CV_CDECL *cvtrackbarcallback) (int pos)function Description:when the trackbar position is changed, the system calls the callback function and sets the parameter pos to the value representing the trackbar position. #include"stdafx.h"#include"iostream"using namespacestd; #include"opencv2/openc

Sub-pixel angular point detection targetIn this tutorial we will cover the following: Use the OPENCV function Cornersubpix to find a more precise corner position (not a position of the integer type, but a more precise floating-point type position). Theoretical codeThe code for this tutorial is shown below. The source code can also be downloaded from this link#include "opencv2/highgui/highgui.h

Opencv 2.4 implements the DPM program in C ++. The main difference between it and the previous C version is that it can detect multiple targets at the same time. During use, you can put the trained model in a folder, and put the image to be detected in another folder for detection. Unfortunately, the accelerated content is not considered. Latent SVM regression ¶ Discriminatively trained Part Based Models f

motion #counterlastuploaded =timestamp Motioncounter=0#Otherwise, the hostel is not occupied Else: Motioncounter=0#Check to see if the frames should is displayed to screen ifconf["Show_video"]: #Display the security feedCv2.imshow ("Security Feed", frame) key= Cv2.waitkey (1) 0xFF#if the ' Q ' key is pressed, break from the Lop ifKey = = Ord ("Q"): Break #clear the stream in preparation for the next frameRawcapture.truncate (0)Conf.json{ "Sh

OPENCV Glass mirror defect detection, defect information marking and extraction Author: scutjy2015@163.com Link: http://blog.csdn.net/scutjy2015/article/details/74011789 Part 1 Partial effect chart Part 2 source program #include Part 3 other experimental pictures

Http://www.cnblogs.com/tiandsp/archive/2013/04/20/3032860.html Three kinds of common edge detection operators. #include "cv.h" #include "highgui.h" using namespace CV; int main (int argc, char* argv[]) { Mat src = imread ("misaka.jpg"); Mat DST; Input image //output image //input Image Color channel number //x direction order number //y Direction order Sobel (Src,dst,src.dept

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.

; - }Wuyi } theprintf"min Distance:%f", mindist); - Wu for(inti =0; i ) { - DoubleDist =matches[i].distance; About if(Dist 1.5*mindist,0.02)) { $ Goodmatches.push_back (Matches[i]); - } - } - ADrawmatches (IMG1, Keypoints_obj, Img2, Keypoints_scene, Goodmatches, Akazematchesimg, Scalar::all (-1), +Scalar::all (-1), vectorChar>(), drawmatchesflags::not_draw_single_points); theImshow ("Good match result", akazematchesimg); - $Waitkey (0); the ret

Modify the adaptiveskindetector. cpp of opencv and remove the complicated command line parameter input. You only need a Network Camera to run it. In terms of principles, I roughly looked at the usage of the color information in the HSV space. The effect is good, but it seems that the detection effect is poor for white walls, especially milky white walls and wall panels. This is the first small item publishe

()) {Foundrect.push_back (R); } } //draw a rectangle, circle a pedestrian for(inti =0; I ) {Rect R=Foundrect[i]; Rectangle (img, r.tl (), R.Br (), Scalar (0,0,255),3); } Namedwindow ("Detecting Pedestrians", cv_window_autosize); Imshow ("Detecting Pedestrians", IMG); Waitkey (0); return 0;}intMain () {Mat image= Imread ("c:\\c_c++ Code\\photo and Video\\text007jpg"); Imshow ("Hog", image); if(Image.empty ()) {cout"Read image failed"Endl; } //1. Defining Hog ObjectsHogdescrip

OPENCV Learning---moving target (foreground) detection 1. Frame Difference method Principle: The video sequence uses pixel-based time difference between two frames or three frames, and the motion region of the image is extracted by the closed-value. Advantages: The algorithm is simple, the computational amount is small, the training background is not necessary, and the light is n

Recently, some OPENCV-based target detection algorithms have been researched, and today is the first day.First download a simple online video of the movement of the two-value display of the code to learn, the following is my understanding, beginners will make a number of mistakes hope that everyone to correct.#include #include "cxcore.h"#include intMainintargc,unsignedChar*argv[]) {　　cvcapture* capture = Cv

Common Methods for moving object detection include optical flow, background subtraction, and frame difference ). The Background Subtraction Method and the interframe difference method are suitable for static cameras, while the optical flow method is used for camera motion, but the calculation is relatively large. The following describes how to use the cvupdatemotionhistory function in opencv to detect motio

transformation matrix. And the online about findhomography introduction is relatively few, so will let people misunderstand findfundamentalmat will calculate the transformation matrix.Try to return the matrix with the Findhomography function, in the template image, the object is already marked with a green box outline, according to the object's four boundary points, and the transformation matrix, you can get the transformed object contour of the four boundary points, the boundary point is conne

Human Eye Detection involves two steps: 1. Face Detection to obtain the rectangular area of a person's face 2. Human Eye detection in the face rectangle area The following are some source code: # Include "stdafx. H "

This tutorial is actually a summary of the previous article, using FEATURES2D and Calib3d modules to discover objects in the scene ~ ~ 1, Create a new console project. Read the input images. Mat img1 = Imread (argv[1], cv_load_image_grayscale); Mat Img2 = Imread (argv[2], cv_load_image_grayscale); 2, Detect keypoints in both images. Detecting Keypointsfastfeaturedetector Detector ();vector 3, Compute descriptors for each of the keypoints.