motion detection opencv - c++

I am attempting to use a straightforward motion detection code to detect movement from a camera. I'm using the OpenCV library and I have some code that takes the difference between two frames to detect a change and then it uses a threshold to create a black/white image of the difference.
My problem: I cannot figure out a simple way to get a true or false output if motion is detected. I got this code from somewhere else and I am not familiar with all the details. I tried to sum the img_diff matrix but it gave me an error. What would be the simplest way to get a 'true' output if motion is detected, meaning that the background difference is not zero? For example, would an if statement comparing two matrices of the current frame and previous frame work?
The code I'm trying to use is below:
int main(int argc, char** argv)
{
const char * _diffType = getCmdOption("-type", "2", argc, argv);
const char * _thresval = getCmdOption("-thr", "60", argc, argv);
int diffType = atoi( _diffType );
int thresval = atoi( _thresval );
VideoCapture cap(0);
if( !cap.isOpened() ) return -1;
Mat cam_frame, img_gray, img_prev, img_diff, img_bin;
const char *win_cam = "Camera input"; namedWindow(win_cam, CV_WINDOW_AUTOSIZE);
const char *win_gray = "Gray image"; namedWindow(win_gray, CV_WINDOW_AUTOSIZE);
const char *win_diff = "Binary diff image"; namedWindow(win_diff, CV_WINDOW_AUTOSIZE);
bool first_frame = true;
while (cvWaitKey(4) == -1) {
cap >> cam_frame;
cvtColor(cam_frame, img_gray, CV_BGR2GRAY);
if (first_frame) {
img_prev=img_gray.clone();
first_frame = false;
continue;
}
absdiff(img_gray, img_prev, img_diff);
threshold(img_diff, img_bin, thresval, 255, THRESH_BINARY);
erode(img_bin, img_bin, Mat(), Point(-1,-1), 3);
dilate(img_bin, img_bin, Mat(), Point(-1,-1), 1);
imshow(win_cam, cam_frame);
imshow(win_gray, img_gray);
imshow(win_diff, img_bin);
if (diffType == 1) img_prev=img_gray.clone();
}
return 0;
}
Any help would be appreciated!

If you are looking for an easy way i would use the average of img_diff as a parameter for motion and just compare the average with a threshold of like 5 or 10 (assuming 8bit gray):
if(mean(img_diff) > thresval){
cout << "motion detected!" << endl;
}
Using this method you don't have to adjust your threshold to the size of the images.
However i see a general problem with detecting motion using only the current and previous frame: it will only detect high frequency motion, or in other words only fast motion. If you want to detect slow motion you need to compare the current frame to an older frame, like 5 or 10 frames before.

Related

OpenCV webcam MJPG low FPS

The problem is that I am keep getting low FPS from Logitech C270 webcam capture in OpenCV3.
Things i've tried are described in code comments
Mat frame;
int main(int argc, char *argv[])
{
// i've tried it this way
//int apiBackend = cv::CAP_DSHOW;
//cv::VideoCapture cap(0+apiBackend);
//and tis way
VideoCapture cap(0);
cap.open(0);
cap.set(CAP_PROP_FOURCC ,cv::VideoWriter::fourcc('M', 'J', 'P', 'G') );
//cap.set(CAP_PROP_EXPOSURE , 1); //changing this gives no result
//cap.set(CAP_PROP_GAIN , 10); // same with this
cap.set(CAP_PROP_FPS, 100);
cap.set(CAP_PROP_FRAME_WIDTH, 640);
cap.set(CAP_PROP_FRAME_HEIGHT, 480);
while(1)
{
float e1 = cv::getTickCount();
cap >> frame; // get a new frame from camera
imshow("frame", frame);
float e2 = cv::getTickCount();
float t = (e2 - e1)/cv::getTickFrequency();
float fps = 1.0 / t;
std::cout << fps << std::endl;
if(waitKey(1) >= 0) break;
}
return 0;
}
Changing CAP_PROP_FPS to 5 works, and FPS drops ok.
Playing with resolution didn't help: from 320*240 to 1280*720 i keep getting about 16 FPS.
Webcam drivers are latest.
Am i missing something?
Thanks fo suggestions everybody!
Looks like the answer is camera-specific: i had to install Logitech Webcam Software and disable RightLight feature, now FPS is about 30.
Maybe there is some way to disable RightLight from OpenCV using cap.set(...), but this is subject for further investigation.

OpenCV Background subtraction by frame averaging

How do i implement background subtraction(background model obtained by averaging first..say 50 frames..) in opencv
I tried looking for some codes but found they were in python..im working in c++(visual studio 2013)
A small working code snippet will help..thanks!
OpenCV provide background subtraction capabilities. See BackgroundSubtractorMOG2, that models the background with a Mixture of Gaussians, and is therefore quite robust to background changes.
The parameters history is the numbers of frame used to build the background model.
#include <opencv2\opencv.hpp>
using namespace cv;
int main(int argc, char *argv[])
{
int history = 50;
float varThreshold = 16.f; // default value
BackgroundSubtractorMOG2 bg = BackgroundSubtractorMOG2(history, varThreshold);
VideoCapture cap(0);
Mat3b frame;
Mat1b fmask;
for (;;)
{
cap >> frame;
bg(frame, fmask, -1);
imshow("frame", frame);
imshow("mask", fmask);
if (cv::waitKey(30) >= 0) break;
}
return 0;
}

Problems with opencv stereoRectifyUncalibrated

I've been trying to rectify and build the disparity mappping for a pair of images using OpenCV stereoRectifyUncalibrated, but I'm not getting very good results. My code is:
template<class T>
T convertNumber(string& number)
{
istringstream ss(number);
T t;
ss >> t;
return t;
}
void readPoints(vector<Point2f>& points, string filename)
{
fstream filest(filename.c_str(), ios::in);
string line;
assert(filest != NULL);
getline(filest, line);
do{
int posEsp = line.find_first_of(' ');
string posX = line.substr(0, posEsp);
string posY = line.substr(posEsp+1, line.size() - posEsp);
float X = convertNumber<float>(posX);
float Y = convertNumber<float>(posY);
Point2f pnt = Point2f(X, Y);
points.push_back(pnt);
getline(filest, line);
}while(!filest.eof());
filest.close();
}
void drawKeypointSequence(Mat lFrame, Mat rFrame, vector<KeyPoint>& lKeyp, vector<KeyPoint>& rKeyp)
{
namedWindow("prevFrame", WINDOW_AUTOSIZE);
namedWindow("currFrame", WINDOW_AUTOSIZE);
moveWindow("prevFrame", 0, 300);
moveWindow("currFrame", 650, 300);
Mat rFrameAux;
rFrame.copyTo(rFrameAux);
Mat lFrameAux;
lFrame.copyTo(lFrameAux);
int size = rKeyp.size();
for(int i=0; i<size; i++)
{
vector<KeyPoint> drawRightKeyp;
vector<KeyPoint> drawleftKeyp;
drawRightKeyp.push_back(rKeyp[i]);
drawleftKeyp.push_back(lKeyp[i]);
cout << rKeyp[i].pt << " <<<>>> " << lKeyp[i].pt << endl;
drawKeypoints(rFrameAux, drawRightKeyp, rFrameAux, Scalar::all(255), DrawMatchesFlags::DRAW_OVER_OUTIMG);
drawKeypoints(lFrameAux, drawleftKeyp, lFrameAux, Scalar::all(255), DrawMatchesFlags::DRAW_OVER_OUTIMG);
imshow("currFrame", rFrameAux);
imshow("prevFrame", lFrameAux);
waitKey(0);
}
imwrite("RightKeypFrame.jpg", rFrameAux);
imwrite("LeftKeypFrame.jpg", lFrameAux);
}
int main(int argc, char* argv[])
{
StereoBM stereo(StereoBM::BASIC_PRESET, 16*5, 21);
double ndisp = 16*4;
assert(argc == 5);
string rightImgFilename(argv[1]); // Right image (current frame)
string leftImgFilename(argv[2]); // Left image (previous frame)
string rightPointsFilename(argv[3]); // Right image points file
string leftPointsFilename(argv[4]); // Left image points file
Mat rightFrame = imread(rightImgFilename.c_str(), 0);
Mat leftFrame = imread(leftImgFilename.c_str(), 0);
vector<Point2f> rightPoints;
vector<Point2f> leftPoints;
vector<KeyPoint> rightKeyp;
vector<KeyPoint> leftKeyp;
readPoints(rightPoints, rightPointsFilename);
readPoints(leftPoints, leftPointsFilename);
assert(rightPoints.size() == leftPoints.size());
KeyPoint::convert(rightPoints, rightKeyp);
KeyPoint::convert(leftPoints, leftKeyp);
// Desenha os keypoints sequencialmente, de forma a testar a consistĂȘncia do matching
drawKeypointSequence(leftFrame, rightFrame, leftKeyp, rightKeyp);
Mat fundMatrix = findFundamentalMat(leftPoints, rightPoints, CV_FM_8POINT);
Mat homRight;
Mat homLeft;
Mat disp16 = Mat(rightFrame.rows, leftFrame.cols, CV_16S);
Mat disp8 = Mat(rightFrame.rows, leftFrame.cols, CV_8UC1);
stereoRectifyUncalibrated(leftPoints, rightPoints, fundMatrix, rightFrame.size(), homLeft, homRight);
warpPerspective(rightFrame, rightFrame, homRight, rightFrame.size());
warpPerspective(leftFrame, leftFrame, homLeft, leftFrame.size());
namedWindow("currFrame", WINDOW_AUTOSIZE);
namedWindow("prevFrame", WINDOW_AUTOSIZE);
moveWindow("currFrame", 650, 300);
moveWindow("prevFrame", 0, 300);
imshow("currFrame", rightFrame);
imshow("prevFrame", leftFrame);
imwrite("RectfRight.jpg", rightFrame);
imwrite("RectfLeft.jpg", leftFrame);
waitKey(0);
stereo(rightFrame, leftFrame, disp16, CV_16S);
disp16.convertTo(disp8, CV_8UC1, 255/ndisp);
FileStorage file("disp_map.xml", FileStorage::WRITE);
file << "disparity" << disp8;
file.release();
imshow("disparity", disp8);
imwrite("disparity.jpg", disp8);
moveWindow("disparity", 0, 0);
waitKey(0);
}
drawKeyPoint sequence is the way I visually check the consistency of the points I have for both images. By drawing each of their keypoints in sequence, I can be sure that keypoint i on image A is keypoint i on image B.
I've also tried playing with the ndisp parameter, but it didn't help much.
I tried it for the following pair of images:
LeftImage
RightImage
got the following rectified pair:
RectifiedLeft
RectifiedRight
and finally, the following disparity map
DisparityMap
Which, as you can see, is quite bad. I've also tried the same pair of images with the following stereoRectifyUncalibrated example: http://programmingexamples.net/wiki/OpenCV/WishList/StereoRectifyUncalibrated and the SBM_Sample.cpp from opencv tutorial code samples to build the disparity map, and got a very similar result.
I'm using opencv 2.4
Thanks in advance!
Besides possible calibration problems, your images clearly lack some texture for the stereo block matching to work.
This algorithm will see many ambiguities and too large disparities on flat (non-tetxured) parts.
Note however that the keypoints seem to match well, so even if the rectification output seems weird it is probably correct.
You can test your code against standard images from the Middlebury stereo page for sanity checks.
I would suggest to do a stereo calibration using the chessboard, or take multiple pictures with a chess board and use stereocalibrate.cpp on your computer. I am saying that because you are using stereorectifyuncalibrated, While the algorithm does not need to know the intrinsic parameters of the cameras, it heavily depends on the epipolar geometry. Therefore, if the camera lenses have a significant distortion, it would be better to correct it before computing the fundamental matrix and calling this function. For example, distortion coefficients can be estimated for each head of stereo camera separately by using calibrateCamera(). Then, the images can be corrected using undistort() , or just the point coordinates can be corrected with undistortPoints().

OpenCV Having issues with cv::FAST

I'm trying to use the open CV FAST algorithim in order to detect corners from a video feed. The method call and set-up seems pretty straight forward yet I'm running into a few problems. When I try and use this code
while(run)
{
clock_t begin,end;
img = cvQueryFrame(capture);
key = cvWaitKey(10);
cvShowImage("stream",img);
//Cv::FAST variables
int threshold=9;
vector<KeyPoint> keypoints;
if(key=='a'){
//begin = clock();
Mat mat(tempImg);
FAST(mat,keypoints,threshold,true);
//end = clock();
//cout << "\n TIME FOR CALCULATION: " << double(diffClock(begin,end)) << "\n" ;
}
I get this error:
OpenCV Error: Assertion failed (image.data && image.type() == CV_8U) in unknown
function, file ........\ocv\opencv\src\cvaux\cvfast.cpp, line 6039
So I figured its a problem with the depth of the image so I when I add this:
IplImage* tempImg = cvCreateImage(Size(img->width,img->height),8,1);
cvCvtColor(img,tempImg,CV_8U);
I get:
OpenCV Error: Bad number of channels (Incorrect number of channels for this conv
ersion code) in unknown function, file ........\ocv\opencv\src\cv\cvcolor.cpp
, line 2238
I've tried using a Mat instead of a IplImage to capture but I keep getting the same kind of errors.
Any suggestions or help?
Thanks in advance.
The entire file just to make it easier for anyone:
#include "cv.h"
#include "cvaux.hpp"
#include "highgui.h"
#include <time.h>
#include <iostream>
double diffClock(clock_t begin, clock_t end);
using namespace std;
using namespace cv;
int main(int argc, char** argv)
{
//Create Mat img for camera capture
IplImage* img;
bool run = true;
CvCapture* capture= 0;
capture = cvCaptureFromCAM(-1);
int key =0;
cvNamedWindow("stream", 1);
while(run)
{
clock_t begin,end;
img = cvQueryFrame(capture);
key = cvWaitKey(10);
cvShowImage("stream",img);
//Cv::FAST variables
int threshold=9;
vector<KeyPoint> keypoints;
if(key=='a'){
//begin = clock();
IplImage* tempImg = cvCreateImage(Size(img->width,img->height),8,1);
cvCvtColor(img,tempImg,CV_8U);
Mat mat(img);
FAST(mat,keypoints,threshold,true);
//end = clock();
//cout << "\n TIME FOR CALCULATION: " << double(diffClock(begin,end)) << "\n" ;
}
else if(key=='x'){
run= false;
}
}
cvDestroyWindow( "stream" );
return 0;
}
Whenever you have a problem using the OpenCV API go check the tests/examples available in the source code: fast.cpp
This practice is extremely useful and educational. Now, if you take a look at that code you will notice that the image gets converted to grayscale before calling cv::FAST() on it:
Mat mat(tempImg);
Mat gray;
cvtColor(mat, gray, CV_BGR2GRAY);
FAST(gray,keypoints,threshold,true);
Seems pretty straight forward, indeed.
You need change this
cvCvtColor(img,tempImg,CV_8U);
To
cvCvtColor(img,tempImg,CV_BGR2GRAY);
You can read this
Good Luck
I started getting the same message with code that had worked previously, and i was certain my Mat was U8 grayscale. It turned out that one of the images i was trying to process was no longer there. So in my case it was a misleading error message.
Take a look at this sample code. The code you are using looks quite outdated opencv, in this sample you will find how feature detectors should be used now.
The sample is generic for several feature detectors (including FAST) so that is like it looks a bit more complicated.
http://code.opencv.org/projects/opencv/repository/entry/branches/2.4/opencv/samples/cpp/matching_to_many_images.cpp
You will also find more samples in the parent directory.
Please follow the following code to have your desired result. For showing an example, I am considering an image only but you can simply use the same idea for video frames
Mat img = imread("IMG.jpg", IMREAD_UNCHANGED);
if( img.empty())
{
cout << "File not available for reading"<<endl;
return -1;
}
Mat grayImage;
if(img.channels() >2){
cvtColor( img, grayImage, CV_BGR2GRAY ); // converting color to gray image
}
else{
grayImage = img;
}
double sigma = 1;
GaussianBlur(grayImage, grayImage, Size(), sigma, sigma); // applying gaussian blur to remove some noise,if present
int thresholdCorner = 40;
vector<KeyPoint> keypointsCorners;
FAST(grayImage,keypointsCorners,thresholdCorner,true); // applying FAST key point detector
if(keypointsCorners.size() > 0){
cout << keypointsCorners.size() << endl;
}
// Drawing a circle around corners
for( int i = 0; i < keypointsCorners.size(); i++ )
{
circle( grayImage, keypointsCorners.at(i).pt, 5, Scalar(0), 2, 8, 0 );
}
cv::namedWindow("Display Image");
cv::imshow("Display Image", grayImage);
cvWaitKey(0);
cvDestroyWindow( "Display Image" );

Masking a blob from a binary image

I am doing motion recognition of walking using openCV and C++ and I would like to create a mask or copied image in order to achieve the effect seen in the picture provided. .The following is an explanation of the images
The resulting blob of the human walking is seen. Then, a mask image or copied image of the original frame is created, the binary human blob is now masked and the non-masked pixels are now set to zero. The result is the extracted human body with a black background. The diagram below shows how the human blob is extracted and then masked.
This is to be done for every 5th frame of a video sequence. My code so far consists of getting every 5th frame, grayscaling it, finding the areas of all the blobs, and applying a threshold value to get a binary image where more or less, only the human blob is white and the rest of the image is black. Now, I am trying to extract the human body but I have no clue how to proceed. Please help me.
#include "cv.h"
#include "highgui.h"
#include "iostream"
using namespace std;
int main( int argc, char* argv ) {
CvCapture *capture = NULL;
capture = cvCaptureFromAVI("C:\\walking\\lady walking.avi");
if(!capture){
return -1;
}
IplImage* color_frame = NULL;
IplImage* gray_frame = NULL ;
int thresh_frame = 28;
CvMoments moments;
int frameCount=0;//Counts every 5 frames
cvNamedWindow( "walking", CV_WINDOW_AUTOSIZE );
while(1) {
color_frame = cvQueryFrame( capture );//Grabs the frame from a file
if( !color_frame ) break;
gray_frame = cvCreateImage(cvSize(color_frame->width, color_frame->height), color_frame->depth, 1);
if( !color_frame ) break;// If the frame does not exist, quit the loop
frameCount++;
if(frameCount==5)
{
cvCvtColor(color_frame, gray_frame, CV_BGR2GRAY);
cvThreshold(gray_frame, gray_frame, thresh_frame, 255, CV_THRESH_BINARY);
cvErode(gray_frame, gray_frame, NULL, 1);
cvDilate(gray_frame, gray_frame, NULL, 1);
cvMoments(gray_frame, &moments, 1);
double m00;
m00 = cvGetCentralMoment(&moments, 0,0);
cvShowImage("walking", gray_frame);
frameCount=0;
}
char c = cvWaitKey(33);
if( c == 27 ) break;
}
double m00 = (double)cvGetCentralMoment(&moments, 0,0);
cout << "Area - : " << m00 << endl;
//area of lady walking = 39696. Therefore, using new threshold area as 30 for this video
//area of walking man = 67929
cvReleaseImage(&color_frame);
cvReleaseImage(&gray_frame);
cvReleaseCapture( &capture );
cvDestroyWindow( "walking" );
return 0;
}
I would also like to upload the video that I am using in the code but I don't know how to upload it here, so if anyone can help me out with that too. I want to provide as much info as possible w.r.t. my question.
the easiest way is to look for the biggest blob in the image (cvfind contours can be the function you need), then you set to blac all the other blobs (scannig all the contours and using cvfloadfill).
finally you scan the entire binary image if the considered pixel is white you do nothing, if the pixel is black you set to black the corresponding pixel of the 5th frame