I am a newbie to openCV. I have installed the opencv library on a ubuntu system, compiled it and trying to look into some image/video processing apps in opencv to understand more.
I am interested to know if OpenCV library has any algorithm/class for removal flicker in captured videos? If yes what document or code should I should look deeper into?
If openCV does not have it, are there any standard implementations in some other Video processing library/SDK/Matlab,.. which provide algorithms for flicker removal from video sequences?
Any pointers would be useful
Thank you.
-AD.
I don't know any standard way to deflicker a video.
But VirtualDub is a Video Processing software which has a Filter for deflickering the video. You can find it's filter source and documents (algorithm description probably) here.
I wrote my own Deflicker C++ function. here it is. You can cut and paste this code as is - no headers needed other than the usual openCV ones.
Mat deflicker(Mat,int);
Mat prevdeflicker;
Mat deflicker(Mat Mat1,int strengthcutoff = 20){ //deflicker - compares each pixel of the frame to a previously stored frame, and throttle small changes in pixels (flicker)
if (prevdeflicker.rows){//check if we stored a previous frame of this name.//if not, theres nothing we can do. clone and exit
int i,j;
uchar* p;
uchar* prevp;
for( i = 0; i < Mat1.rows; ++i)
{
p = Mat1.ptr<uchar>(i);
prevp = prevdeflicker.ptr<uchar>(i);
for ( j = 0; j < Mat1.cols; ++j){
Scalar previntensity = prevp[j];
Scalar intensity = p[j];
int strength = abs(intensity.val[0] - previntensity.val[0]);
if(strength < strengthcutoff){ //the strength of the stimulus must be greater than a certain point, else we do not want to allow the change
//value 25 works good for medium+ light. anything higher creates too much blur around moving objects.
//in low light however this makes it worse, since low light seems to increase contrasts in flicker - some flickers go from 0 to 255 and back. :(
//I need to write a way to track large group movements vs small pixels, and only filter out the small pixel stuff. maybe blur first?
if(intensity.val[0] > previntensity.val[0]){ // use the previous frames value. Change it by +1 - slow enough to not be noticable flicker
p[j] = previntensity.val[0] + 1;
}else{
p[j] = previntensity.val[0] - 1;
}
}
}
}//end for
}
prevdeflicker = Mat1.clone();//clone the current one as the old one.
return Mat1;
}
Call it as: Mat= deflicker(Mat). It needs a loop, and a greyscale image, like so:
for(;;){
cap >> frame; // get a new frame from camera
cvtColor( frame, src_grey, CV_RGB2GRAY ); //convert to greyscale - simplifies everything
src_grey = deflicker(src_grey); // this is the function call
imshow("grey video", src_grey);
if(waitKey(30) >= 0) break;
}
Related
I´m trying to find a objekt in a larger Picture with the findContour/matchShape functions (the object can vary so it´s not possible to look after the color or something similar, Featuredetectors like SIFT also doesn´t work because the object could be symetric)
I have written following code:
Mat scene = imread...
Mat Template = imread...
Mat imagegray1, imagegray2, imageresult1, imageresult2;
int thresh=80;
double ans=0, result=0;
// Preprocess pictures
cvtColor(scene, imagegray1,CV_BGR2GRAY);
cvtColor(Template,imagegray2,CV_BGR2GRAY);
GaussianBlur(imagegray1,imagegray1, Size(5,5),2);
GaussianBlur(imagegray2,imagegray2, Size(5,5),2);
Canny(imagegray1, imageresult1,thresh, thresh*2);
Canny(imagegray2, imageresult2,thresh, thresh*2);
vector<vector <Point> > contours1;
vector<vector <Point> > contours2;
vector<Vec4i>hierarchy1, hierarchy2;
// Template
findContours(imageresult2,contours2,hierarchy2,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE,cvPoint(0,0));
// Szene
findContours(imageresult1,contours1,hierarchy1,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE,cvPoint(0,0));
imshow("template", Template);
double helper = INT_MAX;
int idx_i = 0, idx_j = 0;
// Match all contours with eachother
for(int i = 0; i < contours1.size(); i++)
{
for(int j = 0; j < contours2.size(); j++)
{
ans=matchShapes(contours1[i],contours2[j],CV_CONTOURS_MATCH_I1 ,0);
// find the best matching contour
if((ans < helper) )
{
idx_i = i;
helper = ans;
}
}
}
// draw the best contour
drawContours(scene, contours1, idx_i,
Scalar(255,255,0),3,8,hierarchy1,0,Point());
When I'm using a scene where only the Template is located in, i get a good matching result:
But when there are more objects in the pictures i have trouble detecting the object:
Hope someone can tell me whats the problem with the code i´m using. Thanks
You have a huge amount of contours in the second image (almost each letter).
As the matchShape checks for scale-invariant Hu-moments (http://docs.opencv.org/3.1.0/d3/dc0/group__imgproc__shape.html#gab001db45c1f1af6cbdbe64df04c4e944) also a very small contours may fit the shape you are looking for.
Furthermore, the original shape is not distinguished properly like can be seen when excluding all contours with an area smaller 50.
if(contourArea(contours1[i]) > 50)
drawContours(scene, contours1, i, Scalar(255, 255, 0), 1);
To say it with other words, there is no problem with your code. The contour can simply not be detected very well. I would suggest to have a look at approxCurve and convexHull and try to close the contour this way. Or improve the use of Canny in some way.
Then you could use a priori knowledge to restrict the size (and maybe rotation?) of the contour you are looking for.
So, here is my situation. I have created a object detection program which is based on color object detection. My program detects the color red and it works perfectly. But here is the problems i am facing:-
Whenever there are more than one red object in the surrounding, my program detects them and it cannot really track one object at that time(i.e it tracks other red objects of various sizes in the background. It shows me the error that "too much noise in the background". As you can see in the "threshold image" attached, it detects the round object (which is my tracking object) and my cap which is red in color. I want my program to detect only my tracking object("which is a round shaped coke cap"). How can i achieve that? Please help me out. I have my engineering design contest in few days and i have to demo my program infront of my lecturers. My program should only be able to detect and track the object which i want. Thanks
My code for the objectdetection program is a little long. So, i am hereby explaining the code as follows- I captured a frame from the webcam frame-converted it to HSV- used HSV Inrange filter to filter out the other colors but red- applied morphological operations on the filtered image. This all goes in my main function
I am using a frame resolution of 1280*720 for my webcam frame. It kind of slows down my program but it was a trade off which i had to do for performing gesture controlled operations. Anyways here is my drawobjectfunction and trackfilteredobjectfunction.
int H_MIN = 0;
int H_MAX = 256;
int S_MIN = 0;
int S_MAX = 256;
int V_MIN = 0;
int V_MAX = 256;
//default capture width and height
const int FRAME_WIDTH = 1280;
const int FRAME_HEIGHT = 720;
//max number of objects to be detected in frame
const int MAX_NUM_OBJECTS=50;
//minimum and maximum object area
const int MIN_OBJECT_AREA = 20*20;
const int MAX_OBJECT_AREA = FRAME_HEIGHT*FRAME_WIDTH/1.5;
void drawObject(int x, int y,Mat &frame){
circle(frame,Point(x,y),20,Scalar(0,255,0),2);
if(y-25>0)
line(frame,Point(x,y),Point(x,y-25),Scalar(0,255,0),2);
else line(frame,Point(x,y),Point(x,0),Scalar(0,255,0),2);
if(y+25<FRAME_HEIGHT)
line(frame,Point(x,y),Point(x,y+25),Scalar(0,255,0),2);
else line(frame,Point(x,y),Point(x,FRAME_HEIGHT),Scalar(0,255,0),2);
if(x-25>0)
line(frame,Point(x,y),Point(x-25,y),Scalar(0,255,0),2);
else line(frame,Point(x,y),Point(0,y),Scalar(0,255,0),2);
if(x+25<FRAME_WIDTH)
line(frame,Point(x,y),Point(x+25,y),Scalar(0,255,0),2);
else line(frame,Point(x,y),Point(FRAME_WIDTH,y),Scalar(0,255,0),2);
putText(frame,intToString(x)+","+intToString(y),Point(x,y+30),1,1,Scalar(0,255,0),2);
}
void trackFilteredObject(int &x, int &y, Mat threshold, Mat &cameraFeed){
Mat temp;
threshold.copyTo(temp);
//these two vectors needed for output of findContours
vector< vector<Point> > contours;
vector<Vec4i> hierarchy;
//find contours of filtered image using openCV findContours function
findContours(temp,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE );
//use moments method to find our filtered object
double refArea = 0;
bool objectFound = false;
if (hierarchy.size() > 0) {
int numObjects = hierarchy.size();
//if number of objects greater than MAX_NUM_OBJECTS we have a noisy filter
if(numObjects<MAX_NUM_OBJECTS){
for (int index = 0; index >= 0; index = hierarchy[index][0]) {
Moments moment = moments((cv::Mat)contours[index]);
double area = moment.m00;
//if the area is less than 20 px by 20px then it is probably just noise
//if the area is the same as the 3/2 of the image size, probably just a bad filter
//we only want the object with the largest area so we safe a reference area each
//iteration and compare it to the area in the next iteration.
if(area>MIN_OBJECT_AREA && area<MAX_OBJECT_AREA && area>refArea){
x = moment.m10/area;
y = moment.m01/area;
objectFound = true;
refArea = area;
}else objectFound = false;
}
//let user know you found an object
if(objectFound ==true){
putText(cameraFeed,"Tracking Object",Point(0,50),2,1,Scalar(0,255,0),2);
//draw object location on screen
drawObject(x,y,cameraFeed);}
}else putText(cameraFeed,"TOO MUCH NOISE! ADJUST FILTER",Point(0,50),1,2,Scalar(0,0,255),2);
}
}
Here is the link of the image; as you can see it also detects the red hat in the background along with the red cap of the coke bottle.
My observations:- Here is what i think, to achieve my desired goal of not detecting objects of unknown sizes of red color. I think i have to edit the value of maximum object area which i declared in the above program as (const int MAX_OBJECT_AREA = FRAME_HEIGHT*FRAME_WIDTH/1.5;). I think i have to change this value, that might eliminate the detection of bigger continous red pictures. But also, there is another problem some objects are not completely red in color and they have patches of red and other colors. So, if the detected area is within the range specfied in my program then my program detects those red patches too. What i mean to say is i was wearing a tshirt which has mixed colors and when i tested my program by wearing that tshirt, my program was able to detect the red color out of the other colors. Now, how do i solve this issue?
I think you can try out the following procedure:
obtain a circular kernel having roughly the same area as your object of interest. You can do it like: Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(d, d));
where d is the diameter of the disk.
perform normalized-cross-correlation or convolution of the filtered regions image with this kernel (I think normalized-cross-correlation would be better. And add an empty boarder around the kernel).
the peak of the resulting image should give you the location of the circular region in your filtered image (if you are using normalized-cross-correlation, you'll have to add the shift).
To speed things up, you can perform this at a reduced resolution.
You can filter out non-circular shapes by detecting circles in your thresholded image. OpenCV provides a built-on method to detect circles using Hough transform, more info here. You can take advantage of this function to retain only circles that have a radius in a given range.
Another possibility is to implement connected component labeling (CCL) into your demo program.
I believe that it was removed at some point in verions 2.x of OpenCV, but a basic implementation of the two-pass version is straightforward from the Wikipedia page.
CCL will assign a unique ID for each object after thresholding. You then have to implement matching between the objects at frame (T-1) and objects in frame (T) (for example based on some nearest distance criterion) and possibly trajectory filtering or smoothing, but this would definitely give you some extra-points.
I have written the following program to detect a circle in real time. But it doesn't work.
The compiler doesn't show any error but the problem is that the program doesn't even detect a circle. How can I fix it?
here is my code
using namespace cv;
int main()
{
VideoCapture cap(0);
namedWindow("main",CV_WINDOW_AUTOSIZE);
namedWindow("blur",CV_WINDOW_AUTOSIZE);
Mat img;
Mat img2;
int c;
float radius;
while(1)
{
cap>>img;
imshow("main",img);
cvtColor(img,img2,CV_BGR2GRAY);
GaussianBlur(img2,img2,Size(9,9),2,2);
imshow("blur",img2);
vector <Vec3f> circles;
HoughCircles(img2,circles,CV_HOUGH_GRADIENT,1,img2.rows/8,200,100,0,0);
for(size_t i=0;i<circles.size();i++)
{
Point center(cvRound(circles[i][0]),cvRound(circles[i][1]));
radius = cvRound(circles[i][2]);
circle(img,center,3,Scalar(0,255,0),-1,8,0);
circle(img,center,radius,Scalar(0,0,255),3,8,0);
}
c = waitKey(33);
if(c==27)
break;
}
destroyAllWindows();
return 0;
}
I checked your program, it seems you just forgot to visualize it using imshow() after the detection. You only drew the image before the detection, in this way, you were not able to see the circles (maybe this mistakenly make you think there is no circles detected) even it did detect some circles.
Try to add
imshow("main", img);
right before c = waitKey(33);.
You will see the circles if it does detect some circles.
Edit: to answer your comment for real time circle detection:
Do it in a while loop style will make it work for video frames. However, whether it is real time or not depends on how fast HoughCircles() will work and also other stuff inside the loop despite you setup the proper time for waitKey().
Is there any built in library for sliding a window (custom size) over an image in opencv version 2.x?
I tried to write the algorithm by myself but I found it very painful and probably error-prone.
I need to slide over an image and create histogram for the input of svm.
there is one for HOG Descriptor, which calculates HOG features but I have my own feature set so I just need an algorithm to let me slide over an image.
You can define a Region of Interest (ROI) on a cv::Mat object, which gives you a new Mat object referring to the sub-window. This does not copy the underlying data, merely a new header with the appropriate metadata.
cv::Mat::operator()
See also this other question:
OpenCV C++, getting Region Of Interest (ROI) using cv::Mat
Basic code can looks like. The code is described good enought. I hope.
This is single scale slideing window 60x60 witch Step 30.
Result of this simple example is ROI.
You can visit this basic tutorial Tutorial Here.
// Parameters of your slideing window
int windows_n_rows = 60;
int windows_n_cols = 60;
// Step of each window
int StepSlide = 30;
for (int row = 0; row <= LoadedImage.rows - windows_n_rows; row += StepSlide)
{
for (int col = 0; col <= LoadedImage.cols - windows_n_cols; col += StepSlide)
{
Rect windows(col, row, windows_n_rows, windows_n_cols);
Mat Roi = LoadedImage(windows);
}
}
I'm looking for a way to automatically remove (=make transparent) a "green screen" portrait background from a lot of pictures.
My own attempts this far have been... ehum... less successful.
I'm looking around for any hints or solutions or papers on the subject. Commercial solutions are just fine, too.
And before you comment and say that it is impossible to do this automatically: no it isn't. There actually exists a company which offers exactly this service, and if I fail to come up with a different solution we're going to use them. The problem is that they guard their algorithm with their lives, and therefore won't sell/license their software. Instead we have to FTP all pictures to them where the processing is done and then we FTP the result back home. (And no, they don't have an underpaid staff hidden away in the Philippines which handles this manually, since we're talking several thousand pictures a day...) However, this approach limits its usefulness for several reasons. So I'd really like a solution where this could be done instantly while being offline from the internet.
EDIT: My "portraits" depictures persons, which do have hair - which is a really tricky part since the green background will bleed into hair. Another tricky part is if it is possible to distingush between the green in the background and the same green in peoples clothes. The company I'm talking about above claims that they can do it by figuring out if the green area are in focus (being sharp vs blurred).
Since you didn't provide any image, I selected one from the web having a chroma key with different shades of green and a significant amount of noise due to JPEG compression.
There is no technology specification so I used Java and Marvin Framework.
input image:
The step 1 simply converts green pixels to transparency. Basically it uses a filtering rule in the HSV color space.
As you mentioned, the hair and some boundary pixels presents colors mixed with green. To reduce this problem, in the step 2, these pixels are filtered and balanced to reduce its green proportion.
before:
after:
Finally, in the step 3, a gradient transparency is applied to all boundary pixels. The result will be even better with high quality images.
final output:
Source code:
import static marvin.MarvinPluginCollection.*;
public class ChromaToTransparency {
public ChromaToTransparency(){
MarvinImage image = MarvinImageIO.loadImage("./res/person_chroma.jpg");
MarvinImage imageOut = new MarvinImage(image.getWidth(), image.getHeight());
// 1. Convert green to transparency
greenToTransparency(image, imageOut);
MarvinImageIO.saveImage(imageOut, "./res/person_chroma_out1.png");
// 2. Reduce remaining green pixels
reduceGreen(imageOut);
MarvinImageIO.saveImage(imageOut, "./res/person_chroma_out2.png");
// 3. Apply alpha to the boundary
alphaBoundary(imageOut, 6);
MarvinImageIO.saveImage(imageOut, "./res/person_chroma_out3.png");
}
private void greenToTransparency(MarvinImage imageIn, MarvinImage imageOut){
for(int y=0; y<imageIn.getHeight(); y++){
for(int x=0; x<imageIn.getWidth(); x++){
int color = imageIn.getIntColor(x, y);
int r = imageIn.getIntComponent0(x, y);
int g = imageIn.getIntComponent1(x, y);
int b = imageIn.getIntComponent2(x, y);
double[] hsv = MarvinColorModelConverter.rgbToHsv(new int[]{color});
if(hsv[0] >= 60 && hsv[0] <= 130 && hsv[1] >= 0.4 && hsv[2] >= 0.3){
imageOut.setIntColor(x, y, 0, 127, 127, 127);
}
else{
imageOut.setIntColor(x, y, color);
}
}
}
}
private void reduceGreen(MarvinImage image){
for(int y=0; y<image.getHeight(); y++){
for(int x=0; x<image.getWidth(); x++){
int r = image.getIntComponent0(x, y);
int g = image.getIntComponent1(x, y);
int b = image.getIntComponent2(x, y);
int color = image.getIntColor(x, y);
double[] hsv = MarvinColorModelConverter.rgbToHsv(new int[]{color});
if(hsv[0] >= 60 && hsv[0] <= 130 && hsv[1] >= 0.15 && hsv[2] > 0.15){
if((r*b) !=0 && (g*g) / (r*b) >= 1.5){
image.setIntColor(x, y, 255, (int)(r*1.4), (int)g, (int)(b*1.4));
} else{
image.setIntColor(x, y, 255, (int)(r*1.2), g, (int)(b*1.2));
}
}
}
}
}
public static void main(String[] args) {
new ChromaToTransparency();
}
}
Take a look at this thread:
http://www.wizards-toolkit.org/discourse-server/viewtopic.php?f=2&t=14394&start=0
and the link within it to the tutorial at:
http://tech.natemurray.com/2007/12/convert-white-to-transparent.html
Then it's just a matter of writing some scripts to look through the directory full of images. Pretty simple.
If you know the "green color" you may write a small program in opencv C/C++/Python to do extract that color and replace with transparent pixels.
123 Video Magic Green Screen Background Software and there are a few more just made to remove green screen background hope this helps
PaintShop Pro allows you to remove backgrounds based on picking a color. They also have a Remove Background wand that will remove whatever you touch (converting those pixels to transparent). You can tweak the "tolerance" for the wand, such that it takes out pixels that are similar to the ones you are touching. This has worked pretty well for me in the past.
To automate it, you'd program a script in PSP that does what you want and then call it from your program. This might be a kludgy way to to do automatic replacement, but it would be the cheapest, fastest solution without having to write a bunch of C#/C++ imaging code or pay a commercial agency.
They being said, you pay for what you get.