I am new to OpenCV and i faced some problem while using it.
Currently i am work on Binary Partitioning Tree (BPT) algorithm. Basically I need split the image into many regions, and based on some parameter. 2 regions will merged and form 1 new region, which consists of these 2 regions.
I managed to get initial regions by using cvWatershed. I also created a vector to store these regions, each in 1 vector block. However, I get memory leak when I tried to move the contour information into vector. It says, memory leak.
for (int h = 0; h <compCount; h++) // compCount - Amount of regions found through cvWaterShed
{
cvZero(WSRegion); // clears out an image, used for painting
Region.push_back(EmptyNode); // create an empty vector slot
CvScalar RegionColor = colorTab[h]; // the color of the region in watershed
for (int i = 0; i <WSOut->height; i++)
{
for (int j = 0; j <WSOut->width; j++)
{
CvScalar s = cvGet2D(WSOut, i, j); // get pixel color in watershed image
if (s.val[0] == RegionColor.val[0] && s.val[1] == RegionColor.val[1] && s.val[2] == RegionColor.val[2])
{
cvSet2D(WSRegion, i, j, cvScalarAll(255)); // paint the pixel to white if it has the same color with the region[h]
}
}
}
MemStorage = cvCreateMemStorage(); // create memory storage
cvFindContours(WSRegion, MemStorage, &contours, sizeof(CvContour), CV_RETR_LIST);
Region[h].RegionContour = cvCloneSeq(contours); // clone and store in vector Region[h]
Region[h].RegionContour->h_next = NULL;
}
Is it any ways I can solve this problem? Or is there any alternative that I do not need to create a new memory storage for every region vector？ Thank You in advance
You should create the memory storage only once before the loop, cvFindContours can use that, and after the loop you should release the storage with:
void cvReleaseMemStorage(CvMemStorage** storage)
You can also take a look here for the CvMemStorage specification :
http://opencv.itseez.com/modules/core/doc/dynamic_structures.html?highlight=cvreleasememstorage#CvMemStorage
EDIT:
Your next problem is with cvCloneSeq(). Here are some specifications for it:
CvSeq* cvCloneSeq(const CvSeq* seq, CvMemStorage* storage=NULL )
Parameters:
seq – Sequence
storage – The destination storage block to hold the new sequence header and the copied data, if any. If it is NULL, the function uses the storage block containing the input sequence.
As you can see if you don't specify a different memory storage, it will clone the sequence in the same memory block as the input. When you are releasing the memory storage after the loop you are also releasing the last contour and it's clone that you pushed in the list.
Related
I'm trying to make a simple 3D modeling tool.
there is some work to move a vertex( or vertices ) for transform the model.
I used dynamic vertex buffer because thought it needs much update.
but performance is too low in high polygon model even though I change just one vertex.
is there other methods? or did I wrong way?
here is my D3D11_BUFFER_DESC
Usage = D3D11_USAGE_DYNAMIC;
CPUAccessFlags = D3D11_CPU_ACCESS_WRITE;
BindFlags = D3D11_BIND_VERTEX_BUFFER;
ByteWidth = sizeof(ST_Vertex) * _nVertexCount
D3D11_SUBRESOURCE_DATA d3dBufferData;
d3dBufferData.pSysMem = pVerticesInfo;
hr = pd3dDevice->CreateBuffer(&descBuffer, &d3dBufferData, &_pVertexBuffer);
and my update funtion
D3D11_MAPPED_SUBRESOURCE d3dMappedResource;
pImmediateContext->Map(_pVertexBuffer, 0, D3D11_MAP_WRITE_DISCARD, 0, &d3dMappedResource);
ST_Vertex* pBuffer = (ST_Vertex*)d3dMappedResource.pData;
for (int i = 0; i < vIndice.size(); ++i)
{
pBuffer[vIndice[i]].xfPosition.x = pVerticesInfo[vIndice[i]].xfPosition.x;
pBuffer[vIndice[i]].xfPosition.y = pVerticesInfo[vIndice[i]].xfPosition.y;
pBuffer[vIndice[i]].xfPosition.z = pVerticesInfo[vIndice[i]].xfPosition.z;
}
pImmediateContext->Unmap(_pVertexBuffer, 0);
As mentioned in the previous answer, you are updating your whole buffer every time, which will be slow depending on model size.
The solution is indeed to implement partial updates, there are two possibilities for it, you want to update a single vertex, or you want to update
arbitrary indices (for example, you want to move N vertices in one go, in different locations, like vertex 1,20,23 for example.
The first solution is rather simple, first create your buffer with the following description :
Usage = D3D11_USAGE_DEFAULT;
CPUAccessFlags = 0;
BindFlags = D3D11_BIND_VERTEX_BUFFER;
ByteWidth = sizeof(ST_Vertex) * _nVertexCount
D3D11_SUBRESOURCE_DATA d3dBufferData;
d3dBufferData.pSysMem = pVerticesInfo;
hr = pd3dDevice->CreateBuffer(&descBuffer, &d3dBufferData, &_pVertexBuffer);
This makes sure your vertex buffer is gpu visible only.
Next create a second dynamic buffer which has the size of a single vertex (you do not need any bind flags in that case, as it will be used only for copies)
_pCopyVertexBuffer
Usage = D3D11_USAGE_DYNAMIC; //Staging works as well
CPUAccessFlags = D3D11_CPU_ACCESS_WRITE;
BindFlags = 0;
ByteWidth = sizeof(ST_Vertex);
D3D11_SUBRESOURCE_DATA d3dBufferData;
d3dBufferData.pSysMem = NULL;
hr = pd3dDevice->CreateBuffer(&descBuffer, &d3dBufferData, &_pCopyVertexBuffer);
when you move a vertex, copy the changed vertex in the copy buffer :
ST_Vertex changedVertex;
D3D11_MAPPED_SUBRESOURCE d3dMappedResource;
pImmediateContext->Map(_pVertexBuffer, 0, D3D11_MAP_WRITE_DISCARD, 0, &d3dMappedResource);
ST_Vertex* pBuffer = (ST_Vertex*)d3dMappedResource.pData;
pBuffer->xfPosition.x = changedVertex.xfPosition.x;
pBuffer->.xfPosition.y = changedVertex.xfPosition.y;
pBuffer->.xfPosition.z = changedVertex.xfPosition.z;
pImmediateContext->Unmap(_pVertexBuffer, 0);
Since you use D3D11_MAP_WRITE_DISCARD, make sure to write all attributes there (not only position).
Now once you done, you can use ID3D11DeviceContext::CopySubresourceRegion to only copy the modified vertex in the current location :
I assume that vertexID is the index of the modified vertex :
pd3DeviceContext->CopySubresourceRegion(_pVertexBuffer,
0, //must be 0
vertexID * sizeof(ST_Vertex), //location of the vertex in you gpu vertex buffer
0, //must be 0
0, //must be 0
_pCopyVertexBuffer,
0, //must be 0
NULL //in this case we copy the full content of _pCopyVertexBuffer, so we can set to null
);
Now if you want to update a list of vertices, things get more complicated and you have several options :
-First you apply this single vertex technique in a loop, this will work quite well if your changeset is small.
-If your changeset is very big (close to almost full vertex size, you can probably rewrite the whole buffer instead).
-An intermediate technique is to use compute shader to perform the updates (thats the one I normally use as its the most flexible version).
Posting all c++ binding code would be way too long, but here is the concept :
your vertex buffer must have BindFlags = D3D11_BIND_VERTEX_BUFFER | D3D11_BIND_UNORDERED_ACCESS; //this allows to write wioth compute
you need to create an ID3D11UnorderedAccessView for this buffer (so shader can write to it)
you need the following misc flags : D3D11_RESOURCE_MISC_BUFFER_ALLOW_RAW_VIEWS //this allows to write as RWByteAddressBuffer
you then create two dynamic structured buffers (I prefer those over byteaddress, but vertex buffer and structured is not allowed in dx11, so for the write one you need raw instead)
first structured buffer has a stride of ST_Vertex (this is your changeset)
second structured buffer has a stride of 4 (uint, these are the indices)
both structured buffers get an arbitrary element count (normally i use 1024 or 2048), so that will be the maximum amount of vertices you can update in a single pass.
both structured buffers you need an ID3D11ShaderResourceView (shader visible, read only)
Then update process is the following :
write modified vertices and locations in structured buffers (using map discard, if you have to copy less its ok)
attach both structured buffers for read
attach ID3D11UnorderedAccessView for write
set your compute shader
call dispatch
detach ID3D11UnorderedAccessView for write (this is VERY important)
This is a sample compute shader code (I assume you vertex is position only, for simplicity)
cbuffer cbUpdateCount : register(b0)
{
uint updateCount;
};
RWByteAddressBuffer RWVertexPositionBuffer : register(u0);
StructuredBuffer<float3> ModifiedVertexBuffer : register(t0);
StructuredBuffer<uint> ModifiedVertexIndicesBuffer : register(t0);
//this is the stride of your vertex buffer, since here we use float3 it is 12 bytes
#define WRITE_STRIDE 12
[numthreads(64, 1, 1)]
void CS( uint3 tid : SV_DispatchThreadID )
{
//make sure you do not go part element count, as here we runs 64 threads at a time
if (tid.x >= updateCount) { return; }
uint readIndex = tid.x;
uint writeIndex = ModifiedVertexIndicesBuffer[readIndex];
float3 vertex = ModifiedVertexBuffer[readIndex];
//byte address buffers do not understand float, asuint is a binary cast.
RWVertexPositionBuffer.Store3(writeIndex * WRITE_STRIDE, asuint(vertex));
}
For the purposes of this question I'm going to assume you already have a mechanism for selecting a vertex from a list of vertices based upon ray casting or some other picking method and a mechanism for creating a displacement vector detailing how the vertex was moved in model space.
The method you have for updating the buffer is sufficient for anything less than a few hundred vertices, but on large scale models it becomes extremely slow. This is because you're updating everything, rather than the individual vertices you modified.
To fix this, you should only update the vertices you have changed, and to do that you need to create a change set.
In concept, a change set is nothing more than a set of changes made to the data - a list of the vertices that need to be updated. Since we already know which vertices were modified (otherwise we couldn't have manipulated them), we can map in the GPU buffer, go to that vertex specifically, and copy just those vertices into the GPU buffer.
In your vertex modification method, record the index of the vertex that was modified by the user:
//Modify the vertex coordinates based on mouse displacement
pVerticesInfo[SelectedVertexIndex].xfPosition.x += DisplacementVector.x;
pVerticesInfo[SelectedVertexIndex].xfPosition.y += DisplacementVector.y;
pVerticesInfo[SelectedVertexIndex].xfPosition.z += DisplacementVector.z;
//Add the changed vertex to the list of changes.
changedVertices.add(SelectedVertexIndex);
//And update the GPU buffer
UpdateD3DBuffer();
In UpdateD3DBuffer(), do the following:
D3D11_MAPPED_SUBRESOURCE d3dMappedResource;
pImmediateContext->Map(_pVertexBuffer, 0, D3D11_MAP_WRITE, 0, &d3dMappedResource);
ST_Vertex* pBuffer = (ST_Vertex*)d3dMappedResource.pData;
for (int i = 0; i < changedVertices.size(); ++i)
{
pBuffer[changedVertices[i]].xfPosition.x = pVerticesInfo[changedVertices[i]].xfPosition.x;
pBuffer[changedVertices[i]].xfPosition.y = pVerticesInfo[changedVertices[i]].xfPosition.y;
pBuffer[changedVertices[i]].xfPosition.z = pVerticesInfo[changedVertices[i]].xfPosition.z;
}
pImmediateContext->Unmap(_pVertexBuffer, 0);
changedVertices.clear();
This has the effect of only updating the vertices that have changed, rather than all vertices in the model.
This also allows for some more complex manipulations. You can select multiple vertices and move them all as a group, select a whole face and move all the connected vertices, or move entire regions of the model relatively easily, assuming your picking method is capable of handling this.
In addition, if you record the change sets with enough information (the affected vertices and the displacement index), you can fairly easily implement an undo function by simply reversing the displacement vector and reapplying the selected change set.
I am trying to write a code that uses opencv Mat objects it goes something like this
Mat img;
vector<Mat> images;
for (i = 1; i < 5; i++)
{
img.create(h,w,type) // h,w and type are given correctly
// input an image from somewhere to img correctly.
images.push_back(img);
img.release()
}
for (i = 1; i < 5; i++)
images[i].release();
I however still seem to have memory leakage can anyone tell me why it is so?
I thought that if the refcount of a mat object = 0 then the memory should be automatically deallocated
You rarely need to call release explicitly, since OpenCV Mat objects take automatically care of internal memory.
Also take care that Mat copy just copies creates a new header pointing to the same data. If the original Mat goes out of scope you are left with an invalid matrix. So when you push the image into the vector, use a deep copy (clone()) to avoid that it the image into the vector becomes invalid.
Since you mentioned:
I have a large 3D image stored in a Mat object. I am running over it using for loops. creating a 2D mat called "image" putting the slices into image, pushing back image to vector images. releasing the image. And later doing a for loop on the images vector releasing all the matrices one by one.
You can store all slices into the vector with the following code. To release the images in the vector, just clear the vector.
#include <opencv2/opencv.hpp>
#include <vector>
using namespace cv;
using namespace std;
int main()
{
// Init the multidimensional image
int sizes[] = { 10, 7, 5 };
Mat data(3, sizes, CV_32F);
randu(data, Scalar(0, 0, 0), Scalar(1,1,1));
// Put slices into images
vector<Mat> images;
for (int z = 0; z < data.size[2]; ++z)
{
// Create the slice
Range ranges[] = { Range::all(), Range::all(), Range(z, z + 1) };
Mat slice(data(ranges).clone()); // with clone slice is continuous, but still 3d
Mat slice2d(2, &data.size[0], data.type(), slice.data); // make the slice a 2d image
// Clone the slice into the vector, or it becomes invalid when slice goes of of scope.
images.push_back(slice2d.clone());
}
// You can deallocate the multidimensional matrix now, if needed
data.release();
// Work with slices....
// Release the vector of slices
images.clear();
return 0;
}
Please try this code, which is basically what you do:
void testFunction()
{
// image width/height => 80MB images
int size = 5000;
cv::Mat img = cv::Mat(size, size, CV_8UC3);
std::vector<cv::Mat> images;
for (int i = 0; i < 5; i++)
{
// since image size is the same for i==0 as the initial image, no new data will be allocated in the first iteration.
img.create(size+i,size+i,img.type()); // h,w and type are given correctly
// input an image from somewhere to img correctly.
images.push_back(img);
// release the created image.
img.release();
}
// instead of manual releasing, a images.clear() would have been enough here.
for(int i = 0; i < images.size(); i++)
images[i].release();
images.clear();
}
int main()
{
cv::namedWindow("bla");
cv::waitKey(0);
for(unsigned int i=0; i<100; ++i)
{
testFunction();
std::cout << "another iteration finished" << std::endl;
cv::waitKey(0);
}
std::cout << "end of main" << std::endl;
cv::waitKey(0);
return 0;
}
After the first call of testFunction, memory will be "leaked" so that the application consumes 4 KB more memory on my device. But not more "leaks" after additional calls for me...
So this looks like your code is ok and the "memory leak" isn't related to that matrix creation and releasing, but maybe to some "global" things happening within the openCV library or C++ to optimize future function calls or memory allocations.
I encountered same problems when iterate openCV mat. The memory consumption can be 1.1G, then it stopped by warning that no memory. In my program, there are macro #define new new(FILE, LINE), crashed with some std lib. So I deleted all Overloading Operator about new/delete. When debugging, it has no error. But when it runs, I got "Debug Assertion Failed! Expression: _pFirstBlock == pHead". Following the instruction
Debug Assertion Error in OpenCV
I changed setting from MT (Release)/MTd (Debug)to
Project Properties >> Configuration Properties >> C/C++ >> Code Generation and changing the Runtime Library to:
Multi-threaded Debug DLL (/MDd), if you are building the Debug version of your code.
Multi-threaded DLL(/MD), if you are building the Release version of your code.
The memory leak is gone. The memory consumption is 38M.
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 use OpenCV and cvblob library to play with blob.
Now I want to detect blob in this particular case.
The problem or the difficulty in this case is there are two blobs over a bigger one and other blob that overlap a part of the bigger one.
In cvblob library to detect a blob you must have a binary image.
I think i need to create two or more image to segment color uniform blobs and then binarize them to obtain all the blobs in the image.
How can i do that.
thanks in advance
I'm quite a beginner in OpenCV but I guess that, for that particular case, you should work with cvFindContours with the CV_RETR_EXTERNAL flag (with the CV_RETR_TREE, your yellow blob would be IN the blue one) instead of using cvblob.
It depends if you want to track them or not (cvblob offers a quick and efficient way to track blobs, instead of having to implement camshift).
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* firstContour = cvCreateSeq(CV_SEQ_ELTYPE_POINT, sizeof(CvSeq), sizeof(CvPoint), storage);
cvFindContours(image, storage, &firstContour, sizeof(CvContour), CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// S'il y a un contour
if(firstContour != 0) {
for( CvSeq* c = firstContour; c != NULL; c = c->h_next ) {
for(int i = 0; i < c->total; ++i) {
// Get each point of the current contour
CvPoint* pt = CV_GET_SEQ_ELEM(CvPoint, c, i);
double x = pt->x;
double y = pt->y;
}
}
}
With the information given by the contour you can find easily the centroid, angle and bounding box of your blob.
Tracking these blob might be more difficult as cvblob doesn't like overlapping blobs (as I can see). You may have to implement your own tracking method.
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;
}