Issues with gpu::resize - c++

I am trying to calculate HOG features on GPU for different levels and then I am saving features of each level to a yml file. Below is the function that I am using.
void App::run()
{
unsigned int count = 0;
FileStorage fs;
running = true;
int width = 640;
int height = 480;
Size win_size(args.win_width, args.win_width * 2);
Size win_stride(args.win_stride_width, args.win_stride_height);
cv::gpu::HOGDescriptor gpu_hog(win_size, Size(16, 16), Size(8, 8), Size(8, 8), 9,
cv::gpu::HOGDescriptor::DEFAULT_WIN_SIGMA, 0.2, gamma_corr,
cv::gpu::HOGDescriptor::DEFAULT_NLEVELS);
VideoCapture vc("/home/ubuntu/Desktop/getdescriptor/images/image%d.jpg");
Mat frame;
Mat Left;
Mat img_aux, img, img_to_show, img_new;
cv::Mat temp;
gpu::GpuMat gpu_img, descriptors, new_img;
char cbuff[20];
while (running)
{
vc.read(frame);
if (!frame.empty())
{
workBegin();
sprintf (cbuff, "%04d", count);
// Change format of the image
if (make_gray) cvtColor(frame, img_aux, CV_BGR2GRAY);
else if (use_gpu) cvtColor(frame, img_aux, CV_BGR2BGRA);
else Left.copyTo(img_aux);
// Resize image
if (args.resize_src) resize(img_aux, img, Size(args.width, args.height));
else img = img_aux;
img_to_show = img;
gpu_hog.nlevels = nlevels;
hogWorkBegin();
if (use_gpu)
{
gpu_img.upload(img);
new_img.upload(img_new);
fs.open(cbuff, FileStorage::WRITE);
//double scale = 1.05;
for(int levels = 0; levels < nlevels; levels++)
{
gpu_hog.getDescriptors(gpu_img, win_stride, descriptors, cv::gpu::HOGDescriptor::DESCR_FORMAT_ROW_BY_ROW);
descriptors.download(temp);
printf("size %d %d\n", temp.rows, temp.cols);
fs <<"level" << levels;
fs << "features" << temp;
cout<<"("<<width<<","<<height<<")"<<endl;
width = round(width/scale);
height = round(height/scale);
cout<<"Levels "<<levels<<endl;
if(width < win_size.width || height < win_size.height)
break;
resize(img,img_new,Size(width,height));
scale *= scale;
}
cout<<count<<endl;
count++;
}
hogWorkEnd();
fs.release();
}
else running = false;
}
}
For the first image it is correctly calculating HOG features for all levels but for the next image it takes the old value of width and height and in that case it break the following loop.
if(width < win_size.width || height < win_size.height)
break;
Can some one point my mistake. I tried to debug but unfortunately no success yet.

HOG feature calculation of each image takes old value of the following three parameters.
1. Width
2. Height
3. Scale
And when it calculates HOG features for next image it break the loop immediately. A common programming mistake.

Related

How do you search for images that have a non white background using c++?

I wrote a program that uses the openCV and boost::filesystem libraries, and the program crops images to fit the object in the image. (Photoshop has already been used to replace most of the backgrounds with white). However, I have thousands and thousands of pictures that I need to sort through. I already know how to use the filesystem library and have no issue traversing the system's directories. However, how do I detect images that have a non-white background (missed in the photoshop process)? This incorrect crop is formatted to have a margin and have a 1:1 aspect ratio, but it still has the odd grayish background. The image should end up looking like this correct crop. So, how do I determine if the image has a background like the incorrect crop?
could you try the code below
( to test the code you should create a directory c:/cropping and some subdirs on it. and put some images in the dirs you created.)
hope it will be helpful
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
using namespace cv;
using namespace std;
vector<Rect> divideHW(Mat src, int dim, double threshold1, double threshold2)
{
Mat gray, reduced, canny;
if (src.channels() == 1)
{
gray = src;
}
if (src.channels() == 3)
{
Laplacian(src, gray, CV_8UC1);
cvtColor(gray, gray, COLOR_BGR2GRAY);
imshow("sobel", gray);
}
reduce(gray, reduced, dim, REDUCE_AVG);
Canny(reduced, canny, threshold1, threshold2);
vector<Point> pts;
findNonZero(canny, pts);
vector<Rect> rects;
Rect rect(0, 0, gray.cols, gray.rows);
if (!pts.size())
{
rects.push_back(rect);
}
int ref_x = 0;
int ref_y = 0;
for (size_t i = 0; i< pts.size(); i++)
{
if (dim)
{
rect.height = pts[i].y - ref_y;
rects.push_back(rect);
rect.y = pts[i].y;
ref_y = rect.y;
if (i == pts.size() - 1)
{
rect.height = gray.rows - pts[i].y;
rects.push_back(rect);
}
}
else
{
rect.width = pts[i].x - ref_x;
rects.push_back(rect);
rect.x = pts[i].x;
ref_x = rect.x;
if (i == pts.size() - 1)
{
rect.width = gray.cols - pts[i].x;
rects.push_back(rect);
}
}
}
return rects;
}
int main( int argc, char** argv )
{
int wait_time = 0; // set this value > 0 for not waiting
vector<String> filenames;
String folder = "c:/cropping/*.*"; // you can change this value or set it by argv[1]
glob(folder, filenames, true);
for (size_t i = 0; i < filenames.size(); ++i)
{
Mat src = imread(filenames[i]);
if (src.data)
{
vector<Rect> rects = divideHW(src, 0, 0, 0);
if (rects.size() < 3) continue;
Rect border;
border.x = rects[0].width;
border.width = src.cols - rects[rects.size() - 1].width - border.x;
rects = divideHW(src, 1, 0, 20);
if (rects.size() < 3) continue;
border.y = rects[0].height;
border.height = src.rows - rects[rects.size() - 1].height - border.y;
Mat cropped = src(border).clone();
src(border).setTo(Scalar(255, 255, 255));
Scalar _mean = mean(src);
int mean_total = _mean[0] + _mean[1] + _mean[2];
if (mean_total > 763)
{
imwrite(filenames[i] + ".jpg", cropped);
imshow("cropped", cropped);
waitKey(wait_time);
}
}
}
return 0;
}
I that you can compute the gradient of an ROI of your image (all rows in column 10 to 15 for exemple).
Then you compute the energy of your gradient (sum of all pixels of the gradient image).
If the energy is very low, you have an uniform background (you can't know the background color with this algorithm). Else you have a textured backgroud.
This is a first approach. You can found in OpenCV all the functions required to do that.
A second approach :
If you are sure that your background is white, you can get the ROI of the first approach, then iterate over all pixels, and check for its color. If there are more than "n" pixels with a different color than "255,255,255", you can mark your image as "non white Background".

OpenCV--how to get better hand contour from low quality gray image?

I need to get contour from hand image, usually I process image with 4 steps:
get raw RGB gray image from 3 channels to 1 channel:
cvtColor(sourceGrayImage, sourceGrayImage, COLOR_BGR2GRAY);
use Gaussian blur to filter gray image:
GaussianBlur(sourceGrayImage, sourceGrayImage, Size(3,3), 0);
binary gray image, I split image by height, normally I split image to 6 images by its height, then each one I do threshold process:
// we split source picture to binaryImageSectionCount(here it's 8) pieces by its height,
// then we for every piece, we do threshold,
// and at last we combine them agin to binaryImage
const binaryImageSectionCount = 8;
void GetBinaryImage(Mat &grayImage, Mat &binaryImage)
{
// get every partial gray image's height
int partImageHeight = grayImage.rows / binaryImageSectionCount;
for (int i = 0; i < binaryImageSectionCount; i++)
{
Mat partialGrayImage;
Mat partialBinaryImage;
Rect partialRect;
if (i != binaryImageSectionCount - 1)
{
// if it's not last piece, Rect's height should be partImageHeight
partialRect = Rect(0, i * partImageHeight, grayImage.cols, partImageHeight);
}
else
{
// if it's last piece, Rect's height should be (grayImage.rows - i * partImageHeight)
partialRect = Rect(0, i * partImageHeight, grayImage.cols, grayImage.rows - i * partImageHeight);
}
Mat partialResource = grayImage(partialRect);
partialResource.copyTo(partialGrayImage);
threshold( partialGrayImage, partialBinaryImage, 0, 255, THRESH_OTSU);
// combin partial binary image to one piece
partialBinaryImage.copyTo(binaryImage(partialRect));
///*stringstream resultStrm;
//resultStrm << "partial_" << (i + 1);
//string string = resultStrm.str();
//imshow(string, partialBinaryImage);
//waitKey(0);*/
}
imshow("result binary image.", binaryImage);
waitKey(0);
return;
}
use findcontour to get biggest area contour:
vector<vector<Point> > contours;
findContours(binaryImage, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
normally it works well,
But for some low quality gray image, it doesn't work,like below:
the complete code is here:
#include <opencv2/imgproc/imgproc.hpp>
#include<opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
using namespace std;
using namespace cv;
// we split source picture to binaryImageSectionCount(here it's 8) pieces by its height,
// then we for every piece, we do threshold,
// and at last we combine them agin to binaryImage
const binaryImageSectionCount = 8;
void GetBinaryImage(Mat &grayImage, Mat &binaryImage)
{
// get every partial gray image's height
int partImageHeight = grayImage.rows / binaryImageSectionCount;
for (int i = 0; i < binaryImageSectionCount; i++)
{
Mat partialGrayImage;
Mat partialBinaryImage;
Rect partialRect;
if (i != binaryImageSectionCount - 1)
{
// if it's not last piece, Rect's height should be partImageHeight
partialRect = Rect(0, i * partImageHeight, grayImage.cols, partImageHeight);
}
else
{
// if it's last piece, Rect's height should be (grayImage.rows - i * partImageHeight)
partialRect = Rect(0, i * partImageHeight, grayImage.cols, grayImage.rows - i * partImageHeight);
}
Mat partialResource = grayImage(partialRect);
partialResource.copyTo(partialGrayImage);
threshold( partialGrayImage, partialBinaryImage, 0, 255, THRESH_OTSU);
// combin partial binary image to one piece
partialBinaryImage.copyTo(binaryImage(partialRect));
///*stringstream resultStrm;
//resultStrm << "partial_" << (i + 1);
//string string = resultStrm.str();
//imshow(string, partialBinaryImage);
//waitKey(0);*/
}
imshow("result binary image.", binaryImage);
waitKey(0);
return;
}
int main(int argc, _TCHAR* argv[])
{
// get image path
string imgPath("C:\\Users\\Alfred\\Desktop\\gray.bmp");
// read image
Mat src = imread(imgPath);
imshow("Source", src);
//medianBlur(src, src, 7);
cvtColor(src, src, COLOR_BGR2GRAY);
imshow("gray", src);
// do filter
GaussianBlur(src, src, Size(3,3), 0);
// binary image
Mat threshold_output(src.rows, src.cols, CV_8UC1, Scalar(0, 0, 0));
GetBinaryImage(src, threshold_output);
imshow("binaryImage", threshold_output);
// get biggest contour
vector<vector<Point> > contours;
findContours(threshold_output,contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
int biggestContourIndex = 0;
int maxContourArea = -1000;
for (int i = 0; i < contours.size(); i++)
{
if (contourArea(contours[i]) > maxContourArea)
{
maxContourArea = contourArea(contours[i]);
biggestContourIndex = i;
}
}
// show biggest contour
Mat biggestContour(threshold_output.rows, threshold_output.cols, CV_8UC1, Scalar(0, 0, 0));
drawContours(biggestContour, contours, biggestContourIndex, cv::Scalar(255,255,255), 2, 8, vector<Vec4i>(), 0, Point());
imshow("maxContour", biggestContour);
waitKey(0);
}
could anybody please help me to get a better hand contour result?
thanks!!!
I have the code snippet in python, you can follow the same approach in C:
img = cv2.imread(x, 1)
cv2.imshow("img",img)
imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cv2.imshow("gray",imgray)
#Code for histogram equalization
equ = cv2.equalizeHist(imgray)
cv2.imshow('equ', equ)
#Code for contrast limited adaptive histogram equalization
#clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
#cl2 = clahe.apply(imgray)
#cv2.imshow('clahe2', cl2)
This is the result I obtained:
If you're image is horribly bad you could try the code that I commented involving contrast limited adaptive histogram equalization.

How to remove black borders from a frame in OpenCV using C++?

I would like to know how to remove the black border from the following frame in OpenCV using C++
Original Image
Result
Any help would be really appreciated.
To remove some non-black noise I recommend using cv::threshold and morphology closing. Then you can just remove rows and columns which contains (for example) more than 5% non-black pixels.
I tried following code and it works for your example:
int main()
{
const int threshVal = 20;
const float borderThresh = 0.05f; // 5%
cv::Mat img = cv::imread("img.jpg", cv::IMREAD_GRAYSCALE);
cv::Mat thresholded;
cv::threshold(img, thresholded, threshVal, 255, cv::THRESH_BINARY);
cv::morphologyEx(thresholded, thresholded, cv::MORPH_CLOSE,
cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3)),
cv::Point(-1, -1), 2, cv::BORDER_CONSTANT, cv::Scalar(0));
cv::imshow("thresholded", thresholded);
cv::Point tl, br;
for (int row = 0; row < thresholded.rows; row++)
{
if (cv::countNonZero(thresholded.row(row)) > borderThresh * thresholded.cols)
{
tl.y = row;
break;
}
}
for (int col = 0; col < thresholded.cols; col++)
{
if (cv::countNonZero(thresholded.col(col)) > borderThresh * thresholded.rows)
{
tl.x = col;
break;
}
}
for (int row = thresholded.rows - 1; row >= 0; row--)
{
if (cv::countNonZero(thresholded.row(row)) > borderThresh * thresholded.cols)
{
br.y = row;
break;
}
}
for (int col = thresholded.cols - 1; col >= 0; col--)
{
if (cv::countNonZero(thresholded.col(col)) > borderThresh * thresholded.rows)
{
br.x = col;
break;
}
}
cv::Rect roi(tl, br);
cv::Mat cropped = img(roi);
cv::imwrite("cropped.jpg", cropped);
return 0;
}
Please note that in order to get the best results on all your samples you may need to adjust some parameters: threshVal and borderThresh.
Also you may want to read good tutorials about thresholding and morphology transformations.
From akarsakov's answer. His will crop out the black parts of the input image. But, it will write this cropped image in grayscale. If you are after colour try changing and adding these lines.
#include "opencv2/opencv.hpp"
using namespace cv;
// Read your input image
Mat img = imread("img.jpg");
// Prepare new grayscale image
Mat input_img_gray;
// Convert to img to Grayscale
cvtColor (img, input_img_gray, CV_RGB2GRAY);
Mat thresholded;
// Threshold uses grayscale image
threshold(input_img_gray, thresholded, threshVal, 255, cv::THRESH_BINARY);
I'd recommend ticking akarsakov's answer because it definitely works. This is just for anyone looking to output a coloured image :)

Object tracking delay color tracking OpenCV

I am trying to detect colored balls like ps3 move controller balls from 2 mt distance.I have 10 camera in same room hanging from the ceiling.Room is dark and balls have led inside.I have 4-5 balls.(red,blue,green,yellow,pink). I want track their position with opencv.Whats the right mehtod for doing this in opencv ? Can u give link , example for this ?
I use this code but i have delay problem.When i comment // my trackFilteredObject line there is no lag.But when using this code i have lot latency.I cant understand why happening because my normal cpu usage ~%15 ram usage 6.3GB/15GB (%40) when run this code cpu usage ~20-23 ram usage 6.4GB . I think its not about cpu-ram performance.What am i doing wrong ?
Video: https://www.youtube.com/watch?v=_BKtJpPrkO4 (You can see lag in first 10 sec.After 10 sen i comment tracking codes.)
Note:Kamerasayisi mean cameracount My Track Function:
void trackFilteredObject(Object theObject,Mat threshold,Mat HSV, Mat &cameraFeed){
//max number of objects to be detected in frame
const int FRAME_WIDTH = 5120;
const int FRAME_HEIGHT = 480;
const int MAX_NUM_OBJECTS=50;
//minimum and maximum object area
const int MIN_OBJECT_AREA = 10*10;
const int MAX_OBJECT_AREA = FRAME_HEIGHT*FRAME_WIDTH/1.5;
vector <Object> objects;
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){
Object object;
object.setXPos(moment.m10/area);
object.setYPos(moment.m01/area);
object.setType(theObject.getType());
object.setColor(theObject.getColor());
objects.push_back(object);
objectFound = true;
}else objectFound = false;
}
//let user know you found an object
if(objectFound ==true){
//draw object location on screen
drawObject(objects,cameraFeed,temp,contours,hierarchy);}
}else putText(cameraFeed,"TOO MUCH NOISE! ADJUST FILTER",Point(0,50),1,2,Scalar(0,0,255),2);
}
}
};
Main Code:
void Run()
{
int w, h;
_fps = 30;
IplImage *pCapImage[kameraSayisi];
IplImage *pDisplayImage;
PBYTE pCapBuffer = NULL;
// Create camera instance
for(int i = 0; i < kameraSayisi; i++)
{
_cam[i] = CLEyeCreateCamera(_cameraGUID[i], _mode, _resolution, _fps);
if(_cam[i] == NULL) return;
// Get camera frame dimensions
CLEyeCameraGetFrameDimensions(_cam[i], w, h);
// Create the OpenCV images
pCapImage[i] = cvCreateImage(cvSize(w, h), IPL_DEPTH_8U, 1);
// Set some camera parameters
CLEyeSetCameraParameter(_cam[i], CLEYE_GAIN, 0);
CLEyeSetCameraParameter(_cam[i], CLEYE_EXPOSURE, 511);
// Start capturing
CLEyeCameraStart(_cam[i]);
}
pDisplayImage = cvCreateImage(cvSize(w*kameraSayisi / 2, h * kameraSayisi/4 ), IPL_DEPTH_8U ,1);
if(_cam == NULL) return;
int iLastX = -1;
int iLastY = -1;
//Capture a temporary image from the camera
//program
bool trackObjects = true;
bool useMorphOps = true;
Mat HSV;
//Create a black image with the size as the camera output
Mat imgLines;
// imgLines = Mat::zeros( cvarrToMat(image).size(), CV_8UC3 );;
Mat threshold;
//x and y values for the location of the object
int x=0, y=0;
bool calibrationMode = false;
if(calibrationMode){
//create slider bars for HSV filtering
createTrackbars();
}
// image capturing loop
while(_running)
{
PBYTE pCapBuffer;
// Capture camera images
for(int i = 0; i < kameraSayisi; i++)
{
cvGetImageRawData(pCapImage[i], &pCapBuffer);
CLEyeCameraGetFrame(_cam[i], pCapBuffer, (i==0)?2000:0);
}
// Display stereo image
for(int i = 0; i < kameraSayisi; i++)
{
cvSetImageROI(pDisplayImage, cvRect(w * (i%4) ,i/4 * h, w, h));
cvCopy(pCapImage[i], pDisplayImage);
}
cvResetImageROI(pDisplayImage);
Mat imgOriginal;
Mat imgConverted = cvarrToMat(pDisplayImage);
if(calibrationMode==true)
{
//need to find the appropriate color range values
// calibrationMode must be false
//if in calibration mode, we track objects based on the HSV slider values.
//cvtColor(imgOriginal,imgOriginal,CV_BayerRG2RGB);
cvtColor(imgConverted,imgOriginal,CV_BayerGB2BGR);
cvtColor(imgOriginal,HSV,CV_BGR2HSV);
inRange(HSV,Scalar(H_MIN,S_MIN,V_MIN),Scalar(H_MAX,S_MAX,V_MAX),threshold);
morphOps(threshold);
imshow(_windowName + 'T',threshold);
//the folowing for canny edge detec
/// Create a matrix of the same type and size as src (for dst)
dst.create( imgOriginal.size(), src.type() );
/// Convert the image to grayscale
cvtColor( imgOriginal, src_gray, CV_BGR2GRAY );
/// Create a window
namedWindow( window_name, CV_WINDOW_AUTOSIZE );
/// Create a Trackbar for user to enter threshold
// createTrackbar( "Min Threshold:", window_name, &lowThreshold, max_lowThreshold, CannyThreshold );
/// Show the image
Object a = Object(H_MIN,S_MIN,V_MIN,H_MAX,S_MAX,V_MAX);
trackFilteredObject(a,threshold,HSV,imgOriginal);
}
else{
//we can use their member functions/information
Object blue("blue"), yellow("yellow"), red("red"), orange("orange"),white("white");
cvtColor(imgConverted,imgOriginal,CV_BayerGB2BGR);
//first find blue objects
cvtColor(imgOriginal,HSV,CV_RGB2HSV);
inRange(HSV,blue.getHSVmin(),blue.getHSVmax(),threshold);
morphOps(threshold);
//then yellows
inRange(HSV,yellow.getHSVmin(),yellow.getHSVmax(),threshold);
//then reds
inRange(HSV,red.getHSVmin(),red.getHSVmax(),threshold);
//then white
inRange(HSV,white.getHSVmin(),white.getHSVmax(),threshold);
//then orange
inRange(HSV,orange.getHSVmin(),orange.getHSVmax(),threshold);
trackFilteredObject(yellow,threshold,HSV,imgOriginal);
trackFilteredObject(white,threshold,HSV,imgOriginal);
trackFilteredObject(red,threshold,HSV,imgOriginal);
trackFilteredObject(blue,threshold,HSV,imgOriginal);
trackFilteredObject(orange,threshold,HSV,imgOriginal);
}
//delay 10ms so that screen can refresh.
//image will not appear without this waitKey() command
if (cvWaitKey(30) == 27) //wait for 'esc' key press for 30ms. If 'esc' key is pressed, break loop
{
cout << "esc key is pressed by user" << endl;
break;
}
// cvShowImage(_windowName, image);
imshow(_windowName,imgOriginal);
}
for(int i = 0; i < kameraSayisi; i++)
{
// Stop camera capture
CLEyeCameraStop(_cam[i]);
// Destroy camera object
CLEyeDestroyCamera(_cam[i]);
// Destroy the allocated OpenCV image
cvReleaseImage(&pCapImage[i]);
_cam[i] = NULL;
}
}

Need only one edge in Canny edge algorithm

When i use the canny edge algorithm, it produces the 2 edges opposite the thick colored line as expected, but i want only one edge to be displayed so as to make my line and curve detection algorithm much less complicated, any ideas on how i can make that happen ?
Here is the code :
bool CannyEdgeDetection(DataStructure& col)
{
Mat src, src_gray;
Mat dst, detected_edges, fin;
int WhiteCount = 0, BCount = 0;
char szFil1[32] = "ocv.bmp";
char szFil2[32] = "dst.bmp";
src = imread(szFil1);
dst = imread(szFil1);
blur( src_gray, detected_edges, Size(3,3) );
Canny( src, dst, 100, 200, 3 );
imwrite(szFil2, dst );
IplImage* img = cvLoadImage(szFil2);
int height = img->height;
int width = img->width;
int step = img->widthStep;
int channels = img->nChannels;
uchar * datau = (uchar *)img->imageData;
for(int i=0;i<height;i++){
for(int j=0;j<width;j++){
for(int k=0;k<channels;k++){
datau[i*step+j*channels+k] = 255 - datau[i*step+j*channels+k];
if (datau[i*step+j*channels+k]==0){
WhiteCount++;
col.pixel_col [i][j] = 2;
}
else{BCount++;
col.pixel_col[i][j] = 0;
}
}
}
}
cvSaveImage("img.bmp" ,img);
return 0;
}
This is not the original image but similar :
Which part do i comment out to be able to read black images in white backgrounds ? or any colored image ?
bool done;
do
{
cv::morphologyEx(img, temp, cv::MORPH_OPEN, element);
cv::bitwise_not(temp, temp);
cv::bitwise_and(img, temp, temp);
cv::bitwise_or(skel, temp, skel);
cv::erode(img, img, element);
double max;
cv::minMaxLoc(img, 0, &max);
done = (max == 0);
} while (!done);
That process is called skeletonization or thinning. You can google for that.
Here is a simple method for skeletonization : skeletonization OpenCV In C#
Below is the output I got when applied above method to your image ( Image is inverted before skeletonization because above method work for white images in black background, just opposite case of your input image).