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Hi I am trying to find all the circles in the following image and Identify the defect.
this is my code:
static void findCircles2(const Mat& image)
{
vector<Vec3f> circles;
int thresh1 = 5;
Mat pyr, timg, gray0(image.size(), CV_8U), gray;
pyrDown(image, pyr, Size(image.cols/2, image.rows/2));
pyrUp(pyr, timg, image.size());
for( int c = 0; c < 3; c++ )
{
int ch[] = {c, 0};
mixChannels(&timg, 1, &gray0, 1, ch, 1);
Canny(gray0, gray, 0, thresh1, 5);
//dilate(gray, gray, Mat(), Point(-1,-1));
gray = gray0 >= (1)*255/N;
gray = gray0 >= (2)*255/N;
gray = gray0 >= (6)*255/N;
namedWindow( "Hough Circle Transform Demo 1", CV_WINDOW_AUTOSIZE );
imshow( "Hough Circle Transform Demo 1", gray );
waitKey(0);
HoughCircles( gray, circles, CV_HOUGH_GRADIENT, 1, gray.rows/8, 200, 100, 0, 0 );
cout<<"size of circles: "<<circles.size()<<endl;
for( size_t i = 0; i < circles.size(); i++ )
{
Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
int radius = cvRound(circles[i][2]);
circle( gray, center, 3, Scalar(0,255,0), -1, 8, 0 );
circle( gray, center, radius, Scalar(0,0,255), 3, 8, 0 );
}
/// Show your results
namedWindow( "Hough Circle Transform Demo 2", CV_WINDOW_AUTOSIZE );
imshow( "Hough Circle Transform Demo 2", gray );
waitKey(0);
}
}
Picture:
however the code is unable to find anything, I played arround with the thresholds but it doesnt help. please advise.
Development platform: VS2010, Opencv version: 2.4.10
Because the circles are so small and not that standard, so you cann't just do HoughCircles on the binary image.
An alternative method is to findContours, then filter the contours by ratio between the value of contourArea and the value of minEnclosingCircle.
I am working on reading container code using an IP Camera and I came across #dhanushka's gradient based method for text detection and I've been successful with it as you can see below...
bool debugging = true;
Mat rgb = imread("/home/brian/qt/ANPR/images/0.jpg", 0);
if(debugging) { imshow("Original", rgb); }
Mat grad;
Mat morphKernel = getStructuringElement(MORPH_ELLIPSE, Size(3, 3));
morphologyEx(rgb, grad, MORPH_GRADIENT, morphKernel);
if(debugging) { imshow("gradient morph", grad); }
// binarize
Mat bw;
threshold(grad, bw, 0.0, 255.0, THRESH_BINARY | THRESH_OTSU);
if(debugging) { imshow("threshold", bw); }
// connect horizontally oriented regions
Mat connected;
morphKernel = getStructuringElement(MORPH_RECT, Size(10, 1));
morphologyEx(bw, connected, MORPH_CLOSE, morphKernel);
if(debugging) { imshow("horizontal regions morph", connected); }
// find contours
Mat mask = Mat::zeros(bw.size(), CV_8UC1);
vector<vector<Point> > contours2;
vector<Vec4i> hierarchy;
vector<Rect> txtRect;
vector<vector<Point> > txtContour;
findContours(connected, contours2, hierarchy, CV_RETR_CCOMP,
CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
// filter contours
for(int i = 0; i >= 0; i = hierarchy[i][0]) {
Rect rect = boundingRect(contours2[i]);
Mat maskROI(mask, rect);
maskROI = Scalar(0, 0, 0);
// fill the contour
drawContours(mask, contours2, i, Scalar(255, 255, 255), CV_FILLED);
// ratio of non-zero pixels in the filled region
double r = (double)countNonZero(maskROI)/(rect.width*rect.height);
/* assume at least 45% of the area is filled if it contains text */
if (r > .45 && (rect.height > 10 && rect.width > 10)) {
rectangle(rgb, rect, Scalar(0, 255, 0), 2);
txtRect.push_back(rect);
txtContour.push_back(contours2[i]);
}
}
if(debugging) { imshow("Characters", rgb); }
Mat text(rgb.size(), CV_8U, Scalar(255));
drawContours(text, txtContour, -1, Scalar(0), FILLED, 4);
if(debugging) { imshow("Detected Text", text); }
Step 0: Original Image
Step 1: Morphological gradient
Step 2: Binarized image
Step 3: Horizontally oriented regions
Step 4: Detected Text
Step 5: Extracted Text
The problem is that I have failed to properly extract the detected text so that I can use it in OCR to get the result BSIU225378.
The text I managed to extract is coming from the horizontal connected regions and its unusable for orc, is there a way I can extract text say from the binarized (threshold) image using the contours I found in the horizontal connected regions?
As right now is my school holiday, I decided to pick up some skills thus I'm attempting to learn how to use OpenCV features with visual studio c++ to detect how many cans is in the carton and had to group it 4 by 4.
I have tried various demo codes such as " opencv find:contour " , Template matching(doesn't work well as it cannot detect the rotation of the top lid)
The best method that I found out is that to combine Canny Edge Detection and Hough Transform Circle such that the output result of Canny Edge Detection can be the input image of the Hough Transform Circle,the result is as below.
Unfortunately, not all circles is detected and if i change the
for (int i = 0; i < circles.size(); i++) into
for (int i = 0; i < 24; i++) // 24 is the no. of cans
I will get a Expression: vector subscript out of range. I am not sure why it is only able to detect 21 circles
Source code as below:-
using namespace cv;
using namespace std;
Mat src, src_gray;
int main()
{
Mat src1;
src1 = imread("cans.jpg", CV_LOAD_IMAGE_COLOR);
namedWindow("Original image", CV_WINDOW_AUTOSIZE);
imshow("Original image", src1);
Mat gray, edge, draw;
cvtColor(src1, gray, CV_BGR2GRAY);
Canny(gray, edge,50, 150, 3);
//50,150,3
edge.convertTo(draw, CV_8U);
namedWindow("Canny Edge", CV_WINDOW_AUTOSIZE);
imshow("Canny Edge", draw);
imwrite("output.jpg", draw);
waitKey(500);
/// Read the image
src = imread("output.jpg", 1);
Size size(932, 558);//the dst image size,e.g.100x100
resize(src, src, size);//resize image
/// Convert it to gray
cvtColor(src, src_gray, CV_BGR2GRAY);
/// Reduce the noise so we avoid false circle detection
GaussianBlur(src_gray, src_gray, Size(9, 9), 2, 2);
vector<Vec3f> circles;
/// Apply the Hough Transform to find the circles
HoughCircles(src_gray, circles, CV_HOUGH_GRADIENT, 1, src_gray.rows / 8,200, 100, 0, 0);
/// Draw the circles detected
for (int i = 0; i < circles.size(); i++)
{
printf("are you um?\n");
Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
int radius = cvRound(circles[i][2]);
// circle center
circle(src, center, 3, Scalar(0, 255, 0), -1, 8, 0);
// circle outline
circle(src, center, radius, Scalar(255, 0, 255), 3, 8, 0);
}
// namedWindow("Hough Circle Transform Demo", CV_WINDOW_NORMAL);
imshow("Hough Circle Transform Demo", src);
line(src, Point(0, 288), Point(1024, 288), Scalar(225, 220, 225), 2, 8);
// middle line
line(src, Point(360, 0), Point(360, 576), Scalar(225, 220, 225), 2, 8);
//break cans into 4 by 4
line(src, Point(600, 0), Point(600, 576), Scalar(225, 220, 225), 2, 8);
// x, y
imshow("Lines", src);
imwrite("lineoutput.jpg", src);
waitKey(0);
return 0;
}
I had also manually typed out the coordinates for the lines to group them into 4 x 4.
What should I change in order for it not to have any subscript out of range error and able to detect all circles?
Okay solved my own question. Changed CV_BGR2GRAY to CV_RGB2GRAY,made the file ratio smaller, changing the circles min Radius and applying another threshold to get the circles.
I’m trying to detect some rectangles (white colored) which is drawn on an image. (say using paint or some other image editing tool).
As I’m very much beginner to image processing I searched through net and OpenCV sample program to accomplish the job, but could not get it to working perfectly. I’m using OpenCV C++ library.
Algorithm that I’ve tried
cv::Mat src = cv::imread(argv[1]);
cv::Mat gray;
cv::cvtColor(src, gray, CV_BGR2GRAY);
meanStdDev(gray, mu, sigma);
cv::Mat bw;
cv::Canny(gray, bw, mu.val[0] - sigma.val[0], mu.val[0] + sigma.val[0]);
std::vector<std::vector<cv::Point> > contours;
cv::findContours(bw.clone(), contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
std::vector<cv::Point> approx;
for (int i = 0; i < contours.size(); i++){
cv::approxPolyDP(cv::Mat(contours[i]), approx, cv::arcLength(cv::Mat(contours[i]), true)*0.02, true);
if (approx.size() >= 4 && approx.size() <= 6)
Rect boundRect = boundingRect( Mat(approx) );
rectangle( dst, boundRect.tl(), boundRect.br(), Scalar(255,255,255), 1, 8, 0 );}
Only one rectangle is detected. Can you please guide me or some link for the same.
Input image:
Output image:
I could not compile your code sample because there boundRect is declared within the if-block but rectangle drawing (trying to access boundRect) is outside of the if-block, so I adjusted your code:
int main(int argc, char* argv[])
{
cv::Mat src = cv::imread("C:/StackOverflow/Input/rectangles.png");
cv::Mat dst = src.clone();
cv::Mat gray;
cv::cvtColor(src, gray, CV_BGR2GRAY);
// ADDED: missing declaration of mu and sigma
cv::Scalar mu, sigma;
meanStdDev(gray, mu, sigma);
cv::Mat bw;
cv::Canny(gray, bw, mu.val[0] - sigma.val[0], mu.val[0] + sigma.val[0]);
// ADDED: displaying the canny output
cv::imshow("canny", bw);
std::vector<std::vector<cv::Point> > contours;
cv::findContours(bw.clone(), contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
std::vector<cv::Point> approx;
for (int i = 0; i < contours.size(); i++){
cv::approxPolyDP(cv::Mat(contours[i]), approx, cv::arcLength(cv::Mat(contours[i]), true)*0.02, true);
if (approx.size() >= 4 && approx.size() <= 6)
{
// ADDED: brackets around both lines belonging to the if-block
cv::Rect boundRect = cv::boundingRect(cv::Mat(approx));
cv::rectangle(dst, boundRect.tl(), boundRect.br(), cv::Scalar(255, 255, 255), 3, 8, 0);
}
}
// ADDED: displaying input and results
cv::imshow("input", src);
cv::imshow("dst", dst);
cv::imwrite("C:/StackOverflow/Output/rectangles.png", dst);
cv::waitKey(0);
return 0;
}
with your input image I do get this output:
which is probably not what you expected. See the canny output image (it is always good to have a look at intermediate results for visual debugging!), there are just too many structures in the image and contours will cover all of these, so there are some that will be approximated to polynomes with 4 to 6 elements.
Instead you'll have to become a bit smarter. You could try to extract straight lines with cv::HoughLinesP and connect those lines. Or you could try to segment the image first by finding white areas (if your rectangles are always white).
int main(int argc, char* argv[])
{
cv::Mat src = cv::imread("C:/StackOverflow/Input/rectangles.png");
cv::Mat dst = src.clone();
cv::Mat gray;
cv::cvtColor(src, gray, CV_BGR2GRAY);
cv::Mat mask;
// find "white" pixel
cv::inRange(src, cv::Scalar(230, 230, 230), cv::Scalar(255, 255, 255), mask);
cv::imshow("mask", mask);
std::vector<std::vector<cv::Point> > contours;
cv::findContours(mask, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
std::vector<cv::Point> approx;
for (int i = 0; i < contours.size(); i++){
cv::approxPolyDP(cv::Mat(contours[i]), approx, cv::arcLength(cv::Mat(contours[i]), true)*0.02, true);
if (approx.size() >= 4 && approx.size() <= 6)
{
cv::Rect boundRect = cv::boundingRect(cv::Mat(approx));
cv::rectangle(dst, boundRect.tl(), boundRect.br(), cv::Scalar(255, 255, 255), 1, 8, 0);
}
}
cv::imshow("input", src);
cv::imshow("dst", dst);
cv::imwrite("C:/StackOverflow/Output/rectangles2.png", dst);
cv::waitKey(0);
return 0;
}
gives this result:
As you can see, there are other bright regions near white, too. The polynom approximation does not help much, too.
In general, it's easier to segment a color (even white) in HSV space. With appropriate thresholds:
inRange(hsv, Scalar(0, 0, 220), Scalar(180, 30, 255), mask);
where we don't care about the Hue, and keep only low Saturation and high Value, I get:
Then you can easily find connected components, and discard blobs smaller than a threshold th_blob_size. Resulting rectangles are (in green):
You can eventually apply other filtering stage to account for more difficult situations, but for this image removing small blobs is enough. Please post other images if you need something more robust in general.
Code:
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
int main(int argc, char** argv)
{
Mat3b img = imread("path_to_image");
int th_blob_size = 100;
Mat3b hsv;
cvtColor(img, hsv, COLOR_BGR2HSV);
Mat1b mask;
inRange(hsv, Scalar(0, 0, 220), Scalar(180, 30, 255), mask);
vector<vector<Point>> contours;
findContours(mask.clone(), contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
Mat3b res = img.clone();
for (int i = 0; i < contours.size(); ++i)
{
// Remove small blobs
if (contours[i].size() < th_blob_size)
{
continue;
}
Rect box = boundingRect(contours[i]);
rectangle(res, box, Scalar(0,255,0), 1);
}
imshow("Result", res);
waitKey();
return 0;
}
Are you sure you are only finding one contour or are you only drawing one contour? It doesn't look like you are looping in the drawing routine so you will only ever draw the first one that is found.
I have a blog, long since dead, that may provide you some good direction on this: http://workingwithcomputervision.blogspot.co.uk/2012/09/game-player-step-2-finding-game-board.html
Should the link die I believe this is the most relevant part of the article which relates to drawing contours:
//Draw contours
for (int i = 0; i < contours.size(); i++) {
Scalar color = Scalar(0, 255, 0);
drawContours(drawing, contours, i, color, 2, 8, hierarchy, 0, Point());
}
I notice you are using bounding rectangles for the drawing. Here is an alternative drawing routine, again from the above link, that does this:
Rect bounds;
Mat drawing = Mat::zeros(purpleOnly.size(), CV_8UC3);
int j = 0;
for (int i = 0; i < contours.size(); i++) {
if (arcLength(contours[i], true) > 500){
Rect temp = boundingRect(contours[i]);
rectangle(drawing, temp, Scalar(255, 0, 0), 2, 8);
if (j == 0) {
bounds = temp;
} else {
bounds = bounds | temp;
}
j++;
}
}
Note that I also do some checks on the size of the contour to filter out noise.
My code is the same as this tutorial.
When I see the result image after using cv::watershed(), there is a object(upper-right) that I want to find out, but it's missing.
There are indeed six marks in image after using cv::drawContours().
Is this normal because the inaccuracy of the watershed algorithm exist?
Here is part of my code:
Mat src = imread("result01.png");
Mat gray;
cvtColor(src, gray, COLOR_BGR2GRAY);
Mat thresh;
threshold(gray, thresh, 0, 255, THRESH_BINARY | THRESH_OTSU);
// noise removal
Mat kernel = Mat::ones(3, 3, CV_8UC1);
Mat opening;
morphologyEx(thresh, opening, MORPH_OPEN, kernel, Point(-1, -1), 2);
// Perform the distance transform algorithm
Mat dist_transform;
distanceTransform(opening, dist_transform, CV_DIST_L2, 5);
// Normalize the distance image for range = {0.0, 1.0}
// so we can visualize and threshold it
normalize(dist_transform, dist_transform, 0, 1., NORM_MINMAX);
// Threshold to obtain the peaks
// This will be the markers for the foreground objects
Mat dist_thresh;
threshold(dist_transform, dist_thresh, 0.5, 1., CV_THRESH_BINARY);
Mat dist_8u;
dist_thresh.convertTo(dist_8u, CV_8U);
// Find total markers
vector<vector<Point> > contours;
findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// Create the marker image for the watershed algorithm
Mat markers = Mat::zeros(dist_thresh.size(), CV_32SC1);
// Draw the foreground markers
for (size_t i = 0; i < contours.size(); i++)
drawContours(markers, contours, static_cast<int>(i), Scalar::all(static_cast<int>(i)+1), -1);
// Perform the watershed algorithm
watershed(src, markers);
Original image:
Result after watershed:
You can find original, intermediate and result image here:
Result images after specific process
In your example, what you consider background is given the same label (5) as the "missing" object.
You can easily adjust this by setting a label (>0) to background, too.
You can find what is for sure background dilating and negating the thresh image.
Then, when creating a marker, you define the labels as:
0: unknown
1: background
>1 : your objects
In your output image, markers will have:
-1 : the edges between objects
0: the background (as intended by watershed)
1: the background (as you defined)
>1 : your objects.
This code should help:
#include <opencv2\opencv.hpp>
#include <vector>
using namespace std;
using namespace cv;
int main()
{
Mat3b src = imread("path_to_image");
Mat1b gray;
cvtColor(src, gray, COLOR_BGR2GRAY);
Mat1b thresh;
threshold(gray, thresh, 0, 255, THRESH_BINARY | THRESH_OTSU);
// noise removal
Mat1b kernel = getStructuringElement(MORPH_RECT, Size(3,3));
Mat1b opening;
morphologyEx(thresh, opening, MORPH_OPEN, kernel, Point(-1, -1), 2);
Mat1b kernelb = getStructuringElement(MORPH_RECT, Size(21, 21));
Mat1b background;
morphologyEx(thresh, background, MORPH_DILATE, kernelb);
background = ~background;
// Perform the distance transform algorithm
Mat1f dist_transform;
distanceTransform(opening, dist_transform, CV_DIST_L2, 5);
// Normalize the distance image for range = {0.0, 1.0}
// so we can visualize and threshold it
normalize(dist_transform, dist_transform, 0, 1., NORM_MINMAX);
// Threshold to obtain the peaks
// This will be the markers for the foreground objects
Mat1f dist_thresh;
threshold(dist_transform, dist_thresh, 0.5, 1., CV_THRESH_BINARY);
Mat1b dist_8u;
dist_thresh.convertTo(dist_8u, CV_8U);
// Find total markers
vector<vector<Point> > contours;
findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
// Create the marker image for the watershed algorithm
Mat1i markers(dist_thresh.rows, dist_thresh.cols, int(0));
// Background as 1
Mat1i one(markers.rows, markers.cols, int(1));
bitwise_or(one, markers, markers, background);
// Draw the foreground markers (from 2 up)
for (int i = 0; i < int(contours.size()); i++)
drawContours(markers, contours, i, Scalar(i+2), -1);
// Perform the watershed algorithm
Mat3b dbg;
cvtColor(opening, dbg, COLOR_GRAY2BGR);
watershed(dbg, markers);
Mat res;
markers.convertTo(res, CV_8U);
normalize(res, res, 0, 255, NORM_MINMAX);
return 0;
}
Result:
there is very little emgu cv data on watershed.
here is my translation to this problem using c#. i know it is not the right forum but this answer kips popping up so to all the searchers:
//Mat3b src = imread("path_to_image");
//cvtColor(src, gray, COLOR_BGR2GRAY);
Image<Gray, byte> gray = smallImage.Convert<Gray, byte>();
//threshold(gray, thresh, 0, 255, THRESH_BINARY | THRESH_OTSU);
Image<Gray, byte> thresh = gray.ThresholdBinaryInv(new Gray(55), new Gray(255));
// noise removal
Mat kernel = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(3, 3), new Point(-1, -1));
//Mat1b opening;
//morphologyEx(thresh, opening, MORPH_OPEN, kernel, Point(-1, -1), 2);
Image<Gray, byte> opening = thresh.MorphologyEx(MorphOp.Open, kernel, new Point(-1, -1), 2, BorderType.Default, new MCvScalar(255));
//Mat1b kernelb = getStructuringElement(MORPH_RECT, Size(21, 21));
Mat kernel1 = CvInvoke.GetStructuringElement(ElementShape.Rectangle, new Size(3, 3), new Point(-1, -1));
//Mat1b background;
//morphologyEx(thresh, background, MORPH_DILATE, kernelb);
Image<Gray, byte> background = thresh.MorphologyEx(MorphOp.Dilate, kernel, new Point(-1, -1), 2, BorderType.Default, new MCvScalar(255));
background = ~background;
//// Perform the distance transform algorithm
//Mat1f dist_transform;
//distanceTransform(opening, dist_transform, CV_DIST_L2, 5);
Image<Gray, float> dist_transform = new Image<Gray, float>(opening.Width, opening.Height);
CvInvoke.DistanceTransform(opening, dist_transform, null, DistType.L2, 5);
//// Normalize the distance image for range = {0.0, 1.0}
//// so we can visualize and threshold it
//normalize(dist_transform, dist_transform, 0, 1., NORM_MINMAX);
CvInvoke.Normalize(dist_transform, dist_transform, 0, 1.0, NormType.MinMax, DepthType.Default);
//// Threshold to obtain the peaks
//// This will be the markers for the foreground objects
//Mat1f dist_thresh;
//threshold(dist_transform, dist_thresh, 0.5, 1., CV_THRESH_BINARY);
Image<Gray, float> dist_thresh = new Image<Gray, float>(opening.Width, opening.Height);
CvInvoke.Threshold(dist_transform, dist_thresh, 0.5, 1.0, ThresholdType.Binary);
//Mat1b dist_8u;
//dist_thresh.convertTo(dist_8u, CV_8U);
Image<Gray, Byte> dist_8u = dist_thresh.Convert<Gray, Byte>();
//// Find total markers
//vector<vector<Point>> contours;
//findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
VectorOfVectorOfPoint contours = new VectorOfVectorOfPoint();
CvInvoke.FindContours(dist_8u, contours, null, RetrType.External, ChainApproxMethod.ChainApproxSimple);
//// Create the marker image for the watershed algorithm
//Mat1i markers(dist_thresh.rows, dist_thresh.cols, int(0));
Image<Gray, int> markers = new Image<Gray, int>(dist_thresh.Width, dist_thresh.Height, new Gray(0));
//// Background as 1
//Mat1i one(markers.rows, markers.cols, int(1));
//bitwise_or(one, markers, markers, background);
Image<Gray, int> one = new Image<Gray, int>(markers.Cols, markers.Rows, new Gray(1));
CvInvoke.BitwiseOr(one, markers, markers, background);
//// Draw the foreground markers (from 2 up)
for (int i = 0; i < contours.Size; i++)
// drawContours(markers, contours, i, Scalar(i + 2), -1);
CvInvoke.DrawContours(markers, contours, i, new MCvScalar(i + 2));
//// Perform the watershed algorithm
//Mat3b dbg;
//cvtColor(opening, dbg, COLOR_GRAY2BGR);
//watershed(dbg, markers);
Image<Bgr, byte> dbg = new Image<Bgr, byte>(markers.Cols, markers.Rows);
CvInvoke.CvtColor(opening, dbg, ColorConversion.Gray2Bgr);
CvInvoke.Watershed(dbg, markers);
//Mat res;
//markers.convertTo(res, CV_8U);
//normalize(res, res, 0, 255, NORM_MINMAX);
CvInvoke.Normalize(markers, markers, 0, 255, NormType.MinMax);
//return 0;
to find light objects on dark background replace ThresholdBinaryInv with ThresholdBinary