Find and draw largest rect OpenCV - c++

I need to find the largest contour/rect on this image which should be the card.
I try to use the following code but I get no drawing:
int largest_area=0;
int largest_contour_index=0;
cv::Rect bounding_rect;
Mat thr(src.rows,src.cols,CV_8UC1);
Mat dst(src.rows,src.cols,CV_8UC1,Scalar::all(0));
cvtColor(src,thr,CV_BGR2GRAY); //Convert to gray
threshold(thr, thr,25, 255,THRESH_BINARY); //Threshold the gray
vector<vector<cv::Point>> contours; // Vector for storing contour
vector<Vec4i> hierarchy;
findContours( thr, contours, hierarchy,CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE ); // Find the contours in the image
for( int i = 0; i< contours.size(); i++ ) // iterate through each contour.
{
double a=contourArea( contours[i],false); // Find the area of contour
if(a>largest_area){
largest_area=a;
largest_contour_index=i; //Store the index of largest contour
bounding_rect=boundingRect(contours[i]); // Find the bounding rectangle for biggest contour
}
}
Scalar color( 255,255,255);
drawContours( dst, contours,largest_contour_index, color, CV_FILLED, 8, hierarchy ); // Draw the largest contour using previously stored index.
rectangle(src, bounding_rect, Scalar(0,255,0),1, 8,0);
Could someone provide me with an example using this image to find the largest rect?

You can try yourself by increasing the threshold.
Here You are finding biggest contour on thresholded image, so display thr just after threshold() using imshow() and see what going on , and how it's look like.
See the result by increasing the threshold to little higher value.
threshold(thr, thr,100, 255,THRESH_BINARY); //Threshold the gray
Threshold image
Bounding rect for biggest contour

Related

crop triangle from blackbackground

I'm new to image processing and development.I have a triangle in black background. I want to save that triangle as a Mat object without black pixels[0]. In order to do I tried as below.
Set threshold
find contours
identify contour[0] as trangle // has 2 contours one is triangle other one is backpixels.
save the contour points
crop the image.
My code please find below.
Mat finalImage = imread("test.png, CV_LOAD_IMAGE_GRAYSCALE);
img.copyTo(finalImage, mask);
Mat canny_output;
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
int thresh = 100;
int max_thresh = 255;
RNG rng(12345);
/// Detect edges using canny
Canny(finalImage, canny_output, thresh, thresh * 2, 3);
/// Find contours
findContours(canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0)); //find this method
/// Draw contours
Mat drawing = Mat::zeros(canny_output.size(), CV_8UC1);
for (int i = 0; i< contours.size(); i++)
{
Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
drawContours(drawing, contours, i, color, 2, 8, hierarchy, 0, Point()); // find this method.
}
I have points of contour but by using points of contours i have no idea how to crop only the trangle in input image.
You can get the bounding Rect of the various contours at the same time you are re drawing the contours. So in Your for loop where you are iterating the contours, you may use cv::boundingRect() to get the bounding Rect of the respective contour as:
/// Draw contours
Mat drawing = Mat::zeros(canny_output.size(), CV_8UC1);
for (int i = 0; i< contours.size(); i++)
{
Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
drawContours(drawing, contours, i, color, 2, 8, hierarchy, 0, Point()); // find this method.
cv::Rect boundingRect = cv::boundingRect(contours[i]);
}

how to detect the pallet in fork lift using find contours

Im trying to detect the pallet in forklift. but find contours cant detect the rectangle in a correct way.
how can I detect the large pallet.
I have tried hough transform but it fails of detecting the forklift rectangle, so I'm using findcontours instead.
pallet
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
int main()
{
cv::Mat input = cv::imread("pallet.jpg");
// convert to grayscale (you could load as grayscale instead)
cv::Mat gray;
cv::cvtColor(input,gray, CV_BGR2GRAY);
// compute mask (you could use a simple threshold if the image is always as good as the one you provided)
cv::Mat mask;
cv::threshold(gray, mask, 0, 255, CV_THRESH_BINARY_INV | CV_THRESH_OTSU);
// find contours (if always so easy to segment as your image, you could just add the black/rect pixels to a vector)
std::vector<std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours(mask,contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
/// Draw contours and find biggest contour (if there are other contours in the image, we assume the biggest one is the desired rect)
// drawing here is only for demonstration!
int biggestContourIdx = -1;
float biggestContourArea = 0;
cv::Mat drawing = cv::Mat::zeros( mask.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
{
cv::Scalar color = cv::Scalar(0, 100, 0);
drawContours( drawing, contours, i, color, 1, 8, hierarchy, 0, cv::Point() );
float ctArea= cv::contourArea(contours[i]);
if(ctArea > biggestContourArea)
{
biggestContourArea = ctArea;
biggestContourIdx = i;
}
}
// if no contour found
if(biggestContourIdx < 0)
{
std::cout << "no contour found" << std::endl;
return 1;
}
// compute the rotated bounding rect of the biggest contour! (this is the part that does what you want/need)
cv::RotatedRect boundingBox = cv::minAreaRect(contours[biggestContourIdx]);
// one thing to remark: this will compute the OUTER boundary box, so maybe you have to erode/dilate if you want something between the ragged lines
// draw the rotated rect
cv::Point2f corners[4];
boundingBox.points(corners);
cv::line(drawing, corners[0], corners[1], cv::Scalar(255,255,255));
cv::line(drawing, corners[1], corners[2], cv::Scalar(255,255,255));
cv::line(drawing, corners[2], corners[3], cv::Scalar(255,255,255));
cv::line(drawing, corners[3], corners[0], cv::Scalar(255,255,255));
// display
cv::imshow("input", input);
cv::imshow("drawing", drawing);
cv::waitKey(0);
cv::imwrite("rotatedRect.png",drawing);
return 0;
}

opencv crop a portion of image within contour

I've just started learning OpenCv. i wanted to crop a portion of an image which is a text surrounded by the red circle. can you guys help me to find the solution like what are all the methods i should follow to crop it. I've tried few things and got the red circle cropped and stored it in a mat.
while(1)
{
capture>>img0;
imshow("original", img0);
imwrite("original.jpg", img0);
cv::inRange(img0,cv::Scalar(0,0,100),cv::Scalar(76,85,255),img1);
imshow("threshold.jpg", img1);
imwrite("threshold.jpg", img1);
// find the contours
vector< vector<Point> > contours;
findContours(img1, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
Mat mask = Mat::zeros(img1.rows, img1.cols, CV_8UC1);
drawContours(mask, contours, -1, Scalar(255), CV_FILLED);
Mat crop(img0.rows, img0.cols, CV_8UC3);
crop.setTo(Scalar(255,255,255));
img0.copyTo(crop, mask);
normalize(mask.clone(), mask, 0.0, 255.0, CV_MINMAX, CV_8UC3);
imshow("mask", mask);
imshow("cropped", crop);
imwrite("mask.jpg", mask);
imwrite("cropped.jpg", crop);
if(waitKey(30)=='27')
{
break;
}
}
return 0;`[original image[cropped image][1]`
From this image i wanted to crop a text alone. do help me to find the solution by sharing me the methods or steps to follow.
Thanks in advance
If you wish to extract the text alone, you can try this:-
drawContours(mask, contours, -1, Scalar(255), CV_FILLED);
vector<Rect> boundRect( contours.size() );
for(int i=0;i<contours.size();i++)
{
boundRect[i] = boundingRect(contours[i]);//enclose in Rect
Mat ROI,ROI_txt;
if(boundRect[i].width>30 && boundRect[i].height>30)//ignore noise rects
{
ROI=img0(boundRect[i]);//extract Red circle on ROI
inRange(ROI,Scalar(0,0,0),cv::Scalar(50,50,50),ROI_txt);
//black colour threshold to extract black text
}
}

Converting contours from vector of cv::Point in cv::Mat

Suppose my objective is to extract contours from an initial image. I performed this operations using OpenCV:
cv::Mat gray, edges;
cv::cvtColor(image, gray, CV_BGRA2GRAY);
cv::Canny(gray, edges, 100, 300, 3);
std::vector<std::vector<cv::Point>> contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours(edges, contours, hierarchy, cv::RETR_TREE, cv::CHAIN_APPROX_SIMPLE, cv::Point(0, 0));
Now the contour is contained in an array of Points. I want to rebuild a cv::Mat structure having the same dimensions as the initial image, so as to emphasize the contour by superimposing it to the image.
In particular, I am not interested in drawing immediately the contour. I will perform the following operations:
extract the contour
dilate the contour
superimpose the contour on the image (as you do in edge sharpening)
Thus, the contour has to be a matrix of the same size of the input image.
How can I do that?
Thanks in advance.
You can re-built a Matrix
Mat image_contours_grayscale = Mat::zeros(gray.size(),gray.type());
Scalar color(255);
drawContours( image_contours_grayscale, contours,-1,color,1);
I'm not sure if drawContours works with grayscale image, but if it doesn't, you can try this
Mat image_contours_color = Mat::zeros(image.size(),image.type());
Scalar color(255,255,255);
drawContours( image_contours_color, contours,-1,color,1);
Mat image_contours_grayscale;
cv::cvtColor(image_contours_color, image_contours_grayscale, CV_BGRA2GRAY);
image_contours_grayscale should be a grayscale image, all black with the contours in white.
Let me know if this works, I didn't have the occasion to test this solution !

Calculating the area of Bounding Box

Hello StackOverflowers
I have created an application that Segments an image on the basis of a predefined color using inRange function. I then draw bounding box around the detected object.
My question here is how do I determine region properties such as: area, size, height and with, center point.
Here i placed a screen dump example.....
How should i approach to retrieve region properties of these bounding boxes or any other bounding boxes that get drown.......?
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours(mBlur, contours, hierarchy, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
/// Approximate contours to polygons + get bounding rects and circles
vector<vector<Point> > contours_poly( contours.size() );
vector<Rect> boundRect( contours.size() );
vector<Point2f>center( contours.size() );
vector<float>radius( contours.size() );
for( int i = 0; i < contours.size(); i++ )
{ approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );
boundRect[i] = boundingRect( Mat(contours_poly[i]) );
}
/// Draw polygonal contour + bonding rects
Mat drawing = Mat::zeros( range_out.size(), CV_8UC3 );
for( int i = 0; i< contours.size(); i++ )
{
Scalar color = Scalar(255,0,255);
drawContours( drawing, contours_poly, i, color, 1, 8, vector<Vec4i>(), 0, Point() );
rectangle( drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0 );
}
Regards
You can get the area by using the built in OpenCV function. There are other functions there too to get everything you need.
Just iterate over the 2D coordinates of the segmented shape (the thin pink line in your pictures, you can found this just checking which pixels are not black and looking into its coordinates) and store maximum and minimum X and Y found. Then, width of is maxX - minX and height is maxY - minY
void visualizeSegments(Mat& img, Mat& dst)
{
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(img, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
dst=Mat::zeros(img.size(), CV_8UC3);
for(int i = 0; i < contours.size(); i++)
{
//Moments mu = moments(contours[i], true );
//Point2f centroid(mu.m10/mu.m00,mu.m01/mu.m00);
//double area = fabs(contourArea(Mat(contours[i])));
//vector<Point> contours_poly;
//approxPolyDP(Mat(contours[i]), contours_poly, 3, true);
//Rect boundRect = boundingRect(Mat(contours_poly));
drawContours(dst, contours, i, Scalar(255,0,0), -1, 8, hierarchy);
}
}
As stated befor there are a set of usefull functions in OpenCV
1. double contourArea(InputArray contour, bool oriented=false ) : to comute the area
2. double arcLength(InputArray curve, bool closed) : to compute the perimeter
3. Moments moments(InputArray array, bool binaryImage=false ) : to compute the center of gravity
4. void HuMoments(const Moments& m, OutputArray hu) : if you want additional properties that is usefull for classification