I've this image:
EDIT
Sorry but I had to remove the images!
I need to extract the contour of the non-black picture, so I used findcontour with the CV_RETR_EXTERNAL parameter, but I obtain this:
Here's the code:
static Mat canny_output, grey,draw;
vector<vector<Point>> contours;
cvtColor(final_img, grey, CV_BGR2GRAY);
Canny(grey, canny_output, 100, 200);
findContours(canny_output, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
draw = Mat::zeros(canny_output.size(), CV_8UC3);
for (size_t i = 0; i < contours.size(); i++)
{
drawContours(draw, contours, i, Scalar(255, 0, 0));
}
how can I resolve?
Simply add a binarization with minimal threshold, and remove Canny:
cvtColor(final_img, grey, CV_BGR2GRAY);
//Threshold=1: very low value, anyway the rest of the image is pure black
threshold(grey, binary, 1, 255, CV_THRESH_BINARY);
findContours(binary, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
Related
I've read this post, but even after using cv::threshold to create a really binarry image, I still get ~500 contours. What am I doing wrong?
Shouldn't cv::findContours return only 13 contours since there are clear 13 blobs?
Mat img = imread("img.jpg", CV_LOAD_IMAGE_GRAYSCALE);
Mat img_thresh;
threshold(img, img_thresh, 0, 255, CV_THRESH_BINARY);
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
cv::findContours(img_thresh, contours, hierarchy, RetrievalModes::RETR_TREE, ContourApproximationModes::CHAIN_APPROX_SIMPLE);
RNG rng(12345);
Mat drawing = Mat::zeros(img_thresh.size(), CV_8UC3);
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());
}
imshow("drawing", drawing);
waitKey();
UPDATE1
Using cv::RETR_EXTERNAL instead of cv::RETR_TREE, but still return much more contours than should be.
If you check your binary image you will see there are a lot of independent contours:
So you first need to clean up them by eroding and dilating as below code:
And you will get this result:
Which is cleaner than the original.
It is all the code:
cv::namedWindow("result", cv::WINDOW_FREERATIO);
cv::Mat img = cv::imread(R"(rUYLL.png)");
// to gray
cv::Mat gray;
cv::cvtColor(img, gray, cv::COLOR_BGR2GRAY);
cv::threshold(gray, gray, 0, 255, cv::THRESH_BINARY);
cv::erode(gray, gray, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3)));
cv::dilate(gray, gray, cv::getStructuringElement(cv::MORPH_RECT, cv::Size(3, 3)));
std::vector<std::vector<cv::Point> > contours;
cv::findContours(gray, contours, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
cv::drawContours(img, contours, -1, cv::Scalar(0, 255, 0), 2, 8);
cv::imshow("result", img);
cv::waitKey();
And it is the output:
Hope it helps!
And one simplest way which you can also consider if it works for you, just increase the lower threshold from 0 to 80, and DONE
cv::threshold(gray, gray, 80, 255, cv::THRESH_BINARY);
JUST PLAY WITH THRESHOLD and check the result.
The same output just with changing the threshold value:
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]);
}
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
}
}
I have a video that I want to remove some specific contours on, I capture the video and then draw all the contours;
//Prepare the image for findContours
cv::cvtColor(frame, frame, CV_BGR2GRAY);
cv::threshold(frame, frame, 128, 255, CV_THRESH_BINARY);
//Find the contours. Use the contourOutput Mat so the original image doesn't get overwritten
std::vector<std::vector<cv::Point> > contours;
cv::Mat contourOutput = frame.clone();
cv::findContours( contourOutput, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE );
//Draw the contours
cv::Mat contourImage(frame.size(), CV_8UC3, cv::Scalar(0,0,0));
cv::Scalar colors[3];
colors[0] = cv::Scalar(255, 0, 0);
colors[1] = cv::Scalar(0, 255, 0);
colors[2] = cv::Scalar(0, 0, 255);
for (size_t idx = 0; idx < contours.size(); idx++) {
cv::drawContours(contourImage, contours, idx, colors[idx % 3]);
}
The result looks like this:
Contours
So what I want to do now is to remove or cover objects like the trees and the tall house, basically contours that have a more complex shape than a road sign etc.
A desired result would look like this:
Removed/covered contours
It doesn't have to be exactly like on the picture as long as I can get something I can play with.
I want to combine multiple threshold for detecting different type of red colour. I tried to make 4 type of thresholding and then I combine the result of 4 image into 1. but the result is always pitch black. is there any other way to do it? I guess the way I'm combining the 2 image into 1 is not correct using the addWeighted
Mat img = imread (nameImg);
cvtColor(img , hsv, CV_BGR2HSV);
Mat bw,bw2,bw3,bw4;
inRange(hsv, Scalar(0,28,192), Scalar(4,67,219),bw); // detecting acne 1
inRange(hsv, Scalar(0,40,152), Scalar(8,85,243),bw2); // acne 2
inRange(hsv, Scalar(0,85,202), Scalar(6,146,247),bw3); // acne 3
inRange(hsv, Scalar(156,93,176), Scalar(82,130,255),bw4); // acne 4
vector<vector<Point> > contours;
vector<vector<Point> > contours2;
vector<vector<Point> > contours3;
vector<vector<Point> > contours4;
findContours(bw.clone(), contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
findContours(bw2.clone(), contours2, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
findContours(bw3.clone(), contours3, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
findContours(bw4.clone(), contours4, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
Mat dst = Mat::zeros(img.size(), img.type());
drawContours(dst, contours, -1, Scalar::all(255), CV_FILLED);
Mat dst2 = Mat::zeros(img.size(), img.type());
drawContours(dst2, contours2, -1, Scalar::all(255), CV_FILLED);
Mat dst3 = Mat::zeros(img.size(), img.type());
drawContours(dst3, contours4, -1, Scalar::all(255), CV_FILLED);
Mat dst4 = Mat::zeros(img.size(), img.type());
drawContours(dst4, contours4, -1, Scalar::all(255), CV_FILLED);
Mat dst5, dst6;
imshow("dst",dst);
imshow("dst2",dst2);
imshow("dst3",dst3);
imshow("dst4",dst4);
addWeighted(dst, 0.5, dst2, 0.5, 0,dst5);//combine acne 1 with 2
addWeighted(dst4, 0.5, dst3, 0.5, 0,dst6); //combine acne 3 with 4
addWeighted(dst5, 0.5, dst6, 0.5, 0,out);
imshow("result",out); //the result always black