i want to draw the canni result on the original image. How can i do this? i tried like this, but an error comes out
Mat image;
image = imread("C:\\test.jpg",1);
Mat gray, edqes, out;
cvtColor(image, gray, COLOR_BGR2GRAY);
Canny(gray, edqes, 100, 200, 3);
out.copyTo(image,edqes);
cvNamedWindow("original",CV_WINDOW_NORMAL);
cvNamedWindow("binary",CV_WINDOW_NORMAL);
cvNamedWindow("canny",CV_WINDOW_NORMAL);
cvNamedWindow("out",CV_WINDOW_NORMAL);
imshow("original",image);
imshow("binary", gray);
imshow("canny", edqes);
imshow("out", out);
cvWaitKey(0);
cvDestroyAllWindows();
Try:
cvtColor(edqes, edqes, COLOR_GRAY2BGR);
bitwise_or(edqes,image,out);
Related
I need to detect the defects which are more grayish in color.
I have tried removing noise from the image, thresholding the image but due to gray color of the defect, it becomes invisible or only the boundary remains. I don't know how to detect the defects , if I apply dilation it gets mixed with the surrounding circles.
Images with defects:
Images without defect:
My original image with the red pen marked defect is :
I have tried the below code:
//convert to grayscale
Mat gray_img;
cv::cvtColor(r_img, gray_img, COLOR_BGR2GRAY);
Mat dst1, dst2, dst;
cv::blur(gray_img, dst1, Size(3, 3));
cv::blur(gray_img, dst2, Size(7, 7));
cv::subtract(dst1, dst2, dst);
//thresholding
Mat thresh;
adaptiveThreshold(dst, thresh, 255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 5, 5);
Mat inv_thresh;
bitwise_not(thresh, inv_thresh);
My thresholded image is
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 write a simple app in OpenCV that delete black background of an image and save it with white background in JPG. However, it's always saved with black background.
This is my code:
Mat Imgsrc = imread("../temp/temp1.jpg",1) ;
mat dest;
Mat temp, thr;
cvtColor(Imgsrc, temp, COLOR_BGR2GRAY);
threshold(temp,thr, 0, 255, THRESH_BINARY);
Mat rgb[3];
split(Imgsrc,rgb);
Mat rgba[4] = { rgb[0],rgb[1],rgb[2],thr };
merge(rgba,4,dest);
imwrite("../temp/r5.jpg", dest);
You can simply use setTo with a mask to set some pixels to a specific value according to a mask:
Mat src = imread("../temp/temp1.jpg",1) ;
Mat dst;
Mat gray, thr;
cvtColor(src, gray, COLOR_BGR2GRAY);
// Are you sure to use 0 as threshold value?
threshold(gray, thr, 0, 255, THRESH_BINARY);
// Clone src into dst
dst = src.clone();
// Set to white all pixels that are not zero in the mask
dst.setTo(Scalar(255,255,255) /*white*/, thr);
imwrite("../temp/r5.jpg", dst);
Also a few notes:
You can directly load an image as grayscale using: imread(..., IMREAD_GRAYSCALE);
You can avoid to use all those temporary Mats.
Are you sure you want to use 0 as threshold value? Because in this case you can avoid entirely to apply theshold, and set to white all pixels that are 0 in the grayscale image: dst.setTo(Scalar(255,255,255), gray == 0);
This is how I'd do:
// Load the image
Mat src = imread("path/to/img", IMREAD_COLOR);
// Convert to grayscale
Mat gray;
cvtColor(src, gray, COLOR_BGR2GRAY);
// Set to white all pixels that are 0 in the grayscale image
src.setTo(Scalar(255,255,255), gray == 0)
// Save
imwrite("path/to/other/img", src);
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 convert an rgb image to hsv
I used the function inRange() to detect the red color in the image
Now I want to recognize color instead of detection:
- I want to compare the hue value against the range of red color .. if it lies within it .. print red
else if it lies in blue range say blue .. so on
How to compare Mat hue if it is in the range 100 to 180??
int main()
{
Mat image;
image = imread("C:/Users/Maram/Documents/Visual Studio 2013/Projects/firsttrialw310/x64/Debug/carcolor.png", CV_LOAD_IMAGE_COLOR);
if (!image.data)
{
cout << "Could not open or find the image" << std::endl;
return -1;
}
// Create a new matrix to hold the HSV image
Mat HSV;
// convert RGB image to HSV
cvtColor(image, HSV, CV_RGB2HSV);
namedWindow("Display window", CV_WINDOW_AUTOSIZE);
imshow("Display window", image);
namedWindow("Result window", CV_WINDOW_AUTOSIZE);
imshow("Result window", HSV);
vector<Mat> hsv_planes;
split(HSV, hsv_planes);
Mat h = hsv_planes[0]; // H channel
Mat s = hsv_planes[1]; // S channel
Mat v = hsv_planes[2]; // V channel
namedWindow("hue", CV_WINDOW_AUTOSIZE);
imshow("hue", h);
namedWindow("saturation", CV_WINDOW_AUTOSIZE);
imshow("saturation", s);
namedWindow("value", CV_WINDOW_AUTOSIZE);
imshow("value", v);
//// red color range
Scalar hsv_l(100, 150, 150);
Scalar hsv_h(180, 255, 255);
Mat bw;
inRange(HSV, hsv_l, hsv_h, bw);
imshow("Specific Colour", bw);
////
//black cv::Scalar(0, 0, 0, 0), cv::Scalar(180, 255, 40, 0)
//white cv::Scalar(0, 0, 80, 0), cv::Scalar(180, 255, 255, 0)
waitKey(0);
return 0;
}