I am trying to develop a mobile application on Xamarin. Firstly I'm doing it for the android device. I want the Oncamera function to automatically detect the contours and measure the size of the object. As a primary step I am trying to detect the contours in real-time. Read lot of forms and many documents but nothing helped me
public Mat OnCameraFrame(CameraBridgeViewBase.ICvCameraViewFrame inputFrame)
{
Mat input = inputFrame.Rgba();
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat gray = new Mat();
//Mat hierarchy = new Mat();
Imgproc.CvtColor(p0: input, p1: gray, p2: Imgproc.ColorRgb2gray);
Mat blur = new Mat();
Imgproc.GaussianBlur(gray, blur, new Size(7, 7), -2);
Mat thresh = new Mat();
Imgproc.Threshold(blur, thresh, 127, 250, Imgproc.ThreshBinary);
Mat edged = new Mat();
Imgproc.Canny(thresh, thresh, 25, 50);
Imgproc.Dilate(thresh, thresh, new Mat(), new Point(-1, 1), 1);
Mat hierarchy = thresh.Clone();
Imgproc.FindContours(hierarchy, contours, new Mat(),
Imgproc.RetrExternal, Imgproc.ChainApproxNone);
Java.Lang.JavaSystem.Out.Println("contours" + contours);
if (contours != null)
{
Java.Lang.JavaSystem.Out.Println("found contours");
for (int i = 0; i < contours.Count(); i++)
{
Imgproc.DrawContours(input, contours, i, new Scalar(255, 0, 0), -1);
}
}
else
{
Java.Lang.JavaSystem.Out.Println("no contours");
}
return input;
I used the above logic in the code. But my output in the application is displaying normal image without any contours drawn on it. If I return the "thresh", then canny edge detection is perfectly working. But Drawcontours is not showing up anything.
I used Contours.count() because my Xamarin ide is showing error for contours.Size();
I had a similar problem.
in my case I have replaced
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
with
IList<MatOfPoint> contours = new JavaList<MatOfPoint>();
Related
I want to detect octagonal stop sign like the following image with contours but i can't figure out how it is done
Stop Sign:
I already manage to detect triangles
Mat ROI = new Mat();
Mat bgrClone = bgr.clone();
MatOfPoint approxContour = new MatOfPoint();
MatOfPoint2f approxContour2f = new MatOfPoint2f();
List<MatOfPoint> contourDraw = new ArrayList<MatOfPoint>();
for(int i = 0; i < contourList.size(); i++) {
MatOfPoint2f contour2f = new MatOfPoint2f(contourList.get(i).toArray());
double approxDistance = Imgproc.arcLength(contour2f, true) * 0.02;//0.225
Imgproc.approxPolyDP(contour2f, approxContour2f, approxDistance, true);
approxContour2f.convertTo(approxContour, CvType.CV_32S);
if (approxContour.size().height == 3 && (Imgproc.contourArea(contour2f) > 3000) ) { //&& (Imgproc.contourArea(contour2f) > 5000)
contourDraw.add(approxContour);
Imgproc.drawContours(bgr, contourDraw, -1, new Scalar(0,255,0), 1);
Rect cord = Imgproc.boundingRect(approxContour);
Core.rectangle(bgr, new Point(cord.x, cord.y), new Point(cord.x+cord.width, cord.y+cord.height),new Scalar(0,255,0), 1);
ROI = bgrClone.submat(cord.y, cord.y+cord.height, cord.x, cord.x+cord.width);
showResult(ROI);
}
}
I am using Java but any in any language would be appreciated.
You can perform shape detection using contours with OpenCV. I implemented the following in python.
Steps:
Since you are dealing with traffic signs, most of them would be red in color. In the RGB color space the amount of red is the highest in the red channel. But since white color also comprises of red I did not use it. I rather used the blue channel where red content is minimal.
Blue channel image:
Next I applied Otsu threshold to the image above and inverted it.
Otsu threshold and Inversion:
Finally I found contours and marked those contours having roughly 8 arcs around it.
Final Result:
Code:
import numpy as np
import cv2
import os
path = r'C:\Desktop\Stack\contour\shape_detection'
filename = 'stop.png'
img = cv2.imread(os.path.join(path, filename), 1)
cv2.imshow('img1',img[:,:,0])
ret,thresh1 = cv2.threshold(img[:,:,0], 0, 255,cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
cv2.imshow('thresh1', thresh1)
_, contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for cnt in contours:
approx = cv2.approxPolyDP(cnt,0.01*cv2.arcLength(cnt,True),True)
print len(approx)
if len(approx)==8:
print "octagon"
cv2.drawContours(img, [cnt], 0, (0, 255, 0), 6)
cv2.imshow('sign', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
I'm trying to replicate unsharp mask in Gimp with using Java and OpenCv. I use a grayscale image as input and apply unsharp mask, but results are not even close.
I try to implement this C++ code:
Mat blurred; double sigma = 1, threshold = 5, amount = 1;
GaussianBlur(img, blurred, Size(), sigma, sigma);
Mat lowContrastMask = abs(img - blurred) < threshold;
Mat sharpened = img*(1+amount) + blurred*(-amount);
img.copyTo(sharpened, lowContrastMask);
And this is my Java implementation:
double sigma = 1, threshold = 5, amount = 1;
Mat source = Imgcodecs.imread(input.getName());
Mat destination = new Mat();
Imgproc.GaussianBlur(source, destination, new Size(), sigma, sigma);
Mat lowContrastMask = new Mat();
Core.absdiff(source, destination, lowContrastMask);
Imgproc.threshold(lowContrastMask, lowContrastMask, 0, threshold, Imgproc.THRESH_BINARY);
Mat sharpened = new Mat();
Core.multiply(source, new Scalar(0), sharpened, amount+1);
Mat sharpened2 = new Mat();
Core.multiply(destination, new Scalar(0), sharpened2, -amount);
Core.add(sharpened2, sharpened, sharpened);
source.copyTo(sharpened, lowContrastMask);
Alternative Unsharp Masking method:
Mat source = Imgcodecs.imread(input.getName());
Mat destination = new Mat();
Imgproc.GaussianBlur(source, destination, new Size(0,0), 60);
Core.addWeighted(source, 1.5, destination, -1, 0, destination);
So, both methods are working but results are and not good as gimp result. I'm open to any suggestion. I know it look like a bad implementation. I'm a newbie, I appreciate any help.
I am working on a real time text detection and recognition with OpenCV4Android. Recognition part is totally completed. However, I have to ask question about text detection. I' m using the MSER FeatureDetector for detection text.
This is the real time and calling the method part:
public Mat onCameraFrame(CameraBridgeViewBase.CvCameraViewFrame inputFrame) {
carrierMat = inputFrame.gray();
carrierMat = General.MSER(carrierMat);
return carrierMat;
}
And this is the basic MSER implementation:
private static FeatureDetector fd = FeatureDetector.create(FeatureDetector.MSER);
private static MatOfKeyPoint mokp = new MatOfKeyPoint();
private static Mat edges = new Mat();
public static Mat MSER(Mat mat) {
//for mask
Imgproc.Canny(mat, edges, 400, 450);
fd.detect(mat, mokp, edges);
//for drawing keypoints
Features2d.drawKeypoints(mat, mokp, mat);
return mat;
}
It works fine for finding text with edges mask.
I would like to draw a rectangles for clusters like this:
or this:
You can assume that I have the right points.
As you can see, fd.detect() method is returning a MatOfKeyPoint. Hence I' ve tried this method for drawing rectangle:
public static Mat MSER_(Mat mat) {
fd.detect(mat, mokp);
KeyPoint[] refKp = mokp.toArray();
Point[] refPts = new Point[refKp.length];
for (int i = 0; i < refKp.length; i++) {
refPts[i] = refKp[i].pt;
}
MatOfPoint2f refMatPt = new MatOfPoint2f(refPts);
MatOfPoint2f approxCurve = new MatOfPoint2f();
//Processing on mMOP2f1 which is in type MatOfPoint2f
double approxDistance = Imgproc.arcLength(refMatPt, true) * 0.02;
Imgproc.approxPolyDP(refMatPt, approxCurve, approxDistance, true);
//Convert back to MatOfPoint
MatOfPoint points = new MatOfPoint(approxCurve.toArray());
// Get bounding rect
Rect rect = Imgproc.boundingRect(points);
// draw enclosing rectangle (all same color, but you could use variable i to make them unique)
Imgproc.rectangle(mat, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), Detect_Color_, 5);
//Features2d.drawKeypoints(mat, mokp, mat);
return mat;
}
But when I was trying to Imgproc.arcLength() method, it suddenly stopped. I gave a random approxDistance value for Imgproc.approxPolyDP() method like 0.1, it doesn' t work really efficiently.
So how can I draw rectangle for detected text?
I tested your code and had exactly the same problem.
For now I still can't find the problem within.
But I found a project using both "MSER" and "Morphological".
you can find it here .
The project have very simple structure and the author put the
text detection in "onCameraFrame" method just like you.
I implemented the method from that project and it worked,
but the result was still not very good.
If you seek better text detection tool, here's two of them.
Stroke Width Transform(SWT):
A whole new method for finding text area. It's fast and efficient. however it is only available in c++ or python. you can find some example here.
Class-specific Extremal Regions using class ERFilter:An advanced version of the MSER. Unfortunately, it is only available in OpenCV 3.0.0-dev. You can't use it in current version of OpenCV4Android. The document is here.
To be honest I am new in this area(2 months), but I hope these information can help you finish your project.
(update:2015/9/13)
I've translated a c++ method from a post.
It works far better than the first github project I mentioned.
Here is the code:
public void apply(Mat src, Mat dst) {
if (dst != src) {
src.copyTo(dst);
}
Mat img_gray,img_sobel, img_threshold, element;
img_gray=new Mat();
Imgproc.cvtColor(src, img_gray, Imgproc.COLOR_RGB2GRAY);
img_sobel=new Mat();
Imgproc.Sobel(img_gray, img_sobel, CvType.CV_8U, 1, 0, 3, 1, 0,Core.BORDER_DEFAULT);
img_threshold=new Mat();
Imgproc.threshold(img_sobel, img_threshold, 0, 255, Imgproc.THRESH_OTSU+Imgproc.THRESH_BINARY);
element=new Mat();
element = Imgproc.getStructuringElement(Imgproc.MORPH_RECT, new Size(17, 3) );
Imgproc.morphologyEx(img_threshold, img_threshold, Imgproc.MORPH_CLOSE, element);
//Does the trick
List<MatOfPoint> contours=new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
Imgproc.findContours(img_threshold, contours, hierarchy, 0, 1);
List<MatOfPoint> contours_poly=new ArrayList<MatOfPoint>(contours.size());
contours_poly.addAll(contours);
MatOfPoint2f mMOP2f1,mMOP2f2;
mMOP2f1=new MatOfPoint2f();
mMOP2f2=new MatOfPoint2f();
for( int i = 0; i < contours.size(); i++ )
if (contours.get(i).toList().size()>100)
{
contours.get(i).convertTo(mMOP2f1, CvType.CV_32FC2);
Imgproc.approxPolyDP(mMOP2f1,mMOP2f2, 3, true );
mMOP2f2.convertTo(contours_poly.get(i), CvType.CV_32S);
Rect appRect=Imgproc.boundingRect(contours_poly.get(i));
if (appRect.width>appRect.height)
{
Imgproc.rectangle(dst, new Point(appRect.x,appRect.y) ,new Point(appRect.x+appRect.width,appRect.y+appRect.height), new Scalar(255,0,0));
}
}
}
I'm implementing using Java the OpenCV tutorial for finding an object in a scene using homography http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography.html#feature-homography
Below is my implementation, where img1 is the scene and img2 is the object
FeatureDetector detector = FeatureDetector.create(FeatureDetector.ORB);
DescriptorExtractor descriptor = DescriptorExtractor.create(DescriptorExtractor.ORB);
DescriptorMatcher matcher = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE);
//set up img1 (scene)
Mat descriptors1 = new Mat();
MatOfKeyPoint keypoints1 = new MatOfKeyPoint();
//calculate descriptor for img1
detector.detect(img1, keypoints1);
descriptor.compute(img1, keypoints1, descriptors1);
//set up img2 (template)
Mat descriptors2 = new Mat();
MatOfKeyPoint keypoints2 = new MatOfKeyPoint();
//calculate descriptor for img2
detector.detect(img2, keypoints2);
descriptor.compute(img2, keypoints2, descriptors2);
//match 2 images' descriptors
MatOfDMatch matches = new MatOfDMatch();
matcher.match(descriptors1, descriptors2,matches);
//calculate max and min distances between keypoints
double max_dist=0;double min_dist=99;
List<DMatch> matchesList = matches.toList();
for(int i=0;i<descriptors1.rows();i++)
{
double dist = matchesList.get(i).distance;
if (dist<min_dist) min_dist = dist;
if (dist>max_dist) max_dist = dist;
}
//set up good matches, add matches if close enough
LinkedList<DMatch> good_matches = new LinkedList<DMatch>();
MatOfDMatch gm = new MatOfDMatch();
for (int i=0;i<descriptors2.rows();i++)
{
if(matchesList.get(i).distance<3*min_dist)
{
good_matches.addLast(matchesList.get(i));
}
}
gm.fromList(good_matches);
//put keypoints mats into lists
List<KeyPoint> keypoints1_List = keypoints1.toList();
List<KeyPoint> keypoints2_List = keypoints2.toList();
//put keypoints into point2f mats so calib3d can use them to find homography
LinkedList<Point> objList = new LinkedList<Point>();
LinkedList<Point> sceneList = new LinkedList<Point>();
for(int i=0;i<good_matches.size();i++)
{
objList.addLast(keypoints2_List.get(good_matches.get(i).queryIdx).pt);
sceneList.addLast(keypoints1_List.get(good_matches.get(i).trainIdx).pt);
}
MatOfPoint2f obj = new MatOfPoint2f();
MatOfPoint2f scene = new MatOfPoint2f();
obj.fromList(objList);
scene.fromList(sceneList);
//output image
Mat outputImg = new Mat();
MatOfByte drawnMatches = new MatOfByte();
Features2d.drawMatches(img1, keypoints1, img2, keypoints2, gm, outputImg, Scalar.all(-1), Scalar.all(-1), drawnMatches,Features2d.NOT_DRAW_SINGLE_POINTS);
//run homography on object and scene points
Mat H = Calib3d.findHomography(obj, scene,Calib3d.RANSAC, 5);
Mat tmp_corners = new Mat(4,1,CvType.CV_32FC2);
Mat scene_corners = new Mat(4,1,CvType.CV_32FC2);
//get corners from object
tmp_corners.put(0, 0, new double[] {0,0});
tmp_corners.put(1, 0, new double[] {img2.cols(),0});
tmp_corners.put(2, 0, new double[] {img2.cols(),img2.rows()});
tmp_corners.put(3, 0, new double[] {0,img2.rows()});
Core.perspectiveTransform(tmp_corners,scene_corners, H);
Core.line(outputImg, new Point(scene_corners.get(0,0)), new Point(scene_corners.get(1,0)), new Scalar(0, 255, 0),4);
Core.line(outputImg, new Point(scene_corners.get(1,0)), new Point(scene_corners.get(2,0)), new Scalar(0, 255, 0),4);
Core.line(outputImg, new Point(scene_corners.get(2,0)), new Point(scene_corners.get(3,0)), new Scalar(0, 255, 0),4);
Core.line(outputImg, new Point(scene_corners.get(3,0)), new Point(scene_corners.get(0,0)), new Scalar(0, 255, 0),4);
The program is able to calculate and display feature points from both images. However, the scene_corners returned are 4 points in a close cluster (small green blob)
where they are supposed to represent the 4 corners of the perspective projection of the object onto the scene. I checked double checked to make sure my program is as close to the c++ implementation as possible. What might be causing this?
I checked the homography matrix and it seems the corner coordinates are skewed by 2 very big results from the matrix. Is the homography matrix incorrectly calculated?
I'd appreciate any input, thanks.
Update:
I played about with the filter threshold for good matches and found that 2.75*min_dist seems to work well with this set of images. I can now get good matches with zero outliers. However, the bounding box is still wrong. http://i.imgur.com/fuXeOqL.png
How do I know what value of threshold to use for best matches and how does the homography relate to them? Why was 3*min_dist used in the example?
I managed to solve the problem and use homography correctly while investigating index out of bounds errors. It turns out when I added my good matches to my object and scene lists, I swapped round the query and train indices
objList.addLast(keypoints2_List.get(good_matches.get(i).queryIdx).pt);
sceneList.addLast(keypoints1_List.get(good_matches.get(i).trainIdx).pt);
According to this question OpenCV drawMatches -- queryIdx and trainIdx , since I called
matcher.match(descriptors1, descriptors2,matches);
with descriptor1 first then descriptor2, the correct indices should be:
objList.addLast(keypoints2_List.get(good_matches.get(i).trainIdx).pt);
sceneList.addLast(keypoints1_List.get(good_matches.get(i).queryIdx).pt);
where queryIdx refers to keypoints1_List and trainIdx refers to keypoints2_List.
Here is an example result:
http://i.imgur.com/LZNBjY2.png
Currently I'm also implementing a 2D homography in java and I also found the OpenCV tutorial then your question.
I don't think it'll enhance your results but in the OpenCV tutorial when they compute the min and max distance, they loop with descriptors_object.rows and in your code you do with descriptors1.rows() which is the scene descriptor and not the object descriptor.
Edit: Just also noticed the same with the matcher. For you:
img1/descriptor1 -> Scene
img2/descriptor2 -> the object to find
In the tutorial:
matcher.match( descriptors_object, descriptors_scene, matches );
But in your code:
matcher.match(descriptors1, descriptors2,matches);
And Javadoc:
void org.opencv.features2d.DescriptorMatcher.match(Mat queryDescriptors, Mat trainDescriptors, MatOfDMatch matches)
On Android by using OpenCV I try to draw contours I got from a ROI but I am not able to draw them at the correct place at the image.
Mat resultImage= ...; // source
Rect roi = new Rect(x, y, widht, height);
Mat mat = new Mat(resultImage, roi);
Imgproc.adaptiveThreshold(mat, mat, 255, Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY_INV, 15, 4);
mat.convertTo(mat, CvType.CV_32SC1);
List<MatOfPoint> contours = new ArrayList<MatOfPoint>();
Mat hierarchy = new Mat();
Imgproc.findContours(mat, contours, hierarchy, Imgproc.RETR_FLOODFILL, Imgproc.CHAIN_APPROX_SIMPLE);
mat.release();
Imgproc.drawContours(resultImage, contours, -1, new Scalar(255,0,0), 1);
It draws the contours on the left top corner. But I need it at "x","y" where the ROI is from.
Is there a way to add a offset to any contour?
I got it:
Imgproc.drawContours(resultImage, contours, -1, new Scalar(255,0,0), 1, 8, hierarchy, 1, new Point(x,y));
It is able to add the offset by using the "Point-Class"