I have the following Python code:
ret,thresh = cv2.threshold(gray,5,255,cv2.THRESH_TOZERO)
kernel1 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11))
kernel2 = np.ones((3,3),np.uint8)
erosion = cv2.erode(thresh,kernel2,iterations = 1)
dilation = cv2.dilate(erosion,kernel1,iterations = 7)
I'm trying to convert it to Java. This is my current version:
double thresh = Imgproc.threshold(gray, gray, 5, 255, Imgproc.THRESH_TOZERO);
Mat kernel1 = Imgproc.getStructuringElement(Imgproc.MORPH_ELLIPSE, new Size(11, 11));
Mat kernel2 = Mat.ones(3, 3, CvType.CV_8U);
Mat erosion = new Mat();
Imgproc.erode(gray, erosion, kernel2);
Mat dilation = new Mat();
Imgproc.dilate(erosion, dilation, kernel1);
Right now I'm unable to find a place where thresh parameter should be applied and also I'm not using the iterations parameter for Imgproc.erode and Imgproc.dilate methods because the method signature in this case also requires additional Point anchor parameter which I do not have right now.
How to properly convert this code to Java?
UPDATED
I do the following translation:
Mat erosion = new Mat();
Imgproc.erode(gray, erosion, kernel2, new Point(), 1);
Mat dilation = new Mat();
Imgproc.dilate(erosion, dilation, kernel1, new Point(), 7);
and looks like it is working as expected but I'm not sure about new Point() Is it a correct translation?
Here are the dilate declarations in C++/Python/Java:
C++: void dilate(InputArray src, OutputArray dst, InputArray kernel,
Point anchor=Point(-1,-1),
int iterations=1,
int borderType=BORDER_CONSTANT,
const Scalar& borderValue=morphologyDefaultBorderValue() )
Python: cv2.dilate(src, kernel[, dst
[, anchor[, iterations[, borderType[, borderValue]]]]]) → dst
Java: static void dilate(Mat src, Mat dst, Mat kernel, Point anchor, int iterations)
Because there is no default value for anchor if you want to use the param iterations. So pass new Point(-1,-1) is a better choice.
Imgproc.dilate(src, dst, kernel, new Point(-1,-1), iterations);
Related
I'm new to JavaCv. My task is to find symbols on an image and generate pictures with single symbol on each other.
Firstly, I have this picture:
Then I do thresholding and get this:
I'm trying to use cvFindContours an then draw a rectangle around each symbol, my code:
String filename = "captcha.jpg";
IplImage firstImage=cvLoadImage(filename);
Mat src = imread(filename, CV_LOAD_IMAGE_GRAYSCALE);
Mat dst = new Mat();
threshold(src, dst, 200, 255, 0);
imwrite("out.jpg", dst);
IplImage iplImage=cvLoadImage("out.jpg",CV_8UC1);
CvMemStorage memStorage=CvMemStorage.create();
CvSeq contours=new CvSeq();
cvFindContours(iplImage,memStorage,contours,Loader.sizeof(CvContour.class),CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE,cvPoint(0,0));
CvSeq ptr;
CvRect bb;
for (ptr=contours;ptr!=null;ptr=ptr.h_next()){
bb=cvBoundingRect(ptr);
cvRectangle(firstImage , cvPoint( bb.x(), bb.y() ),
cvPoint( bb.x() + bb.width(), bb.y() + bb.height()),
CvScalar.BLACK, 2, 0, 0 );
}
cvSaveImage("result.jpg",firstImage);
}
I want to get output like this:, but really I get this:
Please, need your help.
You're using "out.jpg" image for findContour().
When you save dst Mat into "out.jpg", JPEG format automatically quantizes your original pixel data and creates noises to your image.
Save dst to "out.png" rather than "out.jpg", or use dst Mat directly into findContour().
Source Code ADDED: C++ version
string filename = "captcha.jpg";
Mat src = imread(filename);
Mat gray;
cvtColor(src, gray, CV_BGR2GRAY);
Mat thres;
threshold(gray, thres, 200, 255, 0);
vector<vector<Point>> contours;
vector<Vec4i> hierarchy;
findContours(thres.clone(), contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE);
Mat firstImage = src.clone();
for(int i=0; i< contours.sizes(); i++)
{
Rect r = boundingRect(contours[i]);
rectangle(firstImage, r, CV_RGB(255, 0, 0), 2);
}
imwrite("result.png", firstImage);
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)
I am trying to do adaptiveThresholding on an image but it gives me this error:
OpenCV Error: Assertion failed (src.type() == CV_8UC1) in adaptiveThreshold
I can't seem to understand why, here is my code:
Mat source = Highgui.imread("camera.jpg",
Highgui.CV_LOAD_IMAGE_COLOR);
Mat destination = new Mat(source.rows(),source.cols(),source.type());
Imgproc.cvtColor(source, destination, Imgproc.COLOR_RGB2GRAY);
Highgui.imwrite("grayscale.jpg", destination);
Mat source2 = Highgui.imread("grayscale.jpg",
Highgui.CV_LOAD_IMAGE_COLOR);
Mat destination2 = new Mat(source.rows(),source.cols(),source.type());
Imgproc.adaptiveThreshold(source2, destination2, 255,
Imgproc.ADAPTIVE_THRESH_MEAN_C, Imgproc.THRESH_BINARY_INV, 15, 4);
For adaptiveThreshold source should be 8-bit single-channel image, but you are loading source2 as colour,
So, Change the line
Mat source2 = Highgui.imread("grayscale.jpg", Highgui.CV_LOAD_IMAGE_COLOR);
to
Mat source2 = Highgui.imread("grayscale.jpg", Highgui.CV_LOAD_IMAGE_GRAYSCALE);
Also why to save and load destination image before adaptiveThreshold, pass it directly to adaptiveThreshold()