I recently started developing an app on Android studio and I just finished writing the code. The accuracy which I get is more than satisfactory but the time taken by the device is a lot. {}I followed some tutorials on how to monitor the performance on android studio and I saw that one small part of my code is taking 6 seconds, which half the time my app takes to display the entire result. I have seen a lot of posts Java OpenCV - extracting good matches from knnMatch , OpenCV filtering ORB matches on OpenCV/JavaCV but haven't come across anyone asking for this problem. The OpenCV link http://docs.opencv.org/2.4/doc/tutorials/features2d/feature_homography/feature_homography.html does provide a good tutorial but the RANSAC function in OpenCV takes different arguments for keypoints as compared to C++.
Here is my code
public Mat ORB_detection (Mat Scene_image, Mat Object_image){
/*This function is used to find the reference card in the captured image with the help of
* the reference card saved in the application
* Inputs - Captured image (Scene_image), Reference Image (Object_image)*/
FeatureDetector orb = FeatureDetector.create(FeatureDetector.DYNAMIC_ORB);
/*1.a Keypoint Detection for Scene Image*/
//convert input to grayscale
channels = new ArrayList<Mat>(3);
Core.split(Scene_image, channels);
Scene_image = channels.get(0);
//Sharpen the image
Scene_image = unsharpMask(Scene_image);
MatOfKeyPoint keypoint_scene = new MatOfKeyPoint();
//Convert image to eight bit, unsigned char
Scene_image.convertTo(Scene_image, CvType.CV_8UC1);
orb.detect(Scene_image, keypoint_scene);
channels.clear();
/*1.b Keypoint Detection for Object image*/
//convert input to grayscale
Core.split(Object_image,channels);
Object_image = channels.get(0);
channels.clear();
MatOfKeyPoint keypoint_object = new MatOfKeyPoint();
Object_image.convertTo(Object_image, CvType.CV_8UC1);
orb.detect(Object_image, keypoint_object);
//2. Calculate the descriptors/feature vectors
//Initialize orb descriptor extractor
DescriptorExtractor orb_descriptor = DescriptorExtractor.create(DescriptorExtractor.ORB);
Mat Obj_descriptor = new Mat();
Mat Scene_descriptor = new Mat();
orb_descriptor.compute(Object_image, keypoint_object, Obj_descriptor);
orb_descriptor.compute(Scene_image, keypoint_scene, Scene_descriptor);
//3. Matching the descriptors using Brute-Force
DescriptorMatcher brt_frc = DescriptorMatcher.create(DescriptorMatcher.BRUTEFORCE_HAMMING);
MatOfDMatch matches = new MatOfDMatch();
brt_frc.match(Obj_descriptor, Scene_descriptor, matches);
//4. Calculating the max and min distance between Keypoints
float max_dist = 0,min_dist = 100,dist =0;
DMatch[] for_calculating;
for_calculating = matches.toArray();
for( int i = 0; i < Obj_descriptor.rows(); i++ )
{ dist = for_calculating[i].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}
System.out.print("\nInterval min_dist: " + min_dist + ", max_dist:" + max_dist);
//-- Use only "good" matches (i.e. whose distance is less than 2.5*min_dist)
LinkedList<DMatch> good_matches = new LinkedList<DMatch>();
double ratio_dist=2.5;
ratio_dist = ratio_dist*min_dist;
int i, iter = matches.toArray().length;
matches.release();
for(i = 0;i < iter; i++){
if (for_calculating[i].distance <=ratio_dist)
good_matches.addLast(for_calculating[i]);
}
System.out.print("\n done Good Matches");
/*Necessary type conversion for drawing matches
MatOfDMatch goodMatches = new MatOfDMatch();
goodMatches.fromList(good_matches);
Mat matches_scn_obj = new Mat();
Features2d.drawKeypoints(Object_image, keypoint_object, new Mat(Object_image.rows(), keypoint_object.cols(), keypoint_object.type()), new Scalar(0.0D, 0.0D, 255.0D), 4);
Features2d.drawKeypoints(Scene_image, keypoint_scene, new Mat(Scene_image.rows(), Scene_image.cols(), Scene_image.type()), new Scalar(0.0D, 0.0D, 255.0D), 4);
Features2d.drawMatches(Object_image, keypoint_object, Scene_image, keypoint_scene, goodMatches, matches_scn_obj);
SaveImage(matches_scn_obj,"drawing_good_matches.jpg");
*/
if(good_matches.size() <= 6){
ph_value = "7";
System.out.println("Wrong Detection");
return Scene_image;
}
else{
//5. RANSAC thresholding for finding the optimum homography
Mat outputImg = new Mat();
LinkedList<Point> objList = new LinkedList<Point>();
LinkedList<Point> sceneList = new LinkedList<Point>();
List<org.opencv.core.KeyPoint> keypoints_objectList = keypoint_object.toList();
List<org.opencv.core.KeyPoint> keypoints_sceneList = keypoint_scene.toList();
//getting the object and scene points from good matches
for(i = 0; i<good_matches.size(); i++){
objList.addLast(keypoints_objectList.get(good_matches.get(i).queryIdx).pt);
sceneList.addLast(keypoints_sceneList.get(good_matches.get(i).trainIdx).pt);
}
good_matches.clear();
MatOfPoint2f obj = new MatOfPoint2f();
obj.fromList(objList);
objList.clear();
MatOfPoint2f scene = new MatOfPoint2f();
scene.fromList(sceneList);
sceneList.clear();
float RANSAC_dist=(float)2.0;
Mat hg = Calib3d.findHomography(obj, scene, Calib3d.RANSAC, RANSAC_dist);
for(i = 0;i<hg.cols();i++) {
String tmp = "";
for ( int j = 0; j < hg.rows(); j++) {
Point val = new Point(hg.get(j, i));
tmp= tmp + val.x + " ";
}
}
Mat scene_image_transformed_color = new Mat();
Imgproc.warpPerspective(original_image, scene_image_transformed_color, hg, Object_image.size(), Imgproc.WARP_INVERSE_MAP);
processing(scene_image_transformed_color, template_match);
return outputImg;
}
} }
and this part is what is taking 6 seconds to implement on runtime -
LinkedList<DMatch> good_matches = new LinkedList<DMatch>();
double ratio_dist=2.5;
ratio_dist = ratio_dist*min_dist;
int i, iter = matches.toArray().length;
matches.release();
for(i = 0;i < iter; i++){
if (for_calculating[i].distance <=ratio_dist)
good_matches.addLast(for_calculating[i]);
}
System.out.print("\n done Good Matches");}
I was thinking may be I can write this part of the code in C++ using NDK but I just wanted to be sure that the language is the problem and not the code itself. Please don't be strict, first question! Any criticism is much appreciated!
