derofim · April 1, 2017 18:07 · derofim · Apr 1, 2017 · derofim · Apr 1, 2017
diff --git a/Readme.md b/Readme.md
diff --git a/source.cpp b/source.cpp
 #include "opencv2/core.hpp"
 #include "opencv2/features2d.hpp"
 #include "opencv2/highgui.hpp"
 #include "opencv2/calib3d.hpp"
 #include "opencv2/xfeatures2d.hpp"
 #include "opencv2/opencv.hpp"
 #include "opencv2/imgcodecs.hpp"

 #include <iostream>
 #include <string>
 #include <vector>

 using namespace cv;
 using namespace cv::xfeatures2d;

 /** @function main */
 int main(int argc, char** argv)
 {
 	std::string fileImgA = "box.png";
 	std::string fileImgB = "box_in_scene.png";

 	Mat img_object = cv::imread(fileImgA, IMREAD_GRAYSCALE);
 	Mat img_scene = imread(fileImgB, IMREAD_GRAYSCALE);

 	if (!img_object.data || !img_scene.data)
 	{
 		std::cout << " --(!) Error reading images " << std::endl; return -1;
 	}

 	//-- Step 1: Detect the keypoints using SURF Detector
 	int minHessian = 400;

 	// Use Ptr as in https://github.com/kyamagu/mexopencv/issues/154
 	Ptr<SurfFeatureDetector> detector = SurfFeatureDetector::create(minHessian);

 	std::vector<KeyPoint> keypoints_object, keypoints_scene;

 	detector->detect(img_object, keypoints_object);
 	detector->detect(img_scene, keypoints_scene);

 	//-- Step 2: Calculate descriptors (feature vectors)
 	// Based on http://docs.opencv.org/3.2.0/d9/d97/tutorial_table_of_content_features2d.html
 	Ptr<SurfDescriptorExtractor> extractor = SurfDescriptorExtractor::create();

 	Mat descriptors_object, descriptors_scene;

 	extractor->compute(img_object, keypoints_object, descriptors_object);
 	extractor->compute(img_scene, keypoints_scene, descriptors_scene);

 	//-- Step 3: Matching descriptor vectors using FLANN matcher
 	FlannBasedMatcher matcher;
 	std::vector< DMatch > matches;
 	matcher.match(descriptors_object, descriptors_scene, matches);

 	double max_dist = 0; double min_dist = 100;

 	//-- Quick calculation of max and min distances between keypoints
 	for (int i = 0; i < descriptors_object.rows; i++)
 	{
 		double dist = matches[i].distance;
 		if (dist < min_dist) min_dist = dist;
 		if (dist > max_dist) max_dist = dist;
 	}

 	printf("-- Max dist : %f \n", max_dist);
 	printf("-- Min dist : %f \n", min_dist);

 	//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
 	std::vector< DMatch > good_matches;

 	for (int i = 0; i < descriptors_object.rows; i++)
 	{
 		if (matches[i].distance < 3 * min_dist)
 		{
 			good_matches.push_back(matches[i]);
 		}
 	}

 	Mat img_matches;
 	drawMatches(img_object, keypoints_object, img_scene, keypoints_scene,
 		good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
 		std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);

 	//-- Localize the object
 	std::vector<Point2f> obj;
 	std::vector<Point2f> scene;

 	for (int i = 0; i < good_matches.size(); i++)
 	{
 		//-- Get the keypoints from the good matches
 		obj.push_back(keypoints_object[good_matches[i].queryIdx].pt);
 		scene.push_back(keypoints_scene[good_matches[i].trainIdx].pt);
 	}

 	Mat H = findHomography(obj, scene, RANSAC);

 	//-- Get the corners from the image_1 ( the object to be "detected" )
 	std::vector<Point2f> obj_corners(4);
 	obj_corners[0] = cvPoint(0, 0); obj_corners[1] = cvPoint(img_object.cols, 0);
 	obj_corners[2] = cvPoint(img_object.cols, img_object.rows); obj_corners[3] = cvPoint(0, img_object.rows);
 	std::vector<Point2f> scene_corners(4);

 	perspectiveTransform(obj_corners, scene_corners, H);

 	//-- Draw lines between the corners (the mapped object in the scene - image_2 )
 	line(img_matches, scene_corners[0] + Point2f(img_object.cols, 0), scene_corners[1] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
 	line(img_matches, scene_corners[1] + Point2f(img_object.cols, 0), scene_corners[2] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
 	line(img_matches, scene_corners[2] + Point2f(img_object.cols, 0), scene_corners[3] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
 	line(img_matches, scene_corners[3] + Point2f(img_object.cols, 0), scene_corners[0] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);

 	//-- Show detected matches
 	namedWindow("image", WINDOW_NORMAL);
 	imshow("image", img_matches);
 	// scale image to fit on screen
 	resizeWindow("image", 800, 600);

 	waitKey(0);
 	return 0;
 }
	#include "opencv2/core.hpp"
	#include "opencv2/features2d.hpp"
	#include "opencv2/highgui.hpp"
	#include "opencv2/calib3d.hpp"
	#include "opencv2/xfeatures2d.hpp"
	#include "opencv2/opencv.hpp"
	#include "opencv2/imgcodecs.hpp"

	#include <iostream>
	#include <string>
	#include <vector>

	using namespace cv;
	using namespace cv::xfeatures2d;

	/** @function main */
	int main(int argc, char** argv)
	{
	std::string fileImgA = "box.png";
	std::string fileImgB = "box_in_scene.png";

	Mat img_object = cv::imread(fileImgA, IMREAD_GRAYSCALE);
	Mat img_scene = imread(fileImgB, IMREAD_GRAYSCALE);

	if (!img_object.data \|\| !img_scene.data)
	{
	std::cout << " --(!) Error reading images " << std::endl; return -1;
	}

	//-- Step 1: Detect the keypoints using SURF Detector
	int minHessian = 400;

	// Use Ptr as in https://github.com/kyamagu/mexopencv/issues/154
	Ptr<SurfFeatureDetector> detector = SurfFeatureDetector::create(minHessian);

	std::vector<KeyPoint> keypoints_object, keypoints_scene;

	detector->detect(img_object, keypoints_object);
	detector->detect(img_scene, keypoints_scene);

	//-- Step 2: Calculate descriptors (feature vectors)
	// Based on http://docs.opencv.org/3.2.0/d9/d97/tutorial_table_of_content_features2d.html
	Ptr<SurfDescriptorExtractor> extractor = SurfDescriptorExtractor::create();

	Mat descriptors_object, descriptors_scene;

	extractor->compute(img_object, keypoints_object, descriptors_object);
	extractor->compute(img_scene, keypoints_scene, descriptors_scene);

	//-- Step 3: Matching descriptor vectors using FLANN matcher
	FlannBasedMatcher matcher;
	std::vector< DMatch > matches;
	matcher.match(descriptors_object, descriptors_scene, matches);

	double max_dist = 0; double min_dist = 100;

	//-- Quick calculation of max and min distances between keypoints
	for (int i = 0; i < descriptors_object.rows; i++)
	{
	double dist = matches[i].distance;
	if (dist < min_dist) min_dist = dist;
	if (dist > max_dist) max_dist = dist;
	}

	printf("-- Max dist : %f \n", max_dist);
	printf("-- Min dist : %f \n", min_dist);

	//-- Draw only "good" matches (i.e. whose distance is less than 3*min_dist )
	std::vector< DMatch > good_matches;

	for (int i = 0; i < descriptors_object.rows; i++)
	{
	if (matches[i].distance < 3 * min_dist)
	{
	good_matches.push_back(matches[i]);
	}
	}

	Mat img_matches;
	drawMatches(img_object, keypoints_object, img_scene, keypoints_scene,
	good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
	std::vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);

	//-- Localize the object
	std::vector<Point2f> obj;
	std::vector<Point2f> scene;

	for (int i = 0; i < good_matches.size(); i++)
	{
	//-- Get the keypoints from the good matches
	obj.push_back(keypoints_object[good_matches[i].queryIdx].pt);
	scene.push_back(keypoints_scene[good_matches[i].trainIdx].pt);
	}

	Mat H = findHomography(obj, scene, RANSAC);

	//-- Get the corners from the image_1 ( the object to be "detected" )
	std::vector<Point2f> obj_corners(4);
	obj_corners[0] = cvPoint(0, 0); obj_corners[1] = cvPoint(img_object.cols, 0);
	obj_corners[2] = cvPoint(img_object.cols, img_object.rows); obj_corners[3] = cvPoint(0, img_object.rows);
	std::vector<Point2f> scene_corners(4);

	perspectiveTransform(obj_corners, scene_corners, H);

	//-- Draw lines between the corners (the mapped object in the scene - image_2 )
	line(img_matches, scene_corners[0] + Point2f(img_object.cols, 0), scene_corners[1] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
	line(img_matches, scene_corners[1] + Point2f(img_object.cols, 0), scene_corners[2] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
	line(img_matches, scene_corners[2] + Point2f(img_object.cols, 0), scene_corners[3] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);
	line(img_matches, scene_corners[3] + Point2f(img_object.cols, 0), scene_corners[0] + Point2f(img_object.cols, 0), Scalar(0, 255, 0), 4);

	//-- Show detected matches
	namedWindow("image", WINDOW_NORMAL);
	imshow("image", img_matches);
	// scale image to fit on screen
	resizeWindow("image", 800, 600);

	waitKey(0);
	return 0;
	}