I need the feature detection algorithm. I'm fed up surfing on the web finding nothing but SURF example and hints how to do that, but I did not find an example with other than patented descriptors like SIFT or SURF.
Can anybody write an example of using the free feature detection algorithm (like ORB/BRISK [as far as I understood SURF and FLAAN are nonfree]) ?
I'm using OpenCV 3.0.0.
Instead of using a SURF keypoint detector and descriptor extractor, just switch to use ORB. You can simply change the string passed to create to have different extractors and descriptors.
The following is valid for OpenCV 2.4.11.
FLANN is not in nonfree. You can use other matchers, however, like BruteForce.
The example below:
#include <iostream>
#include <opencv2\opencv.hpp>
using namespace cv;
/** @function main */
int main(int argc, char** argv)
{
Mat img_object = imread("D:\\SO\\img\\box.png", CV_LOAD_IMAGE_GRAYSCALE);
Mat img_scene = imread("D:\\SO\\img\\box_in_scene.png", CV_LOAD_IMAGE_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
Ptr<FeatureDetector> detector = FeatureDetector::create("ORB");
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)
Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create("ORB");
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
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
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),
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, CV_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
imshow("Good Matches & Object detection", img_matches);
waitKey(0);
return 0;
}
UPDATE
OpenCV 3.0.0 has a different API.
You can find a list of non-patented feature detector and descriptor extractor here.
#include <iostream>
#include <opencv2\opencv.hpp>
using namespace cv;
/** @function main */
int main(int argc, char** argv)
{
Mat img_object = imread("D:\\SO\\img\\box.png", CV_LOAD_IMAGE_GRAYSCALE);
Mat img_scene = imread("D:\\SO\\img\\box_in_scene.png", CV_LOAD_IMAGE_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
Ptr<FeatureDetector> detector = ORB::create();
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)
Ptr<DescriptorExtractor> extractor = ORB::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
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
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, CV_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
imshow("Good Matches & Object detection", img_matches);
waitKey(0);
return 0;
}
