Tag: OpencvProject

This project focuses on the usage of computer vision within the field of board games. We propose a new approach for extracting the position of game board, which consists of the detection of empty fields based on the contour analysis and elipse fitting, locating the key points by using probabilistic Hough lines and of finding the homography by using these key points.

Functions used: Canny, findContours, fitEllipse, HoughLinesP, findHomography, warpPerspective, chamerMatching

Input

The process

1. Canny edge detector
   [c language=”c++”]
   Mat canny;
   Canny(img, canny, 100, 170, 3);
   [/c]
2. Contour analysis – extraction contours and filtering out those that don’t match our criteria
   [c language=”c++”]
   vector<vector<Point>> contours;
   vector<Vec4i> hierarchy;
   findContours(canny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_NONE);
   [/c]
3. Ellipse fitting – further analysis of contours, final extraction of empty fields
   [c language=”c++”]
   RotatedRect e = fitEllipse(contours[i]);
   [/c]
4. Extraction of the game board model – 4 key points are needed for locating this model
5. Locating the key points in the input image – using Hough lines & analysing their intersections
   [c language=”c++”]
   Mat grayCpy;
   vector<Vec4i>& lines;
   HoughLinesP(grayCpy, lines, 1, CV_PI/180, 26, 200, 300);
   [/c]
6. Finding the homography and final projection of the game board model into the input image
   [c language=”c++”]
   findHomography(Mat(modelKeyPoints), Mat(keyPoints));
   warpPerspective(modelImg, newImg, h, Size(imgWithEmptyFieldsDots.cols, imgWithEmptyFieldsDots.rows), CV_INTER_LINEAR + CV_WARP_FILL_OUTLIERS);
   chamerMatching(canny, piece, results, costs, 1.0, 30, 1.0, 3, 3, 5, 0.9, 1.1);
   [/c]

Sample

Result

[c language=”c++”]
Mat findImageContours(const Mat& img, vector<vector<Point> >& contours, vector<Vec4i>& hierarchy)
{
// detect edges using canny:
Mat canny;
Canny(img, canny, 100, 170, 3);

findContours(canny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_NONE);

// draw contours:
Mat imgWithContours = Mat::zeros(canny.size(), CV_8UC3);
for (unsigned int i = 0; i < contours.size(); i++)
{
// process "holes" only:
if (hierarchy[i][3] == -1) continue;
// apply ratio + size + contourArea filters:
if (!checkContour3(contours[i])) continue;

// fit and draw ellipse:
RotatedRect e = fitEllipse(contours[i]);
if (e.size.height < 50)
{
line(imgWithContours, e.center, e.center, Scalar(255, 255, 255),3);
}
}
return imgWithContours;
}
[/c]

This project shows a possible way of finding contour lines on maps. These properties of the contour lines are considered here:

• contour lines are closed or they end at the edges of the map,
• in some sections more neighbor contour lines are nearly parallel,
• they are mainly slightly curved only (the lines do not have large angles like roads or buildings).

The algorithm uses the OpenCV library.

Functions used: cv::medianBlur, cv::Sobel, cv::magnitude

The process

1. Image preprocessing – using median blur
   [c language=”c++”]
   cv::Mat bl;
   cv::medianBlur(input, bl, params_.medianBlurKSize);
   [/c]

2. Detecting lines and their directions – using Sobel filter (magnitudes are obtained using the magnitude function and directions are computed using atan2 from horizontal and vertical gradients)
   [c language=”c++”]
   cv::Mat_<double> grad_x;
   cv::Sobel(beforeSobel, grad_x, CV_64F, 1, 0, params_.sobelKSize);
   cv::Sobel(beforeSobel, grad_y, CV_64F, 0, 1, params_.sobelKSize);
   [/c]
3. Finding some contour line seeds – points at lines with approximately equal directions.
   [c language=”c++”]
   cv::Mat_<double> magnitude;
   cv::magnitude(grad_x, grad_y, magnitude);
   [/c]
4. Tracing lines beginning at the seeds – we are going from each seed to both directions to find the line while checking if the curves do not exceed a threshold (the more curved lines are probably not the contour lines).
5. Filtering of the traced lines – only the lines having both ends at the image boundaries or the closed lines are considered as the map contour lines.

The result image shows a map with some contour lines detected. The seeds and line points are marked as follows:

• yellow – seed points
• red – closed line points
• green – points of the first part of a line ending at the image edge
• blue – points of the second part of a line ending at the image edge

Problems and possible improvements

These algorithm properties cause problems and need to be considered in the algorithm improvements:

• line intersections are not being detected – one line from each pair of the intersecting lines should always be removed,
• the algorithm uses a global magnitude threshold (the threshold determines if a point belongs to a line), but the line intensities change in most images,
• the algorithm has too many parameters which were not generalized to match more possible images,
• some contour lines are not continuous (they are splitted by labels) and thus not being detected by the algorithm.

This project shows object recognition using local features-based methods. We use four methods for keypoints detection and description: SIFT/SIFT, SURF/SURF, FAST/FREAK and ORB/ORB. Keypoints are used to compute homography. Object is located in scene with RANSAC algorithm. RGB and hue-saturation histograms are used for RANSAC verification.

Functions used: FeatureDetector::detect, DescriptorExtractor::compute, knnMatch, findHomography, warp, calcHist, compareHist

Input

The process

1. Keypoints detection
   [c language=”c++”]
   FeatureDetector * detector;
   detector = new SiftFeatureDetector();
   detector->detect( image, key_points_image );

   DescriptorExtractor * extractor;
   extractor = new SiftDescriptorExtractor();
   extractor->compute( image, key_points_image, des_image );
   [/c]

2. Keypoints description
3. Keypoints matching
   [c language=”c++”]
   DescriptorMatcher * matcher;
   matcher = new BruteForceMatcher<L2<float>>();
   matcher->knnMatch(des_object, des_image, matches, 2);
   [/c]
4. Calculating homography
   [c language=”c++”]
   findHomography( obj, scene, CV_RANSAC );
   [/c]
5. Histograms matching
   [c language=”c++”]
   calcHist( &hsv_img_object, 1, channels, Mat(), hist_img_object, 2, histSize, ranges, true, false );
   compareHist( b_hist_object, b_hist_quad, CV_COMP_BHATTACHARYYA );
   [/c]
6. Outline recognized object

Sample

[c language=”c++”]
drawMatches( gray_object, key_points_object, image,
key_points_image, good_matches, img_matches,
Scalar::all(-1), Scalar::all(-1), vector<char>(),
DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

if (good_matches.size() >= 4)
{
for( int i = 0; i < good_matches.size(); i++ )
{
//
obj.push_back( key_points_object[ good_matches[i].queryIdx ].pt );
scene.push_back( key_points_image[ good_matches[i].trainIdx ].pt );
}

H = findHomography( obj, scene, CV_RANSAC );

perspectiveTransform( obj_corners, scene_corners, H);
//*******************************************************

Mat quad = Mat::zeros(rgb_object.rows, rgb_object.cols,
CV_8UC3);

//warping object back to tamplate rotation
warpPerspective(frame, quad, H.inv(), quad.size());

…
[/c]

The project shows detection and recognition of euro money bill from input image (webcam). For each existing euro money bill is chosen template that contains number value of bill and also its structure. For matching templates with input images is used Flann Based matcher of local descriptors extracted by SURF algorithm.

Functions used: medianBlur, FlannBasedmatcher, SerfFeatureDetector, SurfDescriptorExtractor, findHomography

Process

1. Preprocessing – Conversion to grayscale + median filter
   [c language=”c++”]
   cvtColor(input_image_color, input_image, CV_RGB2GRAY);
   medianBlur(input_image, input_image, 3);
   [/c]
2. Compute local descriptors
   [c language=”c++”]
   SurfFeatureDetector detector( minHessian );
   vector<KeyPoint> template_keypoints;
   detector.detect( money_template, template_keypoints );
   SurfDescriptorExtractor extractor;
   extractor.compute( money_template, template_keypoints, template_image );
   detector.detect( input_image, input_keypoints );
   extractor.compute( input_image, input_keypoints, destination_image );
   [/c]
3. Matching local descriptors
   [c language=”c++”]
   FlannBasedMatcher matcher;
   matcher.knnMatch(template_image, destination_image, matches, 2);
   [/c]
4. Finding homography and drawing output
   [c language=”c++”]
   Mat H = findHomography( template_object_points, input_object_points, CV_RANSAC );
   perspectiveTransform( template_corners, input_corners, H);
   drawLinesToOutput(input_corners, img_matches, money_template.cols);
   [/c]

Sample

Project is focused on the image detection which major components are cities and buildings. Buildings and cities detection assumes occurence of the edges as implication of the Windows and walls, as well as presence of the sky. Algorithm creates the feature vector with SVM classification algorithm.

Functions used: HoughLinesP, countNonZero, Sobel, threshold, merge, cvtColor, split, CvSVM

The process

1. Create edge image

   [c language=”c++”]
   cv::Sobel(intput, grad_x, CV_16S, 1, 0, 3, 1, 0, cv::BORDER_DEFAULT);
   cv::Sobel(intput, grad_y, CV_16S, 0, 1, 3, 1, 0, cv::BORDER_DEFAULT);
   [/c]

2. Find lines in the binary edge image

   [c language=”c++”]
   cv::HoughLinesP(edgeImage, edgeLines, 1, CV_PI / 180.0, 1, 10, 0);
   [/c]

3. Count numbers of lines in specified tilt
4. Convert original image to HSV color space and remove saturation and value

   [c language=”c++”]
   cv::cvtColor(src, hsv, CV_BGR2HSV);
   [/c]

5. Process the image from top to bottom , if pixel is not blue then all pixels under him are not sky
6. Classification with SVM

   [c language=”c++”]
   CvSVMParams params;
   params.svm_type = CvSVM::C_SVC;
   params.kernel_type = CvSVM::LINEAR;
   params.term_crit   = cvTermCriteria(CV_TERMCRIT_ITER, 5000, 1e-5);

   float OpencvSVM::predicate(std::vector<float> features)
   {
   std::vector<std::vector<float> > featuresMatrix;
   featuresMatrix.push_back(features);
   cv::Mat featuresMat = createMat(featuresMatrix);
   return SVM.predict(featuresMat);
   }
   [/c]

Example

In computer vision and object recognition, we have three main areas – object classification, detection and segmentation. Classification task deals only with assigning an image to a class (for example bicycle, dog, cactus, etc…), detection task moreover deals with detecting the position of the object in an image and segmentation task deals with finding the detailed contours of the object. Bag of words is a method which belongs to classification problem.

Algorithm steps

1. Find key points in images using Harris detector.
   [c language=”c++”]
   Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
   Ptr<DescriptorExtractor> extractor = DescriptorExtractor::create("SIFT");
   Ptr<FeatureDetector> detector = FeatureDetector::create("HARRIS");
   [/c]
2. Extract SIFT local feature vectors from the set of images.
   [c language=”c++”]
   // Extract SIFT local feature vectors from set of images
   extractTrainingVocabulary("data/train", extractor, detector, bowTrainer);
   [/c]
3. Put all the local feature vectors into a single set.
   [c language=”c++”]
   vector<Mat> descriptors = bowTrainer.getDescriptors();
   [/c]
4. Apply a k-means clustering algorithm over the set of local feature vectors in order to find centroid coordinates. This set of centroids will be the vocabulary.
   [c language=”c++”]
   cout << "Clustering " << count << " features" << endl;
   Mat dictionary = bowTrainer.cluster();

   cout << "dictionary.rows == " << dictionary.rows << ", dictionary.cols == " << dictionary.cols << endl;
   [/c]

5. Compute the histogram that counts how many times each centroid occurred in each image. To compute the histogram find the nearest centroid for each local feature vector.

Histogram

We trained our model on 240 different images from 3 different classes – bonsai, Buddha and porcupine. We then computed the following histogram which counts how many times each centroid occurred in each image. To find the values of the histogram we had to compare the distances of each local feature vector with each centroid and centroid with least difference to local feature vector has incremented in histogram. We used 1000 cluster centers.