The hierarchy returned by findContours has the following form: hierarchy[idx][{0,1,2,3}]={next contour (same level), previous contour (same level), child contour, parent contour} CV_RETR_CCOMP, returns a hierarchy of outer contours and holes. This means elements 2 and 3 of hierarchy[idx] have at most one of these not equal to -1: that is, each element has either no … Read more
1.- The method used is Least Squares. There are more complex algorithms than this one. Still it is the most common one, as the other methods may fail in some cases (i.e. some others fails if points are on plane or on infinite). The method can be found in Multiple View Geometry in Computer Vision … Read more
I can suggest one of the following solutions: Using local high level features: OpenCV includes SURF, so: for each frame, extract SURF features. Then build feature Kd-Tree (also in OpenCV), then match each two consecutive frames to find pairs of corresponding features. Feed those pairs into cvFindHomography to compute the homography between those frames. Warp … Read more
Below is a minimal example: #include <opencv/cv.h> #include <opencv/highgui.h> int main(int argc, const char* argv[]) { const cv::Mat input = cv::imread(“input.jpg”, 0); //Load as grayscale cv::SiftFeatureDetector detector; std::vector<cv::KeyPoint> keypoints; detector.detect(input, keypoints); // Add results to image and save. cv::Mat output; cv::drawKeypoints(input, keypoints, output); cv::imwrite(“sift_result.jpg”, output); return 0; } Tested on OpenCV 2.3
In python opencv you can compute hog like this: import cv2 hog = cv2.HOGDescriptor() im = cv2.imread(sample) h = hog.compute(im)
The feature data you mention (position, laplacian, size, orientation, hessian) is insufficient for your purpose (these are actually the less relevant parts of the descriptor if you want to do matching). The data you want to look at are the “descriptors” (the 4th argument): void cvExtractSURF(const CvArr* image, const CvArr* mask, CvSeq** keypoints, CvSeq** descriptors, … Read more
Amongst these parameters, you need to pay more attention to four of them: scaleFactor – Parameter specifying how much the image size is reduced at each image scale. Basically the scale factor is used to create your scale pyramid. More explanation can be found here. In short, as described here, your model has a fixed … Read more
For anyone who stumbles across this in the future. This can be done with template matching. To summarize (my understanding), template matching looks for an exact match of one image within another image. Here’s an example of how to do it within Python: import cv2 method = cv2.TM_SQDIFF_NORMED # Read the images from the file … Read more
There are multiple ways to go about detecting text in an image. I recommend looking at this question here, for it may answer your case as well. Although it is not in python, the code can be easily translated from c++ to python (Just look at the API and convert the methods from c++ to … Read more
Since AR applications often run on mobile devices, you could consider also other features detector/descriptor: FREAK ORB