I would try to use a skeleton representation of the image. The problem with your canny, here, is that it basically results in two lines because of the width of the line. Then I would apply the Hough transform on it.
Although you already choose BRISK, you might find FREAK interesting. Author claims to have better results than BRISK and ORB. I should also add that ORB is scale-invariant but has some problems in that area. So I would still recommend it for someone to try it. The FREAK source code is compatible with OpenCV (how … Read more
I have achieved my goal and trained good cascade. First you need a couple of original samples (don’t use one and multiply it with create samples). I have used 10 different photos of beer bottles, for each I have created 200 hundred samples, then I have combined all samples in one vector file with 2000 … Read more
I am going to presume that you know the basics of how MSER feature detection works (if not, Wikipedia, and short recap follows). You have two types of MSER regions, positive and negative. First type, you get by thresholding with all intensities (for grayscale images, 0 to 255). E.g. for a threshold T = 100, … 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
Conversion of RGB to LAB(L for lightness and a and b for the color opponents green–red and blue–yellow) will do the work. Apply CLAHE to the converted image in LAB format to only Lightness component and convert back the image to RGB. Here is the snippet. bgr = cv2.imread(image_path) lab = cv2.cvtColor(bgr, cv2.COLOR_BGR2LAB) lab_planes = … Read more
You can see from this answer, they evaluate to identical types. As for why there are two names, if you look at how the #defines are structured (again, see linked answer), a type in OpenCV has 2 parts, the depth, and the number of channels. The system is flexible enough to let you define new … Read more
Try by disabling pre-compiled headers, either from cmake-gui or using the command line parameter -DENABLE_PRECOMPILED_HEADERS=OFF
I tried the following and it worked: Change in FindCUDA.cmake the nppi library to the several splitted ones. This has to be done in 3 places. Remember this change is just to make it work with CUDA 9.0, I am not doing checks for version or anything, which should be done if you plan to … Read more
The naming sheme for the types is CV_<bit-depth>{U|S|F}C<number_of_channels>. So CV_8UC4 translates to: four channels of unsigned char and CV_16S translates to: 1 channel of signed 2-byte integer. Of course the topic is handled in the documentation. Here you can find more information.