Originally, I used the mnemonic from OpenCV online guides: Mat::Mat(Size size, int type, void* data, size_t step=AUTO_STEP) But I didn’t understand what the document meant by size_t step=AUTO_STEP. And this means that I can omit the step argument with which OpenCV will automatically choose AUTO_STEP I’ve tried and this works: A = Mat(2, 5, CV_32FC1, … Read more
I described some tips for preparing images for Tesseract here: Using tesseract to recognize license plates In your example, there are several things going on… You need to get the text to be black and the rest of the image white (not the reverse). That’s what character recognition is tuned on. Grayscale is ok, as … Read more
Perhaps section 3 of this document will help: [link now broken] https://software.intel.com/sites/default/files/managed/2f/19/inde_opencv_3.0_arch_guide.pdf Specifically, section 3.1: A unified abstraction cv::UMat that enables the same APIs to be implemented using CPU or OpenCL code, without a requirement to call OpenCL accelerated version explicitly. These functions use an OpenCL-enabled GPU if exists in the system, and automatically switch … Read more
In OpenCV, if the image is of floating point type, then only those pixels can be visualized using imshow, which have value from 0.0 to 1.0, if the value is greater than 1.0, it will be shown as a white pixel and if less than 0.0, it will be shown as black pixel. To visualize … Read more
You can use hog class in opencv as follows HOGDescriptor hog; vector<float> ders; vector<Point> locs; This function computes the hog features for you hog.compute(grayImg, ders, Size(32, 32), Size(0, 0), locs); The HOG features computed for grayImg are stored in ders vector to make it into a matrix, which can be used later for training. Mat … Read more
Use houghCircle directly on your image, don’t extract edges first. Then test for each detected circle, how much percentage is really present in the image: int main() { cv::Mat color = cv::imread(“../houghCircles.png”); cv::namedWindow(“input”); cv::imshow(“input”, color); cv::Mat canny; cv::Mat gray; /// Convert it to gray cv::cvtColor( color, gray, CV_BGR2GRAY ); // compute canny (don’t blur with … Read more
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
Without any extra assumption on the world scene geometry, you cannot affirm that there is a projective transformation between the two views. This is only true if the scene is planar. A good reference on that topic is the book Multiple View Geometry in Computer Vision by Hartley and Zisserman. If the world scene is … Read more
You say that the matrices are the same dimensions, and yet you are trying to perform matrix multiplication on them. Multiplication of matrices with the same dimension is only possible if they are square. In your case, you get an assertion error, because the dimensions are not square. You have to be careful when multiplying … Read more
Developing from Miki’s answer, In OpenCV 3 definition has moved to modules/core/include/opencv2/core/traits.hpp, where you can find: /** @brief A helper class for cv::DataType The class is specialized for each fundamental numerical data type supported by OpenCV. It provides DataDepth<T>::value constant. */ template<typename _Tp> class DataDepth { public: enum { value = DataType<_Tp>::depth, fmt = DataType<_Tp>::fmt … Read more