edge-detection

I want to use OpenCV's Canny edge detector, such as is outlined in this question . For example: cv::Canny(image,contours,10,350); However, I wish to not only get the final thresholded image out, but I also wish to get the detected edge angle at each pixel. Is this possible in OpenCV? canny doesn't give you this directly. However, you can calculate the angle from the Sobel transform, which is used internally in canny() . Pseudo code: cv::Canny(image,contours,10,350); cv::Sobel(image, dx, CV_64F, 1, 0, 3, 1, 0, cv::BORDER_REPLICATE); cv::Sobel(image, dy, CV_64F, 0, 1, 3, 1, 0, cv::BORDER

I use this both function to find edges on a scale. You have a input image, you apply a mask (for ex. prewitt) to the input image, and get the resultant pic. mypic = imread('examplepic.jpg') hy = fspecial('prewitt') yimfilter = imfilter(mypic,hy) % Using imfilter yconv2 = conv2(mypic,hy) % Using conv2 Which is the theorical difference between these two? I know I got different outputs, but which is the difference? Thanks conv2 outputs the entire 2-D convolution, which means that yconv2 will be bigger than mypic. imfilter, on the other hand, by default trims the edges of the convolution so that

I am new to OpenCv4Android. I am trying to auto detect document using OpenCv4Android sdk. Initially i have gone through issue of landscape opencv camera . Somehow i managed to change the orientation of opencv JavaCameraview to portrait. I made following changes in default classes of opencv sdk to orient opencv camera in portrait : 1) In CameraBridgeViewBase class Matrix matrix = new Matrix(); matrix.setRotate(90f); Bitmap bitmap = Bitmap.createBitmap(mCacheBitmap, 0, 0, mCacheBitmap.getWidth(), mCacheBitmap.getHeight(), matrix, true); 2) now in drawbitmap method replace above bitmap with

I am starting with a nxm boolean array, that defines regions- true if it is in the region, false if not. For example: r = np.array([[ 0, 0, 1, 1, 1], [ 0, 1, 1, 0, 0], [ 0, 1, 1, 1, 0], [ 1, 1, 1, 0, 0], [ 1, 1, 0, 0, 0]]) The line between regions can be defined as an n-1 x m-1 array, which would represent a 'point' in between each set of four values. If all 4 surrounding values are the same, you are not on the edge between regions. If any of the 4 values are different, you are. For r above: l = np.array([[ 1, 1, 1, 1], [ 1, 0, 1, 1], [ 1, 0, 1, 1], [ 0, 1, 1, 0]]) Any thoughts on how this can