2012-07-24 08:58:22 -0500 commented answer Bilinear sampling from a GpuMat It says I need >50 points to accept my answer. I'm new here :) 2012-07-23 13:43:48 -0500 received badge ● Nice Answer (source) 2012-07-23 09:45:32 -0500 received badge ● Teacher (source) 2012-07-23 09:45:32 -0500 received badge ● Self-Learner (source) 2012-07-23 09:40:03 -0500 answered a question Bilinear sampling from a GpuMat I've found the solution: gpu::buildWarpAffineMaps - to build matrices of x and y coordinates containing the lookup coordinates gpu::remap - to sample the image given the lookup coordinates 2012-07-23 04:14:42 -0500 received badge ● Student (source) 2012-07-23 04:04:02 -0500 received badge ● Editor (source) 2012-07-23 03:57:42 -0500 asked a question Bilinear sampling from a GpuMat Hi everyone, I'm writing a GPU-based shape/appearance model, for which I have to crop patches centered on given key points. The patches are square but not necessarily aligned with the image axes, so I cannot just use a rowRange/colRange. My plan is to create a fixed matrix of coordinate offsets, O: O = [x1, x2, ..., xn; y2, y2, ..., yn; 1, 1, ..., 1]  In Homogeneous coordinates. I will store this matrix on the GPU. When I want to sample a patch around X = [x, y, 1]^T, I simply transform the coordinates by a similarity transformation matrix M (which performs translation, rotation and scaling). P = M * O  So P will again have the same layout as O, but with transformed coordinates. Now for the question: Given a matrix P of coordinates, how can I sample an image f(x,y) at the coordinates in P in an efficient manner? The output should be a vector or matrix with the pixel values at the coordinates in P. I want to use bilinear sampling, which is a built in operation on the GPU (so it should be efficient). I suppose I could write a custom kernel for this, but I would think this is already in opencv somewhere. I searched the documentation but didn't find anything. Alternatively, I could rotate/scale the whole image and then crop an axis-aligned patch, but this seems less efficient. Thanks in advance