image-stitching

I use an algorithm of panorama stitching from opencv, in order to stitch 2 or 3 images into one new result image. I have coordinates of points in each source image. I need to calculate what are the new coordinates for these points in the result image. I describe below the algorithm. My code is similar to a sample "stitching_detailed" from opencv (branch 3.4). A result_mask of type Mat is produced, maybe it is the solution? But I don't know how to use it. I found a related question here but not on stitching. Any idea? Here is the algorithm (for detailed code: stitching_detailed.cpp ): Find

I have to stitch two or more images together using python and openCV. I found this code for finding keypoints and matches, but I don't know how to continue. Help me please! import numpy as np import cv2 MIN_MATCH_COUNT = 10 img1 = cv2.imread('a.jpg',0) # queryImage img2 = cv2.imread('b.jpg',0) # trainImage # Initiate SIFT detector sift = cv2.SIFT() # find the keypoints and descriptors with SIFT kp1, des1 = sift.detectAndCompute(img1,None) kp2, des2 = sift.detectAndCompute(img2,None) FLANN_INDEX_KDTREE = 0 index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5) search_params = dict

I'm currently working on Image stitching using OpenCV 2.3.1 on Visual Studio 2010, but I'm having some trouble. Problem Description I'm trying to write a code for stitching multiple images derived from a few cameras(about 3~4), i,e, the code should keep executing image stitching until I ask it to stop. The following is what I've done so far: (For simplification, I'll replace some part of the code with just a few words) 1.Reading frames(images) from 2 cameras (Currently I'm just working on 2 cameras.) 2.Feature detection, descriptor calculation (SURF) 3.Feature matching using FlannBasedMatcher

I am doing image stitching in OpenCV, where I am taking pictures of a planar scene from different locations and try to compose a panorama. I have modified the stitching example to fit my needs. The problem with the openCV stitching pipeline is, that it assumes a pure rotation of the camera, which is not the case for me. When the pictures are taken perfectly orthogonal to the scene (no camera rotation, just translation), the result is quite good, but when there are both, camera rotation and translation the results are not satisfying. I am able to compute the homographies between the camera