![]() Line 4 unpacks the images list (which again, we presume to contain only two images). We can also optionally supply ratio, used for David Lowe’s ratio test when matching features (more on this ratio test later in the tutorial), reprojThresh which is the maximum pixel “wiggle room” allowed by the RANSAC algorithm, and finally showMatches, a boolean used to indicate if the keypoint matches should be visualized or not. The stitch method requires only a single parameter, images, which is the list of (two) images that we are going to stitch together to form the panorama. The Stitcher class will rely on the imutils Python package, so if you don’t already have it installed on your system, you’ll want to go ahead and do that now: # import the necessary packages import numpy as np import imutils import cv2 class Stitcher: def _init_(self): # determine if we are using OpenCV v3.X self.isv3 = imutils.is_cv3(or_better=True) def stitch(self, images, ratio=0.75, reprojThresh=4.0,showMatches=False): # unpack the images, then detect keypoints and extract # local invariant descriptors from them (imageB, imageA) = images (kpsA, featuresA) = tectAndDescribe(imageA) (kpsB, featuresB) = tectAndDescribe(imageB) # match features between the two images M = self.matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh) # if the match is None, then there aren’t enough matched # keypoints to create a panorama if M is None: return None We’ll encapsulate all four of these steps inside panorama.py, where we’ll define a Stitcher class used to construct our panoramas. Step #4: Apply a warping transformation using the homography matrix obtained from Step #3.Step #3: Use the RANSAC algorithm to estimate a homography matrix using our matched feature vectors.Step #2: Match the descriptors between the two images.Step #1: Detect keypoints (DoG, Harris, etc.) and extract local invariant descriptors (SIFT, SURF, etc.) from the two input images.Our panorama stitching algorithm consists of four steps: OpenCV Panorama StitchingĮxample of Keypoints and Local invariant extraction Since there are major differences in how OpenCV 2.4.X and OpenCV 3.X handle keypoint detection and local invariant descriptors (such as SIFT and SURF), I’ve taken special care to provide code that is compatible with both versions (provided that you compiled OpenCV 3 with opencv_contrib support, of course). To construct our image panorama, we’ll utilize computer vision and image processing techniques such as: keypoint detection and local invariant descriptors keypoint matching RANSAC and perspective warping. This article will focus on the basics of Panorama formation using two images, which will be used in the next article where we will be seeing how to stitch together multiple images. Similarly, this article is the first portion of my two-part article series on Panorama / Image stitching. ![]() As some of you may know, I am writing a series of articles explaining various common functionalities of today’s mobile cameras like panorama, HDR, Slow-mo, Ghosting etc.
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