(kpsB, featuresB) = tectAndDescribe(imageB)įeaturesA, featuresB, ratio, reprojThresh) (kpsA, featuresA) = tectAndDescribe(imageA) # unpack the images, then detect keypoints and extract Next up, let’s start working on the stitch method: def stitch(self, images, ratio=0.75, reprojThresh=4.0, Since there are major differences in how OpenCV 2.4 and OpenCV 3 handle keypoint detection and local invariant descriptors, it’s important that we determine the version of OpenCV that we are using. The constructor to Stitcher simply checks which version of OpenCV we are using by making a call to the is_cv3 method. We’ll be using NumPy for matrix/array operations, imutils for a set of OpenCV convenience methods, and finally cv2 for our OpenCV bindings.įrom there, we define the Stitcher class on Line 6. We start off on Lines 2-4 by importing our necessary packages. Self.isv3 = imutils.is_cv3(or_better=True) Let’s go ahead and get started by reviewing panorama.py : # import the necessary packages
Panorama stitcher free install#
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: $ pip install imutils 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.
Our panorama stitching algorithm consists of four steps: Looking for the source code to this post? Jump Right To The Downloads Section OpenCV panorama stitching
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