# Warp source image to destination based on homography H, status = cv2.findHomography(pts_src, pts_dst)
# Four corners of the book in destination image. # Four corners of the book in source image
#Panorama stitcher for four images python open cv how to#
The code below shows how to take four corresponding points in two images and warp image onto the other. can also be generated using the following Python code. WarpPerspective(im_src, im_out, h, im_dst.size()) Warp source image to destination based on homography Mat h = findHomography(pts_src, pts_dst) Four corners of the book in destination image. Four corners of the book in source image Usually, these point correspondences are found automatically by matching features like SIFT or SURF between the images, but in this post we are simply going to click the points by hand.
OpenCV will robustly estimate a homography that best fits all corresponding points. If you have more than 4 corresponding points, it is even better. To calculate a homography between two images, you need to know at least 4 point correspondences between the two images. Creating panoramas will definitely be part of a future post. Is it really that easy ? Nope! (sorry to disappoint) A lot more goes into creating a good panorama, but the basic principle is to align using a homography and stitch intelligently so that you do not see the seams. The two images will share some common regions that can be aligned and stitched and bingo you have a panorama of two images. The two images you just took of a completely arbitrary 3D scene are related by a homography. Next, pan it about the vertical axis and take another picture. In other words you can mount your camera on a tripod and take a picture. It turns out that if you take a picture of any scene ( not just a plane ) and then take a second picture by rotating the camera, the two images are related by a homography! The images had to contain a plane ( the top of a book ), and only the planar part was aligned properly. In the previous section, we learned that if a homography between two images is known, we can warp one image onto the other. Learn More Homography examples using OpenCV – Panorama A Homography is a transformation ( a 3×3 matrix ) that maps the points in one image to the corresponding points in the other image. shows four corresponding points in four different colors - red, green, yellow and orange. In computer vision jargon we call these corresponding points. The red dot represents the same physical point in the two images. What is Homography ?Ĭonsider two images of a plane (top of the book) shown in Figure 1. Yet it failed spectacularly because God confused the language of the human workers and they could not communicate any longer. The project had all the great qualities of having a clear mission, lots of man power, no time constraint and adequate technology ( bricks and mortar ). The Tower of Babel, according to a mythical tale in the Bible, was humans’ first engineering disaster. Homography is a simple concept with a weird name! In this post we will discuss Homography examples using OpenCV. Terms like “Homography” often remind me how we still struggle with communication.
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