Kp1 and kp2 are the main points, des1 and des2 are the descriptions of the respective images. # find the key points and descriptors with SIFT We distinguish the main points and descriptions of both images as follows: sift = _create() These best-matched properties are the basics for image stitching. You can read more OpenCV'sOpenCV's docs on SIFT for Image to understand more about features. SIFT (Scale Invariant Feature Transform) is a very powerful OpenCV algorithm. We shall be using opencv_contrib'sopencv_contrib's SIFT descriptor. We still need to figure out the features that match both photos. Img2 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) Img = cv2.imread('original_image_right.jpg') Img1 = cv2.cvtColor(img_,cv2.COLOR_BGR2GRAY) img_ = cv2.imread('original_image_left.jpg') If you want to resize image size, i.e., by 50%, change from fx=1 to fx=0.5. If you are using large images, I recommend you to use cv2.resize because if you have an older computer, it may be prolonged and take quite some time. So starting from the first step, we are importing these two images and converting them to grayscale.
![panorama stitcher for four images python open cv panorama stitcher for four images python open cv](https://pyimagesearch.com/wp-content/uploads/2016/01/grand_canyon_result_01.jpg)
#Panorama stitcher for four images python open cv install#
If you work with a more recent version, you will be required to build an OpenCV library by yourself to enable the image stitching function, so it's much easier to install an older version: pip install opencv-contrib-python=3.4.2.16 If you have a newer version, first do pip uninstall opencv before installing an older version.
![panorama stitcher for four images python open cv panorama stitcher for four images python open cv](https://i.ytimg.com/vi/33hHRe3j5Os/maxresdefault.jpg)
And finally, we have one beautiful big and a large photograph of the scenic view.įirst, we'll install OpenCV version 3.4.2.16. The entire process of acquiring multiple images and merging them into such panoramas is named image stitching.
![panorama stitcher for four images python open cv panorama stitcher for four images python open cv](https://raw.githubusercontent.com/kushalvyas/Python-Multiple-Image-Stitching/master/test1.jpg)
Such photos of ordered scenes of collections area units refer to as panoramas. So, we can capture multiple images of the entire scene and then put all bits and pieces together into one big picture. If you want to capture a big scene and your camera can only provide an image of a specific resolution and that resolution is 640 by 480, it is certainly not enough to capture the big panoramic view. Let's first understand the concept of image stitching. We essentially create a single stitched image from a group of these images that explains the entire scene in detail. In simple terms, for input, there should be a group of pictures, but at the same time, the logical flow between the images must be preserved.įor example, think about the sea horizon while you are taking few photos of it.
![panorama stitcher for four images python open cv panorama stitcher for four images python open cv](https://i.stack.imgur.com/fhrRC.png)
So what is image stitching? The output is a composite image such that it is a culmination of image scenes. Have you ever wondered how all these features work? So I thought about how difficult it can be to do a panorama merging on my own using the Python language. You probably already know that the Google Photos app has unique auto features like video creation, panoramic image stitching, image collage creation, image sorting, and more.