cv2.createStitcher() not enough keypoints?

Question

I am working on a project to have multiple cameras, each taking an image and then the images will be stitched together. Currently I am trying to use the cv2.createStitcher().stitch(images) function. Below is the code that I use:

import cv2

imageFiles = ['imageCapture1_0.png','imageCapture2_0.png']
images = []
for filename in imageFiles:
    img = cv2.imread(filename)
    images.append(img)

cv2.ocl.setUseOpenCL(False)
stitcher = cv2.createStitcher()
status, result = stitcher.stitch(images)             

cv2.imwrite('result.png',result)

The image input is:

left image:

right image:

However, result output type becomes NoneType with size 1 and value: NoneType object of builtins modules. From what I have googled, the cause of this is because there is not enough matching keypoint to stitch the images together. If so, is there a way to stitch image even with less keypoint? Is there a way to set the parameter? I read through the documentation with no luck trying to find the solution. Thank you in advance

Answer 1

The image stitching operation status, result = stitcher.stitch(images) returns two values, a status indicator and the resulting stitched image. You can check the value of status to determine whether or not the image stitching operation was a success. From the docs it can be one of four variables:

OK = 0: Image stitching was successful.

ERR_NEED_MORE_IMGS = 1: There were not enough keypoints detected in your input images to construct the panorama. You will need more input images.

ERR_HOMOGRAPHY_EST_FAIL = 2: This error occurs when the RANSAC homography estimation fails. Similarly, you may need more input images or the images provided do not have enough distinguishing features for keypoints to be accurately matched.

ERR_CAMERA_PARAMS_ADJUST_FAIL = 3: Usually related to failing to properly estimate camera features from the input images.

For your situation, you can either add more input images so there will be enough keypoints detected or you can look into your own implementation.