def getimage()

in optical_flow_flownet2_homography.py [0:0]

• 27 lines of code
• 5 McCabe index (conditional complexity)

def getimage(img1_path, img2_path, size=None):
    frame1 = cv2.imread(img1_path)
    frame2 = cv2.imread(img2_path)

    if size is not None:
        frame1 = cv2.resize(frame1[:, :, ::-1], (size[1], size[0]))
        frame2 = cv2.resize(frame2[:, :, ::-1], (size[1], size[0]))

    imgH, imgW, _ = frame1.shape

    (kpsA, featuresA) = detectAndDescribe(frame1)
    (kpsB, featuresB) = detectAndDescribe(frame2)
    try:
        (_, H_BA, _) = matchKeypoints(kpsB, kpsA, featuresB, featuresA)
    except Exception:
        H_BA = np.array([1.0, 0, 0, 0, 1.0, 0, 0, 0, 1.0]).reshape(3, 3)

    NoneType = type(None)
    if type(H_BA) == NoneType:
        H_BA = np.array([1.0, 0, 0, 0, 1.0, 0, 0, 0, 1.0]).reshape(3, 3)

    try:
        np.linalg.inv(H_BA)
    except Exception:
        H_BA = np.array([1.0, 0, 0, 0, 1.0, 0, 0, 0, 1.0]).reshape(3, 3)

    img2_registered = cv2.warpPerspective(frame2, H_BA, (imgW, imgH))

    frame1_tensor = torch.from_numpy(frame1).permute(2, 0, 1).contiguous().float()
    frame2_tensor = torch.from_numpy(frame2).permute(2, 0, 1).contiguous().float()
    frame2_reg_tensor = (
        torch.from_numpy(img2_registered).permute(2, 0, 1).contiguous().float()
    )

    return frame1_tensor, frame2_tensor, frame2_reg_tensor, H_BA