cv2.estimateRigidTransform with fullAffine=False
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According to documentation cv2.estimateRigidTransform have parameter fullAffine : fullAffine – If true, the function finds an optimal affine transformation with no additional restrictions (6 degrees of freedom). Otherwise, the class of transformations to choose from is limited to combinations of translation, rotation, and uniform scaling (5 degrees of freedom). I don't understand what is meant by 5 degrees of freedom, as I understand translation, rotation, and uniform scaling can be done with 4 variables (some more info here http://nghiaho.com/?p=2208) By uniform scaling they mean that x and y scale will be the same? I have tried print('cv2.__version__', cv2.__version__) m = cv2.estimateRigidTransform(_prev_pts, _curr_pts, fullAffine=False) print('m.shape...