In many computer vision tasks, the extraction of features invariant to affine transform plays an important role. To achieve better accuracy, region-based approaches usually need expensive computation. Whereas, contour-based methods need less computation, but their performance is strongly dependant on the boundary extraction. A method, generic polar radius integral transform (GPRIT), is proposed to combine region-based and contour-based method together for the extraction of affine invariant features. Polar radius integral transform and central projection transform are all special cases of the proposed GPRIT. With GPRIT, any object is converted into a closed curve for data reduction. Consequently, stationary wavelet transform is conducted to construct affine invariants. Several experiments have been presented to evaluate performance of the proposed GPRIT.