模板匹配可以平移缩放旋转多种匹配

# LogPolarFFTTemplateMatcher This project is an opencv implementation of rotation and scale invariant Log-Polar FFT template matcher. Dependencies: * *opencv* mandatory. * *gtest* if you want build test. Appended canny preprocessing, results become more robust. You can change its parameters in LogPolarFFTTemplateMatch method. Usage example: ``` cpp #include "fftm.hpp" using namespace std; using namespace cv; //----------------------------------------------------------------------------------------------------- // //----------------------------------------------------------------------------------------------------- int main(int argc, unsigned int** argv) { Mat im0 = imread("cat.png", 1); Mat im1 = imread("cat_part.png", 1); imshow("im1", im1); imshow("im0", im0); // As input we need equal sized images, with the same aspect ratio, // scale difference should not exceed 1.8 times. RotatedRect rr = LogPolarFFTTemplateMatch(im0, im1,200,100); // Plot rotated rectangle, to check result correctness Point2f rect_points[4]; rr.points(rect_points); for (int j = 0; j < 4; j++) { line(im0, rect_points[j], rect_points[(j + 1) % 4], Scalar(255, 0, 0), 2, CV_AA); } imshow("result", im0); waitKey(); } ``` Program will show result as below:  References ---------- (1) An FFT-based technique for translation, rotation and scale-invariant image registration. BS Reddy, BN Chatterji. IEEE Transactions on Image Processing, 5, 1266-1271, 1996 (2) An IDL/ENVI implementation of the FFT-based algorithm for automatic image registration. H Xiea, N Hicksa, GR Kellera, H Huangb, V Kreinovich. Computers & Geosciences, 29, 1045-1055, 2003. (3) Image Registration Using Adaptive Polar Transform. R Matungka, YF Zheng, RL Ewing. IEEE Transactions on Image Processing, 18(10), 2009. Based on python version of algorithm: http://www.lfd.uci.edu/~gohlke/code/imreg.py.html