pytorch实现图像remap

def gpu_remap(numpy_img,map_tensor):# 准备图像数据img_tensor = torch.from_numpy(numpy_img).contiguous().cuda(non_blocking=True)img_tensor = img_tensor.permute(2,0,1).unsqueeze(0).float()res = torch.nn.functional.grid_sample(img_tensor,map_tensor,mode='bilinear',padding_mode='zeros',align_corners=None)res = res.char()res = res[0].permute(1,2,0)res = res.cpu()res = res.numpy()res = np.uint8(res)return res

def cpu_remap(numpy_img,mapx,mapy):return cv2.remap(numpy_img,mapx,mapy,cv2.INTER_LINEAR)

if __name__=='__main__':mtx = np.asarray([[1.45400077e+03,0.00000000e+00,8.91370579e+02],[0.00000000e+00,1.20949778e+03,3.22130436e+02],[0.00000000e+00,0.00000000e+00,1.00000000e+00]])dist =np.asarray([[-0.45415106,0.42747461,0.04382156,0.003691,-0.29239333]])w,h = 1920,1080# 准备mapmapx, mapy = cv2.initUndistortRectifyMap(mtx, dist, None, mtx, (w, h), 5)# 准备grid数据mapx_tensor = torch.from_numpy(mapx).unsqueeze(2)/1920*2-1mapy_tensor = torch.from_numpy(mapy).unsqueeze(2)/1080*2-1map_tensor = torch.cat([mapx_tensor,mapy_tensor],dim=2).unsqueeze(0).cuda() img_org = cv2.imread('test.jpg')for i in range(100):cpu_res = cpu_remap(img_org,mapx,mapy)for i in range(100):cpu_res = gpu_remap(img_org,map_tensor)

计算了下耗时，结果发现还是CPU->GPU数据拷贝是瓶颈。。。但是计算耗时确实少很多，后续看看如何优化吧。。。

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