Function image_flow_warp

core/utils/utils.py:344–364

Input: image: HxWx3 numpy flow: HxWx2 torch.Tensor Output: outImg: HxWx3 numpy

(image, flow, padding_mode='zeros')

    return mask

def image_flow_warp(image, flow, padding_mode='zeros'):
    '''
    Input:
        image: HxWx3 numpy
        flow: HxWx2 torch.Tensor
    Output:
        outImg: HxWx3 numpy
    '''
    image = torch.from_numpy(image)
    if image.ndim == 2:
        image = image[None].permute([1,2,0])
    H, W, _ = image.shape
    coords = coords_grid(1, H, W).cuda().float().contiguous()
    flow = flow[None].repeat(1, 1, 1, 1).permute([0, 3, 1, 2]).float().contiguous()
    grid = (flow + coords).permute([0, 2, 3, 1]).contiguous()
    grid[:, :, :, 0] = (grid[:, :, :, 0] * 2 - W + 1) / (W - 1)
    grid[:, :, :, 1] = (grid[:, :, :, 1] * 2 - H + 1) / (H - 1)
    image = image[None].permute([0, 3, 1, 2]).cuda().float()
    outImg = F.grid_sample(image, grid, padding_mode=padding_mode, align_corners=False)[0].cpu().numpy().transpose([1, 2, 0])

    return outImg.astype(np.uint8)

def image_forward_warp(image, flow, padding_mode='zeros'):
    '''