Function inv_warp_image_batch

script/utils/utils.py:270–303

Inverse warp images in batch :param img: batch of images tensor [batch_size, 1, H, W] :param mat_homo_inv: batch of homography matrices tensor [batch_size, 3, 3] :param device: GPU device or CPU :return: batch of warped images

(img, mat_homo_inv, device='cpu', mode='bilinear')

    return warped_points[0,:,:] if no_batches else warped_points

def inv_warp_image_batch(img, mat_homo_inv, device='cpu', mode='bilinear'):
    '''
    Inverse warp images in batch

    :param img:
        batch of images
        tensor [batch_size, 1, H, W]
    :param mat_homo_inv:
        batch of homography matrices
        tensor [batch_size, 3, 3]
    :param device:
        GPU device or CPU
    :return:
        batch of warped images
        tensor [batch_size, 1, H, W]
    '''
    # compute inverse warped points
    if len(img.shape) == 2 or len(img.shape) == 3:
        img = img.view(1,1,img.shape[0], img.shape[1])
    if len(mat_homo_inv.shape) == 2:
        mat_homo_inv = mat_homo_inv.view(1,3,3)

    Batch, channel, H, W = img.shape
    coor_cells = torch.stack(torch.meshgrid(torch.linspace(-1, 1, W), torch.linspace(-1, 1, H), indexing='ij'), dim=2)
    coor_cells = coor_cells.transpose(0, 1)
    coor_cells = coor_cells.to(device)
    coor_cells = coor_cells.contiguous()

    src_pixel_coords = warp_points(coor_cells.view([-1, 2]), mat_homo_inv, device)
    src_pixel_coords = src_pixel_coords.view([Batch, H, W, 2])
    src_pixel_coords = src_pixel_coords.float()

    warped_img = F.grid_sample(img, src_pixel_coords, mode=mode, align_corners=True)
    return warped_img

def compute_valid_mask(image_shape, inv_homography, device='cpu', erosion_radius=0):
    """