utils.py

import numpy as np
import paddle
from paddle.nn import functional as F


def to_tensor(img: np.ndarray):
    """
    Converts a numpy array image (HWC) to a Paddle tensor (NCHW).

    Args:
        img (numpy.ndarray): The input image as a numpy array.

    Returns:
        out (paddle.Tensor): The output tensor.
    """
    img = img[:, :, ::-1]
    img = img.astype("float32") / 255.0
    img = img.transpose(2, 0, 1)
    out: paddle.Tensor = paddle.to_tensor(img)
    out = paddle.unsqueeze(out, axis=0)

    return out


def to_image(x: paddle.Tensor):
    """
    Converts a Paddle tensor (NCHW) to a numpy array image (HWC).

    Args:
        x (paddle.Tensor): The input tensor.

    Returns:
        out (numpy.ndarray): The output image as a numpy array.
    """
    out: np.ndarray = x.squeeze().numpy()
    out = out.transpose(1, 2, 0)
    out = out * 255.0
    out = out.astype("uint8")
    out = out[:, :, ::-1]

    return out


def unwarp(img, bm, bm_data_format="NCHW"):
    """
    Unwarp an image using a flow field.

    Args:
        img (paddle.Tensor): The input image.
        bm (paddle.Tensor): The flow field.

    Returns:
        out (paddle.Tensor): The output image.
    """
    _, _, h, w = img.shape

    if bm_data_format == "NHWC":
        bm = bm.transpose([0, 3, 1, 2])

    # NCHW
    bm = F.upsample(bm, size=(h, w), mode="bilinear", align_corners=True)
    # NHWC
    bm = bm.transpose([0, 2, 3, 1])
    # NCHW
    out = F.grid_sample(img, bm)

    return out