utils/flow_util.py

import cv2
import numpy as np
import os
import torch.nn.functional as F

def resize_flow(flow, newh, neww):
    oldh, oldw = flow.shape[0:2]
    flow = cv2.resize(flow, (neww, newh), interpolation=cv2.INTER_LINEAR)
    flow[:, :, 0] *= newh / oldh
    flow[:, :, 1] *= neww / oldw
    return flow

def resize_flow_pytorch(flow, newh, neww):
    oldh, oldw = flow.shape[-2:]
    flow = F.interpolate(flow, (newh, neww), mode='bilinear')
    flow[:, :, 0] *= newh / oldh
    flow[:, :, 1] *= neww / oldw
    return flow


def imwrite(img, file_path, params=None, auto_mkdir=True):
    if auto_mkdir:
        dir_name = os.path.abspath(os.path.dirname(file_path))
        os.makedirs(dir_name, exist_ok=True)
    return cv2.imwrite(file_path, img, params)


def flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs):
    """Read an optical flow map.

    Args:
        flow_path (ndarray or str): Flow path.
        quantize (bool): whether to read quantized pair, if set to True,
            remaining args will be passed to :func:`dequantize_flow`.
        concat_axis (int): The axis that dx and dy are concatenated,
            can be either 0 or 1. Ignored if quantize is False.

    Returns:
        ndarray: Optical flow represented as a (h, w, 2) numpy array
    """
    if quantize:
        assert concat_axis in [0, 1]
        cat_flow = cv2.imread(flow_path, cv2.IMREAD_UNCHANGED)
        if cat_flow.ndim != 2:
            raise IOError(f'{flow_path} is not a valid quantized flow file, its dimension is {cat_flow.ndim}.')
        assert cat_flow.shape[concat_axis] % 2 == 0
        dx, dy = np.split(cat_flow, 2, axis=concat_axis)
        flow = dequantize_flow(dx, dy, *args, **kwargs)
    else:
        with open(flow_path, 'rb') as f:
            try:
                header = f.read(4).decode('utf-8')
            except Exception:
                raise IOError(f'Invalid flow file: {flow_path}')
            else:
                if header != 'PIEH':
                    raise IOError(f'Invalid flow file: {flow_path}, header does not contain PIEH')

            w = np.fromfile(f, np.int32, 1).squeeze()
            h = np.fromfile(f, np.int32, 1).squeeze()
            # flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2))
            flow = np.fromfile(f, np.float16, w * h * 2).reshape((h, w, 2))

    return flow.astype(np.float32)


def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs):
    """Write optical flow to file.

    If the flow is not quantized, it will be saved as a .flo file losslessly,
    otherwise a jpeg image which is lossy but of much smaller size. (dx and dy
    will be concatenated horizontally into a single image if quantize is True.)

    Args:
        flow (ndarray): (h, w, 2) array of optical flow.
        filename (str): Output filepath.
        quantize (bool): Whether to quantize the flow and save it to 2 jpeg
            images. If set to True, remaining args will be passed to
            :func:`quantize_flow`.
        concat_axis (int): The axis that dx and dy are concatenated,
            can be either 0 or 1. Ignored if quantize is False.
    """
    dir_name = os.path.abspath(os.path.dirname(filename))
    os.makedirs(dir_name, exist_ok=True)
    if not quantize:
        with open(filename, 'wb') as f:
            f.write('PIEH'.encode('utf-8'))
            np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
            # flow = flow.astype(np.float32)
            flow = flow.astype(np.float16)
            flow.tofile(f)
            f.flush()
    else:
        assert concat_axis in [0, 1]
        dx, dy = quantize_flow(flow, *args, **kwargs)
        dxdy = np.concatenate((dx, dy), axis=concat_axis)
        # os.makedirs(os.path.dirname(filename), exist_ok=True)
        cv2.imwrite(filename, dxdy)
        # imwrite(dxdy, filename)


def quantize_flow(flow, max_val=0.02, norm=True):
    """Quantize flow to [0, 255].

    After this step, the size of flow will be much smaller, and can be
    dumped as jpeg images.

    Args:
        flow (ndarray): (h, w, 2) array of optical flow.
        max_val (float): Maximum value of flow, values beyond
                        [-max_val, max_val] will be truncated.
        norm (bool): Whether to divide flow values by image width/height.

    Returns:
        tuple[ndarray]: Quantized dx and dy.
    """
    h, w, _ = flow.shape
    dx = flow[..., 0]
    dy = flow[..., 1]
    if norm:
        dx = dx / w  # avoid inplace operations
        dy = dy / h
    # use 255 levels instead of 256 to make sure 0 is 0 after dequantization.
    flow_comps = [quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]]
    return tuple(flow_comps)


def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
    """Recover from quantized flow.

    Args:
        dx (ndarray): Quantized dx.
        dy (ndarray): Quantized dy.
        max_val (float): Maximum value used when quantizing.
        denorm (bool): Whether to multiply flow values with width/height.

    Returns:
        ndarray: Dequantized flow.
    """
    assert dx.shape == dy.shape
    assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)

    dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]

    if denorm:
        dx *= dx.shape[1]
        dy *= dx.shape[0]
    flow = np.dstack((dx, dy))
    return flow


def quantize(arr, min_val, max_val, levels, dtype=np.int64):
    """Quantize an array of (-inf, inf) to [0, levels-1].

    Args:
        arr (ndarray): Input array.
        min_val (scalar): Minimum value to be clipped.
        max_val (scalar): Maximum value to be clipped.
        levels (int): Quantization levels.
        dtype (np.type): The type of the quantized array.

    Returns:
        tuple: Quantized array.
    """
    if not (isinstance(levels, int) and levels > 1):
        raise ValueError(f'levels must be a positive integer, but got {levels}')
    if min_val >= max_val:
        raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})')

    arr = np.clip(arr, min_val, max_val) - min_val
    quantized_arr = np.minimum(np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1)

    return quantized_arr


def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
    """Dequantize an array.

    Args:
        arr (ndarray): Input array.
        min_val (scalar): Minimum value to be clipped.
        max_val (scalar): Maximum value to be clipped.
        levels (int): Quantization levels.
        dtype (np.type): The type of the dequantized array.

    Returns:
        tuple: Dequantized array.
    """
    if not (isinstance(levels, int) and levels > 1):
        raise ValueError(f'levels must be a positive integer, but got {levels}')
    if min_val >= max_val:
        raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})')

    dequantized_arr = (arr + 0.5).astype(dtype) * (max_val - min_val) / levels + min_val

    return dequantized_arr