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| import cv2
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| import numpy as np
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| import os
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| def flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs):
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| """Read an optical flow map.
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|
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| Args:
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| flow_path (ndarray or str): Flow path.
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| quantize (bool): whether to read quantized pair, if set to True,
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| remaining args will be passed to :func:`dequantize_flow`.
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| concat_axis (int): The axis that dx and dy are concatenated,
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| can be either 0 or 1. Ignored if quantize is False.
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|
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| Returns:
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| ndarray: Optical flow represented as a (h, w, 2) numpy array
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| """
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| if quantize:
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| assert concat_axis in [0, 1]
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| cat_flow = cv2.imread(flow_path, cv2.IMREAD_UNCHANGED)
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| if cat_flow.ndim != 2:
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| raise IOError(f'{flow_path} is not a valid quantized flow file, its dimension is {cat_flow.ndim}.')
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| assert cat_flow.shape[concat_axis] % 2 == 0
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| dx, dy = np.split(cat_flow, 2, axis=concat_axis)
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| flow = dequantize_flow(dx, dy, *args, **kwargs)
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| else:
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| with open(flow_path, 'rb') as f:
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| try:
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| header = f.read(4).decode('utf-8')
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| except Exception:
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| raise IOError(f'Invalid flow file: {flow_path}')
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| else:
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| if header != 'PIEH':
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| raise IOError(f'Invalid flow file: {flow_path}, header does not contain PIEH')
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| w = np.fromfile(f, np.int32, 1).squeeze()
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| h = np.fromfile(f, np.int32, 1).squeeze()
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| flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2))
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| return flow.astype(np.float32)
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| def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs):
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| """Write optical flow to file.
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|
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| If the flow is not quantized, it will be saved as a .flo file losslessly,
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| otherwise a jpeg image which is lossy but of much smaller size. (dx and dy
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| will be concatenated horizontally into a single image if quantize is True.)
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|
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| Args:
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| flow (ndarray): (h, w, 2) array of optical flow.
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| filename (str): Output filepath.
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| quantize (bool): Whether to quantize the flow and save it to 2 jpeg
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| images. If set to True, remaining args will be passed to
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| :func:`quantize_flow`.
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| concat_axis (int): The axis that dx and dy are concatenated,
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| can be either 0 or 1. Ignored if quantize is False.
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| """
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| if not quantize:
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| with open(filename, 'wb') as f:
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| f.write('PIEH'.encode('utf-8'))
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| np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
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| flow = flow.astype(np.float32)
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| flow.tofile(f)
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| f.flush()
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| else:
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| assert concat_axis in [0, 1]
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| dx, dy = quantize_flow(flow, *args, **kwargs)
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| dxdy = np.concatenate((dx, dy), axis=concat_axis)
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| os.makedirs(os.path.dirname(filename), exist_ok=True)
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| cv2.imwrite(filename, dxdy)
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| def quantize_flow(flow, max_val=0.02, norm=True):
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| """Quantize flow to [0, 255].
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|
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| After this step, the size of flow will be much smaller, and can be
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| dumped as jpeg images.
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| Args:
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| flow (ndarray): (h, w, 2) array of optical flow.
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| max_val (float): Maximum value of flow, values beyond
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| [-max_val, max_val] will be truncated.
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| norm (bool): Whether to divide flow values by image width/height.
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|
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| Returns:
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| tuple[ndarray]: Quantized dx and dy.
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| """
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| h, w, _ = flow.shape
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| dx = flow[..., 0]
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| dy = flow[..., 1]
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| if norm:
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| dx = dx / w
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| dy = dy / h
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| flow_comps = [quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]]
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| return tuple(flow_comps)
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|
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| def dequantize_flow(dx, dy, max_val=0.02, denorm=True):
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| """Recover from quantized flow.
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|
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| Args:
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| dx (ndarray): Quantized dx.
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| dy (ndarray): Quantized dy.
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| max_val (float): Maximum value used when quantizing.
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| denorm (bool): Whether to multiply flow values with width/height.
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|
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| Returns:
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| ndarray: Dequantized flow.
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| """
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| assert dx.shape == dy.shape
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| assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1)
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|
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| dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]]
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|
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| if denorm:
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| dx *= dx.shape[1]
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| dy *= dx.shape[0]
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| flow = np.dstack((dx, dy))
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| return flow
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|
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| def quantize(arr, min_val, max_val, levels, dtype=np.int64):
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| """Quantize an array of (-inf, inf) to [0, levels-1].
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|
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| Args:
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| arr (ndarray): Input array.
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| min_val (scalar): Minimum value to be clipped.
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| max_val (scalar): Maximum value to be clipped.
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| levels (int): Quantization levels.
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| dtype (np.type): The type of the quantized array.
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|
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| Returns:
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| tuple: Quantized array.
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| """
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| if not (isinstance(levels, int) and levels > 1):
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| raise ValueError(f'levels must be a positive integer, but got {levels}')
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| if min_val >= max_val:
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| raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})')
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|
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| arr = np.clip(arr, min_val, max_val) - min_val
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| quantized_arr = np.minimum(np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1)
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|
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| return quantized_arr
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|
|
|
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| def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
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| """Dequantize an array.
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|
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| Args:
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| arr (ndarray): Input array.
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| min_val (scalar): Minimum value to be clipped.
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| max_val (scalar): Maximum value to be clipped.
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| levels (int): Quantization levels.
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| dtype (np.type): The type of the dequantized array.
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|
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| Returns:
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| tuple: Dequantized array.
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| """
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| if not (isinstance(levels, int) and levels > 1):
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| raise ValueError(f'levels must be a positive integer, but got {levels}')
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| if min_val >= max_val:
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| raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})')
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| dequantized_arr = (arr + 0.5).astype(dtype) * (max_val - min_val) / levels + min_val
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|
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| return dequantized_arr
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|
|
