basicsr.utils.flow_util

basicsr.utils.flow_util.dequantize(arr, min_val, max_val, levels, dtype=<class 'numpy.float64'>)

Dequantize an array.

Parameters:

• 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:

Dequantized array.

Return type:

tuple

basicsr.utils.flow_util.dequantize_flow(dx, dy, max_val=0.02, denorm=True)

Recover from quantized flow.

Parameters:

• 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:

Dequantized flow.

Return type:

ndarray

basicsr.utils.flow_util.flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs)

Read an optical flow map.

Parameters:

• flow_path (ndarray or str) – Flow path.

• quantize (bool) – whether to read quantized pair, if set to True, remaining args will be passed to 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:

Optical flow represented as a (h, w, 2) numpy array

Return type:

ndarray

basicsr.utils.flow_util.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.)

Parameters:

• 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 quantize_flow().

• concat_axis (int) – The axis that dx and dy are concatenated, can be either 0 or 1. Ignored if quantize is False.

basicsr.utils.flow_util.quantize(arr, min_val, max_val, levels, dtype=<class 'numpy.int64'>)

Quantize an array of (-inf, inf) to [0, levels-1].

Parameters:

• 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:

Quantized array.

Return type:

tuple

basicsr.utils.flow_util.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.

Parameters:

• 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:

Quantized dx and dy.

Return type:

tuple[ndarray]