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| # Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/video/optflow.py # noqa: E501 | |
| import cv2 | |
| import numpy as np | |
| import os | |
| 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)) | |
| 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. | |
| """ | |
| 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.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) | |
| 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 | |