import matplotlib import matplotlib.cm import numpy as np import torch def colorize(value, vmin=None, vmax=None, cmap='jet', invalid_val=-99, invalid_mask=None, background_color=(128, 128, 128, 255), gamma_corrected=False, value_transform=None): """Converts a depth map to a color image. Args: value (torch.Tensor, numpy.ndarry): Input depth map. Shape: (H, W) or (1, H, W) or (1, 1, H, W). All singular dimensions are squeezed vmin (float, optional): vmin-valued entries are mapped to start color of cmap. If None, value.min() is used. Defaults to None. vmax (float, optional): vmax-valued entries are mapped to end color of cmap. If None, value.max() is used. Defaults to None. cmap (str, optional): matplotlib colormap to use. Defaults to 'magma_r'. invalid_val (int, optional): Specifies value of invalid pixels that should be colored as 'background_color'. Defaults to -99. invalid_mask (numpy.ndarray, optional): Boolean mask for invalid regions. Defaults to None. background_color (tuple[int], optional): 4-tuple RGB color to give to invalid pixels. Defaults to (128, 128, 128, 255). gamma_corrected (bool, optional): Apply gamma correction to colored image. Defaults to False. value_transform (Callable, optional): Apply transform function to valid pixels before coloring. Defaults to None. Returns: numpy.ndarray, dtype - uint8: Colored depth map. Shape: (H, W, 4) """ if isinstance(value, torch.Tensor): value = value.detach().cpu().numpy() value = value.squeeze() if invalid_mask is None: invalid_mask = value == invalid_val mask = np.logical_not(invalid_mask) # normalize vmin = np.percentile(value[mask],2) if vmin is None else vmin vmax = np.percentile(value[mask],98) if vmax is None else vmax if vmin != vmax: value = (value - vmin) / (vmax - vmin) # vmin..vmax else: # Avoid 0-division value = value * 0. # squeeze last dim if it exists # grey out the invalid values value[invalid_mask] = np.nan cmapper = matplotlib.cm.get_cmap(cmap) if value_transform: value = value_transform(value) # value = value / value.max() value = cmapper(value, bytes=True) # (nxmx4) # img = value[:, :, :] img = value[...] img[invalid_mask] = background_color # return img.transpose((2, 0, 1)) if gamma_corrected: # gamma correction img = img / 255 img = np.power(img, 2.2) img = img * 255 img = img.astype(np.uint8) return img