Spaces:
Running
on
Zero
Running
on
Zero
| # A reimplemented version in public environments by Xiao Fu and Mu Hu | |
| import matplotlib | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| def resize_max_res(img: Image.Image, max_edge_resolution: int) -> Image.Image: | |
| """ | |
| Resize image to limit maximum edge length while keeping aspect ratio. | |
| Args: | |
| img (`Image.Image`): | |
| Image to be resized. | |
| max_edge_resolution (`int`): | |
| Maximum edge length (pixel). | |
| Returns: | |
| `Image.Image`: Resized image. | |
| """ | |
| original_width, original_height = img.size | |
| downscale_factor = min( | |
| max_edge_resolution / original_width, max_edge_resolution / original_height | |
| ) | |
| new_width = int(original_width * downscale_factor) | |
| new_height = int(original_height * downscale_factor) | |
| resized_img = img.resize((new_width, new_height)) | |
| return resized_img | |
| def colorize_depth_maps( | |
| depth_map, min_depth, max_depth, cmap="Spectral", valid_mask=None | |
| ): | |
| """ | |
| Colorize depth maps. | |
| """ | |
| assert len(depth_map.shape) >= 2, "Invalid dimension" | |
| if isinstance(depth_map, torch.Tensor): | |
| depth = depth_map.detach().clone().squeeze().numpy() | |
| elif isinstance(depth_map, np.ndarray): | |
| depth = depth_map.copy().squeeze() | |
| # reshape to [ (B,) H, W ] | |
| if depth.ndim < 3: | |
| depth = depth[np.newaxis, :, :] | |
| # colorize | |
| cm = matplotlib.colormaps[cmap] | |
| depth = ((depth - min_depth) / (max_depth - min_depth)).clip(0, 1) | |
| img_colored_np = cm(depth, bytes=False)[:, :, :, 0:3] # value from 0 to 1 | |
| img_colored_np = np.rollaxis(img_colored_np, 3, 1) | |
| if valid_mask is not None: | |
| if isinstance(depth_map, torch.Tensor): | |
| valid_mask = valid_mask.detach().numpy() | |
| valid_mask = valid_mask.squeeze() # [H, W] or [B, H, W] | |
| if valid_mask.ndim < 3: | |
| valid_mask = valid_mask[np.newaxis, np.newaxis, :, :] | |
| else: | |
| valid_mask = valid_mask[:, np.newaxis, :, :] | |
| valid_mask = np.repeat(valid_mask, 3, axis=1) | |
| img_colored_np[~valid_mask] = 0 | |
| if isinstance(depth_map, torch.Tensor): | |
| img_colored = torch.from_numpy(img_colored_np).float() | |
| elif isinstance(depth_map, np.ndarray): | |
| img_colored = img_colored_np | |
| return img_colored | |
| def chw2hwc(chw): | |
| assert 3 == len(chw.shape) | |
| if isinstance(chw, torch.Tensor): | |
| hwc = torch.permute(chw, (1, 2, 0)) | |
| elif isinstance(chw, np.ndarray): | |
| hwc = np.moveaxis(chw, 0, -1) | |
| return hwc | |