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| # from https://huggingface.co/spaces/shariqfarooq/ZoeDepth/raw/main/utils.py | |
| # MIT License | |
| # Copyright (c) 2022 Intelligent Systems Lab Org | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| # File author: Shariq Farooq Bhat | |
| import matplotlib | |
| import matplotlib.cm | |
| import numpy as np | |
| import torch | |
| def colorize(value, vmin=None, vmax=None, cmap='magma_r', 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],85) 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 | |
| import os | |
| # bard... | |
| def find_most_recently_created_directory(temp_dir): | |
| """Finds the most recently created directory in a directory. | |
| Args: | |
| temp_dir: The directory to search. | |
| Returns: | |
| The path to the most recently created directory. | |
| """ | |
| directories = os.listdir(temp_dir) | |
| most_recently_created_directory = None | |
| for directory in directories: | |
| path = os.path.join(temp_dir, directory) | |
| st = os.stat(path) | |
| if most_recently_created_directory is None or st.mtime > most_recently_created_directory.mtime: | |
| most_recently_created_directory = path | |
| if most_recently_created_directory is None: | |
| most_recently_created_directory = temp_dir | |
| return most_recently_created_directory | |
| #chatgpt | |
| def get_most_recent_subdirectory(path): | |
| if not os.path.isdir(path): | |
| return path | |
| subdirectories = [f for f in os.listdir(path) if os.path.isdir(os.path.join(path, f))] | |
| if not subdirectories: | |
| return path | |
| most_recent_subdirectory = max(subdirectories, key=lambda d: os.path.getctime(os.path.join(path, d))) | |
| return os.path.join(path, most_recent_subdirectory) | |