66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
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import matplotlib
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import numpy as np
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import torch
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from PIL import Image
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def colorize_depth_maps(depth_map, min_depth, max_depth, cmap='Spectral', valid_mask=None):
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"""
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Colorize depth maps.
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"""
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assert len(depth_map.shape) >= 2, "Invalid dimension"
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if isinstance(depth_map, torch.Tensor):
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depth = depth_map.detach().clone().squeeze().numpy()
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elif isinstance(depth_map, np.ndarray):
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depth = depth_map.copy().squeeze()
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# reshape to [ (B,) H, W ]
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if depth.ndim < 3:
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depth = depth[np.newaxis, :, :]
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# colorize
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cm = matplotlib.colormaps[cmap]
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depth = ((depth - min_depth) / (max_depth - min_depth)).clip(0, 1)
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img_colored_np = cm(depth, bytes=False)[:,:,:,0:3] # value from 0 to 1
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img_colored_np = np.rollaxis(img_colored_np, 3, 1)
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if valid_mask is not None:
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if isinstance(depth_map, torch.Tensor):
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valid_mask = valid_mask.detach().numpy()
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valid_mask = valid_mask.squeeze() # [H, W] or [B, H, W]
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if valid_mask.ndim < 3:
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valid_mask = valid_mask[np.newaxis, np.newaxis, :, :]
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else:
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valid_mask = valid_mask[:, np.newaxis, :, :]
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valid_mask = np.repeat(valid_mask, 3, axis=1)
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img_colored_np[~valid_mask] = 0
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if isinstance(depth_map, torch.Tensor):
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img_colored = torch.from_numpy(img_colored_np).float()
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elif isinstance(depth_map, np.ndarray):
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img_colored = img_colored_np
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return img_colored
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def chw2hwc(chw):
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assert 3 == len(chw.shape)
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if isinstance(chw, torch.Tensor):
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hwc = torch.permute(chw, (1, 2, 0))
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elif isinstance(chw, np.ndarray):
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hwc = np.moveaxis(chw, 0, -1)
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return hwc
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def resize_max_res(img: Image.Image, max_edge_resolution):
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original_width, original_height = img.size
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downscale_factor = min(max_edge_resolution / original_width, max_edge_resolution / original_height)
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new_width = int(original_width * downscale_factor)
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new_height = int(original_height * downscale_factor)
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resized_img = img.resize((new_width, new_height))
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return resized_img
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