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Running
on
Zero
File size: 2,524 Bytes
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# 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
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