Spaces:
Running
on
A10G
Running
on
A10G
File size: 3,051 Bytes
bcec54e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
# ------------------------------------------------------------------------------
# The code is from ZoeDepth (https://github.com/isl-org/ZoeDepth).
# For non-commercial purpose only (research, evaluation etc).
# ------------------------------------------------------------------------------
from scipy import ndimage
import math
import matplotlib
import matplotlib.cm
import numpy as np
import requests
import torch
from PIL import Image
from torchvision.transforms import ToTensor
def colorize(value, vmin=None, vmax=None, cmap='gray_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.colormaps.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, vmin, vmax
|