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on
Zero
Running
on
Zero
File size: 1,587 Bytes
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import torch
from matplotlib import cm
def robust_min_max(tensor, quantile=0.99):
T, H, W = tensor.shape
min_vals = []
max_vals = []
for i in range(T):
min_vals.append(torch.quantile(tensor[i], q=1-quantile, interpolation='nearest').item())
max_vals.append(torch.quantile(tensor[i], q=quantile, interpolation='nearest').item())
return min(min_vals), max(max_vals)
class ColorMapper:
def __init__(self, colormap: str = "inferno"):
self.colormap = torch.tensor(cm.get_cmap(colormap).colors)
def apply(self, image: torch.Tensor, v_min=None, v_max=None):
# assert len(image.shape) == 2
if v_min is None:
v_min = image.min()
if v_max is None:
v_max = image.max()
image = (image - v_min) / (v_max - v_min)
image = (image * 255).long()
colormap = self.colormap.to(image.device)
image = colormap[image]
return image
def color_video_disp(disp):
visualizer = ColorMapper()
disp_img = visualizer.apply(disp, v_min=0, v_max=1)
return disp_img
def pmap_to_disp(point_maps, valid_masks):
disp_map = 1.0 / (point_maps[..., 2] + 1e-4)
min_disparity, max_disparity = robust_min_max(disp_map)
disp_map = torch.clamp((disp_map - min_disparity) / (max_disparity - min_disparity+1e-4), 0, 1)
disp_map = color_video_disp(disp_map)
disp_map[~valid_masks] = 0
return disp_map
# imageio.mimsave(os.path.join(args.save_dir, os.path.basename(args.data[:-4])+'_disp.mp4'), disp, fps=24, quality=9, macro_block_size=1)
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