Spaces:
Running
on
L40S
Running
on
L40S
File size: 3,015 Bytes
55ac26c |
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 75 76 77 78 79 80 81 82 83 |
# This file is originally from DepthCrafter/depthcrafter/utils.py at main · Tencent/DepthCrafter
# SPDX-License-Identifier: MIT License license
#
# This file may have been modified by ByteDance Ltd. and/or its affiliates on [date of modification]
# Original file is released under [ MIT License license], with the full license text available at [https://github.com/Tencent/DepthCrafter?tab=License-1-ov-file].
from typing import Union, List
import tempfile
import numpy as np
import PIL.Image
import matplotlib.cm as cm
import mediapy
import torch
from decord import VideoReader, cpu
def read_video_frames(video_path, process_length, target_fps=-1, max_res=-1, dataset="open"):
vid = VideoReader(video_path, ctx=cpu(0))
original_height, original_width = vid.get_batch([0]).shape[1:3]
height = original_height
width = original_width
if max_res > 0 and max(height, width) > max_res:
scale = max_res / max(original_height, original_width)
height = round(original_height * scale)
width = round(original_width * scale)
vid = VideoReader(video_path, ctx=cpu(0), width=width, height=height)
fps = vid.get_avg_fps() if target_fps == -1 else target_fps
stride = round(vid.get_avg_fps() / fps)
stride = max(stride, 1)
frames_idx = list(range(0, len(vid), stride))
if process_length != -1 and process_length < len(frames_idx):
frames_idx = frames_idx[:process_length]
frames = vid.get_batch(frames_idx).asnumpy()
return frames, fps
def save_video(
video_frames: Union[List[np.ndarray], List[PIL.Image.Image]],
output_video_path: str = None,
fps: int = 10,
crf: int = 18,
) -> str:
if output_video_path is None:
output_video_path = tempfile.NamedTemporaryFile(suffix=".mp4").name
if isinstance(video_frames[0], np.ndarray):
video_frames = [frame.astype(np.uint8) for frame in video_frames]
elif isinstance(video_frames[0], PIL.Image.Image):
video_frames = [np.array(frame) for frame in video_frames]
mediapy.write_video(output_video_path, video_frames, fps=fps, crf=crf)
return output_video_path
class ColorMapper:
# a color mapper to map depth values to a certain colormap
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()
image = self.colormap[image] * 255
return image
def vis_sequence_depth(depths: np.ndarray, v_min=None, v_max=None):
visualizer = ColorMapper()
if v_min is None:
v_min = depths.min()
if v_max is None:
v_max = depths.max()
res = visualizer.apply(torch.tensor(depths), v_min=v_min, v_max=v_max).numpy()
return res
|