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on
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Running
on
Zero
File size: 2,303 Bytes
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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, max_res):
print("==> processing video: ", video_path)
vid = VideoReader(video_path, ctx=cpu(0))
print("==> original video shape: ", (len(vid), *vid.get_batch([0]).shape[1:]))
original_height, original_width = vid.get_batch([0]).shape[1:3]
if max(original_height, original_width) > max_res:
scale = max_res / max(original_height, original_width)
height = round(original_height * scale)
width = round(original_width * scale)
else:
height = original_height
width = original_width
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))
print(
f"==> downsampled shape: {len(frames_idx), *vid.get_batch([0]).shape[1:]}, with stride: {stride}"
)
if process_length != -1 and process_length < len(frames_idx):
frames_idx = frames_idx[:process_length]
print(
f"==> final processing shape: {len(frames_idx), *vid.get_batch([0]).shape[1:]}"
)
frames = vid.get_batch(frames_idx).asnumpy().astype(np.uint8)
frames = [PIL.Image.fromarray(x) for x in frames]
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 * 255).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
def vis_sequence_normal(normals: np.ndarray):
normals = normals.clip(-1., 1.)
normals = normals * 0.5 + 0.5
return normals
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