LiDAR-Perfect-Depth / code /ppd /utils /video_utils.py
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# This file is originally from Video Depth Anything
import numpy as np
import matplotlib.cm as cm
import imageio
import cv2
def read_video_frames(video_path, process_length=-1, target_fps=-1, max_res=-1):
cap = cv2.VideoCapture(video_path)
original_fps = cap.get(cv2.CAP_PROP_FPS)
original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
if max_res > 0 and 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)
fps = original_fps if target_fps < 0 else target_fps
stride = max(round(original_fps / fps), 1)
frames = []
frame_count = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret or (process_length > 0 and frame_count >= process_length):
break
if frame_count % stride == 0:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # Convert BGR to RGB
if max_res > 0 and max(original_height, original_width) > max_res:
frame = cv2.resize(frame, (width, height)) # Resize frame
frames.append(frame)
frame_count += 1
cap.release()
frames = np.stack(frames, axis=0)
return frames, fps
def save_video(frames, output_video_path, fps=10, is_depths=False, grayscale=False):
writer = imageio.get_writer(output_video_path, fps=fps, macro_block_size=1, codec='libx264', ffmpeg_params=['-crf', '18'])
if is_depths:
colormap = np.array(cm.get_cmap("inferno").colors)
d_min, d_max = frames.min(), frames.max()
for i in range(frames.shape[0]):
depth = frames[i]
depth_norm = ((depth - d_min) / (d_max - d_min) * 255).astype(np.uint8)
depth_vis = (colormap[depth_norm] * 255).astype(np.uint8) if not grayscale else depth_norm
writer.append_data(depth_vis)
else:
for i in range(frames.shape[0]):
writer.append_data(frames[i])
writer.close()