Datasets:
Tasks:
Object Detection
Formats:
text
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
Multi-object-tracking
License:
File size: 1,736 Bytes
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import os
import cv2
import numpy as np
# Paths
videos_dir = "test_vid"
frames_dir = "test_frame"
# Define how many frames to extract for each video (should be same in GT file last row)
frames_to_extract = {
"task_day_left_turn-2024_07_31_02_12_05-mot 1.1.mp4": 1965,
# add more here...
}
os.makedirs(frames_dir, exist_ok=True)
for video_file in os.listdir(videos_dir):
if video_file.lower().endswith(".mp4"):
video_path = os.path.join(videos_dir, video_file)
# Subdirectory name (remove .mp4)
subdir_name = os.path.splitext(video_file)[0]
subdir_path = os.path.join(frames_dir, subdir_name)
os.makedirs(subdir_path, exist_ok=True)
cap = cv2.VideoCapture(video_path)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
desired_frames = frames_to_extract.get(video_file, total_frames)
saved = 0
frames = []
while True:
ret, frame = cap.read()
if not ret:
break
frames.append(frame)
cap.release()
if desired_frames > len(frames):
last_frame = frames[-1]
while len(frames) < desired_frames:
frames.append(last_frame)
if desired_frames < len(frames):
indices = np.linspace(0, len(frames) - 1, desired_frames, dtype=int)
frames = [frames[i] for i in indices]
for idx, frame in enumerate(frames):
frame_filename = os.path.join(subdir_path, f"frame_{idx:05d}.jpg")
cv2.imwrite(frame_filename, frame)
saved += 1
print(f"Extracted {saved} frames from {video_file} into {subdir_name}/")
print(" Done extracting frames for all videos.") |