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Extract frames from video files and save them as images using OpenCV
Video Frame Extraction Skill
Purpose
This skill enables extraction of individual frames from video files (MP4, AVI, MOV, etc.) using OpenCV. Extracted frames are saved as image files in a specified output directory. It is suitable for video analysis, creating training datasets, thumbnail generation, and preprocessing video content for further processing.
When to Use
Extracting frames for machine learning training data
Creating image sequences from video content
Generating video thumbnails or preview images
Preprocessing videos for object detection or tracking
Converting video segments to image collections for analysis
Sampling frames at specific intervals for time-lapse effects
Required Libraries
The following Python libraries are required:
import cv2
import os
import json
from pathlib import Path
Input Requirements
File formats: MP4, AVI, MOV, MKV, WMV, FLV, WEBM
Video codec: Must be readable by OpenCV (most common codecs supported)
File access: Read permissions on source video
Output directory: Write permissions on destination folder
Disk space: Ensure sufficient space for extracted frames (uncompressed images)
Output Schema
All extraction results must be returned as valid JSON conforming to this schema:
import cv2
import os
defextract_frames_by_seconds(video_path, output_dir, seconds_interval=1.0):
"""Extract frames at specific time intervals (in seconds)."""
os.makedirs(output_dir, exist_ok=True)
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
frame_interval = int(fps * seconds_interval)
if frame_interval < 1:
frame_interval = 1
frame_index = 0
saved_count = 0whileTrue:
ret, frame = cap.read()
ifnot ret:
breakif frame_index % frame_interval == 0:
filename = os.path.join(output_dir, f"frame_{saved_count:06d}.jpg")
cv2.imwrite(filename, frame)
saved_count += 1
frame_index += 1
cap.release()
return saved_count
Batch Processing Multiple Videos
import cv2
import os
import json
from pathlib import Path
defprocess_video_directory(video_dir, output_base_dir, interval=1):
"""Process all videos in a directory and extract frames."""
video_extensions = {'.mp4', '.avi', '.mov', '.mkv', '.wmv', '.flv', '.webm'}
results = []
for video_file insorted(Path(video_dir).iterdir()):
if video_file.suffix.lower() in video_extensions:
video_output_dir = os.path.join(
output_base_dir,
video_file.stem
)
result = extract_frames_to_json(
str(video_file),
video_output_dir,
interval=interval
)
results.append(result)
print(f"Processed: {video_file.name} -> {result['frames_extracted']} frames")
return results
Extraction Configuration Options
Output Image Formats
# JPEG format (default, good balance of quality and size)
cv2.imwrite("frame.jpg", frame)
# PNG format (lossless, larger files)
cv2.imwrite("frame.png", frame)
# JPEG with custom quality (0-100)
cv2.imwrite("frame.jpg", frame, [cv2.IMWRITE_JPEG_QUALITY, 95])
# PNG with compression level (0-9)
cv2.imwrite("frame.png", frame, [cv2.IMWRITE_PNG_COMPRESSION, 3])
Frame Seeking Methods
# Seek by frame number
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
# Seek by milliseconds
cap.set(cv2.CAP_PROP_POS_MSEC, milliseconds)
# Seek by ratio (0.0 to 1.0)
cap.set(cv2.CAP_PROP_POS_AVI_RATIO, 0.5) # Middle of video
defget_video_info(video_path):
"""Retrieve video metadata."""
cap = cv2.VideoCapture(video_path)
ifnot cap.isOpened():
returnNone
info = {
"total_frames": int(cap.get(cv2.CAP_PROP_FRAME_COUNT)),
"fps": cap.get(cv2.CAP_PROP_FPS),
"width": int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
"height": int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)),
"codec": int(cap.get(cv2.CAP_PROP_FOURCC)),
"duration_seconds": cap.get(cv2.CAP_PROP_FRAME_COUNT) / cap.get(cv2.CAP_PROP_FPS)
}
cap.release()
return info
Specific Frame Extraction
For extracting frames at exact positions:
defextract_specific_frames(video_path, output_dir, frame_numbers):
"""Extract specific frames by their indices."""
os.makedirs(output_dir, exist_ok=True)
cap = cv2.VideoCapture(video_path)
extracted = []
for frame_num insorted(frame_numbers):
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_num)
ret, frame = cap.read()
if ret:
filename = os.path.join(output_dir, f"frame_{frame_num:06d}.jpg")
cv2.imwrite(filename, frame)
extracted.append(frame_num)
cap.release()
return extracted
Error Handling
Common Issues and Solutions
Issue: Video file cannot be opened
cap = cv2.VideoCapture(video_path)
ifnot cap.isOpened():
print(f"Error: Cannot open video file: {video_path}")
print("Check file path, permissions, and codec support")
Issue: Frames read as None
ret, frame = cap.read()
ifnot ret or frame isNone:
print("Failed to read frame - video may be corrupted or ended")
Issue: Codec not supported
# Check if video has valid properties
fps = cap.get(cv2.CAP_PROP_FPS)
if fps == 0:
print("Warning: Could not detect FPS - codec may be unsupported")
Issue: Disk space exhausted
import shutil
defcheck_disk_space(output_dir, required_mb=100):
"""Check available disk space before extraction."""
stat = shutil.disk_usage(output_dir)
available_mb = stat.free / (1024 * 1024)
return available_mb >= required_mb
Quality Self-Check
Before returning results, verify:
Output is valid JSON (use json.loads() to validate)
All required fields are present (success, source_video, frames_extracted, video_metadata)
Output directory was created successfully
Extracted frame count matches expected value based on interval
