Skill: lnj22/pedestrian-traffic-counting-video-frame-extraction (version 0.1.0)

--- name: video-frame-extraction description: 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: ```python 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: ```json { "success": true, "source_video": "sample.mp4", "output_directory": "/path/to/frames", "frames_extracted": 150, "extraction_params": { "interval": 1, "start_frame": 0, "end_frame": null, "output_format": "jpg" }, "video_metadata": { "total_frames": 300, "fps": 30.0, "duration_seconds": 10.0, "resolution": [1920, 1080] }, "output_files": [ "frame_000001.jpg", "frame_000002.jpg" ], "warnings": [] } ``` ### Field Descriptions - `success`: Boolean indicating whether frame extraction completed - `source_video`: Original video filename - `output_directory`: Path where frames were saved - `frames_extracted`: Total number of frames successfully saved - `extraction_params.interval`: Frame sampling interval (1 = every frame, 2 = every other frame, etc.) - `extraction_params.start_frame`: First frame index extracted - `extraction_params.end_frame`: Last frame index extracted (null if extracted to end) - `extraction_params.output_format`: Image format used for saving frames - `video_metadata.total_frames`: Total frame count in source video - `video_metadata.fps`: Frames per second of source video - `video_metadata.duration_seconds`: Video duration in seconds - `video_metadata.resolution`: Video dimensions as [width, height] - `output_files`: List of generated frame filenames - `warnings`: Array of issues encountered during extraction ## Code Examples ### Basic Frame Extraction ```python import cv2 import os def extract_all_frames(video_path, output_dir): """Extract all frames from a video file.""" os.makedirs(output_dir, exist_ok=True) cap = cv2.VideoCapture(video_path) frame_count = 0 while True: ret, frame = cap.read() if not ret: break filename = os.path.join(output_dir, f"frame_{frame_count:06d}.jpg") cv2.imwrite(filename, frame) frame_count += 1 cap.release() return frame_count ``` ### Interval-Based Frame Extraction ```python import cv2 import os def extract_frames_at_interval(video_path, output_dir, interval=1): """Extract frames at specified intervals.""" os.makedirs(output_dir, exist_ok=True) cap = cv2.VideoCapture(video_path) frame_index = 0 saved_count = 0 while True: ret, frame = cap.read() if not ret: break if frame_index % 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 ``` ### Full Extraction with JSON Output ```python import cv2 import os import json from pathlib import Path def extract_frames_to_json(video_path, output_dir, interval=1, start_frame=0, end_frame=None, output_format="jpg"): """Extract frames and return results as JSON.""" video_name = os.path.basename(video_path) warnings = [] output_files = [] try: os.makedirs(output_dir, exist_ok=True) cap = cv2.VideoCapture(video_path) if not cap.isOpened(): raise ValueError(f"Cannot open video: {video_path}") # Get video metadata 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)) duration = total_frames / fps if fps > 0 else 0 # Set end frame if not specified if end_frame is None: end_frame = total_frames # Seek to start frame if start_frame > 0: cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame) frame_index = start_frame saved_count = 0 while frame_index < end_frame: ret, frame = cap.read() if not ret: if frame_index < end_frame: warnings.append(f"Video ended early at frame {frame_index}") break if (frame_index - start_frame) % interval == 0: filename = f"frame_{saved_count:06d}.{output_format}" filepath = os.path.join(output_dir, filename) cv2.imwrite(filepath, frame) output_files.append(filename) saved_count += 1 frame_index += 1 cap.release() result = { "success": True, "source_video": video_name, "output_directory": str(output_dir), "frames_extracted": saved_count, "extraction_params": { "interval": interval, "start_frame": start_frame, "end_frame": end_frame, "output_format": output_format }, "video_metadata": { "total_frames": total_frames, "fps": fps, "duration_seconds": round(duration, 2), "resolution": [width, height] }, "output_files": output_files, "warnings": warnings } except Exception as e: result = { "success": False, "source_video": video_name, "output_directory": str(output_dir), "frames_extracted": 0, "extraction_params": { "interval": interval, "start_frame": start_frame, "end_frame": end_frame, "output_format": output_format }, "video_metadata": { "total_frames": 0, "fps": 0, "duration_seconds": 0, "resolution": [0, 0] }, "output_files": [], "warnings": [f"Extraction failed: {str(e)}"] } return result # Usage result = extract_frames_to_json("video.mp4", "./frames", interval=10) print(json.dumps(result, indent=2)) ``` ### Time-Based Frame Extraction ```python import cv2 import os def extract_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 = 0 while True: ret, frame = cap.read() if not ret: break if 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 ```python import cv2 import os import json from pathlib import Path def process_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 in sorted(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 ```python # 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 ```python # 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 ``` ### Frame Resizing ```python def extract_resized_frames(video_path, output_dir, target_size=(640, 480)): """Extract and resize frames to specified dimensions.""" os.makedirs(output_dir, exist_ok=True) cap = cv2.VideoCapture(video_path) frame_count = 0 while True: ret, frame = cap.read() if not ret: break resized = cv2.resize(frame, target_size) filename = os.path.join(output_dir, f"frame_{frame_count:06d}.jpg") cv2.imwrite(filename, resized) frame_count += 1 cap.release() return frame_count ``` ## Video Metadata Retrieval Extract video properties before processing: ```python def get_video_info(video_path): """Retrieve video metadata.""" cap = cv2.VideoCapture(video_path) if not cap.isOpened(): return None 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: ```python def extract_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 in sorted(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 ```python cap = cv2.VideoCapture(video_path) if not cap.isOpened(): print(f"Error: Cannot open video file: {video_path}") print("Check file path, permissions, and codec support") ``` **Issue**: Frames read as None ```python ret, frame = cap.read() if not ret or frame is None: print("Failed to read frame - video may be corrupted or ended") ``` **Issue**: Codec not supported ```python # 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 ```python import shutil def check_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 - [ ] Warnings array includes all detected issues - [ ] Video was properly released with `cap.release()` - [ ] Frame filenames follow consistent zero-padded numbering ## Limitations - OpenCV may not support all video codecs; install additional codecs if needed - Seeking in variable frame rate videos may be inaccurate - Large videos with high frame counts require significant disk space - Memory usage increases with video resolution - Some container formats (MKV with certain codecs) may have seeking issues - Encrypted or DRM-protected videos cannot be processed - Damaged or partially corrupted videos may extract partial results ## Version History - **1.0.0** (2026-01-21): Initial release with OpenCV video frame extraction


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)