update project structure and improve scripts
This commit is contained in:
Ayomide
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"""Simple video processing for memory module detection."""
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import cv2
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import logging
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from pathlib import Path
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from typing import List, Dict, Any
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from exceptions import VideoProcessingError
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logger = logging.getLogger(__name__)
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class VideoProcessor:
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"""Simple video processor for memory module detection."""
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def __init__(self, detector):
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self.detector = detector
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def process_video(self, video_path: str, fps: int = 1, max_frames: int = 100) -> Dict[str, Any]:
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"""
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Process video file and detect memory modules in frames.
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Args:
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video_path: Path to video file
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fps: Frames per second to process (1 = every second)
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max_frames: Maximum frames to process
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Returns:
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Dictionary with detection results
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"""
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try:
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if not Path(video_path).exists():
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raise FileNotFoundError(f"Video file not found: {video_path}")
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logger.info(f"Processing video: {video_path}")
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# Open video
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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raise VideoProcessingError("Could not open video file")
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# Get video properties
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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video_fps = cap.get(cv2.CAP_PROP_FPS)
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duration = total_frames / video_fps if video_fps > 0 else 0
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# Calculate frame interval
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frame_interval = max(1, int(video_fps / fps)) if video_fps > 0 else 1
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logger.info(f"Video properties: {total_frames} frames, {video_fps:.2f}fps, {duration:.2f}s")
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# Process frames
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detections = []
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frame_count = 0
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processed_frames = 0
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while cap.isOpened() and processed_frames < max_frames:
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ret, frame = cap.read()
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if not ret:
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break
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if frame_count % frame_interval == 0:
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try:
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# Get timestamp
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timestamp = frame_count / video_fps if video_fps > 0 else frame_count
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# Detect memory modules in frame
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result = self.detector._perform_detection(frame)
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# Store frame detection
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frame_detection = {
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'frame_number': frame_count,
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'timestamp': round(timestamp, 2),
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'detections': result['detections'],
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'detection_count': result['detection_count']
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}
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detections.append(frame_detection)
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processed_frames += 1
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logger.info(f"Frame {frame_count}: {result['detection_count']} detections")
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except Exception as e:
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logger.warning(f"Failed to process frame {frame_count}: {e}")
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frame_count += 1
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cap.release()
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# Generate summary
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total_detections = sum(d['detection_count'] for d in detections)
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avg_detections = total_detections / len(detections) if detections else 0
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result = {
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'video_info': {
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'path': video_path,
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'total_frames': total_frames,
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'fps': video_fps,
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'duration': duration
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},
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'processing_info': {
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'frames_processed': processed_frames,
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'frame_interval': frame_interval,
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'target_fps': fps
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},
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'detections': detections,
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'summary': {
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'total_detections': total_detections,
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'avg_detections_per_frame': round(avg_detections, 2),
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'frames_with_detections': len([d for d in detections if d['detection_count'] > 0])
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}
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}
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logger.info(f"Video processing completed: {processed_frames} frames, {total_detections} total detections")
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return result
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except Exception as e:
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logger.error(f"Video processing failed: {e}")
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raise VideoProcessingError(f"Video processing failed: {str(e)}")
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def create_annotated_video(self, video_path: str, output_path: str, fps: int = 1) -> str:
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"""
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Create annotated video with bounding boxes.
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Args:
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video_path: Input video path
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output_path: Output video path
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fps: Processing fps
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Returns:
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Path to annotated video
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"""
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try:
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logger.info(f"Creating annotated video: {video_path} -> {output_path}")
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# Open input video
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cap = cv2.VideoCapture(video_path)
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if not cap.isOpened():
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raise VideoProcessingError("Could not open input video")
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# Get video properties
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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video_fps = cap.get(cv2.CAP_PROP_FPS)
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# Create output video writer
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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out = cv2.VideoWriter(output_path, fourcc, video_fps, (width, height))
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frame_interval = max(1, int(video_fps / fps)) if video_fps > 0 else 1
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frame_count = 0
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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# Process every nth frame for detection
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if frame_count % frame_interval == 0:
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try:
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result = self.detector._perform_detection(frame)
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annotated_frame = result['annotated_image']
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out.write(annotated_frame)
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except Exception as e:
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logger.warning(f"Failed to annotate frame {frame_count}: {e}")
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out.write(frame) # Write original frame if annotation fails
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else:
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out.write(frame) # Write original frame
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frame_count += 1
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cap.release()
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out.release()
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logger.info(f"Annotated video created: {output_path}")
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return output_path
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except Exception as e:
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logger.error(f"Annotated video creation failed: {e}")
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raise VideoProcessingError(f"Annotated video creation failed: {str(e)}")
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def extract_frames(video_path: str, output_dir: str, fps: int = 1) -> List[str]:
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"""
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Extract frames from video for individual processing.
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Args:
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video_path: Path to video file
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output_dir: Directory to save frames
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fps: Frames per second to extract
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Returns:
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List of extracted frame paths
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"""
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try:
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output_path = Path(output_dir)
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output_path.mkdir(parents=True, exist_ok=True)
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cap = cv2.VideoCapture(video_path)
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video_fps = cap.get(cv2.CAP_PROP_FPS)
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frame_interval = max(1, int(video_fps / fps))
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frame_paths = []
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frame_count = 0
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saved_count = 0
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while cap.isOpened():
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ret, frame = cap.read()
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if not ret:
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break
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if frame_count % frame_interval == 0:
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frame_filename = f"frame_{saved_count:04d}.jpg"
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frame_path = output_path / frame_filename
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cv2.imwrite(str(frame_path), frame)
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frame_paths.append(str(frame_path))
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saved_count += 1
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frame_count += 1
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cap.release()
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logger.info(f"Extracted {len(frame_paths)} frames to {output_dir}")
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return frame_paths
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except Exception as e:
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logger.error(f"Frame extraction failed: {e}")
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raise VideoProcessingError(f"Frame extraction failed: {str(e)}")
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