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Optimized Video Processing for DetectifAI
This module contains optimized video processing components focusing on:
- Efficient keyframe extraction for security footage
- Selective frame enhancement only when needed
- Memory-optimized processing for large surveillance videos
"""
import cv2
import numpy as np
import os
import uuid
from typing import Dict, List, Tuple, Optional, Any
from dataclasses import dataclass
import time
import logging
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class FrameData:
"""Data structure for frame information"""
frame_path: str
timestamp: float
frame_number: int
quality_score: float
motion_score: float
burst_active: bool
enhancement_applied: bool
face_count: int = 0
object_count: int = 0
@dataclass
class KeyframeResult:
"""Result structure for keyframe extraction"""
frame_data: FrameData
keyframe_score: float
selection_reason: str
class OptimizedFrameEnhancer:
"""Optimized frame enhancement for DetectifAI - only enhance when necessary"""
def __init__(self, enable_clahe: bool = True, clahe_clip_limit: float = 2.0):
self.enable_clahe = enable_clahe
# Initialize CLAHE (skip denoising for performance)
if enable_clahe:
self.clahe = cv2.createCLAHE(clipLimit=clahe_clip_limit, tileGridSize=(8, 8))
logger.info(f"OptimizedFrameEnhancer initialized - CLAHE: {enable_clahe}")
def enhance_frame_if_needed(self, frame: np.ndarray) -> Tuple[np.ndarray, bool]:
"""
Enhance frame only if quality is poor (DetectifAI optimization)
Args:
frame: Input frame as numpy array
Returns:
Tuple of (enhanced_frame, enhancement_applied)
"""
try:
# Quick quality assessment
if not self._needs_enhancement(frame):
return frame, False
enhanced = frame.copy()
# Apply CLAHE only to L channel for color frames
if len(frame.shape) == 3 and self.enable_clahe:
lab = cv2.cvtColor(enhanced, cv2.COLOR_BGR2LAB)
l_channel = lab[:, :, 0]
l_enhanced = self.clahe.apply(l_channel)
lab[:, :, 0] = l_enhanced
enhanced = cv2.cvtColor(lab, cv2.COLOR_LAB2BGR)
return enhanced, True
elif len(frame.shape) == 2 and self.enable_clahe:
# Grayscale frame
enhanced = self.clahe.apply(enhanced)
return enhanced, True
return frame, False
except Exception as e:
logger.error(f"Error enhancing frame: {e}")
return frame, False
def _needs_enhancement(self, frame: np.ndarray) -> bool:
"""
Quick quality check - only enhance genuinely poor quality frames
"""
try:
# Convert to grayscale for analysis
if len(frame.shape) == 3:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
else:
gray = frame
# Check brightness and contrast
mean_brightness = np.mean(gray)
contrast = np.std(gray)
# Only enhance if frame has quality issues
return (
mean_brightness < 50 or # Too dark
mean_brightness > 200 or # Too bright
contrast < 30 # Low contrast
)
except Exception:
return False
class OptimizedVideoProcessor:
"""
Optimized video processor for DetectifAI surveillance footage
"""
def __init__(self, config=None):
self.config = config
self.frame_enhancer = OptimizedFrameEnhancer(
enable_clahe=getattr(config, 'enable_adaptive_processing', True)
)
# Processing statistics
self.processing_stats = {
'frames_processed': 0,
'frames_enhanced': 0,
'keyframes_extracted': 0,
'total_processing_time': 0.0
}
logger.info("OptimizedVideoProcessor initialized")
def extract_keyframes_optimized(self, video_path: str, output_dir: str,
fps_interval: float = 1.0) -> List[KeyframeResult]:
"""
Extract keyframes with optimized processing for surveillance video
Args:
video_path: Path to input video
output_dir: Directory to save keyframes
fps_interval: Seconds between keyframes (default: 1 frame per second)
Returns:
List of KeyframeResult objects
"""
start_time = time.time()
keyframes = []
try:
# Open video
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
logger.error(f"Could not open video: {video_path}")
return []
# Get video properties
fps = cap.get(cv2.CAP_PROP_FPS)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
duration = total_frames / fps if fps > 0 else 0
logger.info(f"Video properties: {total_frames} frames, {fps:.2f} FPS, {duration:.2f}s")
# Calculate frame interval
frame_interval = int(fps * fps_interval) if fps > 0 else 30
# Create output directory
frames_dir = os.path.join(output_dir, 'frames')
os.makedirs(frames_dir, exist_ok=True)
frame_count = 0
extracted_count = 0
while True:
ret, frame = cap.read()
if not ret:
break
# Extract keyframes at specified intervals
if frame_count % frame_interval == 0:
timestamp = frame_count / fps if fps > 0 else frame_count
# Assess frame quality
quality_score = self._assess_frame_quality(frame)
# Enhance frame if needed
enhanced_frame, enhancement_applied = self.frame_enhancer.enhance_frame_if_needed(frame)
# Use consistent naming pattern for MinIO storage
frame_filename = f"frame_{frame_count:06d}.jpg"
frame_path = os.path.join(frames_dir, frame_filename)
cv2.imwrite(frame_path, enhanced_frame)
# Create frame data
frame_data = FrameData(
frame_path=frame_path,
timestamp=timestamp,
frame_number=frame_count,
quality_score=quality_score,
motion_score=0.0, # Can be calculated if needed
burst_active=False,
enhancement_applied=enhancement_applied
)
keyframe_result = KeyframeResult(
frame_data=frame_data,
keyframe_score=quality_score,
selection_reason="Regular interval extraction"
)
keyframes.append(keyframe_result)
extracted_count += 1
# Update stats
if enhancement_applied:
self.processing_stats['frames_enhanced'] += 1
frame_count += 1
self.processing_stats['frames_processed'] += 1
# Progress logging
if frame_count % 1000 == 0:
progress = (frame_count / total_frames) * 100 if total_frames > 0 else 0
logger.info(f"Progress: {progress:.1f}% ({frame_count}/{total_frames} frames)")
cap.release()
# Update final statistics
processing_time = time.time() - start_time
self.processing_stats['keyframes_extracted'] = extracted_count
self.processing_stats['total_processing_time'] = processing_time
logger.info(f"✅ Keyframe extraction complete:")
logger.info(f" 📊 Extracted {extracted_count} keyframes from {frame_count} frames")
logger.info(f" ⚡ Enhanced {self.processing_stats['frames_enhanced']} frames")
logger.info(f" ⏱️ Processing time: {processing_time:.2f}s")
return keyframes
except Exception as e:
logger.error(f"Error in keyframe extraction: {e}")
return []
def _assess_frame_quality(self, frame: np.ndarray) -> float:
"""
Quick frame quality assessment for keyframe selection
"""
try:
# Convert to grayscale
if len(frame.shape) == 3:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
else:
gray = frame
# Calculate Laplacian variance (focus measure)
laplacian_var = cv2.Laplacian(gray, cv2.CV_64F).var()
# Normalize to 0-1 scale (higher = better quality)
quality_score = min(laplacian_var / 1000.0, 1.0)
return quality_score
except Exception:
return 0.5 # Default quality score
def extract_keyframes(self, video_path: str) -> List[KeyframeResult]:
"""
Main keyframe extraction method for DetectifAI pipeline compatibility
Args:
video_path: Path to input video file
Returns:
List of KeyframeResult objects
"""
if not self.config:
logger.error("No configuration provided for keyframe extraction")
return []
# Use output directory from config
output_dir = getattr(self.config, 'output_base_dir', 'video_processing_outputs')
fps_interval = getattr(self.config, 'keyframe_extraction_fps', 1.0)
return self.extract_keyframes_optimized(video_path, output_dir, fps_interval)
def get_processing_stats(self) -> Dict[str, Any]:
"""Get processing statistics"""
return self.processing_stats.copy()
class StreamingVideoProcessor:
"""
Streaming processor for large surveillance videos to reduce memory usage
"""
def __init__(self, config=None):
self.config = config
self.chunk_size = getattr(config, 'video_chunk_size', 1000) # Process 1000 frames at a time
def process_video_in_chunks(self, video_path: str, output_dir: str,
chunk_processor_func) -> Dict[str, Any]:
"""
Process large videos in chunks to manage memory usage
Args:
video_path: Path to input video
output_dir: Output directory
chunk_processor_func: Function to process each chunk
Returns:
Dictionary with processing results
"""
results = {
'total_chunks': 0,
'processed_chunks': 0,
'total_frames': 0,
'processing_time': 0.0
}
start_time = time.time()
try:
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
logger.error(f"Could not open video: {video_path}")
return results
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = cap.get(cv2.CAP_PROP_FPS)
results['total_frames'] = total_frames
results['total_chunks'] = (total_frames + self.chunk_size - 1) // self.chunk_size
logger.info(f"Processing video in {results['total_chunks']} chunks of {self.chunk_size} frames")
frame_count = 0
chunk_count = 0
while frame_count < total_frames:
# Process chunk
chunk_frames = []
chunk_start = frame_count
# Read chunk frames
for i in range(self.chunk_size):
ret, frame = cap.read()
if not ret:
break
chunk_frames.append({
'frame': frame,
'frame_number': frame_count,
'timestamp': frame_count / fps if fps > 0 else frame_count
})
frame_count += 1
if chunk_frames:
# Process chunk
chunk_processor_func(chunk_frames, chunk_count, output_dir)
chunk_count += 1
results['processed_chunks'] += 1
# Clear memory
del chunk_frames
logger.info(f"Processed chunk {chunk_count}/{results['total_chunks']}")
cap.release()
results['processing_time'] = time.time() - start_time
logger.info(f"✅ Streaming processing complete in {results['processing_time']:.2f}s")
except Exception as e:
logger.error(f"Error in streaming processing: {e}")
return results
def create_optimized_processor(config=None):
"""Factory function to create optimized video processor"""
return OptimizedVideoProcessor(config) |