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#!/usr/bin/env python3
"""
Final test of the optimized Person on Track Detector on all test videos
"""
import sys
import os
from io import BytesIO
import glob
# Add current directory to path
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
def test_person_on_track_final():
"""Test the optimized Person on Track Detector on all test videos"""
print("FINAL PERSON ON TRACK DETECTOR TEST")
print("=" * 50)
print("Testing optimized detector based on comprehensive analysis")
print()
try:
from local_models import get_local_model_manager
from app import extract_frames_from_video, process_image_locally
print("+ Components loaded successfully")
except ImportError as e:
print(f"- Import error: {e}")
return
# Find all test videos
test_videos = glob.glob("test\\*.mp4")
if not test_videos:
print("- No MP4 files found in test folder")
return
print(f"+ Found {len(test_videos)} test videos")
# Initialize models
try:
local_manager = get_local_model_manager()
available_models = local_manager.get_available_models()
print(f"+ Available models: {available_models}")
if "Person on Track Detector" not in available_models:
print("- Person on Track Detector not found!")
return
print("+ Person on Track Detector ready")
except Exception as e:
print(f"- Model initialization error: {e}")
return
all_results = []
# Test each video
for video_idx, video_path in enumerate(test_videos):
video_name = os.path.basename(video_path)
print(f"\n" + "=" * 60)
print(f"TESTING VIDEO {video_idx + 1}: {video_name}")
print("=" * 60)
try:
# Extract frames
with open(video_path, 'rb') as f:
video_data = f.read()
video_file = BytesIO(video_data)
frames = extract_frames_from_video(video_file, fps=0.5)
if not frames:
print(f"- No frames extracted from {video_name}")
continue
print(f"+ Extracted {len(frames)} frames from {video_name}")
# Test first 3 frames
test_frames = frames[:3]
video_results = []
for frame_idx, frame_data in enumerate(test_frames):
frame_num = frame_idx + 1
timestamp = frame_data['timestamp']
print(f"\n Frame {frame_num} ({timestamp:.1f}s):")
print(f" {'-' * 40}")
try:
result = process_image_locally(
frame_data['frame'],
"Track Safety Analysis", # Prompt is ignored for this detector
'Person on Track Detector',
local_manager
)
if 'error' in result:
print(f" ERROR: {result['error']}")
video_results.append({
'video': video_name,
'frame': frame_num,
'timestamp': timestamp,
'on_track': False,
'answer': 'ERROR',
'confidence': 0,
'reasoning': result['error']
})
elif 'person_on_track_detection' in result:
detection = result['person_on_track_detection']
on_track = detection.get('person_on_track', False)
answer = detection.get('answer', 'UNKNOWN')
confidence = detection.get('confidence', 0)
reasoning = detection.get('reasoning', 'No reasoning')
detailed = detection.get('detailed_analysis', {})
# Display results
status = "ON TRACK" if on_track else "CLEAR"
print(f" Result: {status} ({answer})")
print(f" Confidence: {confidence:.0%}")
print(f" Reasoning: {reasoning}")
# Show detailed analysis
if detailed:
print(f" Details: Person={detailed.get('person_keywords_found', 0)}, " +
f"Track={detailed.get('track_keywords_found', 0)}, " +
f"Danger={detailed.get('danger_position_keywords', 0)}, " +
f"Safety={detailed.get('safety_concern_keywords', 0)}")
video_results.append({
'video': video_name,
'frame': frame_num,
'timestamp': timestamp,
'on_track': on_track,
'answer': answer,
'confidence': confidence,
'reasoning': reasoning,
'detailed_analysis': detailed
})
else:
print(f" Unexpected result format: {result}")
video_results.append({
'video': video_name,
'frame': frame_num,
'timestamp': timestamp,
'on_track': False,
'answer': 'UNKNOWN',
'confidence': 0,
'reasoning': 'Unknown result format'
})
except Exception as e:
print(f" ERROR: {e}")
video_results.append({
'video': video_name,
'frame': frame_num,
'timestamp': timestamp,
'on_track': False,
'answer': 'ERROR',
'confidence': 0,
'reasoning': str(e)
})
all_results.extend(video_results)
except Exception as e:
print(f"- Failed to process {video_name}: {e}")
continue
# Comprehensive summary
print(f"\n" + "=" * 80)
print("COMPREHENSIVE RESULTS SUMMARY")
print("=" * 80)
# Results table
print(f"\nDETAILED RESULTS:")
print("-" * 90)
print(f"{'Video':<10} {'Frame':<6} {'Time':<6} {'On Track':<9} {'Answer':<7} {'Confidence':<11} {'Reasoning':<30}")
print("-" * 90)
total_frames = len(all_results)
on_track_count = 0
error_count = 0
total_confidence = 0
for result in all_results:
video = result['video'][:8]
frame = result['frame']
timestamp = result['timestamp']
on_track = "YES" if result['on_track'] else "NO"
answer = result['answer']
confidence = result['confidence']
reasoning = result['reasoning'][:25] + "..." if len(result['reasoning']) > 25 else result['reasoning']
print(f"{video:<10} {frame:<6} {timestamp:<6.1f} {on_track:<9} {answer:<7} {confidence:<11.0%} {reasoning:<30}")
if result['on_track']:
on_track_count += 1
if result['answer'] == 'ERROR':
error_count += 1
total_confidence += confidence
# Overall statistics
print(f"\n" + "=" * 80)
print("OVERALL PERFORMANCE")
print("=" * 80)
print(f"Total frames tested: {total_frames}")
print(f"Videos tested: {len(test_videos)}")
print(f"Person on track detections: {on_track_count}")
print(f"Clear/safe detections: {total_frames - on_track_count - error_count}")
print(f"Error responses: {error_count}")
if total_frames > 0:
detection_rate = on_track_count / total_frames * 100
avg_confidence = total_confidence / total_frames
error_rate = error_count / total_frames * 100
print(f"Detection rate: {detection_rate:.1f}%")
print(f"Average confidence: {avg_confidence:.0%}")
print(f"Error rate: {error_rate:.1f}%")
# Per-video breakdown
print(f"\nPER-VIDEO ANALYSIS:")
print("-" * 50)
for video_path in test_videos:
video_name = os.path.basename(video_path)
video_results = [r for r in all_results if r['video'] == video_name]
if video_results:
on_track_frames = sum(1 for r in video_results if r['on_track'])
total_video_frames = len(video_results)
avg_video_confidence = sum(r['confidence'] for r in video_results) / len(video_results)
print(f"{video_name}: {on_track_frames}/{total_video_frames} frames with person on track "
f"(avg confidence: {avg_video_confidence:.0%})")
print(f"\n" + "=" * 80)
print("FINAL ASSESSMENT")
print("=" * 80)
if error_rate < 10:
print("+ EXCELLENT: Low error rate, detector is working reliably")
elif error_rate < 25:
print("+ GOOD: Acceptable error rate")
else:
print("- HIGH ERROR RATE: Needs improvement")
if avg_confidence > 70:
print("+ HIGH CONFIDENCE: Detector provides confident results")
elif avg_confidence > 50:
print("+ MODERATE CONFIDENCE: Results are reasonably confident")
else:
print("- LOW CONFIDENCE: Results may be unreliable")
print(f"\nRECOMMENDATION:")
if error_rate < 10 and avg_confidence > 70:
print("✅ READY FOR PRODUCTION: Person on Track Detector is highly reliable")
print(" - Use in Streamlit app for real-time track safety monitoring")
print(" - Suitable for automated safety systems")
elif error_rate < 25 and avg_confidence > 50:
print("⚠️ SUITABLE WITH CAUTION: Good performance but monitor results")
print(" - Use for preliminary screening")
print(" - Consider human verification for critical decisions")
else:
print("❌ NEEDS IMPROVEMENT: Not reliable enough for production use")
print(" - Improve keyword detection")
print(" - Adjust confidence thresholds")
print(" - Test with more diverse video content")
print(f"\nNext steps:")
print("1. Open http://localhost:8502")
print("2. Select 'Person on Track Detector' from model dropdown")
print("3. Upload test videos from test/ folder")
print("4. Compare results with this analysis")
return all_results
if __name__ == "__main__":
test_person_on_track_final() |