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#!/usr/bin/env python3
"""
Test the NEW simple but reliable Person on Track Detector
"""
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_simple_detector():
"""Test the new simple detector on multiple videos"""
print("TESTING NEW SIMPLE PERSON ON TRACK DETECTOR")
print("=" * 60)
print("Much simpler approach - only uses Transformer model")
print("Should give more accurate results!")
print()
try:
from local_models import get_local_model_manager
from app import extract_frames_from_video, process_image_locally
print("+ Components loaded")
except ImportError as e:
print(f"- Import error: {e}")
return
# Test multiple videos
test_videos = glob.glob("test\\*.mp4")[:4] # Test first 4 videos
if not test_videos:
print("- No test videos found")
return
print(f"+ Testing {len(test_videos)} videos")
try:
local_manager = get_local_model_manager()
print("+ Simple detector ready")
except Exception as e:
print(f"- Model 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" + "=" * 50)
print(f"VIDEO {video_idx + 1}: {video_name}")
print("=" * 50)
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 from {video_name}")
continue
# Test first frame from each video
frame_data = frames[0]
timestamp = frame_data['timestamp']
print(f"\nFrame 1 ({timestamp:.1f}s):")
print("-" * 30)
try:
result = process_image_locally(
frame_data['frame'],
"Track Safety Analysis",
'Person on Track Detector',
local_manager
)
if 'person_on_track_detection' in result:
detection = result['person_on_track_detection']
people_count = detection.get('people_count', 0)
confidence = detection.get('confidence', 0)
analysis = detection.get('analysis', 'No analysis')
person_on_track = detection.get('person_on_track', False)
# Show detailed analysis
detailed = detection.get('detailed_analysis', {})
scene_desc = detailed.get('scene_description', 'N/A')
person_mentions = detailed.get('person_mentions', 0)
track_mentions = detailed.get('track_mentions', 0)
# Display results
if person_on_track:
print(f"ALERT: {analysis}")
else:
print(f"SAFE: {analysis}")
print(f"People Count: {people_count}")
print(f"Confidence: {confidence:.0%}")
print(f"Scene: '{scene_desc}'")
print(f"Keywords: Person={person_mentions}, Track={track_mentions}")
all_results.append({
'video': video_name,
'on_track': person_on_track,
'people_count': people_count,
'confidence': confidence,
'analysis': analysis,
'scene': scene_desc
})
else:
print(f"ERROR: Unexpected result format")
except Exception as e:
print(f"ERROR: {e}")
except Exception as e:
print(f"- Failed to process {video_name}: {e}")
# Summary
print(f"\n" + "=" * 70)
print("SUMMARY OF NEW SIMPLE DETECTOR")
print("=" * 70)
if all_results:
total = len(all_results)
on_track_count = sum(1 for r in all_results if r['on_track'])
safe_count = total - on_track_count
avg_confidence = sum(r['confidence'] for r in all_results) / total
print(f"Total videos tested: {total}")
print(f"Person on track detections: {on_track_count}")
print(f"Safe detections: {safe_count}")
print(f"Average confidence: {avg_confidence:.0%}")
print(f"\nDETAILED RESULTS:")
for r in all_results:
status = "ON TRACK" if r['on_track'] else "SAFE"
print(f" {r['video']}: {status} - {r['people_count']} people ({r['confidence']:.0%})")
print(f" Scene: {r['scene'][:60]}...")
# Assessment
print(f"\n" + "=" * 70)
print("ASSESSMENT")
print("=" * 70)
if safe_count > 0:
print("+ SUCCESS: Detector now gives SAFE results!")
print("+ No longer stuck on always detecting danger")
else:
print("- STILL PROBLEMATIC: Only danger detections")
if avg_confidence > 60:
print("+ Good confidence levels")
else:
print("- Low confidence, may need adjustment")
print(f"\nThe new simple approach:")
print("1. Uses only reliable Transformer model")
print("2. Simple keyword counting (person + track words)")
print("3. Conservative decision logic")
print("4. Clear scene descriptions for verification")
print(f"\nREADY TO TEST IN STREAMLIT:")
print("Open http://localhost:8502")
print("Select 'Person on Track Detector'")
print("Upload test videos to see improved results")
return all_results
if __name__ == "__main__":
test_simple_detector() |