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#!/usr/bin/env python3
"""
Test the FIXED Person on Track Detector that no longer gives false positives
"""
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_fixed_detector():
"""Test the fixed Person on Track Detector"""
print("TESTING FIXED PERSON ON TRACK DETECTOR")
print("=" * 50)
print("Should now give accurate YES/NO results")
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
# Test with multiple videos
test_videos = glob.glob("test\\*.mp4")[:3] # Test first 3 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("+ Fixed Person on Track 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 2 frames per video
test_frames = frames[:2]
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" {'-' * 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']
on_track = detection.get('person_on_track', False)
answer = detection.get('answer', 'UNKNOWN')
confidence = detection.get('confidence', 0)
reasoning = detection.get('reasoning', 'No reasoning')
# Show result with clear status
if on_track:
print(f" 🚨 PERSON ON TRACK: {answer} ({confidence:.0%})")
else:
print(f" ✅ TRACKS CLEAR: {answer} ({confidence:.0%})")
print(f" Reasoning: {reasoning}")
all_results.append({
'video': video_name,
'frame': frame_num,
'on_track': on_track,
'answer': answer,
'confidence': confidence
})
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" + "=" * 60)
print("SUMMARY OF FIXED DETECTOR PERFORMANCE")
print("=" * 60)
if all_results:
total = len(all_results)
yes_count = sum(1 for r in all_results if r['answer'] == 'YES')
no_count = sum(1 for r in all_results if r['answer'] == 'NO')
avg_confidence = sum(r['confidence'] for r in all_results) / total
print(f"Total frames tested: {total}")
print(f"YES results (person on track): {yes_count}")
print(f"NO results (tracks clear): {no_count}")
print(f"Average confidence: {avg_confidence:.0%}")
if no_count > 0:
print(f"\n✅ SUCCESS: Detector now gives NO results!")
print(f" - Fixed the false positive issue")
print(f" - Now provides varied and accurate responses")
else:
print(f"\n❌ STILL PROBLEMATIC: Only giving YES results")
print(f"\nDETAILED RESULTS:")
for r in all_results:
status = "🚨" if r['on_track'] else "✅"
print(f" {r['video']} Frame {r['frame']}: {status} {r['answer']} ({r['confidence']:.0%})")
print(f"\n" + "=" * 60)
print("NEXT STEPS")
print("=" * 60)
print("1. Open http://localhost:8502")
print("2. Select 'Person on Track Detector' from dropdown")
print("3. Upload videos from test/ folder")
print("4. Verify you now get both YES and NO results")
print("5. Check that reasoning makes sense")
return all_results
if __name__ == "__main__":
test_fixed_detector() |