Delete test_improvements.py

test_improvements.py

@@ -1,103 +0,0 @@
-"""
-Quick test to verify all 9 recommendations are working
-"""
-import cv2
-import numpy as np
-from reid import SQLiteEnhancedReID
-from tracking import DeepSORTTracker
-from detection import DogDetector
-
-def test_recommendations():
-    """Test that all recommendations are properly applied"""
-    
-    print("="*80)
-    print("TESTING RECOMMENDATIONS 1-9")
-    print("="*80)
-    
-    # Test #1: Threshold hierarchy
-    print("\n📊 TEST #1: Threshold Hierarchy")
-    reid = SQLiteEnhancedReID()
-    reid.set_all_thresholds(0.35)
-    
-    assert reid.database_threshold > reid.session_threshold, "❌ DB should be stricter!"
-    assert reid.session_threshold > reid.sleeping_threshold, "❌ Session should be > sleeping!"
-    print(f"  ✅ Correct: DB({reid.database_threshold:.2f}) > "
-          f"Session({reid.session_threshold:.2f}) > "
-          f"Sleeping({reid.sleeping_threshold:.2f})")
-    
-    # Test #2-4: DeepSORT parameters
-    print("\n🎯 TEST #2-4: DeepSORT Parameters")
-    tracker = DeepSORTTracker(
-        max_iou_distance=0.5,
-        max_age=90,
-        n_init=1
-    )
-    
-    assert tracker.n_init == 1, "❌ n_init should be 1"
-    assert tracker.max_age == 90, "❌ max_age should be 90"
-    assert tracker.max_iou_distance == 0.5, "❌ max_iou should be 0.5"
-    print("  ✅ n_init=1, max_age=90, max_iou=0.5")
-    
-    # Test #5-6: Adaptive thresholding
-    print("\n🔄 TEST #5-6: Adaptive Thresholding")
-    metadata = {
-        'deepsort_hits': 15,
-        'deepsort_age': 20,
-        'frames_since_update': 0
-    }
-    
-    adaptive_threshold = reid.compute_adaptive_threshold(metadata)
-    assert adaptive_threshold < reid.session_threshold, "❌ Should reduce threshold"
-    print(f"  ✅ Adaptive: {reid.session_threshold:.2f} → {adaptive_threshold:.2f}")
-    
-    # Test #7: Quality scoring
-    print("\n⭐ TEST #7: Quality Scoring")
-    test_image = np.random.randint(0, 255, (200, 200, 3), dtype=np.uint8)
-    quality, components = reid.compute_feature_quality(
-        test_image, 
-        [50, 50, 150, 150], 
-        confidence=0.8
-    )
-    
-    assert 0 <= quality <= 1, "❌ Quality should be 0-1"
-    assert len(components) == 4, "❌ Should have 4 components"
-    print(f"  ✅ Quality score: {quality:.3f}")
-    print(f"  ✅ Components: {list(components.keys())}")
-    
-    # Test #8: Storage reduction
-    print("\n💾 TEST #8: Storage Reduction")
-    # Simulate adding many features
-    test_features = []
-    for i in range(30):
-        feat = np.random.randn(1536)
-        feat = feat / np.linalg.norm(feat)
-        test_features.append(feat)
-    
-    # Should keep only 20
-    reid.temp_id_features[1] = []
-    for feat in test_features:
-        from reid import DogFeatures
-        dog_feat = DogFeatures(
-            features=feat,
-            quality=np.random.random(),
-            frame_num=i
-        )
-        reid.smart_feature_storage(1, dog_feat)
-    
-    assert len(reid.temp_id_features[1]) <= 20, "❌ Should keep max 20 features"
-    print(f"  ✅ Storage limited: {len(reid.temp_id_features[1])}/20")
-    
-    # Test #9: Mean aggregation
-    print("\n📊 TEST #9: Mean Aggregation")
-    mean_emb = reid.compute_mean_embedding(reid.temp_id_features[1])
-    
-    assert mean_emb is not None, "❌ Should compute mean"
-    assert abs(np.linalg.norm(mean_emb) - 1.0) < 0.01, "❌ Should be normalized"
-    print(f"  ✅ Mean embedding computed, norm={np.linalg.norm(mean_emb):.3f}")
-    
-    print("\n" + "="*80)
-    print("✅ ALL TESTS PASSED - Recommendations 1-9 working!")
-    print("="*80)
-    
-if __name__ == "__main__":
-    test_recommendations()