275 lines
7.5 KiB
Python
275 lines
7.5 KiB
Python
"""
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Tests unitaires pour vision_utils.py
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"""
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import numpy as np
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import sys
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from pathlib import Path
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# Ajouter le répertoire parent au path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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from geniusia2.core.utils.vision_utils import VisionUtils
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from geniusia2.core.models import Detection
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def test_initialization():
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"""Test l'initialisation de VisionUtils"""
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print("Test 1: Initialisation...")
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vision = VisionUtils()
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assert vision.primary_model == "owl-v2"
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assert len(vision.fallback_order) == 3
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assert "owl-v2" in vision.fallback_order
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assert "dino" in vision.fallback_order
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assert "yolo" in vision.fallback_order
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print("✓ Initialisation OK")
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def test_filter_detections():
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"""Test le filtrage des détections"""
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print("\nTest 2: Filtrage des détections...")
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vision = VisionUtils()
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detections = [
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Detection(
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label="button1",
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confidence=0.95,
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bbox=(100, 100, 50, 30),
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embedding=np.random.rand(512),
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model_source="owl-v2"
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),
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Detection(
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label="button2",
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confidence=0.25,
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bbox=(200, 100, 50, 30),
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embedding=np.random.rand(512),
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model_source="owl-v2"
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),
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Detection(
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label="button3",
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confidence=0.75,
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bbox=(300, 100, 50, 30),
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embedding=np.random.rand(512),
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model_source="dino"
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),
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]
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# Filtrer avec seuil de 0.5
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filtered = vision.filter_detections(detections, min_confidence=0.5)
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assert len(filtered) == 2
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assert all(d.confidence >= 0.5 for d in filtered)
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# Vérifier le tri par confiance
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assert filtered[0].confidence >= filtered[1].confidence
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print("✓ Filtrage OK")
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def test_select_best_detection():
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"""Test la sélection de la meilleure détection"""
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print("\nTest 3: Sélection de la meilleure détection...")
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vision = VisionUtils()
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detections = [
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Detection(
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label="button1",
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confidence=0.75,
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bbox=(100, 100, 50, 30),
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embedding=np.random.rand(512),
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model_source="dino"
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),
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Detection(
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label="button2",
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confidence=0.85,
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bbox=(200, 100, 50, 30),
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embedding=np.random.rand(512),
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model_source="owl-v2" # Modèle principal
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),
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]
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# Sans contexte, devrait favoriser owl-v2 avec bonus
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best = vision.select_best_detection(detections)
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assert best is not None
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assert best.model_source == "owl-v2"
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# Avec contexte de position précédente
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context = {"previous_bbox": (95, 95, 50, 30)} # Proche de button1
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best_with_context = vision.select_best_detection(detections, context)
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assert best_with_context is not None
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print("✓ Sélection OK")
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def test_merge_overlapping_detections():
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"""Test la fusion des détections chevauchantes"""
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print("\nTest 4: Fusion des détections chevauchantes...")
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vision = VisionUtils()
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# Créer des détections qui se chevauchent
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detections = [
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Detection(
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label="button",
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confidence=0.95,
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bbox=(100, 100, 50, 30),
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embedding=np.random.rand(512),
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model_source="owl-v2"
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),
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Detection(
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label="button",
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confidence=0.85,
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bbox=(105, 102, 48, 28), # Légèrement décalé, fort chevauchement
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embedding=np.random.rand(512),
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model_source="dino"
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),
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Detection(
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label="button",
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confidence=0.70,
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bbox=(300, 100, 50, 30), # Pas de chevauchement
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embedding=np.random.rand(512),
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model_source="yolo"
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),
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]
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merged = vision.merge_overlapping_detections(detections, iou_threshold=0.5)
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# Devrait fusionner les 2 premières détections
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assert len(merged) == 2
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# La meilleure confiance devrait être conservée
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assert merged[0].confidence == 0.95
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print("✓ Fusion OK")
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def test_get_detection_statistics():
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"""Test le calcul des statistiques"""
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print("\nTest 5: Statistiques des détections...")
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vision = VisionUtils()
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detections = [
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Detection(
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label="button1",
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confidence=0.95,
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bbox=(100, 100, 50, 30),
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embedding=np.random.rand(512),
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model_source="owl-v2"
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),
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Detection(
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label="button2",
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confidence=0.85,
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bbox=(200, 100, 50, 30),
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embedding=np.random.rand(512),
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model_source="owl-v2"
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),
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Detection(
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label="button3",
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confidence=0.75,
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bbox=(300, 100, 50, 30),
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embedding=np.random.rand(512),
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model_source="dino"
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),
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]
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stats = vision.get_detection_statistics(detections)
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assert stats["count"] == 3
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assert stats["max_confidence"] == 0.95
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assert stats["min_confidence"] == 0.75
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assert abs(stats["avg_confidence"] - 0.85) < 0.01
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assert "owl-v2" in stats["models_used"]
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assert "dino" in stats["models_used"]
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assert stats["model_distribution"]["owl-v2"] == 2
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assert stats["model_distribution"]["dino"] == 1
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print("✓ Statistiques OK")
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def test_empty_detections():
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"""Test le comportement avec des listes vides"""
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print("\nTest 6: Gestion des listes vides...")
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vision = VisionUtils()
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# Liste vide
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empty = []
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# Filtrage
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filtered = vision.filter_detections(empty)
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assert len(filtered) == 0
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# Sélection
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best = vision.select_best_detection(empty)
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assert best is None
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# Fusion
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merged = vision.merge_overlapping_detections(empty)
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assert len(merged) == 0
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# Statistiques
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stats = vision.get_detection_statistics(empty)
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assert stats["count"] == 0
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assert stats["avg_confidence"] == 0.0
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print("✓ Gestion des listes vides OK")
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def test_single_detection():
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"""Test le comportement avec une seule détection"""
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print("\nTest 7: Gestion d'une seule détection...")
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vision = VisionUtils()
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single = [
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Detection(
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label="button",
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confidence=0.85,
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bbox=(100, 100, 50, 30),
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embedding=np.random.rand(512),
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model_source="owl-v2"
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)
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]
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# Sélection
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best = vision.select_best_detection(single)
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assert best is not None
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assert best.label == "button"
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# Fusion
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merged = vision.merge_overlapping_detections(single)
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assert len(merged) == 1
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print("✓ Gestion d'une seule détection OK")
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def run_all_tests():
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"""Exécute tous les tests"""
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print("=" * 60)
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print("Tests unitaires de VisionUtils")
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print("=" * 60)
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try:
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test_initialization()
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test_filter_detections()
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test_select_best_detection()
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test_merge_overlapping_detections()
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test_get_detection_statistics()
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test_empty_detections()
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test_single_detection()
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print("\n" + "=" * 60)
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print("✓ TOUS LES TESTS RÉUSSIS!")
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print("=" * 60)
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return True
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except AssertionError as e:
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print(f"\n✗ ÉCHEC DU TEST: {e}")
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return False
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except Exception as e:
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print(f"\n✗ ERREUR: {e}")
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import traceback
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traceback.print_exc()
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return False
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if __name__ == "__main__":
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success = run_all_tests()
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sys.exit(0 if success else 1)
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