After story 4

tests/test_story_04.py

@@ -0,0 +1,98 @@
+import sys
+import tempfile
+import unittest
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
+
+from batcher import DynamicBatcher
+from face_detector import Detection, FaceDetector
+from gpu_manager import GPUMemoryManager
+
+
+class Story04FaceDetectionTests(unittest.TestCase):
+    def test_gpu_manager_respects_memory_thresholds(self):
+        manager = GPUMemoryManager(
+            max_memory_gb=18.0,
+            reduce_threshold_gb=16.0,
+            increase_threshold_gb=10.0,
+            initial_batch_size=16,
+        )
+
+        with patch.object(manager, "get_memory_stats", return_value=(17.0, 18.0)):
+            manager.adjust_batch_size()
+            self.assertLess(manager.current_batch_size, 16)
+
+        with patch.object(manager, "get_memory_stats", return_value=(8.0, 10.0)):
+            manager.adjust_batch_size()
+            self.assertGreater(manager.current_batch_size, manager.min_batch_size)
+
+    def test_dynamic_batcher_flushes_when_batch_full(self):
+        processed_batches = []
+
+        def process_fn(batch):
+            processed_batches.append(batch)
+            return [len(batch)]
+
+        batcher = DynamicBatcher(
+            process_fn=process_fn,
+            max_batch_size=3,
+            batch_timeout_ms=10000.0,
+        )
+
+        for i in range(5):
+            batcher.add(i)
+
+        self.assertEqual(len(processed_batches), 1)
+        self.assertEqual(processed_batches[0], [0, 1, 2])
+        self.assertEqual(batcher.queued_count, 2)
+
+    def test_face_detector_nms_removes_overlapping_boxes(self):
+        detector = FaceDetector(
+            engine_path="/nonexistent/model.trt",
+            confidence_threshold=0.1,
+            iou_threshold=0.45,
+            max_faces_per_frame=10,
+        )
+
+        duplicates = [
+            Detection("frame.jpg", 10, 10, 50, 50, 0.9),
+            Detection("frame.jpg", 12, 12, 48, 48, 0.8),
+            Detection("frame.jpg", 100, 100, 150, 150, 0.75),
+        ]
+
+        kept = detector._nms(duplicates)
+        self.assertEqual(len(kept), 2)
+        self.assertAlmostEqual(kept[0].confidence, 0.9, places=5)
+
+    def test_face_detector_extracts_and_resizes_crops(self):
+        detector = FaceDetector(
+            engine_path="/nonexistent/model.trt",
+            input_size=640,
+            confidence_threshold=0.25,
+        )
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            from PIL import Image
+
+            frame_path = Path(tmpdir) / "frame.jpg"
+            image = Image.new("RGB", (640, 480), color=(100, 150, 200))
+            image.save(frame_path)
+
+            detections = [
+                Detection(str(frame_path), 0, 0, 640, 480, 0.95),
+            ]
+            cropped = detector.extract_crops(detections, output_dir=tmpdir, crop_size=(224, 224))
+
+            self.assertEqual(len(cropped), 1)
+            self.assertTrue(Path(cropped[0].crop_path).exists())
+
+            with Image.open(cropped[0].crop_path) as crop:
+                self.assertEqual(crop.size, (224, 224))
+
+
+if __name__ == "__main__":
+    unittest.main()