Livrare LOT 1 - Didi

ai_platform/modules/extractors/tests/test_detect.py

@@ -0,0 +1,80 @@
+"""Object-detection tests with a fake YOLO predictor (no torch/weights)."""
+
+from __future__ import annotations
+
+import io
+
+from fastapi.testclient import TestClient
+from PIL import Image
+
+from extractors.app import app
+from extractors.features import detect
+
+
+class _Box:
+    def __init__(self, cls_id, conf, xyxy):
+        self.cls = [cls_id]
+        self.conf = [conf]
+        self.xyxy = [xyxy]
+
+
+class _Result:
+    names = {0: "person", 2: "car"}
+
+    def __init__(self, boxes):
+        self.boxes = boxes
+
+
+class FakeYOLO:
+    def __init__(self, boxes):
+        self._boxes = boxes
+
+    def predict(self, img, **kwargs):
+        return [_Result(self._boxes)]
+
+
+def _png() -> bytes:
+    buf = io.BytesIO()
+    Image.new("RGB", (64, 64), (0, 0, 0)).save(buf, format="PNG")
+    return buf.getvalue()
+
+
+def test_summarize_counts():
+    dets = [
+        {"label": "person", "confidence": 0.9, "box": [0, 0, 10, 10]},
+        {"label": "person", "confidence": 0.8, "box": [5, 5, 20, 20]},
+        {"label": "car", "confidence": 0.7, "box": [0, 0, 30, 30]},
+    ]
+    res = detect._summarize(dets)
+    assert res.results["count"] == 3
+    assert res.results["by_label"] == {"person": 2, "car": 1}
+    assert "person×2" in res.evidence[0]
+
+
+def test_analyze_parses_and_sorts():
+    fake = FakeYOLO([_Box(2, 0.6, [1, 1, 9, 9]), _Box(0, 0.95, [0, 0, 8, 8])])
+    res = detect.analyze(_png(), model=fake)
+    assert res.ok is True
+    assert res.results["count"] == 2
+    # sorted by confidence desc → person (0.95) first
+    assert res.results["detections"][0]["label"] == "person"
+    assert res.results["detections"][0]["confidence"] == 0.95
+
+
+def test_analyze_empty_image():
+    res = detect.analyze(b"", model=FakeYOLO([]))
+    assert res.ok is False
+
+
+def test_detect_endpoint_503_without_ml_extra():
+    client = TestClient(app)
+    r = client.post("/v1/detect", files={"file": ("x.png", _png(), "image/png")})
+    assert r.status_code == 503
+
+
+def test_detect_endpoint_with_monkeypatched_model(monkeypatch):
+    monkeypatch.setattr(detect, "get_model", lambda: FakeYOLO([_Box(0, 0.9, [0, 0, 5, 5])]))
+    client = TestClient(app)
+    r = client.post("/v1/detect", files={"file": ("x.png", _png(), "image/png")})
+    assert r.status_code == 200
+    assert r.json()["results"]["by_label"] == {"person": 1}