[AZ-161] [AZ-162] [AZ-163] Add ONNX inference, NMS, annotation queue tests

Made-with: Cursor

tests/test_onnx_inference.py

@@ -0,0 +1,67 @@
+import cv2
+import numpy as np
+import pytest
+
+from inference.inference import Inference
+from inference.onnx_engine import OnnxEngine
+
+
+@pytest.fixture(scope="session")
+def onnx_engine_session(fixture_onnx_model):
+    return OnnxEngine(fixture_onnx_model)
+
+
+def test_bt_inf_01_model_loads(onnx_engine_session):
+    engine = onnx_engine_session
+    assert engine.input_shape is not None
+    assert len(engine.input_shape) >= 4
+    h, w = engine.get_input_shape()
+    assert h > 0 and w > 0
+    assert engine.get_batch_size() > 0
+
+
+def test_bt_inf_02_inference_returns_output(onnx_engine_session, fixture_images_dir):
+    engine = onnx_engine_session
+    imgs = sorted(fixture_images_dir.glob("*.jpg"))
+    assert imgs
+    frame = cv2.imread(str(imgs[0]))
+    assert frame is not None
+    model_height, model_width = engine.get_input_shape()
+    frames = [frame] * engine.get_batch_size()
+    blobs = [
+        cv2.dnn.blobFromImage(f, 1.0 / 255.0, (model_width, model_height), (0, 0, 0), swapRB=True, crop=False)
+        for f in frames
+    ]
+    blob = np.vstack(blobs)
+    out = engine.run(blob)
+    assert isinstance(out, list)
+    assert len(out) > 0
+    assert isinstance(out[0], np.ndarray)
+    assert out[0].ndim == 3
+    assert out[0].shape[0] == engine.get_batch_size()
+    assert out[0].shape[2] >= 6
+
+
+def test_bt_inf_03_postprocess_valid_detections(onnx_engine_session, fixture_images_dir):
+    engine = onnx_engine_session
+    inf = Inference(engine, 0.1, 0.3)
+    imgs = sorted(fixture_images_dir.glob("*.jpg"))
+    frame = cv2.imread(str(imgs[0]))
+    assert frame is not None
+    n = engine.get_batch_size()
+    batch_frames = [frame] * n
+    ts = list(range(n))
+    blob = inf.preprocess(batch_frames)
+    output = engine.run(blob)
+    anns = inf.postprocess(batch_frames, ts, output)
+    assert isinstance(anns, list)
+    assert len(anns) == len(batch_frames)
+    for ann in anns:
+        assert ann.detections is not None
+        for d in ann.detections:
+            assert 0 <= d.x <= 1
+            assert 0 <= d.y <= 1
+            assert 0 <= d.w <= 1
+            assert 0 <= d.h <= 1
+            assert 0 <= d.cls <= 79
+            assert 0 <= d.confidence <= 1