mirror of
https://github.com/yakhyo/uniface.git
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2c78f39e5d
- Add new test files for age_gender, factory, landmark, recognition, scrfd, and utils - Add new scripts for age_gender, landmarks, and video detection - Update documentation in README.md, MODELS.md, QUICKSTART.md - Improve model constants and face utilities - Update detection models (retinaface, scrfd) with enhanced functionality - Update project configuration in pyproject.toml
56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
import numpy as np
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import pytest
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from uniface.constants import RetinaFaceWeights
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from uniface.detection import RetinaFace
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@pytest.fixture
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def retinaface_model():
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return RetinaFace(
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model_name=RetinaFaceWeights.MNET_V2,
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conf_thresh=0.5,
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pre_nms_topk=5000,
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nms_thresh=0.4,
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post_nms_topk=750,
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)
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def test_model_initialization(retinaface_model):
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assert retinaface_model is not None, "Model initialization failed."
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def test_inference_on_640x640_image(retinaface_model):
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mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
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faces = retinaface_model.detect(mock_image)
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assert isinstance(faces, list), "Detections should be a list."
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for face in faces:
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assert isinstance(face, dict), "Each detection should be a dictionary."
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assert "bbox" in face, "Each detection should have a 'bbox' key."
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assert "confidence" in face, "Each detection should have a 'confidence' key."
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assert "landmarks" in face, "Each detection should have a 'landmarks' key."
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bbox = face["bbox"]
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assert len(bbox) == 4, "BBox should have 4 values (x1, y1, x2, y2)."
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landmarks = face["landmarks"]
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assert len(landmarks) == 5, "Should have 5 landmark points."
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assert all(len(pt) == 2 for pt in landmarks), "Each landmark should be (x, y)."
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def test_confidence_threshold(retinaface_model):
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mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
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faces = retinaface_model.detect(mock_image)
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for face in faces:
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confidence = face["confidence"]
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assert confidence >= 0.5, f"Detection has confidence {confidence} below threshold 0.5"
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def test_no_faces_detected(retinaface_model):
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empty_image = np.zeros((640, 640, 3), dtype=np.uint8)
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faces = retinaface_model.detect(empty_image)
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assert len(faces) == 0, "Should detect no faces in a blank image."
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