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https://github.com/yakhyo/uniface.git
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971775b2e8
* docs: Update documentation * fix: Update several missing docs and tests * docs: Clean up and remove redundants * fix: Fix the gaze output formula and change the output order * chore: Update model weights for gaze estimation * release: Update release version to v3.0.0
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
# Copyright 2025-2026 Yakhyokhuja Valikhujaev
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# Author: Yakhyokhuja Valikhujaev
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# GitHub: https://github.com/yakhyo
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from __future__ import annotations
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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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confidence_threshold=0.5,
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pre_nms_topk=5000,
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nms_threshold=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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# Face is a dataclass, check attributes exist
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assert hasattr(face, 'bbox'), "Each detection should have a 'bbox' attribute."
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assert hasattr(face, 'confidence'), "Each detection should have a 'confidence' attribute."
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assert hasattr(face, 'landmarks'), "Each detection should have a 'landmarks' attribute."
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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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