mirror of
https://github.com/yakhyo/uniface.git
synced 2025-12-30 09:02:25 +00:00
0c93598007
* feat: Update linting and type annotations, return types in detect * feat: add face analyzer and face classes * chore: Update the format and clean up some docstrings * docs: Update usage documentation * feat: Change AgeGender model output to 0, 1 instead of string (Female, Male) * test: Update testing code * feat: Add Apple silicon backend for torchscript inference * feat: Add face analyzer example and add run emotion for testing
72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
from uniface.constants import SCRFDWeights
|
|
from uniface.detection import SCRFD
|
|
|
|
|
|
@pytest.fixture
|
|
def scrfd_model():
|
|
return SCRFD(
|
|
model_name=SCRFDWeights.SCRFD_500M_KPS,
|
|
conf_thresh=0.5,
|
|
nms_thresh=0.4,
|
|
)
|
|
|
|
|
|
def test_model_initialization(scrfd_model):
|
|
assert scrfd_model is not None, 'Model initialization failed.'
|
|
|
|
|
|
def test_inference_on_640x640_image(scrfd_model):
|
|
mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
|
|
faces = scrfd_model.detect(mock_image)
|
|
|
|
assert isinstance(faces, list), 'Detections should be a list.'
|
|
|
|
for face in faces:
|
|
assert isinstance(face, dict), 'Each detection should be a dictionary.'
|
|
assert 'bbox' in face, "Each detection should have a 'bbox' key."
|
|
assert 'confidence' in face, "Each detection should have a 'confidence' key."
|
|
assert 'landmarks' in face, "Each detection should have a 'landmarks' key."
|
|
|
|
bbox = face['bbox']
|
|
assert len(bbox) == 4, 'BBox should have 4 values (x1, y1, x2, y2).'
|
|
|
|
landmarks = face['landmarks']
|
|
assert len(landmarks) == 5, 'Should have 5 landmark points.'
|
|
assert all(len(pt) == 2 for pt in landmarks), 'Each landmark should be (x, y).'
|
|
|
|
|
|
def test_confidence_threshold(scrfd_model):
|
|
mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
|
|
faces = scrfd_model.detect(mock_image)
|
|
|
|
for face in faces:
|
|
confidence = face['confidence']
|
|
assert confidence >= 0.5, f'Detection has confidence {confidence} below threshold 0.5'
|
|
|
|
|
|
def test_no_faces_detected(scrfd_model):
|
|
empty_image = np.zeros((640, 640, 3), dtype=np.uint8)
|
|
faces = scrfd_model.detect(empty_image)
|
|
assert len(faces) == 0, 'Should detect no faces in a blank image.'
|
|
|
|
|
|
def test_different_input_sizes(scrfd_model):
|
|
test_sizes = [(480, 640, 3), (720, 1280, 3), (1080, 1920, 3)]
|
|
|
|
for size in test_sizes:
|
|
mock_image = np.random.randint(0, 255, size, dtype=np.uint8)
|
|
faces = scrfd_model.detect(mock_image)
|
|
assert isinstance(faces, list), f'Should return list for size {size}'
|
|
|
|
|
|
def test_scrfd_10g_model():
|
|
model = SCRFD(model_name=SCRFDWeights.SCRFD_10G_KPS, conf_thresh=0.5)
|
|
assert model is not None, 'SCRFD 10G model initialization failed.'
|
|
|
|
mock_image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
|
|
faces = model.detect(mock_image)
|
|
assert isinstance(faces, list), 'SCRFD 10G should return list of detections.'
|