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import subprocess
import pytest
from facefusion import face_classifier, face_detector, face_landmarker, face_recognizer, state_manager
from facefusion.download import conditional_download
from facefusion.face_analyser import get_many_faces, get_one_face
from facefusion.typing import Face
from facefusion.vision import read_static_image
from .helper import get_test_example_file, get_test_examples_directory
@pytest.fixture(scope = 'module', autouse = True)
def before_all() -> None:
conditional_download(get_test_examples_directory(),
[
'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/source.jpg'
])
subprocess.run([ 'ffmpeg', '-i', get_test_example_file('source.jpg'), '-vf', 'crop=iw*0.8:ih*0.8', get_test_example_file('source-80crop.jpg') ])
subprocess.run([ 'ffmpeg', '-i', get_test_example_file('source.jpg'), '-vf', 'crop=iw*0.7:ih*0.7', get_test_example_file('source-70crop.jpg') ])
subprocess.run([ 'ffmpeg', '-i', get_test_example_file('source.jpg'), '-vf', 'crop=iw*0.6:ih*0.6', get_test_example_file('source-60crop.jpg') ])
state_manager.init_item('execution_providers', [ 'cpu' ])
state_manager.init_item('face_detector_angles', [ 0 ])
state_manager.init_item('face_detector_score', 0.5)
state_manager.init_item('face_landmarker_score', 0.5)
face_classifier.pre_check()
face_landmarker.pre_check()
face_recognizer.pre_check()
@pytest.fixture(autouse = True)
def before_each() -> None:
face_classifier.clear_inference_pool()
face_detector.clear_inference_pool()
face_landmarker.clear_inference_pool()
face_recognizer.clear_inference_pool()
def test_get_one_face_with_retinaface() -> None:
state_manager.init_item('face_detector_model', 'retinaface')
state_manager.init_item('face_detector_size', '320x320')
face_detector.pre_check()
source_paths =\
[
get_test_example_file('source.jpg'),
get_test_example_file('source-80crop.jpg'),
get_test_example_file('source-70crop.jpg'),
get_test_example_file('source-60crop.jpg')
]
for source_path in source_paths:
source_frame = read_static_image(source_path)
many_faces = get_many_faces([ source_frame ])
face = get_one_face(many_faces)
assert isinstance(face, Face)
def test_get_one_face_with_scrfd() -> None:
state_manager.init_item('face_detector_model', 'scrfd')
state_manager.init_item('face_detector_size', '640x640')
face_detector.pre_check()
source_paths =\
[
get_test_example_file('source.jpg'),
get_test_example_file('source-80crop.jpg'),
get_test_example_file('source-70crop.jpg'),
get_test_example_file('source-60crop.jpg')
]
for source_path in source_paths:
source_frame = read_static_image(source_path)
many_faces = get_many_faces([ source_frame ])
face = get_one_face(many_faces)
assert isinstance(face, Face)
def test_get_one_face_with_yoloface() -> None:
state_manager.init_item('face_detector_model', 'yoloface')
state_manager.init_item('face_detector_size', '640x640')
face_detector.pre_check()
source_paths =\
[
get_test_example_file('source.jpg'),
get_test_example_file('source-80crop.jpg'),
get_test_example_file('source-70crop.jpg'),
get_test_example_file('source-60crop.jpg')
]
for source_path in source_paths:
source_frame = read_static_image(source_path)
many_faces = get_many_faces([ source_frame ])
face = get_one_face(many_faces)
assert isinstance(face, Face)
def test_get_many_faces() -> None:
source_path = get_test_example_file('source.jpg')
source_frame = read_static_image(source_path)
many_faces = get_many_faces([ source_frame, source_frame, source_frame ])
assert isinstance(many_faces[0], Face)
assert isinstance(many_faces[1], Face)
assert isinstance(many_faces[2], Face)
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