mirror of
https://github.com/facefusion/facefusion.git
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Henry Ruhs
1b151c5c3e
* compose every test fixture via the builder and run_ffmpeg Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Tbcd6VWCiU4BQP1gywPr2a * use loops in tests for ffmpeg stuff --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
115 lines
4.8 KiB
Python
115 lines
4.8 KiB
Python
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import numpy
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import pytest
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from facefusion import face_classifier, face_detector, face_landmarker, face_recognizer, ffmpeg, ffmpeg_builder, process_manager, state_manager
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from facefusion.download import conditional_download
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from facefusion.face_creator import average_face_geometry, get_many_faces, get_one_face, refill_faces
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from facefusion.face_store import clear_faces
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from facefusion.vision import read_static_image
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from .helper import get_test_example_file, get_test_examples_directory
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@pytest.fixture(scope = 'module', autouse = True)
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def before_all() -> None:
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process_manager.start()
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conditional_download(get_test_examples_directory(),
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[
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'https://github.com/facefusion/facefusion-assets/releases/download/examples-3.0.0/source.jpg'
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])
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for crop_scale in [ 80, 70, 60 ]:
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ffmpeg.run_ffmpeg(
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ffmpeg_builder.chain(
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ffmpeg_builder.set_input(get_test_example_file('source.jpg')),
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[
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'-vf',
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'crop=iw*0.' + str(crop_scale) + ':ih*0.' + str(crop_scale)
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],
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ffmpeg_builder.set_output(get_test_example_file('source-' + str(crop_scale) + 'crop.jpg'))
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)
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)
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state_manager.init_item('execution_device_ids', [ 0 ])
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state_manager.init_item('execution_providers', [ 'cpu' ])
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state_manager.init_item('download_providers', [ 'github' ])
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state_manager.init_item('face_detector_angles', [ 0 ])
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state_manager.init_item('face_detector_model', 'many')
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state_manager.init_item('face_detector_size', '640x640')
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state_manager.init_item('face_detector_margin', (0, 0, 0, 0))
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state_manager.init_item('face_detector_score', 0.5)
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state_manager.init_item('face_landmarker_model', 'many')
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state_manager.init_item('face_landmarker_score', 0.5)
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face_classifier.pre_check()
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face_detector.pre_check()
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face_landmarker.pre_check()
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face_recognizer.pre_check()
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@pytest.fixture(autouse = True)
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def before_each() -> None:
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face_classifier.clear_inference_pool()
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face_detector.clear_inference_pool()
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face_landmarker.clear_inference_pool()
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face_recognizer.clear_inference_pool()
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clear_faces()
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def test_get_one_face() -> None:
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source_vision_frame = read_static_image(get_test_example_file('source.jpg'))
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face = get_one_face(get_many_faces([ source_vision_frame ]))
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assert face.bounding_box.size == 4
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def test_get_many_faces() -> None:
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source_path = get_test_example_file('source.jpg')
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source_vision_frame = read_static_image(source_path)
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many_faces = get_many_faces([ source_vision_frame, source_vision_frame, source_vision_frame ])
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assert len(many_faces) == 3
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def test_refill_faces() -> None:
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source_vision_frame = read_static_image(get_test_example_file('source.jpg'))
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face = get_one_face(get_many_faces([ source_vision_frame ]))
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face_first = face._replace(bounding_box = numpy.array([ 0, 0, 10, 10 ]))
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face_middle = face._replace(bounding_box = numpy.array([ 40, 40, 50, 50 ]))
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face_last = face._replace(bounding_box = numpy.array([ 80, 80, 90, 90 ]))
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fill_faces = refill_faces([ face_first, None, face_last ])
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assert fill_faces[0].bounding_box.tolist() == [ 0.0, 0.0, 10.0, 10.0 ]
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assert fill_faces[1].bounding_box.tolist() == [ 40.0, 40.0, 50.0, 50.0 ]
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assert fill_faces[2].bounding_box.tolist() == [ 80.0, 80.0, 90.0, 90.0 ]
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fill_faces = refill_faces([ face_first, None, None, None, face_last ])
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assert fill_faces[0].bounding_box.tolist() == [ 0.0, 0.0, 10.0, 10.0 ]
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assert fill_faces[1].bounding_box.tolist() == [ 20.0, 20.0, 30.0, 30.0 ]
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assert fill_faces[2].bounding_box.tolist() == [ 40.0, 40.0, 50.0, 50.0 ]
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assert fill_faces[3].bounding_box.tolist() == [ 60.0, 60.0, 70.0, 70.0 ]
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assert fill_faces[4].bounding_box.tolist() == [ 80.0, 80.0, 90.0, 90.0 ]
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fill_faces = refill_faces([ face_first, None, face_middle, None, face_last ])
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assert fill_faces[0].bounding_box.tolist() == [ 0.0, 0.0, 10.0, 10.0 ]
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assert fill_faces[1].bounding_box.tolist() == [ 20.0, 20.0, 30.0, 30.0 ]
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assert fill_faces[2].bounding_box.tolist() == [ 40.0, 40.0, 50.0, 50.0 ]
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assert fill_faces[3].bounding_box.tolist() == [ 60.0, 60.0, 70.0, 70.0 ]
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assert fill_faces[4].bounding_box.tolist() == [ 80.0, 80.0, 90.0, 90.0 ]
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def test_average_face_geometry() -> None:
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source_vision_frame = read_static_image(get_test_example_file('source.jpg'))
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face_previous = get_one_face(get_many_faces([ source_vision_frame ]))
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face_next = get_one_face(get_many_faces([ source_vision_frame ]))
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face_previous = face_previous._replace(bounding_box = numpy.array([ 0, 0, 10, 10 ]))
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face_next = face_next._replace(bounding_box = numpy.array([ 80, 80, 90, 90 ]))
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assert average_face_geometry([face_previous, face_next], 0.5).bounding_box.tolist() == [40.0, 40.0, 50.0, 50.0]
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assert average_face_geometry([face_previous, face_next], 0.5).angle == face_next.angle
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assert average_face_geometry([face_previous, face_next], 0.5).embedding is face_next.embedding
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assert average_face_geometry([face_previous, face_next], 0.25).embedding is face_previous.embedding
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