mirror of
https://github.com/facefusion/facefusion.git
synced 2026-07-27 20:41:01 +02:00
Henry Ruhs
130 lines
4.1 KiB
Python
130 lines
4.1 KiB
Python
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import pytest
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from facefusion import face_detector, ffmpeg, ffmpeg_builder, process_manager, state_manager
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from facefusion.download import conditional_download
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from facefusion.face_detector import detect_with_retinaface, detect_with_scrfd, detect_with_yolo_face, detect_with_yunet
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from facefusion.face_helper import apply_nms, get_nms_threshold
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from facefusion.vision import read_static_image
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from .assert_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_score', 0.5)
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face_detector.pre_check()
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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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@pytest.fixture(autouse = True)
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def before_each() -> None:
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face_detector.clear_inference_pool()
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def test_detect_with_retinaface() -> None:
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source_paths =\
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[
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get_test_example_file('source.jpg'),
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get_test_example_file('source-80crop.jpg'),
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get_test_example_file('source-70crop.jpg'),
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get_test_example_file('source-60crop.jpg')
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]
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for source_path in source_paths:
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source_frame = read_static_image(source_path)
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bounding_boxes, face_scores, face_landmarks_5 = detect_with_retinaface(source_frame, '320x320')
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keep_indices = apply_nms(bounding_boxes, face_scores, 0.5, get_nms_threshold('retinaface', [ 0 ]))
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assert len(keep_indices) == 1
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def test_detect_with_scrfd() -> None:
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source_paths =\
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[
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get_test_example_file('source.jpg'),
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get_test_example_file('source-80crop.jpg'),
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get_test_example_file('source-70crop.jpg'),
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get_test_example_file('source-60crop.jpg')
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]
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for source_path in source_paths:
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source_frame = read_static_image(source_path)
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bounding_boxes, face_scores, face_landmarks_5 = detect_with_scrfd(source_frame, '320x320')
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keep_indices = apply_nms(bounding_boxes, face_scores, 0.5, get_nms_threshold('scrfd', [ 0 ]))
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assert len(keep_indices) == 1
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def test_detect_with_yolo_face() -> None:
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source_paths =\
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[
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get_test_example_file('source.jpg'),
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get_test_example_file('source-80crop.jpg'),
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get_test_example_file('source-70crop.jpg'),
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get_test_example_file('source-60crop.jpg')
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]
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for source_path in source_paths:
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source_frame = read_static_image(source_path)
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bounding_boxes, face_scores, face_landmarks_5 = detect_with_yolo_face(source_frame, '640x640')
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keep_indices = apply_nms(bounding_boxes, face_scores, 0.5, get_nms_threshold('yolo_face', [ 0 ]))
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assert len(keep_indices) == 1
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def test_detect_with_yunet() -> None:
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source_paths =\
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[
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get_test_example_file('source.jpg'),
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get_test_example_file('source-80crop.jpg'),
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get_test_example_file('source-70crop.jpg'),
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get_test_example_file('source-60crop.jpg')
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]
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for source_path in source_paths:
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source_frame = read_static_image(source_path)
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bounding_boxes, face_scores, face_landmarks_5 = detect_with_yunet(source_frame, '640x640')
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keep_indices = apply_nms(bounding_boxes, face_scores, 0.5, get_nms_threshold('yunet', [ 0 ]))
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assert len(keep_indices) == 1
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