mirror of
https://github.com/facefusion/facefusion.git
synced 2026-04-23 01:46:09 +02:00
Harisreedhar
ab24cd3f2e
* implement basic webrtc_stream * add aiortc to requirements.txt * update aiortc version * rename variables with rtc_ prefix * changes * changes * change helper to assert_helper and stream_helper * rename variables with rtc_ prefix * add error handling * return whole connection * remove monkey patch and some cleaning * cleanup * tiny adjustments * tiny adjustments * proper typing and naming for rtc offer set * - remove async from on_video_track method - rename source -> target - add audio * audio always before video --------- Co-authored-by: henryruhs <info@henryruhs.com>
134 lines
4.5 KiB
Python
134 lines
4.5 KiB
Python
import subprocess
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import pytest
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from facefusion import face_classifier, face_detector, face_landmarker, face_recognizer, state_manager
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from facefusion.download import conditional_download
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from facefusion.face_analyser import get_many_faces
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from facefusion.face_store import clear_static_faces
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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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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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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') ])
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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') ])
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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') ])
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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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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_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_static_faces()
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def test_get_one_face_with_retinaface() -> None:
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state_manager.init_item('face_detector_model', 'retinaface')
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state_manager.init_item('face_detector_size', '320x320')
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state_manager.init_item('face_detector_margin', (0, 0, 0, 0))
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face_detector.pre_check()
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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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many_faces = get_many_faces([ source_frame ])
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assert len(many_faces) == 1
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def test_get_one_face_with_scrfd() -> None:
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state_manager.init_item('face_detector_model', 'scrfd')
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state_manager.init_item('face_detector_size', '320x320')
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state_manager.init_item('face_detector_margin', (0, 0, 0, 0))
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face_detector.pre_check()
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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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many_faces = get_many_faces([ source_frame ])
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assert len(many_faces) == 1
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def test_get_one_face_with_yoloface() -> None:
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state_manager.init_item('face_detector_model', 'yolo_face')
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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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face_detector.pre_check()
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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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many_faces = get_many_faces([ source_frame ])
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assert len(many_faces) == 1
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def test_get_one_face_with_yunet() -> None:
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state_manager.init_item('face_detector_model', 'yunet')
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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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face_detector.pre_check()
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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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many_faces = get_many_faces([ source_frame ])
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assert len(many_faces) == 1
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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_frame = read_static_image(source_path)
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many_faces = get_many_faces([ source_frame, source_frame, source_frame ])
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assert len(many_faces) == 3
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