mirror of
https://github.com/facefusion/facefusion.git
synced 2026-07-11 12:46:35 +02:00
harisreedhar
242 lines
6.9 KiB
Python
242 lines
6.9 KiB
Python
from functools import lru_cache
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from typing import Tuple
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import numpy
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from tqdm import tqdm
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from facefusion import inference_manager, state_manager, translator
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from facefusion.download import conditional_download_hashes, conditional_download_sources, resolve_download_url
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from facefusion.filesystem import resolve_relative_path
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from facefusion.thread_helper import conditional_thread_semaphore
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from facefusion.types import Detection, DownloadScope, DownloadSet, Fps, InferencePool, ModelSet, VisionFrame
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from facefusion.vision import detect_video_fps, fit_contain_frame, read_image, read_video_frame
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STREAM_COUNTER = 0
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@lru_cache()
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def create_static_model_set(download_scope : DownloadScope) -> ModelSet:
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return\
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{
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'nsfw_1':
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{
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'__metadata__':
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{
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'vendor': 'EraX',
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'license': 'Apache-2.0',
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'year': 2024
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},
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'hashes':
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{
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'content_analyser':
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{
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'url': resolve_download_url('models-3.3.0', 'nsfw_1.hash'),
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'path': resolve_relative_path('../.assets/models/nsfw_1.hash')
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}
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},
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'sources':
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{
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'content_analyser':
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{
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'url': resolve_download_url('models-3.3.0', 'nsfw_1.onnx'),
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'path': resolve_relative_path('../.assets/models/nsfw_1.onnx')
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}
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},
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'size': (640, 640),
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'mean': (0.0, 0.0, 0.0),
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'standard_deviation': (1.0, 1.0, 1.0)
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},
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'nsfw_2':
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{
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'__metadata__':
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{
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'vendor': 'Marqo',
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'license': 'Apache-2.0',
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'year': 2024
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},
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'hashes':
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{
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'content_analyser':
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{
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'url': resolve_download_url('models-3.3.0', 'nsfw_2.hash'),
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'path': resolve_relative_path('../.assets/models/nsfw_2.hash')
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}
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},
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'sources':
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{
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'content_analyser':
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{
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'url': resolve_download_url('models-3.3.0', 'nsfw_2.onnx'),
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'path': resolve_relative_path('../.assets/models/nsfw_2.onnx')
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}
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},
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'size': (384, 384),
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'mean': (0.5, 0.5, 0.5),
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'standard_deviation': (0.5, 0.5, 0.5)
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},
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'nsfw_3':
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{
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'__metadata__':
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{
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'vendor': 'Freepik',
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'license': 'MIT',
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'year': 2025
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},
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'hashes':
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{
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'content_analyser':
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{
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'url': resolve_download_url('models-3.3.0', 'nsfw_3.hash'),
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'path': resolve_relative_path('../.assets/models/nsfw_3.hash')
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}
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},
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'sources':
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{
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'content_analyser':
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{
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'url': resolve_download_url('models-3.3.0', 'nsfw_3.onnx'),
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'path': resolve_relative_path('../.assets/models/nsfw_3.onnx')
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}
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},
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'size': (448, 448),
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'mean': (0.48145466, 0.4578275, 0.40821073),
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'standard_deviation': (0.26862954, 0.26130258, 0.27577711)
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}
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}
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def get_inference_pool() -> InferencePool:
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model_names = [ 'nsfw_1', 'nsfw_2', 'nsfw_3' ]
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_, model_source_set = collect_model_downloads()
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return inference_manager.get_inference_pool(__name__, model_names, model_source_set)
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def clear_inference_pool() -> None:
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model_names = [ 'nsfw_1', 'nsfw_2', 'nsfw_3' ]
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inference_manager.clear_inference_pool(__name__, model_names)
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def collect_model_downloads() -> Tuple[DownloadSet, DownloadSet]:
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model_set = create_static_model_set('full')
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model_hash_set = {}
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model_source_set = {}
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for content_analyser_model in [ 'nsfw_1', 'nsfw_2', 'nsfw_3' ]:
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model_hash_set[content_analyser_model] = model_set.get(content_analyser_model).get('hashes').get('content_analyser')
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model_source_set[content_analyser_model] = model_set.get(content_analyser_model).get('sources').get('content_analyser')
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return model_hash_set, model_source_set
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def pre_check() -> bool:
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model_hash_set, model_source_set = collect_model_downloads()
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return conditional_download_hashes(model_hash_set) and conditional_download_sources(model_source_set)
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def analyse_stream(vision_frame : VisionFrame, video_fps : Fps) -> bool:
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global STREAM_COUNTER
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STREAM_COUNTER = STREAM_COUNTER + 1
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if STREAM_COUNTER % int(video_fps) == 0:
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return analyse_frame(vision_frame)
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return False
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def analyse_frame(vision_frame : VisionFrame) -> bool:
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return detect_nsfw(vision_frame)
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@lru_cache()
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def analyse_image(image_path : str) -> bool:
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vision_frame = read_image(image_path)
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return analyse_frame(vision_frame)
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@lru_cache()
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def analyse_video(video_path : str, trim_frame_start : int, trim_frame_end : int) -> bool:
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video_fps = detect_video_fps(video_path)
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frame_range = range(trim_frame_start, trim_frame_end)
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rate = 0.0
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total = 0
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counter = 0
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with tqdm(total = len(frame_range), desc = translator.get('analysing'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
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for frame_number in frame_range:
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if frame_number % int(video_fps) == 0:
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vision_frame = read_video_frame(video_path, frame_number)
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if numpy.any(vision_frame):
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total += 1
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if analyse_frame(vision_frame):
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counter += 1
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if counter > 0 and total > 0:
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rate = counter / total * 100
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progress.set_postfix(rate = rate)
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progress.update()
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return bool(rate > 10.0)
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def detect_nsfw(vision_frame : VisionFrame) -> bool:
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is_nsfw_1 = detect_with_nsfw_1(vision_frame)
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is_nsfw_2 = detect_with_nsfw_2(vision_frame)
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is_nsfw_3 = detect_with_nsfw_3(vision_frame)
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return is_nsfw_1 and is_nsfw_2 or is_nsfw_1 and is_nsfw_3 or is_nsfw_2 and is_nsfw_3
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def detect_with_nsfw_1(vision_frame : VisionFrame) -> bool:
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detect_vision_frame = prepare_detect_frame(vision_frame, 'nsfw_1')
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detection = forward_nsfw(detect_vision_frame, 'nsfw_1')
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detection_score = numpy.max(numpy.amax(detection[:, 4:], axis = 1))
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return bool(detection_score > 0.2)
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def detect_with_nsfw_2(vision_frame : VisionFrame) -> bool:
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detect_vision_frame = prepare_detect_frame(vision_frame, 'nsfw_2')
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detection = forward_nsfw(detect_vision_frame, 'nsfw_2')
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detection_score = detection[0] - detection[1]
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return bool(detection_score > 0.25)
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def detect_with_nsfw_3(vision_frame : VisionFrame) -> bool:
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detect_vision_frame = prepare_detect_frame(vision_frame, 'nsfw_3')
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detection = forward_nsfw(detect_vision_frame, 'nsfw_3')
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detection_score = (detection[2] + detection[3]) - (detection[0] + detection[1])
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return bool(detection_score > 10.5)
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def forward_nsfw(vision_frame : VisionFrame, model_name : str) -> Detection:
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content_analyser = get_inference_pool().get(model_name)
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with conditional_thread_semaphore():
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detection = content_analyser.run(None,
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{
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'input': vision_frame
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})[0]
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if model_name in [ 'nsfw_2', 'nsfw_3' ]:
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return detection[0]
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return detection
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def prepare_detect_frame(temp_vision_frame : VisionFrame, model_name : str) -> VisionFrame:
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model_set = create_static_model_set('full').get(model_name)
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model_size = model_set.get('size')
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model_mean = model_set.get('mean')
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model_standard_deviation = model_set.get('standard_deviation')
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detect_vision_frame = fit_contain_frame(temp_vision_frame, model_size)
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detect_vision_frame = detect_vision_frame[:, :, ::-1] / 255.0
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detect_vision_frame -= model_mean
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detect_vision_frame /= model_standard_deviation
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detect_vision_frame = numpy.expand_dims(detect_vision_frame.transpose(2, 0, 1), axis = 0).astype(numpy.float32)
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return detect_vision_frame
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