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from functools import lru_cache
import cv2
import numpy
from tqdm import tqdm
from facefusion import inference_manager, state_manager, wording
from facefusion.download import conditional_download_hashes, conditional_download_sources
from facefusion.filesystem import resolve_relative_path
from facefusion.thread_helper import conditional_thread_semaphore
from facefusion.typing import Fps, InferencePool, ModelOptions, ModelSet, VisionFrame
from facefusion.vision import count_video_frame_total, detect_video_fps, get_video_frame, read_image
MODEL_SET : ModelSet =\
{
'open_nsfw':
{
'hashes':
{
'content_analyser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/open_nsfw.hash',
'path': resolve_relative_path('../.assets/models/open_nsfw.hash')
}
},
'sources':
{
'content_analyser':
{
'url': 'https://github.com/facefusion/facefusion-assets/releases/download/models-3.0.0/open_nsfw.onnx',
'path': resolve_relative_path('../.assets/models/open_nsfw.onnx')
}
}
}
}
PROBABILITY_LIMIT = 0.80
RATE_LIMIT = 10
STREAM_COUNTER = 0
def get_inference_pool() -> InferencePool:
model_sources = get_model_options().get('sources')
return inference_manager.get_inference_pool(__name__, model_sources)
def clear_inference_pool() -> None:
inference_manager.clear_inference_pool(__name__)
def get_model_options() -> ModelOptions:
return MODEL_SET.get('open_nsfw')
def pre_check() -> bool:
download_directory_path = resolve_relative_path('../.assets/models')
model_hashes = get_model_options().get('hashes')
model_sources = get_model_options().get('sources')
return conditional_download_hashes(download_directory_path, model_hashes) and conditional_download_sources(download_directory_path, model_sources)
def analyse_stream(vision_frame : VisionFrame, video_fps : Fps) -> bool:
global STREAM_COUNTER
STREAM_COUNTER = STREAM_COUNTER + 1
if STREAM_COUNTER % int(video_fps) == 0:
return analyse_frame(vision_frame)
return False
def analyse_frame(vision_frame : VisionFrame) -> bool:
content_analyser = get_inference_pool().get('content_analyser')
vision_frame = prepare_frame(vision_frame)
with conditional_thread_semaphore():
probability = content_analyser.run(None,
{
'input': vision_frame
})[0][0][1]
return probability > PROBABILITY_LIMIT
def prepare_frame(vision_frame : VisionFrame) -> VisionFrame:
vision_frame = cv2.resize(vision_frame, (224, 224)).astype(numpy.float32)
vision_frame -= numpy.array([ 104, 117, 123 ]).astype(numpy.float32)
vision_frame = numpy.expand_dims(vision_frame, axis = 0)
return vision_frame
@lru_cache(maxsize = None)
def analyse_image(image_path : str) -> bool:
frame = read_image(image_path)
return analyse_frame(frame)
@lru_cache(maxsize = None)
def analyse_video(video_path : str, start_frame : int, end_frame : int) -> bool:
video_frame_total = count_video_frame_total(video_path)
video_fps = detect_video_fps(video_path)
frame_range = range(start_frame or 0, end_frame or video_frame_total)
rate = 0.0
counter = 0
with tqdm(total = len(frame_range), desc = wording.get('analysing'), unit = 'frame', ascii = ' =', disable = state_manager.get_item('log_level') in [ 'warn', 'error' ]) as progress:
for frame_number in frame_range:
if frame_number % int(video_fps) == 0:
frame = get_video_frame(video_path, frame_number)
if analyse_frame(frame):
counter += 1
rate = counter * int(video_fps) / len(frame_range) * 100
progress.update()
progress.set_postfix(rate = rate)
return rate > RATE_LIMIT
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