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Building
| import threading | |
| import numpy | |
| import opennsfw2 | |
| from PIL import Image | |
| from keras import Model | |
| from roop.typing import Frame | |
| PREDICTOR = None | |
| THREAD_LOCK = threading.Lock() | |
| MAX_PROBABILITY = 0.85 | |
| def get_predictor() -> Model: | |
| global PREDICTOR | |
| with THREAD_LOCK: | |
| if PREDICTOR is None: | |
| PREDICTOR = opennsfw2.make_open_nsfw_model() | |
| return PREDICTOR | |
| def clear_predictor() -> None: | |
| global PREDICTOR | |
| PREDICTOR = None | |
| def predict_frame(target_frame: Frame) -> bool: | |
| image = Image.fromarray(target_frame) | |
| image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO) | |
| views = numpy.expand_dims(image, axis=0) | |
| _, probability = get_predictor().predict(views)[0] | |
| return probability > MAX_PROBABILITY | |
| def predict_image(target_path: str) -> bool: | |
| return opennsfw2.predict_image(target_path) > MAX_PROBABILITY | |
| def predict_video(target_path: str) -> bool: | |
| _, probabilities = opennsfw2.predict_video_frames(video_path=target_path, frame_interval=100) | |
| return any(probability > MAX_PROBABILITY for probability in probabilities) | |