| 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) |
|
|