51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
import numpy
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import opennsfw2
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from PIL import Image
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import cv2
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import modules.globals
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from modules.typing import Frame
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MAX_PROBABILITY = 0.85
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# Preload the model once for efficiency
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model = None
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def predict_frame(target_frame: numpy.ndarray) -> bool:
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"""Predict if a frame is NSFW using OpenNSFW2."""
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try:
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# Convert the frame to RGB before processing if color correction is enabled
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if modules.globals.color_correction:
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target_frame = cv2.cvtColor(target_frame, cv2.COLOR_BGR2RGB)
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image = Image.fromarray(target_frame)
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image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
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global model
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if model is None:
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model = opennsfw2.make_open_nsfw_model()
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views = numpy.expand_dims(image, axis=0)
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_, probability = model.predict(views)[0]
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return probability > MAX_PROBABILITY
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except Exception as e:
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print(f"Error in predict_frame: {e}")
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return False
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def predict_image(target_path: str) -> bool:
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"""Predict if an image file is NSFW."""
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try:
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return opennsfw2.predict_image(target_path) > MAX_PROBABILITY
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except Exception as e:
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print(f"Error in predict_image: {e}")
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return False
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def predict_video(target_path: str) -> bool:
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"""Predict if any frame in a video is NSFW."""
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try:
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_, probabilities = opennsfw2.predict_video_frames(video_path=target_path, frame_interval=100)
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return any(probability > MAX_PROBABILITY for probability in probabilities)
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except Exception as e:
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print(f"Error in predict_video: {e}")
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return False
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