mirror of
https://github.com/hacksider/Deep-Live-Cam.git
synced 2026-09-02 14:00:42 +02:00
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
import importlib.util
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import os
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import numpy
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# Keras 3 defaults to the TensorFlow backend, which has no Python 3.14 wheels.
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# opennsfw2 only runs inference, so any installed backend works; pick one that
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# is actually present before opennsfw2 imports keras.
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if "KERAS_BACKEND" not in os.environ:
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for _backend in ("torch", "tensorflow", "jax"):
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if importlib.util.find_spec(_backend) is not None:
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os.environ["KERAS_BACKEND"] = _backend
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break
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import opennsfw2
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from PIL import Image
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import cv2 # Add OpenCV import
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import modules.globals # Import globals to access the color correction toggle
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from modules.gpu_processing import gpu_cvt_color
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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: Frame) -> bool:
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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 = gpu_cvt_color(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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def predict_image(target_path: str) -> bool:
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return opennsfw2.predict_image(target_path) > MAX_PROBABILITY
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def predict_video(target_path: str) -> bool:
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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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