Update roop/predictor.py

roop/predictor.py

@@ -1,43 +1,22 @@
 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
-
+# Define any other necessary variables or constants here
 
 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
-
+    # Modify this function as needed for your specific use case, without NSFW prediction
+    # For example, you can implement custom image analysis or processing here
+    return False
 
 def predict_image(target_path: str) -> bool:
-    return opennsfw2.predict_image(target_path) > MAX_PROBABILITY
-
+    # Modify this function as needed for your specific use case, without NSFW prediction
+    # For example, you can check the image based on your application's requirements
+    return False
 
 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)
+    # Modify this function as needed for your specific use case, without NSFW prediction
+    # For example, you can analyze video frames for other purposes
+    return False