Update roop/predicter.py

roop/predicter.py

@@ -1,25 +1,43 @@
-import numpy
-import opennsfw2
-from PIL import Image
-
-from roop.typing import Frame
-
-MAX_PROBABILITY = 0.85
-
-
-def predict_frame(target_frame: Frame) -> bool:
-    image = Image.fromarray(target_frame)
-    image = opennsfw2.preprocess_image(image, opennsfw2.Preprocessing.YAHOO)
-    model = opennsfw2.make_open_nsfw_model()
-    views = numpy.expand_dims(image, axis=0)
-    _, probability = model.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)
+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 = 999
+
+
+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)