Introduce predicter

roop/core.py

@@ -16,10 +16,10 @@ import argparse
 import torch
 import onnxruntime
 import tensorflow
-from opennsfw2 import predict_video_frames, predict_image
 
 import roop.globals
 import roop.ui as ui
+from roop.predicter import predict_image, predict_video
 from roop.processors.frame.core import get_frame_processors_modules
 from roop.utilities import has_image_extension, is_image, is_video, detect_fps, create_video, extract_frames, get_temp_frame_paths, restore_audio, create_temp, move_temp, clean_temp, normalize_output_path
 
@@ -164,7 +164,7 @@ def start() -> None:
             return
     # process image to image
     if has_image_extension(roop.globals.target_path):
-        if predict_image(roop.globals.target_path) > 0.85:
+        if predict_image(roop.globals.target_path):
             destroy()
         # todo: this needs a temp path for images to work with multiple frame processors
         for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
@@ -177,8 +177,7 @@ def start() -> None:
             update_status('Processing to image failed!')
         return
     # process image to videos
-    seconds, probabilities = predict_video_frames(video_path=roop.globals.target_path, frame_interval=100)
-    if any(probability > 0.85 for probability in probabilities):
+    if predict_video(roop.globals.target_path):
         destroy()
     update_status('Creating temp resources...')
     create_temp(roop.globals.target_path)

roop/predicter.py

@@ -0,0 +1,24 @@
+import numpy
+import opennsfw2
+from PIL import Image
+from opennsfw2 import predict_video_frames
+
+MAX_PROBABILITY = 0.85
+
+
+def predict_frame(target_frame: Image) -> 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 predict_image(target_path) > MAX_PROBABILITY
+
+
+def predict_video(target_path: str) -> bool:
+    _, probabilities = predict_video_frames(video_path=target_path, frame_interval=100)
+    return any(probability > MAX_PROBABILITY for probability in probabilities)

roop/ui.py

@@ -8,6 +8,7 @@ from PIL import Image, ImageOps
 import roop.globals
 from roop.face_analyser import get_one_face
 from roop.capturer import get_video_frame, get_video_frame_total
+from roop.predicter import predict_frame
 from roop.processors.frame.core import get_frame_processors_modules
 from roop.utilities import is_image, is_video, resolve_relative_path
 
@@ -200,6 +201,8 @@ def init_preview() -> None:
 def update_preview(frame_number: int = 0) -> None:
     if roop.globals.source_path and roop.globals.target_path:
         temp_frame = get_video_frame(roop.globals.target_path, frame_number)
+        if predict_frame(temp_frame):
+            quit()
         for frame_processor in get_frame_processors_modules(roop.globals.frame_processors):
             temp_frame = frame_processor.process_frame(
                 get_one_face(cv2.imread(roop.globals.source_path)),