Replace NSFW detector implementation

nsfw_detect.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 
 """
-    Copyright © 2020 Mia Herkt
+    Copyright © 2024 Mia Herkt
     Licensed under the EUPL, Version 1.2 or - as soon as approved
     by the European Commission - subsequent versions of the EUPL
     (the "License");
@@ -18,57 +18,16 @@
     and limitations under the License.
 """
 
-import numpy as np
 import os
 import sys
-from io import BytesIO
 from pathlib import Path
 
-os.environ["GLOG_minloglevel"] = "2"  # seriously :|
-import caffe
 import av
-av.logging.set_level(av.logging.PANIC)
+from transformers import pipeline
 
 class NSFWDetector:
     def __init__(self):
-        npath = Path(__file__).parent / "nsfw_model"
-        self.nsfw_net = caffe.Net(
-            str(npath / "deploy.prototxt"),
-            caffe.TEST,
-            weights = str(npath / "resnet_50_1by2_nsfw.caffemodel")
-        )
-        self.caffe_transformer = caffe.io.Transformer({
-            'data': self.nsfw_net.blobs['data'].data.shape
-        })
-        # move image channels to outermost
-        self.caffe_transformer.set_transpose('data', (2, 0, 1))
-        # subtract the dataset-mean value in each channel
-        self.caffe_transformer.set_mean('data', np.array([104, 117, 123]))
-        # rescale from [0, 1] to [0, 255]
-        self.caffe_transformer.set_raw_scale('data', 255)
-        # swap channels from RGB to BGR
-        self.caffe_transformer.set_channel_swap('data', (2, 1, 0))
-
-    def _compute(self, img):
-        image = caffe.io.load_image(img)
-
-        H, W, _ = image.shape
-        _, _, h, w = self.nsfw_net.blobs["data"].data.shape
-        h_off = int(max((H - h) / 2, 0))
-        w_off = int(max((W - w) / 2, 0))
-        crop = image[h_off:h_off + h, w_off:w_off + w, :]
-
-        transformed_image = self.caffe_transformer.preprocess('data', crop)
-        transformed_image.shape = (1,) + transformed_image.shape
-
-        input_name = self.nsfw_net.inputs[0]
-        output_layers = ["prob"]
-        all_outputs = self.nsfw_net.forward_all(
-            blobs=output_layers, **{input_name: transformed_image})
-
-        outputs = all_outputs[output_layers[0]][0].astype(float)
-
-        return outputs
+        self.classifier = pipeline("image-classification", model="giacomoarienti/nsfw-classifier")
 
     def detect(self, fpath):
         try:
@@ -77,23 +36,13 @@ class NSFWDetector:
                 except: container.seek(0)
 
                 frame = next(container.decode(video=0))
+                img = frame.to_image()
+                res = self.classifier(img)
 
-                if frame.width >= frame.height:
-                    w = 256
-                    h = int(frame.height * (256 / frame.width))
-                else:
-                    w = int(frame.width * (256 / frame.height))
-                    h = 256
-                frame = frame.reformat(width=w, height=h, format="rgb24")
-                img = BytesIO()
-                frame.to_image().save(img, format="ppm")
-
-            scores = self._compute(img)
-        except:
-            return -1.0
-
-        return scores[1]
+                return max([x["score"] for x in res if x["label"] not in ["neutral", "drawings"]])
+        except: pass
 
+        return -1.0
 
 if __name__ == "__main__":
     n = NSFWDetector()