diff --git a/README.rst b/README.rst
index e84f2b1..5b34b50 100644
--- a/README.rst
+++ b/README.rst
@@ -56,7 +56,7 @@ neural network model. This works for images and video files and requires
 the following:
 
 * Caffe Python module (built for Python 3)
-* ``ffmpegthumbnailer`` executable in ``$PATH``
+* `PyAV <https://github.com/PyAV-Org/PyAV>`_
 
 
 Network Security Considerations
diff --git a/nsfw_detect.py b/nsfw_detect.py
index eddf9cb..032f7e4 100755
--- a/nsfw_detect.py
+++ b/nsfw_detect.py
@@ -22,11 +22,12 @@ import numpy as np
 import os
 import sys
 from io import BytesIO
-from subprocess import run, PIPE, DEVNULL
 from pathlib import Path
 
 os.environ["GLOG_minloglevel"] = "2"  # seriously :|
 import caffe
+import av
+av.logging.set_level(av.logging.PANIC)
 
 class NSFWDetector:
     def __init__(self):
@@ -49,7 +50,7 @@ class NSFWDetector:
         self.caffe_transformer.set_channel_swap('data', (2, 1, 0))
 
     def _compute(self, img):
-        image = caffe.io.load_image(BytesIO(img))
+        image = caffe.io.load_image(img)
 
         H, W, _ = image.shape
         _, _, h, w = self.nsfw_net.blobs["data"].data.shape
@@ -71,13 +72,23 @@ class NSFWDetector:
 
     def detect(self, fpath):
         try:
-            ff = run([
-                "ffmpegthumbnailer", "-m", "-o-", "-s256", "-t50%", "-a",
-                "-cpng", "-i", fpath
-            ], stdout=PIPE, stderr=DEVNULL, check=True)
-            image_data = ff.stdout
+            with av.open(fpath) as container:
+                try: container.seek(int(container.duration / 2))
+                except: container.seek(0)
 
-            scores = self._compute(image_data)
+                frame = next(container.decode(video=0))
+
+                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