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#!/usr/bin/env python3
"""
Copyright © 2020 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");
You may not use this work except in compliance with the License.
You may obtain a copy of the license at:
https://joinup.ec.europa.eu/software/page/eupl
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" basis, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
either express or implied.
See the License for the specific language governing permissions
and limitations under the License.
"""
import numpy as np
import os
import sys
from io import BytesIO
from subprocess import run, PIPE, DEVNULL
os.environ["GLOG_minloglevel"] = "2" # seriously :|
import caffe
class NSFWDetector:
def __init__(self):
npath = os.path.join(os.path.dirname(__file__), "nsfw_model")
self.nsfw_net = caffe.Net(os.path.join(npath, "deploy.prototxt"),
os.path.join(npath, "resnet_50_1by2_nsfw.caffemodel"),
caffe.TEST)
self.caffe_transformer = caffe.io.Transformer({'data': self.nsfw_net.blobs['data'].data.shape})
self.caffe_transformer.set_transpose('data', (2, 0, 1)) # move image channels to outermost
self.caffe_transformer.set_mean('data', np.array([104, 117, 123])) # subtract the dataset-mean value in each channel
self.caffe_transformer.set_raw_scale('data', 255) # rescale from [0, 1] to [0, 255]
self.caffe_transformer.set_channel_swap('data', (2, 1, 0)) # swap channels from RGB to BGR
def _compute(self, img):
image = caffe.io.load_image(BytesIO(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
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
except:
return -1.0
scores = self._compute(image_data)
return scores[1]
if __name__ == "__main__":
n = NSFWDetector()
for inf in sys.argv[1:]:
score = n.detect(inf)
print(inf, score)
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