def detect_image(self, image_data):
scores = caffe_preprocess_and_compute(
image_data,
caffe_transformer=self.caffe_transformer,
caffe_net=self.nsfw_net,
output_layers=['prob'])
return scores[1]
def classify(image_data, nsfw_net):
# Classify.
scores = classify_nsfw.caffe_preprocess_and_compute(
image_data,
caffe_transformer=caffe_transformer,
caffe_net=nsfw_net,
output_layers=['prob'])
return scores[1]
def classify(image_data, nsfw_net):
# disable stdout
null_fds = [os.open(os.devnull, os.O_RDWR) for x in range(2)]
save = os.dup(1), os.dup(2)
os.dup2(null_fds[0], 1)
os.dup2(null_fds[1], 2)
scores = classify_nsfw.caffe_preprocess_and_compute(
image_data,
caffe_transformer=caffe_transformer,
caffe_net=nsfw_net,
output_layers=['prob'])
# enable stdout
os.dup2(save[0], 1)
os.dup2(save[1], 2)
os.close(null_fds[0])
os.close(null_fds[1])
return scores
def ck():
u0 = u = ""
try:
u0 = request.args.get('u')
u = urllib.unquote(u0).decode('utf8')
handle = urlopen(u)
image_data = handle.read()
# Classify.
scores = classify_nsfw.caffe_preprocess_and_compute(image_data, caffe_transformer=caffe_transformer, caffe_net=nsfw_net, output_layers=['prob'])
# Scores is the array containing SFW / NSFW image probabilities
# scores[1] indicates the NSFW probability
# print "NSFW score: " , scores[1]
rt = {
"code": 0,
"msg": "",
"data": {
"score" : scores[1]
}
}
return jsonify(rt)
except Exception as e:
msg = "%s, decode: %s, raw: %s" % (str(e), u, u0)
rt = {
"code": 500,
"msg": msg,
"data": {}
}
return jsonify(rt)
def classify(image: bytes) -> np.float64:
scores = caffe_preprocess_and_compute(image,
caffe_transformer=caffe_transformer,
caffe_net=nsfw_net,
output_layers=["prob"])
return scores[1]
def classify(image_path: str) -> np.float64:
with open(image_path, "rb") as image:
scores = caffe_preprocess_and_compute(image.read(), caffe_transformer=caffe_transformer, caffe_net=nsfw_net, output_layers=["prob"])
return scores[1]