推荐SFW 0.98 色情 NSFW 0.015
推荐SFW 0.00 色情 NSFW 0.99 为色情图片
所有代码都应该与Python 3.6and兼容Tensorflow 1.x（用 1.12 测试）。模型实现可以在 中找到model.py
Python 3.6.13 :: Anaconda, Inc.
absl-py==1.1.0
astor==0.8.1
cached-property==1.5.2
certifi==2021.5.30
cycler==0.11.0
dataclasses==0.8
decorator==4.4.2
gast==0.5.3
grpcio==1.46.3
h5py==3.1.0
imageio==2.15.0
importlib-metadata==4.8.3
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.2
kiwisolver==1.3.1
Markdown==3.3.7
matplotlib==3.3.4
networkx==2.5.1
numpy==1.16.2
Pillow==8.4.0
protobuf==4.21.0
pyparsing==3.0.9
python-dateutil==2.8.2
PyWavelets==1.1.1
scikit-image==0.17.2
scipy==1.5.4
six==1.16.0
tensorboard==1.12.2
tensorflow==1.12.0
termcolor==1.1.0
tifffile==2020.9.3
typing_extensions==4.1.1
Werkzeug==2.0.3
wincertstore==0.2
zipp==3.6.0
方法 1.
pip install tensorflow== 1.12
pip installnumpy==1.16.2
pip install scikit-image==0.17.2
方法 2.
pip install -r requirements.txt
这里注意一下只支持.jpg类型图片
python classify_nsfw.py -m data/open_nsfw-weights.npy data/test.jpg
SFW score推荐比例 , NSFW score 不推荐比例
Results for 'test.jpg'
SFW score: 0.9355766177177429
NSFW score: 0.06442338228225708
caffe: TypeError: _open() got an unexpected keyword argument ‘as_grey‘
“as_grey” 实际应当修改为 “as_gray”，原因是 scikit-image 的 0.17.2 版本修改了参数名称
ValueError: Object arrays cannot be loaded when allow_pickle=False
自Numpy 1.16.3版本发行之后，函数 numpy.load() 和 numpy.lib.format.read_array() 采用allow_pickle关键字，现在默认为False以响
pip install numpy=1.16.2
Note: Currently only jpeg images are supported.
classify_nsfw.py accepts some optional parameters you may want to play around with:
usage: classify_nsfw.py [-h] -m MODEL_WEIGHTS [-l {yahoo,tensorflow}]
[-t {tensor,base64_jpeg}]
input_jpeg_file
positional arguments:
input_file Path to the input image. Only jpeg images are
supported.
optional arguments:
-h, --help show this help message and exit
-m MODEL_WEIGHTS, --model_weights MODEL_WEIGHTS
Path to trained model weights file
-l {yahoo,tensorflow}, --image_loader {yahoo,tensorflow}
image loading mechanism
-i {tensor,base64_jpeg}, --input_type {tensor,base64_jpeg}
input type
-l/--image-loader
The classification tool supports two different image loading mechanisms.
yahoo(default) replicates yahoo's original image loading and preprocessing. Use this option if you want the same results as with the original implementationtensorflowis an image loader which uses tensorflow exclusively (no dependencies onPIL,skimage, etc.). Tries to replicate the image loading mechanism used by the original caffe implementation, differs a bit though due to different jpeg and resizing implementations. See this issue for details.
Note: Classification results may vary depending on the selected image loader!
-i/--input_type
Determines if the model internally uses a float tensor (tensor - [None, 224, 224, 3] - default) or a base64 encoded
string tensor (base64_jpeg - [None, ]) as input. If base64_jpeg is used, then the tensorflow image loader will
be used, regardless of the -l/--image-loader argument.
The tools folder contains some utility scripts to test the model.
create_predict_request.py
Takes an input image and generates a json file suitable for prediction requests to a Open NSFW Model deployed
with Google Cloud ML Engine (gcloud ml-engine predict)
or tensorflow-serving.
export_savedmodel.py
Exports the model using the tensorflow serving export api (SavedModel). The export can be used to deploy the model
on Google Cloud ML Engine
, Tensorflow Serving or on mobile (haven't tried that one yet).
export_tflite.py
Exports the model in TFLite format. Use this one if you want to run inference on
mobile or IoT devices. Please note that the base64_jpeg input type does not work with TFLite since the standard
runtime lacks a number of required tensorflow operations.
export_graph.py
Exports the tensorflow graph and checkpoint. Freezes and optimizes the graph per default for improved inference and
deployment usage (e.g. Android, iOS, etc.). Import the graph with tf.import_graph_def.