Package: opennsfw2
IntroductionDetecting Not-Suitable-For-Work (NSFW) content is a high demand task in
computer vision. While there are many types of NSFW content, here we focus on
the pornographic images and videos.TheYahoo Open-NSFW modeloriginally
developed with the Caffe framework has been a favourite choice, but the work
is now discontinued and Caffe is also becoming less popular.
Please see the description on the Yahoo project page for
the context, definitions, and model training details.ThisOpen-NSFW 2project provides a Keras implementation of the
Yahoo model, with references to its previous third-partyTensorFlow 1 implementation.
Note thatKeras 3is compatible with
TensorFlow, JAX, and PyTorch. However, currently this model is
only guaranteed to work with TensorFlow and JAX.A simple API is provided for making predictions on images and videos.InstallationTested with TensorFlow and JAX, for Python 3.9, 3.10, and 3.11.A note on PyTorch:The OpenNSFW 2 model can in fact be run on PyTorch, but the biggest issue is
that the inference output on PyTorch is quite different from
that on TensorFlow and JAX. The reason is still unknown. In addition,
inference is much slower on PyTorch probably because of the issues
discussedhere,
i.e., PyTorch useschannels_firstfor its image data format, but this model
useschannels_last(as in TensorFLow and JAX), hence Keras has to
convert the channel order back and forth at each layer.
Therefore, at the moment it is not recommended to use PyTorch for this model.The best way to install Open-NSFW 2 with its dependencies is from PyPI:python3-mpipinstall--upgradeopennsfw2Alternatively, to obtain the latest version from this repository:gitclonegit@github.com:bhky/opennsfw2.gitcdopennsfw2
python3-mpipinstall.UsageQuick examples for getting started are given below.
For more details, please refer to theAPIsection.Imagesimportopennsfw2asn2# To get the NSFW probability of a single image.image_path="path/to/your/image.jpg"nsfw_probability=n2.predict_image(image_path)# To get the NSFW probabilities of a list of images.# This is better than looping with `predict_image` as the model will only be instantiated once# and batching is used during inference.image_paths=["path/to/your/image1.jpg","path/to/your/image2.jpg",# ...]nsfw_probabilities=n2.predict_images(image_paths)Videoimportopennsfw2asn2# The video can be in any format supported by OpenCV.video_path="path/to/your/video.mp4"# Return two lists giving the elapsed time in seconds and the NSFW probability of each frame.elapsed_seconds,nsfw_probabilities=n2.predict_video_frames(video_path)Lower level with Kerasimportnumpyasnpimportopennsfw2asn2fromPILimportImage# Load and preprocess image.image_path="path/to/your/image.jpg"pil_image=Image.open(image_path)image=n2.preprocess_image(pil_image,n2.Preprocessing.YAHOO)# The preprocessed image is a NumPy array of shape (224, 224, 3).# Create the model.# By default, this call will search for the pre-trained weights file from path:# $HOME/.opennsfw2/weights/open_nsfw_weights.h5# If not exists, the file will be downloaded from this repository.# The model is a `keras_core.Model` object.model=n2.make_open_nsfw_model()# Make predictions.inputs=np.expand_dims(image,axis=0)# Add batch axis (for single image).predictions=model.predict(inputs)# The shape of predictions is (num_images, 2).# Each row gives [sfw_probability, nsfw_probability] of an input image, e.g.:sfw_probability,nsfw_probability=predictions[0]APIpreprocess_imageApply necessary preprocessing to the input image.Parameters:pil_image(PIL.Image): Input as a Pillow image.preprocessing(Preprocessingenum, defaultPreprocessing.YAHOO):
Seepreprocessing details.Return:NumPy array of shape(224, 224, 3).PreprocessingEnum class for preprocessing options.Preprocessing.YAHOOPreprocessing.SIMPLEmake_open_nsfw_modelCreate an instance of the NSFW model, optionally with pre-trained weights from Yahoo.Parameters:input_shape(Tuple[int, int, int], default(224, 224, 3)):
Input shape of the model, this should not be changed.weights_path(Optional[str], default$HOME/.opennsfw/weights/open_nsfw_weights.h5):
Path to the weights in HDF5 format to be loaded by the model.
The weights file will be downloaded if not exists.
IfNone, no weights will be downloaded nor loaded to the model.
Users can provide path if the default is not preferred.
The environment variableOPENNSFW2_HOMEcan also be used to indicate
where the.opennsfw2/directory should be located.name(str, defaultopennsfw2): Model name to be used for the Keras model object.Return:tf.keras.Modelobject.predict_imageEnd-to-end pipeline function from the input image to the predicted NSFW probability.Parameters:image_path(str): Path to the input image file.
The image format must be supported by Pillow.preprocessing: Same as that inpreprocess_image.weights_path: Same as that inmake_open_nsfw_model.grad_cam_path(Optional[str], defaultNone): If notNone, e.g.,cam.jpg,
aGradient-weighted Class Activation Mapping (Grad-CAM)overlay plot will be saved, which highlights the important region(s) of the
(preprocessed) input image that lead to the prediction.
Note that this feature is currently only supported by the TensorFlow backend.alpha(float, default0.8): Opacity of the Grad-CAM layer of the plot,
only valid ifgrad_cam_pathis notNone.Return:nsfw_probability(float): The predicted NSFW probability of the image.predict_imagesEnd-to-end pipeline function from the input images to the predicted NSFW probabilities.Parameters:image_paths(Sequence[str]): List of paths to the input image files.
The image format must be supported by Pillow.batch_size(int, default8): Batch size to be used for model inference.
Choose a value that works the best with your device resources.preprocessing: Same as that inpreprocess_image.weights_path: Same as that inmake_open_nsfw_model.grad_cam_paths(Optional[Sequence[str]], defaultNone): If notNone,
the corresponding Grad-CAM plots for the input images will be saved.
See the description inpredict_image.
Note that this feature is currently only supported by the TensorFlow backend.alpha: Same as that inpredict_image.Return:nsfw_probabilities(List[float]): Predicted NSFW probabilities of the images.AggregationEnum class for aggregation options in video frames prediction.Aggregation.MEANAggregation.MEDIANAggregation.MAXAggregation.MINpredict_video_framesEnd-to-end pipeline function from the input video to predictions.Parameters:video_path(str): Path to the input video source.
The video format must be supported by OpenCV.frame_interval(int, default8): Prediction will be done on every this
number of frames, starting from frame 1, i.e., if this is 8, then
prediction will only be done on frame 1, 9, 17, etc.aggregation_size(int, default8):
Number of frames for which their predicted NSFW probabilities will be aggregated.
For instance, if a prediction will be done "on" frame 9 (decided byframe_interval),
then it actually means prediction will be done onaggregation_sizeframes
starting from frame 9, e.g., frames 9 to 16 if the size is 8.
The predicted probabilities will be aggregated. After aggregation,
each of these frames in that interval will be assumed the same aggregated probability.aggregation(Aggregationenum, defaultAggregation.MEAN):
The aggregation method.batch_size(int, default8, upper-bounded byaggregation_size):
Batch size to be used for model inference. Choose a value that works the best
with your device resources.output_video_path(Optional[str], defaultNone):
If notNone, e.g.,out.mp4,
an output MP4 video with the same frame size and frame rate as
the input video will be saved via OpenCV. The predicted NSFW probability
is printed on the top-left corner of each frame. Be aware that the output
file size could be much larger than the input file size.
This output video is for reference only.preprocessing: Same as that inpreprocess_image.weights_path: Same as that inmake_open_nsfw_model.progress_bar(bool, defaultTrue): Whether to show the progress bar.Return:Tuple ofList[float], each with length equals to the number of video frames.elapsed_seconds: Video elapsed time in seconds at each frame.nsfw_probabilities: NSFW probability of each frame.
For anyframe_interval > 1, all frames without a prediction
will be assumed to have the NSFW probability of the previous predicted frame.Preprocessing detailsOptionsThis implementation provides the following preprocessing options.YAHOO: The default option which was used in the originalYahoo's Caffeand the laterTensorFlow 1implementations. The key steps are:Resize the input Pillow image to(256, 256).Store the image as JPEG in memory and reload it again to a NumPy image
(this step is mysterious, but somehow it really makes a difference).Crop the centre part of the NumPy image with size(224, 224).Swap the colour channels to BGR.Subtract the training dataset mean value of each channel:[104, 117, 123].SIMPLE: A simpler and probably more intuitive preprocessing option is also provided,
but note that the model output probabilities will be different.
The key steps are:Resize the input Pillow image to(224, 224).Convert to a NumPy image.Swap the colour channels to BGR.Subtract the training dataset mean value of each channel:[104, 117, 123].ComparisonUsing 521 private test images, the NSFW probabilities given by
three different settings are compared:TensorFlow 1 implementationwithYAHOOpreprocessing.Keras implementation withYAHOOpreprocessing.Keras implementation withSIMPLEpreprocessing.The following figure shows the result:The current Keras implementation withYAHOOpreprocessing
can totally reproduce the well-tested TensorFlow 1 result,
with small floating point errors only.WithSIMPLEpreprocessing the result is different, where the model tends
to give lower probabilities over the current test images.Note that this comparison does not conclude which preprocessing method is
"better", it only shows their discrepancies. However, users that prefer the
original Yahoo result should go for the defaultYAHOOpreprocessing.