opennsfw2 0.1.2

TensorFlow 2 implementation of the Yahoo Open-NSFW model

Introduction

The Open-NSFW 2 project provides a TensorFlow 2 implementation of the popular Yahoo Open-NSFW model, with references to its previous third-party TensorFlow 1 implementation.

Installation

Python 3 is required.

The best way to install Open-NSFW 2 with its dependencies is from PyPI:

python3 -m pip install --upgrade opennsfw2

Alternatively, to obtain the latest version from this repository:

git clone [e-mail]:bhky/opennsfw2.git
cd opennsfw2
python3 -m pip install .

Usage

import numpy as np
import opennsfw2 as n2

image_path = "path/to/your/image.jpg"
image = n2.load_and_preprocess_image(image_path, n2.Preprocessing.YAHOO)
# The preprocessed image is a NumPy array of shape (224, 224, 3).

model = n2.make_open_nsfw_model()
# By default, this call will download the pre-trained weights to:
# $HOME/.opennsfw2/weights/open_nsfw_weights.h5
#
# The model is an usual tf.keras.Model object.

inputs = np.expand_dims(image, axis=0)  # Add batch axis.
predictions = model.predict(inputs)
# The shape of predictions is (batch_size, 2).
# Each row gives [non_nsfw_probability, nsfw_probability] for each input image,
# e.g.:
non_nsfw_probability, nsfw_probability = predictions[0]

API

load_and_preprocess_image

Load image from given path and apply necessary preprocessing.

• Parameters:
  • image_path (str): Path to input image.
  • preprocessing (Preprocessing enum, default Preprocessing.YAHOO): See preprocessing details.
• Return:
  • NumPy array of shape (224, 224, 3).

Preprocessing

Enum class for preprocessing options.

• Preprocessing.YAHOO
• Preprocessing.SIMPLE

make_open_nsfw_model

Create 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. Users can provide another path if the default is not preferred. If None, no weights will be loaded to the model.
• Return:
  • tf.keras.Model instance.

Preprocessing details

Options

This implementation provides the following preprocessing options.

• YAHOO: The default option which was used in the original Yahoo's Caffe and the later TensorFlow 1 implementations. The key steps are:
  • Load input as a Pillow RGB image.
  • Resize the image to (256, 256).
  • Save the image as JPEG bytes and reload again to an NumPy array (this step is mysterious, but somehow it really makes a difference).
  • Crop the centre part of the image with size (224, 224).
  • Convert the image channels from RGB 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:
  • Load input as a Pillow RGB image.
  • Resize the image to (224, 224).
  • Convert the image to a NumPy array.
  • Convert the image channels from RGB to BGR.
  • Subtract the training dataset mean value of each channel: [104, 117, 123].

Comparison

Using 521 private images, the NSFW probabilities given by three different settings are compared:

• TensorFlow 1 implementation with YAHOO preprocessing.
• TensorFlow 2 implementation with YAHOO preprocessing.
• TensorFlow 2 implementation with SIMPLE preprocessing.

The following figure shows the result:

The current TensorFlow 2 implementation with YAHOO preprocessing can reproduce the well tested TensorFlow 1 probabilities very accurately.

• 504 out of 521 images (~97%) have absolute difference < 0.05.
• Only 3 images with absolute difference > 0.1.

The discrepancies are probably due to floating point errors etc.

With SIMPLE preprocessing, the model tends to give lower probabilities.