Sign Language Recognition Service

This is a Sign Language Recognition service utilizing a deep learning model with Long Short-Term Memory to perform sign language recognition. The service was developed as a part of a bachelor project at Aalborg University.

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Requirements

  • Python 3.7
  • OpenPose 1.6.0
  • CUDA 10.0
  • cuDNN 7.5.0
  • Numpy 1.18.5
  • OpenCV 4.5.1.48
  • Flask 1.1.2
  • Tensorflow 2.0.0
  • Pandas 1.1.5
  • Tensorboard
  • Matplotlib
  • Seaborn
  • Scikit-Learn

How to use

Installing OpenPose

  1. Please install OpenPose 1.6.0 for Python by following the official guide. Note that the newest release on the OpenPose github is 1.7.0 – for this service to work, 1.6.0 must be used.

    A few things to note when installing OpenPose:

    • When cloning the OpenPose repository, use the following git command to get version 1.6.0: 
      git clone --depth 1 --branch v1.6.0 https://github.com/CMU-Perceptual-Computing-Lab/openpose
    • Remember to run the following command on the newly cloned repository: 
      git submodule update --init --recursive --remote
    • Use Visual Studio Enterprise 2017 to build the required files. Install this first if you do not already have it.
    • Install CUDA 10.0 and cuDNN 7.5.0 for CUDA 10.0 after installing Visual Studio Enterprise 2017.
    • When generating the files using CMake, make sure that the BUILD_PYTHON flag is enabled, and that the Python version is set to 3.7. Also make sure that the detected CUDA version is 10.0.
    • After building with Visual Studio Enterprise 2017, make sure that all necessary files have been generated.
      • There should be a openpose.dll in /x64/Release/
      • There should be a openpose.exp and openpose.lib in /src/openpose/Release/
      • There should be a pyopenpose.cp37-win_amd64.pyd in /python/openpose/Release/

  2. Install requirements from requirements.txt

  3. Change the path in main/openpose/paths.py to the path of your OpenPose installation:

    # Change this path so it points to your OpenPose path relative to this file
OPEN_POSE_PATH = get_relative_path(__file__, '../../../../openpose')

  4. If you get any errors related to OpenPose when running the service, please go back and make sure that all instructions have been followed – be particularly careful to install the correct CUDA/cuDNN versions, make sure that the BUILD_PYTHON flag was enabled and that Python 3.7 was used when generating the files.

When OpenPose is successfully installed, you can either use the existing model trained on our dataset, or you can choose to make your own dataset and train a model on this instead.

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Using the service

A singular endpoint ‘/recognize’ has been created in order to perform recognition, which allows for POST requests to be made. The endpoint expects a sequence of base64 images, which will get converted into a suitable format recognizable by the classifier.

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Creating a custom dataset

In order to create a custom dataset, you can access the file create_dataset.py and change the following constant:

DATASET_NAME = 'dsl_dataset'

Such that the path in the constant DATASET_DIR points to a folder where the dataset is located. This folder should contain another folder called ‘src’, which contains folders for all the desired labels in the dataset. Each of these folders should contain videos of the corresponding sign.

Before running the script, the following constants can be tweaked based on the desired settings:

WINDOW_LENGTH = 60
STRIDE = 5
BATCH_SIZE = 512
VAL_SPLIT = 0.2
TEST_SPLIT = 0.1

Finally, the following constant can be changed:

CREATE_RAW_DATA = True

This is because initial feature extraction by OpenPose can be a fairly lengthy process. This allows for the tweaking of the dataset after features have been extracted, by setting this to False. Note that the raw OpenPose data must be created before the actual dataset can be created, so it is necessary to do this at least once.

Training a custom model

In order to train a custom model you can make use of the train_models.py file. Here, the constant DATASET_NAME can be changed to reflect the name of the dataset you wish to use, such that the DATASET_DIR points to the correct folder. Furthermore, you can specify a tensorboard directory:

DATASET_NAME = 'dsl_dataset'
DATASET_DIR = f'.\\main\\algorithm\\datasets\\{DATASET_NAME}'
MODELS_DIR = f'.\\main\\algorithm\\models\\{DATASET_NAME}'
TENSORBOARD_DIR = f'{MODELS_DIR}\\logs'

Before running the script, you can tweak various training settings as well as the hyper parameters of the model by changing the following constants:

MODEL_NAME = "model"
EPOCHS = 25
LAYER_SIZES = [64]
DENSE_LAYERS = [0]
DENSE_ACTIVATION = "relu"
LSTM_LAYERS = [2]
LSTM_ACTIVATION = "tanh"
OUTPUT_ACTIVATION = "softmax"

Note that the trainer can train multiple models depending on these settings. Changing the LAYER_SIZES, DENSE_LAYERS and LSTM_LAYERS to contain several values will result in a model being trained for each possible combination.

After training your model, you should change the paths.py located in main/core/ to reflect the path to the new model by changing the constant MODEL_NAME to the name of your model:

MODEL_NAME = 'dsl_lstm.model'

Finally, it also possible to generate a confusion matrix for your model by using the generate_confusion_matrix.py script. Here, you simply change the constants DATASET_NAME and MODEL_NAME such that the DATASET_DIR points to your dataset directory, and MODEL_DIR points to your model directory, respectively:

DATASET_NAME = "dsl_dataset"
MODEL_NAME = "dsl_lstm"
DATASET_DIR = f"./main/algorithm/datasets/{DATASET_NAME}/{DATASET_NAME}.pickle"
MODEL_DIR = f"./main/algorithm/models/{DATASET_NAME}/{MODEL_NAME}"

Happy signing :O)

Authors

  • Adil Cemalovic
  • Martin Lønne
  • Magnus Helleshøj Lund

GitHub

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