SLR Alphabet Recognizer
This project is a sign language alphabet recognizer using Python, openCV and tensorflow for training InceptionV3 model, a convolutional neural network model for classification.
The framework used for the CNN implementation can be found here:
Simple transfer learning with an Inception V3 architecture model by xuetsing
The project contains the dataset (1Go). If you are only interested in code, you better copy/paste the few files than cloning the entire project.
You can find the demo here
Requirements
This project uses python 3.5 and the PIP following packages:
- opencv
- tensorflow
- matplotlib
- numpy
See requirements.txt and Dockerfile for versions and required APT packages
Using Docker
docker build -t hands-classifier .
docker run -it hands-classifier bash
Install using PIP
pip3 install -r requirements.txt
Training
To train the model, use the following command (see framework github link for more command options):
python3 train.py \
--bottleneck_dir=logs/bottlenecks \
--how_many_training_steps=2000 \
--model_dir=inception \
--summaries_dir=logs/training_summaries/basic \
--output_graph=logs/trained_graph.pb \
--output_labels=logs/trained_labels.txt \
--image_dir=./dataset
If you're using the provided dataset, it may take up to three hours.
Classifying
To test classification, use the following command:
python3 classify.py path/to/image.jpg
Using webcam (demo)
To use webcam, use the following command:
python3 classify_webcam.py
Your hand must be inside the rectangle. Keep position to write word, see demo for deletions.