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deep-hands

The aim of this project is to classify the user-shelf interaction. Three classes are considered:

Neutral Positive Negative
neutral positive negative

In order to generate *.npy files, you can run data.py script. After this, you can use one of the following nets:

  • CNN (train_cnn.py)
  • CNN2 (train_cnn2.py)
  • AlexNet (train_alexnet.py)
  • CaffeNet (train_caffenet.py)
python data.py -images dataset/hands/ [-data data.csv]

python train_cnn.py
python train_cnn2.py
python train_alexnet.py
python train_caffenet.py

annotation-toolbox

This is a useful toolbox to annotate a set of images with different classes.

  1. Positive (hand with product)
  2. Negative (other)
  3. Neutral (only hand)
  4. Skip (bad image)
python annotation-toolbox.py -images dataset/hands/ [-data data.csv]

Python Environment Setup

sudo apt-get install python3-pip python3-dev python-virtualenv # for Python 3.n
virtualenv -p python3 venv
. venv/bin/activate

The preceding command should change your prompt to the following:

(venv)$

Install TensorFlow in the active virtualenv environment:

pip3 install --upgrade tensorflow-gpu # for Python 3.n and GPU

Install the others library:

pip3 install --upgrade keras scikit-learn scikit-image h5py

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