DeepHPD for Smart homes

The project was deployed on hardware stack which consisted of  Raspberry Pi3 running Raspbian 8.0 Jessie and the streaming was done using Basler Dart Camera model daA2500-14uc.
The Software stack includes:
1. Tensorflow

2. CUDA

3. Numpy

4. OpenCV

5. RPi GPIO

The Convolutional neural network used Transfer Learning and was trained using the weights of the InceptionV3 model and used CUDA for GPU based training and achieved a classification accuracy of 96%.

In order to use the components of the project
1. Ensure Python 3 is installed and all software dependencies are installed.

Tensorflow
https://www.tensorflow.org/versions/r0.12/get_started/os_setup

CUDA {For GPU}:
https://developer.nvidia.com/cuda-downloads

OpenCV
pip install opencv-python

RPI GPIO
sudo apt-get install RPi-GPIO

2. Following this you should download the folder from this link for the weights of the trained model from link provided in the GitHub repository

3. Following this, run python script LED-Blink.py, and the classification is generated following which the labels are passed to the Raspberry Pi via RPi-GPI

The camera captures the image and sends the status via labels to the Raspberry Pi3 as an input which is linked to the blink LED program turning it on and off depending on the presence or absence of a human.