Contributers : Sree Harsha Nelaturu, Anith Patel, Avyay Sah
DeepHPD is a novel application which uses Deep Learning in order to detect the presence of a human being. This is done by using a combination of Convolutional Neural Networks and OpenCV which along with a Raspberry pi and Basler Dart Camera, which blinks an LED when it detects human presence.
Software Used :
- Tensorflow
- OpenCV
- Numpy
- RPi-GPIO
- CUDA
The Tensorflow graph also to be placed in the same folder
The two classes provided by the classifier are:
- human
- nohuman
- Inference.py - Uses weights of trained Convolutional Neural Network for classification
- LED-Blink.py - Script to be run to use inferred labels from footage in order to detect humans and blink LED
- retrained_graph.pb - Contains Tensorflow static graph for computation
- inception - Folder which contains the bottlenecks and weights for the moodel
- retrained_labels.txt - Text file that contains the labels of the retrained model.
Sample:
[INPUT]:
Footage via Basler DART is taken via OpenCV
[OUTPUT]:
Label : "human" LED Blinks