LIVE OBJECT DETECTION USING PYTHON
This project implements a live object detection. The windows environment setup and simulation demo are given below.Here are the steps we followed,
Activate the created conda environment to work on that, conda activate object_det_env , Once activated, it will add(object_det_env) to the prefix.
Install Tensorflow, conda install tensorflow, Note: If you are using GPU. Type 'tensorflow-gpu' instead of 'tensorflow'.
Install Keras, conda install keras
Install Matplotlib Library, conda install matplotlib
Install Scikit Learn Library, conda install scikit_learn
Install Opencv using Conda forge, conda install -c conda-forge opencv
Install Pillow library for OpenCV, conda install -c anaconda pillow
Install OpenCV using Python-PIP. It can install all the dependencies for opencv, pip install opencv-python
Check all the libraries are installed and importing properly.
$ python
$ import tensorflow
$ import keras
$ import matplotlib
$ import opencv
# import scikit-learn
Dataset: MS CoCo object detection dataset which can identify up to 60 objects. You can download dataset from http://cocodataset.org/#download
SSD Mobilenet_v2_coco pre-trained model was used to improve the performance of object detection.
Tensorflow object detection API library was used for transfer learning. You can download the library from https://github.com/tensorflow/models/...
The training was done on Google Colaboratory. https://colab.research.google.com
Raspberry Pi 3 B+ was used to run this model.
PC Requirements:
1. Intel i5 or latest.
2. At least 6GB ram.
3. Minimum 500Gb hard disk (SSD is an advantage.)
Request source code for academic purpose, fill REQUEST FORM or contact +91 7904568456 by WhatsApp or info@verilogcourseteam.com, fee applicable.
SIMULATION VIDEO DEMO-Setting Up the Windows Environment
SIMULATION VIDEO DEMO