SDB_Mini_Projects

Self Driving Bike research cluster mini projects

Getting Started

This repo has mini projects used in the sessions of the sdb research cluster

Prerequisites

The code runs on python 3.7 you'll need the following libraries

Numpy

Adds support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

Opencv

This is the library used for real-time computer vision task.

Matplotlib

This is a plotting library for Python and is used with NumPy.

keras, along with tensorflow backend

Keras is an open-source library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library

Installing

make sure you have a python3.7 setup up and running on your machine you can install anaconda and create a new environment using the following link

https://www.geeksforgeeks.org/set-up-virtual-environment-for-python-using-anaconda/

then to install the needed libraries, activate your new environment and use the following commands

pip install numpy opencv-python matplotlib

pip install tensorflow==2.1.0

pip install keras==2.3.1

to make sure everything is up and running, run a python script including the imports of the beforementioned libraries.

Break down into file system and Algorithms used

Every directory holds the code used for each session given along with its documentation

CV lane Detection

Holds The basic lane detection code, the pipeline used in this code is as follows:
1) Turn image into RGB colour space
2) Resize image into a fixed width and height
3) Use a mask to detect the white colour inside the frame
4) Extract the region of interest of the frame
5) Convert to grayscale
6) Extract edges
7) Extract Lines
8) Generate Image with only the lines present in the frame
9) Generate the final outcome with the lanes highlighted on the original frame

Cifar10 Classification

This was done to give you a basic understanding of a normal CNN pipeline. The pipeline used is as follows:
1) Load the dataset
2) Although we did not need to do it here, always clean your data
3) Visualize your data by plotting or showing the images
4) Build your model architecture
5) Compile the model with the needed loss and optimizer
6) Train the model
7) Evaluate the model
8) plot the results

Running the code