Udacity Self Driving Car Nanodegree - Advance Lane Line Finder on a Video Stream

Blind Rectification of Radial Distortion by Line Straightness

SteamVR lens distortion adjustment utility for spherical lenses

Camera calibration using OpenCV

This project is to detect lanes in a video or image and project the results on the output. advanced math and image processing including openCV was used to finish the project.

Images and notebook for camera calibration

Detect lane lines on the road with advanced computer vision techniques

Drawing corners in an image of a chessboard pattern.

The Goal of this Project is to write a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car. The camera calibration images, test road images, and project videos are available in the project repository.

Advanced Lane Finding (project 2 of 9 from Udacity Self-Driving Car Engineer Nanodegree)

Created a pipeline to identify lane lines using various techniques like perspective transform, color gradients, detecting fit for the lanes, detecting curvature and finally warping all to the original image.

Lane finding with traditional CV techniques. Coded in python using OpenCV API's

Developed a Windows tool using PyQt5, integrating K-means clustering for data analysis. The application recommends optimal cluster numbers, identifies cluster members, and allows exporting results to Excel.

Camera Calibration and Distortion Correction

SW Pipeline to identify the lane boundaries from a front-facing camera on a car (use of OpenCV)

Identify lane boundaries in a video and display numerical estimation of lane curvature and vehicle position using camera calibration and perspective transform ("birds-eye view").