camera-calibration's Introduction

Assignment-01

The goal of the assignment is to familiarize you to the process of camera calibration and the critical role it plays in using any measurements of the world from images.

Please raise doubts on the appropriate assignment thread on Moodle.

Instructions

Follow the directory structure as shown below:

├── src           
      ├── Assignment01.ipynb
├── images //your images
├── data  //provided data
└── README.md

src will contain the Jupyter notebook(s) used for the assignment.
images will contain images used for the questions.
data contains images provided to you already, for solving the questions.
Follow this directory structure for all following assignments in this course.
Make sure you run your Jupyter notebook before committing, to save all outputs.

Dataset

black-dots.JPG : for DLT and RANSAC based estimation.
measurement.JPG : Measurements are shown according to scale(in mms). World co-ordinate of every point can be calculated using the unit measurements given from the origin.
checkerboard-[01-15].JPG : for Zhang algorithm based estimation Size of each square on checkerboard 29mmX29mm.

Helper code

Function to get Rotation matrix from Euler angles :

def eulerAnglesToRotationMatrix(theta) :
    R_x = np.array([[1,0,0],[0,math.cos(theta[0]),-math.sin(theta[0])],[0,math.sin(theta[0]), math.cos(theta[0])]])
    R_y = np.array([[math.cos(theta[1]),0,math.sin(theta[1])],[0,1,0],[-math.sin(theta[1]),0,math.cos(theta[1])]])             
    R_z = np.array([[math.cos(theta[2]),-math.sin(theta[2]),0],[math.sin(theta[2]),math.cos(theta[2]),0],[0,0,1]])
    R = np.dot(R_z,np.dot(R_y,R_x))
    return R

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