calibrateCamera.py

# -*- coding: utf-8 -*-
"""
Created on Mon Feb 21 10:12:36 2022

@author: Bimalka Piyaruwan
@script: calibrateCamera.py
@description: This script calibrates the camera and saves the calibration parameters in the specified yaml file.
@sources:
        https://github.com/chenyr0021/camera_calibration_tool/blob/master/calibration.py

"""
# Importing the required libraries
import cv2 as cv
import numpy as np
import glob
import  yaml

# define Number of <<inner>> corners per a chessboard row and column
PatternSize = (9,7)
SquareSize = 30 # size of the PatternSize square in mm

# termination criteria
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)

# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((PatternSize[0] * PatternSize[1], 3), np.float32)
objp[:,:2] = np.mgrid[0:PatternSize[0], 0:PatternSize[1]].T.reshape(-1, 2)
objp = objp * SquareSize # multiply by 30mm to get the real world coordinates

# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.

# Extracting path of individual image stored in a given directory
images = glob.glob('./images-static_camera/*.jpg')
# images = glob.glob('./images-static_checkerboard/*.jpg')

ImageCount = 0
WindowSize = (5,5) # Half of the side length of the search window for cornerSubPix()

for fname in images:
    img = cv.imread(fname)
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    # Find the chess board corners
    # If desired number of corners are found in the image then ret = true
    ret, corners = cv.findChessboardCorners(gray, PatternSize, cv.CALIB_CB_ADAPTIVE_THRESH + cv.CALIB_CB_FAST_CHECK + cv.CALIB_CB_NORMALIZE_IMAGE)
    
    """
    If desired number of corner are detected,
    we refine the pixel coordinates and display 
    them on the images of checker board
    """

    if ret == True:
        print("Chessboard Detected in Image: ", ImageCount)

        objpoints.append(objp)

        # refining pixel coordinates for given 2d points.
        corners2 = cv.cornerSubPix(gray, corners, WindowSize, (-1,-1), criteria)

        imgpoints.append(corners2)

        # Draw and display the corners
        #img = cv.drawChessboardCorners(img, PatternSize, corners2, ret)   
        #cv.imshow('Image' + str(ImageCount),img)
        #cv.waitKey(0)

    ImageCount+=1
cv.destroyAllWindows()

# ++++++++++++++Calibrating the camera++++++++++++++

# height and width of the image: (480, 640, 3)
h,w = img.shape[:2]

"""
Now that we have our object points and image points,
 we are ready to go for calibration. 
 We can use the function, cv.calibrateCamera() which returns the 
 camera matrix, distortion coefficients, rotation and translation vectors etc.
"""

print('\nCalibrating...')
ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
print("\nCalibration Successful!")
# print(type(ret), type(mtx), type(dist), type(rvecs), type(tvecs))
print( mtx.shape, dist.shape)
# Saving the calibration parameters in a yaml file
calibration = {'ret': ret, 'mtx': mtx.tolist(), 'dist': dist.tolist()}

# with open('LogitechC310_static_checkerboard.yaml', 'w') as outfile:
with open('LogitechC310_static_camera.yaml', 'w') as outfile:
        yaml.dump(calibration, outfile)

# print the camera calibration matrix and distortion coefficients
print("\nCamera Matrix: \n", mtx)
print("\nDistortion Coefficients: \n", dist)