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calibrate_camera.py
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calibrate_camera.py
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#------------------------------------------------------------------------
#------------------------------------------------------------------------
#
#
# SCRIPT : calibrate.py
# POURPOSE : Find Intrinsic and Extrinsic camera parameters.
# AUTHOR : Caio Eadi Stringari
# EMAIL : [email protected]
#
# V1.0 : 29/07/2016 [Caio Stringari]
#
#
# The algorithm is based on [Zhang2000] and [BouguetMCT]
#
# Zhang. A Flexible New Technique for Camera Calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1330-1334, 2000.
# Y.Bouguet. MATLAB calibration tool. http://www.vision.caltech.edu/bouguetj/calib_doc/
#
#------------------------------------------------------------------------
#------------------------------------------------------------------------
# system
import os
# arguments
import argparse
# files
from glob import glob
# numpy
import numpy as np
# OpenCV
import cv2
# I/O
from pandas import DataFrame
# Matplotlib
import matplotlib.pyplot as plt
def main():
# camera name
camera = args.camera[0]
# input image path
path = os.path.abspath(args.images[0])
# pattern
n1 = int(args.pattern[0])
n2 = int(args.pattern[1])
images = sorted(glob(path+"/*."+args.format[0]))
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((n1*n2,3), np.float32)
objp[:,:2] = np.mgrid[0:n2,0:n1].T.reshape(-1,2)
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 300, 0.0001)
# 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.
nrets = 0 # counter
# loop over the images trying to identify the chessboard
for k,fname in enumerate(images):
print ("Loading image {} of {}".format(k+1,len(images)))
# read the image as 8 bit
img = cv2.imread(fname)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
# find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (n1,n2),None)
# if found, add object points, image points (after refining them)
if ret == True:
print (" Chessboard pattern found in image {} of {}".format(k+1,len(images)))
# refine corners
corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
# append
objpoints.append(objp)
imgpoints.append(corners2)
# store the last processed images
I = gray
nrets+=1
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (n1,n2), corners2,ret)
cv2.imshow('img',img)
cv2.waitKey(500)
if nrets > 10: break
cv2.destroyAllWindows()
# calculating the calibration
h, w = I.shape[:2]
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
# get the intrinsic camera variables [See Holman et al 1997] for terminology
fx_px = mtx[0,0] # Focal lenght in pixels [x]
fy_px = mtx[1,1] # Focal lenght in pixels [y]
u0 = mtx[0,2]
v0 = mtx[1,2]
k1 = dist[0][0]
k2 = dist[0][1]
p1 = dist[0][2]
p2 = dist[0][3]
k3 = dist[0][4]
ud = np.arange(1,w+1,1)
vd = np.arange(1,h+1,1)
# calculating the error
error = []
for i in range(len(objpoints)):
imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
error.append(cv2.norm(imgpoints[i],imgpoints2, cv2.NORM_L2)/len(imgpoints2))
error = np.array(error)
merror = round(error.mean(),3)
print ("Mean error is {} pixels".format(merror))
# write the parameters to file
f = open(args.output[0],"w")
f.write("### Camera Calibration Results ###\n")
f.write("\n")
f.write("Camera : {}\n".format(camera))
f.write("\n")
f.write("Camera Matrix:\n")
f.write("\n")
df = DataFrame(mtx)
df.to_csv(f,mode="a",header=False,index=False,float_format="%.4f")
f.write("\n")
f.write("Pixel Centers:\n")
f.write("\n")
f.write("U0 : {}\n".format(u0))
f.write("U0 : {}\n".format(v0))
f.write("\n")
f.write("Distortion Coeficients:\n")
f.write("\n")
f.write("k1 : {}\n".format(k1))
f.write("k2 : {}\n".format(k2))
f.write("p1 : {}\n".format(p1))
f.write("p2 : {}\n".format(p2))
f.write("k3 : {}\n".format(k3))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Camera name
parser.add_argument('--camera','-c',
nargs = 1,
action = 'store',
default = ["My Camera"],
dest = 'camera',
required = False,
help = "Camera name.",)
# Input images location
parser.add_argument('--images','-i',
nargs = 1,
action = 'store',
dest = 'images',
required = True,
help = "Input images folder.",)
# Input images location
parser.add_argument('--format','-fmt',
nargs = 1,
action = 'store',
dest = 'format',
required = True,
help = "Input images format. Usually is \'jpg\', \'JPG\', or \'png\'.",)
# Define the pattern to search
parser.add_argument('--pattern','-p',
nargs = 2,
action = 'store',
dest = 'pattern',
required = True,
help = "Number of rows and colums in the chessboard. Ususally 1-ncols and 1-nrows.",)
# Output file
parser.add_argument('--output','-o',
nargs = 1,
action = 'store',
dest = 'output',
required = True,
help = "Output file name.",)
# handle inputs
args = parser.parse_args()
# main call
main()