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#!/usr/bin/env python
'''
camera calibration for distorted images with chess board samples
reads distorted images, calculates the calibration and write undistorted images
usage:
calibrate.py [--debug <output path>] [--square_size] [<image mask>]
default values:
--debug: ./output/
--square_size: 1.0
<image mask> defaults to ../data/left*.jpg
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
# local modules
from common import splitfn
# built-in modules
import os
if __name__ == '__main__':
import sys
import getopt
from glob import glob
args, img_mask = getopt.getopt(sys.argv[1:], '', ['debug=', 'square_size='])
args = dict(args)
args.setdefault('--debug', './output/')
args.setdefault('--square_size', 1.0)
if not img_mask:
img_mask = '../data/left*.jpg' # default
else:
img_mask = img_mask[0]
img_names = glob(img_mask)
debug_dir = args.get('--debug')
if not os.path.isdir(debug_dir):
os.mkdir(debug_dir)
square_size = float(args.get('--square_size'))
pattern_size = (9, 6)
pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2)
pattern_points *= square_size
obj_points = []
img_points = []
h, w = 0, 0
img_names_undistort = []
for fn in img_names:
print('processing %s... ' % fn, end='')
img = cv2.imread(fn, 0)
if img is None:
print("Failed to load", fn)
continue
h, w = img.shape[:2]
found, corners = cv2.findChessboardCorners(img, pattern_size)
if found:
term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1)
cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if debug_dir:
vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
cv2.drawChessboardCorners(vis, pattern_size, corners, found)
path, name, ext = splitfn(fn)
outfile = debug_dir + name + '_chess.png'
cv2.imwrite(outfile, vis)
if found:
img_names_undistort.append(outfile)
if not found:
print('chessboard not found')
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
print('ok')
# calculate camera distortion
rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h), None, None)
print("\nRMS:", rms)
print("camera matrix:\n", camera_matrix)
print("distortion coefficients: ", dist_coefs.ravel())
# undistort the image with the calibration
print('')
for img_found in img_names_undistort:
img = cv2.imread(img_found)
h, w = img.shape[:2]
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(camera_matrix, dist_coefs, (w, h), 1, (w, h))
dst = cv2.undistort(img, camera_matrix, dist_coefs, None, newcameramtx)
# crop and save the image
x, y, w, h = roi
dst = dst[y:y+h, x:x+w]
outfile = img_found + '_undistorted.png'
print('Undistorted image written to: %s' % outfile)
cv2.imwrite(outfile, dst)
cv2.destroyAllWindows()
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