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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
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
from tests_common import NewOpenCVTests
class calibration_test(NewOpenCVTests):
def test_calibration(self):
img_names = []
for i in range(1, 15):
if i < 10:
img_names.append('samples/data/left0{}.jpg'.format(str(i)))
elif i != 10:
img_names.append('samples/data/left{}.jpg'.format(str(i)))
square_size = 1.0
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
for fn in img_names:
img = self.get_sample(fn, 0)
if img is None:
continue
h, w = img.shape[:2]
found, corners = cv.findChessboardCorners(img, pattern_size)
if found:
term = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_COUNT, 30, 0.1)
cv.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
if not found:
continue
img_points.append(corners.reshape(-1, 2))
obj_points.append(pattern_points)
# calculate camera distortion
rms, camera_matrix, dist_coefs, _rvecs, _tvecs = cv.calibrateCamera(obj_points, img_points, (w, h), None, None, flags = 0)
eps = 0.01
normCamEps = 10.0
normDistEps = 0.05
cameraMatrixTest = [[ 532.80992189, 0., 342.4952186 ],
[ 0., 532.93346422, 233.8879292 ],
[ 0., 0., 1. ]]
distCoeffsTest = [ -2.81325576e-01, 2.91130406e-02,
1.21234330e-03, -1.40825372e-04, 1.54865844e-01]
self.assertLess(abs(rms - 0.196334638034), eps)
self.assertLess(cv.norm(camera_matrix - cameraMatrixTest, cv.NORM_L1), normCamEps)
self.assertLess(cv.norm(dist_coefs - distCoeffsTest, cv.NORM_L1), normDistEps)
if __name__ == '__main__':
NewOpenCVTests.bootstrap()
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