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71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
#!/usr/bin/env python
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'''
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camera calibration for distorted images with chess board samples
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reads distorted images, calculates the calibration and write undistorted images
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2
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from tests_common import NewOpenCVTests
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class calibration_test(NewOpenCVTests):
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def test_calibration(self):
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from glob import glob
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img_names = []
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for i in range(1, 15):
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if i < 10:
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img_names.append('samples/data/left0{}.jpg'.format(str(i)))
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elif i != 10:
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img_names.append('samples/data/left{}.jpg'.format(str(i)))
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square_size = 1.0
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pattern_size = (9, 6)
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pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
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pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2)
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pattern_points *= square_size
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obj_points = []
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img_points = []
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h, w = 0, 0
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img_names_undistort = []
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for fn in img_names:
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img = self.get_sample(fn, 0)
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if img is None:
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continue
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h, w = img.shape[:2]
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found, corners = cv2.findChessboardCorners(img, pattern_size)
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if found:
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term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1)
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cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
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if not found:
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continue
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img_points.append(corners.reshape(-1, 2))
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obj_points.append(pattern_points)
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# calculate camera distortion
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rms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points, (w, h), None, None, flags = 0)
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eps = 0.01
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normCamEps = 10.0
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normDistEps = 0.001
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cameraMatrixTest = [[ 532.80992189, 0., 342.4952186 ],
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[ 0., 532.93346422, 233.8879292 ],
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[ 0., 0., 1. ]]
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distCoeffsTest = [ -2.81325576e-01, 2.91130406e-02,
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1.21234330e-03, -1.40825372e-04, 1.54865844e-01]
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self.assertLess(abs(rms - 0.196334638034), eps)
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self.assertLess(cv2.norm(camera_matrix - cameraMatrixTest, cv2.NORM_L1), normCamEps)
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self.assertLess(cv2.norm(dist_coefs - distCoeffsTest, cv2.NORM_L1), normDistEps) |