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Open Source Computer Vision Library
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73 lines
2.3 KiB
73 lines
2.3 KiB
#!/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 as cv |
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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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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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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 = cv.findChessboardCorners(img, pattern_size) |
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if found: |
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term = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_COUNT, 30, 0.1) |
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cv.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 = cv.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 = 0.05 |
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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(cv.norm(camera_matrix - cameraMatrixTest, cv.NORM_L1), normCamEps) |
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self.assertLess(cv.norm(dist_coefs - distCoeffsTest, cv.NORM_L1), normDistEps) |
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if __name__ == '__main__': |
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NewOpenCVTests.bootstrap()
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