78 lines
2.2 KiB
Python
78 lines
2.2 KiB
Python
import numpy as np
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import cv2 as cv
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import glob
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def main():
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# termination criteria
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criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
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# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
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objp = np.zeros((6 * 7, 3), np.float32)
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objp[:, :2] = np.mgrid[0:7, 0:6].T.reshape(-1, 2)
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# Arrays to store object points and image points from all the images.
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objpoints = [] # 3d point in real world space
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imgpoints = [] # 2d points in image plane.
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images = glob.glob('*.jpg')
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for fname in images:
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img = cv.imread(fname)
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gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
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# Find the chess board corners
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ret, corners = cv.findChessboardCorners(gray, (7, 6), None)
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# If found, add object points, image points (after refining them)
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if ret:
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objpoints.append(objp)
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corners2 = cv.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
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imgpoints.append(corners2)
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# Draw and display the corners
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cv.drawChessboardCorners(img, (7, 6), corners2, ret)
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cv.imshow('img', img)
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cv.waitKey(500)
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# Calibration
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ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
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# write to file
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np.savez("results/cam.npz", mtx=mtx, dist=dist, rvecs=rvecs, tvecs=tvecs)
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# Undistortion
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img = cv.imread('checker_cam.jpg')
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h, w = img.shape[:2]
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newcameramtx, roi = cv.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1, (w, h))
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# 1. Using cv.undistort()
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dst = cv.undistort(img, mtx, dist, None, newcameramtx)
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# crop the image
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x, y, w, h = roi
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dst = dst[y:y + h, x:x + w]
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cv.imwrite('results/calibresult_1.png', dst)
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# 2. Undistort using remapping
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mapx, mapy = cv.initUndistortRectifyMap(mtx, dist, None, newcameramtx, (w, h), 5)
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dst = cv.remap(img, mapx, mapy, cv.INTER_LINEAR)
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# crop the image
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x, y, w, h = roi
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dst = dst[y:y + h, x:x + w]
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cv.imwrite('results/calibresult_2.png', dst)
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# Re-projection Error
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mean_error = 0
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for i in range(len(objpoints)):
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imgpoints2, _ = cv.projectPoints(objpoints[i], rvecs[i], tvecs[i], mtx, dist)
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error = cv.norm(imgpoints[i], imgpoints2, cv.NORM_L2) / len(imgpoints2)
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mean_error += error
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print("total error: {}".format(mean_error / len(objpoints)))
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cv.destroyAllWindows()
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if __name__ == '__main__':
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main()
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