chore: import upstream snapshot with attribution
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wehub-resource-sync
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#!/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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usage:
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calibrate.py [--debug <output path>] [-w <width>] [-h <height>] [-t <pattern type>] [--square_size=<square size>]
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[--marker_size=<aruco marker size>] [--aruco_dict=<aruco dictionary name>] [<image mask>]
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usage example:
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calibrate.py -w 4 -h 6 -t chessboard --square_size=50 ../data/left*.jpg
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default values:
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--debug: ./output/
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-w: 4
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-h: 6
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-t: chessboard
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--square_size: 10
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--marker_size: 5
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--aruco_dict: DICT_4X4_50
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--threads: 4
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<image mask> defaults to ../data/left*.jpg
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NOTE: Chessboard size is defined in inner corners. Charuco board size is defined in units.
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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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# local modules
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from common import splitfn
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# built-in modules
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import os
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def main():
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import sys
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import getopt
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from glob import glob
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args, img_names = getopt.getopt(sys.argv[1:], 'w:h:t:', ['debug=','square_size=', 'marker_size=',
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'aruco_dict=', 'threads=', ])
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args = dict(args)
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args.setdefault('--debug', './output/')
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args.setdefault('-w', 4)
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args.setdefault('-h', 6)
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args.setdefault('-t', 'chessboard')
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args.setdefault('--square_size', 10)
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args.setdefault('--marker_size', 5)
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args.setdefault('--aruco_dict', 'DICT_4X4_50')
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args.setdefault('--threads', 4)
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if not img_names:
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img_mask = '../data/left??.jpg' # default
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img_names = glob(img_mask)
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debug_dir = args.get('--debug')
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if debug_dir and not os.path.isdir(debug_dir):
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os.mkdir(debug_dir)
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height = int(args.get('-h'))
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width = int(args.get('-w'))
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pattern_type = str(args.get('-t'))
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square_size = float(args.get('--square_size'))
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marker_size = float(args.get('--marker_size'))
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aruco_dict_name = str(args.get('--aruco_dict'))
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pattern_size = (width, height)
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if pattern_type == 'chessboard':
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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 = cv.imread(img_names[0], cv.IMREAD_GRAYSCALE).shape[:2] # TODO: use imquery call to retrieve results
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aruco_dicts = {
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'DICT_4X4_50': cv.aruco.DICT_4X4_50,
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'DICT_4X4_100': cv.aruco.DICT_4X4_100,
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'DICT_4X4_250': cv.aruco.DICT_4X4_250,
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'DICT_4X4_1000': cv.aruco.DICT_4X4_1000,
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'DICT_5X5_50': cv.aruco.DICT_5X5_50,
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'DICT_5X5_100': cv.aruco.DICT_5X5_100,
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'DICT_5X5_250': cv.aruco.DICT_5X5_250,
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'DICT_5X5_1000': cv.aruco.DICT_5X5_1000,
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'DICT_6X6_50': cv.aruco.DICT_6X6_50,
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'DICT_6X6_100': cv.aruco.DICT_6X6_100,
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'DICT_6X6_250': cv.aruco.DICT_6X6_250,
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'DICT_6X6_1000': cv.aruco.DICT_6X6_1000,
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'DICT_7X7_50': cv.aruco.DICT_7X7_50,
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'DICT_7X7_100': cv.aruco.DICT_7X7_100,
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'DICT_7X7_250': cv.aruco.DICT_7X7_250,
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'DICT_7X7_1000': cv.aruco.DICT_7X7_1000,
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'DICT_ARUCO_ORIGINAL': cv.aruco.DICT_ARUCO_ORIGINAL,
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'DICT_APRILTAG_16h5': cv.aruco.DICT_APRILTAG_16h5,
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'DICT_APRILTAG_25h9': cv.aruco.DICT_APRILTAG_25h9,
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'DICT_APRILTAG_36h10': cv.aruco.DICT_APRILTAG_36h10,
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'DICT_APRILTAG_36h11': cv.aruco.DICT_APRILTAG_36h11,
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'DICT_ARUCO_MIP_36h12': cv.aruco.DICT_ARUCO_MIP_36h12
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}
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if (aruco_dict_name not in set(aruco_dicts.keys())):
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print("unknown aruco dictionary name")
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return None
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aruco_dict = cv.aruco.getPredefinedDictionary(aruco_dicts[aruco_dict_name])
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board = cv.aruco.CharucoBoard(pattern_size, square_size, marker_size, aruco_dict)
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charuco_detector = cv.aruco.CharucoDetector(board)
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def processImage(fn):
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print('processing %s... ' % fn)
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img = cv.imread(fn, cv.IMREAD_GRAYSCALE)
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if img is None:
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print("Failed to load", fn)
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return None
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assert w == img.shape[1] and h == img.shape[0], ("size: %d x %d ... " % (img.shape[1], img.shape[0]))
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found = False
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corners = 0
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if pattern_type == 'chessboard':
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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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frame_img_points = corners.reshape(-1, 2)
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frame_obj_points = pattern_points
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elif pattern_type == 'charucoboard':
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corners, charucoIds, _, _ = charuco_detector.detectBoard(img)
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if (len(corners) > 0):
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frame_obj_points, frame_img_points = board.matchImagePoints(corners, charucoIds)
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found = True
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else:
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found = False
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else:
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print("unknown pattern type", pattern_type)
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return None
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if debug_dir:
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vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
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if pattern_type == 'chessboard':
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cv.drawChessboardCorners(vis, pattern_size, corners, found)
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elif pattern_type == 'charucoboard':
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cv.aruco.drawDetectedCornersCharuco(vis, corners, charucoIds=charucoIds)
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_path, name, _ext = splitfn(fn)
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outfile = os.path.join(debug_dir, name + '_board.png')
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cv.imwrite(outfile, vis)
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if not found:
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print('pattern not found')
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return None
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print(' %s... OK' % fn)
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return (frame_img_points, frame_obj_points)
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threads_num = int(args.get('--threads'))
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if threads_num <= 1:
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chessboards = [processImage(fn) for fn in img_names]
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else:
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print("Run with %d threads..." % threads_num)
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from multiprocessing.dummy import Pool as ThreadPool
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pool = ThreadPool(threads_num)
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chessboards = pool.map(processImage, img_names)
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chessboards = [x for x in chessboards if x is not None]
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for (corners, pattern_points) in chessboards:
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img_points.append(corners)
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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)
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print("\nRMS:", rms)
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print("camera matrix:\n", camera_matrix)
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print("distortion coefficients: ", dist_coefs.ravel())
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# undistort the image with the calibration
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print('')
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for fn in img_names if debug_dir else []:
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_path, name, _ext = splitfn(fn)
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img_found = os.path.join(debug_dir, name + '_board.png')
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outfile = os.path.join(debug_dir, name + '_undistorted.png')
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img = cv.imread(img_found)
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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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newcameramtx, roi = cv.getOptimalNewCameraMatrix(camera_matrix, dist_coefs, (w, h), 1, (w, h))
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dst = cv.undistort(img, camera_matrix, dist_coefs, None, newcameramtx)
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# crop and save 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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print('Undistorted image written to: %s' % outfile)
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cv.imwrite(outfile, dst)
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print('Done')
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if __name__ == '__main__':
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print(__doc__)
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main()
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cv.destroyAllWindows()
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