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Open Source Computer Vision Library
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205 lines
7.1 KiB
205 lines
7.1 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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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: 50 |
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--marker_size: 25 |
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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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''' |
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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 = (height, width) |
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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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elif pattern_type == 'charucoboard': |
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pattern_points = np.zeros((np.prod((height-1, width-1)), 3), np.float32) |
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pattern_points[:, :2] = np.indices((height-1, width-1)).T.reshape(-1, 2) |
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pattern_points *= square_size |
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else: |
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print("unknown pattern") |
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return None |
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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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} |
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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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elif pattern_type == 'charucoboard': |
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corners, _charucoIds, _markerCorners_svg, _markerIds_svg = charuco_detector.detectBoard(img) |
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if (len(corners) == (height-1)*(width-1)): |
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found = True |
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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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cv.drawChessboardCorners(vis, pattern_size, corners, found) |
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_path, name, _ext = splitfn(fn) |
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outfile = os.path.join(debug_dir, name + '_chess.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 (corners.reshape(-1, 2), pattern_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 + '_chess.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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