calibrate.py

import os

import cv2 as cv
import numpy as np


def splitfn(fn):
    path, fn = os.path.split(fn)
    name, ext = os.path.splitext(fn)
    return path, name, ext


if __name__ == '__main__':
    import sys
    import getopt
    from glob import glob

    args, img_mask = getopt.getopt(sys.argv[1:], '', ['debug=', 'square_size=', 'threads='])
    args = dict(args)
    args.setdefault('--debug', './output/')
    args.setdefault('--square_size', 1.0)
    args.setdefault('--threads', 4)
    if not img_mask:
        img_mask = '../data/left??.jpg'  # default
    else:
        img_mask = img_mask[0]

    img_names = glob(img_mask)
    debug_dir = args.get('--debug')
    if debug_dir and not os.path.isdir(debug_dir):
        os.mkdir(debug_dir)
    square_size = float(args.get('--square_size'))

    pattern_size = (9, 6)
    pattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)
    pattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2)
    pattern_points *= square_size

    obj_points = []
    img_points = []
    h, w = cv.imread(img_names[0], cv.IMREAD_GRAYSCALE).shape[:2]  # TODO: use imquery call to retrieve results

    def processImage(fn):
        print('processing %s... ' % fn)
        img = cv.imread(fn, 0)
        if img is None:
            print("Failed to load", fn)
            return None

        assert w == img.shape[1] and h == img.shape[0], ("size: %d x %d ... " % (img.shape[1], img.shape[0]))
        found, corners = cv.findChessboardCorners(img, pattern_size)
        if found:
            term = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_COUNT, 30, 0.1)
            cv.cornerSubPix(img, corners, (5, 5), (-1, -1), term)

        if debug_dir:
            vis = cv.cvtColor(img, cv.COLOR_GRAY2BGR)
            cv.drawChessboardCorners(vis, pattern_size, corners, found)
            _path, name, _ext = splitfn(fn)
            outfile = os.path.join(debug_dir, name + '_chess.png')
            cv.imwrite(outfile, vis)

        if not found:
            print('chessboard not found')
            return None

        print('           %s... OK' % fn)
        return corners.reshape(-1, 2), pattern_points

    threads_num = int(args.get('--threads'))
    if threads_num <= 1:
        chessboards = [processImage(fn) for fn in img_names]
    else:
        print("Run with %d threads..." % threads_num)
        from multiprocessing.dummy import Pool as ThreadPool

        pool = ThreadPool(threads_num)
        chessboards = pool.map(processImage, img_names)

    chessboards = [x for x in chessboards if x is not None]
    for (corners, pattern_points) in chessboards:
        img_points.append(corners)
        obj_points.append(pattern_points)

    # calculate camera distortion
    rms, camera_matrix, dist_coefs, rvecs, tvecs = cv.calibrateCamera(obj_points, img_points, (w, h), None, None)

    print("\nRMS:", rms)
    print("camera matrix:\n", camera_matrix)
    print("distortion coefficients: ", dist_coefs.ravel())

    # undistort the image with the calibration
    print('')
    for fn in img_names if debug_dir else []:
        path, name, ext = splitfn(fn)
        img_found = os.path.join(debug_dir, name + '_chess.png')
        outfile = os.path.join(debug_dir, name + '_undistorted.png')

        img = cv.imread(img_found)
        if img is None:
            continue

        h, w = img.shape[:2]
        newcameramtx, roi = cv.getOptimalNewCameraMatrix(camera_matrix, dist_coefs, (w, h), 1, (w, h))

        dst = cv.undistort(img, camera_matrix, dist_coefs, None, newcameramtx)

        # crop and save the image
        # print(roi)
        x, y, w, h = roi
        dst = dst[y:y + h, x:x + w]

        print('Undistorted image written to: %s' % outfile)
        cv.imwrite(outfile, dst)

    cv.destroyAllWindows()