#!/usr/bin/env python ''' camera calibration for distorted images with chess board samples reads distorted images, calculates the calibration and write undistorted images usage: calibrate.py [--debug ] [--square_size] [] default values: --debug: ./output/ --square_size: 1.0 defaults to ../data/left*.jpg ''' # Python 2/3 compatibility from __future__ import print_function import numpy as np import cv2 as cv # local modules from common import splitfn # built-in modules import os def 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, cv.IMREAD_GRAYSCALE) 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 x, y, w, h = roi dst = dst[y:y+h, x:x+w] print('Undistorted image written to: %s' % outfile) cv.imwrite(outfile, dst) print('Done') if __name__ == '__main__': print(__doc__) main() cv.destroyAllWindows()