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#!/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 <output path>] [--square_size] [<image mask>]
default values:
--debug: ./output/
--square_size: 1.0
<image mask> 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
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], 0).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
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()