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130 lines
4.0 KiB
Python
Executable File
130 lines
4.0 KiB
Python
Executable File
#!/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>] [--square_size] [<image mask>]
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default values:
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--debug: ./output/
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--square_size: 1.0
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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
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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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if __name__ == '__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_mask = getopt.getopt(sys.argv[1:], '', ['debug=', 'square_size=', 'threads='])
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args = dict(args)
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args.setdefault('--debug', './output/')
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args.setdefault('--square_size', 1.0)
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args.setdefault('--threads', 4)
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if not img_mask:
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img_mask = '../data/left??.jpg' # default
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else:
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img_mask = img_mask[0]
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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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square_size = float(args.get('--square_size'))
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pattern_size = (9, 6)
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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 = cv2.imread(img_names[0], 0).shape[:2] # TODO: use imquery call to retrieve results
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def processImage(fn):
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print('processing %s... ' % fn)
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img = cv2.imread(fn, 0)
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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, corners = cv2.findChessboardCorners(img, pattern_size)
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if found:
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term = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_COUNT, 30, 0.1)
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cv2.cornerSubPix(img, corners, (5, 5), (-1, -1), term)
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if debug_dir:
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vis = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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cv2.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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cv2.imwrite(outfile, vis)
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if not found:
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print('chessboard 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 = cv2.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 = cv2.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 = cv2.getOptimalNewCameraMatrix(camera_matrix, dist_coefs, (w, h), 1, (w, h))
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dst = cv2.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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cv2.imwrite(outfile, dst)
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cv2.destroyAllWindows()
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