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Open Source Computer Vision Library
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129 lines
4.0 KiB
129 lines
4.0 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>] [--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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