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3rdparty/opencv-4.5.4/samples/python/calibrate.py 4.12 KB
f4334277   Hu Chunming   提交3rdparty
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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
  
  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, 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)
  
      print('Done')
  
  
  if __name__ == '__main__':
      print(__doc__)
      main()
      cv.destroyAllWindows()