Python cv2.findChessboardCorners() Examples

def get_chessboard(self, columns, rows, show=False):
        """
        Take a picture with a chessboard visible in both captures.

        ``columns`` and ``rows`` should be the number of inside corners in the
        chessboard's columns and rows. ``show`` determines whether the frames
        are shown while the cameras search for a chessboard.
        """
        found_chessboard = [False, False]

        # Placeholder for corners
        found_corners = [None, None]

        while not all(found_chessboard):
            frames = self.get_frames()
            if show:
                self.show_frames(1)
            for i, frame in enumerate(frames):
                (found_chessboard[i],
                 found_corners[i]) = cv2.findChessboardCorners(frame, (columns, rows),
                                                  flags=cv2.CALIB_CB_FAST_CHECK)
        return frames, found_corners 

def find_chessboard(frame):
    chessboard_flags = cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE
    small_frame = cv2.resize(frame, (0, 0), fx=0.3, fy=0.3)
    return cv2.findChessboardCorners(small_frame, (9, 6), chessboard_flags)[0] and \
           cv2.findChessboardCorners(frame, (9, 6), chessboard_flags)[0] 

def cube(img):
    #img_in = cv2.imread("Picture 27.jpg")
    #img = cv2.resize(img_in,None,fx=0.5, fy=0.5, interpolation = cv2.INTER_CUBIC)
        #cv2.imshow('img',img)
    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        #cv2.imshow('gray',gray)
    ret, corners = cv2.findChessboardCorners(gray, (8,7),None)
        # print ret,corners

    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
    objp = np.zeros((7*8,3), np.float32)
    objp[:,:2] = np.mgrid[0:8,0:7].T.reshape(-1,2)

    #axis = np.float32([[3,0,0], [0,3,0], [0,0,-3]]).reshape(-1,3)
    axis = np.float32([[0,0,0], [0,3,0], [3,3,0], [3,0,0],
                       [0,0,-3],[0,3,-3],[3,3,-3],[3,0,-3] ])

    if ret == True:
        cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
            
            
                # Find the rotation and translation vectors.
        rvecs, tvecs, inliers = cv2.solvePnPRansac(objp, corners, mtx, dist)

                # project 3D points to image plane
        imgpts, jac = cv2.projectPoints(axis, rvecs, tvecs, mtx, dist)
        #print imgpts
        img = draw2(img,corners,imgpts)
        
    return img 

def _chessboard_image_points(self,img):
        found, corners = cv2.findChessboardCorners(img,(self.pattern_columns,self.pattern_rows))
        return(found,corners) 

def _get_corners(self, image):
        """Find subpixel chessboard corners in image."""
        temp = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        ret, corners = cv2.findChessboardCorners(temp,
                                                 (self.rows, self.columns))
        if not ret:
            raise ChessboardNotFoundError("No chessboard could be found.")
        cv2.cornerSubPix(temp, corners, (11, 11), (-1, -1),
                         (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS,
                          30, 0.01))
        return corners 

def try_approximate_corners(self, board_dims):
        found, corners = cv2.findChessboardCorners(self.frame, board_dims)
        self.current_image_points = corners
        self.current_board_dims = board_dims
        return found 

def try_approximate_corners_blur(self, board_dims, sharpness_threshold):
        sharpness = cv2.Laplacian(self.frame, cv2.CV_64F).var()
        if sharpness < sharpness_threshold:
            return False
        found, corners = cv2.findChessboardCorners(self.frame, board_dims)
        self.current_image_points = corners
        return found 

def calibrate_camera(nx, ny, basepath):
    """

    :param nx: number of grids in x axis
    :param ny: number of grids in y axis
    :param basepath: path contains the calibration images
    :return: write calibration file into basepath as calibration_pickle.p
    """

    objp = np.zeros((nx*ny,3), np.float32)
    objp[:,:2] = np.mgrid[0:nx,0:ny].T.reshape(-1,2)

    # Arrays to store object points and image points from all the images.
    objpoints = [] # 3d points in real world space
    imgpoints = [] # 2d points in image plane.

    # Make a list of calibration images
    images = glob.glob(path.join(basepath, 'calibration*.jpg'))

    # Step through the list and search for chessboard corners
    for fname in images:
        img = cv2.imread(fname)
        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

        # Find the chessboard corners
        ret, corners = cv2.findChessboardCorners(gray, (nx,ny),None)

        # If found, add object points, image points
        if ret == True:
            objpoints.append(objp)
            imgpoints.append(corners)

            # Draw and display the corners
            img = cv2.drawChessboardCorners(img, (nx,ny), corners, ret)
            cv2.imshow('input image',img)
            cv2.waitKey(500)

    cv2.destroyAllWindows()


    # calibrate the camera
    img_size = (img.shape[1], img.shape[0])
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)

    # Save the camera calibration result for later use (we don't use rvecs / tvecs)
    dist_pickle = {}
    dist_pickle["mtx"] = mtx
    dist_pickle["dist"] = dist
    destnation = path.join(basepath,'calibration_pickle.p')
    pickle.dump( dist_pickle, open( destnation, "wb" ) )
    print("calibration data is written into: {}".format(destnation))

    return mtx, dist 

def calibrate(drawconer=False):
	'''
	read the calibration image and do the camera calibration
	and output the result to a pickle file.
	if drawconer is True, will draw the corner on the chessboard file and save it to another folder.
	'''
	# !!! IMPORTANT, set the nx, ny according the calibration chessboard pictures.
	nx = 9
	ny = 6

	# prepare object points, like (0,0,0), (1,0,0), (2,0,0), ...(6,5,0)
	objp = np.zeros((nx*ny,3), np.float32)
	objp[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1,2)

	# Arrays to store object points and image points from all the images.
	objpoints = [] # 3d points in real world space
	imgpoints = [] # 2d pionts in image plane.

	# Make a list of calibration images
	images = glob.glob('chessboard_img/calibration*.jpg')
	print("Reading the calibration file...")
	# Step through the list and search for chessboard corners
	for idx, fname in enumerate(images):
		img = cv2.imread(fname)
		gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

		# Find the chessboard corners
		print("Searching corners on ", fname, "...")
		ret, corners = cv2.findChessboardCorners(gray, (nx,ny), None)

		# If found, add object points, image points
		if ret == True:
			objpoints.append(objp)
			imgpoints.append(corners)

			if drawconer:
				cv2.drawChessboardCorners(img, (nx,ny), corners, ret)
				write_name = 'corners_found'+str(idx)+'.jpg'
				cv2.imwrite(write_name, img)
				cv2.imshow('img', img)
				cv2.waitKey(500)
	cv2.destroyAllWindows()

	# Get image size
	img_size = (img.shape[1],img.shape[0])

	# Do camera calibration given object points and image points
	ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)

	# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
	print("Saving the parameter to file...>>camera_cal.p")
	dist_pickle = {}
	dist_pickle["mtx"] = mtx
	dist_pickle["dist"] = dist
	pickle_file = open("camera_cal.p", "wb")
	pickle.dump(dist_pickle, pickle_file)
	pickle_file.close() 

def find_chessboard(self, sx=6, sy=9):
        """Finds the corners of an sx X sy chessboard in the image.

        Parameters
        ----------
        sx : int
            Number of chessboard corners in x-direction.
        sy : int
            Number of chessboard corners in y-direction.

        Returns
        -------
        :obj:`list` of :obj:`numpy.ndarray`
            A list containing the 2D points of the corners of the detected
            chessboard, or None if no chessboard found.
        """
        # termination criteria
        criteria = (cv2.TERM_CRITERIA_EPS +
                    cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)

        # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
        objp = np.zeros((sx * sy, 3), np.float32)
        objp[:, :2] = np.mgrid[0:sx, 0:sy].T.reshape(-1, 2)

        # Arrays to store object points and image points from all the images.
        objpoints = []  # 3d point in real world space
        imgpoints = []  # 2d points in image plane.

        # create images
        img = self.data.astype(np.uint8)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

        # Find the chess board corners
        ret, corners = cv2.findChessboardCorners(gray, (sx, sy), None)

        # If found, add object points, image points (after refining them)
        if ret:
            objpoints.append(objp)
            cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
            imgpoints.append(corners)

            if corners is not None:
                return corners.squeeze()
        return None 

def calibrate(dirpath, prefix, image_format, square_size, width=9, height=6):
    """ Apply camera calibration operation for images in the given directory path. """
    # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(8,6,0)
    objp = np.zeros((height*width, 3), np.float32)
    objp[:, :2] = np.mgrid[0:width, 0:height].T.reshape(-1, 2)

    objp = objp * square_size  # Create real world coords. Use your metric.

    # Arrays to store object points and image points from all the images.
    objpoints = []  # 3d point in real world space
    imgpoints = []  # 2d points in image plane.

    # Directory path correction. Remove the last character if it is '/'
    if dirpath[-1:] == '/':
        dirpath = dirpath[:-1]

    # Get the images
    images = glob.glob(dirpath+'/' + prefix + '*.' + image_format)

    # Iterate through the pairs and find chessboard corners. Add them to arrays
    # If openCV can't find the corners in an image, we discard the image.
    for fname in images:
        img = cv2.imread(fname)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        # Find the chess board corners
        ret, corners = cv2.findChessboardCorners(gray, (width, height), None)

        # If found, add object points, image points (after refining them)
        if ret:
            objpoints.append(objp)

            corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
            imgpoints.append(corners2)

            # Draw and display the corners
            # Show the image to see if pattern is found ! imshow function.
            img = cv2.drawChessboardCorners(img, (width, height), corners2, ret)

    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

    return [ret, mtx, dist, rvecs, tvecs] 

def load_frame_images(self):
        """
        Load images (or image pairs) from self.full_frame_folder_path
        """
        print("Loading frames from '{0:s}'".format(self.full_frame_folder_path))
        all_files = [f for f in os.listdir(self.full_frame_folder_path)
                     if osp.isfile(osp.join(self.full_frame_folder_path, f)) and f.endswith(".png")]
        all_files.sort()

        usable_frame_ct = sys.maxsize

        frame_number_sets = []

        for video in self.videos:
            # assume matching numbers in corresponding left & right files
            files = [f for f in all_files if f.startswith(video.name)]
            files.sort()  # added to be explicit

            cam_frame_ct = 0
            frame_numbers = []
            for ix_pair in range(len(files)):
                frame = cv2.imread(osp.join(self.full_frame_folder_path, files[ix_pair]))
                frame_number = int(re.search(r'\d\d\d\d', files[ix_pair]).group(0))
                frame_numbers.append(frame_number)
                found, corners = cv2.findChessboardCorners(frame, self.board_dims)
                if not found:
                    raise ValueError("Could not find corners in image '{0:s}'".format(files[ix_pair]))
                grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
                cv2.cornerSubPix(grey, corners, (11, 11), (-1, -1), self.criteria_subpix)
                video.image_points.append(corners)
                video.usable_frames[frame_number] = ix_pair
                cam_frame_ct += 1
            usable_frame_ct = min(usable_frame_ct, cam_frame_ct)
            frame_number_sets.append(frame_numbers)

        if len(self.videos) > 1:
            # check that all cameras have the same frame number sets
            if len(frame_number_sets[0]) != len(frame_number_sets[1]):
                raise ValueError(
                    "There are some non-paired frames in folder '{0:s}'".format(self.full_frame_folder_path))
            for i_fn in range(len(frame_number_sets[0])):
                fn0 = frame_number_sets[0][i_fn]
                fn1 = frame_number_sets[1][i_fn]
                if fn0 != fn1:
                    raise ValueError("There are some non-paired frames in folder '{0:s}'." +
                                     " Check frame {1:d} for camera {2:s} and frame {3:d} for camera {4:s}."
                                     .format(self.full_frame_folder_path,
                                             fn0, self.videos[0].name,
                                             fn1, self.videos[1].name))

        for i_frame in range(usable_frame_ct):
            self.object_points.append(self.board_object_corner_set)
        return usable_frame_ct 

def get_K_and_D(checkerboard, imgsPath):

    CHECKERBOARD = checkerboard
    subpix_criteria = (cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 30, 0.1)
    calibration_flags = cv2.fisheye.CALIB_RECOMPUTE_EXTRINSIC+cv2.fisheye.CALIB_CHECK_COND+cv2.fisheye.CALIB_FIX_SKEW
    objp = np.zeros((1, CHECKERBOARD[0]*CHECKERBOARD[1], 3), np.float32)
    objp[0,:,:2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)
    _img_shape = None
    objpoints = []
    imgpoints = []
    images = glob.glob(imgsPath + '/*.png')
    for fname in images:
        img = cv2.imread(fname)
        if _img_shape == None:
            _img_shape = img.shape[:2]
        else:
            assert _img_shape == img.shape[:2], "All images must share the same size."

        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
        ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD,cv2.CALIB_CB_ADAPTIVE_THRESH+cv2.CALIB_CB_FAST_CHECK+cv2.CALIB_CB_NORMALIZE_IMAGE)
        if ret == True:
            objpoints.append(objp)
            cv2.cornerSubPix(gray,corners,(3,3),(-1,-1),subpix_criteria)
            imgpoints.append(corners)
    N_OK = len(objpoints)
    K = np.zeros((3, 3))
    D = np.zeros((4, 1))
    rvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_OK)]
    tvecs = [np.zeros((1, 1, 3), dtype=np.float64) for i in range(N_OK)]
    rms, _, _, _, _ = cv2.fisheye.calibrate(
        objpoints,
        imgpoints,
        gray.shape[::-1],
        K,
        D,
        rvecs,
        tvecs,
        calibration_flags,
        (cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-6)
    )
    DIM = _img_shape[::-1]
    print("Found " + str(N_OK) + " valid images for calibration")
    print("DIM=" + str(_img_shape[::-1]))
    print("K=np.array(" + str(K.tolist()) + ")")
    print("D=np.array(" + str(D.tolist()) + ")")
    return DIM, K, D 

def get_calibration_points(images, nx, ny):
    '''
    Generate two lists of calibration points from a set of calibration images
    of chess boards to needed for `cv2.calibrateCamera()`.
    
    It is recommended that `images` contain at least 20 images. All images
    are expected to be of identical size and to contain the same, complete
    chess board pattern.
    
    Args:
        images (array-like): A list of file names of the images to be
            used for calibration.
        nx (int): The number of horizontal inner corners (i.e. corners where two
            white and two black tiles meet) of the chess board.
        ny (int): The number of vertical inner corners (i.e. corners where two
            white and two black tiles meet) of the chess board.
            
    Returns:
        object_points (list): The list of 3-D object points for calibration.
        image_points (list): The list of 2-D image points for calibration.
    '''
    
    image_size = []
    
    # Arrays to store object points and image points
    # of all calibration images for `cv2.calibrateCamera()`.
    object_points = [] # 3-D points in real world space
    image_points = [] # 2-D points in image plane.

    # All calibration images are expected to contain the same calibration pattern,
    # so the object points are the same for all images.
    # Format: (0,0,0), (1,0,0), (2,0,0), ...., (8,5,0)
    # The third coordinate is always zero as the points lie in a plane.
    objp = np.zeros((nx*ny,3), np.float32)
    objp[:,:2] = np.mgrid[0:nx, 0:ny].T.reshape(-1,2)
    
    # Step through the list and search for chess board corners
    for i, fname in enumerate(images):
        img = cv2.imread(fname)
        size = (img.shape[1], img.shape[0])
        if i == 0:
            image_size = size
        if size != image_size:
            raise ValueError("Expected all images to have identical size, but found varying sizes.")
        image_size = size
        
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        # Find the chess board corners
        ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)

        # If found, add object points, image points
        if ret == True:
            object_points.append(objp)
            image_points.append(corners)

    return object_points, image_points, image_size 

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) 

def calibrate_camera(calib_images_dir, verbose=False):
    """
    Calibrate the camera given a directory containing calibration chessboards.

    :param calib_images_dir: directory containing chessboard frames
    :param verbose: if True, draw and show chessboard corners
    :return: calibration parameters
    """

    assert path.exists(calib_images_dir), '"{}" must exist and contain calibration images.'.format(calib_images_dir)

    # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
    objp = np.zeros((6 * 9, 3), np.float32)
    objp[:, :2] = np.mgrid[0:9, 0:6].T.reshape(-1, 2)

    # Arrays to store object points and image points from all the images.
    objpoints = []  # 3d points in real world space
    imgpoints = []  # 2d points in image plane.

    # Make a list of calibration images
    images = glob.glob(path.join(calib_images_dir, 'calibration*.jpg'))

    # Step through the list and search for chessboard corners
    for filename in images:

        img = cv2.imread(filename)
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

        # Find the chessboard corners
        pattern_found, corners = cv2.findChessboardCorners(gray, (9, 6), None)

        if pattern_found is True:
            objpoints.append(objp)
            imgpoints.append(corners)

            if verbose:
                # Draw and display the corners
                img = cv2.drawChessboardCorners(img, (9, 6), corners, pattern_found)
                cv2.imshow('img',img)
                cv2.waitKey(500)

    if verbose:
        cv2.destroyAllWindows()

    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)

    return ret, mtx, dist, rvecs, tvecs 

def calibrate_camera(nx, ny, basepath):
    """

    :param nx: number of grids in x axis
    :param ny: number of grids in y axis
    :param basepath: path contains the calibration images
    :return: write calibration file into basepath as calibration_pickle.p
    """

    objp = np.zeros((nx*ny,3), np.float32)
    objp[:,:2] = np.mgrid[0:nx,0:ny].T.reshape(-1,2)

    # Arrays to store object points and image points from all the images.
    objpoints = [] # 3d points in real world space
    imgpoints = [] # 2d points in image plane.

    # Make a list of calibration images
    images = glob.glob(path.join(basepath, 'calibration*.jpg'))

    # Step through the list and search for chessboard corners
    for fname in images:
        img = cv2.imread(fname)
        gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

        # Find the chessboard corners
        ret, corners = cv2.findChessboardCorners(gray, (nx,ny),None)

        # If found, add object points, image points
        if ret == True:
            objpoints.append(objp)
            imgpoints.append(corners)

            # Draw and display the corners
            img = cv2.drawChessboardCorners(img, (nx,ny), corners, ret)
            cv2.imshow('input image',img)
            cv2.waitKey(500)

    cv2.destroyAllWindows()


    # calibrate the camera
    img_size = (img.shape[1], img.shape[0])
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)

    # Save the camera calibration result for later use (we don't use rvecs / tvecs)
    dist_pickle = {}
    dist_pickle["mtx"] = mtx
    dist_pickle["dist"] = dist
    destnation = path.join(basepath,'calibration_pickle.p')
    pickle.dump( dist_pickle, open( destnation, "wb" ) )
    print("calibration data is written into: {}".format(destnation))

    return mtx, dist 

def process_images(self, filepath):
        """Read images, detect corners, refine corners, and save data."""
        # Arrays to store object points and image points from all the images.
        self.objpoints = []  # 3d point in real world space
        self.imgpoints_l = []  # 2d points in image plane.
        self.imgpoints_r = []  # 2d points in image plane.
        self.calib_successes = [] # polygon ids of left/right image sets with checkerboard corners.

        images_left = glob.glob(filepath + "/left/*")
        images_right = glob.glob(filepath + "/right/*")
        images_left.sort()
        images_right.sort()

        print("\nAttempting to read images for left camera from dir: " +
              filepath + "/left/")
        print("Attempting to read images for right camera from dir: " +
              filepath + "/right/")

        assert len(images_left) != 0, "ERROR: Images not read correctly, check directory"
        assert len(images_right) != 0, "ERROR: Images not read correctly, check directory"

        for image_left, image_right in zip(images_left, images_right):
            img_l = cv2.imread(image_left, 0)
            img_r = cv2.imread(image_right, 0)

            assert img_l is not None, "ERROR: Images not read correctly"
            assert img_r is not None, "ERROR: Images not read correctly"

            print("Finding chessboard corners for %s and %s..." % (os.path.basename(image_left), os.path.basename(image_right)))
            start_time = time.time()

            # Find the chess board corners
            flags = 0
            flags |= cv2.CALIB_CB_ADAPTIVE_THRESH
            flags |= cv2.CALIB_CB_NORMALIZE_IMAGE
            ret_l, corners_l = cv2.findChessboardCorners(img_l, (9, 6), flags)
            ret_r, corners_r = cv2.findChessboardCorners(img_r, (9, 6), flags)

            # termination criteria
            self.criteria = (cv2.TERM_CRITERIA_MAX_ITER +
                             cv2.TERM_CRITERIA_EPS, 30, 0.001)

            # if corners are found in both images, refine and add data
            if ret_l and ret_r:
                self.objpoints.append(self.objp)
                rt = cv2.cornerSubPix(img_l, corners_l, (5, 5),
                                      (-1, -1), self.criteria)
                self.imgpoints_l.append(corners_l)
                rt = cv2.cornerSubPix(img_r, corners_r, (5, 5),
                                      (-1, -1), self.criteria)
                self.imgpoints_r.append(corners_r)
                self.calib_successes.append(polygon_from_image_name(image_left))
                print("\t[OK]. Took %i seconds." % (round(time.time() - start_time, 2)))
            else:
                print("\t[ERROR] - Corners not detected. Took %i seconds." % (round(time.time() - start_time, 2)))

            self.img_shape = img_r.shape[::-1]
        print(str(len(self.objpoints)) + " of " + str(len(images_left)) +
              " images being used for calibration")
        self.ensure_valid_images()