Python cv2.drawChessboardCorners() Examples

def add_corners(self, i_frame, subpixel_criteria, frame_folder_path=None,
                    save_image=False, save_chekerboard_overlay=False):
        grey_frame = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)
        cv2.cornerSubPix(grey_frame, self.current_image_points, (11, 11), (-1, -1), subpixel_criteria)
        if save_image:
            png_path = (os.path.join(frame_folder_path,
                                     "{0:s}{1:04d}{2:s}".format(self.name, i_frame, ".png")))
            cv2.imwrite(png_path, self.frame)
            if save_chekerboard_overlay:
                png_path = (os.path.join(frame_folder_path,
                                         "checkerboard_{0:s}{1:04d}{2:s}".format(self.name, i_frame, ".png")))
                overlay = self.frame.copy()
                cv2.drawChessboardCorners(overlay, self.current_board_dims, self.current_image_points, True)
                cv2.imwrite(png_path, overlay)
        self.usable_frames[i_frame] = len(self.image_points)
        self.image_points.append(self.current_image_points) 

def live_calibrate(camera, pattern_shape, n_matches_needed):
    """ Find calibration parameters as the user moves a checkerboard in front of the camera """
    print("Looking for %s checkerboard" % (pattern_shape,))
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
    example_3d = np.zeros((pattern_shape[0] * pattern_shape[1], 3), np.float32)
    example_3d[:, :2] = np.mgrid[0 : pattern_shape[1], 0 : pattern_shape[0]].T.reshape(-1, 2)
    points_3d = []
    points_2d = []
    while len(points_3d) < n_matches_needed:
        ret, frame = camera.cap.read()
        assert ret
        gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        ret, corners = cv2.findCirclesGrid(
            gray_frame, pattern_shape, flags=cv2.CALIB_CB_ASYMMETRIC_GRID
        )
        cv2.imshow("camera", frame)
        if ret:
            points_3d.append(example_3d.copy())
            points_2d.append(corners)
            print("Found calibration %i of %i" % (len(points_3d), n_matches_needed))
            drawn_frame = cv2.drawChessboardCorners(frame, pattern_shape, corners, ret)
            cv2.imshow("calib", drawn_frame)
        cv2.waitKey(10)
    ret, camera_matrix, distortion_coefficients, _, _ = cv2.calibrateCamera(
        points_3d, points_2d, gray_frame.shape[::-1], None, None
    )
    assert ret
    return camera_matrix, distortion_coefficients 

def _show_corners(self, image, corners):
        """Show chessboard corners found in image."""
        temp = image
        cv2.drawChessboardCorners(temp, (self.rows, self.columns), corners,
                                  True)
        window_name = "Chessboard"
        cv2.imshow(window_name, temp)
        if cv2.waitKey(0):
            cv2.destroyWindow(window_name) 

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 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_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 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(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 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