RoboRally/remote_control/aruco_estimator.py

import pyrealsense2 as rs
import numpy as np
import cv2
import os
import time

from shapely.geometry import LineString
from queue import Queue

import aruco

class ArucoEstimator:
    corner_marker_ids = {
        'a': 0,
        'b': 1,
        'c': 2,
        'd': 3
    }

    corner_estimates = {
        'a': {'pixel_coordinate': None, 'x': None, 'y': None, 'n_estimates': 0},
        'b': {'pixel_coordinate': None, 'x': None, 'y': None, 'n_estimates': 0},
        'c': {'pixel_coordinate': None, 'x': None, 'y': None, 'n_estimates': 0},
        'd': {'pixel_coordinate': None, 'x': None, 'y': None, 'n_estimates': 0},
    }

    def __init__(self, robot_marker_ids=None, use_realsense=True, grid_columns=8, grid_rows=8):
        self.grid_columns = grid_columns
        self.grid_rows = grid_rows

        if robot_marker_ids is None:
            robot_marker_ids = []
        self.robot_marker_ids = robot_marker_ids
        self.robot_marker_estimates = dict([(marker_id, {'t': None, 'x': None, 'y': None, 'angle': None})
                                            for marker_id in self.robot_marker_ids])

        self.draw_grid = False

        self.event_queue = Queue()

        if use_realsense:  # check if realsense camera is connected
            # Configure depth and color streams
            self.pipeline = rs.pipeline()
            config = rs.config()
            #config.enable_stream(rs.stream.color, 1920, 1080, rs.format.bgr8, 30)
            config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8, 30)

            # Start streaming
            self.pipeline.start(config)

            # disable auto exposure
            color_sensor = self.pipeline.get_active_profile().get_device().query_sensors()[1]
            color_sensor.set_option(rs.option.enable_auto_exposure, False)

            camera_intrinsics = self.pipeline.get_active_profile().get_stream(
                rs.stream.color).as_video_stream_profile().get_intrinsics()
            self.camera_matrix = np.zeros((3, 3))
            self.camera_matrix[0][0] = camera_intrinsics.fx
            self.camera_matrix[1][1] = camera_intrinsics.fy
            self.camera_matrix[0][2] = camera_intrinsics.ppx
            self.camera_matrix[1][2] = camera_intrinsics.ppy
            self.dist_coeffs = np.array(camera_intrinsics.coeffs)
            # more info: https://dev.intelrealsense.com/docs/projection-in-intel-realsense-sdk-20
        else:
            # use other camera
            self.cv_camera = cv2.VideoCapture(0)
            self.pipeline = None

        # create detector and get parameters
        self.detector = aruco.MarkerDetector()
        # self.detector.setDictionary('ARUCO_MIP_36h12')
        #self.detector.setDictionary('ARUCO_MIP_16h3')
        # self.detector.setDictionary('ARUCO')
        #self.detector.setDetectionMode(aruco.DM_NORMAL, 0.05)
        self.detector.setDetectionMode(aruco.DM_VIDEO_FAST, 0.05)

        self.detector_params = self.detector.getParameters()

        # print detector parameters
        print("detector params:")
        for val in dir(self.detector_params):
            if not val.startswith("__"):
                print("\t{} : {}".format(val, self.detector_params.__getattribute__(val)))

        self.camparam = aruco.CameraParameters()
        if use_realsense:
            self.camparam.readFromXMLFile(os.path.join(os.path.dirname(__file__), "realsense.yml"))
        else:
            self.camparam.readFromXMLFile(os.path.join(os.path.dirname(__file__), "dfk72_6mm_param2.yml"))
        print(self.camparam)

    def mouse_callback(self, event, px, py, flags, param):
        """
        This function is called for each mouse event inside the opencv window.
        If there are reference markers available we compute the real world position corresponding to the clicked
        position and put it in an event queue.
        :param event: type of event (mouse movement, left click, right click, etc.)
        :param px: x-position of event
        :param py: y-position of event
        """
        if event == cv2.EVENT_LBUTTONDOWN:
            if self.all_corners_detected():
                # inter/extrapolate from clicked point to marker position
                px1 = self.corner_estimates['a']['pixel_coordinate'][0]
                px3 = self.corner_estimates['c']['pixel_coordinate'][0]
                py1 = self.corner_estimates['a']['pixel_coordinate'][1]
                py3 = self.corner_estimates['c']['pixel_coordinate'][1]

                x1 = self.corner_estimates['a']['x']
                x3 = self.corner_estimates['c']['x']
                y1 = self.corner_estimates['a']['y']
                y3 = self.corner_estimates['c']['y']

                alpha = (px - px1) / (px3 - px1)
                beta = (py - py1) / (py3 - py1)

                print(f"alpha = {alpha}, beta = {beta}")

                target_x = x1 + alpha * (x3 - x1)
                target_y = y1 + beta * (y3 - y1)
                target = np.array([target_x, target_y, 0])

                print(f"target = ({target_x},{target_y})")
                self.event_queue.put(('click', target))
            else:
                print("not all markers have been detected!")

    def run_tracking(self, draw_markers=True, draw_marker_coordinate_system=False, invert_grayscale=False):
        """
        Run the marker tracking in a loop
        """
        cv2.namedWindow('RoboRally', cv2.WINDOW_AUTOSIZE)
        cv2.setMouseCallback('RoboRally', self.mouse_callback)

        try:
            running = True
            while running:
                if self.pipeline:
                    frames = self.pipeline.wait_for_frames()
                    color_frame = frames.get_color_frame()
                    if not color_frame:
                        continue
                    # Convert images to numpy arrays
                    color_image = np.asanyarray(color_frame.get_data())
                else:
                    # Capture frame-by-frame
                    ret, color_image = self.cv_camera.read()
                t_image = time.time()

                gray = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
                if invert_grayscale:
                    cv2.bitwise_not(gray, gray)

                # run aruco marker detection
                detected_markers = self.detector.detect(gray)

                # extract data for detected markers
                detected_marker_data = {}
                for marker in detected_markers:
                    detected_marker_data[marker.id] = {'marker_center': marker.getCenter()}
                    if marker.id >= 0:
                        if marker.id in self.corner_marker_ids.values():
                            marker.calculateExtrinsics(0.1, self.camparam)
                        else:
                            marker.calculateExtrinsics(0.07, self.camparam)
                        detected_marker_data[marker.id]['Rvec'] = marker.Rvec
                        detected_marker_data[marker.id]['Tvec'] = marker.Tvec

                    if marker.id >= 0:  # draw markers onto the image
                        if draw_markers:
                            marker.draw(color_image, np.array([255, 255, 255]), 2, True)

                        if draw_marker_coordinate_system:
                            aruco.CvDrawingUtils.draw3dAxis(color_image, self.camparam, marker.Rvec, marker.Tvec, .1)

                # store data
                for marker_id, data in detected_marker_data.items():
                    self.update_estimate(marker_id, data['marker_center'], data['Rvec'], data['Tvec'], t_image)

                # draw grid
                if self.draw_grid:
                    color_image = self.draw_grid_lines(color_image, detected_marker_data)
                color_image = self.draw_robot_pos(color_image, detected_marker_data)

                # Show images
                cv2.imshow('RoboRally', color_image)
                key = cv2.waitKey(1)

                if key > 0:
                    self.event_queue.put(('key', key))
                    if key == ord('g'):
                        self.draw_grid = not self.draw_grid
                    if key == ord('q'):
                        running = False
                    if key == ord('a'):
                        color_sensor = self.pipeline.get_active_profile().get_device().query_sensors()[1]
                        if color_sensor.get_option(rs.option.enable_auto_exposure) == 1.0:
                            color_sensor.set_option(rs.option.enable_auto_exposure, False)
                            print("auto exposure OFF")
                        else:
                            color_sensor.set_option(rs.option.enable_auto_exposure, True)
                            print("auto exposure ON")
        finally:
            cv2.destroyAllWindows()
            if self.pipeline is not None:
                # Stop streaming
                self.pipeline.stop()

    def update_estimate(self, marker_id, pixel_coord_center, rvec, tvec, t_image):
        # update the marker estimate with new data
        if marker_id in self.corner_marker_ids.values():
            # for corner markers we compute the mean of all measurements s.t. the position stabilizes over time
            # (we assume the markers do not move)

            # get corresponding corner to the detected marker
            corner = next(filter(lambda key: self.corner_marker_ids[key] == marker_id, self.corner_marker_ids.keys()))
            old_estimate_x = self.corner_estimates[corner]['x']
            old_estimate_y = self.corner_estimates[corner]['y']
            n_estimates = self.corner_estimates[corner]['n_estimates']

            x = tvec[0][0]
            y = -tvec[1][0]  # flip y coordinate
            if not any([old_estimate_x is None, old_estimate_y is None]):
                new_estimate_x = (n_estimates * old_estimate_x + x) / (n_estimates + 1)  # weighted update
                new_estimate_y = (n_estimates * old_estimate_y + y) / (n_estimates + 1)
            else:
                new_estimate_x = x  # first estimate
                new_estimate_y = y
            self.corner_estimates[corner]['t'] = t_image
            self.corner_estimates[corner]['x'] = new_estimate_x
            self.corner_estimates[corner]['y'] = new_estimate_y
            self.corner_estimates[corner]['n_estimates'] = n_estimates + 1
            self.corner_estimates[corner]['pixel_coordinate'] = pixel_coord_center

        elif marker_id in self.robot_marker_ids:
            # for robot markers we extract x and y position as well as the angle
            # here we could also implement a filter
            x = tvec[0][0]
            y = -tvec[1][0]  # flip y coordinate

            # compute angle
            rot_mat, _ = cv2.Rodrigues(rvec)
            pose_mat = cv2.hconcat((rot_mat, tvec))
            _, _, _, _, _, _, euler_angles = cv2.decomposeProjectionMatrix(pose_mat)
            angle = -euler_angles[2][0] * np.pi / 180.0

            self.robot_marker_estimates[marker_id] = {'t': t_image, 'x': x, 'y': y, 'angle': angle}

    def all_corners_detected(self):
        # checks if all corner markers have been detected at least once
        return not any([estimate['n_estimates'] == 0 for estimate in self.corner_estimates.values()])

    def all_robots_detected(self):
        # checks if all robot markers have been detected at least once
        return not any([estimate['t'] is None for estimate in self.robot_marker_estimates.values()])

    def get_pos_from_grid_point(self, x, y, orientation=None):
        """
         returns the position for the given grid point based on the current corner estimates
        :param x: x position on the grid ( 0 &le x &lt number of grid columns)
        :param y: y position on the grid ( 0 &le x &lt number of grid rows)
        :param orientation: (optional) orientation in the given grid cell (one of ^, >, v, &lt )
        :return: numpy array with corresponding real world x- and y-position
                    if orientation was specified the array also contains the matching angle for the orientation
        """
        assert 0 <= x < self.grid_columns
        assert 0 <= y < self.grid_rows
        assert self.all_corners_detected()

        # compute column line
        a = np.array([self.corner_estimates['a']['x'], self.corner_estimates['a']['y']])
        b = np.array([self.corner_estimates['b']['x'], self.corner_estimates['b']['y']])
        c = np.array([self.corner_estimates['c']['x'], self.corner_estimates['c']['y']])
        d = np.array([self.corner_estimates['d']['x'], self.corner_estimates['d']['y']])
        x_frac = (x + 0.5) / self.grid_columns
        y_frac = (y + 0.5) / self.grid_rows

        vab = b - a
        vdc = c - d
        column_line_top = a + x_frac * vab
        column_line_bottom = d + x_frac * vdc

        vad = d - a
        vbc = c - b
        row_line_top = a + y_frac * vad
        row_line_bottom = b + y_frac * vbc

        column_line = LineString([column_line_top, column_line_bottom])
        row_line = LineString([row_line_top, row_line_bottom])

        int_pt = column_line.intersection(row_line)
        point_of_intersection = np.array([int_pt.x, int_pt.y])

        if orientation is not None:
            # compute angle corresponding to the orientation w.r.t. the grid
            # TODO: test this code
            angle_ab = np.arctan2(vab[1], vab[0])
            angle_dc = np.arctan2(vdc[1], vdc[0])

            angle_ad = np.arctan2(vad[1], vad[0])
            angle_bc = np.arctan2(vbc[1], vbc[0])

            angle = 0.0
            if orientation == '>':
                angle = y_frac * angle_ab + (1 - y_frac) * angle_dc
            elif orientation == '<':
                angle = y_frac * angle_ab + (1 - y_frac) * angle_dc + np.pi
            elif orientation == 'v':
                angle = x_frac * angle_ad + (1 - x_frac) * angle_bc
            elif orientation == '^':
                angle = x_frac * angle_ad + (1 - x_frac) * angle_bc + np.pi

            return np.array((point_of_intersection[0], point_of_intersection[1], angle))
        else:
            return point_of_intersection

    def get_grid_point_from_pos(self):
        # TODO return the nearest grid point for the given position estimate
        pass

    def print_corner_estimates(self):
        for key, value in self.corner_estimates.items():
            if value['n_estimates'] != 0:
                print(f"corner marker {key} at pos ({value['x']},{value['y']})")

    def draw_corner_line(self, frame, corner_1, corner_2):
        # draws a line between the given markers onto the given frame
        corner_1_center = self.corner_estimates[corner_1]['pixel_coordinate']
        corner_2_center = self.corner_estimates[corner_2]['pixel_coordinate']
        if corner_1_center is not None and corner_2_center is not None:
            frame = cv2.line(frame, tuple(corner_1_center), tuple(corner_2_center), color=(0, 0, 255), thickness=2)
        return frame

    def draw_grid_lines(self, frame, detected_marker_data):
        # draws a grid onto the given frame
        frame = self.draw_corner_line(frame, 'a', 'b')
        frame = self.draw_corner_line(frame, 'b', 'c')
        frame = self.draw_corner_line(frame, 'c', 'd')
        frame = self.draw_corner_line(frame, 'd', 'a')

        if not any([estimate['pixel_coordinate'] is None for estimate in self.corner_estimates.values()]):
            # compute outlines of board
            a = self.corner_estimates['a']['pixel_coordinate']
            b = self.corner_estimates['b']['pixel_coordinate']
            c = self.corner_estimates['c']['pixel_coordinate']
            d = self.corner_estimates['d']['pixel_coordinate']

            # draw vertical lines
            vab = b - a
            vdc = c - d
            for x in range(1, self.grid_columns):
                column_line_top = a + x / self.grid_columns * vab
                column_line_bottom = d + x / self.grid_columns * vdc
                frame = cv2.line(frame, tuple(column_line_top), tuple(column_line_bottom), color=(0, 255, 0),
                                 thickness=1)

            # draw horizontal lines
            vad = d - a
            vbc = c - b
            for y in range(1, self.grid_rows):
                row_line_top = a + y / self.grid_rows * vad
                row_line_bottom = b + y / self.grid_rows * vbc
                frame = cv2.line(frame, tuple(row_line_top), tuple(row_line_bottom), color=(0, 255, 0),
                                 thickness=1)

        return frame

    def get_robot_state_estimate(self, marker_id):
        if marker_id in self.robot_marker_estimates:
            if self.robot_marker_estimates[marker_id]['t'] is not None:
                return np.array([self.robot_marker_estimates[marker_id]['x'],
                                 self.robot_marker_estimates[marker_id]['y'],
                                 self.robot_marker_estimates[marker_id]['angle']])
            else:
                print(f"error: no estimate available for robot {marker_id}")
                return None
        else:
            print(f"error: invalid robot id {marker_id}")
            return None

    def draw_robot_pos(self, frame, detected_marker_data):
        # draws information about the robot positions onto the given frame
        robot_corners_pixel_coords = {}
        for marker_id, estimate in self.robot_marker_estimates.items():
            if marker_id in detected_marker_data.keys():
                robot_corners_pixel_coords[marker_id] = tuple(detected_marker_data[marker_id]['marker_center'])

        for marker_id, coord in robot_corners_pixel_coords.items():
            x = self.robot_marker_estimates[marker_id]['x']
            y = self.robot_marker_estimates[marker_id]['y']
            angle = self.robot_marker_estimates[marker_id]['angle']
            #frame = cv2.putText(frame, "pos = ({:5.3f}, {:5.3f}), ang = {:5.3f}".format(x, y, angle), coord,
            #                    cv2.FONT_HERSHEY_SIMPLEX, 0.50, (0, 255, 0))
            frame = cv2.putText(frame, f"{marker_id}", coord, cv2.FONT_HERSHEY_PLAIN, 2, (0, 255, 255), thickness=4)
        return frame


if __name__ == "__main__":
    estimator = ArucoEstimator(use_realsense=True, robot_marker_ids=[11, 12, 13, 14])
    estimator.run_tracking(draw_markers=True, invert_grayscale=True)