RoboRally/remote_control/opencv_viewer_example.py

import pyrealsense2 as rs
import numpy as np
import cv2
from cv2 import aruco
from shapely.geometry import LineString

DS5_product_ids = ["0AD1", "0AD2", "0AD3", "0AD4", "0AD5", "0AF6", "0AFE", "0AFF", "0B00", "0B01", "0B03", "0B07","0B3A"]


def find_device_that_supports_advanced_mode():
    ctx = rs.context()
    ds5_dev = rs.device()
    devices = ctx.query_devices()
    for dev in devices:
        if dev.supports(rs.camera_info.product_id) and str(dev.get_info(rs.camera_info.product_id)) in DS5_product_ids:
            if dev.supports(rs.camera_info.name):
                print("Found device that supports advanced mode:", dev.get_info(rs.camera_info.name))
            return dev
    raise Exception("No device that supports advanced mode was found")


class ArucoEstimator:
    grid_columns = 10
    grid_rows = 8
    corner_marker_ids = {
        'a': 0,
        'b': 1,
        'c': 2,
        'd': 3
    }

    angles = []

    corner_estimates = {
        'a': (None, 0),     # (estimate, n_estimates)
        'b': (None, 0),
        'c': (None, 0),
        'd': (None, 0)
    }

    def __init__(self, robot_marker_ids=[]):
        self.robot_marker_ids = robot_marker_ids
        self.robot_marker_estimates = dict([(id, None) for id in self.robot_marker_ids])

        if False:   # check if realsense camera is connected
            # Configure depth and color streams
            self.pipeline = rs.pipeline()
            config = rs.config()
            # config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
            # config.enable_stream(rs.stream.color, 1920, 1080, rs.format.bgr8, 30)
            config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8, 30)
            # config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)

            # Start streaming
            self.pipeline.start(config)

            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

    def run_tracking(self):
        try:
            while True:
                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()

                gray = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)

                # aruco_dict = aruco.Dictionary_get(aruco.DICT_5X5_250)
                aruco_dict = aruco.Dictionary_get(aruco.DICT_ARUCO_ORIGINAL)
                parameters = aruco.DetectorParameters_create()
                corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict, parameters=parameters)
                frame = aruco.drawDetectedMarkers(color_image.copy(), corners, ids)

                if False:#ids is not None:
                    for id, c in zip(ids, corners):
                        res = aruco.estimatePoseSingleMarkers(c, 0.10, self.camera_matrix, self.dist_coeffs)
                        rvecs = res[0][0][0]
                        tvecs = res[1][0][0]

                        self.update_estimate(id, rvecs, tvecs)

                    frame = self.draw_grid_lines(frame, corners, ids)
                    frame = self.draw_robot_pos(frame, corners, ids)
                else:
                    # pretent we detected some markers
                    pass
                    self.update_estimate(0, rvec=np.array([0,0,0]), tvec=np.array([-1, 1, 0]))
                    self.update_estimate(1, rvec=np.array([0, 0, 0]), tvec=np.array([1, 1.5, 0]))
                    self.update_estimate(2, rvec=np.array([0, 0, 0]), tvec=np.array([1, -1.5, 0]))
                    self.update_estimate(3, rvec=np.array([0, 0, 0]), tvec=np.array([-1, -1, 0]))
                    #import random
                    #self.update_estimate(12, rvec=np.array([0.0, 0.0, 0.0]), tvec=np.array([-1.0 + random.random() * 2, -1.0 + random.random() * 2, 0]))
                    self.update_estimate(12, rvec=np.array([0.0, 0.0, 0.0]),
                                         tvec=np.array([1.2, 0.42, 0]))

                # Show images
                cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)
                cv2.imshow('RealSense', frame)
                cv2.waitKey(1)

        finally:
            if self.pipeline is not None:
                # Stop streaming
                self.pipeline.stop()

    def update_estimate(self, id, rvec, tvec):
        # update the marker estimate with new data
        if 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)
            corner = next(filter(lambda key: self.corner_marker_ids[key] == id, self.corner_marker_ids.keys()))  # get corresponding corner to the detected marker
            old_estimate = self.corner_estimates[corner][0]
            n_estimates = self.corner_estimates[corner][1]
            tvec_proj = tvec[0:2] # projection to get rid of z coordinate
            tvec_proj = np.array((tvec_proj[0], -tvec_proj[1])) # flip y coordinate
            if old_estimate is not None:
                new_estimate = (n_estimates * old_estimate + tvec_proj) / (n_estimates + 1)  # weighted update
            else:
                new_estimate = tvec_proj # first estimate
            self.corner_estimates[corner] = (new_estimate, n_estimates + 1)
        elif 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]
            y = -tvec[1]  # 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.angles.append(angle)
            self.robot_marker_estimates[id] = (x, y, angle)

    def all_corners_detected(self):
        # checks if all corner markers have been detected at least once
        return not any([estimate[0] is None for estimate in self.corner_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 x >= 0 and x < self.grid_columns
        assert y >= 0 and y < self.grid_rows
        assert self.all_corners_detected()

        # compute column line
        a = self.corner_estimates['a'][0]
        b = self.corner_estimates['b'][0]
        c = self.corner_estimates['c'][0]
        d = self.corner_estimates['d'][0]
        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)
            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)

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

    def get_grid_point_from_pos(self):
        # 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[0] is not None:
                print("corner marker {} at pos {}".format(key, value[0]))
        print()

    def draw_corner_line(self, frame, corner_1, corner_2, corner_coords_dict):
        # draws a line between the given markers onto the given frame
        if corner_1 in corner_coords_dict and corner_2 in corner_coords_dict:
            frame = cv2.line(frame, corner_coords_dict[corner_1], corner_coords_dict[corner_2], color=(0, 0, 255),
                         thickness=2)
        return frame

    def draw_grid_lines(self, frame, corners, ids):
        # draws a grid onto the given frame
        board_corners_pixel_coords = {}
        for corner, id in self.corner_marker_ids.items():
            try:
                ind, _ = np.where(ids == id)   # find index
                ind = ind[0]
                board_corners_pixel_coords[corner] = tuple(np.mean(corners[ind][0], axis=0))
            except IndexError:
                pass

        frame = self.draw_corner_line(frame, 'a', 'b', board_corners_pixel_coords)
        frame = self.draw_corner_line(frame, 'b', 'c', board_corners_pixel_coords)
        frame = self.draw_corner_line(frame, 'c', 'd', board_corners_pixel_coords)
        frame = self.draw_corner_line(frame, 'd', 'a', board_corners_pixel_coords)

        if set(board_corners_pixel_coords.keys()) == set(self.corner_marker_ids.keys()): # all markers have been detected
            # compute column line
            a = np.array(board_corners_pixel_coords['a'])
            b = np.array(board_corners_pixel_coords['b'])
            c = np.array(board_corners_pixel_coords['c'])
            d = np.array(board_corners_pixel_coords['d'])

            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=(255, 0, 0),
                         thickness=1)

            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=(255, 0, 0),
                             thickness=1)

        return frame

    def get_robot_state_estimate(self, id):
        if id in self.robot_marker_estimates:
            if self.robot_marker_estimates[id] is not None:
                return self.robot_marker_estimates[id]
            else:
                print(f"error: no estimate available for robot {id}")
        else:
            print(f"error: invalid robot id {id}")

    def draw_robot_pos(self, frame, corners, ids):
        # draws information about the robot positions onto the given frame
        robot_corners_pixel_coords = {}
        for id, estimate in self.robot_marker_estimates.items():
            try:
                ind, _ = np.where(ids == id)   # find index
                ind = ind[0]
                robot_corners_pixel_coords[id] = tuple(np.mean(corners[ind][0], axis=0))
            except IndexError:
                pass

        for id, coord in robot_corners_pixel_coords.items():
            x = self.robot_marker_estimates[id][0]
            y = self.robot_marker_estimates[id][1]
            angle = self.robot_marker_estimates[id][2]
            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))
        return frame


if __name__ == "__main__":
    estimator = ArucoEstimator()