forked from Telos4/RoboRally
360 lines
16 KiB
Python
360 lines
16 KiB
Python
import pyrealsense2 as rs
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import numpy as np
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import cv2
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import os
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from shapely.geometry import LineString
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from queue import Queue
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import aruco
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class ArucoEstimator(socketserver.BaseRequestHandler):
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corner_marker_ids = {
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'a': 15,
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'b': 1,
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'c': 2,
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'd': 3
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}
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angles = []
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corner_estimates = {
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'a': {'pixel_coordinate': None, 'real_world_estimate': None, 'n_estimates': 0 },
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'b': {'pixel_coordinate': None, 'real_world_estimate': None, 'n_estimates': 0 },
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'c': {'pixel_coordinate': None, 'real_world_estimate': None, 'n_estimates': 0 },
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'd': {'pixel_coordinate': None, 'real_world_estimate': None, 'n_estimates': 0 },
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}
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def __init__(self, robot_marker_ids=None, use_realsense=True, grid_columns=8, grid_rows=8):
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self.grid_columns = grid_columns
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self.grid_rows = grid_rows
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if robot_marker_ids is None:
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robot_marker_ids = []
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self.robot_marker_ids = robot_marker_ids
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self.robot_marker_estimates = dict([(id, None) for id in self.robot_marker_ids])
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self.event_queue = Queue()
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if use_realsense: # check if realsense camera is connected
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# Configure depth and color streams
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self.pipeline = rs.pipeline()
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config = rs.config()
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# config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
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#config.enable_stream(rs.stream.color, 1920, 1080, rs.format.bgr8, 30)
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config.enable_stream(rs.stream.color, 1280, 720, rs.format.bgr8, 30)
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# config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
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# Start streaming
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self.pipeline.start(config)
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camera_intrinsics = self.pipeline.get_active_profile().get_stream(
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rs.stream.color).as_video_stream_profile().get_intrinsics()
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self.camera_matrix = np.zeros((3, 3))
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self.camera_matrix[0][0] = camera_intrinsics.fx
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self.camera_matrix[1][1] = camera_intrinsics.fy
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self.camera_matrix[0][2] = camera_intrinsics.ppx
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self.camera_matrix[1][2] = camera_intrinsics.ppy
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self.dist_coeffs = np.array(camera_intrinsics.coeffs)
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# more info: https://dev.intelrealsense.com/docs/projection-in-intel-realsense-sdk-20
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else:
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# use other camera
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self.cv_camera = cv2.VideoCapture(0)
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self.pipeline = None
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# create detector and get parameters
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self.detector = aruco.MarkerDetector()
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#self.detector.setDictionary('ARUCO_MIP_36h12')
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#self.detector.setDictionary('ARUCO_MIP_16h3')
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#self.detector.setDictionary('ARUCO')
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#self.detector.setDetectionMode(aruco.DM_NORMAL, 0.05)
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#self.detector.setDetectionMode(aruco.DM_VIDEO_FAST, 0.05)
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self.detector_params = self.detector.getParameters()
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# print detector parameters
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print("detector params:")
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for val in dir(self.detector_params):
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if not val.startswith("__"):
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print("\t{} : {}".format(val, self.detector_params.__getattribute__(val)))
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self.camparam = aruco.CameraParameters()
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self.camparam.readFromXMLFile(os.path.join(os.path.dirname(__file__), "dfk72_6mm_param2.yml"))
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def mouse_callback(self, event, px, py, flags, param):
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if event == 1:
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if self.all_corners_detected():
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# inter/extrapolate from clicked point to marker position
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px1 = self.corner_estimates['a']['pixel_coordinate'][0]
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px3 = self.corner_estimates['c']['pixel_coordinate'][0]
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py1 = self.corner_estimates['a']['pixel_coordinate'][1]
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py3 = self.corner_estimates['c']['pixel_coordinate'][1]
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x1 = self.corner_estimates['a']['real_world_estimate'][0]
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x3 = self.corner_estimates['c']['real_world_estimate'][0]
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y1 = self.corner_estimates['a']['real_world_estimate'][1]
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y3 = self.corner_estimates['c']['real_world_estimate'][1]
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alpha = (px - px1) / (px3 - px1)
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beta = (py - py1) / (py3 - py1)
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print(f"alpha = {alpha}, beta = {beta}")
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target_x = x1 + alpha * (x3 - x1)
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target_y = y1 + beta * (y3 - y1)
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target = np.array([target_x, target_y, 0])
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print(f"target = ({target_x},{target_y})")
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self.event_queue.put(('click', target))
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else:
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print("not all markers have been detected!")
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def run_tracking(self):
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cv2.namedWindow('RoboRally', cv2.WINDOW_AUTOSIZE)
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cv2.setMouseCallback('RoboRally', self.mouse_callback)
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try:
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running = True
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while running:
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if self.pipeline:
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frames = self.pipeline.wait_for_frames()
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color_frame = frames.get_color_frame()
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if not color_frame:
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continue
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# Convert images to numpy arrays
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color_image = np.asanyarray(color_frame.get_data())
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else:
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# Capture frame-by-frame
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ret, color_image = self.cv_camera.read()
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gray = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
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# run aruco marker detection
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detected_markers = self.detector.detect(gray)
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# extract data for detected markers
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detected_marker_data = {}
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for marker in detected_markers:
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detected_marker_data[marker.id] = {'center': marker.getCenter()}
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if marker.id > 0:
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if marker.id in self.corner_marker_ids.values():
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marker.calculateExtrinsics(0.1, self.camparam)
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else:
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marker.calculateExtrinsics(0.07, self.camparam)
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detected_marker_data[marker.id]['Rvec'] = marker.Rvec
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detected_marker_data[marker.id]['Tvec'] = marker.Tvec
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if marker.id > 0: # draw markers onto the image
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marker.draw(color_image, np.array([255, 255, 255]), 2, True)
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aruco.CvDrawingUtils.draw3dAxis(color_image, self.camparam, marker.Rvec, marker.Tvec, .1)
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# store data
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for id, data in detected_marker_data.items():
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self.update_estimate(id, data['center'], data['Rvec'], data['Tvec'])
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# draw grid
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color_image = self.draw_grid_lines(color_image, detected_marker_data)
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color_image = self.draw_robot_pos(color_image, detected_marker_data)
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# Show images
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cv2.imshow('RoboRally', color_image)
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key = cv2.waitKey(1)
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if key > 0:
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self.event_queue.put(('key', key))
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if key == ord('q'):
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running = False
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finally:
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cv2.destroyAllWindows()
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if self.pipeline is not None:
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# Stop streaming
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self.pipeline.stop()
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def update_estimate(self, id, pixel_coord, rvec, tvec):
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# update the marker estimate with new data
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if id in self.corner_marker_ids.values():
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# for corner markers we compute the mean of all measurements s.t. the position stabilizes over time
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# (we assume the markers do not move)
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corner = next(filter(lambda key: self.corner_marker_ids[key] == id, self.corner_marker_ids.keys())) # get corresponding corner to the detected marker
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old_estimate = self.corner_estimates[corner]['real_world_estimate']
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n_estimates = self.corner_estimates[corner]['n_estimates']
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x = tvec[0][0]
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y = -tvec[1][0] # flip y coordinate
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tvec_proj = np.array([x, y])
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if old_estimate is not None:
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new_estimate = (n_estimates * old_estimate + tvec_proj) / (n_estimates + 1) # weighted update
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else:
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new_estimate = tvec_proj # first estimate
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self.corner_estimates[corner]['real_world_estimate'] = new_estimate
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self.corner_estimates[corner]['n_estimates'] = n_estimates + 1
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self.corner_estimates[corner]['pixel_coordinate'] = pixel_coord
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elif id in self.robot_marker_ids:
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# for robot markers we extract x and y position as well as the angle
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# here we could also implement a filter
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x = tvec[0][0]
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y = -tvec[1][0] # flip y coordinate
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# compute angle
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rot_mat, _ = cv2.Rodrigues(rvec)
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pose_mat = cv2.hconcat((rot_mat, tvec))
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_, _, _, _, _, _, euler_angles = cv2.decomposeProjectionMatrix(pose_mat)
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angle = -euler_angles[2][0] * np.pi / 180.0
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self.angles.append(angle)
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self.robot_marker_estimates[id] = (x, y, angle)
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def all_corners_detected(self):
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# checks if all corner markers have been detected at least once
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return not any([estimate['n_estimates'] == 0 for estimate in self.corner_estimates.values()])
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def all_robots_detected(self):
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# checks if all robot markers have been detected at least once
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return not any([estimate is None for estimate in self.robot_marker_estimates.values()])
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def get_pos_from_grid_point(self, x, y, orientation=None):
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"""
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returns the position for the given grid point based on the current corner estimates
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:param x: x position on the grid ( 0 &le x < number of grid columns)
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:param y: y position on the grid ( 0 &le x < number of grid rows)
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:param orientation: (optional) orientation in the given grid cell (one of ^, >, v, < )
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:return: numpy array with corresponding real world x- and y-position
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if orientation was specified the array also contains the matching angle for the orientation
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"""
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assert x >= 0 and x < self.grid_columns
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assert y >= 0 and y < self.grid_rows
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assert self.all_corners_detected()
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# compute column line
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a = self.corner_estimates['a']['real_world_estimate']
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b = self.corner_estimates['b']['real_world_estimate']
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c = self.corner_estimates['c']['real_world_estimate']
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d = self.corner_estimates['d']['real_world_estimate']
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x_frac = (x + 0.5) / self.grid_columns
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y_frac = (y + 0.5) / self.grid_rows
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vab = b - a
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vdc = c - d
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column_line_top = a + x_frac * vab
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column_line_bottom = d + x_frac * vdc
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vad = d - a
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vbc = c - b
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row_line_top = a + y_frac * vad
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row_line_bottom = b + y_frac * vbc
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column_line = LineString([column_line_top, column_line_bottom])
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row_line = LineString([row_line_top, row_line_bottom])
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int_pt = column_line.intersection(row_line)
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point_of_intersection = np.array([int_pt.x, int_pt.y])
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if orientation is not None:
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# compute angle corresponding to the orientation w.r.t. the grid
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# TODO: test this code
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angle_ab = np.arctan2(vab[1], vab[0])
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angle_dc = np.arctan2(vdc[1], vdc[0])
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angle_ad = np.arctan2(vad[1], vad[0])
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angle_bc = np.arctan2(vbc[1], vbc[0])
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angle = 0.0
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if orientation == '>':
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angle = y_frac * angle_ab + (1 - y_frac) * angle_dc
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elif orientation == '<':
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angle = y_frac * angle_ab + (1 - y_frac) * angle_dc + np.pi
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elif orientation == 'v':
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angle = x_frac * angle_ad + (1 - x_frac) * angle_bc
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elif orientation == '^':
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angle = x_frac * angle_ad + (1 - x_frac) * angle_bc + np.pi
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return np.array((point_of_intersection[0], point_of_intersection[1], angle))
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else:
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return point_of_intersection
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def get_grid_point_from_pos(self):
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# return the nearest grid point for the given position estimate
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pass
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def print_corner_estimates(self):
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for key, value in self.corner_estimates.items():
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if value['n_estimates'] != 0:
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print("corner marker {} at pos {}".format(key, value['real_world_estimate']))
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print()
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def draw_corner_line(self, frame, corner_1, corner_2, corner_coords_dict):
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# draws a line between the given markers onto the given frame
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if corner_1 in corner_coords_dict and corner_2 in corner_coords_dict:
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frame = cv2.line(frame, corner_coords_dict[corner_1], corner_coords_dict[corner_2], color=(0, 0, 255),
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thickness=2)
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return frame
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def draw_grid_lines(self, frame, detected_marker_data):
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# draws a grid onto the given frame
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board_corners_pixel_coords = {}
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for corner, id in self.corner_marker_ids.items():
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if id in detected_marker_data.keys():
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board_corners_pixel_coords[corner] = tuple(detected_marker_data[id]['center'])
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frame = self.draw_corner_line(frame, 'a', 'b', board_corners_pixel_coords)
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frame = self.draw_corner_line(frame, 'b', 'c', board_corners_pixel_coords)
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frame = self.draw_corner_line(frame, 'c', 'd', board_corners_pixel_coords)
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frame = self.draw_corner_line(frame, 'd', 'a', board_corners_pixel_coords)
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if set(board_corners_pixel_coords.keys()) == set(self.corner_marker_ids.keys()): # all markers have been detected
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# compute column line
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a = np.array(board_corners_pixel_coords['a'])
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b = np.array(board_corners_pixel_coords['b'])
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c = np.array(board_corners_pixel_coords['c'])
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d = np.array(board_corners_pixel_coords['d'])
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vab = b - a
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vdc = c - d
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for x in range(1,self.grid_columns):
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column_line_top = a + x / self.grid_columns * vab
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column_line_bottom = d + x / self.grid_columns * vdc
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frame = cv2.line(frame, tuple(column_line_top), tuple(column_line_bottom), color=(0, 255, 0),
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thickness=1)
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vad = d - a
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vbc = c - b
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for y in range(1, self.grid_rows):
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row_line_top = a + y / self.grid_rows * vad
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row_line_bottom = b + y / self.grid_rows * vbc
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frame = cv2.line(frame, tuple(row_line_top), tuple(row_line_bottom), color=(0, 255, 0),
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thickness=1)
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return frame
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def get_robot_state_estimate(self, id):
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if id in self.robot_marker_estimates:
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if self.robot_marker_estimates[id] is not None:
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return np.array(self.robot_marker_estimates[id])
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else:
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print(f"error: no estimate available for robot {id}")
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return None
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else:
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print(f"error: invalid robot id {id}")
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return None
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def draw_robot_pos(self, frame, detected_marker_data):
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# draws information about the robot positions onto the given frame
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robot_corners_pixel_coords = {}
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for id, estimate in self.robot_marker_estimates.items():
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if id in detected_marker_data.keys():
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robot_corners_pixel_coords[id] = tuple(detected_marker_data[id]['center'])
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for id, coord in robot_corners_pixel_coords.items():
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x = self.robot_marker_estimates[id][0]
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y = self.robot_marker_estimates[id][1]
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angle = self.robot_marker_estimates[id][2]
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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))
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return frame
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if __name__ == "__main__":
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estimator = ArucoEstimator(use_realsense=False)
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estimator.run_tracking()
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