568 lines
26 KiB
Python
568 lines
26 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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import time
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import math
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from pyqtgraph.Qt import QtCore, QtGui
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from pyqtgraph.parametertree import Parameter, ParameterTree, parameterTypes
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import pyqtgraph as pg
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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 FPSCounter:
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def __init__(self):
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self.fps_start_time = time.time()
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self.fps_counter = 0
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self.fps_display_rate = 1.0
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def get_fps(self):
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self.fps_counter += 1
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delta_t = time.time() - self.fps_start_time
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if delta_t > self.fps_display_rate:
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self.fps_counter = 0
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self.fps_start_time = time.time()
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return self.fps_counter / delta_t
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## test add/remove
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## this group includes a menu allowing the user to add new parameters into its child list
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class RobotMarkerGroup(parameterTypes.GroupParameter):
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def __init__(self, **opts):
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opts['type'] = 'group'
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opts['addText'] = "Add Marker"
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opts['addList'] = ['Robot']
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parameterTypes.GroupParameter.__init__(self, **opts)
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def addNew(self, typ):
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current_robots = self.children()
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current_indices = [int(r.name().split(' ')[1]) for r in current_robots]
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new_index = len(current_indices) + 1
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val = 0
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self.addChild(dict(name=f"Robot {new_index}", type='int', value=val, removable=False,
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renamable=True))
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class CornerMarkerGroup(parameterTypes.GroupParameter):
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def __init__(self, **opts):
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opts['type'] = 'group'
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opts['addText'] = "Add Marker"
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opts['addList'] = ['Corner']
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parameterTypes.GroupParameter.__init__(self, **opts)
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def addNew(self, typ):
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current_corners = self.children()
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current_chars = [str(r.name().split(' ')[1]) for r in current_corners]
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new_char = chr(ord(current_chars[-1]) + 1)
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val = 0
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self.addChild(dict(name=f"Corner {new_char}", type='int', value=val, removable=False))
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class ArucoEstimator:
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def __init__(self, robot_marker_ids=None, use_realsense=True, grid_columns=7, grid_rows=4):
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self.app = QtGui.QApplication([])
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## Create window with GraphicsView widget
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self.win = QtGui.QWidget()
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self.layout = QtGui.QGridLayout()
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self.win.setLayout(self.layout)
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self.win.keyPressEvent = self.handleKeyPressEvent
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self.win.setWindowTitle('ArucoEstimator')
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self.plotwidget = pg.PlotWidget()
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self.layout.addWidget(self.plotwidget)
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## lock the aspect ratio so pixels are always square
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self.plotwidget.setAspectLocked(True)
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self.plotwidget.getPlotItem().getAxis('left').hide()
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self.plotwidget.getPlotItem().getAxis('bottom').hide()
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## Create image item
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self.img = pg.ImageItem(border='w')
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self.img.setLevels([[0, 255], [0, 255], [0, 255]])
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self.img.mouseClickEvent = self.handleMouseEvent
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self.plotwidget.addItem(self.img)
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# fps display
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self.fps_counter = FPSCounter()
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self.fps_overlay = pg.TextItem('fps = 0', color=(255, 255, 0), anchor=(0,1))
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self.plotwidget.addItem(self.fps_overlay)
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self.invert_grayscale = False
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self.draw_grid = True
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self.draw_markers = True
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self.draw_marker_coordinate_system = False
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self.corner_marker_size = 0.075
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self.corner_marker_ids = {
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'a': 0,
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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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self.corner_estimates = {
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'a': {'pixel_coordinate': None, 'x': None, 'y': None, 'n_estimates': 0},
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'b': {'pixel_coordinate': None, 'x': None, 'y': None, 'n_estimates': 0},
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'c': {'pixel_coordinate': None, 'x': None, 'y': None, 'n_estimates': 0},
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'd': {'pixel_coordinate': None, 'x': None, 'y': None, 'n_estimates': 0},
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}
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self.grid_columns = grid_columns
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self.grid_rows = grid_rows
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self.robot_marker_size = 0.07
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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([(marker_id, {'t': None, 'x': None, 'y': None, 'angle': None})
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for marker_id in self.robot_marker_ids])
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robot_marker_group = [{'name': f'Robot {ind}', 'type': 'int', 'value': marker_id} for ind, marker_id in
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enumerate(self.robot_marker_ids, 1)]
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corner_marker_group = [{'name': f'Corner {letter}', 'type': 'int', 'value': marker_id} for letter, marker_id in
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self.corner_marker_ids.items()]
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self.threshold = 10.0
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# parameter
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params_spec = [
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{'name': 'Corner marker size', 'type': 'float', 'value': self.corner_marker_size, 'siPrefix': True,
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'suffix': 'm', 'limits': (0.01, 1), 'step': 0.001},
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{'name': 'Robot marker size', 'type': 'float', 'value': self.robot_marker_size, 'siPrefix': True,
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'suffix': 'm', 'limits': (0.01, 1), 'step': 0.001},
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{'name': 'Draw markers', 'type': 'bool', 'value': self.draw_markers},
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{'name': 'Draw marker coordinate system', 'type': 'bool', 'value': self.draw_marker_coordinate_system},
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{'name': 'Threshold', 'type': 'float', 'value': self.threshold},
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{'name': 'Invert grayscale', 'type': 'bool', 'value': self.invert_grayscale, 'tip': "Invert grayscale image before marker detection"},
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{'name': 'Show FPS', 'type': 'bool', 'value': True, 'tip': "Display frames per second counter"},
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{'name': 'Draw grid', 'type': 'bool', 'value': self.draw_grid, 'tip': "Draw grid spanned by the markers 0 - 3"},
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{'name': 'Grid columns', 'type': 'int', 'value': self.grid_columns, 'tip': "Number of columns for the grid"},
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{'name': 'Grid rows', 'type': 'int', 'value': self.grid_rows, 'tip': "Number of rows for the grid"},
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{'name': 'Display mode', 'type': 'list', 'values': ['color', 'grayscale'], 'value': 'color', 'tip': "Display mode for the video"},
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{'name': 'Autoexposure', 'type': 'bool', 'value': True},
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{'name': 'Controlled robot', 'type': 'list', 'values': self.robot_marker_ids, 'tip': 'Robot to control'},
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RobotMarkerGroup(name="Robot markers", children=robot_marker_group),
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CornerMarkerGroup(name="Corner markers", children=corner_marker_group),
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]
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self.params = Parameter.create(name='params', type='group', children=params_spec)
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self.params.param('Invert grayscale').sigValueChanged.connect(lambda _, v: self.__setattr__('invert_grayscale', v))
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self.params.param('Threshold').sigValueChanged.connect(lambda _, v: self.__setattr__('threshold', v))
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self.params.param('Draw markers').sigValueChanged.connect(lambda _, v: self.__setattr__('draw_markers', v))
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self.params.param('Draw marker coordinate system').sigValueChanged.connect(lambda _, v: self.__setattr__('draw_marker_coordinate_system', v))
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self.params.param('Draw grid').sigValueChanged.connect(lambda _, v: self.__setattr__('draw_grid', v))
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self.params.param('Grid columns').sigValueChanged.connect(lambda _, v: self.__setattr__('grid_columns', v))
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self.params.param('Grid rows').sigValueChanged.connect(lambda _, v: self.__setattr__('grid_rows', v))
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self.params.param('Corner marker size').sigValueChanged.connect(lambda _, v: self.__setattr__('corner_marker_size', v))
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self.params.param('Robot marker size').sigValueChanged.connect(lambda _, v: self.__setattr__('robot_marker_size', v))
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self.params.param('Show FPS').sigValueChanged.connect(lambda _, v: self.fps_overlay.show() if v else self.fps_overlay.hide())
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self.params.param('Autoexposure').sigValueChanged.connect(lambda _, v: self.set_autoexposure(v))
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self.params.param('Controlled robot').sigValueChanged.connect(lambda _, v: self.event_queue.put(('controlled_robot',
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{'robot_id': v})))
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self.paramtree = ParameterTree()
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self.paramtree.setParameters(self.params, showTop=False)
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self.layout.addWidget(self.paramtree)
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self.win.show() ## show widget alone in its own window
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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.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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# Start streaming
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self.pipeline.start(config)
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# enable auto exposure
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self.set_autoexposure(self.params['Autoexposure'])
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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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# print detector parameters
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# print("detector params:")
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# print(self.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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if use_realsense:
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self.camparam.readFromXMLFile(os.path.join(os.path.dirname(__file__), "realsense.yml"))
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else:
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self.camparam.readFromXMLFile(os.path.join(os.path.dirname(__file__), "dfk72_6mm_param2.yml"))
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self.drag_line_end = None
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self.drag_line_start = None
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self.previous_click = None
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def set_autoexposure(self, v):
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if self.pipeline is not None:
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color_sensor = self.pipeline.get_active_profile().get_device().query_sensors()[1]
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color_sensor.set_option(rs.option.enable_auto_exposure, v)
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print(color_sensor.get_option(rs.option.enable_auto_exposure))
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def compute_clicked_position(self, px, py):
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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']['x']
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x3 = self.corner_estimates['c']['x']
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y1 = self.corner_estimates['a']['y']
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y3 = self.corner_estimates['c']['y']
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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])
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else:
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print("not all markers have been detected!")
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target = np.array([px, -py])
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return target
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def handleMouseEvent(self, event):
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# get click position as distance to top-left corner of the image
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px = event.pos().x()
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py = self.img.height() - event.pos().y()
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print(f"px = {px}, py = {py}")
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if event.button() == QtCore.Qt.MouseButton.LeftButton:
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# self.drag_line_start = (px, py)
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# elif event == cv2.EVENT_LBUTTONUP:
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self.drag_line_end = (px, py)
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self.drag_line_start = (px, py)
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target_pos = self.compute_clicked_position(self.drag_line_start[0], self.drag_line_start[1])
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if self.drag_line_start != self.drag_line_end:
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# compute target angle for clicked position
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facing_pos = self.compute_clicked_position(px, py)
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v = facing_pos - target_pos
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target_angle = math.atan2(v[1], v[0])
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else:
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# determine angle from previously clicked pos (= self.drag_line_end)
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if self.previous_click is not None:
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previous_pos = self.compute_clicked_position(self.previous_click[0], self.previous_click[1])
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v = target_pos - previous_pos
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target_angle = math.atan2(v[1], v[0])
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else:
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target_angle = 0.0
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target = np.array([target_pos[0], target_pos[1], target_angle])
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print(target)
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self.previous_click = (px, py)
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self.event_queue.put(('click', {'x': target[0], 'y': target[1], 'angle': target[2]}))
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self.drag_line_start = None
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elif event == cv2.EVENT_MOUSEMOVE:
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if self.drag_line_start is not None:
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self.drag_line_end = (px, py)
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def process_frame(self):
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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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# 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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t_image = time.time()
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gray = cv2.cvtColor(color_image, cv2.COLOR_BGR2GRAY)
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if self.invert_grayscale:
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cv2.bitwise_not(gray, gray)
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detector = aruco.MarkerDetector()
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detector.setDetectionMode(aruco.DM_VIDEO_FAST, 0.01)
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#detector_params = detector.getParameters()
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# run aruco marker detection
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detected_markers = detector.detect(gray)
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# detected_markers2 = detector.detect(gray)
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#gray = detector.getThresholdedImage()
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display_mode = self.params.param('Display mode').value()
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#print(f"detected_markers = {[marker.id for marker in detected_markers]}")
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#print("threshold = ", self.threshold)
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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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if marker.id >= 0:
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detected_marker_data[marker.id] = {'marker_center': marker.getCenter()}
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if marker.id in self.corner_marker_ids.values():
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marker.calculateExtrinsics(self.corner_marker_size, self.camparam)
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else:
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marker.calculateExtrinsics(self.robot_marker_size, 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 self.draw_markers:
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marker.draw(color_image, np.array([255, 255, 255]), 2, True)
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if self.draw_marker_coordinate_system:
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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 marker_id, data in detected_marker_data.items():
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self.update_estimate(marker_id, data['marker_center'], data['Rvec'], data['Tvec'], t_image)
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# draw grid
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if self.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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# draw drag
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if self.drag_line_start is not None and self.drag_line_end is not None:
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color_image = cv2.line(color_image, self.drag_line_start, self.drag_line_end, color=(0, 0, 255), thickness=2)
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# compute frame rate
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self.fps_overlay.setText(f"fps = {self.fps_counter.get_fps():.1f}")
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# Show images
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color_image_rgb = cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB) # convert to RGB
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if display_mode == 'color':
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self.img.setImage(np.transpose(np.flipud(color_image_rgb), axes=(1, 0, 2)))
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elif display_mode == 'grayscale':
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self.img.setImage(np.transpose(np.flipud(gray)))
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QtCore.QTimer.singleShot(1, self.process_frame)
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def handleKeyPressEvent(self, ev):
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key = ev.key()
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self.event_queue.put(('key', key))
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if key == QtCore.Qt.Key_Q:
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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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self.app.quit()
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elif key == QtCore.Qt.Key_I:
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self.invert_grayscale = not self.invert_grayscale
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def update_estimate(self, marker_id, pixel_coord_center, rvec, tvec, t_image):
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# update the marker estimate with new data
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if marker_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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# get corresponding corner to the detected marker
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corner = next(filter(lambda key: self.corner_marker_ids[key] == marker_id, self.corner_marker_ids.keys()))
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old_estimate_x = self.corner_estimates[corner]['x']
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old_estimate_y = self.corner_estimates[corner]['y']
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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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if not any([old_estimate_x is None, old_estimate_y is None]):
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new_estimate_x = (n_estimates * old_estimate_x + x) / (n_estimates + 1) # weighted update
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new_estimate_y = (n_estimates * old_estimate_y + y) / (n_estimates + 1)
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else:
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new_estimate_x = x # first estimate
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new_estimate_y = y
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self.corner_estimates[corner]['t'] = t_image
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self.corner_estimates[corner]['x'] = new_estimate_x
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self.corner_estimates[corner]['y'] = new_estimate_y
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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_center
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elif marker_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.robot_marker_estimates[marker_id] = {'t': float(t_image), 'x': float(x), 'y': float(y), 'angle': float(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['t'] is None for estimate in self.robot_marker_estimates.values()])
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def get_pos_from_grid_point(self, x, y, dimx, dimy, 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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if not self.all_corners_detected():
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#raise RuntimeError("not all corner markers have been detected yet")
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a = np.array([0,1])
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b = np.array([1,1])
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c = np.array([1,0])
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d = np.array([0,0])
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else:
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# compute column line
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a = np.array([self.corner_estimates['a']['x'], self.corner_estimates['a']['y']])
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b = np.array([self.corner_estimates['b']['x'], self.corner_estimates['b']['y']])
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c = np.array([self.corner_estimates['c']['x'], self.corner_estimates['c']['y']])
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d = np.array([self.corner_estimates['d']['x'], self.corner_estimates['d']['y']])
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|
|
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x_frac = (x + 0.5) / dimx
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y_frac = (y + 0.5) / dimy
|
|
|
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vab = b - a
|
|
vdc = c - d
|
|
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
|
|
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.astype(int)), tuple(corner_2_center.astype(int)), 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.astype(int)), tuple(column_line_bottom.astype(int)), 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.astype(int)), tuple(row_line_bottom.astype(int)), 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.process_frame()
|
|
|
|
import sys
|
|
|
|
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
|
|
QtGui.QApplication.instance().exec_() |