RoboRally/remote_control/opencv_viewer_example.py

311 lines
13 KiB
Python

## License: Apache 2.0. See LICENSE file in root directory.
## Copyright(c) 2015-2017 Intel Corporation. All Rights Reserved.
###############################################
## Open CV and Numpy integration ##
###############################################
import pyrealsense2 as rs
import numpy as np
import cv2
from cv2 import aruco
from shapely.geometry import LineString
import time
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
}
robot_marker_ids = [12]
robot_marker_estimates = dict([(id, None) for id in robot_marker_ids])
angles = []
corner_estimates = {
'a': (None, 0), # (estimate, n_estimates)
'b': (None, 0),
'c': (None, 0),
'd': (None, 0)
}
def __init__(self):
if True: # 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
# array containing pose estimates for each marker
estimates = {}
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 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]
tvecs = res[1]
self.update_estimate(id, rvecs, tvecs)
frame = self.draw_grid_lines(frame, corners, ids)
frame = self.draw_robot_pos(frame, corners, ids)
# Show images
cv2.namedWindow('RealSense', cv2.WINDOW_AUTOSIZE)
cv2.imshow('RealSense', frame)
cv2.waitKey(1)
finally:
# Stop streaming
self.pipeline.stop()
# import matplotlib.pyplot as plt
# plt.plot(playboard.angles)
# plt.show()
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][0][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][0][0]
y = -tvec[0][0][1] # flip y coordinate
# compute angle
rot_mat, _ = cv2.Rodrigues(rvec[0][0])
pose_mat = cv2.hconcat((rot_mat, tvec[0][0]))
_, _, _, _, _, _, euler_angles = cv2.decomposeProjectionMatrix(pose_mat)
angle = -euler_angles[2][0] * np.pi / 180.0
self.angles.append(angle)
self.robot_marker_estimates[id[0]] = (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__":
playboard = Board()