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navOCC_R.py
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navOCC_R.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
# FINAL FOR NAVIGATION AND MAPPING
import rospy
from nav_msgs.msg import Odometry
from nav_msgs.msg import OccupancyGrid
from sensor_msgs.msg import LaserScan
from tf.transformations import euler_from_quaternion
from geometry_msgs.msg import Twist
import math
import cmath
import numpy as np
import time
from PIL import Image
import matplotlib.pyplot as plt
import tf2_ros
import cv2
#from sound_play.msg import SoundRequest
#from sound_play.libsoundplay import SoundClient
laser_range = np.array([])
occdata = []
odata=[]
msg_d=[]
yaw = 0.0
yawtf=0.0
t_yaw=0.0
rotate_speed = 0.15
linear_speed = 0.11
stop_distance = 0.50
occ_bins = [-1, 0, 100, 101]
front_angle = 5
front_angles = range(-front_angle,front_angle+1,1)
side_angles_f = range(43,48,1)
side_angles_b = range(133,138,1)
grid_x_legit=0
grid_y_legit=0
height=0
width=0
rotated=0
r=[]
def get_odom_dir(msg):
global yaw
orientation_quat = msg.pose.pose.orientation
orientation_list = [orientation_quat.x, orientation_quat.y, orientation_quat.z, orientation_quat.w]
(roll, pitch, yaw) = euler_from_quaternion(orientation_list)
def get_laserscan(msg):
global laser_range
# create numpy array
laser_range = np.array([msg.ranges])
def get_occupancy(msg, tfBuffer):
global yawtf
global grid_x_legit
global grid_y_legit
global width
global height
global occdata
global rotated
# create numpy array
occdata = np.array([msg.data])
# compute histogram to identify percent of bins with -1
occ_counts = np.histogram(occdata,occ_bins)
# calculate total number of bins
total_bins = msg.info.width * msg.info.height
# log the info
# rospy.loginfo('Width: %i Height: %i',msg.info.width,msg.info.height)
# rospy.loginfo('Unmapped: %i Unoccupied: %i Occupied: %i Total: %i', occ_counts[0][0], occ_counts[0][1], occ_counts[0][2], total_bins)
# find transform to convert map coordinates to base_link coordinates
# lookup_transform(target_frame, source_frame, time)
trans = tfBuffer.lookup_transform('map', 'base_link', rospy.Time(0))
cur_pos = trans.transform.translation
cur_rot = trans.transform.rotation
# rospy.loginfo(['Trans: ' + str(cur_pos)])
# rospy.loginfo(['Rot: ' + str(cur_rot)])
# get map resolution
map_res = msg.info.resolution
# get map origin struct has fields of x, y, and z
map_origin = msg.info.origin.position
# get map grid positions for x, y position
grid_x_legit = round((cur_pos.x - map_origin.x) / map_res)
grid_y_legit = round(((cur_pos.y - map_origin.y) / map_res))
# rospy.loginfo(['Grid Y: ' + str(grid_y) + ' Grid X: ' + str(grid_x)])
# make occdata go from 0 instead of -1, reshape into 2D
# oc2 = occdata + 1
## odata = np.uint8(oc2.reshape(msg.info.height,msg.info.width,order='F'))
#
# # set all values above 1 (i.e. above 0 in the original map data, representing occupied locations)
# oc3 = (oc2>1).choose(oc2,2)
# # reshape to 2D array using column order
# ondata = np.uint8(oc3.reshape(msg.info.height,msg.info.width,order='F'))
# # set current robot location to 0
# ondata[grid_x_legit][grid_y_legit] = 0
# # create image from 2D array using PIL
# img = Image.fromarray(ondata.astype(np.uint8))
# find center of image
width = msg.info.width
height = msg.info.height
# translate by curr_pos - centerxy to make sure the rotation is performed
# with the robot at the center
# using tips from:
# https://stackabuse.com/affine-image-transformations-in-python-with-numpy-pillow-and-opencv/
# translation_m = np.array([[1, 0, (i_centerx-grid_y_legit)],
# [0, 1, (i_centery-grid_x_legit)],
# [0, 0, 1]])
# # Image.transform function requires the matrix to be inverted
# tm_inv = np.linalg.inv(translation_m)
# # translate the image so that the robot is at the center of the image
# img_transformed = img.transform((msg.info.height, msg.info.width),
# Image.AFFINE,
# data=tm_inv.flatten()[:6],
# resample=Image.NEAREST)
# convert quaternion to Euler angles
orientation_list = [cur_rot.x, cur_rot.y, cur_rot.z, cur_rot.w]
(roll, pitch, yawtf) = euler_from_quaternion(orientation_list)
# rospy.loginfo(['Yaw: R: ' + str(yawtf) + ' D: ' + str(np.degrees(yawtf))])
# rotate by 180 degrees to invert map so that the forward direction is at the top of the image
# rotated = img_transformed.rotate(np.degrees(-yawtf)+180)
# # we should now be able to access the map around the robot by converting
# # back to a numpy array: im2arr = np.array(rotated)
#
# # show image using grayscale map
# plt.imshow(rotated,cmap='gray')
# plt.draw_all()
# # pause to make sure the plot gets created
# plt.pause(0.00000000001)
def retRotated():
global occdata
global odata
global msg_d
global grid_x_legit
global grid_y_legit
global width
global height
global yawtf
oc2 = occdata + 1
odata = np.uint8(oc2.reshape(height,width,order='F'))
# set all values above 1 (i.e. above 0 in the original map data, representing occupied locations)
oc3 = (oc2>1).choose(oc2,2)
# reshape to 2D array using column order
ondata = np.uint8(oc3.reshape(height,width,order='F'))
# set current robot location to 0
ondata[grid_x_legit][grid_y_legit] = 0
# create image from 2D array using PIL
img = Image.fromarray(ondata.astype(np.uint8))
# find center of image
i_centerx = width/2
i_centery = height/2
# translate by curr_pos - centerxy to make sure the rotation is performed
# with the robot at the center
# using tips from:
# https://stackabuse.com/affine-image-transformations-in-python-with-numpy-pillow-and-opencv/
translation_m = np.array([[1, 0, (i_centerx-grid_y_legit)],
[0, 1, (i_centery-grid_x_legit)],
[0, 0, 1]])
# Image.transform function requires the matrix to be inverted
tm_inv = np.linalg.inv(translation_m)
# translate the image so that the robot is at the center of the image
img_transformed = img.transform((height, width),
Image.AFFINE,
data=tm_inv.flatten()[:6],
resample=Image.NEAREST)
rotated = img_transformed.rotate(np.degrees(-yawtf)+180)
# we should now be able to access the map around the robot by converting
# back to a numpy array: im2arr = np.array(rotated)
# show image using grayscale map
plt.imshow(rotated,cmap='gray')
plt.draw_all()
# pause to make sure the plot gets created
plt.pause(0.00000000001)
return rotated
#def get_occupancy(msg, tfBuffer):
# global msg_d
# global yawtf
# global grid_x_legit
# global grid_y_legit
#
# global odata
# global rotated
#
# # create numpy array
# occdata = np.array([msg.data])
# # compute histogram to identify percent of bins with -1
# occ_counts = np.histogram(occdata,occ_bins)
# # calculate total number of bins
# total_bins = msg.info.width * msg.info.height
# # log the info
## rospy.loginfo('Width: %i Height: %i',msg.info.width,msg.info.height)
## rospy.loginfo('Unmapped: %i Unoccupied: %i Occupied: %i Total: %i', occ_counts[0][0], occ_counts[0][1], occ_counts[0][2], total_bins)
#
# # find transform to convert map coordinates to base_link coordinates
# # lookup_transform(target_frame, source_frame, time)
# trans = tfBuffer.lookup_transform('map', 'base_link', rospy.Time(0))
# cur_pos = trans.transform.translation
# cur_rot = trans.transform.rotation
## rospy.loginfo(['Trans: ' + str(cur_pos)])
## rospy.loginfo(['Rot: ' + str(cur_rot)])
#
# # get map resolution
# map_res = msg.info.resolution
# # get map origin struct has fields of x, y, and z
# map_origin = msg.info.origin.position
# # get map grid positions for x, y position
# grid_x_legit = round((cur_pos.x - map_origin.x) / map_res)
# grid_y_legit = round(((cur_pos.y - map_origin.y) / map_res))
## rospy.loginfo(['Grid Y: ' + str(grid_y) + ' Grid X: ' + str(grid_x)])
#
# # make occdata go from 0 instead of -1, reshape into 2D
# oc2 = occdata + 1
# odata = np.uint8(oc2.reshape(msg.info.height,msg.info.width,order='F'))
#
# # set all values above 1 (i.e. above 0 in the original map data, representing occupied locations)
# oc3 = (oc2>1).choose(oc2,2)
# # reshape to 2D array using column order
# ondata = np.uint8(oc3.reshape(msg.info.height,msg.info.width,order='F'))
# # set current robot location to 0
# ondata[grid_x_legit][grid_y_legit] = 0
# # create image from 2D array using PIL
# img = Image.fromarray(ondata.astype(np.uint8))
# # find center of image
# i_centerx = msg.info.width/2
# i_centery = msg.info.height/2
# # translate by curr_pos - centerxy to make sure the rotation is performed
# # with the robot at the center
# # using tips from:
# # https://stackabuse.com/affine-image-transformations-in-python-with-numpy-pillow-and-opencv/
# translation_m = np.array([[1, 0, (i_centerx-grid_y_legit)],
# [0, 1, (i_centery-grid_x_legit)],
# [0, 0, 1]])
# # Image.transform function requires the matrix to be inverted
# tm_inv = np.linalg.inv(translation_m)
# # translate the image so that the robot is at the center of the image
# img_transformed = img.transform((msg.info.height, msg.info.width),
# Image.AFFINE,
# data=tm_inv.flatten()[:6],
# resample=Image.NEAREST)
#
# # convert quaternion to Euler angles
# orientation_list = [cur_rot.x, cur_rot.y, cur_rot.z, cur_rot.w]
# (roll, pitch, yawtf) = euler_from_quaternion(orientation_list)
## rospy.loginfo(['Yaw: R: ' + str(yawtf) + ' D: ' + str(np.degrees(yawtf))])
#
# # rotate by 180 degrees to invert map so that the forward direction is at the top of the image
# rotated = img_transformed.rotate(np.degrees(-yawtf)+180)
# # we should now be able to access the map around the robot by converting
# # back to a numpy array: im2arr = np.array(rotated)
#
# # show image using grayscale map
# plt.imshow(rotated,cmap='gray')
# plt.draw_all()
# # pause to make sure the plot gets created
# plt.pause(0.00000000001)
def rotatebotSlow(rot_angle):
global yaw
# create Twist object
twist = Twist()
# set up Publisher to cmd_vel topic
pub = rospy.Publisher('cmd_vel', Twist, queue_size=10)
# set the update rate to 1 Hz
rate = rospy.Rate(1000)
# get current yaw angle
current_yaw = np.copy(yaw)
# log the info
# rospy.loginfo(['Current: ' + str(math.degrees(current_yaw))])
# we are going to use complex numbers to avoid problems when the angles go from
# 360 to 0, or from -180 to 180
c_yaw = complex(math.cos(current_yaw),math.sin(current_yaw))
# calculate desired yaw
target_yaw = current_yaw + math.radians(rot_angle)
# convert to complex notation
c_target_yaw = complex(math.cos(target_yaw),math.sin(target_yaw))
# rospy.loginfo(['Desired: ' + str(math.degrees(cmath.phase(c_target_yaw)))])
# divide the two complex numbers to get the change in direction
c_change = c_target_yaw / c_yaw
# get the sign of the imaginary component to figure out which way we have to turn
c_change_dir = np.sign(c_change.imag)
# set linear speed to zero so the TurtleBot rotates on the spot
twist.linear.x = 0.0
# set the direction to rotate
twist.angular.z = c_change_dir * 0.08
# start rotation
pub.publish(twist)
# we will use the c_dir_diff variable to see if we can stop rotating
c_dir_diff = c_change_dir
# rospy.loginfo(['c_change_dir: ' + str(c_change_dir) + ' c_dir_diff: ' + str(c_dir_diff)])
# if the rotation direction was 1.0, then we will want to stop when the c_dir_diff
# becomes -1.0, and vice versa
while(c_change_dir * c_dir_diff > 0):
# get current yaw angle
current_yaw = np.copy(yaw)
# get the current yaw in complex form
c_yaw = complex(math.cos(current_yaw),math.sin(current_yaw))
# rospy.loginfo('While Yaw: %f Target Yaw: %f', math.degrees(current_yaw), math.degrees(target_yaw))
# get difference in angle between current and target
c_change = c_target_yaw / c_yaw
# get the sign to see if we can stop
c_dir_diff = np.sign(c_change.imag)
# rospy.loginfo(['c_change_dir: ' + str(c_change_dir) + ' c_dir_diff: ' + str(c_dir_diff)])
rate.sleep()
# rospy.loginfo(['End Yaw: ' + str(math.degrees(current_yaw))])
# set the rotation speed to 0
twist.angular.z = 0.0
# stop the rotation
time.sleep(1)
pub.publish(twist)
def rotatebot(rot_angle):
global yaw
# create Twist object
twist = Twist()
# set up Publisher to cmd_vel topic
pub = rospy.Publisher('cmd_vel', Twist, queue_size=10)
# set the update rate to 1 Hz
rate = rospy.Rate(1000)
# get current yaw angle
current_yaw = np.copy(yaw)
# log the info
# rospy.loginfo(['Current: ' + str(math.degrees(current_yaw))])
# we are going to use complex numbers to avoid problems when the angles go from
# 360 to 0, or from -180 to 180
c_yaw = complex(math.cos(current_yaw),math.sin(current_yaw))
# calculate desired yaw
target_yaw = current_yaw + math.radians(rot_angle)
# convert to complex notation
c_target_yaw = complex(math.cos(target_yaw),math.sin(target_yaw))
# rospy.loginfo(['Desired: ' + str(math.degrees(cmath.phase(c_target_yaw)))])
# divide the two complex numbers to get the change in direction
c_change = c_target_yaw / c_yaw
# get the sign of the imaginary component to figure out which way we have to turn
c_change_dir = np.sign(c_change.imag)
# set linear speed to zero so the TurtleBot rotates on the spot
twist.linear.x = 0.0
# set the direction to rotate
twist.angular.z = c_change_dir * rotate_speed
# start rotation
pub.publish(twist)
# we will use the c_dir_diff variable to see if we can stop rotating
c_dir_diff = c_change_dir
# rospy.loginfo(['c_change_dir: ' + str(c_change_dir) + ' c_dir_diff: ' + str(c_dir_diff)])
# if the rotation direction was 1.0, then we will want to stop when the c_dir_diff
# becomes -1.0, and vice versa
while(c_change_dir * c_dir_diff > 0):
# get current yaw angle
current_yaw = np.copy(yaw)
# get the current yaw in complex form
c_yaw = complex(math.cos(current_yaw),math.sin(current_yaw))
# rospy.loginfo('While Yaw: %f Target Yaw: %f', math.degrees(current_yaw), math.degrees(target_yaw))
# get difference in angle between current and target
c_change = c_target_yaw / c_yaw
# get the sign to see if we can stop
c_dir_diff = np.sign(c_change.imag)
# rospy.loginfo(['c_change_dir: ' + str(c_change_dir) + ' c_dir_diff: ' + str(c_dir_diff)])
rate.sleep()
# rospy.loginfo(['End Yaw: ' + str(math.degrees(current_yaw))])
# set the rotation speed to 0
twist.angular.z = 0.0
# stop the rotation
time.sleep(1)
pub.publish(twist)
def rotatebotWhileMove(rot_angle):
global yaw
global laser_range
# create Twist object
twist = Twist()
# set up Publisher to cmd_vel topic
pub = rospy.Publisher('cmd_vel', Twist, queue_size=10)
# set the update rate to 1 Hz
rate = rospy.Rate(50)
# get current yaw angle
current_yaw = np.copy(yaw)
# log the info
#rospy.loginfo(['Current: ' + str(math.degrees(current_yaw))])
# we are going to use complex numbers to avoid problems when the angles go from
# 360 to 0, or from -180 to 180
c_yaw = complex(math.cos(current_yaw),math.sin(current_yaw))
# calculate desired yaw
target_yaw = current_yaw + math.radians(rot_angle)
# convert to complex notation
c_target_yaw = complex(math.cos(target_yaw),math.sin(target_yaw))
#rospy.loginfo(['Desired: ' + str(math.degrees(target_yaw))])
# divide the two complex numbers to get the change in direction
c_change = c_target_yaw / c_yaw
# get the sign of the imaginary component to figure out which way we have to turn
c_change_dir = np.sign(c_change.imag)
# set linear speed to zero so the TurtleBot rotates on the spot
twist.linear.x = 0.6*linear_speed
# set the direction to rotate
twist.angular.z = c_change_dir * rotate_speed
# start rotation
time.sleep(0.2)
pub.publish(twist)
# we will use the c_dir_diff variable to see if we can stop rotating
c_dir_diff = c_change_dir
# rospy.loginfo(['c_change_dir: ' + str(c_change_dir) + ' c_dir_diff: ' + str(c_dir_diff)])
# if the rotation direction was 1.0, then we will want to stop when the c_dir_diff
# becomes -1.0, and vice versa
while(c_change_dir * c_dir_diff > 0):
# get current yaw angle
current_yaw = np.copy(yaw)
if abs(current_yaw - target_yaw)<5:
rate=rospy.Rate(1000)
# get the current yaw in complex form
c_yaw = complex(math.cos(current_yaw),math.sin(current_yaw))
#rospy.loginfo('While Yaw: %f Target Yaw: %f', math.degrees(current_yaw), math.degrees(target_yaw))
# get difference in angle between current and target
c_change = c_target_yaw / c_yaw
# get the sign to see if we can stop
c_dir_diff = np.sign(c_change.imag)
# rospy.loginfo(['c_change_dir: ' + str(c_change_dir) + ' c_dir_diff: ' + str(c_dir_diff)])
rate.sleep()
#rospy.loginfo(['End Yaw: ' + str(math.degrees(current_yaw))])
# set the rotation speed to 0
twist.linear.x=linear_speed
twist.angular.z = 0.0
# stop the rotation
time.sleep(0.01)
pub.publish(twist)
def preciseRot():
global laser_range
twist = Twist();
pub=rospy.Publisher("cmd_vel",Twist,queue_size=10)
rate=rospy.Rate(50)
rate.sleep()
twist.angular.z=-rotate_speed
time.sleep(0.3)
pub.publish(twist)
sideF=np.amin(laser_range[0,range(42,48,1)])
sideB=np.amin(laser_range[0,range(122,128,1)])
if abs(sideF-sideB)<0.02:
twist.angular.z=0
time.sleep(0.1)
pub.publish(twist)
def findWall():
global laser_range
global t_yaw
# rospy.init_node('findWall', anonymous=True)
#
# # subscribe to odometry data
# rospy.Subscriber('odom', Odometry, get_odom_dir)
# # subscribe to LaserScan data
# rospy.Subscriber('scan', LaserScan, get_laserscan)
# # subscribe to map occupancy data
# rospy.Subscriber('map', OccupancyGrid, get_occupancy)
#
rate = rospy.Rate(200) # 5 Hz
#
rospy.loginfo("In findWall().")
rate.sleep()
# turn 90 degrees to the left first
rotatebot(t_yaw+90)
t_yaw+=90
rospy.loginfo(str(yaw))
pub = rospy.Publisher('cmd_vel', Twist, queue_size=10)
twist=Twist()
a=0
if np.amin(laser_range[0,front_angles])<=0.25:
twist.linear.x=-linear_speed
a=1
else:
twist.linear.x=linear_speed
a=2
twist.angular.z=0.0
time.sleep(0.5) #1
pub.publish(twist)
while True:
# check distances in front of bot
fd=laser_range[0,front_angles]
#rospy.loginfo(str(fd))
#rospy.loginfo(str(np.amin(fd)))
if a==1:
if np.amin(fd)>0.20:
break
else:
if np.amin(fd)<0.25:
break
twist.linear.x=0
time.sleep(0.001)
pub.publish(twist)
# rotatebot(-90)
# rospy.loginfo(laser_range[0,[-43,-44,-45,-46,-47]])
# rospy.loginfo(laser_range[0,[-133,-134,-135,-136,-137]])
# rospy.loginfo(laser_range[0,[43,44,45,46,47]])
# rospy.loginfo(laser_range[0,[133,134,135,136,137]])
# rospy.loginfo(laser_range[0,range(0,181,1)])
# preciseRot()
rotatebot(t_yaw-90)
t_yaw-=90
def moveTo(D):
global laser_range
pub = rospy.Publisher('cmd_vel', Twist, queue_size=10)
rate = rospy.Rate(200)
rospy.loginfo("In moveTo")
rate.sleep()
twist=Twist()
# rotatebot(-5)
# if laser_range[0,0]>D+0.1:
# D=laser_range[0,0]
# else:
# rotatebot(10)
# if laser_range[0,0]>D+0.1:
# D=laser_range[0,0]
# else:
# rotatebot(-5)
twist.linear.x=linear_speed
twist.angular.z=0
time.sleep(0.1)
pub.publish(twist)
rospy.loginfo(" D = " +str(D))
rospy.loginfo(" time.sleep = " + str((D/linear_speed) *0.6))
time.sleep((D/linear_speed) *0.6)
#time.sleep(1)
twist.linear.x=0
time.sleep(0.1)
pub.publish(twist)
if np.amin(laser_range[0,range(-3,4)])<0.2:
twist.linear.x=-linear_speed
time.sleep(0.1)
pub.publish(twist)
time.sleep(1)
twist.linear.x=0
time.sleep(0.1)
pub.publish(twist)
def getUA():
global rotated
r=np.array(rotated)
y_count=-1
x1=0
x2=0
y1=0
y2=0
grid_x=191.5
grid_y=191.5
for row in r:
prev=-1
y_count+=1
x_count=-1
b=False
for num in row:
x_count+=1
if prev==0 and num==1 and 165<x_count<235 and 165<y_count<235:
if x1==0:
x1=x_count
x2=x_count
if y1==0:
y1=y_count
y2=y_count
a=True
b=True
prev=num
if a==True and b==False:
# rospy.loginfo("x2-x1 = " +str(x2-x1) + " y2-y1 = " +str(y2-y1))
# if abs(x2-x1)<3 and abs(y2-y1)<3:
if abs(x2-x1)**2 + abs(y2-y1)**2<16:
x1=0
x2=0
y1=0
y2=0
continue
break
a1=0
a2=0
b1=0
b2=0
c=False
y_count=-1
for row in r:
prev=-1
y_count+=1
x_count=-1
d=False
for num in row:
x_count+=1
if prev==1 and num==0 and 165<x_count<235 and 165<y_count<235:
if a1==0:
a1=x_count
a2=x_count
if b1==0:
b1=y_count
b2=y_count
c=True
d=True
prev=num
if c==True and d==False:
# if abs(a2-a1)<3 and abs(b2-b1)<3:
if abs(a2-a1)**2 + abs(b2-b1)**2<16:
a1=0
a2=0
b1=0
b2=0
continue
break
def closure(mapdata):
# This function checks if mapdata contains a closed contour. The function
# assumes that the raw map data from SLAM has been modified so that
# -1 (unmapped) is now 0, and 0 (unoccupied) is now 1, and the occupied
# values go from 1 to 101.
# According to: https://stackoverflow.com/questions/17479606/detect-closed-contours?rq=1
# closed contours have larger areas than arc length, while open contours have larger
# arc length than area. But in my experience, open contours can have areas larger than
# the arc length, but closed contours tend to have areas much larger than the arc length
# So, we will check for contour closure by checking if any of the contours
# have areas that are more than 10 times larger than the arc length
# This value may need to be adjusted with more testing.
ALTHRESH = 10
# We will slightly fill in the contours to make them easier to detect
DILATE_PIXELS = 3
# assumes mapdata is uint8 and consists of 0 (unmapped), 1 (unoccupied),
# and other positive values up to 101 (occupied)
# so we will apply a threshold of 2 to create a binary image with the
# occupied pixels set to 255 and everything else is set to 0
# we will use OpenCV's threshold function for this
ret,img2 = cv2.threshold(mapdata,2,255,0)
# we will perform some erosion and dilation to fill out the contours a
# little bit
element = cv2.getStructuringElement(cv2.MORPH_CROSS,(DILATE_PIXELS,DILATE_PIXELS))
# img3 = cv2.erode(img2,element)
img4 = cv2.dilate(img2,element)
# use OpenCV's findContours function to identify contours
# OpenCV version 3 changed the number of return arguments, so we
# need to check the version of OpenCV installed so we know which argument
# to grab
fc = cv2.findContours(img4, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
(major, minor, _) = cv2.__version__.split(".")
if(major == '3'):
contours = fc[1]
else:
contours = fc[0]
# find number of contours returned
lc = len(contours)
# rospy.loginfo('# Contours: %s', str(lc))
# create array to compute ratio of area to arc length
cAL = np.zeros((lc,2))
for i in range(lc):
cAL[i,0] = cv2.contourArea(contours[i])
cAL[i,1] = cv2.arcLength(contours[i], True)
# closed contours tend to have a much higher area to arc length ratio,
# so if there are no contours with high ratios, we can safely say
# there are no closed contours
cALratio = cAL[:,0]/cAL[:,1]
rospy.loginfo('Closure: %s', str(cALratio))
if cALratio[0] > ALTHRESH:
return True
else:
return False
def move():
global laser_range
global yawtf
global yaw
global rotated
global msg_d
global grid_y_legit
global grid_x_legit
rospy.init_node("move", anonymous=True)
pub=rospy.Publisher("cmd_vel",Twist,queue_size=10)
# subscribe to odometry data
rospy.Subscriber('odom', Odometry, get_odom_dir)
# subscribe to LaserScan data
rospy.Subscriber('scan', LaserScan, get_laserscan)
# subscribe to map occupancy data
tfBuffer = tf2_ros.Buffer()
tfListener = tf2_ros.TransformListener(tfBuffer)
rospy.sleep(1.0)
# subscribe to map occupancy data
rospy.Subscriber('map', OccupancyGrid, get_occupancy, tfBuffer)
rate=rospy.Rate(200)
rate.sleep()
t_yaw=math.degrees(yaw)
time.sleep(1)
twist=Twist()
# twist.linear.x=0.1
# twist.angular.z=0
# time.sleep(0.1)
# pub.publish(twist)
# rospy.loginfo(np.argmin(laser_range))
# rotatebot(-170)
# while True:
# rospy.loginfo(" grid_x_legit = " + str(grid_x_legit) + " grid_y_legit = " + str(grid_y_legit))
contourCheck=1
start_time = time.time()
try:
while contourCheck==1:
r=np.array(retRotated())
# closure(odata)
y_count=-1
x1=0
x2=0
y1=0
y2=0
a=False
#the loop below is basically checking the grid
# for row in r:
# y_count+=1
# x_count=-1
# check=False
# for num in row:
# x_count+=1
## if num == 3:
## rospy.loginfo("Bot Coordinates received.")
## rospy.loginfo("grid_x_legit = " + str(grid_x_legit) + " grid_y_legit = " + str(grid_y_legit))
## grid_x=x_count
## grid_y=y_count
## check=True
## break
## if check==True:
## break
# rospy.loginfo(" columns = " + str(x_count))
# rospy.loginfo(" rows = " + str(y_count))
y_count=-1
grid_x=191.5
grid_y=191.5
for row in r:
prev=-1
y_count+=1
x_count=-1
b=False
for i,num in enumerate(row):
x_count+=1
if prev==0 and num==1 and 165<x_count<235 and 165<y_count<235 and row[i+1]==1 and row[i+2]==1 and row[i+3]==1 and row[i+4]==1:
if x1==0:
x1=x_count
x2=x_count
if y1==0:
y1=y_count
y2=y_count
a=True
b=True
prev=num
if a==True and b==False:
# rospy.loginfo("x2-x1 = " +str(x2-x1) + " y2-y1 = " +str(y2-y1))
# if abs(x2-x1)<3 and abs(y2-y1)<3:
if abs(x2-x1)**2 + abs(y2-y1)**2<16:
x1=0
x2=0
y1=0
y2=0
continue
break
a1=0
a2=0
b1=0
b2=0
c=False
y_count=-1
for row in r:
prev=-1
y_count+=1
x_count=-1
d=False
for i,num in enumerate(row):
x_count+=1
if prev==1 and num==0 and 165<x_count<235 and 165<y_count<235 and row[i-1]==1 and row[i-2]==1 and row[i-3]==1 and row[i-4]==1:
if a1==0:
a1=x_count
a2=x_count
if b1==0:
b1=y_count
b2=y_count
c=True
d=True
prev=num
if c==True and d==False:
# if abs(a2-a1)<3 and abs(b2-b1)<3:
if abs(a2-a1)**2 + abs(b2-b1)**2<16:
a1=0
a2=0
b1=0
b2=0
continue
break
# x_count=-1
# i1=0
# i2=0
# j1=0
# j2=0
# e=False
# for column in r.T:
# x_count+=1
# prev=-1
# y_count=-1
# f=False
# for num in column:
# y_count+=1
# if prev==1 and num==0:
# if i1==0:
# i1=x_count
# i2=x_count
# if j1==0:
# j1=y_count
# j2=y_count
# e=True
# f=True
# prev=num
# if e==True and f==False:
# break
rospy.loginfo("Reached.")
rospy.loginfo("x1 = " + str(x1) + " x2 = " + str(x2) + " y1 = " +str(y1) + " y2 = " + str(y2))
rospy.loginfo("a1 = " + str(a1) + " a2 = " + str(a2) + " b1 = " +str(b1) + " b2 = " + str(b2))
# rospy.loginfo("i1 = " + str(i1) + " i2 = " + str(i2) + " j1 = " + str(j1) + " j2 = " + str(j2))
if (x2-x1)!=0 and (y2-y1)!=0: #and (y1<=b1 if b1>0 else True):
x=((x2-x1)/2)+x1
y=((y2-y1)/2)+y1
# y2=383-y2
# y1=383-y1
# x=0
# y=0
# if (x1-grid_x)**2 + (y1-grid_y)**2 < (x2-grid_x)**2 + (y2-grid_y)**2:
# x=x1
# y=y1
# else:
# x=x2
# y=y2
special=False
angle=0
if x<grid_x and y<grid_y:
#angle=90-math.degrees(math.atan(float(abs(y-grid_y))/abs(x-grid_x)))
angle=90-math.degrees(math.atan(float(abs(y2-y1))/abs(x2-x1)))
if abs(y-grid_y)<5:
special=True
elif x<grid_x and y>grid_y:
#angle=90+math.degrees(math.atan(float(abs(y-grid_y))/abs(x-grid_x)))
angle=90+math.degrees(math.atan(float(abs(y2-y1))/abs(x2-x1)))
elif x>grid_x and y<grid_y:
#angle = -(90-math.degrees(math.atan(float(abs(y-grid_y))/abs(x-grid_x))))
angle=-(90-math.degrees(math.atan(float(abs(y2-y1))/abs(x2-x1))))
elif x>grid_x and y>grid_y:
#angle = -(90+math.degrees(math.atan(float(abs(y-grid_y))/abs(x-grid_x))))
angle=-(90+math.degrees(math.atan(float(abs(y2-y1))/abs(x2-x1))))
special=True
rospy.loginfo("x = " + str(x) + " x_grid = " + str(grid_x) + " y = " +str(y) + " y_grid = " + str(grid_y))
try:
rospy.loginfo("angle = " + str(angle))
#rospy.loginfo(laser_range[0,range(int(angle)-25,int(angle)+21)])
# lr2i = np.argmax(laser_range[0,range(int(angle)-20,int(angle)-10)+range(int(angle)+10,int(angle)+21)])
# D=laser_range[0,lr2i]
# rospy.loginfo("D = " + str(D))
# rotatebot(float(lr2i))
# rospy.loginfo(range(int(angle)-25,int(angle)-20)+range(int(angle)+20,int(angle)+26))
arr=[]
if special==True:
if angle>0:
arr=range(int(angle)-25,int(angle)-20)
else:
arr=range(int(angle)+20,int(angle)+26)
else:
# arr=range(int(angle)-25,int(angle)-20)+range(int(angle)+20,int(angle)+26)
if angle<0:
if angle-25<-180:
arr=range(int(angle)+20,int(angle)+26)
else:
arr=range(int(angle)-25,int(angle)-20)+range(int(angle)+20,int(angle)+26)
elif angle>0:
if angle+25>180:
arr=range(int(angle)-25,int(angle)-20)
else:
arr=range(int(angle)-25,int(angle)-20)+range(int(angle)+20,int(angle)+26)
rospy.loginfo(arr)
distarray=[]
max1=0
arg=400
zerocheck=False
for w in arr:
dist=laser_range[0,w]
if dist==0 or dist==float("inf"):
rospy.loginfo("Found infinite distance at arg = " + str(w))
arg=w
zerocheck=True
break
distarray.append(dist)
if dist>max1:
max1=dist
arg=w
rospy.loginfo("arg = " + str(arg))
rotatebot(float(arg))
time.sleep(0.1)
if arg>0:
if arg>90:
rotatebotSlow(-3)
else:
rotatebotSlow(-(arg//50)-1)
else:
if arg<-90:
rotatebotSlow(3)
else:
rotatebotSlow(-(arg//50)+1)
D=laser_range[0,0]
# rotatebot(-5)
# if laser_range[0,0]>D+0.1 or (laser_range[0,0]==0) or (laser_range[0,0]==float("inf")):
# D=laser_range[0,0]
# else:
# rotatebot(10)
# if laser_range[0,0]>D+0.1 or (laser_range[0,0]==0) or (laser_range[0,0]==float("inf")):
# D=laser_range[0,0]
# else:
# rotatebot(-5)
rospy.loginfo("D = " + str(D))
if D==0 or D==float("inf"):
rospy.loginfo("In D==inf")
# moveTo(2)