mirror of
https://github.com/elephantrobotics/mycobot_ros.git
synced 2026-07-05 19:47:04 +00:00
588 lines
21 KiB
Python
Executable file
588 lines
21 KiB
Python
Executable file
# encoding:utf-8
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#!/usr/bin/env python2
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from tokenize import Pointfloat
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import cv2
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import numpy as np
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import time
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import json
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import os
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import rospy
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from visualization_msgs.msg import Marker
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from PIL import Image
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from threading import Thread
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import tkFileDialog as filedialog
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import Tkinter as tk
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from moving_utils import Movement
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IS_CV_4 = cv2.__version__[0] == '4'
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__version__ = "1.0" # Adaptive seeed
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class Object_detect(Movement):
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def __init__(self, camera_x=150, camera_y=-10):
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# inherit the parent class
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super(Object_detect, self).__init__()
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# get path of file
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dir_path = os.path.dirname(__file__)
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# 移动角度
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self.move_angles = [
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[-7.11, -6.94, -55.01, -24.16, 0, 15], # init the point
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[-1.14, -10.63, -87.8, 9.05, -3.07, 15], # point to grab
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[17.4, -10.1, -87.27, 5.8, -2.02, 15], # point to grab
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]
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# 移动坐标
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self.move_coords = [
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[120.1, -141.6, 240.9, -173.34, -8.15, -110.11], # above the red bucket
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# above the yello bucket
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#[208.2, -127.8, 260.9, -157.51, -17.5, -71.18],
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[205.6, -130.5, 263.0, -150.99, -0.07, -107.35],
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[209.7, -18.6, 230.4, -168.48, -9.86, -39.38],
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[196.9, -64.7, 232.6, -166.66, -9.44, -52.47],
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[126.6, -118.1, 305.0, -157.57, -13.72, -75.3],
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]
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# 判断连接设备:ttyUSB*为M5，ttyACM*为seeed
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self.robot_m5 = os.popen("ls /dev/ttyUSB*").readline()[:-1]
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self.robot_wio = os.popen("ls /dev/ttyACM*").readline()[:-1]
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self.robot_raspi = os.popen("ls /dev/ttyAMA*").readline()[:-1]
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self.robot_jes = os.popen("ls /dev/ttyTHS1").readline()[:-1]
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self.raspi = False
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if "dev" in self.robot_m5:
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self.Pin = [2, 5]
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elif "dev" in self.robot_wio:
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self.Pin = [20, 21]
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for i in self.move_coords:
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i[2] -= 20
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elif "dev" in self.robot_raspi or "dev" in self.robot_jes:
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import RPi.GPIO as GPIO
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self.GPIO = GPIO
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GPIO.setwarnings(False)
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GPIO.setmode(GPIO.BCM)
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GPIO.setup(20, GPIO.OUT)
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GPIO.setup(21, GPIO.OUT)
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self.raspi = True
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if self.raspi:
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self.gpio_status(False)
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self.Pin = [2, 5]
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# choose place to set cube
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self.color = 0
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# parameters to calculate camera clipping parameters
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self.x1 = self.x2 = self.y1 = self.y2 = 0
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# set cache of real coord
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self.cache_x = self.cache_y = 0
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# load model of img recognition
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# self.model_path = os.path.join(dir_path, "frozen_inference_graph.pb")
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# self.pbtxt_path = os.path.join(dir_path, "graph.pbtxt")
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# self.label_path = os.path.join(dir_path, "labels.json")
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# load class labels
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# self.labels = json.load(open(self.label_path))
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# use to calculate coord between cube and mycobot
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self.sum_x1 = self.sum_x2 = self.sum_y2 = self.sum_y1 = 0
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# The coordinates of the grab center point relative to the mycobot
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self.camera_x, self.camera_y = camera_x, camera_y
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# The coordinates of the cube relative to the mycobot
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self.c_x, self.c_y = 0, 0
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# The ratio of pixels to actual values
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self.ratio = 0
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# Get ArUco marker dict that can be detected.
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self.aruco_dict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_6X6_250)
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# Get ArUco marker params.
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self.aruco_params = cv2.aruco.DetectorParameters_create()
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# if IS_CV_4:
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# self.net = cv2.dnn.readNetFromTensorflow(self.model_path, self.pbtxt_path)
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# else:
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# print('Load tensorflow model need the version of opencv is 4.')
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# exit(0)
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# init a node and a publisher
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rospy.init_node("marker", anonymous=True)
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self.pub = rospy.Publisher('/cube', Marker, queue_size=1)
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# init a Marker
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self.marker = Marker()
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self.marker.header.frame_id = "/joint1"
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self.marker.ns = "cube"
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self.marker.type = self.marker.CUBE
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self.marker.action = self.marker.ADD
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self.marker.scale.x = 0.04
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self.marker.scale.y = 0.04
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self.marker.scale.z = 0.04
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self.marker.color.a = 1.0
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self.marker.color.g = 1.0
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self.marker.color.r = 1.0
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# marker position initial
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self.marker.pose.position.x = 0
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self.marker.pose.position.y = 0
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self.marker.pose.position.z = 0.03
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self.marker.pose.orientation.x = 0
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self.marker.pose.orientation.y = 0
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self.marker.pose.orientation.z = 0
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self.marker.pose.orientation.w = 1.0
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self.cache_x = self.cache_y = 0
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# publish marker
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def pub_marker(self, x, y, z=0.03):
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self.marker.header.stamp = rospy.Time.now()
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self.marker.pose.position.x = x
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self.marker.pose.position.y = y
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self.marker.pose.position.z = z
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self.marker.color.g = self.color
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self.pub.publish(self.marker)
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def gpio_status(self, flag):
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if flag:
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self.GPIO.output(20, 0)
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self.GPIO.output(21, 0)
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else:
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self.GPIO.output(20, 1)
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self.GPIO.output(21, 1)
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# Grasping motion
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def move(self, x, y, color):
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# send Angle to move mycobot
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self.pub_angles(self.move_angles[0], 20)
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time.sleep(1.5)
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self.pub_angles(self.move_angles[1], 20)
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time.sleep(1.5)
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self.pub_angles(self.move_angles[2], 20)
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time.sleep(1.5)
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# send coordinates to move mycobot
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self.pub_coords([x, y, 165, -178.9, -1.57, -66], 20, 1)
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time.sleep(1.5)
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# 根据不同底板机械臂，调整吸泵高度
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if "dev" in self.robot_m5:
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# m5 and jetson nano
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self.pub_coords([x, y, 90, -178.9, -1.57, -66], 25, 1)
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elif "dev" in self.robot_wio:
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h = 0
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if 165 < x < 180:
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h = 10
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elif x > 180:
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h = 20
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elif x < 135:
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h = -20
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self.pub_coords([x, y, 31.9+h, -178.9, -1, -66], 20, 1)
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elif "dev" in self.robot_jes:
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h = 0
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if x<130:
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h=15
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self.pub_coords([x, y, 90-h, -178.9, -1.57, -66], 25, 1)
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time.sleep(1.5)
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# open pump
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if self.raspi:
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self.gpio_status(True)
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else:
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self.pub_pump(True, self.Pin)
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time.sleep(0.5)
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self.pub_angles(self.move_angles[2], 20)
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time.sleep(3)
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self.pub_marker(
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self.move_coords[2][0]/1000.0, self.move_coords[2][1]/1000.0, self.move_coords[2][2]/1000.0)
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self.pub_angles(self.move_angles[1], 20)
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time.sleep(1.5)
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self.pub_marker(
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self.move_coords[3][0]/1000.0, self.move_coords[3][1]/1000.0, self.move_coords[3][2]/1000.0)
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self.pub_angles(self.move_angles[0], 20)
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time.sleep(1.5)
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self.pub_marker(
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self.move_coords[4][0]/1000.0, self.move_coords[4][1]/1000.0, self.move_coords[4][2]/1000.0)
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print self.move_coords[color]
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self.pub_coords(self.move_coords[color], 20, 1)
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self.pub_marker(self.move_coords[color][0]/1000.0, self.move_coords[color]
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[1]/1000.0, self.move_coords[color][2]/1000.0)
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time.sleep(2)
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# close pump
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if self.raspi:
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self.gpio_status(False)
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else:
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self.pub_pump(False, self.Pin)
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time.sleep(1)
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if color == 1:
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self.pub_marker(
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self.move_coords[color][0]/1000.0+0.04, self.move_coords[color][1]/1000.0-0.02)
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elif color == 0:
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self.pub_marker(
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self.move_coords[color][0]/1000.0+0.03, self.move_coords[color][1]/1000.0)
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self.pub_angles(self.move_angles[0], 20)
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time.sleep(3)
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# decide whether grab cube
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def decide_move(self, x, y, color):
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print(x, y, self.cache_x, self.cache_y)
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# detect the cube status move or run
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if (abs(x - self.cache_x) + abs(y - self.cache_y)) / 2 > 5: # mm
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self.cache_x, self.cache_y = x, y
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return
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else:
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self.cache_x = self.cache_y = 0
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# 调整吸泵吸取位置，y增大,向左移动;y减小,向右移动;x增大,前方移动;x减小,向后方移动
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if "dev" in self.robot_wio:
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if (y < -30 and x > 140) or (x > 150 and y < -10):
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x -= 10
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y += 10
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elif y > -10:
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y += 10
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elif x > 170:
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x -= 10
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y += 10
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elif "dev" in self.robot_m5:
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y += 10
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x -= 15
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if y < -20:
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y += 5
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# print x,y
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elif "dev" in self.robot_jes:
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if y<0:
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x+=5
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y+=3
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y+=10
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print x,y
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self.move(x, y, color)
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# init mycobot
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def run(self):
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if not self.raspi:
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self.pub_pump(False, self.Pin)
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for _ in range(5):
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self.pub_angles([-7.11, -6.94, -55.01, -24.16, 0, -15], 20)
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print(_)
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time.sleep(0.5)
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# draw aruco
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def draw_marker(self, img, x, y):
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# draw rectangle on img
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cv2.rectangle(
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img,
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(x - 20, y - 20),
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(x + 20, y + 20),
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(0, 255, 0),
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thickness=2,
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lineType=cv2.FONT_HERSHEY_COMPLEX,
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)
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# add text on rectangle
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cv2.putText(img, "({},{})".format(x, y), (x, y),
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cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (243, 0, 0), 2,)
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# get points of two aruco
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def get_calculate_params(self, img):
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# Convert the image to a gray image
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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# Detect ArUco marker.
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corners, ids, rejectImaPoint = cv2.aruco.detectMarkers(
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gray, self.aruco_dict, parameters=self.aruco_params
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)
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"""
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Two Arucos must be present in the picture and in the same order.
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There are two Arucos in the Corners, and each aruco contains the pixels of its four corners.
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Determine the center of the aruco by the four corners of the aruco.
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"""
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if len(corners) > 0:
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if ids is not None:
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if len(corners) <= 1 or ids[0] == 1:
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return None
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x1 = x2 = y1 = y2 = 0
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point_11, point_21, point_31, point_41 = corners[0][0]
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x1, y1 = int((point_11[0] + point_21[0] + point_31[0] + point_41[0]) / 4.0), int(
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(point_11[1] + point_21[1] + point_31[1] + point_41[1]) / 4.0)
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point_1, point_2, point_3, point_4 = corners[1][0]
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x2, y2 = int((point_1[0] + point_2[0] + point_3[0] + point_4[0]) / 4.0), int(
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(point_1[1] + point_2[1] + point_3[1] + point_4[1]) / 4.0)
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return x1, x2, y1, y2
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return None
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# set camera clipping parameters
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def set_cut_params(self, x1, y1, x2, y2):
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self.x1 = int(x1)
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self.y1 = int(y1)
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self.x2 = int(x2)
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self.y2 = int(y2)
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print(self.x1, self.y1, self.x2, self.y2)
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# set parameters to calculate the coords between cube and mycobot
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def set_params(self, c_x, c_y, ratio):
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self.c_x = c_x
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self.c_y = c_y
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self.ratio = 220.0/ratio
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# calculate the coords between cube and mycobot
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def get_position(self, x, y):
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return ((y - self.c_y)*self.ratio + self.camera_x), ((x - self.c_x)*self.ratio + self.camera_y)
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"""
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Calibrate the camera according to the calibration parameters.
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Enlarge the video pixel by 1.5 times, which means enlarge the video size by 1.5 times.
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If two ARuco values have been calculated, clip the video.
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"""
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def transform_frame(self, frame):
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# enlarge the image by 1.5 times
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fx = 1.5
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fy = 1.5
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frame = cv2.resize(frame, (0, 0), fx=fx, fy=fy,
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interpolation=cv2.INTER_CUBIC)
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if self.x1 != self.x2:
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# the cutting ratio here is adjusted according to the actual situation
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frame = frame[int(self.y2*0.2):int(self.y1*1.15),
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int(self.x1*0.7):int(self.x2*1.15)]
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return frame
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# according the class_id to get object name
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def id_class_name(self, class_id):
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for key, value in self.labels.items():
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if class_id == int(key):
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return value
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# detect object
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def obj_detect(self, img, goal):
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# rows, cols = frame.shape[:-1]
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# Resize image and swap BGR to RGB.
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# blob = cv2.dnn.blobFromImage(
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# frame,
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# size=(300, 300),
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# mean=(0, 0, 0),
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# swapRB=True,
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# crop=False,
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# )
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# Detecting.
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# self.net.setInput(blob)
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# out = self.net.forward()
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# x, y = 0, 0
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# Processing result.
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# for detection in out[0, 0, :, :]:
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# score = float(detection[2])
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# if score > 0.3:
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# class_id = detection[1]
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# left = detection[3] * cols
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# top = detection[4] * rows
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# right = detection[5] * cols
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# bottom = detection[6] * rows
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# if abs(right + bottom - left - top) > 380:
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# continue
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# x, y = (left + right) / 2.0, (top + bottom) / 2.0
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# cv2.rectangle(
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# frame,
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# (int(left), int(top)),
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# (int(right), int(bottom)),
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# (0, 230, 0),
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# thickness=2,
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# )
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# cv2.putText(
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# frame,
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# "{}: {}%".format(self.id_class_name(class_id),round(score * 100, 2)),
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# (int(left), int(top) - 10),
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# cv2.FONT_HERSHEY_COMPLEX_SMALL,
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# 1,
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# (243, 0, 0),
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# 2,
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# )
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i = 0
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MIN_MATCH_COUNT = 10
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sift = cv2.xfeatures2d.SIFT_create()
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# find the keypoints and descriptors with SIFT
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kp = []
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des = []
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for i in goal:
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kp0, des0 = sift.detectAndCompute(i, None)
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kp.append(kp0)
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des.append(des0)
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# kp1, des1 = sift.detectAndCompute(goal, None)
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kp2, des2 = sift.detectAndCompute(img, None)
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# FLANN parameters
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FLANN_INDEX_KDTREE = 0
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index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
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search_params = dict(checks=50) # or pass empty dictionary
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flann = cv2.FlannBasedMatcher(index_params, search_params)
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x, y = 0, 0
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try:
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for i in range(len(des)):
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matches = flann.knnMatch(des[i], des2, k=2)
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# store all the good matches as per Lowe's ratio test. 根据Lowe比率测试存储所有良好匹配项。
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good = []
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for m, n in matches:
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if m.distance < 0.7*n.distance:
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good.append(m)
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# When there are enough robust matching point pairs 当有足够的健壮匹配点对（至少个MIN_MATCH_COUNT）时
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if len(good) > MIN_MATCH_COUNT:
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# extract corresponding point pairs from matching 从匹配中提取出对应点对
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# query index of small objects, training index of scenarios 小对象的查询索引，场景的训练索引
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src_pts = np.float32(
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[kp[i][m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
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dst_pts = np.float32(
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[kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
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# Using matching points to find homography matrix in cv2.ransac 利用匹配点找到CV2.RANSAC中的单应矩阵
|
||
M, mask = cv2.findHomography(
|
||
src_pts, dst_pts, cv2.RANSAC, 5.0)
|
||
matchesMask = mask.ravel().tolist()
|
||
# Calculate the distortion of image, that is the corresponding position in frame 计算图1的畸变，也就是在图2中的对应的位置
|
||
h, w, d = goal[i].shape
|
||
pts = np.float32(
|
||
[[0, 0], [0, h-1], [w-1, h-1], [w-1, 0]]).reshape(-1, 1, 2)
|
||
dst = cv2.perspectiveTransform(pts, M)
|
||
ccoord = (dst[0][0]+dst[1][0]+dst[2][0]+dst[3][0])/4.0
|
||
cv2.putText(img, "{}".format(ccoord), (50, 60), fontFace=None,
|
||
fontScale=1, color=(0, 255, 0), lineType=1)
|
||
print(format(dst[0][0][0]))
|
||
x = (dst[0][0][0]+dst[1][0][0] +
|
||
dst[2][0][0]+dst[3][0][0])/4.0
|
||
y = (dst[0][0][1]+dst[1][0][1] +
|
||
dst[2][0][1]+dst[3][0][1])/4.0
|
||
|
||
# bound box 绘制边框
|
||
img = cv2.polylines(
|
||
img, [np.int32(dst)], True, 244, 3, cv2.LINE_AA)
|
||
# cv2.polylines(mixture, [np.int32(dst)], True, (0, 255, 0), 2, cv2.LINE_AA)
|
||
except Exception as e:
|
||
pass
|
||
|
||
# else:
|
||
# if(len(good) < MIN_MATCH_COUNT):
|
||
|
||
# i += 1
|
||
# if(i % 10 == 0):
|
||
# print("Not enough matches are found - %d/%d" %
|
||
# (len(good), MIN_MATCH_COUNT))
|
||
|
||
# matchesMask = None
|
||
if x+y > 0:
|
||
return x, y
|
||
else:
|
||
return None
|
||
|
||
|
||
def run():
|
||
|
||
# Object_detect().take_photo()
|
||
# Object_detect().cut_photo()
|
||
# goal = Object_detect().distinguist()
|
||
goal = []
|
||
path = os.getcwd()+'/local_photo/img'
|
||
|
||
for i, j, k in os.walk(path):
|
||
for l in k:
|
||
goal.append(cv2.imread('local_photo/img/{}'.format(l)))
|
||
|
||
cap_num = 0
|
||
cap = cv2.VideoCapture(cap_num, cv2.CAP_V4L)
|
||
if not cap.isOpened():
|
||
cap.open()
|
||
# init a class of Object_detect
|
||
detect = Object_detect()
|
||
# init mycobot
|
||
detect.run()
|
||
|
||
_init_ = 20 #
|
||
init_num = 0
|
||
nparams = 0
|
||
num = 0
|
||
real_sx = real_sy = 0
|
||
while cv2.waitKey(1) < 0:
|
||
# read camera
|
||
_, frame = cap.read()
|
||
# deal img
|
||
frame = detect.transform_frame(frame)
|
||
|
||
if _init_ > 0:
|
||
_init_ -= 1
|
||
continue
|
||
# calculate the parameters of camera clipping
|
||
if init_num < 20:
|
||
if detect.get_calculate_params(frame) is None:
|
||
cv2.imshow("figure", frame)
|
||
continue
|
||
else:
|
||
x1, x2, y1, y2 = detect.get_calculate_params(frame)
|
||
detect.draw_marker(frame, x1, y1)
|
||
detect.draw_marker(frame, x2, y2)
|
||
detect.sum_x1 += x1
|
||
detect.sum_x2 += x2
|
||
detect.sum_y1 += y1
|
||
detect.sum_y2 += y2
|
||
init_num += 1
|
||
continue
|
||
elif init_num == 20:
|
||
detect.set_cut_params(
|
||
(detect.sum_x1)/20.0,
|
||
(detect.sum_y1)/20.0,
|
||
(detect.sum_x2)/20.0,
|
||
(detect.sum_y2)/20.0,
|
||
)
|
||
detect.sum_x1 = detect.sum_x2 = detect.sum_y1 = detect.sum_y2 = 0
|
||
init_num += 1
|
||
continue
|
||
|
||
# calculate params of the coords between cube and mycobot
|
||
if nparams < 10:
|
||
if detect.get_calculate_params(frame) is None:
|
||
cv2.imshow("figure", frame)
|
||
continue
|
||
else:
|
||
x1, x2, y1, y2 = detect.get_calculate_params(frame)
|
||
detect.draw_marker(frame, x1, y1)
|
||
detect.draw_marker(frame, x2, y2)
|
||
detect.sum_x1 += x1
|
||
detect.sum_x2 += x2
|
||
detect.sum_y1 += y1
|
||
detect.sum_y2 += y2
|
||
nparams += 1
|
||
continue
|
||
elif nparams == 10:
|
||
nparams += 1
|
||
# calculate and set params of calculating real coord between cube and mycobot
|
||
detect.set_params(
|
||
(detect.sum_x1+detect.sum_x2)/20.0,
|
||
(detect.sum_y1+detect.sum_y2)/20.0,
|
||
abs(detect.sum_x1-detect.sum_x2)/10.0 +
|
||
abs(detect.sum_y1-detect.sum_y2)/10.0
|
||
)
|
||
print "ok"
|
||
continue
|
||
# get detect result
|
||
detect_result = detect.obj_detect(frame, goal)
|
||
if detect_result is None:
|
||
cv2.imshow("figure", frame)
|
||
continue
|
||
else:
|
||
x, y = detect_result
|
||
# calculate real coord between cube and mycobot
|
||
real_x, real_y = detect.get_position(x, y)
|
||
if num == 5:
|
||
detect.pub_marker(real_sx/5.0/1000.0, real_sy/5.0/1000.0)
|
||
detect.decide_move(real_sx/5.0, real_sy/5.0, detect.color)
|
||
num = real_sx = real_sy = 0
|
||
|
||
else:
|
||
num += 1
|
||
real_sy += real_y
|
||
real_sx += real_x
|
||
|
||
cv2.imshow("figure", frame)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
run()
|
||
# Object_detect().take_photo()
|
||
# Object_detect().cut_photo()
|