From 2e6482823bba39fec9ca7babfb5600a920c6e387 Mon Sep 17 00:00:00 2001 From: wangWking <842749351@qq.com> Date: Thu, 19 May 2022 15:43:35 +0800 Subject: [PATCH] optimize image recoginition of /scripts/detect_obj_img_folder_opt.py --- mycobot_ai/launch/Readme.md | 37 - mycobot_ai/launch/pi.launch | 28 - mycobot_ai/launch/seeed.launch | 27 - mycobot_ai/launch/vision.launch | 6 +- .../launch/{jsnn.launch => vision_wio.launch} | 6 +- mycobot_ai/local_photo/goal5.jpeg | Bin 0 -> 4413 bytes mycobot_ai/local_photo/img/goal02.jpeg | Bin 6300 -> 0 bytes mycobot_ai/local_photo/img/goal04.jpeg | Bin 7054 -> 0 bytes mycobot_ai/local_photo/img/goal1.jpeg | Bin 5145 -> 0 bytes mycobot_ai/local_photo/img/goal2.jpeg | Bin 6483 -> 5168 bytes mycobot_ai/local_photo/img/goal3.jpeg | Bin 4927 -> 0 bytes mycobot_ai/local_photo/img/goal4.jpeg | Bin 4532 -> 0 bytes mycobot_ai/local_photo/img/goal5.jpeg | Bin 4435 -> 0 bytes mycobot_ai/local_photo/takephoto.jpeg | Bin 0 -> 51011 bytes mycobot_ai/prof.calltree | 21703 ++++++++++++++++ mycobot_ai/prof.out | Bin 0 -> 554273 bytes mycobot_ai/res/blue/goal1.jpeg | Bin 4413 -> 5352 bytes mycobot_ai/res/blue/goal2.jpeg | Bin 0 -> 6099 bytes mycobot_ai/res/gray/goal1.jpeg | Bin 6314 -> 5421 bytes mycobot_ai/res/gray/goal2.jpeg | Bin 0 -> 4736 bytes mycobot_ai/res/green/goal1.jpeg | Bin 5066 -> 5717 bytes mycobot_ai/res/green/goal2.jpeg | Bin 0 -> 5205 bytes mycobot_ai/res/green/goal3.jpeg | Bin 0 -> 6116 bytes mycobot_ai/res/green/goal4.jpeg | Bin 0 -> 4585 bytes mycobot_ai/res/red/goal1.jpeg | Bin 4769 -> 7027 bytes mycobot_ai/res/takephoto.jpeg | Bin 47911 -> 44103 bytes mycobot_ai/scripts/add_img.py | 17 +- mycobot_ai/scripts/ai_windows.py | 182 - mycobot_ai/scripts/detect_encode.py | 0 mycobot_ai/scripts/detect_obj_color.py | 50 +- mycobot_ai/scripts/detect_obj_color_wio.py | 393 + mycobot_ai/scripts/detect_obj_img.py | 29 +- mycobot_ai/scripts/detect_obj_img_folder.py | 16 +- .../scripts/detect_obj_img_folder_opt.py | 648 + mycobot_ai/scripts/detect_obj_img_wio.py | 406 + mycobot_ai/scripts/graph.pbtxt | 5364 ++-- mycobot_ai/scripts/labels.json | 162 +- mycobot_ai/scripts/local_photo/takephoto.jpeg | Bin 0 -> 50575 bytes mycobot_ai/scripts/moving_utils.py | 0 mycobot_ai/scripts/openVideo.py | 0 mycobot_ai/scripts/pump.py | 37 +- mycobot_ai/scripts/scripts.tar.gz | Bin 0 -> 12204 bytes mycobot_ai/scripts/send_maker.py | 0 mycobot_ai/scripts/test.py | 104 + mycobot_ai/scripts/tools.py | 10 +- 45 files changed, 26085 insertions(+), 3140 deletions(-) delete mode 100644 mycobot_ai/launch/Readme.md delete mode 100644 mycobot_ai/launch/pi.launch delete mode 100644 mycobot_ai/launch/seeed.launch rename mycobot_ai/launch/{jsnn.launch => vision_wio.launch} (84%) create mode 100644 mycobot_ai/local_photo/goal5.jpeg delete mode 100644 mycobot_ai/local_photo/img/goal02.jpeg delete mode 100644 mycobot_ai/local_photo/img/goal04.jpeg delete mode 100644 mycobot_ai/local_photo/img/goal1.jpeg delete mode 100644 mycobot_ai/local_photo/img/goal3.jpeg delete mode 100644 mycobot_ai/local_photo/img/goal4.jpeg delete mode 100644 mycobot_ai/local_photo/img/goal5.jpeg create mode 100644 mycobot_ai/local_photo/takephoto.jpeg create mode 100644 mycobot_ai/prof.calltree create mode 100644 mycobot_ai/prof.out create mode 100644 mycobot_ai/res/blue/goal2.jpeg create mode 100644 mycobot_ai/res/gray/goal2.jpeg create mode 100644 mycobot_ai/res/green/goal2.jpeg create mode 100644 mycobot_ai/res/green/goal3.jpeg create mode 100644 mycobot_ai/res/green/goal4.jpeg mode change 100644 => 100755 mycobot_ai/scripts/add_img.py delete mode 100644 mycobot_ai/scripts/ai_windows.py mode change 100644 => 100755 mycobot_ai/scripts/detect_encode.py mode change 100644 => 100755 mycobot_ai/scripts/detect_obj_color.py create mode 100755 mycobot_ai/scripts/detect_obj_color_wio.py mode change 100644 => 100755 mycobot_ai/scripts/detect_obj_img.py mode change 100644 => 100755 mycobot_ai/scripts/detect_obj_img_folder.py create mode 100644 mycobot_ai/scripts/detect_obj_img_folder_opt.py create mode 100755 mycobot_ai/scripts/detect_obj_img_wio.py create mode 100644 mycobot_ai/scripts/local_photo/takephoto.jpeg mode change 100644 => 100755 mycobot_ai/scripts/moving_utils.py mode change 100644 => 100755 mycobot_ai/scripts/openVideo.py mode change 100644 => 100755 mycobot_ai/scripts/pump.py create mode 100644 mycobot_ai/scripts/scripts.tar.gz mode change 100644 => 100755 mycobot_ai/scripts/send_maker.py create mode 100755 mycobot_ai/scripts/test.py mode change 100644 => 100755 mycobot_ai/scripts/tools.py diff --git a/mycobot_ai/launch/Readme.md b/mycobot_ai/launch/Readme.md deleted file mode 100644 index 128053a..0000000 --- a/mycobot_ai/launch/Readme.md +++ /dev/null @@ -1,37 +0,0 @@ -### Tips： - -1. 请使用与机械臂相同型号的文件名文件。 - -2. 请先执行以下操作: - + 1. 打开一个新的终端 - + 2. 输入命令 -```bash -chmod +x /home/h/catkin_mycobot/src/mycobot_ros/mycobot_communication/scripts/xxx.py - # 此处为各个新增文件的文件名 -``` -3. jetson nano的文件还没有使用机械臂进行过测试，可能存在问题。 - -4. 数莓派版本的使用: - + 1. 打开VScode,新建一个文件，复制以下内容（请确保电脑与数莓派机械臂已经连接）并运行 - ```bash - from pymycobot import MyCobotSocket - - mc = MyCobotSocket("192.168.10.10","9000") - mc.connect() - ``` - - + 2. 打开“网络与internet”设置 - 更改适配器选项 - 右键打开数莓派的以太网属性 - 打开 “internet协议版本4” 的属性 - 选择 “使用下面的IP地址” - IP地址为 ： 192.168.10.100 （最后一位非10都可） - 子网掩码为： 255.255.255.0 - 确认 - - - - - - - diff --git a/mycobot_ai/launch/pi.launch b/mycobot_ai/launch/pi.launch deleted file mode 100644 index 4c8ada4..0000000 --- a/mycobot_ai/launch/pi.launch +++ /dev/null @@ -1,28 +0,0 @@ - - - - - - - - - - - - - - ["joint_states"] - - - - - - - - - - - - - - diff --git a/mycobot_ai/launch/seeed.launch b/mycobot_ai/launch/seeed.launch deleted file mode 100644 index bc451ab..0000000 --- a/mycobot_ai/launch/seeed.launch +++ /dev/null @@ -1,27 +0,0 @@ - - - - - - - - - - - - - ["joint_states"] - - - - - - - - - - - - - - diff --git a/mycobot_ai/launch/vision.launch b/mycobot_ai/launch/vision.launch index 654f540..a01ed45 100644 --- a/mycobot_ai/launch/vision.launch +++ b/mycobot_ai/launch/vision.launch @@ -1,6 +1,6 @@ - - - + + + diff --git a/mycobot_ai/launch/jsnn.launch b/mycobot_ai/launch/vision_wio.launch similarity index 84% rename from mycobot_ai/launch/jsnn.launch rename to mycobot_ai/launch/vision_wio.launch index 157b08f..654f540 100644 --- a/mycobot_ai/launch/jsnn.launch +++ b/mycobot_ai/launch/vision_wio.launch @@ -1,8 +1,8 @@ - + - + @@ -14,7 +14,7 @@ - + diff --git a/mycobot_ai/local_photo/goal5.jpeg b/mycobot_ai/local_photo/goal5.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..42767ceeb08a25e9beba673dd8de81bedd8741fb GIT binary patch literal 4413 zcmbW2bx_pbyT?Dv((}HJBt%3&VoB*10b%Lx zZlo9Xhu`1Kz5m=l?!E6b&ok%DdCr;lYo0T6uoKt?fc%O2V|4%o0szqM0I<`5Du4s} ztG^lNuW-SCGafE37#AN8AOD{qAR@#kAR@rWC%i*QMEqB`Gm<;RB!3rwFZs_=96T@> zkC*_T;GdHJQ^K|Y6oh~)-~?c*C3 z{x%{qDjJcL{4OOm?fr-J+`RmP&xK!#zE)ILp{i?Y>+0LuJ370%dwPEkqen)^#wRAH z7MGS+R@c@yHn$Fsj{lsTo}FJ@{^bGz;QwIV=Kqn4;+6~NZx0Cma)EHXZUan#i^nF6 zPpPa!U+mnyKV<(qu+aY(*?)okH`f$E z0tVeS4@?0l0v9aHSRh7ROV)Tqd=T}Rlhy@BLd$^^&c;nr_hv*o9&Kkw*Qd0a`Na`) z4?Z8~M7FP#X`OFFqUpjtT%63!LYt>);ax5obM!?EHp@(2Wo#plA{USY74@}(&0CA< zG`L7NqB4;r14ccg@-3cL-S~*r8l*z2%V{k}+;Nyc0IeXrN7~+RZh@GZU5O!Sf~YbI z{epgJLAIzNR@gFp?TmwG2Zu{1e_8iQ27}poe`|f2^PM-(BDr4yyKA3}dp!=&Y0$=AxT@*lNZS;qJy=Nsk%v`GUHkBuG ztDDq5tm!(=a;|&UE_C)_kWkdPLJFXe^beZfLRRy$l|)z>mHaM5 z=&tv^1=RbEZnKd3Go!)=lG}Yl&0kL+J%7B4cfzItTi1CkMG^Y#Q}n{feeqs;BfJ2p zaT~`arZi6JmBu(72emXQj$;`9)!_?|H{NT~%hEvRL7hCk1ES8_rO%HXJ`P$3OdVR6RG$wc$?<)Js5?j~GKK308tkk1g2N)7Xxv z*W=;4=2eo($MSe3oC3aZFy#Vrv9RuDj?D1U2d{x=_}o z9R)9$f7`@7tK|$;>+g%Ah;)WAAvTlkn0g%tY&v44xEjfko2vQcy@?D157ha`{%B9E zFLieAr!7s#NvhOLC(A<9?q@4ScLmm#hO$h8TM{(m%$8o1z~64Vd~T)Zp3dT9*61{6 z%AH&kNPJ%dTOo0oa@5!O93^;EyY;zoSaDKwQKXtI=7-vLB9pws@lDShUMjo$e$?eJ z8!Rw5`VN|RB%c25{lzEic1o#}updkd63WQoGR0@$1-_iq=%Re~%KgULiH^mY0$ zZ=rPt^~a$D#ZFCi7l^Bfmt%QyX3(H0^0yJ&q6OILMr3^9;P$=j3_CT1g{?9137usy z1KZELJ0{6a{fBmEf~xY`)@xEHOPf!h4lh+$PO6fFo8T>+>=F z7x_iS%t7t2;AXNw+IC2Zj}xS(B-1BtD*VY|$NVfpAs9S_6rXS|fm2X9lVQ zMv5L^81kor--1hYBza;@vXO7hkIL@tyxN%_%{bbUq$5P9(hsV1AX>7nn?_nRlm}G} z1yE{$FxsKiUvuM+@FO6bKiB-x14r%%h6^|MhUgTeQ`4fK?D5d-w)$X-sZb8OyjzdaUZu1 zq7h5Z)**;}0Fn`BKQ~j+llX|04}gq*&3I76>>=pg-KI#|#Pcb#D~leL<@Q0dhmE|S z;)JA8dmM2f6f+NqBO^AZ#y)ynf68lE6f_C)Aq{$ah`!!S*{2=fu<)%vY?Zo6DSmxv zSm!)-ugo#obe2qTl{C;+{t(T9a@YGh7Cu{0cW9VLmIhkO=fq-6nckZf!4H!srxDv0?uS6$pA~I6L{G_ zj;4d0Kssqv1H&O6y})d_F(-*ek$2mtorOdCP1NsqsV8Q*8Y;wRNwd0?rA!f8`We2+ zx$2V0YC`2_aa1;<)J9@JHLL038;VoR+KW0bHVe@em16;4(QGVW;B!JJ*fo@L zrXKiFru*J(d*%_}eJB>t-kJvGv`}3SQR~BYo z^Qx0>EYm6v^DBsK50`O3C(a>(2N~4co-@gzmnx zPUs&iLMO-^)cu?u80e7<7EKZz`_?Tw^Wfan$z!A79wLR1@M}X zsYco5)VS;NW!Zl*zx;SMrOm2o-1nWA>VXu4)N&crsr^Luc!daDI^#}|j9k^TNeK

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zV3G7Wuhe`vntxcP{{XxCp9#khZiP9oKhVOs7{6lRgMXTiaCcw~XT49Up-oai-2?UO zRpvd|@#$Zr!$_Yq8?>1kf~OhJ(x!)p1d@5EV`#|7e$^^C^D&I}p*H2Aq^{BE-wfo$ zUo#Qg0M|qDg5Epdhc_%w%+};>^sZ0gtob_>=jH>BYq9a*H=YEZ5IOQ#56YG'.format( - port) - # 根据通信口修改ros启动文件 - os.system( - "sed -i '2c {}' ~/catkin_ws/src/mycobot_ros/mycobot_ai/launch/vision.launch" - .format(self.command)) - - def change_file(self, port): - command1 = ''.format(port) - command2 = ''.format(1000000) - # 根据通信口修改ros启动文件 - os.system( - "sed -i '2c {}' ~/catkin_ws/src/mycobot_ros/mycobot_ai/launch/vision.launch".format(command1)) - os.system( - "sed -i '3c {}' ~/catkin_ws/src/mycobot_ros/mycobot_ai/launch/vision.launch".format(command2)) - - def start_run(self): - try: - print(u"开始运行") - one = self.myCombox.get() - if one == u"颜色识别": - self.run_py = "detect_obj_color.py" - t2 = threading.Thread(target=self.open_py1) - t2.setDaemon(True) - t2.start() - elif one == u"物体识别": - self.run_py = "detect_obj_img.py" - t3 = threading.Thread(target=self.open_py) - t3.setDaemon(True) - t3.start() - elif one == u"二维码识别": - self.run_py = "detect_encode.py" - t3 = threading.Thread(target=self.open_py2) - t3.setDaemon(True) - t3.start() - except Exception as e: - self.tips = str(e) - self.log_data.insert(END, self.tips) - - def open_py(self): - os.system( - "python ~/catkin_ws/src/mycobot_ros/mycobot_ai/scripts/detect_obj_img.py" - ) - - def open_py1(self): - os.system( - "python ~/catkin_ws/src/mycobot_ros/mycobot_ai/scripts/detect_obj_color.py" - ) - - def open_py2(self): - os.system( - "python ~/catkin_ws/src/mycobot_ros/mycobot_ai/scripts/detect_encode.py" - ) - - def add_img(self): - os.system( - "python ~/catkin_ws/src/mycobot_ros/mycobot_ai/scripts/add_img.py" - ) - - def open_ros(self): - t1 = threading.Thread(target=self.ross) - t1.setDaemon(True) - t1.start() - self.ros = True - - def ross(self): - os.system( - "roslaunch ~/catkin_ws/src/mycobot_ros/mycobot_ai/launch/vision.launch" - ) - - def close_py(self): - t1 = threading.Thread(target=self.close_p) - t1.setDaemon(True) - t1.start() - - def close_p(self): - # 关闭ai程序 - os.system("ps -ef | grep -E " + self.run_py + - " | grep -v 'grep' | awk '{print $2}' | xargs kill -9") - - def get_current_time(self): - # 日志时间 - """Get current time with format.""" - current_time = time.strftime("%Y-%m-%d %H:%M:%S", - time.localtime(time.time())) - return current_time - - def write_log_to_Text(self, logmsg): - # 设置日志函数 - global LOG_NUM - current_time = self.get_current_time() - logmsg_in = str(current_time) + " " + str(logmsg) + "\n" # 换行 - - if LOG_NUM <= 18: - self.log_data_Text.insert(END, logmsg_in) - LOG_NUM += len(logmsg_in.split("\n")) - # print(LOG_NUM) - else: - self.log_data_Text.insert(END, logmsg_in) - self.log_data_Text.yview("end") - - def run(self): - self.win.mainloop() - - -if __name__ == "__main__": - mc = Application() - mc.run() diff --git a/mycobot_ai/scripts/detect_encode.py b/mycobot_ai/scripts/detect_encode.py old mode 100644 new mode 100755 diff --git a/mycobot_ai/scripts/detect_obj_color.py b/mycobot_ai/scripts/detect_obj_color.py old mode 100644 new mode 100755 index 6d1a5b5..26032a4 --- a/mycobot_ai/scripts/detect_obj_color.py +++ b/mycobot_ai/scripts/detect_obj_color.py @@ -64,6 +64,7 @@ class Object_detect(Movement): self.raspi = True if self.raspi: self.gpio_status(False) + self.Pin = [2, 5] # choose place to set cube self.color = 0 @@ -147,20 +148,10 @@ class Object_detect(Movement): # send coordinates to move mycobot self.pub_coords([x, y, 165, -178.9, -1.57, -25.95], 20, 1) time.sleep(1.5) - if "dev" in self.robot_m5 or self.raspi: - self.pub_coords([x, y, 90, -178.9, -1.57, -25.95], 20, 1) - elif "dev" in self.robot_wio: - h = 0 - - if 165 < x < 180: - h = 10 - elif x > 180: - h = 20 - elif x < 135: - h = -20 - self.pub_coords([x, y, 31.9+h, -178.9, -1, -25.95], 20, 1) - - time.sleep(1.5) + + self.pub_coords([x, y, 80, -178.9, -1.57, -25.95], 20, 1) + + time.sleep(1) # open pump if self.raspi: self.gpio_status(True) @@ -213,34 +204,9 @@ class Object_detect(Movement): else: self.cache_x = self.cache_y = 0 # 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 - if "dev" in self.robot_wio: - - if (y < -30 and x > 140) or (x > 150 and y < -10): - x -= 10 - y += 10 - elif y > -10: - y += 10 - elif x > 170: - x -= 10 - y += 10 - elif "dev" in self.robot_raspi: - if x > 160: - y += 10 - elif y < -20: - x -= 10 - y += 10 - elif "dev" in self.robot_jes: - y += 13 - x += 4 - - elif "dev" in self.robot_m5: - x -= 10 - if y < 0: - y += 10 - if y < -30: - y += 7 - print x, y - self.move(x, y, color) + + + self.move(x,y, color) # init mycobot def run(self): diff --git a/mycobot_ai/scripts/detect_obj_color_wio.py b/mycobot_ai/scripts/detect_obj_color_wio.py new file mode 100755 index 0000000..fa7b0bf --- /dev/null +++ b/mycobot_ai/scripts/detect_obj_color_wio.py @@ -0,0 +1,393 @@ +#encoding:utf-8 + +from tokenize import Pointfloat +import cv2 +import numpy as np +import time +import json +import os +import rospy +from visualization_msgs.msg import Marker + +from moving_utils import Movement + +IS_CV_4 = cv2.__version__[0] == '4' + +class Object_detect(Movement): + + def __init__(self, camera_x=150, camera_y=-10): + # inherit the parent class + super(Object_detect, self).__init__() + # get path of file + dir_path = os.path.dirname(__file__) + # choose place to set cube + self.color = 0 + # parameters to calculate camera clipping parameters + self.x1 = self.x2 = self.y1 = self.y2 =0 + # set cache of real coord + self.cache_x = self.cache_y = 0 + # set color HSV + self.HSV = { + "yellow": [np.array([11, 115, 70]), np.array([40, 255, 245])], + "red": [np.array([0, 43, 46]), np.array([8, 255, 255])], + } + # use to calculate coord between cube and mycobot + self.sum_x1= self.sum_x2= self.sum_y2= self.sum_y1= 0 + # The coordinates of the grab center point relative to the mycobot + self.camera_x, self.camera_y = camera_x, camera_y + # The coordinates of the cube relative to the mycobot + self.c_x, self.c_y = 0,0 + # The ratio of pixels to actual values + self.ratio = 0 + # Get ArUco marker dict that can be detected. + self.aruco_dict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_6X6_250) + # Get ArUco marker params. + self.aruco_params = cv2.aruco.DetectorParameters_create() + + # init a node and a publisher + rospy.init_node("marker", anonymous=True) + self.pub = rospy.Publisher('/cube', Marker, queue_size=1) + # init a Marker + self.marker = Marker() + self.marker.header.frame_id = "/joint1" + self.marker.ns = "cube" + self.marker.type = self.marker.CUBE + self.marker.action = self.marker.ADD + self.marker.scale.x = 0.04 + self.marker.scale.y = 0.04 + self.marker.scale.z = 0.04 + self.marker.color.a = 1.0 + self.marker.color.g = 1.0 + self.marker.color.r = 1.0 + + + # marker position initial + self.marker.pose.position.x = 0 + self.marker.pose.position.y = 0 + self.marker.pose.position.z = 0.03 + self.marker.pose.orientation.x = 0 + self.marker.pose.orientation.y = 0 + self.marker.pose.orientation.z = 0 + self.marker.pose.orientation.w = 1.0 + + # publish marker + def pub_marker(self, x, y , z=0.03): + self.marker.header.stamp = rospy.Time.now() + self.marker.pose.position.x = x + self.marker.pose.position.y = y + self.marker.pose.position.z = z + self.marker.color.g = self.color + self.pub.publish(self.marker) + + + + # Grasping motion + def move(self, x,y,color): + angles = [ + [-7.11, -6.94, -55.01, -24.16, 0, -38.84], # init the point + [-1.14, -10.63, -87.8, 9.05, -3.07, -37.7], # point to grab + [17.4, -10.1, -87.27, 5.8, -2.02, -37.7], # point to grab + ] + + coords = [ + [106.1, -141.6, 240.9, -173.34, -8.15, -83.11], # above the red bucket + [208.2, -127.8, 246.9, -157.51, -17.5, -71.18], # above the green bucket + [209.7, -18.6, 230.4, -168.48, -9.86, -39.38], + [196.9, -64.7, 232.6, -166.66, -9.44, -52.47], + [126.6, -118.1, 305.0, -157.57, -13.72, -75.3], + + ] + + # send Angle to move mycobot + self.pub_angles(angles[0], 20) + time.sleep(1.5) + self.pub_angles(angles[1], 20) + time.sleep(1.5) + self.pub_angles(angles[2], 20) + time.sleep(1.5) + # send coordinates to move mycobot + self.pub_coords([x, y, 165, -178.9, -1.57, -25.95], 20, 1) + time.sleep(1.5) + self.pub_coords([x, y, 40, -178.9, -1.57, -25.95], 20, 1) + time.sleep(1.5) + # open pump + self.pub_pump(True,[20,21]) + time.sleep(0.5) + self.pub_angles(angles[2], 20) + time.sleep(3) + self.pub_marker(coords[2][0]/1000.0, coords[2][1]/1000.0, coords[2][2]/1000.0) + + self.pub_angles(angles[1], 20) + time.sleep(1.5) + self.pub_marker(coords[3][0]/1000.0, coords[3][1]/1000.0, coords[3][2]/1000.0) + + self.pub_angles(angles[0], 20) + time.sleep(1.5) + self.pub_marker(coords[4][0]/1000.0, coords[4][1]/1000.0, coords[4][2]/1000.0) + + self.pub_coords(coords[color], 20, 1) + self.pub_marker(coords[color][0]/1000.0, coords[color][1]/1000.0, coords[color][2]/1000.0) + time.sleep(2) + # close pump + self.pub_pump(False,[20,21]) + if color==1: + self.pub_marker(coords[color][0]/1000.0+0.04, coords[color][1]/1000.0-0.02) + elif color==0: + self.pub_marker(coords[color][0]/1000.0+0.03, coords[color][1]/1000.0) + self.pub_angles(angles[0], 20) + time.sleep(3) + + + # decide whether grab cube + def decide_move(self, x, y, color): + + + print(x, y,self.cache_x, self.cache_y) + # detect the cube status move or run + if (abs(x - self.cache_x) + abs(y - self.cache_y)) / 2 > 5: # mm + self.cache_x, self.cache_y = x, y + return + else: + self.cache_x = self.cache_y = 0 + self.move(x-10,y+10,color) + + # init mycobot + def run(self): + + for _ in range(10): + self.pub_angles([-7.11, -6.94, -55.01, -24.16, 0, -38.84], 20) + print(_) + time.sleep(0.5) + self.pub_pump(False,[20,21]) + + # draw aruco + def draw_marker(self,img,x,y): + # draw rectangle on img + cv2.rectangle( + img, + (x - 20, y - 20), + (x + 20, y + 20), + (0, 255, 0), + thickness=2, + lineType=cv2.FONT_HERSHEY_COMPLEX, + ) + # add text on rectangle + cv2.putText(img,"({},{})".format(x,y),(x,y),cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (243, 0, 0), 2,) + + # get points of two aruco + def get_calculate_params(self,img): + # Convert the image to a gray image + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # Detect ArUco marker. + corners, ids, rejectImaPoint = cv2.aruco.detectMarkers( + gray, self.aruco_dict, parameters=self.aruco_params + ) + + """ + Two Arucos must be present in the picture and in the same order. + There are two Arucos in the Corners, and each aruco contains the pixels of its four corners. + Determine the center of the aruco by the four corners of the aruco. + """ + if len(corners) > 0: + if ids is not None: + if len(corners) <= 1 or ids[0]==1: + return None + x1=x2=y1=y2 = 0 + point_11,point_21,point_31,point_41 = corners[0][0] + x1, y1 = int((point_11[0] + point_21[0] + point_31[0] + point_41[0]) / 4.0), int((point_11[1] + point_21[1] + point_31[1] + point_41[1]) / 4.0) + point_1,point_2,point_3,point_4 = corners[1][0] + x2, y2 = int((point_1[0] + point_2[0] + point_3[0] + point_4[0]) / 4.0), int((point_1[1] + point_2[1] + point_3[1] + point_4[1]) / 4.0) + return x1,x2,y1,y2 + return None + + # set camera clipping parameters + def set_cut_params(self, x1, y1, x2, y2): + self.x1 = int(x1) + self.y1 = int(y1) + self.x2 = int(x2) + self.y2 = int(y2) + print(self.x1,self.y1,self.x2,self.y2) + + # set parameters to calculate the coords between cube and mycobot + def set_params(self, c_x, c_y, ratio): + self.c_x = c_x + self.c_y = c_y + self.ratio = 220.0/ratio + + # calculate the coords between cube and mycobot + def get_position(self, x, y): + return ((y - self.c_y)*self.ratio + self.camera_x), ((x - self.c_x)*self.ratio + self.camera_y) + + """ + Calibrate the camera according to the calibration parameters. + Enlarge the video pixel by 1.5 times, which means enlarge the video size by 1.5 times. + If two ARuco values have been calculated, clip the video. + """ + def transform_frame(self, frame): + # enlarge the image by 1.5 times + fx = 1.5 + fy = 1.5 + frame = cv2.resize(frame, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_CUBIC) + if self.x1 != self.x2: + # the cutting ratio here is adjusted according to the actual situation + frame = frame[int(self.y2*0.2):int(self.y1*1.15), int(self.x1*0.7):int(self.x2*1.15)] + return frame + + # detect cube color + def color_detect(self, img): + # set the arrangement of color'HSV + x = y = 0 + for mycolor, item in self.HSV.items(): + redLower = np.array(item[0]) + redUpper = np.array(item[1]) + # transfrom the img to model of gray + hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) + # wipe off all color expect color in range + mask = cv2.inRange(hsv, item[0], item[1]) + # a etching operation on a picture to remove edge roughness + erosion = cv2.erode(mask, np.ones((1, 1), np.uint8), iterations=2) + # the image for expansion operation, its role is to deepen the color depth in the picture + dilation =cv2.dilate(erosion, np.ones((1, 1), np.uint8), iterations=2) + # adds pixels to the image + target = cv2.bitwise_and(img, img, mask=dilation) + # the filtered image is transformed into a binary image and placed in binary + ret, binary = cv2.threshold(dilation, 127, 255, cv2.THRESH_BINARY) + # get the contour coordinates of the image, where contours is the coordinate value, here only the contour is detected + contours, hierarchy = cv2.findContours( + dilation, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + + if len(contours) > 0: + # do something about misidentification + boxes = [ + box + for box in [cv2.boundingRect(c) for c in contours] + if min(img.shape[0], img.shape[1]) / 10 + < min(box[2], box[3]) + < min(img.shape[0], img.shape[1]) / 1 + ] + if boxes: + for box in boxes: + x, y, w, h = box + # find the largest object that fits the requirements + c = max(contours, key=cv2.contourArea) + # get the lower left and upper right points of the positioning object + x, y, w, h = cv2.boundingRect(c) + # locate the target by drawing rectangle + cv2.rectangle(img, (x, y), (x+w, y+h), (153, 153, 0), 2) + # calculate the rectangle center + x, y = (x*2+w)/2, (y*2+h)/2 + # calculate the real coordinates of mycobot relative to the target + if mycolor == "yellow": + self.color = 1 + elif mycolor == "red": + self.color = 0 + + if abs(x) + abs(y) > 0: + return x, y + else: + return None + + + + +if __name__ == "__main__": + # open the camera + cap_num = 0 + cap = cv2.VideoCapture(cap_num) + 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.color_detect(frame) + 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 == 20: + detect.pub_marker(real_sx/20.0/1000.0, real_sy/20.0/1000.0) + detect.decide_move(real_sx/20.0, real_sy/20.0, detect.color) + num = real_sx = real_sy = 0 + + else: + num += 1 + real_sy += real_y + real_sx += real_x + + cv2.imshow("figure",frame) + + + diff --git a/mycobot_ai/scripts/detect_obj_img.py b/mycobot_ai/scripts/detect_obj_img.py old mode 100644 new mode 100755 index eb7f4be..47bc937 --- a/mycobot_ai/scripts/detect_obj_img.py +++ b/mycobot_ai/scripts/detect_obj_img.py @@ -25,6 +25,8 @@ class Object_detect(Movement): super(Object_detect, self).__init__() # get path of file dir_path = os.path.dirname(__file__) + + # 移动角度 self.move_angles = [ [-7.11, -6.94, -55.01, -24.16, 0, 15], # init the point @@ -64,7 +66,7 @@ class Object_detect(Movement): self.raspi = True if self.raspi: self.gpio_status(False) - + self.Pin = [2, 5] # choose place to set cube self.color = 0 # parameters to calculate camera clipping parameters @@ -77,6 +79,10 @@ class Object_detect(Movement): # self.label_path = os.path.join(dir_path, "labels.json") # load class labels # self.labels = json.load(open(self.label_path)) + + + + # use to calculate coord between cube and mycobot self.sum_x1 = self.sum_x2 = self.sum_y2 = self.sum_y1 = 0 # The coordinates of the grab center point relative to the mycobot @@ -121,6 +127,8 @@ class Object_detect(Movement): self.marker.pose.orientation.w = 1.0 self.cache_x = self.cache_y = 0 + + # publish marker def pub_marker(self, x, y, z=0.03): @@ -152,8 +160,8 @@ class Object_detect(Movement): self.pub_coords([x, y, 165, -178.9, -1.57, -66], 20, 1) time.sleep(1.5) # 根据不同底板机械臂，调整吸泵高度 - if "dev" in self.robot_m5 or "dev" in self.robot_raspi: - # m5 and raspi + if "dev" in self.robot_m5: + # m5 and jetson nano self.pub_coords([x, y, 90, -178.9, -1.57, -66], 25, 1) elif "dev" in self.robot_wio: h = 0 @@ -166,8 +174,8 @@ class Object_detect(Movement): self.pub_coords([x, y, 31.9+h, -178.9, -1, -66], 20, 1) elif "dev" in self.robot_jes: h = 0 - if x < 130: - h = 15 + if x<130: + h=15 self.pub_coords([x, y, 90-h, -178.9, -1.57, -66], 25, 1) time.sleep(1.5) # open pump @@ -237,11 +245,11 @@ class Object_detect(Movement): y += 5 # print x,y elif "dev" in self.robot_jes: - if y < 0: - x += 5 - y += 3 - y += 10 - print x, y + if y<0: + x+=5 + y+=3 + y+=10 + print x,y self.move(x, y, color) # init mycobot @@ -476,6 +484,7 @@ def run(): 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(): diff --git a/mycobot_ai/scripts/detect_obj_img_folder.py b/mycobot_ai/scripts/detect_obj_img_folder.py old mode 100644 new mode 100755 index d2d1029..2fa3d92 --- a/mycobot_ai/scripts/detect_obj_img_folder.py +++ b/mycobot_ai/scripts/detect_obj_img_folder.py @@ -315,7 +315,7 @@ class Object_detect(Movement): # detect object def obj_detect(self, img, goal): i = 0 - MIN_MATCH_COUNT = 10 + MIN_MATCH_COUNT = 5 sift = cv2.xfeatures2d.SIFT_create() # find the keypoints and descriptors with SIFT @@ -430,20 +430,21 @@ def run(): # init mycobot detect.run() - _init_ = 20 # +# _init_ = 20 # init_num = 0 nparams = 0 num = 0 real_sx = real_sy = 0 while cv2.waitKey(1) < 0: + start_time = time.time() # read camera _, frame = cap.read() # deal img frame = detect.transform_frame(frame) - if _init_ > 0: - _init_ -= 1 - continue +# if _init_ > 0: +# _init_ -= 1 +# continue # calculate the parameters of camera clipping if init_num < 20: if detect.get_calculate_params(frame) is None: @@ -519,8 +520,11 @@ def run(): cv2.imshow("figure", frame) + end_time = time.time() + print("loop_time = ", end_time - start_time) + if __name__ == "__main__": run() # Object_detect().take_photo() - # Object_detect().cut_photo() \ No newline at end of file + # Object_detect().cut_photo() diff --git a/mycobot_ai/scripts/detect_obj_img_folder_opt.py b/mycobot_ai/scripts/detect_obj_img_folder_opt.py new file mode 100644 index 0000000..1ed9909 --- /dev/null +++ b/mycobot_ai/scripts/detect_obj_img_folder_opt.py @@ -0,0 +1,648 @@ +# encoding:utf-8 +#!/usr/bin/env python2 + +from multiprocessing import Process, Pipe + +from cgi import parse +from difflib import restore +# import queue +from sys import path +from tokenize import Pointfloat +from turtle import color +# from typing_extensions import Self +import cv2 +import numpy as np +import time +import json +import os +import rospy +from visualization_msgs.msg import Marker +from PIL import Image +from threading import Thread +import tkFileDialog as filedialog +import Tkinter as tk +from moving_utils import Movement + +IS_CV_4 = cv2.__version__[0] == '4' +__version__ = "1.0" # Adaptive seeed + + +class Object_detect(Movement): + + def __init__(self, camera_x=150, camera_y=-10): + # inherit the parent class + super(Object_detect, self).__init__() + # get path of file + dir_path = os.path.dirname(__file__) + + # 移动角度 + self.move_angles = [ + [-26.11, -6.94, -55.01, -24.16, 0, 15], # init the point + [-1.14, 0.63, -87.8, 9.05, -3.07, 15], # point to grab + [17.4, -10.1, -87.27, 5.8, -2.02, 15], # point to grab + ] + + # 移动坐标 + self.move_coords = [ + [120.1, -141.6, 240.9, -173.34, -8.15, -110.11], # above the red bucket + # above the yello bucket + #[208.2, -127.8, 260.9, -157.51, -17.5, -71.18], + [205.6, -130.5, 263.0, -150.99, -0.07, -107.35], # above the green bucket + [-20.0, 176.7, 242.6, -166.66, -9.44, -52.47], # above the gray bucket + [104.9, 176.7, 242.6, -166.66, -9.44, -52.47], # above the blue bucket + [126.6, -118.1, 305.0, -157.57, -13.72, -75.3], + ] + + # 判断连接设备:ttyUSB*为M5，ttyACM*为seeed + + self.raspi = False + import RPi.GPIO as GPIO + self.GPIO = GPIO + GPIO.setwarnings(False) + GPIO.setmode(GPIO.BCM) + GPIO.setup(20, GPIO.OUT) + GPIO.setup(21, GPIO.OUT) + + self.gpio_status(False) + + self.Pin = [2, 5] + # choose place to set cube + self.color = 0 + # parameters to calculate camera clipping parameters + self.x1 = self.x2 = self.y1 = self.y2 = 0 + # set cache of real coord + self.cache_x = self.cache_y = 0 + # load model of img recognition + # self.model_path = os.path.join(dir_path, "frozen_inference_graph.pb") + # self.pbtxt_path = os.path.join(dir_path, "graph.pbtxt") + # self.label_path = os.path.join(dir_path, "labels.json") + # # load class labels + # self.labels = json.load(open(self.label_path)) + + # use to calculate coord between cube and mycobot + self.sum_x1 = self.sum_x2 = self.sum_y2 = self.sum_y1 = 0 + # The coordinates of the grab center point relative to the mycobot + self.camera_x, self.camera_y = camera_x, camera_y + # The coordinates of the cube relative to the mycobot + self.c_x, self.c_y = 0, 0 + # The ratio of pixels to actual values + self.ratio = 0 + # Get ArUco marker dict that can be detected. + self.aruco_dict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_6X6_250) + # Get ArUco marker params. + self.aruco_params = cv2.aruco.DetectorParameters_create() + + # if IS_CV_4: + # self.net = cv2.dnn.readNetFromTensorflow(self.model_path, self.pbtxt_path) + # else: + # print('Load tensorflow model need the version of opencv is 4.') + # exit(0) + # init a node and a publisher + rospy.init_node("marker", anonymous=True) + self.pub = rospy.Publisher('/cube', Marker, queue_size=1) + # init a Marker + self.marker = Marker() + self.marker.header.frame_id = "/joint1" + self.marker.ns = "cube" + self.marker.type = self.marker.CUBE + self.marker.action = self.marker.ADD + self.marker.scale.x = 0.04 + self.marker.scale.y = 0.04 + self.marker.scale.z = 0.04 + self.marker.color.a = 1.0 + self.marker.color.g = 1.0 + self.marker.color.r = 1.0 + + # marker position initial + self.marker.pose.position.x = 0 + self.marker.pose.position.y = 0 + self.marker.pose.position.z = 0.03 + self.marker.pose.orientation.x = 0 + self.marker.pose.orientation.y = 0 + self.marker.pose.orientation.z = 0 + self.marker.pose.orientation.w = 1.0 + + self.cache_x = self.cache_y = 0 + + # publish marker + + def pub_marker(self, x, y, z=0.03): + self.marker.header.stamp = rospy.Time.now() + self.marker.pose.position.x = x + self.marker.pose.position.y = y + self.marker.pose.position.z = z + self.marker.color.g = self.color + self.pub.publish(self.marker) + + def gpio_status(self, flag): + if flag: + self.GPIO.output(20, 0) + self.GPIO.output(21, 0) + else: + self.GPIO.output(20, 1) + self.GPIO.output(21, 1) + + # Grasping motion + def move(self, x, y, color): + # send Angle to move mycobot + self.pub_angles(self.move_angles[0], 20) + time.sleep(1.5) + self.pub_angles(self.move_angles[1], 20) + time.sleep(1.5) + self.pub_angles(self.move_angles[2], 20) + time.sleep(1.5) + # send coordinates to move mycobot + self.pub_coords([x, y, 165, -178.9, -1.57, -66], 20, 1) + time.sleep(1.5) + # 根据不同底板机械臂，调整吸泵高度 + self.pub_coords([x, y, 90, -178.9, -1.57, -66], 25, 1) + time.sleep(1.5) + # open pump + self.gpio_status(True) + + time.sleep(0.5) + self.pub_angles(self.move_angles[2], 20) + time.sleep(3) + self.pub_marker(self.move_coords[2][0] / 1000.0, + self.move_coords[2][1] / 1000.0, + self.move_coords[2][2] / 1000.0) + + self.pub_angles(self.move_angles[1], 20) + time.sleep(1.5) + self.pub_marker(self.move_coords[3][0] / 1000.0, + self.move_coords[3][1] / 1000.0, + self.move_coords[3][2] / 1000.0) + + self.pub_angles(self.move_angles[0], 20) + time.sleep(1.5) + self.pub_marker(self.move_coords[4][0] / 1000.0, + self.move_coords[4][1] / 1000.0, + self.move_coords[4][2] / 1000.0) + + print(self.move_coords[color]) + self.pub_coords(self.move_coords[color], 20, 1) + self.pub_marker(self.move_coords[color][0] / 1000.0, + self.move_coords[color][1] / 1000.0, + self.move_coords[color][2] / 1000.0) + time.sleep(4) + # close pump + self.gpio_status(False) + + time.sleep(1) + self.pub_marker(self.move_coords[color][0] / 1000.0 + 0.04, + self.move_coords[color][1] / 1000.0 - 0.02) + self.pub_angles(self.move_angles[0], 20) + time.sleep(3) + + # decide whether grab cube + def decide_move(self, x, y, color): + print(x, y, self.cache_x, self.cache_y) + # detect the cube status move or run + if (abs(x - self.cache_x) + abs(y - self.cache_y)) / 2 > 5: # mm + self.cache_x, self.cache_y = x, y + return + else: + self.cache_x = self.cache_y = 0 + # 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 + print(x, y) + self.move(x, y, color) + + # init mycobot + def run(self): + for _ in range(5): + self.pub_angles([-26.11, -6.94, -55.01, -24.16, 0, -15], 20) + print(_) + time.sleep(0.5) + + # draw aruco + + def draw_marker(self, img, x, y): + # draw rectangle on img + cv2.rectangle( + img, + (x - 20, y - 20), + (x + 20, y + 20), + (0, 255, 0), + thickness=2, + lineType=cv2.FONT_HERSHEY_COMPLEX, + ) + # add text on rectangle + cv2.putText( + img, + "({},{})".format(x, y), + (x, y), + cv2.FONT_HERSHEY_COMPLEX_SMALL, + 1, + (243, 0, 0), + 2, + ) + + # get points of two aruco + def get_calculate_params(self, img): + # Convert the image to a gray image + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # Detect ArUco marker. + corners, ids, rejectImaPoint = cv2.aruco.detectMarkers( + gray, self.aruco_dict, parameters=self.aruco_params) + """ + Two Arucos must be present in the picture and in the same order. + There are two Arucos in the Corners, and each aruco contains the pixels of its four corners. + Determine the center of the aruco by the four corners of the aruco. + """ + if len(corners) > 0: + if ids is not None: + if len(corners) <= 1 or ids[0] == 1: + return None + x1 = x2 = y1 = y2 = 0 + point_11, point_21, point_31, point_41 = corners[0][0] + x1, y1 = int( + (point_11[0] + point_21[0] + point_31[0] + point_41[0]) / + 4.0), int( + (point_11[1] + point_21[1] + point_31[1] + point_41[1]) + / 4.0) + point_1, point_2, point_3, point_4 = corners[1][0] + x2, y2 = int( + (point_1[0] + point_2[0] + point_3[0] + point_4[0]) / + 4.0), int( + (point_1[1] + point_2[1] + point_3[1] + point_4[1]) / + 4.0) + return x1, x2, y1, y2 + return None + + # set camera clipping parameters + def set_cut_params(self, x1, y1, x2, y2): + self.x1 = int(x1) + self.y1 = int(y1) + self.x2 = int(x2) + self.y2 = int(y2) + print(self.x1, self.y1, self.x2, self.y2) + + # set parameters to calculate the coords between cube and mycobot + def set_params(self, c_x, c_y, ratio): + self.c_x = c_x + self.c_y = c_y + self.ratio = 220.0 / ratio + + # calculate the coords between cube and mycobot + def get_position(self, x, y): + return ((y - self.c_y) * self.ratio + + self.camera_x), ((x - self.c_x) * self.ratio + self.camera_y) + + """ + Calibrate the camera according to the calibration parameters. + Enlarge the video pixel by 1.5 times, which means enlarge the video size by 1.5 times. + If two ARuco values have been calculated, clip the video. + """ + + def transform_frame(self, frame): + # enlarge the image by 1.5 times + fx = 1.5 + fy = 1.5 + frame = cv2.resize(frame, (0, 0), + fx=fx, + fy=fy, + interpolation=cv2.INTER_CUBIC) + if self.x1 != self.x2: + # the cutting ratio here is adjusted according to the actual situation + frame = frame[int(self.y2 * 0.2):int(self.y1 * 1.15), + int(self.x1 * 0.7):int(self.x2 * 1.15)] + return frame + + # according the class_id to get object name + def id_class_name(self, class_id): + for key, value in self.labels.items(): + if class_id == int(key): + return value + + # detect object + def obj_detect(self, img, goal, kp_img, desc_img, kp_list, desc_list, connection): + i = 0 + MIN_MATCH_COUNT = 5 + # sift = cv2.xfeatures2d.SIFT_create() + + # find the keypoints and descriptors with SIFT + # kp = [] + # des = [] + kp = kp_list + des = desc_list + + # for i in goal: + # kp0, des0 = sift.detectAndCompute(i, None) + # kp.append(kp0) + # des.append(des0) + + # kp1, des1 = sift.detectAndCompute(goal, None) + # kp2, des2 = sift.detectAndCompute(img, None) + kp2, des2 = kp_img, desc_img + + # FLANN parameters + FLANN_INDEX_KDTREE = 0 + index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5) + search_params = dict(checks=50) # or pass empty dictionary + flann = cv2.FlannBasedMatcher(index_params, search_params) + + x, y = 0, 0 + try: + for i in range(len(des)): + matches = flann.knnMatch(des[i], des2, k=2) + # 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。 + good = [] + for m, n in matches: + if m.distance < 0.7 * n.distance: + good.append(m) + + # 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 + if len(good) > MIN_MATCH_COUNT: + + # extract corresponding point pairs from matching 从匹配中提取出对应点对 + # query index of small objects, training index of scenarios 小对象的查询索引，场景的训练索引 + src_pts = np.float32([kp[i][m.queryIdx].pt + for m in good]).reshape(-1, 1, 2) + dst_pts = np.float32([kp2[m.trainIdx].pt + for m in good]).reshape(-1, 1, 2) + + # 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) + coord = (dst[0][0] + dst[1][0] + dst[2][0] + + dst[3][0]) / 4.0 + connection.send((DRAW_COORDS, coord)) + # cv2.putText(img, "{}".format(coord), (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) + connection.send((DRAW_RECT, dst)) + # 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 + +# The path to save the image folder +def parse_folder(folder): + restore = [] + path = '/home/ubuntu/catkin_ws/src/mycobot_ros/mycobot_ai/' + folder + for i, j, k in os.walk(path): + for l in k: + restore.append(cv2.imread(folder + '/{}'.format(l))) + return restore + + +def compute_keypoints_and_descriptors(sift, images_lists): + kp_list = [] + desc_list = [] + for images in images_lists: + kp_tmp = [] + desc_tmp = [] + for img in images: + kp, desc = sift.detectAndCompute(img, None) + kp_tmp.append(kp) + desc_tmp.append(desc) + kp_list.append(kp_tmp) + desc_list.append(desc_tmp) + + return kp_list, desc_list + + +GET_FRAME = 1 +STOP_PROCESSING = 2 +DRAW_COORDS = 3 +DRAW_RECT = 4 +CLEAR_DRAW = 5 +CROP_FRAME = 6 + + +def get_frame(connection): + connection.send(GET_FRAME) + frame = connection.recv() + return frame + + +def process_transform_frame(frame, x1, y1, x2, y2): + # enlarge the image by 1.5 times + fx = 1.5 + fy = 1.5 + frame = cv2.resize(frame, (0, 0), + fx=fx, + fy=fy, + interpolation=cv2.INTER_CUBIC) +# if x1 != x2: + # the cutting ratio here is adjusted according to the actual situation +# frame = frame[int(y2 * 0.2):int(y1 * 1.15), +# int(x1 * 0.7):int(x2 * 1.15)] + return frame + + +def process_display_frame(connection): + cap_num = 0 + coord = None + dst = None + x1 = 0 + y1 = 0 + x2 = 0 + y2 = 0 + cap = cv2.VideoCapture(cap_num, cv2.CAP_V4L) + if not cap.isOpened(): + cap.open() + while cv2.waitKey(1) < 0: + _, frame = cap.read() + frame = process_transform_frame(frame, x1, y1, x2, y2) + if connection.poll(): + request = connection.recv() + if request == GET_FRAME: + connection.send(frame) + elif request == CLEAR_DRAW: + coord = None + dst = None + elif type(request) is tuple: + if request[0] == DRAW_COORDS: + coord = request[1] + elif request[0] == DRAW_RECT: + dst = request[1] + elif request[0] == CROP_FRAME: + x1 = request[1] + y1 = request[2] + x2 = request[3] + y2 = request[4] + + if not coord is None: + cv2.putText(frame, "{}".format(coord), (50, 60), fontFace=None, + fontScale=1, color=(0, 255, 0), lineType=1) + if not dst is None: + frame = cv2.polylines(frame, [np.int32(dst)], True, 244, 3, cv2.LINE_AA) + cv2.imshow("figure", frame) + time.sleep(0.04) + connection.send(STOP_PROCESSING) + + +def run(): + parent_conn, child_conn = Pipe() + + child = Process(target=process_display_frame, args=(child_conn,)) + child.start() + + # Object_detect().take_photo() + # Object_detect().cut_photo() + # goal = Object_detect().distinguist() + + res_queue = [[], [], [], []] + res_queue[0] = parse_folder('res/red') + res_queue[1] = parse_folder('res/green') + res_queue[2] = parse_folder('res/gray') + res_queue[3] = parse_folder('res/blue') + + # res_queue = [] + # res_queue.extend(parse_folder('res/red')) + # res_queue.extend(parse_folder('res/green')) + # res_queue.extend(parse_folder('res/gray')) + # res_queue.extend(parse_folder('res/blue')) + + sift = cv2.xfeatures2d.SIFT_create() + kp_list, desc_list = compute_keypoints_and_descriptors(sift, res_queue) + + # 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 True: + start_time = time.time() + if parent_conn.poll(): + data = parent_conn.recv() + if data == STOP_PROCESSING: + break + # read camera + frame = get_frame(parent_conn) + # 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, + ) + parent_conn.send((CROP_FRAME, + (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 + print "ok" + 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 + + kp_img, desc_img = sift.detectAndCompute(frame, None) + + frame = get_frame(parent_conn) + for i, v in enumerate(res_queue): + # HACK: to update frame every time + detect_result = detect.obj_detect(frame, v, kp_img, desc_img, kp_list[i], desc_list[i], parent_conn) + if detect_result: + x, y = detect_result + # calculate real coord between cube and mycobot + real_x, real_y = detect.get_position(x, y) + detect.color = i + detect.pub_marker(real_x / 1000.0, real_y / 1000.0) + detect.decide_move(real_x, real_y, detect.color) + # if num == 5: + # detect.color = i + # 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 + parent_conn.send(CLEAR_DRAW) + + # cv2.imshow("figure", frame) + time.sleep(0.05) + end_time = time.time() + print("loop_time = ", end_time - start_time) + + child.join() + + +if __name__ == "__main__": + run() + # Object_detect().take_photo() + # Object_detect().cut_photo() diff --git a/mycobot_ai/scripts/detect_obj_img_wio.py b/mycobot_ai/scripts/detect_obj_img_wio.py new file mode 100755 index 0000000..c0e30ff --- /dev/null +++ b/mycobot_ai/scripts/detect_obj_img_wio.py @@ -0,0 +1,406 @@ +#encoding:utf-8 + +from tokenize import Pointfloat +import cv2 +import numpy as np +import time +import json +import os +import rospy +from visualization_msgs.msg import Marker + +from moving_utils import Movement + +IS_CV_4 = cv2.__version__[0] == '4' + +class Object_detect(Movement): + + def __init__(self, camera_x=150, camera_y=-10): + # inherit the parent class + super(Object_detect, self).__init__() + # get path of file + dir_path = os.path.dirname(__file__) + # choose place to set cube + self.color = 0 + # parameters to calculate camera clipping parameters + self.x1 = self.x2 = self.y1 = self.y2 =0 + # set cache of real coord + self.cache_x = self.cache_y = 0 + # load model of img recognition + self.model_path = os.path.join(dir_path, "frozen_inference_graph.pb") + self.pbtxt_path = os.path.join(dir_path, "graph.pbtxt") + self.label_path = os.path.join(dir_path, "labels.json") + # load class labels + self.labels = json.load(open(self.label_path)) + # use to calculate coord between cube and mycobot + self.sum_x1= self.sum_x2= self.sum_y2= self.sum_y1= 0 + # The coordinates of the grab center point relative to the mycobot + self.camera_x, self.camera_y = camera_x, camera_y + # The coordinates of the cube relative to the mycobot + self.c_x, self.c_y = 0,0 + # The ratio of pixels to actual values + self.ratio = 0 + # Get ArUco marker dict that can be detected. + self.aruco_dict = cv2.aruco.Dictionary_get(cv2.aruco.DICT_6X6_250) + # Get ArUco marker params. + self.aruco_params = cv2.aruco.DetectorParameters_create() + + if IS_CV_4: + self.net = cv2.dnn.readNetFromTensorflow(self.model_path, self.pbtxt_path) + else: + print('Load tensorflow model need the version of opencv is 4.') + exit(0) + # init a node and a publisher + rospy.init_node("marker", anonymous=True) + self.pub = rospy.Publisher('/cube', Marker, queue_size=1) + # init a Marker + self.marker = Marker() + self.marker.header.frame_id = "/joint1" + self.marker.ns = "cube" + self.marker.type = self.marker.CUBE + self.marker.action = self.marker.ADD + self.marker.scale.x = 0.04 + self.marker.scale.y = 0.04 + self.marker.scale.z = 0.04 + self.marker.color.a = 1.0 + self.marker.color.g = 1.0 + self.marker.color.r = 1.0 + + + # marker position initial + self.marker.pose.position.x = 0 + self.marker.pose.position.y = 0 + self.marker.pose.position.z = 0.03 + self.marker.pose.orientation.x = 0 + self.marker.pose.orientation.y = 0 + self.marker.pose.orientation.z = 0 + self.marker.pose.orientation.w = 1.0 + + self.cache_x = self.cache_y = 0 + # publish marker + def pub_marker(self, x, y , z=0.03): + self.marker.header.stamp = rospy.Time.now() + self.marker.pose.position.x = x + self.marker.pose.position.y = y + self.marker.pose.position.z = z + self.marker.color.g = self.color + self.pub.publish(self.marker) + + # Grasping motion + def move(self, x,y,color): + angles = [ + [-7.11, -6.94, -55.01, -24.16, 0, -38.84], # init the point + [-1.14, -10.63, -87.8, 9.05, -3.07, -37.7], # point to grab + [17.4, -10.1, -87.27, 5.8, -2.02, -37.7], # point to grab + ] + + coords = [ + [106.1, -141.6, 240.9, -173.34, -8.15, -83.11], # above the red bucket + [208.2, -127.8, 246.9, -157.51, -17.5, -71.18], # above the green bucket + [209.7, -18.6, 230.4, -168.48, -9.86, -39.38], + [196.9, -64.7, 232.6, -166.66, -9.44, -52.47], + [126.6, -118.1, 305.0, -157.57, -13.72, -75.3], + + ] + + # send Angle to move mycobot + self.pub_angles(angles[0], 20) + time.sleep(1.5) + self.pub_angles(angles[1], 20) + time.sleep(1.5) + self.pub_angles(angles[2], 20) + time.sleep(1.5) + # send coordinates to move mycobot + self.pub_coords([x, y, 165, -178.9, -1.57, -25.95], 20, 1) + time.sleep(1.5) + self.pub_coords([x, y, 40, -178.9, -1.57, -25.95], 20, 1) + time.sleep(1.5) + # open pump + self.pub_pump(True) + time.sleep(0.5) + self.pub_angles(angles[2], 20) + time.sleep(3) + self.pub_marker(coords[2][0]/1000.0, coords[2][1]/1000.0, coords[2][2]/1000.0) + + self.pub_angles(angles[1], 20) + time.sleep(1.5) + self.pub_marker(coords[3][0]/1000.0, coords[3][1]/1000.0, coords[3][2]/1000.0) + + self.pub_angles(angles[0], 20) + time.sleep(1.5) + self.pub_marker(coords[4][0]/1000.0, coords[4][1]/1000.0, coords[4][2]/1000.0) + + self.pub_coords(coords[color], 20, 1) + self.pub_marker(coords[color][0]/1000.0, coords[color][1]/1000.0, coords[color][2]/1000.0) + time.sleep(2) + # close pump + self.pub_pump(False) + if color==1: + self.pub_marker(coords[color][0]/1000.0+0.04, coords[color][1]/1000.0-0.02) + elif color==0: + self.pub_marker(coords[color][0]/1000.0+0.03, coords[color][1]/1000.0) + self.pub_angles(angles[0], 20) + time.sleep(3) + + + # decide whether grab cube + def decide_move(self, x, y, color): + + + print(x, y,self.cache_x, self.cache_y) + # detect the cube status move or run + if (abs(x - self.cache_x) + abs(y - self.cache_y)) / 2 > 5: # mm + self.cache_x, self.cache_y = x, y + return + else: + self.cache_x = self.cache_y = 0 + self.move(x,y,color) + + # init mycobot + def run(self): + + for _ in range(10): + self.pub_angles([-7.11, -6.94, -55.01, -24.16, 0, -38.84], 20) + print(_) + time.sleep(0.5) + self.pub_pump(False) + + # draw aruco + def draw_marker(self,img,x,y): + # draw rectangle on img + cv2.rectangle( + img, + (x - 20, y - 20), + (x + 20, y + 20), + (0, 255, 0), + thickness=2, + lineType=cv2.FONT_HERSHEY_COMPLEX, + ) + # add text on rectangle + cv2.putText(img,"({},{})".format(x,y),(x,y),cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (243, 0, 0), 2,) + + # get points of two aruco + def get_calculate_params(self,img): + # Convert the image to a gray image + gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) + # Detect ArUco marker. + corners, ids, rejectImaPoint = cv2.aruco.detectMarkers( + gray, self.aruco_dict, parameters=self.aruco_params + ) + + """ + Two Arucos must be present in the picture and in the same order. + There are two Arucos in the Corners, and each aruco contains the pixels of its four corners. + Determine the center of the aruco by the four corners of the aruco. + """ + if len(corners) > 0: + if ids is not None: + if len(corners) <= 1 or ids[0]==1: + return None + x1=x2=y1=y2 = 0 + point_11,point_21,point_31,point_41 = corners[0][0] + x1, y1 = int((point_11[0] + point_21[0] + point_31[0] + point_41[0]) / 4.0), int((point_11[1] + point_21[1] + point_31[1] + point_41[1]) / 4.0) + point_1,point_2,point_3,point_4 = corners[1][0] + x2, y2 = int((point_1[0] + point_2[0] + point_3[0] + point_4[0]) / 4.0), int((point_1[1] + point_2[1] + point_3[1] + point_4[1]) / 4.0) + return x1,x2,y1,y2 + return None + + # set camera clipping parameters + def set_cut_params(self, x1, y1, x2, y2): + self.x1 = int(x1) + self.y1 = int(y1) + self.x2 = int(x2) + self.y2 = int(y2) + print(self.x1,self.y1,self.x2,self.y2) + + # set parameters to calculate the coords between cube and mycobot + def set_params(self, c_x, c_y, ratio): + self.c_x = c_x + self.c_y = c_y + self.ratio = 220.0/ratio + + # calculate the coords between cube and mycobot + def get_position(self, x, y): + return ((y - self.c_y)*self.ratio + self.camera_x), ((x - self.c_x)*self.ratio + self.camera_y) + + """ + Calibrate the camera according to the calibration parameters. + Enlarge the video pixel by 1.5 times, which means enlarge the video size by 1.5 times. + If two ARuco values have been calculated, clip the video. + """ + def transform_frame(self, frame): + # enlarge the image by 1.5 times + fx = 1.5 + fy = 1.5 + frame = cv2.resize(frame, (0, 0), fx=fx, fy=fy, interpolation=cv2.INTER_CUBIC) + if self.x1 != self.x2: + # the cutting ratio here is adjusted according to the actual situation + frame = frame[int(self.y2*0.2):int(self.y1*1.15), int(self.x1*0.7):int(self.x2*1.15)] + return frame + + # according the class_id to get object name + def id_class_name(self, class_id): + for key, value in self.labels.items(): + if class_id == int(key): + return value + + + # detect object + def obj_detect(self, frame): + rows, cols = frame.shape[:-1] + + # Resize image and swap BGR to RGB. + blob = cv2.dnn.blobFromImage( + frame, + size=(300, 300), + mean=(0, 0, 0), + swapRB=True, + crop=False, + ) + + # Detecting. + self.net.setInput(blob) + out = self.net.forward() + x, y = 0, 0 + + # Processing result. + for detection in out[0, 0, :, :]: + score = float(detection[2]) + if score > 0.3: + class_id = detection[1] + left = detection[3] * cols + top = detection[4] * rows + right = detection[5] * cols + bottom = detection[6] * rows + if abs(right + bottom - left - top) > 380: + continue + x, y = (left + right) / 2.0, (top + bottom) / 2.0 + cv2.rectangle( + frame, + (int(left), int(top)), + (int(right), int(bottom)), + (0, 230, 0), + thickness=2, + ) + cv2.putText( + frame, + "{}: {}%".format(self.id_class_name(class_id),round(score * 100, 2)), + (int(left), int(top) - 10), + cv2.FONT_HERSHEY_COMPLEX_SMALL, + 1, + (243, 0, 0), + 2, + ) + + if x+y > 0: + return x, y + else: + return None + + + + + + +if __name__ == "__main__": + # open the camera + cap_num = 0 + cap = cv2.VideoCapture(cap_num) + 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) + 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) + + + + diff --git a/mycobot_ai/scripts/graph.pbtxt b/mycobot_ai/scripts/graph.pbtxt index 9791f03..ce68612 100644 --- a/mycobot_ai/scripts/graph.pbtxt +++ b/mycobot_ai/scripts/graph.pbtxt @@ -1,2682 +1,2682 @@ -node { - name: "image_tensor" - op: "Placeholder" - attr { - key: "dtype" - value { - type: DT_UINT8 - } - } - attr { - key: "shape" - value { - shape { - dim { - size: -1 - } - dim { - size: 300 - } - dim { - size: 300 - } - dim { - size: 3 - } - } - } - } -} -node { - name: "Preprocessor/mul" - op: "Mul" - input: "image_tensor" - input: "Preprocessor/mul/x" -} -node { - name: "Preprocessor/sub" - op: "Sub" - input: "Preprocessor/mul" - input: "Preprocessor/sub/y" -} -node { - name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1" - op: "Conv2D" - input: "Preprocessor/sub" - input: "FeatureExtractor/MobilenetV1/Conv2d_0/weights/read/_104__cf__107" - input: "^FeatureExtractor/Assert/Assert" - attr { - key: "data_format" - value { - s: "NHWC" - } - } - attr { - key: "padding" - value { - s: "SAME" - } - } - attr { - key: "strides" - value { - list { - i: 1 - i: 2 - i: 2 - i: 1 - } - } - } -} -node { - name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/add_1" - op: "Add" - input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1" - input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/sub/_103__cf__106" -} -node { - name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6" - op: "Relu6" - input: 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"FeatureExtractor/MobilenetV1/Conv2d_3_pointwise/weights/read/_86__cf__89" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/sub/_85__cf__88" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_4_depthwise/depthwise_weights/read/_83__cf__86" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 2 + i: 2 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/depthwise" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/mul/_81__cf__84" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/sub/_82__cf__85" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/mul_1" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_4_pointwise/weights/read/_80__cf__83" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/sub/_79__cf__82" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_5_depthwise/depthwise_weights/read/_77__cf__80" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/depthwise" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/mul/_75__cf__78" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/sub/_76__cf__79" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/mul_1" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_5_pointwise/weights/read/_74__cf__77" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/sub/_73__cf__76" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_6_depthwise/depthwise_weights/read/_71__cf__74" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 2 + i: 2 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/depthwise" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/mul/_69__cf__72" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/sub/_70__cf__73" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/mul_1" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_6_pointwise/weights/read/_68__cf__71" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/sub/_67__cf__70" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_7_depthwise/depthwise_weights/read/_65__cf__68" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/depthwise" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/mul/_63__cf__66" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/sub/_64__cf__67" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/mul_1" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_7_pointwise/weights/read/_62__cf__65" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/sub/_61__cf__64" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_8_depthwise/depthwise_weights/read/_59__cf__62" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/depthwise" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/mul/_57__cf__60" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/sub/_58__cf__61" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/mul_1" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_8_pointwise/weights/read/_56__cf__59" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/sub/_55__cf__58" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_9_depthwise/depthwise_weights/read/_53__cf__56" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/depthwise" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/mul/_51__cf__54" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/sub/_52__cf__55" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/mul_1" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_9_pointwise/weights/read/_50__cf__53" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/sub/_49__cf__52" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_10_depthwise/depthwise_weights/read/_47__cf__50" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/depthwise" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/mul/_45__cf__48" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/sub/_46__cf__49" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm/batchnorm/mul_1" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_10_pointwise/weights/read/_44__cf__47" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/BatchNorm/batchnorm/add_1" + op: 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"FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6" + input: "BoxPredictor_0/ClassPredictor/weights/read/_178__cf__181" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "BoxPredictor_0/ClassPredictor/BiasAdd" + op: "BiasAdd" + input: "BoxPredictor_0/ClassPredictor/Conv2D" + input: "BoxPredictor_0/ClassPredictor/biases/read/_177__cf__180" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "BoxPredictor_0/BoxEncodingPredictor/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6" + input: "BoxPredictor_0/BoxEncodingPredictor/weights/read/_116__cf__119" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "loc_pred_transposed" + value { + b: true + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "BoxPredictor_0/BoxEncodingPredictor/BiasAdd" + op: "BiasAdd" + input: "BoxPredictor_0/BoxEncodingPredictor/Conv2D" + input: "BoxPredictor_0/BoxEncodingPredictor/biases/read/_115__cf__118" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_12_depthwise/depthwise_weights/read/_35__cf__38" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 2 + i: 2 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/depthwise" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/mul/_33__cf__36" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/sub/_34__cf__37" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/mul_1" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_12_pointwise/weights/read/_32__cf__35" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/sub/_31__cf__34" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/depthwise" + op: "DepthwiseConv2dNative" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_13_depthwise/depthwise_weights/read/_29__cf__32" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/mul_1" + op: "Mul" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/depthwise" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/mul/_27__cf__30" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/sub/_28__cf__31" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/mul_1" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise/weights/read/_26__cf__29" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/sub/_25__cf__28" +} +node { + name: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/BatchNorm/batchnorm/add_1" +} +node { + name: "BoxPredictor_1/ClassPredictor/Conv2D" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6" + input: "BoxPredictor_1/ClassPredictor/weights/read/_176__cf__179" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "BoxPredictor_1/ClassPredictor/BiasAdd" + op: "BiasAdd" + input: "BoxPredictor_1/ClassPredictor/Conv2D" + input: "BoxPredictor_1/ClassPredictor/biases/read/_175__cf__178" + attr { + key: "data_format" + value { + s: "NHWC" + } + } +} +node { + name: "BoxPredictor_1/BoxEncodingPredictor/Conv2D" + op: "Conv2D" + input: 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key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 1 + i: 1 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/sub/_22__cf__25" +} +node { + name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/BatchNorm/batchnorm/add_1" +} +node { + name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/mul_1" + op: "Conv2D" + input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_256/Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/weights/read/_20__cf__23" + attr { + key: "data_format" + value { + s: "NHWC" + } + } + attr { + key: "padding" + value { + s: "SAME" + } + } + attr { + key: "strides" + value { + list { + i: 1 + i: 2 + i: 2 + i: 1 + } + } + } +} +node { + name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/add_1" + op: "Add" + input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/mul_1" + input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/sub/_19__cf__22" +} +node { + name: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/Relu6" + op: "Relu6" + input: "FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_512/BatchNorm/batchnorm/add_1" +} +node { + name: "BoxPredictor_2/ClassPredictor/Conv2D" + op: "Conv2D" + input: 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float_val: 201.52543263816605 + float_val: 493.63448015713 + float_val: 164.53659927201608 + float_val: 292.4038303442689 + tensor_shape { + dim { + size: 6 + } + } + } + } + } +} +node { + name: "PriorBox/concat" + op: "ConcatV2" + input: "PriorBox_0" + input: "PriorBox_1" + input: "PriorBox_2" + input: "PriorBox_3" + input: "PriorBox_4" + input: "PriorBox_5" + input: "concat/axis_flatten" +} +node { + name: "ClassPredictor/concat3d/shape" + op: "Const" + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + int_val: 0 + int_val: -1 + int_val: 91 + tensor_shape { + dim { + size: 3 + } + } + } + } + } +} +node { + name: "ClassPredictor/concat3d" + op: "Reshape" + input: "ClassPredictor/concat" + input: "ClassPredictor/concat3d/shape" +} +node { + name: "ClassPredictor/concat/sigmoid" + op: "Sigmoid" + input: "ClassPredictor/concat3d" +} +node { + name: "ClassPredictor/concat/sigmoid/Flatten" + op: "Flatten" + input: "ClassPredictor/concat/sigmoid" +} +node { + name: "detection_out" + op: "DetectionOutput" + input: "BoxEncodingPredictor/concat" + input: "ClassPredictor/concat/sigmoid/Flatten" + input: "PriorBox/concat" + attr { + key: "background_label_id" + value { + i: 0 + } + } + attr { + key: "code_type" + value { + s: "CENTER_SIZE" + } + } + attr { + key: "confidence_threshold" + value { + f: 1e-08 + } + } + attr { + key: "keep_top_k" + value { + i: 100 + } + } + attr { + key: "nms_threshold" + value { + f: 0.6 + } + } + attr { + key: "num_classes" + value { + i: 91 + } + } + attr { + key: "share_location" + value { + b: true + } + } + attr { + key: "top_k" + value { + i: 100 + } + } +} diff --git a/mycobot_ai/scripts/labels.json b/mycobot_ai/scripts/labels.json index c8926c6..570e618 100644 --- a/mycobot_ai/scripts/labels.json +++ b/mycobot_ai/scripts/labels.json @@ -1,82 +1,82 @@ -{ - "1": "person", - "2": "bicycle", - "3": "car", - "4": "motorcycle", - "5": "airplane", - "6": "bus", - "7": "train", - "8": "truck", - "9": "boat", - "10": "traffic light", - "11": "fire hydrant", - "13": "stop sign", - "14": "parking meter", - "15": "bench", - "16": "bird", - "17": "cat", - "18": "dog", - "19": "horse", - "20": "sheep", - "21": "cow", - "22": "elephant", - "23": "bear", - "24": "zebra", - "25": "giraffe", - "27": "backpack", - "28": "umbrella", - "31": "handbag", - "32": "tie", - "33": "suitcase", - "34": "frisbee", - "35": "skis", - "36": "snowboard", - "37": "sports ball", - "38": "kite", - "39": "baseball bat", - "40": "baseball glove", - "41": "skateboard", - "42": "surfboard", - "43": "tennis racket", - "44": "bottle", - "46": "wine glass", - "47": "cup", - "48": "fork", - "49": "knife", - "50": "spoon", - "51": "bowl", - "52": "banana", - "53": "apple", - "54": "sandwich", - "55": "orange", - "56": "broccoli", - "57": "carrot", - "58": "hot dog", - "59": "pizza", - "60": "donut", - "61": "cake", - "62": "chair", - "63": "couch", - "64": "potted plant", - "65": "bed", - "67": "dining table", - "70": "toilet", - "72": "tv", - "73": "laptop", - "74": "mouse", - "75": "remote", - "76": "keyboard", - "77": "cell phone", - "78": "microwave", - "79": "oven", - "80": "toaster", - "81": "sink", - "82": "refrigerator", - "84": "book", - "85": "clock", - "86": "vase", - "87": "scissors", - "88": "teddy bear", - "89": "hair drier", - "90": "toothbrush" +{ + "1": "person", + "2": "bicycle", + "3": "car", + "4": "motorcycle", + "5": "airplane", + "6": "bus", + "7": "train", + "8": "truck", + "9": "boat", + "10": "traffic light", + "11": "fire hydrant", + "13": "stop sign", + "14": "parking meter", + "15": "bench", + "16": "bird", + "17": "cat", + "18": "dog", + "19": "horse", + "20": "sheep", + "21": "cow", + "22": "elephant", + "23": "bear", + "24": "zebra", + "25": "giraffe", + "27": "backpack", + "28": "umbrella", + "31": "handbag", + "32": "tie", + "33": "suitcase", + "34": "frisbee", + "35": "skis", + "36": "snowboard", + "37": "sports ball", + "38": "kite", + "39": "baseball bat", + "40": "baseball glove", + "41": "skateboard", + "42": "surfboard", + "43": "tennis racket", + "44": "bottle", + "46": "wine glass", + "47": "cup", + "48": "fork", + "49": "knife", + "50": "spoon", + "51": "bowl", + "52": "banana", + "53": "apple", + "54": "sandwich", + "55": "orange", + "56": "broccoli", + "57": "carrot", + "58": "hot dog", + "59": "pizza", + "60": "donut", + "61": "cake", + "62": "chair", + "63": "couch", + "64": "potted plant", + "65": "bed", + "67": "dining table", + "70": "toilet", + "72": "tv", + "73": "laptop", + "74": "mouse", + "75": "remote", + "76": "keyboard", + "77": "cell phone", + "78": "microwave", + "79": "oven", + "80": "toaster", + "81": "sink", + "82": "refrigerator", + "84": "book", + "85": "clock", + "86": "vase", + "87": "scissors", + "88": "teddy bear", + "89": "hair drier", + "90": "toothbrush" } \ No newline at end of file diff --git a/mycobot_ai/scripts/local_photo/takephoto.jpeg b/mycobot_ai/scripts/local_photo/takephoto.jpeg new file mode 100644 index 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gpiod(self): - import RPi.GPIO as GPIO - GPIO.setwarnings(False) - GPIO.setmode(GPIO.BCM) - GPIO.setup(20, GPIO.OUT) - GPIO.setup(21, GPIO.OUT) - GPIO.output(20, 0) - GPIO.output(21, 0) - time.sleep(3) - print "close" - GPIO.output(20, 1) - GPIO.output(21, 1) + def __init__(self): + super(Pump, self).__init__() + rospy.init_node("pump", anonymous=True) + def run(self): + self.pub_pump(False) + time.sleep(1) + self.pub_pump(True) + time.sleep(5) + self.pub_pump(False) if __name__ == "__main__": - pump = Pump() - pump.gpiod() + pump = Pump() + pump.run() \ No newline at end of file diff --git a/mycobot_ai/scripts/scripts.tar.gz b/mycobot_ai/scripts/scripts.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..5e01d6b89c06f07e2671a8a2ca56886c5247e04a GIT binary patch literal 12204 zcmV;dFH_JTiwFSFS$AOo1MPina~sEz@O>43#TtBc1VnI&T@WP25>iD`l&m_v6(#4A zqFAUUuq4+6u<-7J1myEmN*vo#Y)4Mww>ZudJ156Zt}HoEe70WgA9DgJeez$p?w;A* 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