Welcome to DOFBOT-Pi repository

1.Gesture recognition stack blocks

Gesture Recognition Stacking Blocks

1. Gesture Recognition Instructions

This routine uses gesture recognition based on Baidu Smart Cloud platform service, which provides 50,000 free uses per day, for educational purposes only, not for commercial use. If long-term use is required, please purchase related services. Our company is not responsible for any issues.
For KEY application instructions and API documentation, please refer to the previous section of the course content.
Supported gestures and example images for gesture recognition

2. Experiment Setup

Place the experiment setup according to the following: yellow blocks in the yellow area, red blocks in the red area, green blocks in the green area, blue blocks in the blue area.

3. Code Content

Start Docker

After entering the main board desktop, open the terminal and run the following command to start the Dofbot corresponding container:


./Docker_Ros.sh

Then enter the following command in the docker terminal to start jupyter-lab,


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cd
jupyter-lab --allow-root

Then enter the main board's IP address plus :9999 in the browser,


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IP:9999         // Example: 192.168.1.11:9999

Code path: /root/Dofbot/6.AI_Visual/2.gesture_stack.ipynb


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#bgr8 to jpeg format conversion
import enum
import cv2
def bgr8_to_jpeg(value, quality=75):
    return bytes(cv2.imencode('.jpg', value)[1])


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# Import related modules
import   threading
import   time
from   Arm_Lib import Arm_Device
#   Create robotic arm object
Arm   = Arm_Device()
time.sleep(.1)


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#   Define gesture recognition function part
import   cv2
import   time
import   demjson
import   pygame
from   aip import AipBodyAnalysis
from   aip import AipSpeech
from   PIL import Image, ImageDraw, ImageFont
import   numpy
import   ipywidgets.widgets as widgets
#   For specific gestures, please refer to the official documentation https://ai.baidu.com/ai-doc/BODY/4k3cpywrv
hand={'One':'数字1','Two':'数字2','Three':'数字3','Four':'数字4',
      'Five':'数字5', 'Six':'数字6','Seven':'数字7',
      'Eight':'数字8','Nine':'数字9','Fist':'拳头','Ok':'OK',
      'Prayer':'祈祷','Congratulation':'作揖','Honour':'作别',
      'Heart_single':'比心心','Thumb_up':'点赞','Thumb_down':'Diss',
      'ILY':'我爱你','Palm_up':'掌心向上','Heart_1':'双手比心1',
      'Heart_2':'双手比心2','Heart_3':'双手比心3','Rock':'Rock',
      'Insult':'竖中指','Face':'脸'}
#   Replace the key below with your own  
"""   Body Analysis   APPID AK SK """
APP_ID   = '18550528'
API_KEY   = 'K6PWqtiUTKYK1fYaz13O8E3i'
SECRET_KEY   = 'IDBUII1j6srF1XVNDX32I2WpuwBWczzK'
client   = AipBodyAnalysis(APP_ID, API_KEY, SECRET_KEY)
g_camera   = cv2.VideoCapture(0)
g_camera.set(3,   640)
g_camera.set(4,   480)
g_camera.set(5,   30)  #Set frame rate
g_camera.set(cv2.CAP_PROP_FOURCC,   cv2.VideoWriter.fourcc('M', 'J', 'P', 'G'))
g_camera.set(cv2.CAP_PROP_BRIGHTNESS,   40) #Set brightness -64 - 64  0.0
g_camera.set(cv2.CAP_PROP_CONTRAST,   50) #Set contrast -64 - 64  2.0
g_camera.set(cv2.CAP_PROP_EXPOSURE,   156) #Set exposure value 1.0 - 5000  156.0
ret,   frame = g_camera.read()


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#   Define camera display component
image_widget   = widgets.Image(format='jpeg', width=600, height=500)  #Set camera display component
display(image_widget)  
image_widget.value   = bgr8_to_jpeg(frame)


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#   Define function to display Chinese text
def   cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=20):
    if (isinstance(img, numpy.ndarray)):  # Check if it's OpenCV image type
        img =   Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    # Create an object that can draw on the given image
    draw = ImageDraw.Draw(img)
    # Font format
    fontStyle = ImageFont.truetype(
        "simhei.ttf", textSize,   encoding="utf-8")
    # Draw text
    draw.text((left, top), text, textColor,   font=fontStyle)
    # Convert back to OpenCV format
    return cv2.cvtColor(numpy.asarray(img),   cv2.COLOR_RGB2BGR)


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#   Define variable parameters for different positions
look_at   = [90, 164, 18, 0, 90, 90]
p_top   = [90, 80, 50, 50, 270]
p_Yellow   = [65, 22, 64, 56, 270]
p_Red   = [118, 19, 66, 56, 270]
p_Green   = [136, 66, 20, 29, 270]
p_Blue   = [44, 66, 20, 28, 270]
p_layer_4   = [90, 76, 40, 17, 270]
p_layer_3   = [90, 65, 44, 17, 270]
p_layer_2   = [90, 65, 25, 36, 270]
p_layer_1   = [90, 48, 35, 30, 270]
p_push_over_1   = [90, 90, 5, 0, 90, 150]
p_push_over_2   = [90, 90, 0, 50, 90, 150]
#Define block grabbing status
yellow_grabbed   = 0
red_grabbed   = 0
green_grabbed   = 0
blue_grabbed   = 0
#Define gesture recognition count
Count_One   = 0
Count_Two   = 0
Count_Three   = 0
Count_Four   = 0
Count_Fist   = 0


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#   Define robotic arm movement function, control servos 1-6 simultaneously, p=[S1,S2,S3,S4,S5,S6]
def   arm_move_6(p, s_time = 500):
    for i in range(6):
        id = i + 1
        Arm.Arm_serial_servo_write(id, p[i],   s_time)
        time.sleep(.01)
    time.sleep(s_time/1000)
   
#   Define robotic arm movement function, control servos 1-5 simultaneously, p=[S1,S2,S3,S4,S5]
def   arm_move(p, s_time = 500):
    for i in range(5):
        id = i + 1
        if id == 5:
            time.sleep(.1)
            Arm.Arm_serial_servo_write(id,   p[i], int(s_time*1.2))
        elif id == 1 :
            Arm.Arm_serial_servo_write(id,   p[i], int(3*s_time/4))
        else:
            Arm.Arm_serial_servo_write(id,   p[i], int(s_time))
        time.sleep(.01)
    time.sleep(s_time/1000)
   
#   Define block clamping function, enable=1: clamp, =0: release
def   arm_clamp_block(enable):
    if enable == 0:
        Arm.Arm_serial_servo_write(6, 60,   400)
    else:
        Arm.Arm_serial_servo_write(6, 130,   400)
    time.sleep(.5)


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#Number function definition
def   number_action(index):
    if index == 1:
        # Grab the yellow block
        arm_move(p_top, 1000)
        arm_move(p_Yellow, 1000)
        arm_clamp_block(1)
#         time.sleep(.5)
        arm_move(p_top, 1000)
    elif index == 2:
        # Grab the red block
        arm_move(p_top, 1000)
        arm_move(p_Red, 1000)
        arm_clamp_block(1)
        arm_move(p_top, 1000)
    elif index == 3:
        # Grab the green block
        arm_move(p_top, 1000)
        arm_move(p_Green, 1000)
        arm_clamp_block(1)
        arm_move(p_top, 1000)
    elif index == 4:
        # Grab the blue block
        arm_move(p_top, 1000)
        arm_move(p_Blue, 1000)
        arm_clamp_block(1)
        arm_move(p_top, 1000)
 
   
def   put_down_block(layer):
   
    if layer == 1:
        arm_move(p_layer_1, 1000)
        arm_clamp_block(0)
        arm_move_6(look_at, 1000)
    elif layer == 2:
        arm_move(p_layer_2, 1000)
        arm_clamp_block(0)
        arm_move_6(look_at, 1000)
    elif layer == 3:
        arm_move(p_layer_3, 1000)
        arm_clamp_block(0)
        arm_move_6(look_at, 1000)
    elif layer == 4:
        arm_move(p_layer_4, 1000)
        time.sleep(.1)
        arm_clamp_block(0)
        arm_move_6(look_at, 1000)
#   Push over blocks
def   push_over_block():
    arm_move_6(p_push_over_1, 1000)
    time.sleep(.2)
    arm_move_6(p_push_over_2, 1000)
    time.sleep(.1)
    arm_move_6(look_at, 1000)
    time.sleep(1)
    global g_layer
    g_layer = 0


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#Move the robotic arm to the camera forward-looking position
arm_move_6(look_at,   1000)
time.sleep(1)


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global   g_state_arm
g_state_arm   = 0
global   g_layer
g_layer   = 0
def   ctrl_arm_move(index):
    global g_layer
    g_layer = g_layer + 1
    if g_layer >= 5:
        g_layer = 1
    arm_clamp_block(0)
    if index == 1:
        number_action(index)
        put_down_block(g_layer)
    elif index == 2:
        number_action(index)
        put_down_block(g_layer)
    elif index == 3:
        number_action(index)
        put_down_block(g_layer)
    elif index == 4:
        number_action(index)
        put_down_block(g_layer)
    elif index == 5:
        time.sleep(1)
        push_over_block()
  
    global g_state_arm
    g_state_arm = 0


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def   start_move_arm(index):
    # Start robotic arm control thread
    global g_state_arm
    if g_state_arm == 0:
        closeTid = threading.Thread(target =   ctrl_arm_move, args = [index])
        closeTid.setDaemon(True)
        closeTid.start()
        g_state_arm = 1


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try:
    Arm.Arm_Buzzer_On(1)
    s_time = 300
    Arm.Arm_serial_servo_write(4, 10, s_time)
    time.sleep(s_time/1000)
    Arm.Arm_serial_servo_write(4, 0, s_time)
    time.sleep(s_time/1000)
    Arm.Arm_serial_servo_write(4, 10, s_time)
    time.sleep(s_time/1000)
    Arm.Arm_serial_servo_write(4, 0, s_time)
    time.sleep(s_time/1000)
   
    while True:
        """1. Take photo   """
        ret, frame = g_camera.read()
 
        """ 2. Call gesture recognition   """
        raw =   str(client.gesture(image_widget.value))
        text = demjson.decode(raw)
        try:
            res =   text['result'][0]['classname']
        except:
    #           print('Recognition result: Nothing recognized~' )
    #           img = cv2ImgAddText(frame, "Not recognized", 250, 30, (0, 0 , 255), 30)
            img = frame
        else:
    #           print('Recognition result:' + hand[res])
    #           img = cv2ImgAddText(frame, hand[res], 250, 30, (0, 255 , 0), 30)
            if res == 'One':
                Count_One = Count_One + 1
                Count_Two = 0
                Count_Three = 0
                Count_Four = 0
                Count_Fist = 0
                if Count_One >= 3:
                    print('识别结果：' + hand[res])
                    img = cv2ImgAddText(frame,   hand[res], 250, 30, (0, 255 , 0), 30)
                    if yellow_grabbed == 0:
                        start_move_arm(1)
    #                     global yellow_grabbed
                        yellow_grabbed = 1
            elif res == 'Two':
                Count_Two = Count_Two + 1
                Count_Three = 0
                Count_Four = 0
                Count_Fist = 0
                if Count_Two >= 3:
                    print('识别结果：' + hand[res])
                    img =   cv2ImgAddText(frame, hand[res], 250, 30, (0, 255 , 0), 30)
                    if red_grabbed == 0:
                        start_move_arm(2)
    #                     global red_grabbed
                        red_grabbed = 1
            elif res == 'Three':
                Count_Three = Count_Three + 1
                Count_Two = 0
                Count_Four = 0
                Count_Fist = 0
                if Count_Three >= 3:
                    print('识别结果：' + hand[res])
                    img = cv2ImgAddText(frame,   hand[res], 250, 30, (0, 255 , 0), 30)
                    if green_grabbed == 0:
                        start_move_arm(3)
    #                     global green_grabbed
                        green_grabbed = 1
              elif res == 'Four':
                Count_Four = Count_Four + 1
                Count_Two = 0
                Count_Three = 0
                Count_Fist = 0
                if Count_Four >= 3:
                    print('识别结果：' + hand[res])
                    img = cv2ImgAddText(frame,   hand[res], 250, 30, (0, 255 , 0), 30)
                    if blue_grabbed == 0:
                        start_move_arm(4)
    #                     global blue_grabbed
                        blue_grabbed = 1
            elif res == 'Fist': #Fist
                Count_Fist = Count_Fist + 1
                Count_One = 0
                Count_Two = 0
                Count_Three = 0
                Count_Four = 0
                if Count_Fist >= 3:
                    print('识别结果：' + hand[res])
                    img =   cv2ImgAddText(frame, hand[res], 250, 30, (0, 255 , 0), 30)
                    start_move_arm(5)
                    yellow_grabbed = 0
                    red_grabbed = 0
                    green_grabbed = 0
                    blue_grabbed = 0
                    Count_Fist = 0
            else:
                img = frame
        image_widget.value =   bgr8_to_jpeg(img)
except   KeyboardInterrupt:
    print(" Program closed! ")
    pass


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If the set gesture is recognized, the robotic arm will perform the corresponding action. When the robotic arm recognizes a number for the first time, it grabs a block and places it on the first layer; when it recognizes a gesture number for the second time, it grabs a block and places it on the second layer; when it recognizes a gesture number for the third time, it grabs a block and places it on the third layer; when it recognizes a gesture number for the fourth time, it grabs a block and places it on the fourth layer. The color of the block grabbed by the robotic arm each time is determined by the recognized number: number 1 represents yellow, number 2 represents red, number 3 represents green, number 4 represents blue. When this number is recognized again, it will not grab again unless a fist is recognized, which will push over all blocks, clear the records, and start over.
The gestures and corresponding actions in this routine are as follows:

Gesture recognition fixed action

Color recognition grab blocks