Introduction
This project demonstrates how to use Machine Learning Environment to make a machine–learning model that identifies hand gestures and makes the quadruped move accordingly.
To test the model, simply enter the input values in the “Testing” panel and click on the “Predict” button.
The model will return the probability of the input belonging to the classes.
Export in Python Coding
Click on the “Export Model” button on the top right of the Testing box, and PictoBlox will load your model into the Python Coding Environment if you have opened the ML Environment in Python Coding.
code
The following code appears in the Python Editor of the selected sprite.
####################imports####################
# Do not change
import numpy as np
import tensorflow as tf
import time
quarky=Quarky
quad=Quadruped(4,1,8,5,3,2,7,6)
# Do not change
####################imports####################
#Following are the model and video capture configurations
# Do not change
model=tf.keras.models.load_model(
"num_model.h5",
custom_objects=None,
compile=True,
options=None)
pose = Posenet() # Initializing Posenet
pose.enablebox() # Enabling video capture box
pose.video("on",0) # Taking video input
class_list=['Forward','Backward','Left','Right','Stop'] # List of all the classes
# Do not change
###############################################
#This is the while loop block, computations happen here
# Do not change
while True:
pose.analysehand() # Using Posenet to analyse hand pose
coordinate_xy=[]
# for loop to iterate through 21 points of recognition
for i in range(21):
if(pose.gethandposition(1,i,0)!="NULL" or pose.gethandposition(2,i,0)!="NULL"):
coordinate_xy.append(int(240+float(pose.gethandposition(1,i,0))))
coordinate_xy.append(int(180-float(pose.gethandposition(2,i,0))))
else:
coordinate_xy.append(0)
coordinate_xy.append(0)
coordinate_xy_tensor = tf.expand_dims(coordinate_xy, 0) # Expanding the dimension of the coordinate list
predict=model.predict(coordinate_xy_tensor) # Making an initial prediction using the model
predict_index=np.argmax(predict[0], axis=0) # Generating index out of the prediction
predicted_class=class_list[predict_index] # Tallying the index with class list
print(predicted_class)
# Do not change
Logic
- If the identified class from the analyzed image is “forward,” the Quadruped will move forward at a specific speed.
- If the identified class is “backward,” the Quadruped will move backward.
- If the identified class is “left,” the Quadruped will move left.
- If the identified class is “right,” the Quadruped will move right.
- Otherwise, the Quadruped will be in the home position.
Add this code in
def runQuarky(predicted_class):
if pose.ishanddetected():
if predicted_class == "Forward":
quad.move("forward",1000,1)
elif predicted_class == "Backward":
quad.move("backward",1000,1)
elif predicted_class == "Left":
quad.move("turn left",1000,1)
elif predicted_class == "Right":
quad.move("turn right",1000,1)
elif predicted_class == "Stop":
quad.home()
Final code
####################imports####################
# Do not change
import numpy as np
import tensorflow as tf
import time
quarky=Quarky
quad=Quadruped(4,1,8,5,3,2,7,6)
# Do not change
####################imports####################
#Following are the model and video capture configurations
# Do not change
model=tf.keras.models.load_model(
"num_model.h5",
custom_objects=None,
compile=True,
options=None)
pose = Posenet() # Initializing Posenet
pose.enablebox() # Enabling video capture box
pose.video("on",0) # Taking video input
class_list=['Forward','Backward','Left','Right','Stop'] # List of all the classes
def runQuarky(predicted_class):
if pose.ishanddetected():
if predicted_class == "Forward":
quad.move("forward",1000,1)
elif predicted_class == "Backward":
quad.move("backward",1000,1)
elif predicted_class == "Left":
quad.move("turn left",1000,1)
elif predicted_class == "Right":
quad.move("turn right",1000,1)
elif predicted_class == "Stop":
quad.home()
# Do not change
###############################################
#This is the while loop block, computations happen here
# Do not change
while True:
pose.analysehand() # Using Posenet to analyse hand pose
coordinate_xy=[]
# for loop to iterate through 21 points of recognition
for i in range(21):
if(pose.gethandposition(1,i,0)!="NULL" or pose.gethandposition(2,i,0)!="NULL"):
coordinate_xy.append(int(240+float(pose.gethandposition(1,i,0))))
coordinate_xy.append(int(180-float(pose.gethandposition(2,i,0))))
else:
coordinate_xy.append(0)
coordinate_xy.append(0)
coordinate_xy_tensor = tf.expand_dims(coordinate_xy, 0) # Expanding the dimension of the coordinate list
predict=model.predict(coordinate_xy_tensor) # Making an initial prediction using the model
predict_index=np.argmax(predict[0], axis=0) # Generating index out of the prediction
predicted_class=class_list[predict_index] # Tallying the index with class list
print(predicted_class)
runQuarky(predicted_class)
# Do not change
Output
The destination devices could be
It could be
The actions based on the information and data could be
