nextbackdrop()

Code

sprite = Sprite('Tobi')

fd = FaceDetection()

# Enable Bounding Box on the stage
fd.enablebox()

# Set Theshold of the stage
fd.setthreshold(0.9)

fd.analysestage()

sprite.say(str(fd.count()) + " Faces Detected")

Output

The example demonstrates the application of face recognition with stage. Following are the key steps happening:

1. Initializing the program with parameters for the sprite and face detection library.
2. Saving Chris's face as class 1.
3. Saving Robert's face as class 2.
4. Running face recognition and placing the square box sprite on the faces of Chris and Robert.

Code

sprite = Sprite('Square Box')
fd = FaceDetection()
import time

fd.setthreshold(0.5)
fd.enablebox()

# Reset Database
fd.deleteallclass()

# Adding Chirs face to database
sprite.switchbackdrop("Chris")
time.sleep(0.5)
fd.addclassfromstage(1, "Chris")

# Adding Robert face to database
sprite.switchbackdrop("Robert")
time.sleep(0.5)
fd.addclassfromstage(2, "Robert")

sprite.switchbackdrop("Robert and Chris")

while True:
  fd.recognisefromstage()
  
  print(fd.count())
  for i in range(fd.count()):
    sprite.setx(fd.x(i+1))
    sprite.sety(fd.y(i+1))
    sprite.setsize(fd.width(i+1))
    sprite.say(getclassname(i+1))
    time.sleep(1)

Result

The example demonstrates the application of face recognition with camera feed. Following are the key steps happening:

1. Initializing the program with parameters for the sprite and face detection library.
2. Saving the face showing in the camera as class 1.
3. Running face recognition and reporting whether class 1 is detected or not.

Code

sprite = Sprite('Tobi')

fd = FaceDetection()
import time

fd.setthreshold(0.5)
fd.video("on", 0)
fd.enablebox()
time.sleep(2)

fd.deleteallclass()

# Adding face 1 to database
fd.addclassfromstage(1, "Face 1")

while True:
  fd.recognisefromcamera()
  
  if fd.isclassdetected(1):
    sprite.say("Face 1 Recognised")
  else:
    sprite.say("Face 1 Missing")

Output

The example demonstrates the application of face detection with a stage feed. Following are the key steps happening:

1. Initializing the program with parameters for the sprite and face detection library.
2. Running face detection
3. Running the loop to show every face and its expression

Code

sprite = Sprite('Square Box')
import time
fd = FaceDetection()

# Disable Bounding Box on the stage
fd.disablebox()

# Set Theshold of the stage
fd.setthreshold(0.4)

fd.analysestage()

print(fd.count())

for i in range(fd.count()):
  sprite.setx(fd.x(i + 1))
  sprite.sety(fd.y(i + 1))
  sprite.setsize(fd.width(i + 1))
  sprite.say("Face " + str(i + 1) + ": " + fd.expression(i + 1))
  time.sleep(1)

Output

The example demonstrates how to use face landmarks in the projects. Following are the key steps happening:

1. Initializing the program with parameters for the sprite, pen, and face detection library.
2. Running face detection.
3. Running the loop to show every landmark on the face.

Code

sprite = Sprite('Ball')
fd = FaceDetection()
import time
pen = Pen()

pen.clear()
sprite.setsize(10)

fd.enablebox()

fd.analysestage()

for i in range(68):
  sprite.setx(fd.landmarksx(1, i+1))
  sprite.sety(fd.landmarksy(1, i+1))
  pen.stamp()
  time.sleep(0.2)

Output

The example demonstrates how to use face detection with a camera feed. Following are the key steps happening:

1. Initializing the program with parameters for the sprite, and face detection library.
2. Running face detection.
3. Running the loop to show every face and expression.

Code

sprite = Sprite('Square Box')
import time
fd = FaceDetection()

fd.video("on", 0)

# Enable Bounding Box on the stage
fd.enablebox()

# Set Theshold of the stage
fd.setthreshold(0.5)

while True:
  fd.analysestage()

  for i in range(fd.count()):
    sprite.setx(fd.x(i + 1))
    sprite.sety(fd.y(i + 1))
    sprite.setsize(fd.width(i + 1))
    sprite.say(fd.expression(i + 1))

Output

Code

sprite = Sprite('Tobi')
quarky = Quarky()

import time

while True:
	quarky.drawpattern("jjbjbjjjbbbbbjjbbbbbjjjbbbjjjjjbjjj")
	time.sleep(0.4)
	quarky.drawpattern("jjjjjjjjjbjbjjjjbbbjjjjjbjjjjjjjjjj")
	time.sleep(0.4)

Code

from quarky import *
import time

while True:
	quarky.drawpattern("jjbjbjjjbbbbbjjbbbbbjjjbbbjjjjjbjjj")
	time.sleep(1)
	quarky.drawpattern("jjjjjjjjjbjbjjjjbbbjjjjjbjjjjjjjjjj")
	time.sleep(1)

Script

Python Code Generated

# This python code is generated by PictoBlox

from quarky import *
# This python code is generated by PictoBlox

# imported modules
import time



while True:
	quarky.drawpattern("jjbjbjjjbbbbbjjbbbbbjjjbbbjjjjjbjjj")
	time.sleep(1)
	quarky.drawpattern("jjjjjjjjjbjbjjjjbbbjjjjjbjjjjjjjjjj")
	time.sleep(1)
	
	

Script

Script

Image Classification Model

Code

####################imports####################
#do not change

import cv2
import numpy as np
import tensorflow as tf

sprite = Sprite("Tobi")

#do not change
####################imports####################

#Following are the model and video capture configurations
#do not change

model = tf.keras.models.load_model('saved_model.h5',
                                   custom_objects=None,
                                   compile=True,
                                   options=None)

cap = cv2.VideoCapture(0)  # Using device's camera to capture video
text_color = (206, 235, 135)
org = (50, 50)
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 1
thickness = 3

class_list = ['Mask Off', 'Mask On', 'Mask Wrong']  # List of all the classes

#do not change
###############################################


def checkmask(predicted_class):
  if predicted_class == 'Mask On':
    sprite.say("Thank you for wearing the mask")
  elif predicted_class == 'Mask Off':
    sprite.say("Please wear a mask")
  else:
    sprite.say("Please wear the mask propertly")


#This is the while loop block, computations happen here

while True:
  ret, image_np = cap.read()  # Reading the captured images
  image_np = cv2.flip(image_np, 1)
  image_resized = cv2.resize(image_np, (224, 224))
  img_array = tf.expand_dims(image_resized,
                             0)  # Expanding the image array dimensions
  predict = model.predict(img_array)  # Making an initial model prediction
  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

  image_np = cv2.putText(
      image_np, "Image Classification Output: " + str(predicted_class), org,
      font, fontScale, text_color, thickness, cv2.LINE_AA)

  print(predict)
  cv2.imshow("Image Classification Window",
             image_np)  # Displaying the classification window
  checkmask(predicted_class)

  if cv2.waitKey(25) & 0xFF == ord(
      'q'):  # Press 'q' to close the classification window
    break

cap.release()  # Stops taking video input
cv2.destroyAllWindows()  #Closes input window

Image Classification Model

Code

####################imports####################
#do not change

import cv2
import numpy as np
import tensorflow as tf

#do not change
####################imports####################

#Following are the model and video capture configurations
#do not change

model = tf.keras.models.load_model('saved_model.h5',
                                   custom_objects=None,
                                   compile=True,
                                   options=None)

text_color = (206, 235, 135)
org = (50, 50)
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 0.5
thickness = 1

class_list = ['Bacteria', 'Normal', 'Virus']  # List of all the classes

#do not change
###############################################

image_np = cv2.imread("test.jpg", cv2.IMREAD_COLOR)
image_resized = cv2.resize(image_np, (224, 224))
img_array = tf.expand_dims(image_resized,
                           0)  # Expanding the image array dimensions
predict = model.predict(img_array)  # Making an initial model prediction
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

image_np = cv2.putText(image_np,
                       "Image Classification Output: " + str(predicted_class),
                       org, font, fontScale, text_color, thickness,
                       cv2.LINE_AA)

print(predict)
cv2.imshow("Image Classification Window",
           image_np)  # Displaying the classification window

cv2.imwrite("TestResult.jpg", image_np)
cv2.waitKey(0)
cv2.destroyAllWindows()

Script

Script

Output

Script

Output

Script

Exmaple

The example demonstrates how to use face landmarks in the projects. Following are the key steps happening:

1. Initializing the program with parameters for the sprite, pen, and face detection library.
2. Running face detection.
3. Running the loop to show every landmark on the face.

Script

Output

The example demonstrates the application of face recognition with a camera feed. Following are the key steps happening:

1. Initializing the program with parameters for the sprite and face detection library.
2. Saving the face showing in the camera as class 1.
3. Running face recognition and reporting whether class 1 is detected or not.

Script

Output

The example demonstrates the application of face recognition with stage. Following are the key steps happening:

1. Initializing the program with parameters for the sprite and face detection library.
2. Saving Chris’s face as class 1.
3. Saving Robert’s face as class 2.
4. Running face recognition and placing the square box sprite on the faces of Chris and Robert.

Script

Output

The example demonstrates the use of clone and gliding function in Sprite:

1. Whenever the sprite is clicked, a clone is created.
2. When a clone is created its position is set to a random position on the top of the stage and then it glides down to the bottom.
3. When it reaches the bottom, the clone is deleted.

Script

Output

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Output

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Output

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Output

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Output

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Output

Script

1. Main Script to change the speed and position parameters of the ball.
2. Custom function to initialize the ball position and speed with random variables.
3. Custom function to check the boundary conditions and set the rules.

Output

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Output

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Output

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Output