This function is used to match the input image from the stage with the stored classes previously stored in the database.
The function also stores all the face data in PictoBlox for access by other functions.
Function Definition: recognisefromstage()
This function is used to match the input image from the stage with the stored classes previously stored in the database.
The function also stores all the face data in PictoBlox for access by other functions.
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")
The example demonstrates the application of face recognition with stage. Following are the key steps happening:
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)
The example demonstrates the application of face recognition with camera feed. Following are the key steps happening:
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")
The example demonstrates the application of face detection with a stage feed. Following are the key steps happening:
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)
The example demonstrates how to use face landmarks in the projects. Following are the key steps happening:
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)
The example demonstrates how to use face detection with a camera feed. Following are the key steps happening:
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))
sprite = Sprite('Tobi')
quarky = Quarky()
import time
while True:
quarky.drawpattern("jjbjbjjjbbbbbjjbbbbbjjjbbbjjjjjbjjj")
time.sleep(0.4)
quarky.drawpattern("jjjjjjjjjbjbjjjjbbbjjjjjbjjjjjjjjjj")
time.sleep(0.4)
from quarky import *
import time
while True:
quarky.drawpattern("jjbjbjjjbbbbbjjbbbbbjjjbbbjjjjjbjjj")
time.sleep(1)
quarky.drawpattern("jjjjjjjjjbjbjjjjbbbjjjjjbjjjjjjjjjj")
time.sleep(1)
# 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)
####################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
####################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()
The example demonstrates how to use face landmarks in the projects. Following are the key steps happening:
The example demonstrates the application of face recognition with a camera feed. Following are the key steps happening:
The example demonstrates the application of face recognition with stage. Following are the key steps happening:
The example demonstrates the use of clone and gliding function in Sprite: