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