integration plate recognition with the game, add popup

main.py

@@ -1,8 +1,10 @@
 import secrets
 import sys
+from random import randrange
 
 from src.decisionTree import *
-#from src.SubprojectMaksymilianKierski.PlateRecognition import use_model_to_predict
+
+from src.SubprojectMaksymilianKierski.PlateRecognition import use_model_to_predict, text_speech
 
 if __name__ == "__main__":
     # SETUP
@@ -18,6 +20,7 @@ if __name__ == "__main__":
 
     goal = None
     path = ''
+
     while True:
 
         for event in pygame.event.get():
@@ -43,11 +46,12 @@ if __name__ == "__main__":
                     tree.print()
 
                 if event.key == pygame.K_m:
+
                     tabPos = [[1, 2], [1, 4], [1, 5], [1, 7], [1, 8], [1, 10], [5, 4], [5, 8]]
                     print(tabPos[0][0], tabPos[0][1])
 
                     if not [waiter.X, waiter.Y] in tabPos:
-                        #save_dataset()
+                        # save_dataset()
                         x = tabPos[0][0]
                         y = tabPos[0][1]
 
@@ -55,7 +59,15 @@ if __name__ == "__main__":
                         path = waiter.findPath(goal)
                         path = waiter.translatePath(path)
                     else:
-                        print('hello')
+                        predict = use_model_to_predict('test-{}'.format(randrange(20)))
+                        if predict == 1:
+                            predict = 'This plate is empty'
+                        else:
+                            predict = 'This plate has food'
+                        text_speech('arialnarrow.ttf', 30, predict, (255, 255, 255), (0, 128, 0),
+                                    (waiter.X*100), (waiter.Y*50), False, False, screen=graphics.screen)
+                        pygame.display.flip()
+
 
                 if event.key == pygame.K_r:
                     temp = False

src/SubprojectMaksymilianKierski/PlateRecognition.py

@@ -16,6 +16,8 @@ from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D,
 from tensorflow.keras.callbacks import TensorBoard  # tensorboard to analysing our models in period of time
 import time
 
+import pygame
+
 current_path = os.path.dirname(__file__)
 relative_path = os.path.join(current_path, 'Data/')
 
@@ -28,6 +30,7 @@ training_data = []
 X = []  # feature set
 y = []  # label set
 
+
 def create_training_data():
     for category in CATEGORIES:
         path = os.path.join(DATADIR, category)  # path to plate images dir
@@ -60,20 +63,20 @@ def pack_dataset():
 def save_dataset():
     create_training_data()
     X = pack_dataset()
-    pickle_out = open(relative_path+'SavedData/X.pickle', 'wb')
+    pickle_out = open(relative_path + 'SavedData/X.pickle', 'wb')
     pickle.dump(X, pickle_out)
     pickle_out.close()
 
-    pickle_out = open(relative_path+'SavedData/y.pickle', 'wb')
+    pickle_out = open(relative_path + 'SavedData/y.pickle', 'wb')
     pickle.dump(y, pickle_out)
     pickle_out.close()
 
 
 def load_dataset():
-    pickle_in = open(relative_path+'SavedData/X.pickle', 'rb')
+    pickle_in = open(relative_path + 'SavedData/X.pickle', 'rb')
     X = pickle.load(pickle_in)
 
-    pickle_in = open(relative_path+'SavedData/y.pickle', 'rb')
+    pickle_in = open(relative_path + 'SavedData/y.pickle', 'rb')
     y = pickle.load(pickle_in)
 
     return X, y
@@ -112,7 +115,7 @@ def creat_model():
 
     model.fit(X, y, batch_size=32, epochs=10, validation_split=0.1, callbacks=[tenserboard])
 
-    model.save(relative_path+'SavedModels/plate-64x2-cnn.model')
+    model.save(relative_path + 'SavedModels/plate-64x2-cnn.model')
 
 
 def use_model_to_predict(name):
@@ -121,16 +124,37 @@ def use_model_to_predict(name):
     def prepare(filepath):
         img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
         new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))  # The same which in model
-        plt.imshow(new_array, cmap=plt.cm.binary)
+        plt.imshow(img_array, cmap=plt.cm.binary)
         plt.show()
         return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)
 
-    model = tf.keras.models.load_model(relative_path+'SavedModels/plate-64x2-cnn.model')
-    prediction = model.predict([prepare(relative_path+'TestData/' + name + '.jpg')])
+    model = tf.keras.models.load_model(relative_path + 'SavedModels/plate-64x2-cnn.model')
+    prediction = model.predict([prepare(relative_path + 'TestData/' + name + '.jpg')])
 
-    print(CATEGORIES[int(prediction[0][0])])
     print(prediction)
+    print(CATEGORIES[int(prediction[0][0])])
+    return int(prediction[0][0])
 
-#save_dataset()
-use_model_to_predict('test-full-5')
-#creat_model()
+
+def text_speech(font: str, size: int, text: str, color, background, x, y, bold: bool, bubble: bool, screen):
+    global textRect
+    font = pygame.font.SysFont(None, 40)
+    # font = pygame.font.Font(font,size)
+    font.set_bold(bold)
+    textSurf = font.render(text, True, color).convert_alpha()
+    if bubble:
+        textSize = textSurf.get_size()
+        bubbleSurf = pygame.Surface((textSize[0] * 2., textSize[1] * 2))
+        textRect = bubbleSurf.get_rect()
+        bubbleSurf.fill(background)
+        bubbleSurf.blit(textSurf, textSurf.get_rect(center=textRect.center))
+        textRect.center = (x, y)
+        screen.blit(bubbleSurf, textRect)
+    else:
+        textRect = textSurf.get_rect()
+        textRect.center = (x, y)
+        screen.blit(textSurf, textRect)
+
+# save_dataset()
+# use_model_to_predict('test-full-5')
+# creat_model()