Improving model, fix plate prediction

src/SubprojectMaksymilianKierski/PlateRecognition.py

@@ -22,14 +22,21 @@ current_path = os.path.dirname(__file__)
 relative_path = os.path.join(current_path, 'Data/')
 
 DATADIR = relative_path + 'LearningData'
-CATEGORIES = ['Full', 'Empty']
-IMG_SIZE = 70  # specify on which size we want to transform the photos for example 50x50px
+CATEGORIES = ['Full', 'Empty']  # labels
+IMG_SIZE = 150  # specify on which size we want to transform the photos for example 50x50px
 NAME = 'plate-cnn-64x2-{}'.format(int(time.time()))
 
 training_data = []
 X = []  # feature set
 y = []  # label set
 
+'''
+layer size | conv leyer |
+     64    |     1      | loss: 0.0443 - accuracy: 0.9942 - val_loss: 0.3614 - val_accuracy: 0.7692
+     64    |     2      | loss: 0.0931 - accuracy: 0.9625 - val_loss: 0.4772 - val_accuracy: 0.8462
+     64    |     3      |
+'''
+
 
 def create_training_data():
     for category in CATEGORIES:
@@ -115,7 +122,7 @@ 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/{}.model'.format(NAME))
 
 
 def use_model_to_predict(name):
@@ -128,7 +135,7 @@ def use_model_to_predict(name):
         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')
+    model = tf.keras.models.load_model(relative_path + 'SavedModels/plate-cnn-64x1-1590497461.model')
     prediction = model.predict([prepare(relative_path + 'TestData/' + name + '.jpg')])
 
     print(prediction)
@@ -155,6 +162,7 @@ def text_speech(font: str, size: int, text: str, color, background, x, y, bold:
         textRect.center = (x, y)
         screen.blit(textSurf, textRect)
 
+
 # save_dataset()
-# use_model_to_predict('test-full-5')
-# creat_model()
+#use_model_to_predict('test-0')
+creat_model()