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a/src/SubprojectMaksymilianKierski/PlateRecognition.py b/src/SubprojectMaksymilianKierski/PlateRecognition.py index 3fe3eda..2e4ca9a 100644 --- a/src/SubprojectMaksymilianKierski/PlateRecognition.py +++ b/src/SubprojectMaksymilianKierski/PlateRecognition.py @@ -24,17 +24,20 @@ relative_path = os.path.join(current_path, 'Data/') DATADIR = relative_path + 'LearningData' 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())) +NAME = 'plate-cnn-64x3x1-{}'.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 | +layer size | conv layer | Dense layer | + 64 | 1 | 0 | loss: 0.0443 - accuracy: 0.9942 - val_loss: 0.3614 - val_accuracy: 0.7692 + 64 | 2 | 0 | loss: 0.0931 - accuracy: 0.9625 - val_loss: 0.4772 - val_accuracy: 0.8462 + 64 | 3 | 0 | loss: 0.2491 - accuracy: 0.9020 - val_loss: 0.3762 - val_accuracy: 0.7949 + 64 | 1 | 1 | loss: 0.0531 - accuracy: 0.9971 - val_loss: 0.4176 - val_accuracy: 0.8205 -> + 64 | 2 | 1 | loss: 0.0644 - accuracy: 0.9798 - val_loss: 0.5606 - val_accuracy: 0.8462 + 64 | 3 | 1 | loss: 0.1126 - accuracy: 0.9625 - val_loss: 0.5916 - val_accuracy: 0.8205 ''' @@ -110,6 +113,10 @@ def creat_model(): model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) + # model.add(Conv2D(64, (3, 3))) + # model.add(Activation('relu')) + # model.add(MaxPooling2D(pool_size=(2, 2))) + model.add(Flatten()) # converts our 3D feature maps to 1D feature vectors model.add(Dense(1)) # output layer 1 output @@ -135,11 +142,12 @@ 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-cnn-64x1-1590497461.model') + 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) - print(CATEGORIES[int(prediction[0][0])]) + #print(prediction) + #print(CATEGORIES[int(prediction[0][0])]) return int(prediction[0][0]) @@ -164,5 +172,8 @@ def text_speech(font: str, size: int, text: str, color, background, x, y, bold: # save_dataset() -#use_model_to_predict('test-0') -creat_model() +# for x in range(10): +# print(CATEGORIES[use_model_to_predict('test-{}'.format(x))]) +#creat_model() + +#python -m tensorboard.main --logdir=Data/logs \ No newline at end of file