Merge branch 'master' of gitlab.cci.drexel.edu:kpp55/Autonomous_Vehicles_with_embedded_intelligence

keras_training.py

-import tensorflow as tf
-from tensorflow import keras
-import numpy as np
-import matplotlib.pyplot as plt
-import serial
-import time
-ser = serial.Serial(
-    port='/dev/ttyACM0',
-    baudrate=19200,
-    parity=serial.PARITY_ODD,
-    stopbits=serial.STOPBITS_TWO,
-    bytesize=serial.SEVENBITS
-)
-plt.rcParams['interactive'] == True
-fashion_mnist = keras.datasets.fashion_mnist
-(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
-class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 
-               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
-train_images = train_images / 255.0
-test_images = test_images / 255.0
-plt.figure(figsize=(10,10))
-for i in range(25):
-    plt.subplot(5,5,i+1)
-    plt.xticks([])
-    plt.yticks([])
-    plt.grid(False)
-    plt.imshow(train_images[i], cmap=plt.cm.binary)
-    plt.xlabel(class_names[train_labels[i]])
-plt.show()
-model = keras.Sequential([
-    keras.layers.Flatten(input_shape=(28, 28)), #Transforms the format of the images from a 2d-array (of 28 by 28 pixels), to a 1d-array of 28 * 28 = 784 pixels
-    keras.layers.Dense(128, activation=tf.nn.relu), #This is the fully connected layers
-    keras.layers.Dense(10, activation=tf.nn.softmax) #Output layer
-])
-model.compile(optimizer=tf.train.AdamOptimizer(), 
-              loss='sparse_categorical_crossentropy',
-              metrics=['accuracy'])
-model.fit(train_images, train_labels, epochs=5)
-test_loss, test_acc = model.evaluate(test_images, test_labels)
-
-print('Test accuracy:', test_acc)
-predictions = model.predict(test_images)
-np.argmax(predictions[0])
-def plot_image(i, predictions_array, true_label, img):
-  predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
-  plt.grid(False)
-  plt.xticks([])
-  plt.yticks([])
-  
-  plt.imshow(img, cmap=plt.cm.binary)
-
-  predicted_label = np.argmax(predictions_array)
-  if predicted_label == true_label:
-    color = 'blue'
-  else:
-    color = 'red'
-  
-  plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
-                                100*np.max(predictions_array),
-                                class_names[true_label]),
-                                color=color)
-
-def plot_value_array(i, predictions_array, true_label):
-  predictions_array, true_label = predictions_array[i], true_label[i]
-  plt.grid(False)
-  plt.xticks([])
-  plt.yticks([])
-  thisplot = plt.bar(range(10), predictions_array, color="#777777")
-  plt.ylim([0, 1]) 
-  predicted_label = np.argmax(predictions_array)
- 
-  thisplot[predicted_label].set_color('red')
-  thisplot[true_label].set_color('blue')
-i = 0
-plt.figure(figsize=(6,3))
-plt.subplot(1,2,1)
-plot_image(i, predictions, test_labels, test_images)
-plt.subplot(1,2,2)
-plot_value_array(i, predictions,  test_labels)
-plt.show()
-num_rows = 5
-num_cols = 3
-num_images = num_rows*num_cols
-plt.figure(figsize=(2*2*num_cols, 2*num_rows))
-for i in range(num_images):
-  plt.subplot(num_rows, 2*num_cols, 2*i+1)
-  plot_image(i, predictions, test_labels, test_images)
-  plt.subplot(num_rows, 2*num_cols, 2*i+2)
-  plot_value_array(i, predictions, test_labels)
-# Single image testing
-img = test_images[0]
-print(img.shape)
-img = (np.expand_dims(img,0))
-print(img.shape)
-predictions_single = model.predict(img)
-print(predictions_single)
-print(np.argmax(predictions_single[0]))
-for i in range(0,len(test_images)):
-  img = test_images[i]
-  img = (np.expand_dims(img,0))
-  predictions_single = model.predict(img)
-  predict = int(np.argmax(predictions_single[0]))
-  time.sleep(3)
-  ser.write(b'%d' % predict)
\ No newline at end of file

testing.ino

-int incomingByte = 0;
-
-void setup() {
-  Serial.begin(19200);
-  pinMode(13,OUTPUT);
-  pinMode(12,OUTPUT);
-  pinMode(11,OUTPUT);
-  pinMode(10,OUTPUT);
-  pinMode(9,OUTPUT);
-  pinMode(8,OUTPUT);
-  pinMode(7,OUTPUT);
-  pinMode(6,OUTPUT);
-  pinMode(5,OUTPUT);
-  pinMode(4,OUTPUT);
-}
-
-void loop() {
-     // send data only when you receive data:
-     if (Serial.available() > 0) {
-        // read the incoming byte:
-        incomingByte = Serial.read();
-        // say what you got:
-        Serial.print("I received: ");
-        Serial.println(incomingByte, DEC);
-     }
-     if(incomingByte == 57)
-     {
-        digitalWrite(13,HIGH);
-        delay(1000);
-        digitalWrite(13,LOW);
-        delay(1000);
-     }
-     else if(incomingByte == 56)
-     {
-        digitalWrite(12,HIGH);
-        delay(1000);
-        digitalWrite(12,LOW);
-        delay(1000);
-     }
-     else if(incomingByte == 55)
-     {
-        digitalWrite(11,HIGH);
-        delay(1000);
-        digitalWrite(11,LOW);
-        delay(1000);
-     }
-     
-     else if(incomingByte == 54)
-     {
-        digitalWrite(10,HIGH);
-        delay(1000);
-        digitalWrite(10,LOW);
-        delay(1000);
-     }
-     
-     else if(incomingByte == 53)
-     {
-        digitalWrite(9,HIGH);
-        delay(1000);
-        digitalWrite(9,LOW);
-        delay(1000);
-     }
-
-     else if(incomingByte == 52)
-     {
-        digitalWrite(8,HIGH);
-        delay(1000);
-        digitalWrite(8,LOW);
-        delay(1000);
-     }
-
-     else if(incomingByte == 51)
-     {
-        digitalWrite(7,HIGH);
-        delay(1000);
-        digitalWrite(7,LOW);
-        delay(1000);
-     }
-
-     else if(incomingByte == 50)
-     {
-        digitalWrite(6,HIGH);
-        delay(1000);
-        digitalWrite(6,LOW);
-        delay(1000);
-     }
-
-     else if(incomingByte == 49)
-     {
-        digitalWrite(5,HIGH);
-        delay(1000);
-        digitalWrite(5,LOW);
-        delay(1000);
-     }
-     
-     else if(incomingByte == 48)
-     {
-        digitalWrite(4,HIGH);
-        delay(1000);
-        digitalWrite(4,LOW);
-        delay(1000);
-     }
-}