Traffic_Sign_Classifier


Self-Driving Car Engineer Nanodegree

Deep Learning

Project: Build a Traffic Sign Recognition Classifier

In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary.

Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.

In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a write up template that can be used to guide the writing process. Completing the code template and writeup template will cover all of the rubric points for this project.

The rubric contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this Ipython notebook and also discuss the results in the writeup file.

Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.


Step 0: Load The Data


In [1]:
# Load pickled data
import pickle

# TODO: Fill this in based on where you saved the training and testing data

training_file = 'trafficsign/train.p'
validation_file='trafficsign/valid.p'
testing_file = 'trafficsign/test.p'

with open(training_file, mode='rb') as f:
    train = pickle.load(f)
with open(validation_file, mode='rb') as f:
    valid = pickle.load(f)
with open(testing_file, mode='rb') as f:
    test = pickle.load(f)
    
X_train, y_train = train['features'], train['labels']
X_valid, y_valid = valid['features'], valid['labels']
X_test, y_test = test['features'], test['labels']

Step 1: Dataset Summary & Exploration

The pickled data is a dictionary with 4 key/value pairs:

  • 'features' is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).
  • 'labels' is a 1D array containing the label/class id of the traffic sign. The file signnames.csv contains id -> name mappings for each id.
  • 'sizes' is a list containing tuples, (width, height) representing the original width and height the image.
  • 'coords' is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES

Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the pandas shape method might be useful for calculating some of the summary results.

Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas


In [2]:
### Replace each question mark with the appropriate value. 
### Use python, pandas or numpy methods rather than hard coding the results

# TODO: Number of training examples
n_train = X_train.shape[0]

# TODO: Number of validation examples
n_validation = X_valid.shape[0]

# TODO: Number of testing examples.
n_test = X_test.shape[0]

# TODO: What's the shape of an traffic sign image?
image_shape = (X_train.shape[1],X_train.shape[2],X_train.shape[3])

# TODO: How many unique classes/labels there are in the dataset.
n_classes = len(set(y_test))

print("Number of training examples =", n_train)
print("Number of testing examples =", n_test)
print("Number of validatio examples =", n_validation)
print("Image data shape =", image_shape)
print("Number of classes =", n_classes)


Number of training examples = 34799
Number of testing examples = 12630
Number of validatio examples = 4410
Image data shape = (32, 32, 3)
Number of classes = 43

Include an exploratory visualization of the dataset

Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.

The Matplotlib examples and gallery pages are a great resource for doing visualizations in Python.

NOTE: It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?


In [3]:
### Data exploration visualization code goes here.
### Feel free to use as many code cells as needed.
import matplotlib.pyplot as plt
import pandas as pd
# Visualizations will be shown in the notebook.
%matplotlib inline
pd.DataFrame.hist(pd.DataFrame(y_train))
plt.title('Distribution of the different classes in the Training data ')
pd.DataFrame.hist(pd.DataFrame(y_valid))
plt.title('Distribution of the different classes in the Validation data ')
pd.DataFrame.hist(pd.DataFrame(y_test))
plt.title('Distribution of the different classes in the Test data ')
plt.plot()


Out[3]:
[]

In [4]:
import numpy as np
%matplotlib inline

signNames = pd.read_csv("signnames.csv")
fig = plt.figure(figsize=(32,32), tight_layout={'h_pad':4})
for index in range(0,43,1):
    image_index = (np.nonzero(y_train==index))[0][0]
    
    ax=plt.subplot(11,4,index+1) 
    ax.imshow(X_train[image_index],interpolation=None)
    ax.set_title( str(list(((signNames[signNames['ClassId']==index]))['SignName'])) )
plt.show()


/home/carnd/anaconda3/envs/carnd-term1/lib/python3.5/site-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
  warnings.warn("This figure includes Axes that are not "

Step 2: Design and Test a Model Architecture

Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the German Traffic Sign Dataset.

The LeNet-5 implementation shown in the classroom at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!

With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission.

There are various aspects to consider when thinking about this problem:

  • Neural network architecture (is the network over or underfitting?)
  • Play around preprocessing techniques (normalization, rgb to grayscale, etc)
  • Number of examples per label (some have more than others).
  • Generate fake data.

Here is an example of a published baseline model on this problem. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.

Pre-process the Data Set (normalization, grayscale, etc.)

Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, (pixel - 128)/ 128 is a quick way to approximately normalize the data and can be used in this project.

Other pre-processing steps are optional. You can try different techniques to see if it improves performance.

Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.


In [5]:
#for gray_sclae images
import cv2
def gray(image):
    return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
def RGB_gray(X_data):
    X_out_data=np.zeros((len(X_data),32,32,1))
    for i in range(len(X_data)):
        X_out_data[i,:,:,0]=gray(X_data[i,:,:,:])
    return X_out_data

def norm(image):
    dest = np.zeros((32,32,1))
    return cv2.normalize(image,dest ,alpha=0, beta=1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
def normalize(X_data):
    X_out_data=np.zeros((len(X_data),32,32,X_data.shape[3]))
    for i in range(len(X_data)):
        X_out_data[i,:,:,0]=norm(X_data[i,:,:,0])
    return X_out_data

train_gray = RGB_gray(X_train)
valid_gray = RGB_gray(X_valid)
test_gray = RGB_gray(X_test)

X_train = (train_gray-128)/128
X_valid = (valid_gray-128)/128
X_test = (test_gray-128)/128
#X_train = normalize(train_gray)
#X_valid = normalize(valid_gray)
#X_test = normalize(test_gray)

In [6]:
import numpy as np
%matplotlib inline

signNames = pd.read_csv("signnames.csv")
fig = plt.figure(figsize=(32,32), tight_layout={'h_pad':4})
for index in range(0,43,1):
    image_index = (np.nonzero(y_train==index))[0][0]
    
    ax=plt.subplot(11,4,index+1) 
    ax.imshow(np.squeeze(X_train[image_index]),interpolation=None)
    ax.set_title( str(list(((signNames[signNames['ClassId']==index]))['SignName'])) )
plt.show()


/home/carnd/anaconda3/envs/carnd-term1/lib/python3.5/site-packages/matplotlib/figure.py:1742: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.
  warnings.warn("This figure includes Axes that are not "

SetUp TensorFlow


In [7]:
import tensorflow as tf
from tensorflow.contrib.layers import flatten
EPOCHS = 20
BATCH_SIZE=128

Model Architecture


In [8]:
def LaNet(x):
    mu = 0
    sigma = 0.1
    
    #Every x is a batch with shape (128,32,32,1)
    #Fallowing LANET Architecture after first convolution the ouput will be (128,28,28,6).
    #The filter will be of size (5,5,1,6) so conv1_W = (5,5,1,6) and conv1_b = 6
    
    tf.contrib.layers.xavier_initializer()
    conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5,1 , 6), mean = mu, stddev = sigma))
    conv1_b = tf.Variable(tf.zeros(6))
    conv1   = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b
    # The activation function is relu
    conv1 = tf.nn.relu(conv1)
    # We will apply max_pooling after applying max_pool our dimensions are (128,14,14,6) 
    conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', name='convolution1')

    
    #2nd convolution
    #after applyig 2nd convolution our output should be (128,10,10,16)
    #Here we have (128,14,14,6) as input so the conv2_w =(5,5,6,16) and conv2_b=(16)
    conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))
    conv2_b = tf.Variable(tf.zeros(16))
    conv2   = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') +conv2_b
    
    #activation
    conv2 = tf.nn.relu(conv2)
    
    # we apply max_pooling to the (128,10,10,16) to make it (128,5,5,16)
    conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID', name='convolution2')
    # There will be 16 feature maps each with dimesion 5*5
    #print(conv2)
    
    #  Flatten the output shape of the final pooling layer such that it's 1D instead of 3D. 
    #The easiest way to do is by using tf.contrib.layers.flatten. this will convert (128,5,5,16) to (128,400)
    fc0   = flatten(conv2)
    
    
    h_fc0_drop = tf.nn.dropout(fc0, keep_prob)
    
    #print(fully_connected_layer0)
    #This fully connected layer is connected to next layer with 120 neurons. So, the number of weights will be (400,120)
    fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))
    fc1_b = tf.Variable(tf.zeros(120))
    fc1   = tf.matmul(h_fc0_drop, fc1_W) + fc1_b
    fc1    = tf.nn.relu(fc1)
    # fully_connected_layer1 is of dimension(400,120). fully_connected_layer2 will have 84 activations 
    fc2_W  = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))
    fc2_b  = tf.Variable(tf.zeros(84))
    fc2    = tf.matmul(fc1, fc2_W) + fc2_b
    fc2    = tf.nn.relu(fc2)
    
    #fully_connected_layer1 is of dimension(128,84). fully_connected_layer3 will have 43 activations 
    fc3_W  = tf.Variable(tf.truncated_normal(shape=(84, 43), mean = mu, stddev = sigma))
    fc3_b  = tf.Variable(tf.zeros(43))
    logits = tf.matmul(fc2, fc3_W) + fc3_b
    return logits

Features and Labels


In [9]:
x = tf.placeholder(tf.float32,(None,32,32,1))
y=tf.placeholder(tf.int32,(None))
one_hot_y = tf.one_hot(y,43)

Training Pipeline


In [10]:
rate=0.001
keep_prob = tf.placeholder(tf.float32)
logits = LaNet(x)
CrossEntropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y,logits=logits)
Loss = tf.reduce_mean(CrossEntropy)
optimize = tf.train.AdamOptimizer(learning_rate=rate).minimize(Loss)

Evaluation PipeLine


In [11]:
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))
accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

saver = tf.train.Saver()

def evaluate(X_data, y_data):
    num_examples = len(X_data)
    total_accuracy = 0
    sess = tf.get_default_session()
    for offset in range(0, num_examples, BATCH_SIZE):
        batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]
        accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y, keep_prob:1.0})
        total_accuracy += (accuracy * len(batch_x))
    return total_accuracy / num_examples

Train Model

  1. start session
  2. initialize all variables
  3. loop on the number of epoches
     shuffle the training data randomly and create batches
     loop on the batches 
         call optimize for stochastic gradient descent 

In [12]:
from sklearn.utils import shuffle
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    num_examples = len(X_train)
    
    print("Training...")
    print()
    for i in range(EPOCHS):
        X_train, y_train = shuffle(X_train, y_train)
        for offset in range(0, num_examples, BATCH_SIZE):
            end = offset + BATCH_SIZE
            batch_x, batch_y = X_train[offset:end], y_train[offset:end]
            sess.run(optimize, feed_dict={x: batch_x, y: batch_y, keep_prob:0.6})
            
        training_accuracy = evaluate(X_train, y_train)
        validation_accuracy = evaluate(X_valid, y_valid)
        print("EPOCH {} ...".format(i+1))
        print("Training Accuracy = {:.3f}".format(training_accuracy))
        print()
        print("Validation Accuracy = {:.3f}".format(validation_accuracy))
        print()
        
    saver.save(sess, './lenet')
    print("Model saved")


Training...

EPOCH 1 ...
Training Accuracy = 0.787

Validation Accuracy = 0.722

EPOCH 2 ...
Training Accuracy = 0.914

Validation Accuracy = 0.849

EPOCH 3 ...
Training Accuracy = 0.946

Validation Accuracy = 0.881

EPOCH 4 ...
Training Accuracy = 0.962

Validation Accuracy = 0.891

EPOCH 5 ...
Training Accuracy = 0.972

Validation Accuracy = 0.910

EPOCH 6 ...
Training Accuracy = 0.978

Validation Accuracy = 0.905

EPOCH 7 ...
Training Accuracy = 0.979

Validation Accuracy = 0.916

EPOCH 8 ...
Training Accuracy = 0.984

Validation Accuracy = 0.929

EPOCH 9 ...
Training Accuracy = 0.986

Validation Accuracy = 0.941

EPOCH 10 ...
Training Accuracy = 0.990

Validation Accuracy = 0.934

EPOCH 11 ...
Training Accuracy = 0.986

Validation Accuracy = 0.930

EPOCH 12 ...
Training Accuracy = 0.990

Validation Accuracy = 0.928

EPOCH 13 ...
Training Accuracy = 0.992

Validation Accuracy = 0.934

EPOCH 14 ...
Training Accuracy = 0.994

Validation Accuracy = 0.948

EPOCH 15 ...
Training Accuracy = 0.995

Validation Accuracy = 0.944

EPOCH 16 ...
Training Accuracy = 0.995

Validation Accuracy = 0.942

EPOCH 17 ...
Training Accuracy = 0.995

Validation Accuracy = 0.949

EPOCH 18 ...
Training Accuracy = 0.996

Validation Accuracy = 0.943

EPOCH 19 ...
Training Accuracy = 0.996

Validation Accuracy = 0.940

EPOCH 20 ...
Training Accuracy = 0.997

Validation Accuracy = 0.951

Model saved

Evaluating model on the test data

The model saved will be used on test data.


In [13]:
with tf.Session() as sess:
    saver.restore(sess,tf.train.latest_checkpoint('.'))
    test_accuracy = evaluate(X_test,y_test)
    print(test_accuracy)


0.940617576971

Step 3: Test a Model on New Images

To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.

You may find signnames.csv useful as it contains mappings from the class id (integer) to the actual sign name.

Load and Output the Images


In [14]:
import os
import matplotlib.image as mpimg
import cv2
test_image =[]

def Resize(img):
    
    #resize image into (32,32) dimension
    resize_img = cv2.resize(img,(32,32))
    #Convert to 3 channels
    #processed_img = cv2.cvtColor(resize_img, cv2.COLOR_BGRA2BGR)
    test_image.append(resize_img)
    
    return resize_img

 
#fig = plt.figure(figsize=(32,32), tight_layout={'h_pad':4})
i = 0
for file in os.listdir('testImages'):
    if '.jpg' in file:
        plt.figure(figsize=(1,1)) 
        img = mpimg.imread('testImages/' + file)
        plt.title(file)
        plt.imshow(Resize(img))
        plt.figure()
        plt.title(file+(' Original'))
        plt.imshow(img)    
        i+=1

test_image=normalize(RGB_gray(np.array(test_image)))