training.py

#%%
import numpy as np
import pandas as pd
import os
import cv2
import keras as ks
from PIL import Image
from sklearn.model_selection import train_test_split
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import Adam
from sklearn.metrics import accuracy_score
np.random.seed(42);
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight');
#%%
datadir="D:\Python\classi\gtsrb-german-traffic-sign/";
train_path="D:\Python\classi\gtsrb-german-traffic-sign\Train/";
test_path="D:\Python\classi\gtsrb-german-traffic-sign\Test/";
#%%TOTAL NUMBER OF CLASSES
num_categories=len(os.listdir(train_path));
classes = { 0:'Speed limit (20km/h)',
            1:'Speed limit (30km/h)', 
            2:'Speed limit (50km/h)', 
            3:'Speed limit (60km/h)', 
            4:'Speed limit (70km/h)', 
            5:'Speed limit (80km/h)', 
            6:'End of speed limit (80km/h)', 
            7:'Speed limit (100km/h)', 
            8:'Speed limit (120km/h)', 
            9:'No passing', 
            10:'No passing veh over 3.5 tons', 
            11:'Right-of-way at intersection', 
            12:'Priority road', 
            13:'Yield', 
            14:'Stop', 
            15:'No vehicles', 
            16:'Veh > 3.5 tons prohibited', 
            17:'No entry', 
            18:'General caution', 
            19:'Dangerous curve left', 
            20:'Dangerous curve right', 
            21:'Double curve', 
            22:'Bumpy road', 
            23:'Slippery road', 
            24:'Road narrows on the right', 
            25:'Road work', 
            26:'Traffic signals', 
            27:'Pedestrians', 
            28:'Children crossing', 
            29:'Bicycles crossing', 
            30:'Beware of ice/snow',
            31:'Wild animals crossing', 
            32:'End speed + passing limits', 
            33:'Turn right ahead', 
            34:'Turn left ahead', 
            35:'Ahead only', 
            36:'Go straight or right', 
            37:'Go straight or left', 
            38:'Keep right', 
            39:'Keep left', 
            40:'Roundabout mandatory', 
            41:'End of no passing', 
            42:'End no passing veh > 3.5 tons' }
#%%TRAINING DATA VISUALIZATION
folders=os.listdir(train_path);
train_number=[];
class_num=[];
for folder in folders:
    train_files=os.listdir(train_path+"/"+folder);
    train_number.append(len(train_files));
    class_num.append(classes[int(folder)]);
ziplist=zip(train_number,class_num);
sortedpairs=sorted(ziplist);
tupes=zip(*sortedpairs);
train_number,class_num=[list(tuple) for tuple in tupes];

plt.figure(figsize=(21,10));
plt.bar(class_num,train_number);
plt.xticks(class_num,rotation="vertical");
plt.show();



# %%TRAINING DATA COLLECTION
image_data = []
image_labels = []

for i in range(num_categories):
    path = datadir + '/Train/' + str(i)
    images = os.listdir(path)

    for img in images:
        try:
            image = cv2.imread(path + '/' + img)
            image_fromarray = Image.fromarray(image, 'RGB')
            resize_image = image_fromarray.resize((30, 30))
            image_data.append(np.array(resize_image))
            image_labels.append(i)
        except:
            print("Error in " + img)

# Changing the list to numpy array
image_data = np.array(image_data)
image_labels = np.array(image_labels)

print(image_data.shape, image_labels.shape)
# %%SHUFFLING THE DATA
shuffle_indexes = np.arange(image_data.shape[0])
np.random.shuffle(shuffle_indexes)
image_data = image_data[shuffle_indexes]
image_labels = image_labels[shuffle_indexes]

#%%SPLITTING DATA INTO TRAINING AND VALIDATION SETS
X_train, X_val, y_train, y_val = train_test_split(image_data, image_labels, test_size=0.3, random_state=42, shuffle=True)

X_train = X_train/255 
X_val = X_val/255

print("X_train.shape", X_train.shape)
print("X_valid.shape", X_val.shape)
print("y_train.shape", y_train.shape)
print("y_valid.shape", y_val.shape)

#%%
y_train = ks.utils.to_categorical(y_train, num_categories)
y_val = ks.utils.to_categorical(y_val, num_categories)

print(y_train.shape)
print(y_val.shape)

#%%MODEL CREATION
model = ks.models.Sequential([    
    ks.layers.Conv2D(filters=16, kernel_size=(3,3), activation='relu', input_shape=(30,30,3)),
    ks.layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu'),
    ks.layers.MaxPool2D(pool_size=(2, 2)),
    ks.layers.BatchNormalization(axis=-1),
    
    ks.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu'),
    ks.layers.Conv2D(filters=128, kernel_size=(3,3), activation='relu'),
    ks.layers.MaxPool2D(pool_size=(2, 2)),
    ks.layers.BatchNormalization(axis=-1),
    
    ks.layers.Flatten(),
    ks.layers.Dense(512, activation='relu'),
    ks.layers.BatchNormalization(),
    ks.layers.Dropout(rate=0.5),
    
    ks.layers.Dense(43, activation='softmax')
])
#%%
lr = 0.001
epochs = 30

opt = Adam(lr=lr, decay=lr / (epochs * 0.5))
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])

#%%AUGUMENTING DATA AND TRAINING MODEL
aug = ImageDataGenerator(
    rotation_range=10,
    zoom_range=0.15,
    width_shift_range=0.1,
    height_shift_range=0.1,
    shear_range=0.15,
    horizontal_flip=False,
    vertical_flip=False,
    fill_mode="nearest")

history = model.fit(aug.flow(X_train, y_train, batch_size=32), epochs=epochs, validation_data=(X_val, y_val))
#%%
model.save("model.h5")
model.save_weights("D:\Python\classi/nig")
dir(aug)
#%%MODEL EVALUATION
pd.DataFrame(history.history).plot(figsize=(8, 5))
plt.grid(True)
plt.gca().set_ylim(0, 1)
plt.show()

#%%MODEL ACCURACY
test = pd.read_csv(datadir + '/Test.csv')

labels = test["ClassId"].values
imgs = test["Path"].values

data =[]

for img in imgs:
    try:
        image = cv2.imread(datadir + '/' +img)
        image_fromarray = Image.fromarray(image, 'RGB')
        resize_image = image_fromarray.resize((30, 30))
        data.append(np.array(resize_image))
    except:
        print("Error in " + img)
X_test = np.array(data)
X_test = X_test/255

pred=np.argmax(model.predict(X_test), axis=-1)
#Accuracy with the test data
print('Test Data accuracy: ',accuracy_score(labels, pred)*100)
# %%MODEL TESTING

plt.figure(figsize = (25, 25))

start_index = 0
for i in range(25):
    plt.subplot(5, 5, i + 1)
    plt.grid(False)
    plt.xticks([])
    plt.yticks([])
    prediction = pred[start_index + i]
    actual = labels[start_index + i]
    col = 'g'
    if prediction != actual:
        col = 'r'
    plt.xlabel('Actual={} || Pred={}'.format(classes[actual],classes[prediction]), color = col)
    plt.imshow(X_test[start_index + i])
plt.show()
# %%