import pandas as pd

import numpy as np

import os

import cv2

import matplotlib.pyplot as plt

import random

data = r"D:\users\new owner\Desktop\TKS\Christmas Break\gtsrb-german-traffic-sign"

img_size = 32

def load_data(dataset):

rows = pd.read_csv(dataset)

rows = rows.sample(frac=1).reset_index(drop=True)

images = []

classes = []

for i, row in rows.iterrows():

img_class = row["ClassId"]

img_path = row["Path"]

image = os.path.join(data, img_path)

image = cv2.imread(image)

image_rs = cv2.resize(image, (img_size, img_size), 3)

R, G, B = cv2.split(image_rs)

img_r = cv2.equalizeHist(R)

img_g = cv2.equalizeHist(G)

img_b = cv2.equalizeHist(B)

new_image = cv2.merge((img_r, img_g, img_b))

images.append(new_image)

classes.append(img_class)

if i % 500 == 0:

print(f"loaded: {i}")

X = np.array(images)

y = np.array(classes)

return(X, y)

train_data = r"D:\users\new owner\Desktop\TKS\Christmas Break\gtsrb-german-traffic-sign\Train.csv"

test_data = r"D:\users\new owner\Desktop\TKS\Christmas Break\gtsrb-german-traffic-sign\Test.csv"

(trainX, trainY) = load_data(train_data)

(testX, testY) = load_data(test_data)

import tensorflow as tf

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import BatchNormalization

from tensorflow.keras.layers import Conv2D

from tensorflow.keras.layers import MaxPooling2D

from tensorflow.keras.layers import Activation

from tensorflow.keras.layers import Flatten

from tensorflow.keras.layers import Dropout

from tensorflow.keras.layers import Dense

from tensorflow.keras.utils import to_categorical

from tensorflow.keras.preprocessing.image import ImageDataGenerator

from tensorflow.keras.optimizers import Adam

train_X = trainX / 255.0

test_X = testX / 255.0

num_labels = len(np.unique(trainY))

print(num_labels)

train_Y = to_categorical(trainY, num_labels)

test_Y = to_categorical(trainY, num_labels)

class_totals = train_Y.sum(axis=0)

class_weight = class_totals.max() / class_totals

class RoadSignClassifier:

def createCNN(width, height, depth, classes):

model = Sequential()

inputShape = (height, width, depth)

#1st convolution layer

model.add(Conv2D(8, (5, 5), input_shape=inputShape, activation="relu"))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(BatchNormalization())

model.add(Conv2D(16, (3, 3), activation="relu"))

model.add(BatchNormalization())

model.add(Conv2D(16, (3, 3), activation="relu"))

model.add(BatchNormalization())

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3), padding="same", activation="relu"))

model.add(BatchNormalization())

model.add(Conv2D(32, (3, 3), padding="same", activation="relu"))

model.add(BatchNormalization())

model.add(MaxPooling2D(pool_size=(2, 2)))

#fully connected layer

model.add(Flatten())

model.add(Dropout(0.5))

model.add(Dense(128, activation="relu"))

#output

model.add(Dense(classes, activation="softmax"))

return model

epochs = 20

batch_size = 64

learning_rate = 0.001

data_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)

model = RoadSignClassifier.createCNN(width=img_size, height=img_size, depth=3, classes=43)

optimizer = Adam(lr=learning_rate, decay=learning_rate / (epochs))

model.compile(optimizer=optimizer, loss="categorical_crossentropy", metrics=["accuracy"])

fit = model.fit_generator(

data_aug.flow(train_X, train_Y, batch_size=batch_size),

epochs=epochs,

validation_data=(test_X, test_Y),

class_weight=class_weight,

verbose=1)