main.py

import random
import cv2
import matplotlib.pyplot as plt
import pandas as pd
import os
import numpy as np
from sklearn.utils import shuffle
import matplotlib.image as mpimg
from imgaug import augmenters as iaa
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Convolution2D,Flatten,Dense
from tensorflow.keras.optimizers import Adam


def getName(filePath):
    return filePath.split('\\')[-1]

def importDataInfo(path):
    columns = ['Center', 'Left', 'Right','Steering', 'Throttle', 'Brake', 'Speed']
    data = pd.read_csv(os.path.join(path,'driving_log.csv'),names = columns)
    # print(data['Center'][0])
    # print(getName(data['Center'][0]))
    data['Center'] = data['Center'].apply(getName)
   # print(data.head())
    print('Total Images Imported:',data.shape[0])
    return data

def balanceData(data,display=True):
    nbins = 31
    samplesPerBin =500
    hist,bins = np.histogram(data['Steering'],nbins)
    # print(bins)
    if display:
       center = (bins[:-1]+bins[1:])*0.5

    # print(center)
       plt.bar(center,hist,width = 0.06)
       plt.plot((-1,1),(samplesPerBin,samplesPerBin))
       #plt.show()

    removeIndexList = []
    for j in range(nbins):
        binDataList = []
        for i in range(len(data['Steering'])):
            if data['Steering'][i] >= bins[j] and data['Steering'][i]<=bins[j+1]:
                binDataList.append(i)
        binDataList =shuffle(binDataList)
        binDataList = binDataList[samplesPerBin:]
        removeIndexList.extend(binDataList)
 #   print('Removed Images:',len(removeIndexList))
    data.drop(data.index[removeIndexList], inplace=True)
  #  print('Remaining Images:', len(data))

    if display:
        hist, _ = np.histogram(data['Steering'], nbins)
        plt.bar(center, hist, width=0.06)
        plt.plot((-1, 1), (samplesPerBin, samplesPerBin))
    #    plt.show()

    return data

def loadData(path,data):
    imagesPath = []
    steering = []

    for i in range(len(data)):
        indexedData = data.iloc[i]
        print(indexedData)
        imagesPath.append(os.path.join(path,'IMG',indexedData[0]))
        print(os.path.join(path,'IMG',indexedData[0]))
        steering.append(float(indexedData[3]))
    imagesPath = np.asarray(imagesPath)
    steering = np.asarray(steering)
    return imagesPath,steering

def augmentImage(imgPath,steering):
  img = mpimg.imread(imgPath)
  if np.random.rand()<0.5:

    pan = iaa.Affine(translate_percent={'x':(-0.1,-0.1),'y':(-0.1,-0.1)})
    img = pan.augment_image(img)

  if np.random.rand() < 0.5:

    zoom = iaa.Affine(scale=(1,1.2))
    img = zoom.augment_image(img)

  if np.random.rand()<0.5:

    brightness = iaa.Multiply((0.4,1.2))
    img = brightness.augment_image(img)

  if np.random.rand()<0.5:

    img = cv2.flip(img,1)
    steering = -steering

  return img,steering

#imgRe, st = augmentImage('demo.jpg',0)
#lt.imshow(imgRe)
#plt.show()

def preProcessing(img):
    img = img[60:135,:,:]
    img = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
    img = cv2.GaussianBlur(img,(3,3),0)
    img = cv2.resize(img,(200,66))
    img = img/255
    return img

imgRe = preProcessing(mpimg.imread('demo.jpg'))
#plt.imshow(imgRe)
#plt.show()

def batchGen(imagesPath,steeringList,batchSize,trainFlag):
    while True:
        imgBatch=[]
        steeringBatch = []

        for i in range(batchSize):
            index = random.randint(0,len(imagesPath)-1)
            if trainFlag:
              img, steering = augmentImage(imagesPath[index],steeringList[index])
            else:
                img = mpimg.imread(imagesPath[index])
                steering = steeringList[index]
            img = preProcessing(img)
            imgBatch.append(img)
            steeringBatch.append(steering)
        yield(np.asarray(imgBatch),np.asarray(steeringBatch))

def createModel():
    model = Sequential()

    model.add(Convolution2D(24,(5,5),(2,2),input_shape=(66,200,3),activation='elu'))
    model.add(Convolution2D(36,(5,5),(2,2),input_shape=(66,200,3),activation='elu'))
    model.add(Convolution2D(48,(5,5),(2,2),input_shape=(66,200,3),activation='elu'))
    model.add(Convolution2D(64,(3,3),(1,1),input_shape=(66,200,3),activation='elu'))
    model.add(Convolution2D(64,(3,3),(1,1),input_shape=(66,200,3),activation='elu'))

    model.add(Flatten())
    model.add(Dense(100,activation='elu'))
    model.add(Dense(50, activation='elu'))
    model.add(Dense(10, activation='elu'))
    model.add(Dense(1))

    model.compile(Adam(lr=0.0001),loss='mse')

    return model

if __name__=='__main__':
    print('connected')