大众实习任务
修改英伟达的端对端神经网络，在树莓派上实现智能小车
1.图像处理

# -*- coding: utf-8 -*-
"""
Created on Tue Jul 17 13:38:23 2018

@author: 51207
"""

import os
from PIL import Image
import numpy as np
import xlrd
import pickle

def loadimg_infile(imgdir):
    imgname=[]
    imgname_sorted=[]
    filename=os.listdir(imgdir)
    for file in filename:
        if 'jpg' in file:
            imgname.append(imgdir+file)
    s=len(imgname)
    for i in range(s):
        imgname_sorted.append(imgdir+'/img_'+str(i)+'.jpg')
    return imgname_sorted


def imglist_to_nparray(imglist):
    result=np.zeros((len(imglist),90,160,3),dtype=np.uint8)
    i=0
    for img in imglist:
        image=np.array(Image.open(img))
        result[i]=image
        i+=1
    return result

imgdir='D:/mywork/aidrive/intern/smallcar/data/shoudong/image3'
x=loadimg_infile(imgdir)
X=imglist_to_nparray(x)
ydata=imgdir+'/ydata.xlsx'
workbook=xlrd.open_workbook(ydata)
sheet=workbook.sheet_by_name('Sheet1')
Y=np.array(sheet.col_values(0),dtype=np.uint8)

with open('datashoudong3.pkl','wb') as fp:
    pickle.dump([X,Y],fp)      

2.模型训练

# -*- coding: utf-8 -*-
"""
Created on Tue Jul  3 10:43:21 2018

@author: 51207
"""

import os
import pickle
import matplotlib
from matplotlib.pyplot import imshow

file_path="D:/mywork/aidrive/intern/smallcar/data/alldata.pkl"

#提取数据
with open(file_path,'rb') as f:
    X,Y=pickle.load(f)

#划分数据集
import numpy as np

def unison_shuffled_copies(X,Y):
    assert len(X)==len(Y)
    p=np.random.permutation(len(X))
    return X[p], Y[p]

shuffled_X,shuffled_Y=unison_shuffled_copies(X,Y)

test_cutoff=int(len(X)* .8)
val_cutoff=test_cutoff+int(len(X)* .1)
train_X,train_Y=shuffled_X[:test_cutoff],shuffled_Y[:test_cutoff]
val_X, val_Y = shuffled_X[test_cutoff:val_cutoff], shuffled_Y[test_cutoff:val_cutoff]
test_X, test_Y = shuffled_X[val_cutoff:], shuffled_Y[val_cutoff:]

'''
#增强训练集
X_flipped = np.array([np.fliplr(i) for i in train_X])
Y_flipped = np.array([-i for i in train_Y])
train_X = np.concatenate([train_X, X_flipped])
train_Y = np.concatenate([train_Y, Y_flipped])
'''

#建模型
from keras.models import Model, load_model
from keras.layers import Input, Convolution2D, MaxPooling2D, Activation, Dropout, Flatten, Dense
img_in=Input(shape=(90,160,3),name='img_in')
angle_in=Input(shape=(1,),name='angle_in')

x=Convolution2D(8,3,3)(img_in)
x=Activation('relu')(x)
x=MaxPooling2D(pool_size=(2,2))(x)

x=Convolution2D(16,3,3)(x)
x=Activation('relu')(x)
x=MaxPooling2D(pool_size=(2,2))(x)

x=Convolution2D(32,3,3)(x)
x=Activation('relu')(x)
x=MaxPooling2D(pool_size=(2,2))(x)

merged=Flatten()(x)

x=Dense(256)(merged)
x=Activation('linear')(x)
x=Dropout(.2)(x)

angle_out=Dense(1,name='angle_out')(x)

model=Model(input=[img_in],output=[angle_out])
model.compile(optimizer='adam',loss='mean_squared_error')
model.summary()

from keras import callbacks

model_path = os.path.expanduser('~/best_autopilot.hdf5')
save_best = callbacks.ModelCheckpoint(model_path, monitor='val_loss', verbose=1,save_best_only=True, mode='min')
early_stop = callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=5,verbose=0, mode='auto')
callbacks_list = [save_best, early_stop]
model.fit(train_X, train_Y, batch_size=50, nb_epoch=20, validation_data=(val_X, val_Y), callbacks=callbacks_list)

import pandas as pd

model = load_model(model_path)
test_P = model.predict(test_X)
test_P = test_P.reshape((test_P.shape[0],)) 

df = pd.DataFrame({'predicted':test_P, 'actual':test_Y})
ax = df.plot.scatter('predicted', 'actual')

P = model.predict(X[:150])
#predict outputs nested arrays so we need to reshape to plot.
P = P.reshape((P.shape[0],)) 

ax = pd.DataFrame({'predicted':P, 'actual':Y[:150]}).plot()
ax.set_ylabel("steering angle")

3.树莓派上运行

# -*- coding: utf-8 -*  
import RPi.GPIO as GPIO
GPIO.setmode(GPIO.BOARD)
GPIO.setup(32, GPIO.IN)                          # right US Echo
GPIO.setup(36, GPIO.OUT)                         # right US Trig
GPIO.setup(15, GPIO.IN)                          # front US Echo
GPIO.setup(38, GPIO.OUT)                         # front US Trig
GPIO.setup(37, GPIO.OUT,initial = GPIO.HIGH)     # red1
GPIO.setup(35, GPIO.OUT,initial = GPIO.HIGH)     # red2
GPIO.setup(33, GPIO.OUT,initial = GPIO.LOW)      # yellow_right
GPIO.setup(31, GPIO.OUT,initial = GPIO.LOW)      # yellow_left
GPIO.setup(11, GPIO.OUT,initial = GPIO.HIGH)     # Beep
from picamera.array import PiRGBArray
from picamera import PiCamera
import serial  
import time
import cv2
import numpy as np
from keras.models import load_model

ser = serial.Serial("/dev/ttyUSB0", 9600)

def set_picamera():
    camera = PiCamera()
    camera.resolution = (640,360)
    camera.framerate = 30
    camera.brightness=60
    camera.shutter_speed = 10000
    camera.exposure_mode = 'night'
    camera.iso=800
    time.sleep(5)
    g = camera.awb_gains
    camera.awb_mode='off'
    camera.awb_gains = g
    rawCapture = PiRGBArray(camera,size=(640,360))
    
    return camera,rawCapture

def start_Beep():
    for i in range(0,5):
        GPIO.output(11,GPIO.LOW)
        time.sleep(0.1)
        GPIO.output(11,GPIO.HIGH)
        time.sleep(0.1)

def get_angle(imageCap):
    result=np.zeros((1,90,160,3),dtype=np.uint8)
    image=np.array(imageCap)
    result[0]=image
    return int(model.predict(result)[0])

def normalize_angle(angle):
    return (int(angle/5.0+0.5))*5

if __name__ == '__main__':
    try:
        model_path='ml_model_retrain.hdf5'
        #model_path='ml_model_pbs.hdf5'
        model = load_model(model_path)
        #start_Beep()
        camera,raw = set_picamera()
        for frame in camera.capture_continuous(raw,format = "bgr",use_video_port=True):
            img = frame.array
            imageCap = cv2.resize(img,(160,90))
            angle=get_angle(imageCap)
            #angle=normalize_angle(get_angle(imageCap))
            ser.write("1,13,"+str(angle))

    except KeyboardInterrupt:  
        if ser != None:
            str1 = "1,0,90"
            n = ser.write(str1)
            time.sleep(0.2)
            print ("\nfinish")
            ser.close()

未附上图像采集等其他程序
最终效果：
https://pan.baidu.com/s/1dZfu7dOQvmXW-ESvyUX0tQ

d1420180907_165649.gif