实战端到端深度学习模拟无人驾驶
一、理论知识
参考NVIDIA的论文 https://arxiv.org/pdf/1604.07316.pdf
这个实验的目的是使用卷积神经网络（CNN）将从前向摄像机得到的原始图像映射成自动驾驶汽车的驾驶命令。
这个过程需要先采集数据，原理是这样的：
一共有左、中、右3个摄像头，负责采集视频数据。在训练后我们只需要中间的摄像头采集的数据用于驾驶决策，那为什么需要3个采集训练数据呢，因为左右两个采集特定偏离中心的变化图像，这样只有来自人类驾驶员的数据是不足以用来训练的；网络还必须学习如何从任何错误中恢复，否则该汽车就将慢慢偏移道路。因此训练数据还扩充了额外的图像，这些图像显示了远离车道中心的偏离程度以及不同道路方向上的转动。左右两个摄像头采集负责特定偏离中心的变化图像，用这个方法来扩充额外图像数据
下面是训练系统框图。训练用图像被送入一个卷积神经网络，然后计算一个被预测出的转向命令。这个被转向命令会与该图像的期望命令相比较，卷积神经网络的权重就会被调整以使其实际输出更接近期望输出。
一旦训练完成，网络就能够从中摄像机的视频图像中生成转向命令
图像数据需要从RGB转成YUV格式，传入一个9 层网络架构的神经网络，其中包括一个归一化层、5 个卷积层和 3 个完全连接的层。
二、无人驾驶模拟环境搭建
这里我们使用Udacity的自动驾驶汽车模拟器，下载地址是 https://github.com/udacity/self-driving-car-sim
下载后运行程序选择Training Mode，就可以开始模拟驾驶了，玩得差不多了，就可以录制训练数据了
决定录制的时候在训练模式下点Record进行录制，第一次需要选择用于存放3个摄像头的图片与driving_log.csv数据
这是中间摄像头的图像
找两张分别是左、右摄像头的图像
好吧，看得出来我开得不好，其实我是让他帮我开的
三、训练程序
数据预处理
csv文件的各个字段含义是：center,left,right,steering,throttle,reverse,speed，我们需要的训练数据（center，left，right）和标签数据（steering）都在里面
#加载训练数据并将其分解为训练和验证集
def load_data(self):
#从csv读取数据
data_df = pd.read_csv(os.path.join(os.getcwd(), self.data_path, 'driving_log.csv')
,names=['center', 'left', 'right', 'steering', 'throttle', 'reverse', 'speed'])
X = data_df[['center', 'left', 'right']].values
y = data_df['steering'].values
#随机划分训练集和测试集
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=self.test_size, random_state=0)
return X_train, X_valid, y_train, y_valid图片的处理
首先需要取一部分图片进行增强处理（这里是60%的概率）
进行增强处理流程是：先平均概率随机取一个摄像头的图片，如果取得的不是中间的摄像头，对于的steering angle也进行相应的调整
#从中心、左或右随机选择一个图像，并进行调整
def choose_image(self, center, left, right, steering_angle):
choice = np.random.choice(3)
if choice == 0:
return self.load_image(left), steering_angle + 0.2
elif choice == 1:
return self.load_image(right), steering_angle - 0.2
return self.load_image(center), steering_angle接下来是进行随机反转，这个步骤是为了防止训练数据过少，比如都是偏左或都是右拐的数据，翻转的时候一样要翻转转向角
#随机反转图片
def random_flip(self, image, steering_angle):
if np.random.rand() < 0.5:
image = cv2.flip(image, 1)
steering_angle = -steering_angle
return image, steering_angle然后是使用cv2.warpAffine()函数随机平移变换，第三个参数是输出图像的大小。第二个参数是变换矩阵
#随机平移变换
def random_translate(self, image, steering_angle, range_x, range_y):
trans_x = range_x * (np.random.rand() - 0.5)
trans_y = range_y * (np.random.rand() - 0.5)
steering_angle += trans_x * 0.002
trans_m = np.float32([[1, 0, trans_x], [0, 1, trans_y]])
height, width = image.shape[:2]
image = cv2.warpAffine(image, trans_m, (width, height))
return image, steering_angle在对图片随机加入阴影，并对路上的白线进行加强，这里放几个对比图能更直观感受到
中间为了方便处理图片，把RGB格式转换为HLS格式和HSV格式
#添加随机阴影
def random_shadow(self, image):
x1, y1 = self.IMAGE_WIDTH * np.random.rand(), 0
x2, y2 = self.IMAGE_WIDTH * np.random.rand(), self.IMAGE_HEIGHT
#xm, ym = np.mgrid[0:self.IMAGE_HEIGHT, 0:self.IMAGE_WIDTH]
xm, ym = np.mgrid[0:image.shape[0], 0:image.shape[1]]
mask = np.zeros_like(image[:, :, 1])
mask[(ym - y1) * (x2 - x1) - (y2 - y1) * (xm - x1) > 0] = 1
cond = mask == np.random.randint(2)
s_ratio = np.random.uniform(low=0.2, high=0.5)
hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
hls[:, :, 1][cond] = hls[:, :, 1][cond] * s_ratio
return cv2.cvtColor(hls, cv2.COLOR_HLS2RGB)
#随机调整亮度
def random_brightness(self, image):
hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
ratio = 1.0 + 0.4 * (np.random.rand() - 0.5)
hsv[:, :, 2] = hsv[:, :, 2] * ratio
return cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
#除去顶部的天空和底部的汽车正面
def crop(self, image):
return image[60:-25, :, :]
#调整图像大小
def resize(self, image):
return cv2.resize(image, (self.IMAGE_WIDTH, self.IMAGE_HEIGHT), cv2.INTER_AREA)
#转换RGB为YUV格式
def rgb2yuv(self, image):
return cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
神经网络模型
好了，准备好数据接下来就是我们的模型了，按论文进行构建就可以了，稍微可以做些自己的调整，比如这里加了Dropout
#构建模型
def build_model(self):
model = Sequential()
model.add(Lambda(lambda x: x / 127.5 - 1.0, input_shape=(self.IMAGE_HEIGHT, self.IMAGE_WIDTH, self.IMAGE_CHANNELS)))
model.add(Conv2D(24, (5, 5), activation='elu', strides=2))
model.add(Conv2D(36, (5, 5), activation='elu', strides=2))
model.add(Conv2D(48, (5, 5), activation='elu', strides=2))
model.add(Conv2D(64, (3, 3), activation='elu'))
model.add(Conv2D(64, (3, 3), activation='elu'))
model.add(Dropout(self.keep_prob))
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.summary()
return model=================================================================
lambda_1 (Lambda) (None, 66, 200, 3) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 31, 98, 24) 1824
_________________________________________________________________
conv2d_2 (Conv2D) (None, 14, 47, 36) 21636
_________________________________________________________________
conv2d_3 (Conv2D) (None, 5, 22, 48) 43248
_________________________________________________________________
conv2d_4 (Conv2D) (None, 3, 20, 64) 27712
_________________________________________________________________
conv2d_5 (Conv2D) (None, 1, 18, 64) 36928
_________________________________________________________________
dropout_1 (Dropout) (None, 1, 18, 64) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 1152) 0
_________________________________________________________________
dense_1 (Dense) (None, 100) 115300
_________________________________________________________________
dense_2 (Dense) (None, 50) 5050
_________________________________________________________________
dense_3 (Dense) (None, 10) 510
_________________________________________________________________
dense_4 (Dense) (None, 1) 11
=================================================================
Total params: 252,219
训练
可以开始训练了，这里用fit_generator，这样可以CPU实时图像增强同时并行在GPU训练模型
# CPU做图像的实时数据增强,并行在GPU训练模型
model.fit_generator(self.batch_generator(X_train, y_train, True),
steps_per_epoch = self.steps_per_epoch,
epochs = self.epochs,
#max_queue_size=1,
validation_data=self.batch_generator(X_valid, y_valid, False),
validation_steps=len(X_valid),
callbacks=[checkpoint],
verbose=1)四、模拟自动驾驶
Python使用socketio通过4567端口连接到UDACITY模拟器，当收到数据后，就根据当前的中心摄像头图像进行预测转向，然后根据转向角度与速度计算油门
# 汽车的当前转向角
#steering_angle = float(data["steering_angle"])
# 汽车的油门
#throttle = float(data["throttle"])
# 当前的速度
speed = float(data["speed"])
# 中心摄像头
image = Image.open(BytesIO(base64.b64decode(data["image"])))
try:
image = np.asarray(image) # from PIL image to numpy array
image = preprocess(image) # apply the preprocessing
image = np.array([image]) # the model expects 4D array
# 预测图像的转向
steering_angle = float(model.predict(image, batch_size=1))
# 根据速度调整油门，如果大于最大速度就减速，如果小于最低速度就加加速
if speed > MAX_SPEED:
speed_limit = MIN_SPEED # slow down
else:
speed_limit = MAX_SPEED
throttle = 1.0 - steering_angle ** 2 - (speed / speed_limit) ** 2
print('{} {} {}'.format(steering_angle, throttle, speed))
send_control(steering_angle, throttle)
except Exception as e:
print(e)在UDACITY模拟器里选择Autonomous Mode，运行起模拟驾驶程序就可以了
五、完整程序
训练程序，里面的参数可以随意实验调整看看
PowerMode_autopilot.py
# -*- coding: utf-8 -*-
import pandas as pd
import cv2, os
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Lambda, Conv2D, Dropout, Dense, Flatten
from keras.callbacks import ModelCheckpoint
import numpy as np
import matplotlib.image as mpimg
from keras.optimizers import Adam
from keras.models import load_model
class PowerMode_autopilot:
def __init__(self, data_path = 'data', learning_rate = 1.0e-4, keep_prob = 0.5 , batch_size = 40,
save_best_only = True, test_size = 0.2, steps_per_epoch = 20000, epochs = 10):
self.IMAGE_HEIGHT, self.IMAGE_WIDTH, self.IMAGE_CHANNELS = 66, 200, 3
self.data_path = data_path
self.learning_rate = learning_rate
self.keep_prob = keep_prob
self.save_best_only = save_best_only
self.batch_size = batch_size
self.test_size = test_size
self.steps_per_epoch = steps_per_epoch
self.epochs = epochs
#加载训练数据并将其分解为训练和验证集
def load_data(self):
#从csv读取数据
data_df = pd.read_csv(os.path.join(os.getcwd(), self.data_path, 'driving_log.csv')
,names=['center', 'left', 'right', 'steering', 'throttle', 'reverse', 'speed'])
X = data_df[['center', 'left', 'right']].values
y = data_df['steering'].values
#随机划分训练集和测试集
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=self.test_size, random_state=0)
return X_train, X_valid, y_train, y_valid
#构建模型
def build_model(self):
model = Sequential()
model.add(Lambda(lambda x: x / 127.5 - 1.0, input_shape=(self.IMAGE_HEIGHT, self.IMAGE_WIDTH, self.IMAGE_CHANNELS)))
model.add(Conv2D(24, (5, 5), activation='elu', strides=2))
model.add(Conv2D(36, (5, 5), activation='elu', strides=2))
model.add(Conv2D(48, (5, 5), activation='elu', strides=2))
model.add(Conv2D(64, (3, 3), activation='elu'))
model.add(Conv2D(64, (3, 3), activation='elu'))
model.add(Dropout(self.keep_prob))
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.summary()
return model
#加载图片
def load_image(self, image_file):
return mpimg.imread(os.path.join(self.data_path, image_file.strip()))
# ---------增强处理-------
#从中心、左或右随机选择一个图像，并进行调整
def choose_image(self, center, left, right, steering_angle):
choice = np.random.choice(3)
if choice == 0:
return self.load_image(left), steering_angle + 0.2
elif choice == 1:
return self.load_image(right), steering_angle - 0.2
return self.load_image(center), steering_angle
#随机反转图片
def random_flip(self, image, steering_angle):
if np.random.rand() < 0.5:
image = cv2.flip(image, 1)
steering_angle = -steering_angle
return image, steering_angle
#随机平移变换model.summary()
def random_translate(self, image, steering_angle, range_x, range_y):
trans_x = range_x * (np.random.rand() - 0.5)
trans_y = range_y * (np.random.rand() - 0.5)
steering_angle += trans_x * 0.002
trans_m = np.float32([[1, 0, trans_x], [0, 1, trans_y]])
height, width = image.shape[:2]
image = cv2.warpAffine(image, trans_m, (width, height))
return image, steering_angle
#添加随机阴影
def random_shadow(self, image):
x1, y1 = self.IMAGE_WIDTH * np.random.rand(), 0
x2, y2 = self.IMAGE_WIDTH * np.random.rand(), self.IMAGE_HEIGHT
#xm, ym = np.mgrid[0:self.IMAGE_HEIGHT, 0:self.IMAGE_WIDTH]
xm, ym = np.mgrid[0:image.shape[0], 0:image.shape[1]]
mask = np.zeros_like(image[:, :, 1])
mask[(ym - y1) * (x2 - x1) - (y2 - y1) * (xm - x1) > 0] = 1
cond = mask == np.random.randint(2)
s_ratio = np.random.uniform(low=0.2, high=0.5)
hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
hls[:, :, 1][cond] = hls[:, :, 1][cond] * s_ratio
return cv2.cvtColor(hls, cv2.COLOR_HLS2RGB)
#随机调整亮度
def random_brightness(self, image):
hsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
ratio = 1.0 + 0.4 * (np.random.rand() - 0.5)
hsv[:, :, 2] = hsv[:, :, 2] * ratio
return cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)
#产生一个增强图像和调整转向角
def augument(self, center, left, right, steering_angle, range_x=100, range_y=10):
image, steering_angle = self.choose_image(center, left, right, steering_angle) #随机选择一个图像，并进行调整
image, steering_angle = self.random_flip(image, steering_angle) #翻转
image, steering_angle = self.random_translate(image, steering_angle, range_x, range_y) #移动
image = self.random_shadow(image) #加阴影
image = self.random_brightness(image) #亮度
return image, steering_angle
#------图像预处理-------------
#除去顶部的天空和底部的汽车正面
def crop(self, image):
return image[60:-25, :, :]
#调整图像大小
def resize(self, image):
return cv2.resize(image, (self.IMAGE_WIDTH, self.IMAGE_HEIGHT), cv2.INTER_AREA)
#转换RGB为YUV格式
def rgb2yuv(self, image):
return cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
#图像预处理
def preprocess(self, image):
image = self.crop(image)
image = self.resize(image)
image = self.rgb2yuv(image)
return image
#-------生成训练图像--------------
#生成训练图像，给出图像路径和相关的转向角
def batch_generator(self, image_paths, steering_angles, is_training):
images = np.empty([self.batch_size, self.IMAGE_HEIGHT, self.IMAGE_WIDTH, self.IMAGE_CHANNELS])
steers = np.empty(self.batch_size)
while True:
i = 0
for index in np.random.permutation(image_paths.shape[0]):
center, left, right = image_paths[index]
steering_angle = steering_angles[index]
# 0.6的概率做图像增强
if is_training and np.random.rand() < 0.6:
image, steering_angle = self.augument(center, left, right, steering_angle)
else:
image = self.load_image(center)
#将图像和转向角度添加到批处理中
images[i] = self.preprocess(image)
steers[i] = steering_angle
i += 1
if i == self.batch_size:
break
yield images, steers
#训练数据
def train_model(self, model, X_train, X_valid, y_train, y_valid):
#用户每次epoch之后保存模型数据
checkpoint = ModelCheckpoint('model-{epoch:03d}.h5',
monitor='val_loss',
verbose=0,
# 如果save_best_only = True，则最近验证误差最好的模型数据会被保存下来
save_best_only=self.save_best_only,
mode='auto')
model.compile(loss='mean_squared_error', optimizer=Adam(lr=self.learning_rate))
# CPU做图像的实时数据增强,并行在GPU训练模型
model.fit_generator(self.batch_generator(X_train, y_train, True),
steps_per_epoch = self.steps_per_epoch,
epochs = self.epochs,
#max_queue_size=1,
validation_data=self.batch_generator(X_valid, y_valid, False),
validation_steps=len(X_valid),
callbacks=[checkpoint],
verbose=1)
def main():
autopilot = PowerMode_autopilot(data_path = 'data', learning_rate = 1.0e-4, keep_prob = 0.5 , batch_size = 40,
save_best_only = True, test_size = 0.2, steps_per_epoch = 2000, epochs = 10)
data = autopilot.load_data()
model = autopilot.build_model()
#model = load_model('model-xx.h5')
#model.summary()
autopilot.train_model(model, *data)
if __name__ == '__main__':
main()PowerDirve.py
# -*- coding: utf-8 -*-
import os, cv2, socketio, base64, shutil, eventlet.wsgi
import numpy as np
from keras.models import load_model
from flask import Flask
from PIL import Image
from io import BytesIO
from datetime import datetime
from keras.preprocessing.image import array_to_img
# socketio
sio = socketio.Server()
# ------图像预处理-------------
# 除去顶部的天空和底部的汽车正面
def crop(image):
return image[60:-25, :, :]
# 调整图像大小
def resize(image):
return cv2.resize(image, (IMAGE_WIDTH, IMAGE_HEIGHT), cv2.INTER_AREA)
# 转换RGB为YUV格式
def rgb2yuv(image):
return cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
# 图像预处理
def preprocess(image):
image = crop(image)
image = resize(image)
image = rgb2yuv(image)
return image
@sio.on('telemetry')
def telemetry(sid, data):
if data:
# 汽车的当前转向角
#steering_angle = float(data["steering_angle"])
# 汽车的油门
#throttle = float(data["throttle"])
# 当前的速度
speed = float(data["speed"])
# 中心摄像头
image = Image.open(BytesIO(base64.b64decode(data["image"])))
try:
image = np.asarray(image) # from PIL image to numpy array
image = preprocess(image) # apply the preprocessing
image = np.array([image]) # the model expects 4D array
# 预测图像的转向
steering_angle = float(model.predict(image, batch_size=1))
# 根据速度调整油门，如果大于最大速度就减速，如果小于最低速度就加加速
if speed > MAX_SPEED:
speed_limit = MIN_SPEED # slow down
else:
speed_limit = MAX_SPEED
throttle = 1.0 - steering_angle ** 2 - (speed / speed_limit) ** 2
print('{} {} {}'.format(steering_angle, throttle, speed))
send_control(steering_angle, throttle)
except Exception as e:
print(e)
# save frame
if image_folder != '':
timestamp = datetime.utcnow().strftime('%Y_%m_%d_%H_%M_%S_%f')[:-3]
image_filename = os.path.join(image_folder, timestamp)
array_to_img(image[0]).save('{}.jpg'.format(image_filename))
else:
sio.emit('manual', data={}, skip_sid=True)
@sio.on('connect')
def connect(sid, environ):
print("connect ", sid)
send_control(0, 0)
def send_control(steering_angle, throttle):
sio.emit(
"steer",
data={
'steering_angle': steering_angle.__str__(),
'throttle': throttle.__str__()
},
skip_sid=True)
if __name__ == '__main__':
IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_CHANNELS = 66, 200, 3
MAX_SPEED, MIN_SPEED = 25, 10
# 载入模型
model = load_model('model-xx.h5')
image_folder = ''
# 设定图片缓存目录
if image_folder != '':
if os.path.exists(image_folder):
shutil.rmtree(image_folder)
os.makedirs(image_folder)
app = Flask(__name__)
app = socketio.Middleware(sio, app)
eventlet.wsgi.server(eventlet.listen(('', 4567)), app)