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Repository: VendettaMask/AutoRun-Car
Branch: master
Commit: c614e30f331c
Files: 12
Total size: 780.6 KB
Directory structure:
gitextract_24x63za3/
├── .gitignore
├── LICENSE
├── README.md
├── 树莓派端/
│ ├── README.md
│ ├── rc_control1.py
│ ├── stream_client.py
│ └── ultrasonic_client.py
└── 电脑端/
├── README.md
├── cascade_xml/
│ ├── fullbody.xml
│ ├── stop_sign.xml
│ └── traffic_light.xml
└── rc_driver.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
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================================================
FILE: LICENSE
================================================
GNU LESSER GENERAL PUBLIC LICENSE
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================================================
FILE: README.md
================================================
# AutoRun-Car
基于树莓派图像识别的智能循迹避障小车</br>
作者：三无小组</br>
视频请点击:https://youtu.be/4E3luEluiFE</br>
## 基本需求
raspberry pi3</br>
小车模块</br>
笔记本电脑</br>
树莓派官方摄像头</br>
## 环境要求
树莓派：rasbian系统</br>
电脑：opencv2.45</br>
numpy</br>
## 具体原理
### 道路检测
本程序所使用的道路检测算法为最基础的检测算法，可升级至深度学习算法，但是没时间搞~~主要是不会~~
对于道路检测而言，最基本的就是在图像上随机抽取图像上下部等宽区域，将图像的灰度中心计算出来，由此可以看出，当上半部图像的灰度中心与下半部图像中心的位置差超出阈值时，判定道路出现了转弯，以图像中心为坐标原点，当插值大于0时说明要右转，反之左转。可自由设置。
### 标识牌检测
使用opencv自带的训练网络，虽然实际训练了，但是结果较差，采用了国外训练好的图像模型，在代码中提供了。
### 障碍物检测
超声波检测，虽然没啥用，毕竟我们的障碍物是停止标志，图像的精度目前看来比超声波准，不过本着买了就要用的原则，用了
### 距离测算
采用单目视觉，首先对相机进行标定，然后利用角度计算得出前方的实际距离。但是相机在运动过程晃动较大，最后还是热熔胶枪粘的，这个功能的演示就弃了。
## 代码使用
注释有写代码的具体使用，请自行阅读。
================================================
FILE: 树莓派端/README.md
================================================
## 树莓派端运行这三个程序。
stream_client用于获取摄像头数据并发送至电脑。
ultrasonic_client用于获取超声波数据并发送至电脑。
rc_control用于运动控制，接收电脑端发来的命令后使小车转弯、前进等。
================================================
FILE: 树莓派端/rc_control1.py
================================================
#coding=utf-8
import socket
from socket import *
import RPi.GPIO as GPIO
import time
IN1 = 20
IN2 = 21
IN3 = 19
IN4 = 26
ENA = 16
ENB = 13
GPIO.setmode(GPIO.BCM) #设置GPIO口为BCM编码方式
GPIO.setwarnings(False) #忽略警告信息
t1 = 0.03
class rpiGPIOHelper(object):
def __init__(self):
global pwm_ENA
global pwm_ENB
global delaytime
GPIO.setup(ENA,GPIO.OUT,initial=GPIO.HIGH)
GPIO.setup(IN1,GPIO.OUT,initial=GPIO.LOW)
GPIO.setup(IN2,GPIO.OUT,initial=GPIO.LOW)
GPIO.setup(ENB,GPIO.OUT,initial=GPIO.HIGH)
GPIO.setup(IN3,GPIO.OUT,initial=GPIO.LOW)
GPIO.setup(IN4,GPIO.OUT,initial=GPIO.LOW)
#设置pwm引脚和频率为2000hz
pwm_ENA = GPIO.PWM(ENA, 2000)
pwm_ENB = GPIO.PWM(ENB, 2000)
pwm_ENA.start(0)
pwm_ENB.start(0)
#小车右转
def right(self,delaytime):
GPIO.output(IN1, GPIO.HIGH)
GPIO.output(IN2, GPIO.LOW)
GPIO.output(IN3, GPIO.LOW)
GPIO.output(IN4, GPIO.LOW)
pwm_ENA.ChangeDutyCycle(30)
pwm_ENB.ChangeDutyCycle(30)
print "pi car right."
time.sleep(delaytime)
#小车左转
def left(self,delaytime):
GPIO.output(IN1, GPIO.LOW)
GPIO.output(IN2, GPIO.LOW)
GPIO.output(IN3, GPIO.HIGH)
GPIO.output(IN4, GPIO.LOW)
pwm_ENA.ChangeDutyCycle(30)
pwm_ENB.ChangeDutyCycle(30)
print "pi car left."
time.sleep(delaytime)
#小车前进
def run(self,delaytime):
GPIO.output(IN1, GPIO.HIGH)
GPIO.output(IN2, GPIO.LOW)
GPIO.output(IN3, GPIO.HIGH)
GPIO.output(IN4, GPIO.LOW)
pwm_ENA.ChangeDutyCycle(30)
pwm_ENB.ChangeDutyCycle(30)
print "pi car forwarding."
time.sleep(delaytime)
#小车后退
def back(self,delaytime):
GPIO.output(IN1, GPIO.LOW)
GPIO.output(IN2, GPIO.HIGH)
GPIO.output(IN3, GPIO.LOW)
GPIO.output(IN4, GPIO.HIGH)
pwm_ENA.ChangeDutyCycle(30)
pwm_ENB.ChangeDutyCycle(30)
print "pi car backward"
time.sleep(delaytime)
#小车停止
def brake(self,delaytime):
GPIO.output(IN1, GPIO.LOW)
GPIO.output(IN2, GPIO.LOW)
GPIO.output(IN3, GPIO.LOW)
GPIO.output(IN4, GPIO.LOW)
pwm_ENA.ChangeDutyCycle(30)
pwm_ENB.ChangeDutyCycle(30)
time.sleep(delaytime)
# ============socket================ #
client_socket = socket(AF_INET, SOCK_STREAM)
client_socket.connect(('192.168.43.61', 8004))
# ============socket================ #
try:
gpio_helper = rpiGPIOHelper()
while True:
pre_data = client_socket.recv(1024)
print "pre_data=====================",pre_data
data = pre_data.split('O')[0]
if not data: continue
if data=="run":
gpio_helper.run(0.03)
elif data=="left":
gpio_helper.left(0.03)
elif data=="right":
gpio_helper.right(0.03)
elif data=="back":
gpio_helper.back(0.03)
else:
data = "brake"
gpio_helper.brake(0.03)
print 555555555555555555555555555555555555555
client_socket.sendall(data + " had recvied!")
except KeyboardInterrupt:
pass
pwm_ENA.stop()
pwm_ENB.stop()
GPIO.cleanup()
================================================
FILE: 树莓派端/stream_client.py
================================================
import io
import socket
import struct
import time
import picamera
# create socket and bind host
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client_socket.connect(('192.168.43.61', 8000))
connection = client_socket.makefile('wb')
try:
with picamera.PiCamera() as camera:
camera.resolution = (320, 240) # pi camera resolution
camera.framerate = 10 # 10 frames/sec
time.sleep(2) # give 2 secs for camera to initilize
start = time.time()
stream = io.BytesIO()
# send jpeg format video stream
for foo in camera.capture_continuous(stream, 'jpeg', use_video_port = True):
connection.write(struct.pack('<L', stream.tell()))
connection.flush()
stream.seek(0)
connection.write(stream.read())
if time.time() - start > 600:
break
stream.seek(0)
stream.truncate()
connection.write(struct.pack('<L', 0))
finally:
connection.close()
client_socket.close()
================================================
FILE: 树莓派端/ultrasonic_client.py
================================================
from socket import *
import time
import RPi.GPIO as GPIO
GPIO.setwarnings(False)
# create a socket and bind socket to the host
client_socket = socket(AF_INET, SOCK_STREAM)
client_socket.connect(('192.168.43.61', 8002))
def measure():
GPIO.output(GPIO_TRIGGER, True)
time.sleep(0.00001)
GPIO.output(GPIO_TRIGGER, False)
start = time.time()
while GPIO.input(GPIO_ECHO)==0:
start = time.time()
while GPIO.input(GPIO_ECHO)==1:
stop = time.time()
elapsed = stop-start
distance = (elapsed * 34300)/2
return distance
# referring to the pins by GPIO numbers
GPIO.setmode(GPIO.BCM)
# define pi GPIO
GPIO_TRIGGER = 1
GPIO_ECHO = 0
# output pin: Trigger
GPIO.setup(GPIO_TRIGGER,GPIO.OUT)
# input pin: Echo
GPIO.setup(GPIO_ECHO,GPIO.IN)
# initialize trigger pin to low
GPIO.output(GPIO_TRIGGER, False)
try:
while True:
distance = measure()
print "Distance : %.1f cm" % distance
# send data to the host every 0.5 sec
client_socket.send(str(distance))
time.sleep(0.5)
finally:
client_socket.close()
GPIO.cleanup()
================================================
FILE: 电脑端/README.md
================================================
在电脑端运行rc_driver，功能在代码注释里有简单解释。
文件夹cascade_xml中存放的是别人训练好的stop和红绿灯文件，rc_driver代码中第119和120行用到，如果缺少这些文件就会导致无法识别出stop和红绿灯。
================================================
FILE: 电脑端/cascade_xml/fullbody.xml
================================================
<?xml version="1.0"?>
<!--
14x28 fullbody detector (see the detailed description below).
//////////////////////////////////////////////////////////////////////////
| Contributors License Agreement
| IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
| By downloading, copying, installing or using the software you agree
| to this license.
| If you do not agree to this license, do not download, install,
| copy or use the software.
|
| Copyright (c) 2004, Hannes Kruppa and Bernt Schiele (ETH Zurich, Switzerland).
| All rights reserved.
|
| Redistribution and use in source and binary forms, with or without
| modification, are permitted provided that the following conditions are
| met:
|
| * Redistributions of source code must retain the above copyright
| notice, this list of conditions and the following disclaimer.
| * Redistributions in binary form must reproduce the above
| copyright notice, this list of conditions and the following
| disclaimer in the documentation and/or other materials provided
| with the distribution.
| * The name of Contributor may not used to endorse or promote products
| derived from this software without specific prior written permission.
|
| THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
| "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
| LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
| A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
| CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
| EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
| PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
| PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
| LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
| NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
| SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. Back to
| Top
//////////////////////////////////////////////////////////////////////////
"Haar"-based Detectors For Pedestrian Detection
===============================================
by Hannes Kruppa and Bernt Schiele, ETH Zurich, Switzerland
This archive provides the following three detectors:
- upper body detector (most fun, useful in many scenarios!)
- lower body detector
- full body detector
These detectors have been successfully applied to pedestrian detection
in still images. They can be directly passed as parameters to the
program HaarFaceDetect.
NOTE: These detectors deal with frontal and backside views but not
with side views (also see "Known limitations" below).
RESEARCHERS:
If you are using any of the detectors or involved ideas please cite
this paper (available at www.vision.ethz.ch/publications/):
@InProceedings{Kruppa03-bmvc,
author = "Hannes Kruppa, Modesto Castrillon-Santana and Bernt Schiele",
title = "Fast and Robust Face Finding via Local Context."
booktitle = "Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance"
year = "2003",
month = "October"
}
COMMERCIAL:
If you have any commercial interest in this work please contact
hkruppa@inf.ethz.ch
ADDITIONAL INFORMATION
======================
Check out the demo movie, e.g. using mplayer or any (Windows/Linux-) player
that can play back .mpg movies.
Under Linux that's:
> ffplay demo.mpg
or:
> mplayer demo.mpg
The movie shows a person walking towards the camera in a realistic
indoor setting. Using ffplay or mplayer you can pause and continue the
movie by pressing the space bar.
Detections coming from the different detectors are visualized using
different line styles:
upper body : dotted line
lower body : dashed line
full body : solid line
You will notice that successful detections containing the target do
not sit tightly on the body but also include some of the background
left and right. This is not a bug but accurately reflects the
employed training data which also includes portions of the background
to ensure proper silhouette representation. If you want to get a
feeling for the training data check out the CBCL data set:
http://www.ai.mit.edu/projects/cbcl/software-datasets/PedestrianData.html
There is also a small number of false alarms in this sequence.
NOTE: This is per frame detection, not tracking (which is also one of
the reasons why it is not mislead by the person's shadow on the back
wall).
On an Intel Xeon 1.7GHz machine the detectors operate at something
between 6Hz to 14 Hz (on 352 x 288 frames per second) depending on the
detector. The detectors work as well on much lower image resolutions
which is always an interesting possibility for speed-ups or
"coarse-to-fine" search strategies.
Additional information e.g. on training parameters, detector
combination, detecting other types of objects (e.g. cars) etc. is
available in my PhD thesis report (available end of June). Check out
www.vision.ethz.ch/kruppa/
KNOWN LIMITATIONS
==================
1) the detectors only support frontal and back views but not sideviews.
Sideviews are trickier and it makes a lot of sense to include additional
modalities for their detection, e.g. motion information. I recommend
Viola and Jones' ICCV 2003 paper if this further interests you.
2) dont expect these detectors to be as accurate as a frontal face detector.
A frontal face as a pattern is pretty distinct with respect to other
patterns occuring in the world (i.e. image "background"). This is not so
for upper, lower and especially full bodies, because they have to rely
on fragile silhouette information rather than internal (facial) features.
Still, we found especially the upper body detector to perform amazingly well.
In contrast to a face detector these detectors will also work at very low
image resolutions
Acknowledgements
================
Thanks to Martin Spengler, ETH Zurich, for providing the demo movie.
-->
<opencv_storage>
<haarcascade_fullbody type_id="opencv-haar-classifier">
<size>14 28</size>
<stages>
<_>
<!-- stage 0 -->
<trees>
<_>
<!-- tree 0 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>1 5 12 21 -1.</_>
<_>5 5 4 21 3.</_></rects>
<tilted>0</tilted></feature>
<threshold>-0.0558205693960190</threshold>
<left_val>0.5869792103767395</left_val>
<right_val>-0.6281142234802246</right_val></_></_>
<_>
<!-- tree 1 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>9 2 3 26 -1.</_>
<_>9 15 3 13 2.</_></rects>
<tilted>0</tilted></feature>
<threshold>-0.0388611815869808</threshold>
<left_val>-0.7091681957244873</left_val>
<right_val>0.2682121098041534</right_val></_></_>
<_>
<!-- tree 2 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>1 4 12 23 -1.</_>
<_>5 4 4 23 3.</_></rects>
<tilted>0</tilted></feature>
<threshold>-0.2674087882041931</threshold>
<left_val>0.8308296203613281</left_val>
<right_val>-0.2259958982467651</right_val></_></_>
<_>
<!-- tree 3 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>1 7 12 9 -1.</_>
<_>4 7 6 9 2.</_></rects>
<tilted>0</tilted></feature>
<threshold>0.0964197367429733</threshold>
<left_val>-0.1169784963130951</left_val>
<right_val>0.8725455999374390</right_val></_></_>
<_>
<!-- tree 4 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>3 12 3 16 -1.</_>
<_>3 20 3 8 2.</_></rects>
<tilted>0</tilted></feature>
<threshold>-0.0107987103983760</threshold>
<left_val>-0.5721974968910217</left_val>
<right_val>0.2532565891742706</right_val></_></_>
<_>
<!-- tree 5 -->
<_>
<!-- root node -->
<feature>
<rects>
<_>4 8 6 6 -1.</_>
<_>4 11 6 3 2.</_></rects>
<tilted>0</tilted></feature>
<threshold>0.0113656399771571</threshold>
<left_val>0.1965083032846451</left_val>
<right_val>-0.7274463772773743</right_val></_></_>
<_>
<!-- tree 6 -->
<_>
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<feature>
<rects>
<_>1 25 12 3 -1.</_>
<_>5 25 4 3 3.</_></rects>
<tilted>0</tilted></feature>
<threshold>-5.0216919044032693e-004</threshold>
<left_val>0.2443515956401825</left_val>
<right_val>-0.5197358131408691</right_val></_></_>
<_>
<!-- tree 7 -->
<_>
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<feature>
<rects>
<_>6 25 8 3 -1.</_>
<_>6 25 4 3 2.</_></rects>
<tilted>0</tilted></feature>
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<l
gitextract_24x63za3/
├── .gitignore
├── LICENSE
├── README.md
├── 树莓派端/
│ ├── README.md
│ ├── rc_control1.py
│ ├── stream_client.py
│ └── ultrasonic_client.py
└── 电脑端/
├── README.md
├── cascade_xml/
│ ├── fullbody.xml
│ ├── stop_sign.xml
│ └── traffic_light.xml
└── rc_driver.py
Condensed preview — 12 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (824K chars).
[
{
"path": ".gitignore",
"chars": 1203,
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packagi..."
},
{
"path": "LICENSE",
"chars": 7651,
"preview": " GNU LESSER GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007..."
},
{
"path": "README.md",
"chars": 645,
"preview": "# AutoRun-Car\n基于树莓派图像识别的智能循迹避障小车</br>\n作者：三无小组</br>\n视频请点击:https://youtu.be/4E3luEluiFE</br>\n## 基本需求\nraspberry pi3</br>\n小车..."
},
{
"path": "树莓派端/README.md",
"chars": 122,
"preview": "## 树莓派端运行这三个程序。\n\nstream_client用于获取摄像头数据并发送至电脑。\n\nultrasonic_client用于获取超声波数据并发送至电脑。\n\nrc_control用于运动控制，接收电脑端发来的命令后使小车转弯、前进等..."
},
{
"path": "树莓派端/rc_control1.py",
"chars": 3451,
"preview": "#coding=utf-8\r\nimport socket\r\nfrom socket import *\r\nimport RPi.GPIO as GPIO\r\nimport time\r\n\r\n\r\nIN1 = 20\r\nIN2 = 21\r\nIN3 =..."
},
{
"path": "树莓派端/stream_client.py",
"chars": 1087,
"preview": "import io\nimport socket\nimport struct\nimport time\nimport picamera\n\n\n# create socket and bind host\nclient_socket = socket..."
},
{
"path": "树莓派端/ultrasonic_client.py",
"chars": 1120,
"preview": "from socket import *\nimport time\nimport RPi.GPIO as GPIO\n\n\nGPIO.setwarnings(False)\n\n# create a socket and bind socket to..."
},
{
"path": "电脑端/README.md",
"chars": 119,
"preview": "在电脑端运行rc_driver，功能在代码注释里有简单解释。\n\n文件夹cascade_xml中存放的是别人训练好的stop和红绿灯文件，rc_driver代码中第119和120行用到，如果缺少这些文件就会导致无法识别出stop和红绿灯。\n"
},
{
"path": "电脑端/cascade_xml/fullbody.xml",
"chars": 636650,
"preview": "<?xml version=\"1.0\"?>\n<!--\n 14x28 fullbody detector (see the detailed description below). \n\n//////////////////////////..."
},
{
"path": "电脑端/cascade_xml/stop_sign.xml",
"chars": 85463,
"preview": "<?xml version=\"1.0\"?>\n<opencv_storage>\n<cascade>\n <stageType>BOOST</stageType>\n <featureType>HAAR</featureType>\n <hei..."
},
{
"path": "电脑端/cascade_xml/traffic_light.xml",
"chars": 50553,
"preview": "<?xml version=\"1.0\"?>\n<opencv_storage>\n<cascade>\n <stageType>BOOST</stageType>\n <featureType>HAAR</featureType>\n <hei..."
},
{
"path": "电脑端/rc_driver.py",
"chars": 11237,
"preview": "#coding=utf-8\nimport threading\nimport SocketServer\nimport cv2\nimport numpy as np\nimport math\nimport socket\nimport time..."
}
]
About this extraction
This page contains the full source code of the VendettaMask/AutoRun-Car GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 12 files (780.6 KB), approximately 254.6k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.