AI视觉处理--运动物体检测
硬件平台：树莓派4B
工作流程：
读取图像-->灰度-->计算差值->开操作去除噪声（先侵蚀再膨胀）-->检测轮廓-->计算轮廓面积
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@功能描述： 运动识别
@实现方法： 计算两帧之间的差别，查找轮廓
@author: Alex.Duan
"""
import cv2
import numpy as np
# 载入图片
#img1 = cv2.imread("1.png")
#img2 = cv2.imread("2.png")
#cv2.imshow('img1',img1)
#cv2.imshow('img2',img2)
# 从摄像机读入图像
cap = cv2.VideoCapture(0)
cap.set(3,640)
cap.set(4,480)
ret, img2 = cap.read()
img1 = img2
while True:
ret, img2 = cap.read()
# 转换为灰度图
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
img1 = img2
# 计算绝对差，并进行二值化（差值部分为白色)
diff = cv2.absdiff(gray1, gray2);
reval,diff = cv2.threshold(diff, 45, 255, cv2.THRESH_BINARY)
# 开操作（先侵蚀后膨胀）
# 侵蚀: 去除干扰点, 5x5矩形核
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
diff = cv2.erode(diff,kernel)
#cv2.imshow('erode',diff)
# 膨胀: 11x11矩形核（侵蚀与膨胀均对白色部分）
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (11, 11))
diff = cv2.dilate(diff,kernel)
cv2.imshow('dilate',diff)
# 查找轮廓
image, contours, hierarchy = cv2.findContours(diff, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
# 计算轮廓面积
area = 0
for i in np.arange(len(contours)):
area += cv2.contourArea(contours[i])
# 通过轮廓面积大小来确定事件
if area > 1000:
print("Area is %f" %(area))
# 绘制轮廓
out = cv2.drawContours(img2, contours,-1, (0,255,0),thickness = 2)
cv2.imshow('diff',out)
#cv2.imshow('image',image)
k = cv2.waitKey(10)
if k == 27:
break
# 关闭窗口
cv2.destroyAllWindows()
AI视觉处理--全景图像拼接
硬件平台：树莓派4B
拼接模块：Stitcher.py
import numpy as np
import cv2
class Stitcher:
#----------------------------------------------------
# 拼接函数
def stitch(self, images, ratio=0.75, reprojThresh=4.0, showMatches=False):
# 获取输入图片
(imageB, imageA) = images
# 检测A、B图片的SIFT关键特征点，并计算特征描述子
(kpsA, featuresA) = self.detectAndDescribe(imageA)
(kpsB, featuresB) = self.detectAndDescribe(imageB)
# 匹配两张图片的所有特征点，返回匹配结果
M = self.matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh)
# 如果没有匹配成功的特征点，退出算法
if M is None:
return None
# 提取匹配结果：H是3x3视角变换矩阵
(matches, H, status) = M
# 将图片A进行视角变换，result是变换后图片
result = cv2.warpPerspective(imageA, H, (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
#self.cv_show('result', result)
# 将图片B传入result图片最左端
result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
#self.cv_show('result', result)
# 检测是否需要显示图片匹配
if showMatches:
# 生成匹配图片
vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches, status)
# 返回结果
return (result, vis)
# 返回匹配结果
return result
#----------------------------------------------------
# 检测关键特征点，并计算特征描述子
def detectAndDescribe(self, image):
# 将彩色图片转换成灰度图
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 建立SIFT生成器
descriptor = cv2.xfeatures2d.SIFT_create()
# 检测SIFT特征点，并计算描述子
(kps, features) = descriptor.detectAndCompute(gray, None)
# (898,) (898, 128)
# print(np.array(kps).shape, np.array(features).shape)
# <KeyPoint 000002062C605DE0>
# print(kps[0])
# (3.369633913040161, 178.34132385253906)
# print(kps[0].pt)
# 将结果转换成NumPy数组，并将kp转换成float32类型，便于接下来做计算。
kps = np.float32([kp.pt for kp in kps]) # (898, 2)
# 返回特征点集，及对应的描述特征
return (kps, features)
#----------------------------------------------------------------
# 匹配两张图片的所有特征点，返回匹配结果
def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh):
# 建立暴力匹配器
matcher = cv2.BFMatcher()
# 使用KNN检测来自A、B图的SIFT特征匹配对，K=2
rawMatches = matcher.knnMatch(featuresA, featuresB, 2) # (898, 2)检测出每个点，匹配的2个点
# 返回的M结果为[(1, 6), ..,(112, 113)]等等，里面的数字为第几个特征点
matches = []
for m in rawMatches:
# 当最近距离跟次近距离的比值小于ratio值时，保留此匹配对
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
# 存储两个点在featuresA, featuresB中的索引值
matches.append((m[0].trainIdx, m[0].queryIdx))
# 显示匹配列表(小括号中，后者为距离)
# [(458, 16), (454, 18),...,(464, 25), (465, 26)]
# print(matches)
# 当筛选后的匹配对数量大于4时，计算视角变换矩阵
if len(matches) > 4: # 实际计算出(148, 2)
# 获取匹配对的点坐标（float32型）
ptsA = np.float32([kpsA[i] for (_, i) in matches])
print(ptsA.shape) # (148, 2)
ptsB = np.float32([kpsB[i] for (i, _) in matches])
# 计算视角变换矩阵（把RANSAC和计算H矩阵合并到了一起）
(H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, reprojThresh)
# 该函数的作用就是先用RANSAC选择最优的四组配对点，再计算H矩阵。H为3*3矩阵
print(status.shape) # (148, 1)
# 返回结果
return (matches, H, status)
# 如果匹配对小于4时，返回None
return None
def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):
# 初始化可视化图片，将A、B图左右连接到一起
(hA, wA) = imageA.shape[:2]
(hB, wB) = imageB.shape[:2]
vis = np.zeros((max(hA, hB), wA + wB, 3), dtype="uint8")
vis[0:hA, 0:wA] = imageA
vis[0:hB, wA:] = imageB
# 联合遍历，画出匹配对
for ((trainIdx, queryIdx), s) in zip(matches, status):
# 当点对匹配成功时，画到可视化图上
if s == 1:
# 画出匹配对
ptA = (int(kpsA[queryIdx][0]), int(kpsA[queryIdx][1]))
ptB = (int(kpsB[trainIdx][0]) + wA, int(kpsB[trainIdx][1]))
cv2.line(vis, ptA, ptB, (0, 255, 0), 1)
# 返回可视化结果
return vis
测试程序：ImageStitcher.py
from Stitcher import Stitcher
import cv2
# 读取拼接图片（注意图片左右的放置）
imageL = cv2.imread("2.jpg") # 左
imageR = cv2.imread("1.jpg") # 右，对右边的图形做变换
# 把图片拼接成全景图
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageL, imageR], showMatches=True)
# 保存图片
# cv2.imwrite("3.png", vis)
# cv2.imwrite("4.png", result)
# 显示原图
#cv2.imshow("Right", imageR)
#cv2.imshow("Left" , imageL)
# 显示匹配图
#cv2.imshow("Keypoint Matches", vis)
# 显示合成结果
cv2.imshow("Result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
安装必要的库：
$ sudo apt-get install libatlas-base-dev libjasper-dev
$ sudo apt-get install libqtgui4 libqttest4
否则会报以下错误：
ImportError: libcblas.so.3: cannot open shared object file: No such file or directory
ImportError: libjasper.so.1: cannot open shared object file: No such file or directory
ImportError: libQtGui.so.4: cannot open shared object file: No such file or directory
ImportError: libQtTest.so.4: cannot open shared object file: No such file or directory
问题描述： AttributeError: module 'cv2' has no attribute 'xfeatures2d'
解决方法：
sudo pip3 install opencv-contrib-python
问题描述： ImportError: /usr/local/lib/python3.7/dist-packages/cv2/cv2.cpython-37m-arm-linux-gnueabihf.so: undefined symbol: __atomic_fetch_add_8
解决方法：
LD_PRELOAD=/usr/lib/arm-linux-gnueabihf/libatomic.so.1
问题描述： This algorithm is patented and is excluded in this configuration; Set OPENCV_ENABLE_NONFREE CMake option and rebuild the library in function 'create'
解决方案： 采用低版本的库即可
$ pip3 install --user opencv-contrib-python==3.3.0.10
Linux应用笔记--deb包制作及apt源搭建
操作系统： raspbian/debian
制作如下目录及文件
mydeb
|----DEBIAN
|------- control （描述文件）
|------- postinst （安装后执行有脚本）
|------- postrm （卸载时执行的脚本）
|---- usr/local/bin
|----- helloworld.sh （待安装的文件）
|---- lib/systemd/system
|----- helloworld.service （启动服务，可选）
control文件 内容如下：
Package: helloworld
Version: 1.0
Section: free
Prioritt: optional
Architecture: armhf
Maintainer: admin@oroct.com
Description: make deb package example
postinst文件： 软件安装后执行的脚本（可选）
# !/bin/sh
echo "install mydeb" >> /var/log/mydeb.log
postrm文件：软件删除后执行的脚本（可选）
# !/bin/sh
echo "remove mydeb" >> /var/log/mydeb.log
生成deb安装包： 第一个参数为将要打包的目录名，第二个参数为生成包的名称
$ dpkg -b mydeb mydeb.deb
安装测试：
$ dpkg -i mydeb.deb
删除测试：
$ dpkg -r mydeb.deb
一键创建相关文件脚本：
#!/bin/sh
PKGNAME=helloworld
VERSION=1.0
# 创建相关目录
if [ ! -d DEBIAN ] ; then
mkdir DEBIAN
fi
if [ ! -d usr/local/bin ] ; then
mkdir -p usr/local/bin
fi
if [ ! -d lib/systemd/system ] ; then
mkdir -p lib/systemd/system
fi
# 创建相关文件
echo "#!/bin/sh" > DEBIAN/postinst
echo "systemctl enable ${PKGNAME}.service" >> DEBIAN/postinst
echo "systemctl start ${PKGNAME}.service" >> DEBIAN/postinst
echo "#!/bin/sh" > DEBIAN/postrm
echo "systemctl stop ${PKGNAME}.service" >> DEBIAN/postrm
echo "systemctl disable ${PKGNAME}.service" >> DEBIAN/postrm
echo "Package: ${PKGNAME}" > DEBIAN/control
echo "Version: ${VERSION}" >> DEBIAN/control
echo "Section: free " >> DEBIAN/control
echo "Prioritt: optional" >> DEBIAN/control
echo "Architecture: armhf" >> DEBIAN/control
echo "Maintainer: admin@oroct.com" >> DEBIAN/control
echo "Description: " >> DEBIAN/control
https://www.cnblogs.com/xiang--liu/p/12994429.html
Jetson Nano笔记--VNC
文章摘要： 解决了不接显示屏时VNC黑屏的问题
硬件平台：Jetson Nano
操作系统：Ubuntu 18.04
安装vino:
sudo apt install vino
配置VNC server:
gsettings set org.gnome.Vino prompt-enabled false
gsettings set org.gnome.Vino require-encryption false
设置enabled参数： /usr/share/glib-2.0/schemas/org.gnome.Vino.gschema.xml
<key name='enabled' type='b'>
<summary>Enable remote access to the desktop</summary>
<description>
If true, allows remote access to the desktop via the RFB
protocol. Users on remote machines may then connect to the
desktop using a VNC viewer.
</description>
<default>false</default>
</key>
设置为Gnome模式:
sudo glib-compile-schemas /usr/share/glib-2.0/schemas
设置VNC登陆密码： 123456
$ gsettings set org.gnome.Vino authentication-methods "['vnc']"
$ gsettings set org.gnome.Vino vnc-password $(echo -n '123456'|base64)
设置开机自启动VNC
gsettings set org.gnome.Vino enabled true
设置自动启动脚本：
在~/.config/autostart或者/etc//autostart中新建文件vino-server.desktop
[Desktop Entry]
Type=Application
Name=Vino VNC server
Exec=/usr/lib/vino/vino-server
NoDisplay=true
注意事项： 必须在autostart中启动，而不能在systemd中启动。
设置用户自动登陆：
修改/etc/gdm3/custom.conf文件
[daemon]
# Enabling automatic login
AutomaticLoginEnable = true
AutomaticLogin = nvidia