#!/bin/sh #HOME=/ BIN=PLCXPro stoprun() { PID=`ps -ef --cols=200|grep -v stopapp |grep "$1" | awk '{print $2 " " $8}'|sed -e '/grep/d' -e 's/^ *//' -e 's/ .*//'` if [ -n "$PID" ] then echo "$1 id is $PID, kill normal process!" kill -3 $PID else echo "$1 id $PID is down! it's over!" fi } stoprunstart() { PID=`ps -ef --cols=200|grep -v stopapp |grep "$1"|grep start | awk '{print $2 " " $8}'|sed -e '/grep/d' -e 's/^ *//' -e 's/ .*//'` if [ -n "$PID" ] then echo "$1 id is $PID, kill normal process!" kill -9 $PID else echo "$1 id $PID is down! it's over!" fi } runtest() { PID=`ps -ef --cols=200|grep -v stopapp |grep "$1" | awk '{print $2 " " $8}'|sed -e '/grep/d' -e 's/^ *//' -e 's/ .*//'` if [ -n "$PID" ] then ## echo $1 id is $PID, sys normal process! sleep 30 else echo $1 is down! reload the process! date echo nohup $BIN & nohup ./$BIN > /dev/null & fi }怎么开启或者重启这个PLCXPro

可以运行以下命令来重启 PLCXPro 进程：

./stopapp.sh PLCXPro

该命令会执行 stoprun 函数，查找并终止名为 PLCXPro 的进程。

然后再执行以下命令来启动 PLCXPro 进程：

./runapp.sh PLCXPro

该命令会执行 runtest 函数，查找名为 PLCXPro 的进程是否已经存在，若不存在则启动 PLCXPro 进程。

相关问题

def medianBlur(image,winSize): # 图像的高、宽 rows,cols = image.shape[:2] # 窗口的高、宽，均为奇数 winH,winW = winSize halfWinH = (winH-1)/2 halfWinW = (winW-2)/2 # 中值滤波后的输出图像 medianBlurImg = np.zeros(image.shape,image.dtype) for r in range(rows): for c in range(cols): # 判断边界 rTop = 0 if r-halfWinH<0 else r-halfWinH rBottom = rows-1 if r+halfWinH > rows-1 else r+halfWinH cLeft = 0 if c-halfWinW < 0 else c-halfWinW cRight = cols-1 if c+halfWinW > cols-1 else c+halfWinW print(rTop,rBottom,cLeft,cRight) # 取邻域 region = image[rTop:rBottom,cLeft:int(cRight)] # 求中值 medianBlurImg[r][c] = np.median(region) return medianBlurImg。出现错误：TypeError: slice indices must be integers or None or have an index method

看起来是在取邻域时出现了问题。错误提示中说“slice indices must be integers or None or have an index method”，也就是说切片的索引必须是整数或者是 `None`，或者是具有 `__index__` 方法的对象。

在你的代码中，`cLeft` 和 `cRight` 的计算中出现了一个小错误。应该改为：

cLeft = 0 if c-halfWinW < 0 else c-halfWinW
cRight = cols-1 if c+halfWinW > cols-1 else c+halfWinW+1  # 注意这里加 1

原因是切片操作是左闭右开区间，所以右边界要加 1。

另外，`halfWinH` 和 `halfWinW` 的计算也应该改为：

halfWinH = (winH-1) // 2
halfWinW = (winW-1) // 2

这样可以保证窗口的大小为奇数。

修改后的代码如下：

def medianBlur(image, winSize):
    # 图像的高、宽
    rows, cols = image.shape[:2]
    # 窗口的高、宽，均为奇数
    winH, winW = winSize
    halfWinH = (winH-1) // 2
    halfWinW = (winW-1) // 2
    # 中值滤波后的输出图像
    medianBlurImg = np.zeros(image.shape, image.dtype)
    for r in range(rows):
        for c in range(cols):
            # 判断边界
            rTop = 0 if r-halfWinH < 0 else r-halfWinH
            rBottom = rows-1 if r+halfWinH > rows-1 else r+halfWinH
            cLeft = 0 if c-halfWinW < 0 else c-halfWinW
            cRight = cols-1 if c+halfWinW > cols-1 else c+halfWinW+1
            # 取邻域
            region = image[rTop:rBottom, cLeft:cRight]
            # 求中值
            medianBlurImg[r][c] = np.median(region)
    return medianBlurImg

希望这个答案能够帮到你！

create_walls(){ const g = []; for(let r = 0; r < this.rows; r ++ ){ g[r] = []; for(let c = 0; c < this.cows; c ++ ){ g[r][c] = false; } } //四周加墙 for(let r = 0; r < this.rows; r ++ ){ g[r][0] = g[r][this.cols - 1] = true; } for(let c = 0; c < this.cols; c ++ ){ g[0][c] = g[this.rows - 1][c] = true; } //随机化 for(let i = 0; i < this.inner_walls_count; i ++){ for (let j = 0; j < 1000; j ++){ let r = parseInt(Math.random() * this.rows); let c = parseInt(Math.random() * this.cols); if(g[r][c] || r == this.rows - 2 && c == 1 || c == this.cols - 2 && r == 1){ continue; } g[r][c] = g[this.rows - 1 - r][this.cols - 1 -c] = true; break; } } const copy_g = JSON.parse(JSON.stringify(g));//深拷贝 if(!this.check_connectivity(copy_g, this.rows - 2, 1, 1, this.cols - 2)){ return false; } for(let r = 0; r < this.rows; r ++ ){ for(let c = 0; c < this.cols; c ++ ){ if(g[r][c]){ this.walls.push(new Wall(r, c, this)); } } } return true; }翻译成Java

以下是将 JavaScript 代码转换为 Java 代码后的结果：

public boolean createWalls() {
boolean[][] g = new boolean[this.rows][this.cols];
for (int r = 0; r < this.rows; r++) {
for (int c = 0; c < this.cols; c++) {
g[r][c] = false;
}
}

// 四周加墙
for (int r = 0; r < this.rows; r++) {
g[r][0] = g[r][this.cols - 1] = true;
}
for (int c = 0; c < this.cols; c++) {
g[0][c] = g[this.rows - 1][c] = true;
}

// 随机化
for (int i = 0; i < this.inner_walls_count; i++) {
for (int j = 0; j < 1000; j++) {
int r = (int) (Math.random() * this.rows);
int c = (int) (Math.random() * this.cols);
if (g[r][c] || (r == this.rows - 2 && c == 1) || (c == this.cols - 2 && r == 1)) {
continue;
}
g[r][c] = g[this.rows - 1 - r][this.cols - 1 - c] = true;
break;
}
}

boolean[][] copy_g = new boolean[this.rows][this.cols];
for (int r = 0; r < this.rows; r++) {
for (int c = 0; c < this.cols; c++) {
copy_g[r][c] = g[r][c];
}
}

if (!this.checkConnectivity(copy_g, this.rows - 2, 1, 1, this.cols - 2)) {
return false;
}

for (int r = 0; r < this.rows; r++) {
for (int c = 0; c < this.cols; c++) {
if (g[r][c]) {
this.walls.add(new Wall(r, c, this));
}
}
}

return true;
}