解释这段代码:void algorithm_getpara(uint16_t* pu16AlgImgBuf, uint16_t u16AlgCol, uint16_t u16AlgRow, uint16_t* pu16Dmin, uint16_t* pu16Dmax, uint32_t* pu32Dfactor) { int16_t i16Algx, i16Algy, i16Algi; uint16_t u16YSta, u16XSta, u16YEnd, u16XEnd, u16YStep, u16XStep; uint32_t u32AreaOffsetY, u32AreaOffsetX; uint32_t u32Oxi_Arg[6][4]; int iWinWidth = 2550; memset((uint8_t*)&u32Oxi_Arg, 0, sizeof(u32Oxi_Arg)); for (i16Algi = 0; i16Algi < 6; i16Algi++) { u16YSta = g_stAlgArea[i16Algi].u16CdtRow_Sta; u16YEnd = u16YSta + g_stAlgArea[i16Algi].u16CdtRow_Len; u16YStep = g_stAlgArea[i16Algi].u16CdtRow_Step; u16XSta = g_stAlgArea[i16Algi].u16CdtCol_Sta; u16XEnd = u16XSta + g_stAlgArea[i16Algi].u16CdtCol_Len; u16XStep = g_stAlgArea[i16Algi].u16CdtCol_Step; // DBG("%d:u16XSta=%d u16XEnd=%d u16XStep=%d u16YSta=%d u16YEnd=%d, u16YStep=%d\r\n", i16Algi, u16XSta, u16XEnd, u16XStep, u16YSta, u16YEnd, u16YStep); for (i16Algy = u16YSta; i16Algy < u16YEnd; i16Algy += u16YStep) { u32AreaOffsetY = i16Algy * u16AlgCol; for (i16Algx = u16XSta; i16Algx < u16XEnd; i16Algx += u16XStep) { u32AreaOffsetX = u32AreaOffsetY + i16Algx; u32Oxi_Arg[i16Algi][0] += pu16AlgImgBuf[u32AreaOffsetX]; } } u32Oxi_Arg[i16Algi][0] = (u32Oxi_Arg[i16Algi][0] >> 10); //agv,total / (32 * 32) u32Oxi_Arg[i16Algi][1] = ((u32Oxi_Arg[i16Algi][0] << 1) - iWinWidth) >> 1; //min u32Oxi_Arg[i16Algi][2] = ((u32Oxi_Arg[i16Algi][0] << 1) + iWinWidth) >> 1; //max if (u32Oxi_Arg[i16Algi][1] != u32Oxi_Arg[i16Algi][2]) u32Oxi_Arg[i16Algi][3] = (((uint32_t)255) << FACTOR_SHIFT) / (u32Oxi_Arg[i16Algi][2] - u32Oxi_Arg[i16Algi][1]);//factor } u32AreaOffsetX = 0xFFFFFFFF; i16Algx = 0; for (i16Algi = 0; i16Algi < 6; i16Algi++) { if (u32Oxi_Arg[i16Algi][0] < u32AreaOffsetX) { u32AreaOffsetX = u32Oxi_Arg[i16Algi][0]; i16Algx = i16Algi; } } *pu16Dmin = (uint16_t)u32Oxi_Arg[i16Algx][1]; *pu16Dmax = (uint16_t)u32Oxi_Arg[i16Algx][2]; *pu32Dfactor = u32Oxi_Arg[i16Algx][3]; }并说明u32Oxi_Arg[i16Algi][1] = ((u32Oxi_Arg[i16Algi][0] << 1) - iWinWidth) >> 1; //min u32Oxi_Arg[i16Algi][2] = ((u32Oxi_Arg[i16Algi][0] << 1) + iWinWidth) >> 1; //max 这两句为什么要左移与右移
时间: 2024-03-29 17:41:05 浏览: 55
这段代码是一个函数algorithm_getpara,它的功能是对一个二维数组进行区域划分,并计算每个区域的最小值、最大值和一个因子,并将结果存储在u32Oxi_Arg数组中。最后,在u32Oxi_Arg数组中找到最小值,然后将最小值的对应的最小值、最大值和因子存储在pu16Dmin、pu16Dmax和pu32Dfactor中。
关于u32Oxi_Arg[i16Algi][1]和u32Oxi_Arg[i16Algi][2]这两句代码:这两行代码是计算当前区域的最小值和最大值,并将其存储在u32Oxi_Arg数组中。其中,u32Oxi_Arg[i16Algi][0]是当前区域的总和,左移一位相当于除以2,再减去iWinWidth的一半(iWinWidth是2550,即窗口的宽度),就可以得到当前区域的最小值。同理,左移一位再加上iWinWidth的一半,就可以得到当前区域的最大值。左移与右移的操作可以提高代码的运行效率,因为位移运算比乘除法和加减法运算要快得多。
相关问题
c++ QT QMap<int, QImage> algorithm::projectionTo2D( void *data,uint32_t dimx,uint32_t dimy, uint32_t dimz, const QList<QPair<uint32_t, uint32_t>>& projection)
这是一个 C++ 的函数,使用了 QT 库。函数的作用是将三维数据投影到二维平面上,并返回 QMap<int, QImage> 类型的结果。下面是函数的代码实现:
```cpp
QMap<int, QImage> algorithm::projectionTo2D(void *data, uint32_t dimx, uint32_t dimy, uint32_t dimz, const QList<QPair<uint32_t, uint32_t>>& projection) {
QMap<int, QImage> result;
// 遍历每个投影
for (auto& proj : projection) {
uint32_t direction = proj.first;
uint32_t position = proj.second;
QImage image(dimx, dimy, QImage::Format_RGB32);
QRgb* line = reinterpret_cast<QRgb*>(image.bits());
// 根据投影方向,计算每个像素的值,并将其存储到 image 中
for (uint32_t i = 0; i < dimx; ++i) {
for (uint32_t j = 0; j < dimy; ++j) {
uint32_t k = 0;
switch (direction) {
case 0: // 投影到 XY 平面
k = position;
break;
case 1: // 投影到 XZ 平面
k = j;
break;
case 2: // 投影到 YZ 平面
k = i;
break;
default:
break;
}
uint8_t value = *reinterpret_cast<uint8_t*>(data) + k * dimx * dimy + j * dimx + i;
line[j * dimx + i] = qRgb(value, value, value);
}
}
result[direction] = image;
}
return result;
}
```
使用这个函数的方法可以参考我之前回答的问题。
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