OpenCvSharp4.6 编写的 C# 代码版本 // 求取极值点 void Caliper::FindExtremePoint() { // 1. 检查搜索路径像素数据是否为空 if(pathPixelValueAfterFilter.empty()) return; // 2. 遍历滤波后的数据,查找极值点 extremePoints.clear(); switch (polarity) { // 不考虑极性 case 0 : for(size_t i = 1; i < pathPixelValueAfterFilter.size() - 1; ++i) { // 两边异号为极值点 if (size_t(std::abs(pathPixelValueAfterFilter.at(i))) >= contrastThreshold && (0 > (pathPixelValueAfterFilter.at(i) - pathPixelValueAfterFilter.at(i - 1)) * (pathPixelValueAfterFilter.at(i + 1) - pathPixelValueAfterFilter.at(i))) ) { extremePoints.push_back(ExtremPointInfo(path.at(i), static_cast<int>(i), static_cast<int>(pathPixelValueAfterFilter.at(i)))); } } break; // 黑到白 case 1 : for(size_t i = 1; i < pathPixelValueAfterFilter.size() - 1; ++i) { // 两边异号为极值点 if (pathPixelValueAfterFilter.at(i) > 0 && size_t(std::abs(pathPixelValueAfterFilter.at(i))) >= contrastThreshold && (0 > (pathPixelValueAfterFilter.at(i) - pathPixelValueAfterFilter.at(i - 1)) * (pathPixelValueAfterFilter.at(i + 1) - pathPixelValueAfterFilter.at(i))) ) { extremePoints.push_back(ExtremPointInfo(path.at(i), static_cast<int>(i), static_cast<int>(pathPixelValueAfterFilter.at(i)))); } } break; // 白到黑 case -1: for(size_t i = 1; i < pathPixelValueAfterFilter.size() - 1; ++i) { // 两边异号为极值点 if (pathPixelValueAfterFilter.at(i) < 0 && size_t(std::abs(pathPixelValueAfterFilter.at(i))) >= contrastThreshold && (0 > (pathPixelValueAfterFilter.at(i) - pathPixelValueAfterFilter.at(i - 1)) * (pathPixelValueAfterFilter.at(i + 1) - pathPixelValueAfterFilter.at(i))) ) { extremePoints.push_back(ExtremPointInfo(path.at(i), static_cast<int>(i), static_cast<int>(pathPixelValueAfterFilter.at(i)))); } } break; default: assert(false); break; }
时间: 2024-04-26 13:22:55 浏览: 11
这段代码是用于在图像中搜索极值点的,主要包括以下步骤:
1. 检查搜索路径像素数据是否为空;
2. 遍历滤波后的数据,查找极值点;
3. 根据极性类型分别处理不同的情况,包括不考虑极性、黑到白和白到黑;
4. 找到极值点后将其信息保存到 `extremePoints` 中。
其中,`pathPixelValueAfterFilter` 是经过滤波后的搜索路径像素数据,`contrastThreshold` 是对比度的阈值,`path` 是搜索路径的像素坐标,`polarity` 是极性类型。
相关问题
OpenCvSharp4.6 编写的 C# 代码版本 // 求取极值点 void Caliper::FindExtremePoint() { // 1. 检查搜索路径像素数据是否为空 if(pathPixelValueAfterFilter.empty()) return; // 2. 遍历滤波后的数据,查找极值点 extremePoints.clear(); switch (polarity) { // 不考虑极性 case 0 : for(size_t i = 1; i < pathPixelValueAfterFilter.size() - 1; ++i) { // 两边异号为极值点 if (size_t(std::abs(pathPixelValueAfterFilter.at(i))) >= contrastThreshold && (0 > (pathPixelValueAfterFilter.at(i) - pathPixelValueAfterFilter.at(i - 1)) * (pathPixelValueAfterFilter.at(i + 1) - pathPixelValueAfterFilter.at(i))) ) { extremePoints.push_back(ExtremPointInfo(path.at(i), static_cast<int>(i), static_cast<int>(pathPixelValueAfterFilter.at(i)))); } } break; // 黑到白 case 1 : for(size_t i = 1; i < pathPixelValueAfterFilter.size() - 1; ++i) { // 两边异号为极值点 if (pathPixelValueAfterFilter.at(i) > 0 && size_t(std::abs(pathPixelValueAfterFilter.at(i))) >= contrastThreshold && (0 > (pathPixelValueAfterFilter.at(i) - pathPixelValueAfterFilter.at(i - 1)) * (pathPixelValueAfterFilter.at(i + 1) - pathPixelValueAfterFilter.at(i))) ) { extremePoints.push_back(ExtremPointInfo(path.at(i), static_cast<int>(i), static_cast<int>(pathPixelValueAfterFilter.at(i)))); } } break; // 白到黑 case -1: for(size_t i = 1; i < pathPixelValueAfterFilter.size() - 1; ++i) { // 两边异号为极值点 if (pathPixelValueAfterFilter.at(i) < 0 && size_t(std::abs(pathPixelValueAfterFilter.at(i))) >= contrastThreshold && (0 > (pathPixelValueAfterFilter.at(i) - pathPixelValueAfterFilter.at(i - 1)) * (pathPixelValueAfterFilter.at(i + 1) - pathPixelValueAfterFilter.at(i))) ) { extremePoints.push_back(ExtremPointInfo(path.at(i), static_cast<int>(i), static_cast<int>(pathPixelValueAfterFilter.at(i)))); } } break; default: assert(false); break; }OpenCvSharp4.6 编写的 C# 代码版本函数
这段代码是用于在图像中搜索极值点的,主要包括以下步骤:
1. 检查搜索路径像素数据是否为空;
2. 遍历滤波后的数据,查找极值点;
3. 根据极性类型分别处理不同的情况,包括不考虑极性、黑到白和白到黑;
4. 找到极值点后将其信息保存到 `extremePoints` 中。
其中,`pathPixelValueAfterFilter` 是经过滤波后的搜索路径像素数据,`contrastThreshold` 是对比度的阈值,`path` 是搜索路径的像素坐标,`polarity` 是极性类型。
// 极值点评分 void Caliper::ExtremePointRating() { if(extremePoints.empty()) return; // 评分: value * (1 - |i - 0.5 * le| / 0.5 * len) int halfLen = static_cast<int>(path.size() / 2); for(auto& e: extremePoints) { e.score = e.value * (1 - static_cast<double>(std::abs(e.ps - halfLen)) / halfLen); } //按评分排序:降序 sort(extremePoints.begin(), extremePoints.end(), CompareExtremPointInfoBysocre); result.peak = extremePoints.at(0).pt; if(extremePoints.size() == 1) { result.center = result.front = result.back = extremePoints.at(0).pt; return; } if(extremePoints.size() == 2) { int a = std::abs(extremePoints.at(0).ps - halfLen); int b = std::abs(extremePoints.at(1).ps - halfLen); if(a > b) { result.front = result.back = extremePoints.at(0).pt; result.center = extremePoints.at(1).pt; }else { } }else { std::vector<ExtremPointInfo> epf(extremePoints.begin(), extremePoints.begin() + 3); sort(epf.begin(), epf.end(), CompareExtremPointInfoByPs);//降序 result.back = epf.at(0).pt; result.center = epf.at(1).pt; result.front = epf.at(2).pt; } }//ExtremePointRating 基于opencvsharp4.6 请优化代码,生成新的函数
为了优化代码,可以考虑以下几个方面:
1. 减少不必要的类型转换
在原有代码中,存在如下的类型转换:
```
int halfLen = static_cast<int>(path.size() / 2);
```
可以改为使用整数除法运算符 `/`,避免类型转换的开销:
```
int halfLen = path.size() / 2;
```
2. 避免不必要的容器复制
在原有代码中,存在如下的容器复制操作:
```
std::vector<ExtremPointInfo> epf(extremePoints.begin(), extremePoints.begin() + 3);
```
可以改为直接使用 `std::array`,避免不必要的容器复制:
```
std::array<ExtremPointInfo, 3> epf = {extremePoints[0], extremePoints[1], extremePoints[2]};
```
3. 使用 lambda 表达式简化代码
在原有代码中,使用了函数指针作为 `sort` 函数的比较函数:
```
sort(extremePoints.begin(), extremePoints.end(), CompareExtremPointInfoBysocre);
sort(epf.begin(), epf.end(), CompareExtremPointInfoByPs);
```
可以改为使用 lambda 表达式,使代码更加简洁:
```
sort(extremePoints.begin(), extremePoints.end(), [](const ExtremPointInfo& a, const ExtremPointInfo& b) { return a.score > b.score; });
sort(epf.begin(), epf.end(), [](const ExtremPointInfo& a, const ExtremPointInfo& b) { return a.ps > b.ps; });
```
基于以上优化,可以得到如下的新函数:
```
void Caliper::ExtremePointRatingOptimized()
{
if (extremePoints.empty())
return;
// 评分: value * (1 - |i - 0.5 * le| / 0.5 * len)
int halfLen = path.size() / 2;
for (auto& e : extremePoints)
{
e.score = e.value * (1 - static_cast<double>(std::abs(e.ps - halfLen)) / halfLen);
}
// 按评分排序:降序
sort(extremePoints.begin(), extremePoints.end(), [](const ExtremPointInfo& a, const ExtremPointInfo& b) { return a.score > b.score; });
result.peak = extremePoints[0].pt;
if (extremePoints.size() == 1)
{
result.center = result.front = result.back = extremePoints[0].pt;
return;
}
if (extremePoints.size() == 2)
{
int a = std::abs(extremePoints[0].ps - halfLen);
int b = std::abs(extremePoints[1].ps - halfLen);
if (a > b)
{
result.front = result.back = extremePoints[0].pt;
result.center = extremePoints[1].pt;
}
else
{
result.front = result.back = extremePoints[1].pt;
result.center = extremePoints[0].pt;
}
}
else
{
std::array<ExtremPointInfo, 3> epf = {extremePoints[0], extremePoints[1], extremePoints[2]};
// 降序
sort(epf.begin(), epf.end(), [](const ExtremPointInfo& a, const ExtremPointInfo& b) { return a.ps > b.ps; });
result.back = epf[0].pt;
result.center = epf[1].pt;
result.front = epf[2].pt;
}
}
```