mxx_ru 安装依赖
时间: 2023-10-02 17:12:56 浏览: 45
如果你想在 Python 3 上安装 mxx_ru 的依赖,可以使用 pip 命令来安装。在终端中输入以下命令:
```
pip3 install -r requirements.txt
```
这将会自动安装所有在 requirements.txt 文件中列出的依赖项。如果你还没有安装 pip,可以在终端中输入以下命令来安装:
```
sudo apt-get install python3-pip
```
如果你在 Windows 上使用 Python 3,则可以使用以下命令安装依赖项:
```
pip install -r requirements.txt
```
如果你还没有安装 pip,可以从官方网站下载并安装 pip:https://pip.pypa.io/en/stable/installing/
相关问题
解释 int nSize = pdPoints.size(); if (nSize < 3) { return; } vector<double>vdX; vector<double>vdY; double dMeanX = 0, dMeanY = 0; for (Point2d p : pdPoints) { vdX.push_back(p.x); vdY.push_back(p.y); dMeanX += p.x; dMeanY += p.y; } dMeanX /= (nSize * 1.); dMeanY /= (nSize * 1.); double Xi = 0, Yi = 0, Zi = 0; double Mz = 0, Mxy = 0, Mxx = 0, Myy = 0, Mxz = 0, Myz = 0, Mzz = 0, Cov_xy = 0, Var_z=0; double A0 = 0, A1 = 0, A2 = 0, A22 = 0; double Dy = 0, xnew = 0, x = 0, ynew = 0, y = 0; double DET = 0, Xcenter = 0, Ycenter = 0; for (int i = 0; i < nSize; i++) { Xi = vdX[i] - dMeanX; // centered x-coordinates Yi = vdY[i] - dMeanY; // centered y-coordinates Zi = Xi * Xi + Yi * Yi; Mxy += Xi * Yi; Mxx += Xi * Xi; Myy += Yi * Yi; Mxz += Xi * Zi; Myz += Yi * Zi; Mzz += Zi * Zi; } Mxx /= (nSize * 1.); Myy /= (nSize * 1.); Mxy /= (nSize * 1.); Mxz /= (nSize * 1.); Myz /= (nSize * 1.); Mzz /= (nSize * 1.); Mz = Mxx + Myy; Cov_xy = Mxx * Myy - Mxy * Mxy; Var_z = Mzz - Mz * Mz; A2 = 4.0 * Cov_xy - 3.0 * Mz * Mz - Mzz; A1 = Var_z * Mz + 4.0 * Cov_xy * Mz - Mxz * Mxz - Myz * Myz; A0 = Mxz * (Mxz * Myy - Myz * Mxy) + Myz * (Myz * Mxx - Mxz * Mxy) - Var_z * Cov_xy; A22 = A2 + A2; // finding the root of the characteristic polynomial // using Newton's method starting at x=0 // (it is guaranteed to converge to the right root) x = 0., y = A0; for (int i = 0; i < 99; i++) // usually, 4-6 iterations are enough { Dy = A1 + x * (A22 + 16. * x * x); xnew = x - y / Dy; if ((xnew == x) || (!isfinite(xnew))) { break; } ynew = A0 + xnew * (A1 + xnew * (A2 + 4.0 * xnew * xnew)); if (abs(ynew) >= abs(y)) { break; } x = xnew; y = ynew; } DET = x * x - x * Mz + Cov_xy; Xcenter = (Mxz * (Myy - x) - Myz * Mxy) / DET / 2.0; Ycenter = (Myz * (Mxx - x) - Mxz * Mxy) / DET / 2.0; dRadius = sqrt(Xcenter * Xcenter + Ycenter * Ycenter + Mz - x - x); pdCenter = Point2d(Xcenter + dMeanX, Ycenter + dMeanY);
这段代码实现了一个使用最小二乘法进行圆形拟合的算法。给定一组二维坐标点,该算法的目标是找到一个最优的圆形,使得该圆形与这些点的距离的平方和最小。
具体来说,这段代码的实现步骤如下:
1. 如果输入的二维坐标点个数小于3,则直接返回。
2. 将所有二维坐标点的x、y坐标分别存储到两个vector类型的数组vdX、vdY中,并计算这些点的重心坐标。
3. 根据平移后的二维坐标点,计算它们的协方差矩阵,并计算出该矩阵的特征向量和特征值。
4. 根据特征向量和特征值计算出一个最优的圆心坐标和半径长度。
5. 将计算出的圆心坐标和半径长度还原为原始坐标系中的坐标和长度。
在具体实现过程中,该算法使用了一些变量来存储计算过程中的中间结果,并采用了牛顿迭代法来寻找特征值的根。最终,该算法将圆形的半径和圆心坐标存储在dRadius和pdCenter参数中,以便后续使用。
使用getRotationMatrix2D创建变换矩阵,通过SIMD指令集加速warpAffine算子参数WARP_INVERSE_MAP效果的C++例程
以下是一个使用getRotationMatrix2D创建变换矩阵,并通过SIMD指令集加速warpAffine算子参数WARP_INVERSE_MAP效果的C++例程:
```c++
#include <opencv2/opencv.hpp>
#include <opencv2/core/hal/intrin.hpp>
using namespace cv;
void warpAffineInverseMap(const Mat& src, Mat& dst, const Mat& M)
{
CV_Assert(src.type() == CV_8UC1);
int rows = src.rows, cols = src.cols;
int drows = dst.rows, dcols = dst.cols;
CV_Assert(rows > 0 && cols > 0 && drows > 0 && dcols > 0);
const float* m = M.ptr<float>(0);
__m128 mm0 = _mm_set_ps(m[0], m[1], m[2], 0);
__m128 mm1 = _mm_set_ps(m[3], m[4], m[5], 0);
__m128 mm2 = _mm_set_ps(0, 0, 1, 0);
__m128i vddx = _mm_set_epi32(3, 2, 1, 0);
__m128i vddy = _mm_set_epi32(dcols + 3, dcols + 2, dcols + 1, dcols + 0);
for (int y = 0; y < drows; ++y) {
float* pdst = dst.ptr<float>(y);
int* pdx = (int*)pdst;
int* pdy = pdx + 4;
for (int x = 0; x < dcols; x += 4) {
__m128i vmx = _mm_set_epi32(x + 3, x + 2, x + 1, x + 0);
__m128i vmy = _mm_set1_epi32(y);
__m128 mx = _mm_cvtepi32_ps(vmx);
__m128 my = _mm_cvtepi32_ps(vmy);
__m128 mxx = _mm_mul_ps(mm0, mx);
__m128 mxy = _mm_mul_ps(mm1, my);
__m128 mxs = _mm_add_ps(mxx, mxy);
__m128 mys = _mm_add_ps(_mm_mul_ps(mm1, mx), _mm_mul_ps(mm0, my));
__m128 mzs = _mm_add_ps(_mm_mul_ps(mm2, mx), _mm_mul_ps(mm2, my));
__m128i vixs = _mm_cvtps_epi32(mxs);
__m128i viys = _mm_cvtps_epi32(mys);
__m128i vidx = _mm_cvtps_epi32(_mm_div_ps(_mm_castsi128_ps(_mm_sub_epi32(vixs, vmx)), mzs));
__m128i vidy = _mm_cvtps_epi32(_mm_div_ps(_mm_castsi128_ps(_mm_sub_epi32(viys, vmy)), mzs));
__m128i vmaskx = _mm_cmplt_epi32(_mm_add_epi32(vixs, vddx), _mm_set1_epi32(cols));
__m128i vmasky = _mm_cmplt_epi32(_mm_add_epi32(viys, vddy), _mm_set1_epi32(rows));
__m128i vmask = _mm_and_si128(vmaskx, vmasky);
__m128i vidx2 = _mm_and_si128(vidx, vmask);
__m128i vidy2 = _mm_and_si128(vidy, vmask);
pdx[x + 0] = vidx2.m128i_i32[0];
pdx[x + 1] = vidx2.m128i_i32[1];
pdx[x + 2] = vidx2.m128i_i32[2];
pdx[x + 3] = vidx2.m128i_i32[3];
pdy[x + 0] = vidy2.m128i_i32[0];
pdy[x + 1] = vidy2.m128i_i32[1];
pdy[x + 2] = vidy2.m128i_i32[2];
pdy[x + 3] = vidy2.m128i_i32[3];
}
}
for (int y = 0; y < drows; ++y) {
const uchar* psrc = src.ptr(y);
float* pdst = dst.ptr<float>(y);
for (int x = 0; x < dcols; ++x) {
int sx = pdst[x * 2];
int sy = pdst[x * 2 + 1];
if (sx >= 0 && sy >= 0 && sx < cols && sy < rows) {
pdst[x] = psrc[sy * cols + sx];
}
else {
pdst[x] = 0;
}
}
}
}
int main()
{
Mat src = imread("lena.png", IMREAD_GRAYSCALE);
Mat dst(src.size(), CV_8UC1);
double angle = 30;
double scale = 1;
Point2f center(src.cols / 2.f, src.rows / 2.f);
Mat M = getRotationMatrix2D(center, angle, scale);
warpAffineInverseMap(src, dst, M);
imshow("src", src);
imshow("dst", dst);
waitKey();
}
```
该例程中使用了SSE指令集进行加速,因此需要确保编译器开启了SSE指令集的支持。该例程中的warpAffineInverseMap函数实现了warpAffine算子参数WARP_INVERSE_MAP的效果,即通过变换矩阵M计算出每个像素在源图像中的位置,然后从源图像中取出该位置的像素值,赋值给目标图像。