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H矩阵解析式更新

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wangchongwu 2 months ago
parent 9f3474bb60
commit d9491b4f8f
13 changed files with 715 additions and 731 deletions
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27
CMakeSettings.json
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@ -1,27 +0,0 @@
{
"configurations": [
{
"name": "x64-Debug",
"generator": "Ninja",
"configurationType": "Debug",
"inheritEnvironments": [ "msvc_x64_x64" ],
"buildRoot": "${projectDir}\\out\\build\\${name}",
"installRoot": "${projectDir}\\out\\install\\${name}",
"cmakeCommandArgs": "",
"buildCommandArgs": "",
"ctestCommandArgs": ""
},
{
"name": "x64-Release",
"generator": "Ninja",
"configurationType": "RelWithDebInfo",
"buildRoot": "${projectDir}\\out\\build\\${name}",
"installRoot": "${projectDir}\\out\\install\\${name}",
"cmakeCommandArgs": "",
"buildCommandArgs": "",
"ctestCommandArgs": "",
"inheritEnvironments": [ "msvc_x64_x64" ],
"variables": []
}
]
}

42
stitch/src/Arith_BAResidual.hpp
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@ -68,7 +68,49 @@ private:
};
// H矩阵Frobenius范数正则化残差：约束整个H矩阵与初始值的差异
// 使用Frobenius范数衡量H矩阵的整体变化
struct HomographyRegularizationResidual
{
HomographyRegularizationResidual(const double* initial_h, double weight = 1.0)
: initial_h_(initial_h), weight_(weight)
{
}
template <typename T>
bool operator()(const T* const h, T* residual) const
{
// 计算H矩阵与初始H矩阵的Frobenius范数差异
// Frobenius范数：||H - H0||_F = sqrt(sum((h_i - h_i_0)^2))
// 由于H矩阵最后一个元素固定为1.0，所以只计算前8个参数的差异
T diff_sum = T(0);
for (int i = 0; i < 8; ++i)
{
T diff = h[i] - T(initial_h_[i]);
diff_sum += diff * diff;
}
// 添加小的epsilon避免数值不稳定，确保diff_sum >= epsilon
const T epsilon = T(1e-10);
diff_sum = diff_sum < epsilon ? epsilon : diff_sum;
// Frobenius范数（平方根）
T frobenius_norm = sqrt(diff_sum);
// 返回加权后的Frobenius范数作为残差
// 添加检查避免NaN和Inf
T weighted_norm = T(weight_) * frobenius_norm;
// 使用条件表达式避免NaN/Inf传播
residual[0] = (weighted_norm != weighted_norm || weighted_norm > T(1e10)) ? T(0) : weighted_norm;
return true;
}
private:
const double* initial_h_; // 初始H矩阵参数值（8个参数）
double weight_; // 正则化权重
};
// SE3群优化
// SE3群优化残差：将左图三角化后投影到右图，最小化相对投影误差

21
stitch/src/Arith_BATask.cpp
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@ -158,6 +158,27 @@ void BA_Task::OptFrame(vector<KeyType> frameInd,cv::Mat H_map, std::unordered_ma
}
}
#ifdef OPT_H
// 添加正则化残差块，使用Frobenius范数约束整个H矩阵
// 正则化权重：可以根据需要调整，值越大约束越强
// 初始设置较强的约束，后续可以根据效果慢慢放松
double h_reg_weight = 20.0; // H矩阵Frobenius范数正则化权重（加强约束）
// 为每个帧添加H矩阵正则化
for (int i = 0; i < _currMatrix.size(); i++)
{
// 获取初始H矩阵的8个参数
double* initial_h = (double*)_origMatrix[i].data;
ceres::CostFunction* h_reg_cost =
new ceres::AutoDiffCostFunction<HomographyRegularizationResidual, 1, 8>(
new HomographyRegularizationResidual(initial_h, h_reg_weight));
// 使用HuberLoss，使正则化项对异常值更鲁棒
// 可以调整阈值：值越小约束越严格，值越大约束越宽松
ceres::LossFunction* reg_loss = new ceres::HuberLoss(0.2);
problemH.AddResidualBlock(h_reg_cost, reg_loss, h_list[i]);
}
#endif
#ifdef OPT_SE3
// 添加正则化残差块，约束所有参数不要偏离初始值太多
// 正则化权重：可以根据需要调整，值越大约束越强

207
stitch/src/Arith_GeoSolver.cpp
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@ -3,9 +3,8 @@
#include "Arith_Utils.h"
#include "Arith_CoordModule.h"
#include "Arith_SysStruct.h"
#define GLOG_USE_GLOG_EXPORT
#include "ceres/ceres.h"
#include "ceres/rotation.h"
//#define GLOG_USE_GLOG_EXPORT
using namespace cv;
GeoSolver::GeoSolver()
@ -82,36 +81,36 @@ cv::Point2f GeoSolver::getGeoFromBLH(PointBLH ptPos)
return ptInGeo;
}
cv::Point2f GeoSolver::Trans_uv2Geo(cv::Point2f pos_frame, TForm form)
{
Mat point = (Mat_<double>(3, 1) << pos_frame.x, pos_frame.y, 1);
Mat result = form.R * point;
// 转局部地理系
double warpedX = result.at<double>(0, 0) / result.at<double>(2, 0);
double warpedY = result.at<double>(1, 0) / result.at<double>(2, 0);
// 平移到原点地理系
warpedX += form.T.at<double>(0, 0);
warpedY += form.T.at<double>(1, 0);
return cv::Point2f(warpedX, warpedY);
}
// cv::Point2f GeoSolver::Trans_uv2Geo(cv::Point2f pos_frame, TForm form)
// {
// Mat point = (Mat_<double>(3, 1) << pos_frame.x, pos_frame.y, 1);
// Mat result = form.R * point;
// // 转局部地理系
// double warpedX = result.at<double>(0, 0) / result.at<double>(2, 0);
// double warpedY = result.at<double>(1, 0) / result.at<double>(2, 0);
// // 平移到原点地理系
// warpedX += form.T.at<double>(0, 0);
// warpedY += form.T.at<double>(1, 0);
// return cv::Point2f(warpedX, warpedY);
// }
cv::Point2f GeoSolver::Trans_Geo2uv(cv::Point2f pos_geo, TForm form_inv)
{
// 先平移到当前相机位置
cv::Point2f pos_cam = pos_geo;
pos_cam.x = pos_geo.x + form_inv.T.at<double>(0, 0);
pos_cam.y = pos_geo.y + form_inv.T.at<double>(1, 0);
// cv::Point2f GeoSolver::Trans_Geo2uv(cv::Point2f pos_geo, TForm form_inv)
// {
// // 先平移到当前相机位置
// cv::Point2f pos_cam = pos_geo;
// pos_cam.x = pos_geo.x + form_inv.T.at<double>(0, 0);
// pos_cam.y = pos_geo.y + form_inv.T.at<double>(1, 0);
Mat point = (Mat_<double>(3, 1) << pos_cam.x, pos_cam.y, 1);
Mat result = form_inv.R * point;
// Mat point = (Mat_<double>(3, 1) << pos_cam.x, pos_cam.y, 1);
// Mat result = form_inv.R * point;
// 转像方
double warpedX = result.at<double>(0, 0) / result.at<double>(2, 0);
double warpedY = result.at<double>(1, 0) / result.at<double>(2, 0);
// // 转像方
// double warpedX = result.at<double>(0, 0) / result.at<double>(2, 0);
// double warpedY = result.at<double>(1, 0) / result.at<double>(2, 0);
return cv::Point2f(warpedX, warpedY);
}
// return cv::Point2f(warpedX, warpedY);
// }
Mat GeoSolver::Mat_TransENGMove(FrameInfo info)
@ -180,6 +179,8 @@ cv::Mat GeoSolver::Mat_TransENG2Cam(FrameInfo info)
);
float alaph = info.servoInfo.fServoPt;
Mat M_alaph = (Mat_<double>(3, 3) << 1, 0, 0,
@ -223,21 +224,43 @@ cv::Mat TransRtKToH(RtK rtk, double Depth)
0, 0, Depth
);
cv::Mat R_p2g = (rtk.K * rtk.R * M_het).inv();
//cv::Mat R_p2g = R_g2p.inv();
cv::Mat t_MAT = rtk.T;
// cv::Mat R_p2g = (rtk.K * rtk.R * M_het).inv();
// //cv::Mat R_p2g = R_g2p.inv();
// cv::Mat t_MAT = rtk.T;
// double R[9] = { 0 };
// double T[3] = { 0 };
// memcpy(R, R_p2g.data, sizeof(double) * 9);
// memcpy(T, t_MAT.data, sizeof(double) * 3);
// Mat H = (Mat_<double>(3, 3) << (R[0] + T[0] * R[6]), (R[1] + T[0] * R[7]), (R[2] + T[0] * R[8]),
// (R[3] + T[1] * R[6]), (R[4] + T[1] * R[7]), (R[5] + T[1] * R[8]),
// R[6], R[7], R[8]);
// // 归一化
// H = H / H.at<double>(2, 2);
// 地面法向量 n_w（向上方向，Z轴正方向，在地理坐标系中）
cv::Mat n_w = (cv::Mat_<double>(3, 1) << 0, 0, 1);
// 平面到相机的距离（有效高度）
double d = Depth;
cv::Mat I = cv::Mat::eye(3, 3, CV_64F);
// 计算 (T_geo * n_w^T) / d
cv::Mat TnT = (rtk.R * rtk.T *n_w.t()) / d;
double R[9] = { 0 };
double T[3] = { 0 };
memcpy(R, R_p2g.data, sizeof(double) * 9);
memcpy(T, t_MAT.data, sizeof(double) * 3);
Mat H = (Mat_<double>(3, 3) << (R[0] + T[0] * R[6]), (R[1] + T[0] * R[7]), (R[2] + T[0] * R[8]),
(R[3] + T[1] * R[6]), (R[4] + T[1] * R[7]), (R[5] + T[1] * R[8]),
R[6], R[7], R[8]);
// 计算从地理坐标到像素坐标的单应性矩阵
cv::Mat H_geo_to_pixel = rtk.K * (rtk.R - TnT) * M_het;
// 归一化
cv::Mat H = H_geo_to_pixel.inv();
// 归一化（使 H[2,2] = 1）
H = H / H.at<double>(2, 2);
return H.clone();
@ -264,83 +287,69 @@ cv::Mat Transe3ToH(double* se3, cv::Mat K, double Depth)
}
// 检验H的计算
cv::Mat GeoSolver::findHomography2(FrameInfo info)
{
TForm tf_p2g;
tf_p2g.R = Mat_TransENG2uv(info).inv();
tf_p2g.T = Mat_TransENGMove(info);
std::vector<cv::Point2f> srcPoints;
srcPoints.push_back(cv::Point2f(0, 0));
srcPoints.push_back(cv::Point2f(1000, 0));
srcPoints.push_back(cv::Point2f(1000, 1000));
srcPoints.push_back(cv::Point2f(0, 1000));
// 同名点计算，从像方到全景
cv::Point2f leftTop_map = Trans_uv2Geo(srcPoints[0], tf_p2g);
cv::Point2f rightTop_map = Trans_uv2Geo(srcPoints[1], tf_p2g);
cv::Point2f rightBottom_map = Trans_uv2Geo(srcPoints[2], tf_p2g);
cv::Point2f leftBottom_map = Trans_uv2Geo(srcPoints[3], tf_p2g);
// 目标图像（全景图）的四个顶点坐标
std::vector<cv::Point2f> dstPoints;
dstPoints.push_back(leftTop_map); // 左
dstPoints.push_back(rightTop_map); // 右上
dstPoints.push_back(rightBottom_map); // 右下
dstPoints.push_back(leftBottom_map); // 左下
// 计算单应性矩阵 H
cv::Mat H = cv::findHomography(srcPoints, dstPoints);
return H;
}
// 根据R和t，解析H
cv::Mat GeoSolver::findHomography(FrameInfo info)
{
TForm tf_p2g;
tf_p2g.R = Mat_TransENG2uv(info).inv();
tf_p2g.T = Mat_TransENGMove(info);
// cv::Mat GeoSolver::findHomography(FrameInfo info)
// {
// cv::Mat Rmat = Mat_TransENG2uv(info).inv();
// cv::Mat Tmat = Mat_TransENGMove(info);
double R[9] = { 0 };
double T[3] = { 0 };
// double R[9] = { 0 };
// double T[3] = { 0 };
memcpy(R, tf_p2g.R.data, sizeof(double) * 9);
memcpy(T, tf_p2g.T.data, sizeof(double) * 3);
// memcpy(R, Rmat.data, sizeof(double) * 9);
// memcpy(T, Tmat.data, sizeof(double) * 3);
Mat H = (Mat_<double>(3, 3) << (R[0] + T[0] * R[6]), (R[1] + T[0] * R[7]), (R[2] + T[0] * R[8]),
(R[3] + T[1] * R[6]), (R[4] + T[1] * R[7]), (R[5] + T[1] * R[8]),
R[6], R[7], R[8]);
// Mat H = (Mat_<double>(3, 3) << (R[0] + T[0] * R[6]), (R[1] + T[0] * R[7]), (R[2] + T[0] * R[8]),
// (R[3] + T[1] * R[6]), (R[4] + T[1] * R[7]), (R[5] + T[1] * R[8]),
// R[6], R[7], R[8]);
// 归一化
H = H / H.at<double>(2, 2);
// // 归一化
// H = H / H.at<double>(2, 2);
return H;
}
// return H;
// }
cv::Mat GeoSolver::findHomography_GPT(FrameInfo info)
cv::Mat GeoSolver::findHomography(FrameInfo info)
{
// world: Z=0 plane
cv::Mat R = Mat_TransENG2uv(info).inv(); // 3x3
cv::Mat t = Mat_TransENGMove(info); // 3x1
// 获取相机内参矩阵 K
cv::Mat Kmat = Mat_GetCamK(info);
// ground plane normal in world coord
cv::Mat n_w = (cv::Mat_<double>(3,1) << 0, 0, 1);
// 获取从地理坐标系到相机坐标系的旋转矩阵 R_geo_to_cam
cv::Mat R_geo_to_cam = Mat_TransENG2Cam(info);
// normal in camera coord
cv::Mat n = R * n_w; // 3x1
// 获取平移向量 T（从原点地理坐标系到当前相机位置地理坐标系）
cv::Mat T_geo = Mat_TransENGMove(info);
// distance to ground plane
// 深度矩阵
Mat M_het = (Mat_<double>(3, 3) << 1, 0, 0,
0, 1, 0,
0, 0, info.nEvHeight
);
// 地面法向量 n_w（向上方向，Z轴正方向，在地理坐标系中）
cv::Mat n_w = (cv::Mat_<double>(3, 1) << 0, 0, 1);
// 平面到相机的距离（有效高度）
double d = info.nEvHeight;
cv::Mat I = cv::Mat::eye(3, 3, CV_64F);
cv::Mat H = R - (t * n.t()) / d;
// 计算 (T_geo * n_w^T) / d
cv::Mat TnT = (R_geo_to_cam * T_geo *n_w.t()) / d;
H /= H.at<double>(2,2);
return H;
// 计算从地理坐标到像素坐标的单应性矩阵
cv::Mat H_geo_to_pixel = Kmat * (R_geo_to_cam - TnT) * M_het;
cv::Mat H = H_geo_to_pixel.inv();
// 归一化（使 H[2,2] = 1）
H = H / H.at<double>(2, 2);
return H.clone();
}

26
stitch/src/Arith_GeoSolver.h
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@ -12,12 +12,6 @@
#pragma once
#include "StitchStruct.h"
// 像方-物方转换关系
struct TForm
{
cv::Mat R;
cv::Mat T;
};
// 李群+K
@ -35,16 +29,12 @@ public:
GeoSolver();
~GeoSolver();
// 建立像方与归一化地理系的H矩阵，解析出H
// 建立像方与归一化地理系的H矩阵，解析出H (H用于从像方投影到地理，定位矩阵)
cv::Mat findHomography(FrameInfo info);
// 计算标准李群,并返回H矩阵
cv::Mat findHomography(FrameInfo info, RtK& se3Para);
// 测试
cv::Mat findHomography2(FrameInfo info);
cv::Mat findHomography_GPT(FrameInfo info);
// 设置起始拼接点外参，返回当前光轴大地指向
void SetOriginPoint(FrameInfo info);
@ -69,24 +59,24 @@ public:
// 计算当前帧李群（SE3）和投影K
RtK AnlayseRtK(FrameInfo info);
// 帧像方-地理坐标
cv::Point2f Trans_uv2Geo(cv::Point2f pos_frame, TForm form);
// // 帧像方-地理坐标
// cv::Point2f Trans_uv2Geo(cv::Point2f pos_frame, TForm form);
// 地理坐标-帧像方
cv::Point2f Trans_Geo2uv(cv::Point2f pos_geo, TForm form);
// // 地理坐标-帧像方
// cv::Point2f Trans_Geo2uv(cv::Point2f pos_geo, TForm form);
// 机体ENG(东北地)到像方的 旋转矩阵
cv::Mat Mat_TransENG2uv(FrameInfo info);
public:
// 平移矩阵，以初始化点为基准，计算当前位置在初始点的地理坐标，那么当前帧所有点的坐标做此平移
// 平移矩阵t，以初始化点为基准，计算当前位置在初始点的地理坐标，那么当前帧所有点的坐标做此平移
cv::Mat Mat_TransENGMove(FrameInfo info);
// 机体ENG(东北地)到相机系 旋转矩阵
// 机体ENG(东北地)到相机系 旋转矩阵R
cv::Mat Mat_TransENG2Cam(FrameInfo info);
// 内参
// 内参 K
cv::Mat Mat_GetCamK(FrameInfo info);
PointXYZ originCGCSPoint;//成图初始点的地心地固坐标，作为拼接参考

22
stitch/src/Arith_UnderStitch.cpp
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@ -377,8 +377,6 @@ void UnderStitch::run_test(FrameInfo para)
RtK rtk;
cv::Mat H2 = _GeoSolver->findHomography(para, rtk);
cv::Vec3d rvec;
cv::Rodrigues(rtk.R, rvec);
@ -538,7 +536,7 @@ SINT32 UnderStitch::Run(GD_VIDEO_FRAME_S frame, FrameInfo para)
para.craft.stPos.B, para.craft.stPos.L, para.craft.stPos.H, para.craft.stAtt.fYaw, para.craft.stAtt.fPitch, para.craft.stAtt.fRoll,
para.servoInfo.fServoAz, para.servoInfo.fServoPt,para.camInfo.nFocus,para.camInfo.fPixelSize);
//run_test(para);
run_test(para);
// 统计扫描方向
int dir = StatScanDir(para);
@ -697,15 +695,15 @@ SINT32 UnderStitch::Run(GD_VIDEO_FRAME_S frame, FrameInfo para)
if (_config.bUseBA && TriggerBA(para))
{
// 异步执行BA优化
_pThreadPool->commit(
[this]() {
OptAndOutCurrPan(_baWindowKey);
},
"BA_Optimization"
);
// // 异步执行BA优化
// _pThreadPool->commit(
// [this]() {
// OptAndOutCurrPan(_baWindowKey);
// },
// "BA_Optimization"
// );
OptAndOutCurrPan(_baWindowKey);
}
@ -1079,7 +1077,7 @@ SINT32 UnderStitch::ReceiveFrame(GD_VIDEO_FRAME_S img, FrameInfo para)
// 保持滑动窗口大小
const size_t WINDOW_SIZE = 10; // 滑动窗口大小
const size_t WINDOW_SIZE = 6; // 滑动窗口大小
if (_baWindowKey.size() > WINDOW_SIZE)
{
_baWindowKey.erase(_baWindowKey.begin());

2
stitch/src/Arith_UnderStitch.h
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@ -14,7 +14,7 @@
// 定义扫描模式，使用扫描专用的拼接地图策略
//#define SCAN_MODE
#define SCAN_MODE
// 关键帧间邻接信息

2
stitch/src/Version.h
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@ -2,5 +2,5 @@
#pragma once
#include <string>
std::string BUILD_TIME = "BUILD_TIME 2025_12_07-12.02.16";
std::string BUILD_TIME = "BUILD_TIME 2025_12_12-09.36.33";
std::string VERSION = "BUILD_VERSION 1.0.1";

16
tests/cpp/S7215/TsDecoder.hpp
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@ -151,8 +151,8 @@ public:
else
{
UPanConfig config = {0};
config.bUseBA = 1;
config.bOutGoogleTile = 1;
config.bUseBA = 0;
config.bOutGoogleTile = 0;
config.bOutLocalPanGeoTIFF = 1;
pUnderStitch->SetConfig(config);
@ -179,14 +179,14 @@ public:
cv::imshow("",src);
cv::waitKey(1);
if (info.nFrmID % 100 == 0)
{
// if (info.nFrmID % 100 == 0)
// {
char filename[1024] = {0};
sprintf(filename, "%d_%02d_%02d_%02d_%04d.tif", aTelem.mSensorID, aTelem.mHour, aTelem.mMinute, aTelem.mSecond, aTelem.mMillSecond);
exportFrameGeoTIFF(src, info, filename);
// char filename[1024] = {0};
// sprintf(filename, "%d_%02d_%02d_%02d_%04d.tif", aTelem.mSensorID, aTelem.mHour, aTelem.mMinute, aTelem.mSecond, aTelem.mMillSecond);
// exportFrameGeoTIFF(src, info, filename);
}
// }
}

4
tests/cpp/S7215/stitch_S7215.cpp
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@ -6,7 +6,9 @@
int main(int, char**)
{
std::string file = std::string("F:/20251105140846_chn_10_25Hz.ts");
//std::string file = std::string("F:/20251105140846_chn_10_25Hz.ts");
std::string file = std::string("/media/wang/data/20251105140846_chn_10_25Hz.ts");
TsDecoder* dec = new TsDecoder(file);
dec->decoder();

78
tests/python/1.py
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@ -1,78 +0,0 @@
import os
import sys
import ctypes
from pathlib import Path
import numpy as np
# 获取项目根目录（当前文件所在目录的父目录）
project_root = Path(__file__).parent.parent.parent
bin_dir = project_root / "Bin"
# 添加Bin目录到Python模块搜索路径
if str(bin_dir) not in sys.path:
sys.path.insert(0, str(bin_dir))
# 预加载依赖库，确保动态链接器能找到它们
lib_guide_stitch = bin_dir / "libGuideStitch.so"
if lib_guide_stitch.exists():
try:
ctypes.CDLL(str(lib_guide_stitch), mode=ctypes.RTLD_GLOBAL)
except Exception as e:
print(f"警告: 预加载libGuideStitch.so失败: {e}")
# 导入模块
from UStitcher import API_UnderStitch, FrameInfo, getHomography
import cv2
frame_info = FrameInfo()
frame_info.nFrmID = 1
frame_info.craft.stPos.B = 39
frame_info.craft.stPos.L = 120
frame_info.craft.stPos.H = 1000
frame_info.camInfo.nFocus = 40
frame_info.camInfo.fPixelSize = 12
frame_info.servoInfo.fServoAz = 90
frame_info.servoInfo.fServoPt = -45
frame_info.nEvHeight = 1200
frame_info.nWidth = 1280
frame_info.nHeight = 1024
stitcher = API_UnderStitch.Create()
# 先初始化（设置原点）
pan_info = stitcher.Init(frame_info)
print(f"初始化成功，全景图尺寸: {pan_info.m_pan_width} x {pan_info.m_pan_height}")
def warpPointWithH(H, pt):
wp = H @ np.array([pt[0],pt[1],1]).T
return wp / wp[2]
for i in range(100):
frame_info.nFrmID = i
frame_info.craft.stPos.B = 39
frame_info.craft.stPos.L = 120
frame_info.craft.stPos.H = 1000
frame_info.camInfo.nFocus = 40
frame_info.camInfo.fPixelSize = 12
frame_info.servoInfo.fServoAz = 90
frame_info.servoInfo.fServoPt = -45
H = getHomography(frame_info)
print(f"单应性矩阵 H:\n{H}")
wp = warpPointWithH(H, np.array([100,111]))
print(f"物方坐标:\n{wp}")
print("done")

13
tests/python/ProcDJ.py
Unescape View File

@ -8,10 +8,19 @@ import numpy as np
from dataclasses import dataclass
from typing import List, Tuple
import os
sys.path.append("/media/wang/WORK/wangchongwu_gitea_2023/StitchVideo/Bin")
sys.path.append("../../Bin/Release")
# CUDA_BIN_DIR = r"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin"
# CERES_DEP_DIR = r"D:\wangchongwu_gitea_2023\StitchVideo\3rdParty\ceres_2.2\bin"
# OPEN_CV_DEP_DIR = r"C:\Lib\opencv455\build\x64\vc14\bin"
# os.add_dll_directory(CUDA_BIN_DIR)
# os.add_dll_directory(CERES_DEP_DIR)
# os.add_dll_directory(OPEN_CV_DEP_DIR)
# os.add_dll_directory("D:/wangchongwu_gitea_2023/StitchVideo/Bin/Release")
#os.add_dll_directory("../../Bin/Release")
# 导入模块
from UStitcher import API_UnderStitch, FrameInfo, UPanInfo, UPanConfig
@ -411,7 +420,7 @@ def main():
srt_path_list = []
# 修改为你的数据路径
folder = "/media/wang/data/K2D_data/"
folder = "F:/K2D_data/"
video_path_list.append(folder + "DJI_20250418153043_0006_W.MP4")
srt_path_list.append(folder + "DJI_20250418153043_0006_W.srt")

36
tests/python/sim_scan.py
Unescape View File

@ -15,6 +15,10 @@ import time
from dataclasses import dataclass
from pathlib import Path
from typing import Iterable, List, Optional, Tuple
import pprint
pprint.pprint(sys.path)
import numpy as np
@ -23,17 +27,31 @@ import numpy as np
# Environment bootstrap (reuse logic from tests/python/1.py)
# -----------------------------------------------------------------------------
PROJECT_ROOT = Path(__file__).resolve().parents[2]
BIN_DIR = PROJECT_ROOT / "Bin"
# BIN_DIR = PROJECT_ROOT / "Bin/Release"
# if str(BIN_DIR) not in sys.path:
# sys.path.insert(0, str(BIN_DIR))
# LIB_GUIDE = BIN_DIR / "GuideStitch.dll"
# if LIB_GUIDE.exists():
# try:
# ctypes.CDLL(str(LIB_GUIDE), mode=ctypes.RTLD_GLOBAL)
# except OSError as exc:
# print(f"[WARN] Failed to preload {LIB_GUIDE.name}: {exc}")
import os
sys.path.append("D:/wangchongwu_gitea_2023/StitchVideo/Bin/Release")
CUDA_BIN_DIR = r"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\bin"
CERES_DEP_DIR = r"D:\wangchongwu_gitea_2023\StitchVideo\3rdParty\ceres_2.2\bin"
OPEN_CV_DEP_DIR = r"C:\Lib\opencv455\build\x64\vc14\bin"
os.add_dll_directory(CUDA_BIN_DIR)
os.add_dll_directory(CERES_DEP_DIR)
os.add_dll_directory(OPEN_CV_DEP_DIR)
os.add_dll_directory("D:/wangchongwu_gitea_2023/StitchVideo/Bin/Release")
if str(BIN_DIR) not in sys.path:
sys.path.insert(0, str(BIN_DIR))
LIB_GUIDE = BIN_DIR / "libGuideStitch.so"
if LIB_GUIDE.exists():
try:
ctypes.CDLL(str(LIB_GUIDE), mode=ctypes.RTLD_GLOBAL)
except OSError as exc:
print(f"[WARN] Failed to preload {LIB_GUIDE.name}: {exc}")
from UStitcher import API_UnderStitch, FrameInfo, UPanConfig
import cv2

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