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优化

master
hesuicong 3 weeks ago
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bccd31fea6
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b95b9ca27a
1 changed files with 205 additions and 38 deletions
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  1. 217
      libs/MVS/SceneTexture.cpp

217
libs/MVS/SceneTexture.cpp
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@ -4301,9 +4301,10 @@ bool MeshTexture::CreateVirtualFaces62(FaceDataViewArr& facesDatas, FaceDataView
} else { } else {
IIndexArr selectedCams = SelectBestViews(centerFaceDatas, virtualFaceCenterFaceID, minCommonCameras, ratioAngleToQuality); IIndexArr selectedCams = SelectBestViews(centerFaceDatas, virtualFaceCenterFaceID, minCommonCameras, ratioAngleToQuality);
//*
// 获取中心面片的法线 (注意变量名是 normalCenter, 不是 centerNormal) // 获取中心面片的法线 (注意变量名是 normalCenter, 不是 centerNormal)
const Normal& normalCenter = scene.mesh.faceNormals[virtualFaceCenterFaceID]; const Normal& normalCenter = scene.mesh.faceNormals[virtualFaceCenterFaceID];
//*
// 过滤selectedCams：只保留夹角小于30度的视图 // 过滤selectedCams：只保留夹角小于30度的视图
IIndexArr filteredCams; // 用于存储过滤后的视图索引 IIndexArr filteredCams; // 用于存储过滤后的视图索引
for (IIndex idxView : selectedCams) { for (IIndex idxView : selectedCams) {
@ -4359,35 +4360,97 @@ bool MeshTexture::CreateVirtualFaces62(FaceDataViewArr& facesDatas, FaceDataView
if (angleDeg <= 45.0f) if (angleDeg <= 45.0f)
{ {
// filteredCams.push_back(idxView); filteredCams.push_back(idxView);
//* // float brightnessScore = CalculateBrightnessScore(imageData); // 亮度评分函数
float brightnessScore = CalculateBrightnessScore(imageData); // 亮度评分函数 // float angleScore = 1.0f - (angleDeg / 45.0f);
float angleScore = 1.0f - (angleDeg / 45.0f); // float qualityScore = 0.0f;
float qualityScore = 0.0f; // const FaceDataArr& centerFaceDatas = facesDatas[virtualFaceCenterFaceID];
const FaceDataArr& centerFaceDatas = facesDatas[virtualFaceCenterFaceID]; // for (const FaceData& fd : centerFaceDatas) {
for (const FaceData& fd : centerFaceDatas) { // if (fd.idxView == idxView) {
if (fd.idxView == idxView) { // qualityScore = fd.quality;
qualityScore = fd.quality; // break;
break; // }
// }
// qualityScore = std::max(0.0f, std::min(1.0f, qualityScore));
// float overallScore = 0.5f * angleScore + 0.3f * brightnessScore + 0.2f * qualityScore;
// if (overallScore > 0.35f) {
// filteredCams.push_back(idxView);
// }
} }
} }
qualityScore = std::max(0.0f, std::min(1.0f, qualityScore)); else
float overallScore = 0.5f * angleScore + 0.3f * brightnessScore + 0.2f * qualityScore; {
if (overallScore > 0.35f) {
filteredCams.push_back(idxView); filteredCams.push_back(idxView);
// float brightnessScore = CalculateBrightnessScore(imageData); // 亮度评分函数
// float angleScore = 1.0f - (angleDeg / 45.0f);
// float qualityScore = 0.0f;
// const FaceDataArr& centerFaceDatas = facesDatas[virtualFaceCenterFaceID];
// for (const FaceData& fd : centerFaceDatas) {
// if (fd.idxView == idxView) {
// qualityScore = fd.quality;
// break;
// }
// }
// qualityScore = std::max(0.0f, std::min(1.0f, qualityScore));
// float overallScore = 0.5f * angleScore + 0.3f * brightnessScore + 0.2f * qualityScore;
// if (overallScore > 0.35f) {
// filteredCams.push_back(idxView);
// }
} }
//*/
} }
}
//*/
/*
struct CameraInfo {
float brightness;
float angleDeg;
float qualityScore;
bool isEdge;
};
// 修改后的代码，使用亮度统计分布过滤异常值
IIndexArr filteredCams; // 用于存储过滤后的视图索引
// 步骤1: 收集所有候选相机的亮度值
std::vector<float> allBrightnessScores;
std::vector<std::pair<IIndex, CameraInfo>> cameraInfos; // 存储相机ID和相关信息
for (IIndex idxView : selectedCams) {
const Image& imageData = images[idxView];
// 计算相机朝向向量
const RMatrix& R = imageData.camera.R;
Point3f localForward(0.0f, 0.0f, -1.0f);
Point3f cameraForward;
cameraForward.x = R(0,0) * localForward.x + R(0,1) * localForward.y + R(0,2) * localForward.z;
cameraForward.y = R(1,0) * localForward.x + R(1,1) * localForward.y + R(1,2) * localForward.z;
cameraForward.z = R(2,0) * localForward.x + R(2,1) * localForward.y + R(2,2) * localForward.z;
// 归一化
float norm = std::sqrt(cameraForward.x * cameraForward.x +
cameraForward.y * cameraForward.y +
cameraForward.z * cameraForward.z);
if (norm > 0.0f) {
cameraForward.x /= norm;
cameraForward.y /= norm;
cameraForward.z /= norm;
} else {
cameraForward = Point3f(0, 0, -1);
} }
else
{
// filteredCams.push_back(idxView);
//* // 计算夹角
float brightnessScore = CalculateBrightnessScore(imageData); // 亮度评分函数 Point3f normalPoint(normalCenter.x, normalCenter.y, normalCenter.z);
float angleScore = 1.0f - (angleDeg / 45.0f); float cosAngle = cameraForward.dot(normalPoint);
float angleDeg = std::acos(cosAngle) * 180.0f / M_PI;
// 计算亮度分数
float brightnessScore = CalculateBrightnessScore(imageData);
allBrightnessScores.push_back(brightnessScore);
// 获取质量分数
float qualityScore = 0.0f; float qualityScore = 0.0f;
const FaceDataArr& centerFaceDatas = facesDatas[virtualFaceCenterFaceID]; const FaceDataArr& centerFaceDatas = facesDatas[virtualFaceCenterFaceID];
for (const FaceData& fd : centerFaceDatas) { for (const FaceData& fd : centerFaceDatas) {
@ -4397,15 +4460,120 @@ bool MeshTexture::CreateVirtualFaces62(FaceDataViewArr& facesDatas, FaceDataView
} }
} }
qualityScore = std::max(0.0f, std::min(1.0f, qualityScore)); qualityScore = std::max(0.0f, std::min(1.0f, qualityScore));
float overallScore = 0.5f * angleScore + 0.3f * brightnessScore + 0.2f * qualityScore;
if (overallScore > 0.35f) { // 检查是否为边缘
std::string strPath = imageData.name;
size_t lastSlash = strPath.find_last_of("/\\");
if (lastSlash == std::string::npos) lastSlash = 0;
else lastSlash++;
size_t lastDot = strPath.find_last_of('.');
if (lastDot == std::string::npos) lastDot = strPath.size();
std::string strName = strPath.substr(lastSlash, lastDot - lastSlash);
bool isEdge = false;
if (!scene.is_face_delete_edge(strName, virtualFaceCenterFaceID)) {
isEdge = scene.is_face_edge(strName, virtualFaceCenterFaceID);
}
// 存储相机信息
cameraInfos.emplace_back(idxView, CameraInfo{
.brightness = brightnessScore,
.angleDeg = angleDeg,
.qualityScore = qualityScore,
.isEdge = isEdge
});
}
float medianBrightness = 0.0f;
// 步骤2: 计算亮度分布的统计特征
if (!allBrightnessScores.empty()) {
// 方法1: 使用中位数和MAD（绝对中位差） - 对异常值更鲁棒
std::vector<float> sortedBrightness = allBrightnessScores;
std::sort(sortedBrightness.begin(), sortedBrightness.end());
medianBrightness = sortedBrightness[sortedBrightness.size() / 2];
// 计算MAD
std::vector<float> deviations;
for (float brightness : allBrightnessScores) {
deviations.push_back(std::abs(brightness - medianBrightness));
}
std::sort(deviations.begin(), deviations.end());
float mad = deviations[deviations.size() / 2];
// 使用3倍MAD作为阈值（通常2-3倍MAD是常用阈值）
float lowerThreshold = medianBrightness - 2.5f * mad;
float upperThreshold = medianBrightness + 2.5f * mad;
// 方法2: 使用四分位距(IQR) - 备选方法
if (sortedBrightness.size() >= 4) {
int q1_index = sortedBrightness.size() / 4;
int q3_index = 3 * sortedBrightness.size() / 4;
float q1 = sortedBrightness[q1_index];
float q3 = sortedBrightness[q3_index];
float iqr = q3 - q1;
// 使用1.5倍IQR规则
float iqrLower = q1 - 1.5f * iqr;
float iqrUpper = q3 + 1.5f * iqr;
// 也可以结合两种方法
lowerThreshold = std::max(lowerThreshold, iqrLower);
upperThreshold = std::min(upperThreshold, iqrUpper);
}
// 步骤3: 根据统计特征过滤相机
for (const auto& [idxView, info] : cameraInfos) {
// 检查亮度是否在正常范围内
if (info.brightness < lowerThreshold || info.brightness > upperThreshold) {
// printf("过滤相机 %d: 亮度 %.3f 超出范围 [%.3f, %.3f]\n",
// idxView, info.brightness, lowerThreshold, upperThreshold);
continue;
}
// 根据是否为边缘面使用不同的参数
float angleLimit = info.isEdge ? 45.0f : 60.0f;
float scoreThreshold = info.isEdge ? 0.4f : 0.3f;
if (info.angleDeg <= angleLimit) {
float angleScore = 1.0f - (info.angleDeg / angleLimit);
float overallScore = 0.5f * angleScore + 0.3f * info.brightness + 0.2f * info.qualityScore;
if (overallScore > scoreThreshold) {
filteredCams.push_back(idxView); filteredCams.push_back(idxView);
} }
//*/
} }
} }
} }
// 如果没有足够的相机满足统计条件，可以放宽阈值或使用原始方法
if (filteredCams.size() < 3 && !cameraInfos.empty()) {
// 回退到简单方法：选择亮度最接近中位数的前N个相机
std::vector<std::pair<IIndex, float>> brightnessDifferences;
for (const auto& cameraInfoPair : cameraInfos) {
IIndex idxView = cameraInfoPair.first;
CameraInfo info = cameraInfoPair.second;
if (info.isEdge ? (info.angleDeg <= 45.0f) : (info.angleDeg <= 60.0f)) {
// 计算亮度与中位数的差异
float brightnessDiff = std::abs(info.brightness - medianBrightness);
brightnessDifferences.emplace_back(idxView, brightnessDiff);
}
}
// 按亮度差异排序，选择差异最小的
std::sort(brightnessDifferences.begin(), brightnessDifferences.end(),
[](const auto& a, const auto& b) { return a.second < b.second; });
int neededCount = std::min(5, (int)brightnessDifferences.size());
for (int i = 0; i < neededCount; i++) {
filteredCams.push_back(brightnessDifferences[i].first);
}
}
//*/
// 确保 selectedCams 是非 const 的，才能对其进行赋值 // 确保 selectedCams 是非 const 的，才能对其进行赋值
// 例如，其声明应为：IIndexArr selectedCams = ...; (不能是 const IIndexArr) // 例如，其声明应为：IIndexArr selectedCams = ...; (不能是 const IIndexArr)
if (filteredCams.empty()) { if (filteredCams.empty()) {
@ -4420,7 +4588,6 @@ bool MeshTexture::CreateVirtualFaces62(FaceDataViewArr& facesDatas, FaceDataView
selectedCams = filteredCams; selectedCams = filteredCams;
isVirtualFace[virtualFaceCenterFaceID] = true; isVirtualFace[virtualFaceCenterFaceID] = true;
} }
//*/
currentVirtualFaceQueue.AddTail(virtualFaceCenterFaceID); currentVirtualFaceQueue.AddTail(virtualFaceCenterFaceID);
queuedFaces.clear(); queuedFaces.clear();

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