处理色彩斑驳的问题

libs/MVS/SceneTexture.cpp

@@ -4301,10 +4301,9 @@ bool MeshTexture::CreateVirtualFaces62(FaceDataViewArr& facesDatas, FaceDataView
 		} else {
 			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)
 			const Normal& normalCenter = scene.mesh.faceNormals[virtualFaceCenterFaceID];
-
-			//*
 			// 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
 			for (IIndex idxView : selectedCams) {
@@ -4360,97 +4359,10 @@ bool MeshTexture::CreateVirtualFaces62(FaceDataViewArr& facesDatas, FaceDataView
 
 						if (angleDeg <= 45.0f) 
 						{
-							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);
-							// }
+							//*
-						}
+							float brightnessScore = CalculateBrightnessScore(imageData); // 亮度评分函数
-					}
+							float angleScore = 1.0f - (angleDeg / 45.0f);
-					else
-					{
-						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);
-				}
-				
-				// 计算夹角
-				Point3f normalPoint(normalCenter.x, normalCenter.y, normalCenter.z);
-				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;
 							const FaceDataArr& centerFaceDatas = facesDatas[virtualFaceCenterFaceID];
 							for (const FaceData& fd : centerFaceDatas) {
@@ -4460,119 +4372,38 @@ bool MeshTexture::CreateVirtualFaces62(FaceDataViewArr& facesDatas, FaceDataView
 								}
 							}
 							qualityScore = std::max(0.0f, std::min(1.0f, qualityScore));
-				
+							float overallScore = 0.5f * angleScore + 0.3f * brightnessScore + 0.2f * qualityScore;
-				// 检查是否为边缘
+							if (overallScore > 0.2f) {
-				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);
 							}
-					}
+							//*/
-				}
 						}
 
-			// 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()) {
+					else
-				// 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;
+						// filteredCams.push_back(idxView);
 
-				for (const auto& cameraInfoPair : cameraInfos) {
+						//*
-					IIndex idxView = cameraInfoPair.first;
+						float brightnessScore = CalculateBrightnessScore(imageData); // 亮度评分函数
-					CameraInfo info = cameraInfoPair.second;
+						float angleScore = 1.0f - (angleDeg / 45.0f);
-					if (info.isEdge ? (info.angleDeg <= 45.0f) : (info.angleDeg <= 60.0f)) {
+						float qualityScore = 0.0f;
-						// 计算亮度与中位数的差异
+						const FaceDataArr& centerFaceDatas = facesDatas[virtualFaceCenterFaceID];
-						float brightnessDiff = std::abs(info.brightness - medianBrightness);
+						for (const FaceData& fd : centerFaceDatas) {
-						brightnessDifferences.emplace_back(idxView, brightnessDiff);
+							if (fd.idxView == idxView) {
+								qualityScore = fd.quality;
+								break;
 							}
 						}
-				
+						qualityScore = std::max(0.0f, std::min(1.0f, qualityScore));
-				// 按亮度差异排序，选择差异最小的
+						float overallScore = 0.5f * angleScore + 0.3f * brightnessScore + 0.2f * qualityScore;
-				std::sort(brightnessDifferences.begin(), brightnessDifferences.end(),
+						if (overallScore > 0.2f) {
-					[](const auto& a, const auto& b) { return a.second < b.second; });
+							filteredCams.push_back(idxView);
-				
-				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
 			// 例如，其声明应为：IIndexArr selectedCams = ...; (不能是 const IIndexArr)
@@ -4588,6 +4419,7 @@ bool MeshTexture::CreateVirtualFaces62(FaceDataViewArr& facesDatas, FaceDataView
 				selectedCams = filteredCams;
 				isVirtualFace[virtualFaceCenterFaceID] = true;
 			}
+			//*/
 
 			currentVirtualFaceQueue.AddTail(virtualFaceCenterFaceID);
 			queuedFaces.clear();
@@ -4641,6 +4473,7 @@ bool MeshTexture::CreateVirtualFaces62(FaceDataViewArr& facesDatas, FaceDataView
 				}
 			} while (!currentVirtualFaceQueue.IsEmpty());
 
+			// compute virtual face quality and create virtual face
 			for (IIndex idxView: selectedCams) {
 				FaceData& virtualFaceData = virtualFaceDatas.emplace_back();
 				virtualFaceData.quality = 0;
@@ -4648,30 +4481,218 @@ bool MeshTexture::CreateVirtualFaces62(FaceDataViewArr& facesDatas, FaceDataView
 				#if TEXOPT_FACEOUTLIER != TEXOPT_FACEOUTLIER_NA
 				virtualFaceData.color = Point3f::ZERO;
 				#endif
+				int invalidQuality = 0;
+				Color invalidColor = Point3f::ZERO;
 				unsigned processedFaces(0);
 				bool bInvalidFacesRelative = false;
 				int invalidCount = 0;
 				for (FIndex fid : virtualFace) {
 					const FaceDataArr& faceDatas = facesDatas[fid];
 					for (FaceData& faceData: faceDatas) {
-						if (faceData.idxView == idxView) {
+
-							virtualFaceData.quality += faceData.quality;
+						int nViewCount = 0;
+					    if (faceData.idxView == idxView) 
+						{
+							for (const FaceData& fd : faceDatas) 
+							{
+								if ( faceData.bInvalidFacesRelative)
+								{
+									++nViewCount;
+								}
+							}
+							// if (faceData.bInvalidFacesRelative)
+							if (bHasInvalidView)
+							{
+								// invalidQuality += faceData.quality;
+								// #if TEXOPT_FACEOUTLIER != TEXOPT_FACEOUTLIER_NA
+								// invalidColor += faceData.color;
+								// #endif
+				
+								++processedFaces;
+							}
+							else
+							{
+								// virtualFaceData.quality += faceData.quality;
 								#if TEXOPT_FACEOUTLIER != TEXOPT_FACEOUTLIER_NA
-							virtualFaceData.color += faceData.color;
+								// virtualFaceData.color += faceData.color;
 								#endif
 								++processedFaces;
-							if (faceData.bInvalidFacesRelative)
+								// break;
-								++invalidCount;
-							break;
 							}
 						}
 					}
+				}
+
+				float maxLuminance = 120.0f;
+				float minLuminance = 90.0f;
+				int validViewsSize = validViews.size();
+				// bHasInvalidView = true;
+				if (bHasInvalidView)
+				{
+					// 使用鲁棒的统计方法计算颜色和亮度的中心值
+					const Color medianColor = ComputeMedianColorAndQuality(sortedViews).color;
+					const float medianQuality = ComputeMedianColorAndQuality(sortedViews).quality;
+					const float medianLuminance = ComputeMedianLuminance(sortedViews);
+
+					// 计算颜色和亮度的绝对中位差(MAD)作为偏差阈值
+					const float colorMAD = ComputeColorMAD(sortedViews, medianColor);
+					const float luminanceMAD = ComputeLuminanceMAD(sortedViews, medianLuminance);
+					
+					// 基于MAD设置动态阈值（3倍MAD是统计学上常用的异常值阈值）
+					const float maxColorDeviation = 0.01f * colorMAD;
+					const float maxLuminanceDeviation = 0.01f * luminanceMAD;
+
+					std::vector<int> validIndices;
+					for (int n = 0; n < sortedViews.size(); ++n) {
+						const Color& viewColor = sortedViews[n].second;
+						const float viewLuminance = MeshTexture::GetLuminance(viewColor);
+						
+						const float colorDistance = cv::norm(viewColor - medianColor);
+						const float luminanceDistance = std::abs(viewLuminance - medianLuminance);
+						
+						if (colorDistance <= maxColorDeviation && 
+							luminanceDistance <= maxLuminanceDeviation)
+						{
+							validIndices.push_back(n);
+						}
+						else
+						{
+							const FIndex currentFaceId = currentVirtualFaceQueue.GetHead();
+							const Normal& faceNormal = scene.mesh.faceNormals[currentFaceId];
+							const float cosFaceToCenter(ComputeAngleN(normalCenter.ptr(), faceNormal.ptr()));
+
+							bool bColorSimilarity = true;
+							// Check color similarity
+							const Color& centerColor = faceColors[virtualFaceCenterFaceID];
+							const Color& currentColor = faceColors[currentFaceId];
+
+							float colorDistance = cv::norm(centerColor - currentColor);
+							// printf("1colorDistance=%f\n", colorDistance);
+							if (colorDistance > thMaxColorDeviation) {
+								// printf("2colorDistance=%f\n", colorDistance);
+								bColorSimilarity = false;
+							}
+
+							// if ((cosFaceToCenter<dynamicCosTh) || !IsFaceVisible(facesDatas[currentFaceId], selectedCams))
+							if (cosFaceToCenter<dynamicCosTh)
+							{
+								if (nInvalidViewCount<=2)
+									validIndices.push_back(n);
+								else
+								{
+									// if ((colorDistance <= 350.0f))
+										validIndices.push_back(n);
+								}
+							}
+							else
+							{
+								if (nInvalidViewCount<=2)
+									validIndices.push_back(n);
+								else
+								{
+									// if (bColorSimilarity)
+										validIndices.push_back(n);
+								}
+							}
+
+						}
+					}
+
+					if (validIndices.empty()) {
+						for (int n = 0; n < sortedViews.size(); ++n) {
+							const Color& viewColor = sortedViews[n].second;
+							const float viewLuminance = MeshTexture::GetLuminance(viewColor);
+							
+							const float colorDistance = cv::norm(viewColor - medianColor);
+							const float luminanceDistance = std::abs(viewLuminance - medianLuminance);
+							
+							if (colorDistance <= maxColorDeviation)
+							{
+								// validIndices.push_back(n);
+							}
+						}
+					}
+
+					if (validIndices.empty()) {
+						for (int n = 0; n < sortedViews.size(); ++n) {
+							const Color& viewColor = sortedViews[n].second;
+							const float viewLuminance = MeshTexture::GetLuminance(viewColor);
+							
+							const float colorDistance = cv::norm(viewColor - medianColor);
+							const float luminanceDistance = std::abs(viewLuminance - medianLuminance);
+							
+							if (luminanceDistance <= maxLuminanceDeviation)
+							{
+								// validIndices.push_back(n);
+							}
+						}
+					}
+
+					{				
 						ASSERT(processedFaces > 0);
-				virtualFaceData.quality /= processedFaces;
+						// virtualFaceData.quality /= processedFaces;
 						#if TEXOPT_FACEOUTLIER != TEXOPT_FACEOUTLIER_NA
-				virtualFaceData.color /= processedFaces;
+						// virtualFaceData.color /= processedFaces;
 						#endif
-				virtualFaceData.bInvalidFacesRelative = (invalidCount > processedFaces / 2);
+
+						virtualFaceData.quality = 0;
+						#if TEXOPT_FACEOUTLIER != TEXOPT_FACEOUTLIER_NA
+						virtualFaceData.color = Point3f::ZERO;
+						#endif
+					}
+				}
+				else
+				{
+					// 使用鲁棒的统计方法计算颜色和亮度的中心值
+					const Color medianColor = ComputeMedianColorAndQuality(sortedViews).color;
+					const float medianQuality = ComputeMedianColorAndQuality(sortedViews).quality;
+					const float medianLuminance = ComputeMedianLuminance(sortedViews);
+
+					// 计算颜色和亮度的绝对中位差(MAD)作为偏差阈值
+					const float colorMAD = ComputeColorMAD(sortedViews, medianColor);
+					const float luminanceMAD = ComputeLuminanceMAD(sortedViews, medianLuminance);
+					
+					// 基于MAD设置动态阈值（3倍MAD是统计学上常用的异常值阈值）
+					const float maxColorDeviation = 0.01f * colorMAD;
+					// const float maxLuminanceDeviation = 0.01f * luminanceMAD;
+					const float maxLuminanceDeviation = 0.05f * luminanceMAD;
+
+					std::vector<int> validIndices;
+					for (int n = 0; n < sortedViews.size(); ++n) {
+						const Color& viewColor = sortedViews[n].second;
+						const float viewLuminance = MeshTexture::GetLuminance(viewColor);
+						
+						const float colorDistance = cv::norm(viewColor - medianColor);
+						const float luminanceDistance = std::abs(viewLuminance - medianLuminance);
+						
+						// if (colorDistance <= maxColorDeviation && 
+						// 	luminanceDistance <= maxLuminanceDeviation)
+						// if (luminanceDistance <= maxLuminanceDeviation)
+						{
+							validIndices.push_back(n);
+						}
+					}
+
+					if (validIndices.empty()) {
+
+						virtualFaceData.quality = medianQuality;
+						virtualFaceData.color = medianColor;
+					} 
+					else {
+						// 使用过滤后的视图重新计算平均值
+						float totalQuality2 = 0.0f;
+						Color totalColor2 = Color(0,0,0);
+						for (int idx : validIndices) {
+							totalQuality2 += validViews[idx].first;
+							totalColor2 += validViews[idx].second;
+						}
+						virtualFaceData.quality = totalQuality2 / validIndices.size();
+						virtualFaceData.color = totalColor2 / validIndices.size();
+					}
+				}
+
+				// virtualFaceData.bInvalidFacesRelative = (invalidCount > 1);
+				// virtualFaceData.bInvalidFacesRelative = (invalidCount > processedFaces * 2 / 3);
 			}
 			ASSERT(!virtualFaceDatas.empty());
 		}