openMVS/libs/MVS/ColorComparisonFace.cpp

#include "ColorComparisonFace.h"
#include "Mesh.h"  // 在cpp中包含完整定义
#include <opencv2/opencv.hpp>

#include <cstdlib>
#include <cstdio>
#include <algorithm>
#include <sys/stat.h>
#include <iomanip>
#include <sstream>

using namespace MVS;

// 辅助函数：检查目录是否存在
static bool directoryExists(const std::string& path) {
    struct stat info;
    if (stat(path.c_str(), &info) != 0) {
        return false;
    }
    return (info.st_mode & S_IFDIR) != 0;
}

// 辅助函数：创建目录
static bool createDirectory(const std::string& path) {
    #ifdef _WIN32
        return _mkdir(path.c_str()) == 0;
    #else
        return mkdir(path.c_str(), 0755) == 0;
    #endif
}

// 辅助函数：递归创建目录
static bool createDirectories(const std::string& path) {
    size_t pos = 0;
    std::string dir;
    
    while ((pos = path.find_first_of("/\\", pos + 1)) != std::string::npos) {
        dir = path.substr(0, pos);
        if (!directoryExists(dir)) {
            if (!createDirectory(dir)) {
                return false;
            }
        }
    }
    
    // 创建最终目录
    if (!directoryExists(path)) {
        return createDirectory(path);
    }
    
    return true;
}

// 构造函数
ColorComparisonFace::ColorComparisonFace(const std::string& dir) : outputDir(dir) {
    // 确保输出目录以斜杠结尾
    if (!outputDir.empty() && outputDir.back() != '/' && outputDir.back() != '\\') {
        outputDir += '/';
    }
    
    // 创建输出目录
    if (!createDirectories(outputDir)) {
        printf("⚠️ 无法创建目录: %s\n", outputDir.c_str());
    } else {
        printf("✅ 输出目录: %s\n", outputDir.c_str());
    }
}

// 添加颜色信息（带图像区域）
void ColorComparisonFace::addColorInfo(int faceId, 
                     const MeshColor& gaussianColor, 
                     const cv::Mat& imageRegion,
                     float distance, float threshold,
                     const std::string& filename) {
    ColorInfo info = {faceId, gaussianColor, imageRegion.clone(), distance, threshold, filename};
    faceViewColorMap[faceId][filename].push_back(info);
	printf("addColorInfo faceId=%d", faceId);
}

// 获取总face数
int ColorComparisonFace::getTotalFaces() const {
    return faceViewColorMap.size();
}

// 获取总记录数
int ColorComparisonFace::getTotalRecords() const {
    int total = 0;
    for (const auto& faceEntry : faceViewColorMap) {
        for (const auto& viewEntry : faceEntry.second) {
            total += viewEntry.second.size();
        }
    }
    return total;
}

// 获取faceid列表
std::vector<int> ColorComparisonFace::getFaceIds() const {
    std::vector<int> faceIds;
    for (const auto& faceEntry : faceViewColorMap) {
        faceIds.push_back(faceEntry.first);
    }
    return faceIds;
}

// 创建实际图像区域对比图
void ColorComparisonFace::createBatchComparison(int maxBlocksPerRow, int maxFacesPerImage) {
    if (faceViewColorMap.empty()) {
        printf("⚠️ 没有颜色信息可生成\n");
        return;
    }
    
    printf("正在创建实际图像区域对比图...\n");
    printf("总face数: %zu\n", faceViewColorMap.size());
    printf("总记录数: %d\n", getTotalRecords());
    
    // 块参数
    int imageRegionSize = 200;     // 图像区域显示大小
    int colorBlockSize = 100;      // 高斯颜色块大小
    int blockMargin = 20;          // 块之间的边距
    int infoHeight = 100;          // 信息区域高度
    
    // 限制处理的face数
    int facesToProcess = std::min((int)faceViewColorMap.size(), maxFacesPerImage);
    
    // 处理前N个face
    int faceCount = 0;
    for (const auto& faceEntry : faceViewColorMap) {
        if (faceCount >= facesToProcess) break;
        
        int faceId = faceEntry.first;
        const auto& viewMap = faceEntry.second;
        
        printf("处理 face %d (%zu 个视图)...\n", faceId, viewMap.size());
        
        // 计算需要的行数和列数
        int numBlocks = 0;
        for (const auto& viewEntry : viewMap) {
            numBlocks += viewEntry.second.size();
        }
        
        int numCols = std::min(maxBlocksPerRow, numBlocks);
        int numRows = (numBlocks + numCols - 1) / numCols;
        
        // 计算每个块的宽度和高度
        int blockWidth = colorBlockSize + imageRegionSize + blockMargin * 3;
        int blockHeight = imageRegionSize + blockMargin + infoHeight;
        
        // 图片总尺寸
        int totalWidth = numCols * (blockWidth + blockMargin) + blockMargin;
        int totalHeight = 60 + (numRows * (blockHeight + blockMargin)) + blockMargin;
        
        // 创建大图
        cv::Mat faceImage(totalHeight, totalWidth, CV_8UC3, cv::Scalar(245, 245, 245));
        
        // 添加标题
        std::string title = cv::format("Face %d - Image Region Comparison", faceId);
        cv::putText(faceImage, title, 
                    cv::Point(20, 30), 
                    cv::FONT_HERSHEY_SIMPLEX, 0.8, cv::Scalar(0, 0, 0), 2);
        
        // 添加统计信息
        int totalRecords = 0;
        int needFixCount = 0;
        for (const auto& viewEntry : viewMap) {
            totalRecords += viewEntry.second.size();
            for (const auto& info : viewEntry.second) {
                if (info.distance > info.threshold) needFixCount++;
            }
        }
        
        std::string stats = cv::format("Views: %zu, Records: %d, Need Fix: %d", 
                                       viewMap.size(), totalRecords, needFixCount);
        cv::putText(faceImage, stats, 
                    cv::Point(20, 55), 
                    cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0), 1);
        
        int blockIndex = 0;
        int currentRow = 0;
        int currentCol = 0;
        
        // 遍历这个face的所有视图
        for (const auto& viewEntry : viewMap) {
            const std::string& viewName = viewEntry.first;
            const std::vector<ColorInfo>& infos = viewEntry.second;
            
            for (const auto& info : infos) {
                // 计算当前块的位置
                int blockX = blockMargin + currentCol * (blockWidth + blockMargin);
                int blockY = 60 + blockMargin + currentRow * (blockHeight + blockMargin);
                
                // 绘制块背景
                cv::rectangle(faceImage, 
                            cv::Rect(blockX, blockY, blockWidth, blockHeight), 
                            cv::Scalar(255, 255, 255), -1);
                cv::rectangle(faceImage, 
                            cv::Rect(blockX, blockY, blockWidth, blockHeight), 
                            cv::Scalar(200, 200, 200), 2);
                
                // 绘制高斯颜色块
                int gaussianX = blockX + blockMargin;
                int gaussianY = blockY + blockMargin;
                
                cv::Scalar gaussianBGR(info.gaussianColor[2], info.gaussianColor[1], info.gaussianColor[0]);
                cv::rectangle(faceImage, 
                            cv::Rect(gaussianX, gaussianY, colorBlockSize, colorBlockSize), 
                            gaussianBGR, -1);
                cv::rectangle(faceImage, 
                            cv::Rect(gaussianX, gaussianY, colorBlockSize, colorBlockSize), 
                            cv::Scalar(0, 0, 0), 2);
                
                // 添加高斯标签
                cv::putText(faceImage, "GAUSSIAN", 
                           cv::Point(gaussianX + 10, gaussianY - 5), 
                           cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0), 1);
                
                // 绘制图像区域
                int imageX = blockX + blockMargin + colorBlockSize + blockMargin;
                int imageY = blockY + blockMargin;
                
                if (!info.imageRegion.empty()) {
                    // 调整图像区域大小
                    cv::Mat resizedRegion;
                    cv::resize(info.imageRegion, resizedRegion, cv::Size(imageRegionSize, imageRegionSize));
                    
                    // 将图像区域绘制到指定位置
                    resizedRegion.copyTo(faceImage(cv::Rect(imageX, imageY, imageRegionSize, imageRegionSize)));
                    
                    cv::rectangle(faceImage, 
                                cv::Rect(imageX, imageY, imageRegionSize, imageRegionSize), 
                                cv::Scalar(0, 0, 0), 2);
                    
                    // 添加原始图像标签
                    cv::putText(faceImage, "ORIGINAL", 
                               cv::Point(imageX + 10, imageY - 5), 
                               cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 0, 0), 1);
                } else {
                    // 如果没有图像区域，绘制占位符
                    cv::rectangle(faceImage, 
                                cv::Rect(imageX, imageY, imageRegionSize, imageRegionSize), 
                                cv::Scalar(200, 200, 200), -1);
                    cv::rectangle(faceImage, 
                                cv::Rect(imageX, imageY, imageRegionSize, imageRegionSize), 
                                cv::Scalar(150, 150, 150), 2);
                    
                    cv::putText(faceImage, "NO IMAGE", 
                               cv::Point(imageX + imageRegionSize/2 - 40, imageY + imageRegionSize/2), 
                               cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(100, 100, 100), 1);
                }
                
                // 绘制信息区域
                int infoY = blockY + blockMargin + imageRegionSize;
                cv::rectangle(faceImage, 
                            cv::Rect(blockX, infoY, blockWidth, infoHeight), 
                            cv::Scalar(240, 240, 240), -1);
                cv::rectangle(faceImage, 
                            cv::Rect(blockX, infoY, blockWidth, infoHeight), 
                            cv::Scalar(200, 200, 200), 1);
                
                // 添加视图信息
                std::string displayName = viewName;
                if (displayName.length() > 20) {
                    displayName = displayName.substr(0, 18) + "...";
                }
                
                cv::putText(faceImage, cv::format("View: %s", displayName.c_str()), 
                           cv::Point(blockX + 10, infoY + 20), 
                           cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(0, 0, 0), 1);
                
                cv::putText(faceImage, cv::format("Face: %d", info.faceId), 
                           cv::Point(blockX + 10, infoY + 40), 
                           cv::FONT_HERSHEY_SIMPLEX, 0.4, cv::Scalar(0, 0, 0), 1);
                
                // 添加颜色值
                cv::putText(faceImage, 
                           cv::format("Gaussian: R=%d, G=%d, B=%d", 
                                     info.gaussianColor[0], info.gaussianColor[1], info.gaussianColor[2]),
                           cv::Point(blockX + 10, infoY + 60), 
                           cv::FONT_HERSHEY_SIMPLEX, 0.35, cv::Scalar(0, 0, 0), 1);
                
                // 添加距离和阈值
                cv::putText(faceImage, 
                           cv::format("Distance: %.4f", info.distance),
                           cv::Point(blockX + 10, infoY + 80), 
                           cv::FONT_HERSHEY_SIMPLEX, 0.35, cv::Scalar(0, 0, 0), 1);
                
                cv::putText(faceImage, 
                           cv::format("Threshold: %.4f", info.threshold),
                           cv::Point(blockX + 10, infoY + 95), 
                           cv::FONT_HERSHEY_SIMPLEX, 0.35, cv::Scalar(0, 0, 0), 1);
                
                // 添加判断结果
                int resultX = blockX + blockWidth - 60;
                int resultY = infoY + 25;
                
                std::string resultText = (info.distance > info.threshold) ? "FIX" : "OK";
                cv::Scalar resultColor = (info.distance > info.threshold) ? cv::Scalar(0, 0, 255) : cv::Scalar(0, 180, 0);
                
                cv::putText(faceImage, resultText, 
                           cv::Point(resultX, resultY), 
                           cv::FONT_HERSHEY_SIMPLEX, 0.5, resultColor, 1);
                
                // 更新行和列索引
                blockIndex++;
                currentCol++;
                if (currentCol >= numCols) {
                    currentCol = 0;
                    currentRow++;
                }
                
                // 如果已经达到最大块数，跳出循环
                if (blockIndex >= maxBlocksPerRow * numRows) {
                    break;
                }
            }
            
            // 如果已经达到最大块数，跳出循环
            if (blockIndex >= maxBlocksPerRow * numRows) {
                break;
            }
        }
        
        // 保存图片
        std::string outputPath = outputDir + cv::format("face_%d_image_comparison.png", faceId);
        
        if (!cv::imwrite(outputPath, faceImage)) {
            printf("❌ 无法保存face %d的图像区域对比图: %s\n", faceId, outputPath.c_str());
        } else {
            printf("✅ face %d图像区域对比图已保存: %s\n", faceId, outputPath.c_str());
            printf("   尺寸: %d x %d 像素, 视图数: %zu, 记录数: %d\n", 
                   totalWidth, totalHeight, viewMap.size(), totalRecords);
        }
        
        faceCount++;
    }
    
    // 保存颜色信息到CSV文件
    saveColorInfoToFile();
}

// 保存颜色信息到CSV文件
void ColorComparisonFace::saveColorInfoToFile() {
    // 为每个face保存单独的CSV
    for (const auto& faceEntry : faceViewColorMap) {
        int faceId = faceEntry.first;
        const auto& viewMap = faceEntry.second;
        
        if (viewMap.empty()) {
            continue;
        }
        
        std::string filePath = outputDir + cv::format("face_%d_colors.csv", faceId);
        FILE* fp = fopen(filePath.c_str(), "w");
        
        if (!fp) {
            printf("❌ 无法创建文件: %s\n", filePath.c_str());
            continue;
        }
        
        // 写入CSV标题
        fprintf(fp, "FaceID,View,Gaussian_R,Gaussian_G,Gaussian_B,Distance,Threshold,NeedsFix\n");
        
        for (const auto& viewEntry : viewMap) {
            const std::string& viewName = viewEntry.first;
            const std::vector<ColorInfo>& infos = viewEntry.second;
            
            for (int i = 0; i < (int)infos.size(); i++) {
                const ColorInfo& info = infos[i];
                bool needsFix = (info.distance > info.threshold);
                fprintf(fp, "%d,%s,%d,%d,%d,%.4f,%.4f,%s\n",
                        faceId,
                        viewName.c_str(),
                        info.gaussianColor[0], info.gaussianColor[1], info.gaussianColor[2],
                        info.distance, info.threshold,
                        needsFix ? "YES" : "NO");
            }
        }
        
        fclose(fp);
        printf("✅ face %d颜色信息已保存到: %s\n", faceId, filePath.c_str());
    }
    
    // 保存汇总统计信息
    std::string summaryPath = outputDir + "image_comparison_summary.csv";
    FILE* summaryFp = fopen(summaryPath.c_str(), "w");
    
    if (summaryFp) {
        fprintf(summaryFp, "FaceID,Views,TotalRecords,NeedFixRecords,NeedFix%%,MinDistance,MaxDistance,AvgDistance\n");
        
        for (const auto& faceEntry : faceViewColorMap) {
            int faceId = faceEntry.first;
            const auto& viewMap = faceEntry.second;
            
            int totalRecords = 0;
            int needFixCount = 0;
            float minDist = std::numeric_limits<float>::max();
            float maxDist = 0.0f;
            float sumDist = 0.0f;
            
            for (const auto& viewEntry : viewMap) {
                const std::vector<ColorInfo>& infos = viewEntry.second;
                totalRecords += infos.size();
                
                for (const auto& info : infos) {
                    if (info.distance > info.threshold) needFixCount++;
                    minDist = std::min(minDist, info.distance);
                    maxDist = std::max(maxDist, info.distance);
                    sumDist += info.distance;
                }
            }
            
            float avgDist = (totalRecords > 0) ? (sumDist / totalRecords) : 0.0f;
            float fixPercentage = (totalRecords > 0) ? (needFixCount * 100.0f / totalRecords) : 0.0f;
            
            fprintf(summaryFp, "%d,%zu,%d,%d,%.2f%%,%.4f,%.4f,%.4f\n",
                    faceId,
                    viewMap.size(),
                    totalRecords,
                    needFixCount,
                    fixPercentage,
                    minDist,
                    maxDist,
                    avgDist);
        }
        
        fclose(summaryFp);
        printf("✅ 汇总统计信息已保存到: %s\n", summaryPath.c_str());
    }
}