image_processor.cpp

#include <iostream>
#include <vector>
#include <string>
#include <cmath>
#include <algorithm>
#include <numeric>
#include <fstream>
#include <sstream>
#include <map>
#include <memory>
#include <mutex>
#include <thread>
#include <queue>
#include <functional>
#include <chrono>
#include <exception>
#include <cstring>
#include <cstdio>
#include <cstdlib>

// ==================== 图像处理核心模块 ====================

struct ImagePixel {
    unsigned char r, g, b, a;
};

struct ImageData {
    int width;
    int height;
    int channels;
    std::vector<ImagePixel> pixels;
    std::string format;
    std::string source_url;
};

struct FeatureDescriptor {
    std::vector<double> color_histogram;
    std::vector<double> texture_features;
    std::vector<double> edge_features;
    std::vector<double> shape_descriptors;
    double quality_score;
    std::map<std::string, double> metadata;
};

class ImageProcessor {
private:
    std::mutex process_lock;
    
    // 图像质量评估
    double calculateSharpness(const ImageData& img) {
        if (img.width < 3 || img.height < 3) return 0.0;
        
        double sharpness = 0.0;
        int count = 0;
        
        for (int y = 1; y < img.height - 1; ++y) {
            for (int x = 1; x < img.width - 1; ++x) {
                int idx = y * img.width + x;
                double gradient_x = std::abs(img.pixels[idx + 1].r - img.pixels[idx - 1].r);
                double gradient_y = std::abs(img.pixels[idx + img.width].r - img.pixels[idx - img.width].r);
                sharpness += gradient_x + gradient_y;
                count++;
            }
        }
        
        return count > 0 ? sharpness / count : 0.0;
    }
    
    // 颜色直方图计算
    std::vector<double> calculateColorHistogram(const ImageData& img, int bins = 32) {
        std::vector<double> histogram(bins * 3, 0.0);
        double bin_size = 256.0 / bins;
        
        for (const auto& pixel : img.pixels) {
            int r_bin = std::min(bins - 1, static_cast<int>(pixel.r / bin_size));
            int g_bin = std::min(bins - 1, static_cast<int>(pixel.g / bin_size));
            int b_bin = std::min(bins - 1, static_cast<int>(pixel.b / bin_size));
            
            histogram[r_bin]++;
            histogram[bins + g_bin]++;
            histogram[bins * 2 + b_bin]++;
        }
        
        // 归一化
        double total = img.pixels.size();
        for (auto& val : histogram) {
            val /= total;
        }
        
        return histogram;
    }
    
    // 边缘检测（Sobel算子）
    std::vector<double> detectEdges(const ImageData& img) {
        std::vector<double> edges;
        if (img.width < 3 || img.height < 3) return edges;
        
        int sobel_x[3][3] = {{-1, 0, 1}, {-2, 0, 2}, {-1, 0, 1}};
        int sobel_y[3][3] = {{-1, -2, -1}, {0, 0, 0}, {1, 2, 1}};
        
        for (int y = 1; y < img.height - 1; ++y) {
            for (int x = 1; x < img.width - 1; ++x) {
                double gx = 0, gy = 0;
                
                for (int ky = -1; ky <= 1; ++ky) {
                    for (int kx = -1; kx <= 1; ++kx) {
                        int idx = (y + ky) * img.width + (x + kx);
                        double intensity = (img.pixels[idx].r + img.pixels[idx].g + img.pixels[idx].b) / 3.0;
                        gx += intensity * sobel_x[ky + 1][kx + 1];
                        gy += intensity * sobel_y[ky + 1][kx + 1];
                    }
                }
                
                edges.push_back(std::sqrt(gx * gx + gy * gy));
            }
        }
        
        return edges;
    }
    
    // 纹理特征提取（灰度共生矩阵简化版）
    std::vector<double> extractTextureFeatures(const ImageData& img) {
        std::vector<double> features;
        
        // 计算灰度图像
        std::vector<double> gray(img.pixels.size());
        for (size_t i = 0; i < img.pixels.size(); ++i) {
            gray[i] = (img.pixels[i].r + img.pixels[i].g + img.pixels[i].b) / 3.0;
        }
        
        // 对比度
        double contrast = 0.0;
        int pairs = 0;
        for (size_t i = 0; i + 1 < gray.size(); ++i) {
            contrast += std::pow(gray[i] - gray[i + 1], 2);
            pairs++;
        }
        features.push_back(pairs > 0 ? contrast / pairs : 0.0);
        
        // 能量
        double energy = 0.0;
        for (double val : gray) {
            energy += val * val;
        }
        features.push_back(energy / gray.size());
        
        // 熵
        std::map<int, int> freq;
        for (double val : gray) {
            freq[static_cast<int>(val)]++;
        }
        double entropy = 0.0;
        for (const auto& pair : freq) {
            double p = pair.second / static_cast<double>(gray.size());
            if (p > 0) entropy -= p * std::log2(p);
        }
        features.push_back(entropy);
        
        return features;
    }

public:
    FeatureDescriptor extractFeatures(const ImageData& img) {
        std::lock_guard<std::mutex> lock(process_lock);
        FeatureDescriptor features;
        
        features.color_histogram = calculateColorHistogram(img);
        features.texture_features = extractTextureFeatures(img);
        features.edge_features = detectEdges(img);
        features.quality_score = calculateSharpness(img);
        
        features.metadata["width"] = img.width;
        features.metadata["height"] = img.height;
        features.metadata["total_pixels"] = img.pixels.size();
        features.metadata["avg_brightness"] = calculateAverageBrightness(img);
        
        return features;
    }
    
    double calculateSimilarity(const FeatureDescriptor& f1, const FeatureDescriptor& f2) {
        // 计算特征向量的余弦相似度
        double dot_product = 0.0, norm1 = 0.0, norm2 = 0.0;
        
        // 颜色直方图相似度
        for (size_t i = 0; i < f1.color_histogram.size() && i < f2.color_histogram.size(); ++i) {
            dot_product += f1.color_histogram[i] * f2.color_histogram[i];
            norm1 += f1.color_histogram[i] * f1.color_histogram[i];
            norm2 += f2.color_histogram[i] * f2.color_histogram[i];
        }
        
        return (norm1 > 0 && norm2 > 0) ? dot_product / (std::sqrt(norm1) * std::sqrt(norm2)) : 0.0;
    }
    
    ImageData resizeImage(const ImageData& src, int new_width, int new_height) {
        ImageData result;
        result.width = new_width;
        result.height = new_height;
        result.channels = src.channels;
        result.format = src.format;
        
        double x_ratio = src.width / static_cast<double>(new_width);
        double y_ratio = src.height / static_cast<double>(new_height);
        
        for (int y = 0; y < new_height; ++y) {
            for (int x = 0; x < new_width; ++x) {
                int src_x = static_cast<int>(x * x_ratio);
                int src_y = static_cast<int>(y * y_ratio);
                
                src_x = std::min(src_x, src.width - 1);
                src_y = std::min(src_y, src.height - 1);
                
                result.pixels.push_back(src.pixels[src_y * src.width + src_x]);
            }
        }
        
        return result;
    }
    
private:
    double calculateAverageBrightness(const ImageData& img) {
        double sum = 0.0;
        for (const auto& pixel : img.pixels) {
            sum += (pixel.r + pixel.g + pixel.b) / 3.0;
        }
        return img.pixels.empty() ? 0.0 : sum / img.pixels.size();
    }
};

// ==================== 文物图像识别引擎 ====================

struct RecognitionResult {
    std::string artifact_type;
    double confidence;
    std::string dynasty;
    std::string material;
    std::map<std::string, double> type_probabilities;
    std::vector<std::string> characteristics;
    std::string authentication_advice;
};

class ArtifactRecognitionEngine {
private:
    std::map<std::string, std::vector<FeatureDescriptor>> known_artifacts;
    std::mutex recognition_lock;
    
    std::string classifyByFeatures(const FeatureDescriptor& features) {
        // 基于特征的分类逻辑（简化版）
        double texture_energy = features.texture_features.size() > 1 ? features.texture_features[1] : 0.0;
        double edge_density = features.edge_features.empty() ? 0.0 : 
                             std::accumulate(features.edge_features.begin(), features.edge_features.end(), 0.0) / features.edge_features.size();
        
        if (texture_energy > 5000 && edge_density > 100) {
            return "古钱币";
        } else if (texture_energy > 3000 && edge_density > 80) {
            return "瓷器";
        } else if (edge_density > 120) {
            return "青铜器";
        } else {
            return "其他文物";
        }
    }
    
    std::string identifyDynasty(const FeatureDescriptor& features) {
        double quality = features.quality_score;
        if (quality > 200) return "清代";
        else if (quality > 150) return "明代";
        else if (quality > 100) return "宋代";
        else if (quality > 50) return "唐代";
        else return "古代";
    }

public:
    RecognitionResult recognize(const ImageData& img) {
        std::lock_guard<std::mutex> lock(recognition_lock);
        
        RecognitionResult result;
        ImageProcessor processor;
        FeatureDescriptor features = processor.extractFeatures(img);
        
        result.artifact_type = classifyByFeatures(features);
        result.dynasty = identifyDynasty(features);
        result.confidence = std::min(0.95, features.quality_score / 300.0);
        result.material = result.artifact_type == "古钱币" ? "铜" : 
                         result.artifact_type == "瓷器" ? "瓷" : "金属";
        
        result.type_probabilities[result.artifact_type] = result.confidence;
        result.type_probabilities["其他"] = 1.0 - result.confidence;
        
        result.characteristics.push_back("纹理复杂度: " + std::to_string(features.texture_features.size()));
        result.characteristics.push_back("边缘密度: " + std::to_string(features.edge_features.size()));
        result.characteristics.push_back("质量评分: " + std::to_string(features.quality_score));
        
        if (result.confidence > 0.7) {
            result.authentication_advice = "高置信度识别，建议进一步专家鉴定";
        } else {
            result.authentication_advice = "低置信度，需要人工审核";
        }
        
        return result;
    }
    
    bool learnArtifact(const std::string& type, const FeatureDescriptor& features) {
        std::lock_guard<std::mutex> lock(recognition_lock);
        known_artifacts[type].push_back(features);
        return true;
    }
};

// ==================== 测试主函数 ====================

int main() {
    std::cout << "========================================" << std::endl;
    std::cout << "  文物图像处理与识别模块" << std::endl;
    std::cout << "  Artifact Image Processing & Recognition" << std::endl;
    std::cout << "========================================" << std::endl;
    std::cout << std::endl;
    
    // 创建测试图像数据（模拟）
    ImageData test_image;
    test_image.width = 100;
    test_image.height = 100;
    test_image.channels = 3;
    test_image.format = "RGB";
    
    for (int i = 0; i < 100 * 100; ++i) {
        ImagePixel pixel;
        pixel.r = static_cast<unsigned char>(rand() % 256);
        pixel.g = static_cast<unsigned char>(rand() % 256);
        pixel.b = static_cast<unsigned char>(rand() % 256);
        pixel.a = 255;
        test_image.pixels.push_back(pixel);
    }
    
    std::cout << "[INFO] 加载测试图像: " << test_image.width << "x" << test_image.height << std::endl;
    
    // 特征提取
    ImageProcessor processor;
    auto features = processor.extractFeatures(test_image);
    
    std::cout << std::endl;
    std::cout << "=== 特征提取结果 ===" << std::endl;
    std::cout << "颜色直方图维度: " << features.color_histogram.size() << std::endl;
    std::cout << "纹理特征数量: " << features.texture_features.size() << std::endl;
    std::cout << "边缘特征数量: " << features.edge_features.size() << std::endl;
    std::cout << "图像质量评分: " << features.quality_score << std::endl;
    
    // 文物识别
    ArtifactRecognitionEngine recognizer;
    auto recognition = recognizer.recognize(test_image);
    
    std::cout << std::endl;
    std::cout << "=== 识别结果 ===" << std::endl;
    std::cout << "文物类型: " << recognition.artifact_type << std::endl;
    std::cout << "置信度: " << (recognition.confidence * 100) << "%" << std::endl;
    std::cout << "推测朝代: " << recognition.dynasty << std::endl;
    std::cout << "材质: " << recognition.material << std::endl;
    std::cout << std::endl;
    std::cout << "鉴定建议: " << recognition.authentication_advice << std::endl;
    
    std::cout << std::endl;
    std::cout << "=== C++图像处理模块运行成功 ===" << std::endl;
    
    return 0;
}