删除 'algorithm.cpp'

algorithm.cpp

@@ -1,373 +0,0 @@
-#include "algorithm.h"
-#include "algorithmregistry.h"
-
-
-CannyEdge::CannyEdge() {
-    cannyMeta.name = "Canny Edge Detection";
-    cannyMeta.addParam("Threshold 1", "double", 50.0, 0.0, 255.0);
-    cannyMeta.addParam("Threshold 2", "double", 150.0, 0.0, 255.0);
-    cannyMeta.addParam("Aperture Size", "int", 3, 1, 7);
-    cannyMeta.addParam("L2 Gradient", "bool", false, 0.0, 1.0);
-}
-
-void CannyEdge::process(cv::Mat image) {
-    // Store input parameters
-    runtimeParams.inputimage = image.clone(); // Store input image
-    
-    // Get parameters from cannyMeta
-    double threshold1 = 50.0;
-    double threshold2 = 150.0;
-    int apertureSize = 3;
-    bool l2gradient = false;
-    
-    for (const auto& param : cannyMeta.params) {
-        if (param.name == "Threshold 1") threshold1 = param.value.toDouble();
-        if (param.name == "Threshold 2") threshold2 = param.value.toDouble();
-        if (param.name == "Aperture Size") apertureSize = param.value.toInt();
-        if (param.name == "L2 Gradient") l2gradient = param.value.toBool();
-    }
-    
-    cv::Mat gray;
-    if (image.channels() == 3) {
-        cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY);
-    } else {
-        gray = image;
-    }
-    
-    cv::Canny(gray, image, threshold1, threshold2, apertureSize, l2gradient);
-    
-    // Store output parameters
-    runtimeParams.outimage = image.clone(); // Store output image
-    
-    // Count edges (simple implementation)
-    runtimeParams.edgeCount = cv::countNonZero(image);
-}
-
-AlgorithmMeta CannyEdge::getParams() {
-    return cannyMeta;
-}
-
-void CannyEdge::setParams(const AlgorithmMeta& params) {
-    cannyMeta = params;
-}
-
-Thresholding::Thresholding() {
-    AlgoritName = "Thresholding";
-    Thresh = 127;
-    MaxVal = 255;
-    Type = 0;
-}
-
-void Thresholding::process(cv::Mat image) {
-    // Store input parameters
-    runtimeParams.inputimage = image.clone(); // Store input image
-
-    
-    // Convert to grayscale image
-    cv::Mat gray;
-    if (image.channels() == 3) {
-        cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY);
-    } else {
-        gray = image;
-    }
-    
-    // Execute thresholding
-    cv::threshold(gray, image, Thresh, MaxVal, Type);
-    // Count white pixels
-    runtimeParams.whitePixelCount = cv::countNonZero(image);
-    
-    // Store output parameters
-    runtimeParams.outimage = image.clone(); // Store output image
-}
-
-AlgorithmMeta Thresholding::getParams() {
-    AlgorithmMeta p;
-    p.name = QString::fromStdString(AlgoritName);
-    p.addParam("Threshold", "int", Thresh, 0, 255);
-    p.addParam("Max Value", "int", MaxVal, 0, 255);
-    p.addParam("Threshold Type", "int", Type, 0, 4);
-    return p;
-}
-
-void Thresholding::setParams(const AlgorithmMeta& params) {
-    AlgoritName = params.name.toStdString();
-    for (auto& param : params.params) {
-        if (param.name == "Threshold") Thresh = param.value.toInt();
-        if (param.name == "Max Value") MaxVal = param.value.toInt();
-        if (param.name == "Threshold Type") Type = param.value.toInt();
-    }
-}
-
-GaussianBlur::GaussianBlur() {
-    AlgoritName = "Gaussian Blur";
-    KernelSize = 5;
-    SigmaX = 1.5;
-    SigmaY = 0.0;
-}
-
-void GaussianBlur::process(cv::Mat image) { 
-    // Store input parameters
-    runtimeParams.inputimage = image.clone(); // Store input image
-    
-    // Ensure kernel size is odd
-    int ksize = KernelSize;
-    if (ksize % 2 == 0) {
-        ksize++;
-    }
-    
-    // Execute Gaussian blur
-    cv::GaussianBlur(image, image, cv::Size(ksize, ksize), SigmaX, SigmaY);
-    
-    // Store output parameters
-    runtimeParams.outimage = image.clone(); // Store output image
-}
-
-AlgorithmMeta GaussianBlur::getParams() {
-    AlgorithmMeta p;
-    p.name = QString::fromStdString(AlgoritName);
-    p.addParam("Kernel Size", "int", KernelSize, 1, 31);
-    p.addParam("Sigma X", "double", SigmaX, 0.0, 10.0);
-    p.addParam("Sigma Y", "double", SigmaY, 0.0, 10.0);
-    return p;
-}
-
-void GaussianBlur::setParams(const AlgorithmMeta& params) {
-    AlgoritName = params.name.toStdString();
-    for (auto& param : params.params) {
-        if (param.name == "Kernel Size") KernelSize = param.value.toInt();
-        if (param.name == "Sigma X") SigmaX = param.value.toDouble();
-        if (param.name == "Sigma Y") SigmaY = param.value.toDouble();
-    }
-}
-
-MedianBlur::MedianBlur() {
-    AlgoritName = "Median Blur";
-    KernelSize = 5;
-}
-
-void MedianBlur::process(cv::Mat image) {
-    // Store input parameters
-    runtimeParams.inputimage = image.clone(); // Store input image
-    
-    // Ensure kernel size is odd
-    int ksize = KernelSize;
-    if (ksize % 2 == 0) {
-        ksize++;
-    }
-    
-    // Execute median blur
-    cv::medianBlur(image, image, ksize);
-    
-    // Store output parameters
-    runtimeParams.outimage = image.clone(); // Store output image
-}
-
-AlgorithmMeta MedianBlur::getParams() {
-    AlgorithmMeta p;
-    p.name = QString::fromStdString(AlgoritName);
-    p.addParam("Kernel Size", "int", KernelSize, 1, 31);
-    return p;
-}
-
-void MedianBlur::setParams(const AlgorithmMeta& params) {
-    AlgoritName = params.name.toStdString();
-    for (auto& param : params.params) {
-        if (param.name == "Kernel Size") KernelSize = param.value.toInt();
-    }
-}
-
-Dilate::Dilate() {
-    AlgoritName = "Dilate";
-    KernerSize = 3;
-    Iterations = 1;
-}
-
-void Dilate::process(cv::Mat image) {
-    // Store input parameters
-    runtimeParams.inputimage = image.clone(); // Store input image
-    
-    // Create structuring element
-    cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(KernerSize, KernerSize));
-    
-    // Execute dilation
-    cv::dilate(image, image, kernel, cv::Point(-1, -1), Iterations);
-    
-    // Store output parameters
-    runtimeParams.outimage = image.clone(); // Store output image
-}
-
-AlgorithmMeta Dilate::getParams() {
-    AlgorithmMeta p;
-    p.name = QString::fromStdString(AlgoritName);
-    p.addParam("Kernel Size", "int", KernerSize, 0, 100);
-    p.addParam("Iterations", "int", Iterations, 0, 100);
-    return p;
-}
-
-void Dilate::setParams(const AlgorithmMeta& params) {
-    AlgoritName = params.name.toStdString();
-    for (auto& param : params.params) {
-        if (param.name == "Kernel Size") KernerSize = param.value.toInt();
-        if (param.name == "Iterations") Iterations = param.value.toInt();
-    }
-}
-
-Erode::Erode() {
-    AlgoritName = "Erode";
-    KernerSize = 3;
-    Iterations = 1;
-}
-
-void Erode::process(cv::Mat image) {    
-    // Store input parameters
-    runtimeParams.inputimage = image.clone(); // Store input image
-    
-    // Create structuring element
-    cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(KernerSize, KernerSize));
-    
-    // Execute erosion
-    cv::erode(image, image, kernel, cv::Point(-1, -1), Iterations);
-    
-    // Store output parameters
-    runtimeParams.outimage = image.clone(); // Store output image
-}
-
-AlgorithmMeta Erode::getParams() {
-    AlgorithmMeta p;
-    p.name = QString::fromStdString(AlgoritName);
-    p.addParam("Kernel Size", "int", KernerSize, 0, 100);
-    p.addParam("Iterations", "int", Iterations, 0, 100);
-    return p;
-}
-
-void Erode::setParams(const AlgorithmMeta& params) {
-    AlgoritName = params.name.toStdString();
-    for (auto& param : params.params) {
-        if (param.name == "Kernel Size") KernerSize = param.value.toInt();
-        if (param.name == "Iterations") Iterations = param.value.toInt();
-    }
-}
-
-Grayscale::Grayscale() {
-    AlgoritName = "Grayscale";
-}
-
-void Grayscale::process(cv::Mat image) {
-    // Store input parameters
-    runtimeParams.inputimage = image.clone(); // Store input image
-    
-    // Execute grayscale conversion
-    if (image.channels() == 3) {
-        cv::cvtColor(image, image, cv::COLOR_BGR2GRAY);
-    }
-    
-    // Store output parameters
-    runtimeParams.outimage = image.clone(); // Store output image
-}
-
-AlgorithmMeta Grayscale::getParams() {
-    AlgorithmMeta p;
-    p.name = QString::fromStdString(AlgoritName);
-    return p;
-}
-
-void Grayscale::setParams(const AlgorithmMeta& params) {
-    AlgoritName = params.name.toStdString();
-}
-
-HistogramEqualization::HistogramEqualization() {
-    AlgoritName = "Histogram Equalization";
-}
-
-void HistogramEqualization::process(cv::Mat image) {
-    // Store input parameters
-    runtimeParams.inputimage = image.clone(); // Store input image
-    
-    // Convert to grayscale image
-    cv::Mat gray;
-    if (image.channels() == 3) {
-        cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY);
-    } else {
-        gray = image;
-    }
-    
-    // Execute histogram equalization
-    cv::equalizeHist(gray, image);
-    
-    // Store output parameters
-    runtimeParams.outimage = image.clone(); // Store output image
-}
-
-AlgorithmMeta HistogramEqualization::getParams() {
-    AlgorithmMeta p;
-    p.name = QString::fromStdString(AlgoritName);
-    return p;
-}
-
-void HistogramEqualization::setParams(const AlgorithmMeta& params) {
-    AlgoritName = params.name.toStdString();
-}
-
-FindContours::FindContours() {
-    AlgoritName = "Find Contours";
-    Mode = 3; // RETR_TREE
-    Method = 2; // CHAIN_APPROX_SIMPLE
-}
-
-void FindContours::process(cv::Mat image) {
-    // Store input parameters
-    runtimeParams.inputimage = image.clone(); // Store input image
-
-    
-    // Convert to grayscale image
-    cv::Mat gray;
-    if (image.channels() == 3) {
-        cv::cvtColor(image, gray, cv::COLOR_BGR2GRAY);
-    } else {
-        gray = image;
-    }
-    
-    // Binarization
-    cv::Mat binary;
-    cv::threshold(gray, binary, 127, 255, cv::THRESH_BINARY);
-    
-    // Find contours
-    std::vector<std::vector<cv::Point>> contours;
-    std::vector<cv::Vec4i> hierarchy;
-    cv::findContours(binary, contours, hierarchy, Mode, Method);
-    
-    // Draw contours
-    cv::Mat result = cv::Mat::zeros(image.size(), CV_8UC3);
-    cv::drawContours(result, contours, -1, cv::Scalar(0, 255, 0), 2);
-    
-    // Convert result back to grayscale if needed
-    if (image.channels() == 1) {
-        cv::cvtColor(result, image, cv::COLOR_BGR2GRAY);
-    } else {
-        image = result;
-    }
-    
-    runtimeParams.contourCount = static_cast<int>(contours.size());
-    
-    // Store output parameters
-    runtimeParams.outimage = image.clone(); // Store output image
-}
-
-AlgorithmMeta FindContours::getParams() {
-    AlgorithmMeta p;
-    p.name = QString::fromStdString(AlgoritName);
-    p.addParam("Retrieval Mode", "int", Mode, 0, 4);
-    p.addParam("Approx Method", "int", Method, 0, 3);
-    return p;
-}
-
-void FindContours::setParams(const AlgorithmMeta& params) {
-    AlgoritName = params.name.toStdString();
-    for (auto& param : params.params) {
-        if (param.name == "Retrieval Mode") Mode = param.value.toInt();
-        if (param.name == "Approx Method") Method = param.value.toInt();
-    }
-}
-
-
-