mirror of
https://github.com/OrcaSlicer/OrcaSlicer.git
synced 2026-10-08 16:21:14 +00:00
HanifKoh
8a6377f087
* Add Missing Includes Across src/libslic3r Every libslic3r source and header now directly includes the headers declaring what it uses, rather than relying on the precompiled header or transitive includes. Generated with clang-tidy misc-include-cleaner, with libslic3r headers spelled libslic3r/... so they resolve outside the library's private include paths. MultiMaterialSegmentation.hpp, Support/SupportParameters.hpp and Format/STEP.hpp are made self-contained by hand. * Make the libslic3r Headers Compile on Their Own Each now includes, or forward-declares, what it uses instead of relying on what its includers happened to include first. Left out: I18N.hpp, which errors on purpose when included from GUI code, and VoxelizeCSGMesh.hpp and SLA/bicubic.h, which nothing includes and which no longer compile at all. * Add the Includes Missing From the Hand-Fixed libslic3r Headers clang-tidy would not edit these headers while they failed to compile on their own, so the first pass skipped them. With the headers now self-contained, a second pass adds the rest. * Keep Windows Setup Ahead of the Added libslic3r Includes Print.cpp and Thread.cpp open with a _WIN32 block that has to come first; without the precompiled header, Print.cpp otherwise reaches windows.h through OCCT with NONLS defined and boost/regex fails. OpenVDBUtils.cpp and SLA/SupportTreeBuilder.cpp had includes inside #ifndef NOMINMAX, which libslic3r defines on Windows, so those were skipped there. .clang-tidy also ignores the MSVC STL and UCRT internals, Boost.Multiprecision's fwd.hpp and CPython's Windows include directory. * Re-Add libslic3r Includes After the Clipper2 2.0.1 Migration Rebasing onto main took main's version of the files the Clipper2 migration rewrote, so their added includes are restored here, along with includes for main's new code. Clipper2's individual headers are now ignored by clang-tidy: they only build the Z variant through clipper2_z.hpp, which defines USINGZ first, so including clipper.core.h and the like directly broke ClipperZUtils.cpp.
275 lines
12 KiB
C++
275 lines
12 KiB
C++
#pragma once
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#include <cmath>
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#include <array>
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#include <iostream>
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#include <ctime>
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#include <opencv2/core/mat.hpp>
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#include <opencv2/core/matx.hpp>
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#include <opencv2/core.hpp>
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#include <vector>
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#include <opencv2/imgproc.hpp>
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#include "opencv2/opencv.hpp"
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#include "libslic3r/Color.hpp"
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class QuantKMeans
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{
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public:
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int m_alpha_thres;
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cv::Mat m_flatten_labels;
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cv::Mat m_centers8UC3;
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QuantKMeans(int alpha_thres = 10) : m_alpha_thres(alpha_thres) {}
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void apply(cv::Mat &ori_image, cv::Mat &new_image, int num_cluster, int color_space)
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{
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cv::Mat image;
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convert_color_space(ori_image, image, color_space);
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cv::Mat flatten_image = flatten(image);
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apply(flatten_image, num_cluster, color_space);
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replace_centers(ori_image, new_image);
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}
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void apply_aplha(cv::Mat &ori_image, cv::Mat &new_image, int num_cluster, int color_space)
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{
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// cout << " *** DoAlpha *** " << endl;
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cv::Mat flatten_image8UC3 = flatten_alpha(ori_image);
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cv::Mat image8UC3;
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convert_color_space(flatten_image8UC3, image8UC3, color_space);
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cv::Mat image32FC3(image8UC3.rows, 1, CV_32FC3);
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for (int i = 0; i < image8UC3.rows; i++)
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image32FC3.at<cv::Vec3f>(i, 0) = image8UC3.at<cv::Vec3b>(i, 0);
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apply(image32FC3, num_cluster, color_space);
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repalce_centers_aplha(ori_image, new_image);
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}
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void apply(cv::Mat &flatten_image, int num_cluster, int color_space)
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{
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cv::Mat centers32FC3;
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num_cluster = fmin(flatten_image.rows, num_cluster);
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kmeans(flatten_image, num_cluster, this->m_flatten_labels, cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 300, 0.5), 3, cv::KMEANS_PP_CENTERS,
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centers32FC3);
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this->m_centers8UC3 = cv::Mat(num_cluster, 1, CV_8UC3);
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for (int i = 0; i < num_cluster; i++) this->m_centers8UC3.at<cv::Vec3b>(i) = centers32FC3.at<cv::Vec3f>(i);
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convert_color_space(this->m_centers8UC3, this->m_centers8UC3, color_space, true);
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}
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void apply(const std::vector<std::array<float, 4>> &ori_colors,
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std::vector<std::array<float, 4>> & cluster_results,
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std::vector<int> & labels,
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int num_cluster = -1,
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int max_cluster = 15,
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int color_space = 2)
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{
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// 0~255
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cv::Mat flatten_image8UC3 = flatten_vector(ori_colors);
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this->apply(flatten_image8UC3, cluster_results, labels, num_cluster, max_cluster, color_space);
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}
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void apply(const cv::Mat & flatten_image8UC3,
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std::vector<std::array<float, 4>> &cluster_results,
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std::vector<int> & labels,
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int num_cluster = -1,
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int max_cluster = 15,
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int color_space = 2)
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{
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cv::Mat image8UC3;
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convert_color_space(flatten_image8UC3, image8UC3, color_space);
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cv::Mat image32FC3(image8UC3.rows, 1, CV_32FC3);
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for (int i = 0; i < image8UC3.rows; i++)
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image32FC3.at<cv::Vec3f>(i, 0) = image8UC3.at<cv::Vec3b>(i, 0);
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int best_cluster = 1;
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double cur_score = 0, best_score = 100;
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num_cluster = fmin(num_cluster, max_cluster);
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if (num_cluster < 1) {
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if (!this->more_than_request(image8UC3, max_cluster)) max_cluster = compute_num_colors(image8UC3);
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num_cluster = fmin(num_cluster, max_cluster);
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cur_score = cv::kmeans(image32FC3, 1, this->m_flatten_labels, cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 300, 0.5), 3, cv::KMEANS_PP_CENTERS);
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best_score = cur_score;
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for (int cur_cluster = 2; cur_cluster < max_cluster + 1; cur_cluster++) {
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cv::Mat centers32FC3;
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cur_score = cv::kmeans(image32FC3, cur_cluster, this->m_flatten_labels, cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 300, 0.5), 3,
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cv::KMEANS_PP_CENTERS, centers32FC3);
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if (this->repeat_center(cur_cluster, centers32FC3, color_space))
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break;
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best_cluster = cur_score < best_score ? cur_cluster : best_cluster;
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best_score = cur_score < best_score ? cur_score : best_score;
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}
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} else if (this->more_than_request(image8UC3, num_cluster))
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best_cluster = num_cluster;
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else {
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best_cluster = compute_num_colors(image8UC3);
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std::cout << "num of image color is " << best_cluster << ", less than custom number " << num_cluster << std::endl;
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}
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cv::Mat centers32FC3;
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cv::kmeans(image32FC3, best_cluster, this->m_flatten_labels, cv::TermCriteria(cv::TermCriteria::EPS + cv::TermCriteria::COUNT, 300, 0.5), 3, cv::KMEANS_PP_CENTERS,
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centers32FC3);
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this->m_centers8UC3 = cv::Mat(best_cluster, 1, CV_8UC3);
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for (int i = 0; i < best_cluster; i++) {
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auto center = centers32FC3.row(i);
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this->m_centers8UC3.at<cv::Vec3b>(i) = {uchar(center.at<float>(0)), uchar(center.at<float>(1)), uchar(center.at<float>(2))};
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}
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convert_color_space(this->m_centers8UC3, this->m_centers8UC3, color_space, true);
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cluster_results.clear();
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labels.clear();
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for (int i = 0; i < flatten_image8UC3.rows; i++)
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labels.emplace_back(this->m_flatten_labels.at<int>(i, 0));
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for (int i = 0; i < best_cluster; i++) {
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cv::Vec3f center = this->m_centers8UC3.at<cv::Vec3b>(i, 0);
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cluster_results.emplace_back(std::array<float, 4>{center[0] / 255.f, center[1] / 255.f, center[2] / 255.f, 1.f});
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}
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}
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bool more_than_request(const cv::Mat &image8UC3, int target_num)
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{
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std::vector<cv::Vec3b> uniqueImage;
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cv::Vec3b cur_color;
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for (int i = 0; i < image8UC3.rows; i++) {
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cur_color = image8UC3.at<cv::Vec3b>(i, 0);
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if (!is_in(cur_color, uniqueImage)) {
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uniqueImage.emplace_back(cur_color);
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if (uniqueImage.size() >= target_num) return true;
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}
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}
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return false;
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}
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int compute_num_colors(const cv::Mat &image8UC3)
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{
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std::vector<cv::Vec3b> uniqueImage;
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cv::Vec3b cur_color;
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for (int i = 0; i < image8UC3.rows; i++) {
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cur_color = image8UC3.at<cv::Vec3b>(i, 0);
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if (!is_in(cur_color, uniqueImage)) uniqueImage.emplace_back(cur_color);
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}
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return uniqueImage.size();
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}
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bool is_in(const cv::Vec3b &cur_color, const std::vector<cv::Vec3b> &uniqueImage)
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{
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for (auto &color : uniqueImage)
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if (cur_color[0] == color[0] && cur_color[1] == color[1] && cur_color[2] == color[2]) return true;
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return false;
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}
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bool repeat_center(int cur_cluster, const cv::Mat ¢ers32FC3, int color_space)
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{
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cv::Mat centers8UC3 = cv::Mat(cur_cluster, 1, CV_8UC3);
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for (int i = 0; i < cur_cluster; i++) {
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auto center = centers32FC3.row(i);
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centers8UC3.at<cv::Vec3b>(i) = {uchar(center.at<float>(0)), uchar(center.at<float>(1)), uchar(center.at<float>(2))};
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}
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convert_color_space(centers8UC3, centers8UC3, color_space, true);
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std::vector<cv::Vec3b> unique_centers;
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cv::Vec3b cur_center;
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for (int i = 0; i < cur_cluster; i++) {
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cur_center = centers8UC3.at<cv::Vec3b>(i, 0);
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if (!is_in(cur_center, unique_centers))
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unique_centers.emplace_back(cur_center);
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else
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return true;
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}
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return false;
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}
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void replace_centers(cv::Mat &ori_image, cv::Mat &new_image)
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{
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for (int i = 0; i < ori_image.rows; i++) {
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for (int j = 0; j < ori_image.cols; j++) {
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int idx = this->m_flatten_labels.at<int>(i * ori_image.cols + j, 0);
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cv::Vec3b pixel = this->m_centers8UC3.at<cv::Vec3b>(idx);
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new_image.at<cv::Vec3b>(i, j) = pixel;
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}
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}
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}
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void repalce_centers_aplha(cv::Mat &ori_image, cv::Mat &new_image)
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{
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int cnt = 0;
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int idx;
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cv::Vec3b center;
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for (int i = 0; i < ori_image.rows; i++) {
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for (int j = 0; j < ori_image.cols; j++) {
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cv::Vec4b pixel = ori_image.at<cv::Vec4b>(i, j);
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if ((int) pixel[3] < this->m_alpha_thres)
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new_image.at<cv::Vec4b>(i, j) = pixel;
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else {
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idx = this->m_flatten_labels.at<int>(cnt++, 0);
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center = this->m_centers8UC3.at<cv::Vec3b>(idx);
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new_image.at<cv::Vec4b>(i, j) = cv::Vec4b(center[0], center[1], center[2], pixel[3]);
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}
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}
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}
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}
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void convert_color_space(const cv::Mat &ori_image, cv::Mat &image, int color_space, bool reverse = false)
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{
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switch (color_space) {
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case 0: image = ori_image; break;
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case 1:
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if (reverse)
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cvtColor(ori_image, image, cv::COLOR_HSV2BGR);
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else
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cvtColor(ori_image, image, cv::COLOR_BGR2HSV);
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break;
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case 2:
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if (reverse)
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cvtColor(ori_image, image, cv::COLOR_Lab2BGR);
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else
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cvtColor(ori_image, image, cv::COLOR_BGR2Lab);
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break;
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default: break;
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}
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}
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cv::Mat flatten(cv::Mat &image)
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{
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int num_pixels = image.rows * image.cols;
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cv::Mat img(num_pixels, 1, CV_32FC3);
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for (int i = 0; i < image.rows; i++) {
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for (int j = 0; j < image.cols; j++) {
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cv::Vec3f pixel = image.at<cv::Vec3b>(i, j);
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img.at<cv::Vec3f>(i * image.cols + j, 0) = pixel;
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}
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}
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return img;
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}
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cv::Mat flatten_alpha(cv::Mat &image)
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{
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int num_pixels = image.rows * image.cols;
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for (int i = 0; i < image.rows; i++)
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for (int j = 0; j < image.cols; j++) {
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cv::Vec4b pixel = image.at<cv::Vec4b>(i, j);
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if ((int) pixel[3] < this->m_alpha_thres) num_pixels--;
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}
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cv::Mat img(num_pixels, 1, CV_8UC3);
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int cnt = 0;
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for (int i = 0; i < image.rows; i++) {
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for (int j = 0; j < image.cols; j++) {
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cv::Vec4b pixel = image.at<cv::Vec4b>(i, j);
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if ((int) pixel[3] >= this->m_alpha_thres) img.at<cv::Vec3b>(cnt++, 0) = cv::Vec3b(pixel[0], pixel[1], pixel[2]);
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}
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}
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return img;
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}
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cv::Mat flatten_vector(const std::vector<std::array<float, 4>> &ori_colors)
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{
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int num_pixels = ori_colors.size();
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cv::Mat image8UC3(num_pixels, 1, CV_8UC3);
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for (int i = 0; i < num_pixels; i++) {
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std::array<float, 4> pixel = ori_colors[i];
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image8UC3.at<cv::Vec3b>(i, 0) = cv::Vec3b((int) (pixel[0] * 255.f), (int) (pixel[1] * 255.f), (int) (pixel[2] * 255.f));
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}
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return image8UC3;
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}
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};
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bool obj_color_deal_algo(std::vector<Slic3r::RGBA> &input_colors,
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std::vector<Slic3r::RGBA>& cluster_colors_from_algo,
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std::vector<int>& cluster_labels_from_algo,
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char & cluster_number,
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int max_cluster); |