Files
gray_match/main.cpp
2025-05-08 17:56:22 +08:00

142 lines
4.6 KiB
C++

#include "grayMatch.h"
#include <fstream>
#include <iostream>
#include <opencv2/core/utility.hpp>
#include <opencv2/opencv.hpp>
int main(int argc, const char *argv[]) {
const std::string keys = "{model m || model image}"
"{scene s || scene image}"
"{view v || view result}"
"{threshold t | 0.7 | match minium score}"
"{bench b || match benchmark}"
"{help h || print this help}";
cv::CommandLineParser cmd(argc, argv, keys);
if (!cmd.check()) {
cmd.printErrors();
return -1;
}
if (cmd.has("help")) {
cmd.printMessage();
return 0;
}
auto srcFile = std::string(IMG_DIR) + "/3.bmp";
auto dstFile = std::string(IMG_DIR) + "/h.bmp";
if (cmd.has("model"))
srcFile = cmd.get<std::string>("model");
if (cmd.has("scene"))
dstFile = cmd.get<std::string>("scene");
auto src = cv::imread(srcFile, cv::IMREAD_GRAYSCALE);
auto dst = cv::imread(dstFile, cv::IMREAD_GRAYSCALE);
if (src.empty() || dst.empty()) {
return -1;
}
const std::string modelName("model.bin");
{
auto t0 = cv::getTickCount();
auto model = trainModel(src.data, src.cols, src.rows, src.channels(),
static_cast<int>(src.step), 0, 0, src.cols, src.rows, -1);
auto t1 = cv::getTickCount();
// get size
int size;
serialize(model, nullptr, &size);
// serialize to buffer
std::vector<uchar> buffer(size);
serialize(model, buffer.data(), &size);
// write to file
std::ofstream ofs(modelName, std::ios::binary | std::ios::out);
if (!ofs.is_open()) {
return -1;
}
ofs.write(reinterpret_cast<const char *>(buffer.data()), size);
freeModel(&model);
auto trainCost = static_cast<double>(t1 - t0) / cv::getTickFrequency();
std::cout << "train(s):" << trainCost << std::endl;
}
int count = 70;
std::vector<Pose> poses(count);
Model_t model;
auto score = cmd.get<float>("threshold");
{
// open file
std::ifstream ifs(modelName, std::ios::binary | std::ios::in);
if (!ifs.is_open()) {
return -2;
}
// get size
ifs.seekg(0, std::ios::end);
auto size = ifs.tellg();
ifs.seekg(0, std::ios::beg);
// read to buffer
std::vector<uchar> buffer(size);
ifs.read(reinterpret_cast<char *>(buffer.data()), size);
// deserialize from buffer
model = deserialize(buffer.data(), static_cast<int>(buffer.size()));
auto t2 = cv::getTickCount();
matchModel(dst.data, dst.cols, dst.rows, dst.channels(), static_cast<int>(dst.step), 0, 0,
dst.cols, dst.rows, model, &count, poses.data(), -1, 0, 360, 0, score, 1);
auto t3 = cv::getTickCount();
auto matchCost = static_cast<double>(t3 - t2) / cv::getTickFrequency();
std::cout << "match(s):" << matchCost << std::endl;
}
for (int i = 0; i < count; i++) {
const auto &pose = poses[ i ];
std::cout << pose.x << "," << pose.y << "," << pose.angle << "," << pose.score << std::endl;
}
if (cmd.has("bench")) {
constexpr int times = 100;
auto start = cv::getTickCount();
for (int i = 0; i < times; i++) {
matchModel(dst.data, dst.cols, dst.rows, dst.channels(), static_cast<int>(dst.step), 0,
0, dst.cols, dst.rows, model, &count, poses.data(), -1, 0, 360, 0, score, 1);
count = 70;
}
auto end = cv::getTickCount();
const auto cost = static_cast<double>(end - start) / cv::getTickFrequency() / times;
std::cout << "match bench avg(s):" << cost << std::endl;
}
if (cmd.has("view")) {
cv::Mat color;
cv::cvtColor(dst, color, cv::COLOR_GRAY2RGB);
for (int i = 0; i < count; i++) {
const auto &pose = poses[ i ];
cv::RotatedRect rect(cv::Point2f(pose.x, pose.y), src.size(), -pose.angle);
cv::Point2f pts[ 4 ];
rect.points(pts);
cv::line(color, pts[ 0 ], pts[ 1 ], cv::Scalar(255, 0, 0), 1, cv::LINE_AA);
cv::line(color, pts[ 1 ], pts[ 2 ], cv::Scalar(255, 0, 0), 1, cv::LINE_AA);
cv::line(color, pts[ 2 ], pts[ 3 ], cv::Scalar(255, 0, 0), 1, cv::LINE_AA);
cv::line(color, pts[ 3 ], pts[ 0 ], cv::Scalar(255, 0, 0), 1, cv::LINE_AA);
}
cv::imshow("img", color);
cv::waitKey();
}
return 0;
}