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#include "line2Dup.h"
#include <memory>
#include <iostream>
#include <assert.h>
#include <chrono>
using namespace std;
using namespace cv;
static std::string prefix = "/home/meiqua/shape_based_matching/test/";
class Timer
{
public:
Timer() : beg_(clock_::now()) {}
void reset() { beg_ = clock_::now(); }
double elapsed() const {
return std::chrono::duration_cast<second_>
(clock_::now() - beg_).count(); }
void out(std::string message = ""){
double t = elapsed();
std::cout << message << "\nelasped time:" << t << "s" << std::endl;
reset();
}
private:
typedef std::chrono::high_resolution_clock clock_;
typedef std::chrono::duration<double, std::ratio<1> > second_;
std::chrono::time_point<clock_> beg_;
};
// NMS, got from cv::dnn so we don't need opencv contrib
// just collapse it
namespace cv_dnn {
namespace
{
template <typename T>
static inline bool SortScorePairDescend(const std::pair<float, T>& pair1,
const std::pair<float, T>& pair2)
{
return pair1.first > pair2.first;
}
} // namespace
inline void GetMaxScoreIndex(const std::vector<float>& scores, const float threshold, const int top_k,
std::vector<std::pair<float, int> >& score_index_vec)
{
for (size_t i = 0; i < scores.size(); ++i)
{
if (scores[i] > threshold)
{
score_index_vec.push_back(std::make_pair(scores[i], i));
}
}
std::stable_sort(score_index_vec.begin(), score_index_vec.end(),
SortScorePairDescend<int>);
if (top_k > 0 && top_k < (int)score_index_vec.size())
{
score_index_vec.resize(top_k);
}
}
template <typename BoxType>
inline void NMSFast_(const std::vector<BoxType>& bboxes,
const std::vector<float>& scores, const float score_threshold,
const float nms_threshold, const float eta, const int top_k,
std::vector<int>& indices, float (*computeOverlap)(const BoxType&, const BoxType&))
{
CV_Assert(bboxes.size() == scores.size());
std::vector<std::pair<float, int> > score_index_vec;
GetMaxScoreIndex(scores, score_threshold, top_k, score_index_vec);
// Do nms.
float adaptive_threshold = nms_threshold;
indices.clear();
for (size_t i = 0; i < score_index_vec.size(); ++i) {
const int idx = score_index_vec[i].second;
bool keep = true;
for (int k = 0; k < (int)indices.size() && keep; ++k) {
const int kept_idx = indices[k];
float overlap = computeOverlap(bboxes[idx], bboxes[kept_idx]);
keep = overlap <= adaptive_threshold;
}
if (keep)
indices.push_back(idx);
if (keep && eta < 1 && adaptive_threshold > 0.5) {
adaptive_threshold *= eta;
}
}
}
// copied from opencv 3.4, not exist in 3.0
template<typename _Tp> static inline
double jaccardDistance__(const Rect_<_Tp>& a, const Rect_<_Tp>& b) {
_Tp Aa = a.area();
_Tp Ab = b.area();
if ((Aa + Ab) <= std::numeric_limits<_Tp>::epsilon()) {
// jaccard_index = 1 -> distance = 0
return 0.0;
}
double Aab = (a & b).area();
// distance = 1 - jaccard_index
return 1.0 - Aab / (Aa + Ab - Aab);
}
template <typename T>
static inline float rectOverlap(const T& a, const T& b)
{
return 1.f - static_cast<float>(jaccardDistance__(a, b));
}
void NMSBoxes(const std::vector<Rect>& bboxes, const std::vector<float>& scores,
const float score_threshold, const float nms_threshold,
std::vector<int>& indices, const float eta=1, const int top_k=0)
{
NMSFast_(bboxes, scores, score_threshold, nms_threshold, eta, top_k, indices, rectOverlap);
}
}
void scale_test(string mode = "test"){
int num_feature = 150;
// feature numbers(how many ori in one templates?)
// two pyramids, lower pyramid(more pixels) in stride 4, lower in stride 8
line2Dup::Detector detector(num_feature, {4, 8});
// mode = "test";
if(mode == "train"){
Mat img = cv::imread(prefix+"case0/templ/circle.png");
assert(!img.empty() && "check your img path");
shape_based_matching::shapeInfo_producer shapes(img);
shapes.scale_range = {0.1f, 1};
shapes.scale_step = 0.01f;
shapes.produce_infos();
std::vector<shape_based_matching::shapeInfo_producer::Info> infos_have_templ;
string class_id = "circle";
for(auto& info: shapes.infos){
// template img, id, mask,
//feature numbers(missing it means using the detector initial num)
int templ_id = detector.addTemplate(shapes.src_of(info), class_id, shapes.mask_of(info),
int(num_feature*info.scale));
std::cout << "templ_id: " << templ_id << std::endl;
// may fail when asking for too many feature_nums for small training img
if(templ_id != -1){ // only record info when we successfully add template
infos_have_templ.push_back(info);
}
}
// save templates
detector.writeClasses(prefix+"case0/%s_templ.yaml");
// save infos,
// in this simple case infos are not used
shapes.save_infos(infos_have_templ, prefix + "case0/circle_info.yaml");
std::cout << "train end" << std::endl << std::endl;
}else if(mode=="test"){
std::vector<std::string> ids;
// read templates
ids.push_back("circle");
detector.readClasses(ids, prefix+"case0/%s_templ.yaml");
Mat test_img = imread(prefix+"case0/1.jpg");
assert(!test_img.empty() && "check your img path");
// make the img having 32*n width & height
// at least 16*n here for two pyrimads with strides 4 8
int stride = 32;
int n = test_img.rows/stride;
int m = test_img.cols/stride;
Rect roi(0, 0, stride*m , stride*n);
Mat img = test_img(roi).clone();
assert(img.isContinuous());
Timer timer;
// match, img, min socre, ids
auto matches = detector.match(img, 90, ids);
// one output match:
// x: top left x
// y: top left y
// template_id: used to find templates
// similarity: scores, 100 is best
timer.out();
std::cout << "matches.size(): " << matches.size() << std::endl;
size_t top5 = 5;
if(top5>matches.size()) top5=matches.size();
for(size_t i=0; i<top5; i++){
auto match = matches[i];
auto templ = detector.getTemplates("circle",
match.template_id);
// template:
// nums: num_pyramids * num_modality (modality, depth or RGB, always 1 here)
// template[0]: lowest pyrimad(more pixels)
// template[0].width: actual width of the matched template
// template[0].tl_x / tl_y: topleft corner when cropping templ during training
// In this case, we can regard width/2 = radius
int x = templ[0].width/2 + match.x;
int y = templ[0].height/2 + match.y;
int r = templ[0].width/2;
Scalar color(255, rand()%255, rand()%255);
cv::putText(img, to_string(int(round(match.similarity))),
Point(match.x+r-10, match.y-3), FONT_HERSHEY_PLAIN, 2, color);
cv::circle(img, {x, y}, r, color, 2);
}
imshow("img", img);
waitKey(0);
std::cout << "test end" << std::endl << std::endl;
}
}
void angle_test(string mode = "test", bool use_rot = true){
line2Dup::Detector detector(128, {4, 8});
if(mode != "test"){
Mat img = imread(prefix+"case1/train.png");
assert(!img.empty() && "check your img path");
Rect roi(130, 110, 270, 270);
img = img(roi).clone();
Mat mask = Mat(img.size(), CV_8UC1, {255});
// padding to avoid rotating out
int padding = 100;
cv::Mat padded_img = cv::Mat(img.rows + 2*padding, img.cols + 2*padding, img.type(), cv::Scalar::all(0));
img.copyTo(padded_img(Rect(padding, padding, img.cols, img.rows)));
cv::Mat padded_mask = cv::Mat(mask.rows + 2*padding, mask.cols + 2*padding, mask.type(), cv::Scalar::all(0));
mask.copyTo(padded_mask(Rect(padding, padding, img.cols, img.rows)));
shape_based_matching::shapeInfo_producer shapes(padded_img, padded_mask);
shapes.angle_range = {0, 360};
shapes.angle_step = 1;
shapes.scale_range = {1}; // support just one
shapes.produce_infos();
std::vector<shape_based_matching::shapeInfo_producer::Info> infos_have_templ;
string class_id = "test";
bool is_first = true;
// for other scales you want to re-extract points:
// set shapes.scale_range then produce_infos; set is_first = false;
int first_id = 0;
float first_angle = 0;
for(auto& info: shapes.infos){
Mat to_show = shapes.src_of(info);
std::cout << "\ninfo.angle: " << info.angle << std::endl;
int templ_id;
if(is_first){
templ_id = detector.addTemplate(shapes.src_of(info), class_id, shapes.mask_of(info));
first_id = templ_id;
first_angle = info.angle;
if(use_rot) is_first = false;
}else{
templ_id = detector.addTemplate_rotate(class_id, first_id,
info.angle-first_angle,
{shapes.src.cols/2.0f, shapes.src.rows/2.0f});
}
auto templ = detector.getTemplates("test", templ_id);
for(int i=0; i<templ[0].features.size(); i++){
auto feat = templ[0].features[i];
cv::circle(to_show, {feat.x+templ[0].tl_x, feat.y+templ[0].tl_y}, 3, {0, 0, 255}, -1);
}
// will be faster if not showing this
imshow("train", to_show);
waitKey(1);
std::cout << "templ_id: " << templ_id << std::endl;
if(templ_id != -1){
infos_have_templ.push_back(info);
}
}
detector.writeClasses(prefix+"case1/%s_templ.yaml");
shapes.save_infos(infos_have_templ, prefix + "case1/test_info.yaml");
std::cout << "train end" << std::endl << std::endl;
}else if(mode=="test"){
std::vector<std::string> ids;
ids.push_back("test");
detector.readClasses(ids, prefix+"case1/%s_templ.yaml");
// angle & scale are saved here, fetched by match id
auto infos = shape_based_matching::shapeInfo_producer::load_infos(prefix + "case1/test_info.yaml");
Mat test_img = imread(prefix+"case1/test.png");
assert(!test_img.empty() && "check your img path");
int padding = 250;
cv::Mat padded_img = cv::Mat(test_img.rows + 2*padding,
test_img.cols + 2*padding, test_img.type(), cv::Scalar::all(0));
test_img.copyTo(padded_img(Rect(padding, padding, test_img.cols, test_img.rows)));
int stride = 16;
int n = padded_img.rows/stride;
int m = padded_img.cols/stride;
Rect roi(0, 0, stride*m , stride*n);
Mat img = padded_img(roi).clone();
assert(img.isContinuous());
// cvtColor(img, img, CV_BGR2GRAY);
std::cout << "test img size: " << img.rows * img.cols << std::endl << std::endl;
Timer timer;
auto matches = detector.match(img, 90, ids);
timer.out();
if(img.channels() == 1) cvtColor(img, img, CV_GRAY2BGR);
std::cout << "matches.size(): " << matches.size() << std::endl;
size_t top5 = 1;
if(top5>matches.size()) top5=matches.size();
for(size_t i=0; i<top5; i++){
auto match = matches[i];
auto templ = detector.getTemplates("test",
match.template_id);
// 270 is width of template image
// 100 is padding when training
// tl_x/y: template croping topleft corner when training
float r_scaled = 270/2.0f*infos[match.template_id].scale;
// scaling won't affect this, because it has been determined by warpAffine
// cv::warpAffine(src, dst, rot_mat, src.size()); last param
float train_img_half_width = 270/2.0f + 100;
float train_img_half_height = 270/2.0f + 100;
// center x,y of train_img in test img
float x = match.x - templ[0].tl_x + train_img_half_width;
float y = match.y - templ[0].tl_y + train_img_half_height;
cv::Vec3b randColor;
randColor[0] = rand()%155 + 100;
randColor[1] = rand()%155 + 100;
randColor[2] = rand()%155 + 100;
for(int i=0; i<templ[0].features.size(); i++){
auto feat = templ[0].features[i];
cv::circle(img, {feat.x+match.x, feat.y+match.y}, 3, randColor, -1);
}
cv::putText(img, to_string(int(round(match.similarity))),
Point(match.x+r_scaled-10, match.y-3), FONT_HERSHEY_PLAIN, 2, randColor);
cv::RotatedRect rotatedRectangle({x, y}, {2*r_scaled, 2*r_scaled}, -infos[match.template_id].angle);
cv::Point2f vertices[4];
rotatedRectangle.points(vertices);
for(int i=0; i<4; i++){
int next = (i+1==4) ? 0 : (i+1);
cv::line(img, vertices[i], vertices[next], randColor, 2);
}
std::cout << "\nmatch.template_id: " << match.template_id << std::endl;
std::cout << "match.similarity: " << match.similarity << std::endl;
}
imshow("img", img);
waitKey(0);
std::cout << "test end" << std::endl << std::endl;
}
}
void noise_test(string mode = "test"){
line2Dup::Detector detector(30, {4, 8});
if(mode == "train"){
Mat img = imread(prefix+"case2/train.png");
assert(!img.empty() && "check your img path");
Mat mask = Mat(img.size(), CV_8UC1, {255});
shape_based_matching::shapeInfo_producer shapes(img, mask);
shapes.angle_range = {0, 360};
shapes.angle_step = 1;
shapes.produce_infos();
std::vector<shape_based_matching::shapeInfo_producer::Info> infos_have_templ;
string class_id = "test";
for(auto& info: shapes.infos){
imshow("train", shapes.src_of(info));
waitKey(1);
std::cout << "\ninfo.angle: " << info.angle << std::endl;
int templ_id = detector.addTemplate(shapes.src_of(info), class_id, shapes.mask_of(info));
std::cout << "templ_id: " << templ_id << std::endl;
if(templ_id != -1){
infos_have_templ.push_back(info);
}
}
detector.writeClasses(prefix+"case2/%s_templ.yaml");
shapes.save_infos(infos_have_templ, prefix + "case2/test_info.yaml");
std::cout << "train end" << std::endl << std::endl;
}else if(mode=="test"){
std::vector<std::string> ids;
ids.push_back("test");
detector.readClasses(ids, prefix+"case2/%s_templ.yaml");
Mat test_img = imread(prefix+"case2/test.png");
assert(!test_img.empty() && "check your img path");
// cvtColor(test_img, test_img, CV_BGR2GRAY);
int stride = 16;
int n = test_img.rows/stride;
int m = test_img.cols/stride;
Rect roi(0, 0, stride*m , stride*n);
test_img = test_img(roi).clone();
Timer timer;
auto matches = detector.match(test_img, 90, ids);
timer.out();
std::cout << "matches.size(): " << matches.size() << std::endl;
size_t top5 = 500;
if(top5>matches.size()) top5=matches.size();
vector<Rect> boxes;
vector<float> scores;
vector<int> idxs;
for(auto match: matches){
Rect box;
box.x = match.x;
box.y = match.y;
auto templ = detector.getTemplates("test",
match.template_id);
box.width = templ[0].width;
box.height = templ[0].height;
boxes.push_back(box);
scores.push_back(match.similarity);
}
cv_dnn::NMSBoxes(boxes, scores, 0, 0.5f, idxs);
for(auto idx: idxs){
auto match = matches[idx];
auto templ = detector.getTemplates("test",
match.template_id);
int x = templ[0].width + match.x;
int y = templ[0].height + match.y;
int r = templ[0].width/2;
cv::Vec3b randColor;
randColor[0] = rand()%155 + 100;
randColor[1] = rand()%155 + 100;
randColor[2] = rand()%155 + 100;
for(int i=0; i<templ[0].features.size(); i++){
auto feat = templ[0].features[i];
cv::circle(test_img, {feat.x+match.x, feat.y+match.y}, 2, randColor, -1);
}
cv::putText(test_img, to_string(int(round(match.similarity))),
Point(match.x+r-10, match.y-3), FONT_HERSHEY_PLAIN, 2, randColor);
cv::rectangle(test_img, {match.x, match.y}, {x, y}, randColor, 2);
std::cout << "\nmatch.template_id: " << match.template_id << std::endl;
std::cout << "match.similarity: " << match.similarity << std::endl;
}
imshow("img", test_img);
waitKey(0);
std::cout << "test end" << std::endl << std::endl;
}
}
void MIPP_test(){
std::cout << "MIPP tests" << std::endl;
std::cout << "----------" << std::endl << std::endl;
std::cout << "Instr. type: " << mipp::InstructionType << std::endl;
std::cout << "Instr. full type: " << mipp::InstructionFullType << std::endl;
std::cout << "Instr. version: " << mipp::InstructionVersion << std::endl;
std::cout << "Instr. size: " << mipp::RegisterSizeBit << " bits" << std::endl;
std::cout << "Instr. lanes: " << mipp::Lanes << std::endl;
std::cout << "64-bit support: " << (mipp::Support64Bit ? "yes" : "no") << std::endl;
std::cout << "Byte/word support: " << (mipp::SupportByteWord ? "yes" : "no") << std::endl;
#ifndef has_max_int8_t
std::cout << "in this SIMD, int8 max is not inplemented by MIPP" << std::endl;
#endif
#ifndef has_shuff_int8_t
std::cout << "in this SIMD, int8 shuff is not inplemented by MIPP" << std::endl;
#endif
std::cout << "----------" << std::endl << std::endl;
}
int main(){
// scale_test("test");
angle_test("test", true); // test or train
// noise_test("test");
return 0;
}
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