root
284 lines
9.3 KiB
C++
284 lines
9.3 KiB
C++
#define STB_IMAGE_IMPLEMENTATION
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#include <iostream>
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#include <signal.h>
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#include <stdlib.h> /* srand, rand */
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#ifdef __linux__
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#include <unistd.h>
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#endif
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#define STB_IMAGE_WRITE_IMPLEMENTATION
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#include "stb_image_write.h"
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#include "stb_image.h"
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#include <mutex>
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#include "utils.h"
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#include "baggageDetect.hpp"
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#include "handler.h"
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#include <vector>
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#include <random>
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#include <climits>
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#include <algorithm>
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#include <functional>
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#include <string>
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#include <fstream>
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#include <stdio.h>
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/videoio.hpp>
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#include <opencv2/imgproc/imgproc.hpp>
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#include "Yolo3Detection.h"
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//#include "CenternetDetection.h"
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//#include "MobilenetDetection.h"
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#include "evaluation.h"
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#include <chrono>
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#include <cstdint>
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#include <iostream>
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using namespace std;
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using namespace cv;
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#include <fstream>
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#include <iostream>
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#include <string>
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#include "image.h"
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <unistd.h>
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#include<sys/socket.h> //socket
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#include<sys/types.h>
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#include<netinet/in.h>
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image make_empty_image(int w, int h, int c)
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{
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image out;
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out.data = 0;
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out.h = h;
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out.w = w;
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out.c = c;
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return out;
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}
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image make_image(int w, int h, int c)
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{
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image out = make_empty_image(w,h,c);
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out.data = (float*)calloc(h * w * c, sizeof(float));
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return out;
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}
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int check_mistakes = 0;
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image load_image_file(unsigned char *image_data, int channels, int antilog, int gray, int width, int height)
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{
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int w, h, c;
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unsigned char *data = image_data;
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w = width;
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h = height;
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c = channels;
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if (!image_data) {
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if (check_mistakes) getchar();
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return make_image(10, 10, 3);
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}
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if (channels) c = channels;
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int i,j,k;
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image im = make_image(w, h, c);
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for(k = 0; k < c; ++k){
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for(j = 0; j < h; ++j){
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for(i = 0; i < w; ++i){
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int dst_index = i + w*j + w*h*k;
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int src_index = k + c*i + c*w*j;
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(im).data[dst_index] = (float)image_data[src_index]/255.;
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}
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}
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}
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//free(data);
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return im;
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}
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cv::Mat image_to_mat(image img)
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{
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int channels = img.c;
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int width = img.w;
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int height = img.h;
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cv::Mat mat = cv::Mat(height, width, CV_8UC(channels));
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int step = mat.step;
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for (int y = 0; y < img.h; ++y) {
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for (int x = 0; x < img.w; ++x) {
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for (int c = 0; c < img.c; ++c) {
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float val = img.data[c*img.h*img.w + y*img.w + x];
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mat.data[y*step + x*img.c + c] = (unsigned char)(val * 255);
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}
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}
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}
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return mat;
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}
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char ntype = 'y';
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const char *config_filename = "../demo/config.yaml";
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const char * net = "../demo/yolo4_fp32.rt";
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// const char * img_path = "../demo/demo.jpg";
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char * img_data;
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bool show = false;
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bool verbose;
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int classes, map_points, map_levels;
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float map_step, IoU_thresh, conf_thresh;
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tk::dnn::Yolo3Detection yolo;
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// tk::dnn::CenternetDetection cnet;
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// tk::dnn::MobilenetDetection mbnet;
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tk::dnn::DetectionNN *detNN;
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int n_classes = classes;
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std::vector<tk::dnn::Frame> images;
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std::vector<tk::dnn::box> detected_bbox;
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tk::dnn::Frame f;
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//read parametersi
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handler::handler(utility::string_t url):m_listener(url)
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{
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m_listener.support(methods::POST, bind(&handler::handle_post, this, placeholders::_1));
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}
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string name_from_path(string path)
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{
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return path.substr(path.find_last_of("/\\")+1);
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}
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void handler::init_bag(){tk::dnn::readmAPParams(config_filename, classes, map_points, map_levels, map_step,
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IoU_thresh, conf_thresh, verbose);
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//extract network name from rt path
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std::string net_name;
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removePathAndExtension(net, net_name);
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std::cout<<"Network: "<<net_name<<std::endl;
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//open files (if needed)
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//std::ofstream times, memory, coco_json;
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int n_classes = classes;
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// float conf_threshold=0.001;
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detNN = &yolo;
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detNN->init(net, n_classes, 1, conf_thresh);
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//read images
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// std::ifstream all_labels(labels_path);
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// std::cout << timeSinceEpochMillisec() << std::endl;
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std::string l_filename;
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if(show)
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cv::namedWindow("detection", cv::WINDOW_NORMAL);
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return;}
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// init_bag();
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// int images_done;
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// for (images_done=0 ; std::getline(all_labels, l_filename) && images_done < n_images ; ++images_done) {
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// std::cout <<COL_ORANGEB<< "Images done:\t" << images_done<< "\n"<<COL_END;
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void handler::handle_post(http_request request){
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// init_bag();
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// const char * img_path=
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//tk::dnn::Frame f;
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BOOST_LOG_TRIVIAL(info) << "[" << name_from_path(string(__FILE__)) << " " << __LINE__ << "] " << request.to_string();
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map<utility::string_t, utility::string_t> http_get_vars = uri::split_query(request.request_uri().query());
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map<utility::string_t, utility::string_t>::iterator it = http_get_vars.find("name");
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// std::cout<<request<<"\n";
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//If 'name' is not in the query.
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int len;
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if(it == http_get_vars.end())
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{
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BOOST_LOG_TRIVIAL(error) << "[" << name_from_path(string(__FILE__)) << " " << __LINE__ << "] " << "Image name not passed in query.";
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request.reply(status_codes::UnprocessableEntity,"Please pass image name in the query.");
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return;
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}https://github.com/baggageai/baggageai-code-one.git
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std::cout<<http_get_vars["name"]<<"\n";
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string image_name = (string)http_get_vars["name"];
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string ustring;
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//reading binary data and storing it in a pointer
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// std::vector<std::string> img_data=request.extract_vector();
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request.extract_vector().then([image_name, &ustring, &len](vector<unsigned char> v) {
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ustring = {v.begin(),v.end()};
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len = ustring.size();
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}).wait();
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//printSize(ustring);
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BOOST_LOG_TRIVIAL(info) << "[" << name_from_path(string(__FILE__)) << " " << __LINE__ << "] " << "Detection Started";
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unsigned char * sockData = (unsigned char *)ustring.c_str();
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// std::cout<<ch<<"char"<<len<<"len"<<"\n";
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// cv::Mat frame = cv::imread("demo.jpg", cv::IMREAD_COLOR);
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int channels;
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int h=0,w=0;
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image im;
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unsigned char *data = stbi_load_from_memory(sockData, len, &w, &h, &channels, 0);
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im = load_image_file(data, channels, 0, 0, w, h);
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// float *data = stbi_loadf_from_memory(ch, len, &w, &h, &channels, 0);
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std::cout<<"imagedata"<<h<<"w"<<w<<"ch"<<channels<<"\n";
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string outputPath = "/home/baggageai/files/build/testp.png";
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// Write pictures
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// stbi_write_png(outputPath.c_str(), w, h, channels, data, 0);
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//cv::Mat frame=image_to_mat(data,h,w,channels);
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// cv::Mat frame = cv::Mat(data,h, w, CV_16UC(channels));
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// cv::imwrite("/home/baggageai/files/build/test.png", img);
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// cv::Mat gray;
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cv::Mat gray=image_to_mat(im);
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cv::Mat frame;
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cv::Mat in[] = {gray, gray,gray};
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cv::merge(in, 3, frame);
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cv::imwrite("/home/baggageai/files/build/test1.png", frame);
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std::cout<<frame.channels();
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std::vector<cv::Mat> batch_frames;
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batch_frames.push_back(frame);
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int height = frame.rows;
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int width = frame.cols;
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std::cout<<height<<"width"<<width<<"\n";
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// if(!frame.data)
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// break;
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std::vector<cv::Mat> batch_dnn_input;
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batch_dnn_input.push_back(frame.clone());
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std::cout<<"test1"<<"\n";
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//inference
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detected_bbox.clear();
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detNN->update(batch_dnn_input,1);
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detNN->draw(batch_frames);
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detected_bbox = detNN->detected;
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std::cout<<"test2"<<"\n";
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try{
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json::value response;
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vector<json::value> jsonArray;
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// save detections labels
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for(auto d:detected_bbox){
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//convert detected bb in the same format as label
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//<x_center>/<image_width> <y_center>/<image_width> <width>/<image_width> <height>/<image_width>
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tk::dnn::BoundingBox b;
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b.x = (d.x + d.w/2) / width;
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b.y = (d.y + d.h/2) / height;
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b.w = d.w / width;
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b.h = d.h / height;
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b.prob = d.prob;
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b.cl = d.cl;
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//f.det.push_back(b);
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json::value detection;
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detection["label"] = json::value::number(b.cl);
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detection["x"] = json::value::number(b.x);
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detection["y"] = json::value::number(b.y);
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detection["w"] = json::value::number(b.w);
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detection["h"] = json::value::number(b.h);
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detection["prob"] = json::value::number(b.prob);
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jsonArray.push_back(detection);
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std::cout<< d.cl << " "<< d.prob << " "<< b.x << " "<< b.y << " "<< b.w << " "<< b.h <<"\n";
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if(show)// draw rectangle for detection
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cv::rectangle(batch_frames[0], cv::Point(d.x, d.y), cv::Point(d.x + d.w, d.y + d.h), cv::Scalar(0, 0, 255), 2);
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}
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//images.push_back(f);
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if(show){
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cv::imshow("detection", batch_frames[0]);
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cv::waitKey(0);
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}
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response["detections"] = json::value::array(jsonArray); //JSON Response
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request.reply(status_codes::OK,response.serialize());
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// free(detectboxes);
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BOOST_LOG_TRIVIAL(info) << "[" << name_from_path(string(__FILE__)) << " " << __LINE__ << "] " << "Detection Completed and Response sent";
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}
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catch (exception const& e) {
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BOOST_LOG_TRIVIAL(error) << "[" << name_from_path(string(__FILE__)) << " " << __LINE__ << "] " << e.what();
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request.reply(status_codes::BadRequest, e.what());
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}
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// std::cout << timeSinceEpochMillisec() << std::endl;
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return ;
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}
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