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Added support DLL (dynamic link library) - yolo_cpp_dll.dll

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This commit is contained in:
AlexeyAB
2017-03-15 23:39:18 +03:00
parent a71bdd7a83
commit a6cbaeecde
9 changed files with 828 additions and 2 deletions
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28
build/darknet/yolo_console_dll.sln Normal file
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11
src/network.h
View File

@ -2,8 +2,13 @@
#ifndef NETWORK_H
#define NETWORK_H
#include "image.h"
#include "layer.h"
#ifdef __cplusplus
extern "C" {
#endif
#include "image.h"
#include "data.h"
#include "tree.h"
@ -125,5 +130,9 @@ float get_network_cost(network net);
int get_network_nuisance(network net);
int get_network_background(network net);
#ifdef __cplusplus
}
#endif
#endif

2
src/yolo.c
View File

@ -6,6 +6,7 @@
#include "box.h"
#include "demo.h"
#ifdef OPENCV
#pragma comment(lib, "opencv_core249.lib")
#pragma comment(lib, "opencv_imgproc249.lib")
#pragma comment(lib, "opencv_highgui249.lib")
@ -13,7 +14,6 @@
//#pragma comment(lib, "opencv_photo249.lib")
//#pragma comment(lib, "opencv_video249.lib")
#ifdef OPENCV
#include "opencv2/highgui/highgui_c.h"
#endif

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#include <iostream>
#include <string>
#include <vector>
//#define OPENCV
#include "yolo_v2_class.hpp" // imported functions from DLL
#ifdef OPENCV
#include <opencv2/opencv.hpp> // C++
#pragma comment(lib, "opencv_core249.lib")
#pragma comment(lib, "opencv_imgproc249.lib")
#pragma comment(lib, "opencv_highgui249.lib")
void draw_boxes(cv::Mat mat_img, std::vector<bbox_t> result_vec) {
for (auto &i : result_vec) {
cv::rectangle(mat_img, cv::Rect(i.x, i.y, i.w, i.h), cv::Scalar(50, 200, 50), 3);
}
cv::imshow("window name", mat_img);
cv::waitKey(0);
}
#endif // OPENCV
void show_result(std::vector<bbox_t> result_vec) {
for (auto &i : result_vec) {
std::cout << "obj_id = " << i.obj_id << " - x = " << i.x << ", y = " << i.y
<< ", w = " << i.w << ", h = " << i.h
<< ", prob = " << i.prob << std::endl;
}
}
int main()
{
Detector detector("yolo-voc.cfg", "yolo-voc.weights");
while (true)
{
std::string filename;
std::cout << "input image filename: ";
std::cin >> filename;
if (filename.size() == 0) break;
#ifdef OPENCV
cv::Mat mat_img = cv::imread(filename);
std::vector<bbox_t> result_vec = detector.detect(mat_img);
draw_boxes(mat_img, result_vec);
#else
std::vector<bbox_t> result_vec = detector.detect(filename);
#endif
show_result(result_vec);
}
return 0;
}

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#include "yolo_v2_class.hpp"
#include "network.h"
extern "C" {
#include "detection_layer.h"
#include "region_layer.h"
#include "cost_layer.h"
#include "utils.h"
#include "parser.h"
#include "box.h"
#include "image.h"
#include "demo.h"
#include "option_list.h"
}
//#include <sys/time.h>
#include <vector>
#include <iostream>
#define FRAMES 3
#define ROI_PER_DETECTOR 100
struct detector_gpu_t{
float **probs;
box *boxes;
network net;
//image det;
//image det_s;
image images[FRAMES];
float *avg;
float *predictions[FRAMES];
};
YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id)
{
int old_gpu_index;
cudaGetDevice(&old_gpu_index);
detector_gpu_ptr = std::make_shared<detector_gpu_t>();
detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
cudaSetDevice(gpu_id);
network &net = detector_gpu.net;
net.gpu_index = gpu_id;
//gpu_index = i;
char *cfgfile = const_cast<char *>(cfg_filename.data());
char *weightfile = const_cast<char *>(weight_filename.data());
net = parse_network_cfg(cfgfile);
if (weightfile) {
load_weights(&net, weightfile);
}
set_batch_network(&net, 1);
net.gpu_index = gpu_id;
layer l = net.layers[net.n - 1];
int j;
detector_gpu.avg = (float *)calloc(l.outputs, sizeof(float));
for (j = 0; j < FRAMES; ++j) detector_gpu.predictions[j] = (float *)calloc(l.outputs, sizeof(float));
for (j = 0; j < FRAMES; ++j) detector_gpu.images[j] = make_image(1, 1, 3);
detector_gpu.boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box));
detector_gpu.probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *));
for (j = 0; j < l.w*l.h*l.n; ++j) detector_gpu.probs[j] = (float *)calloc(l.classes, sizeof(float));
cudaSetDevice(old_gpu_index);
}
YOLODLL_API Detector::~Detector()
{
detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
layer l = detector_gpu.net.layers[detector_gpu.net.n - 1];
free(detector_gpu.boxes);
free(detector_gpu.avg);
free(detector_gpu.predictions);
for (int j = 0; j < l.w*l.h*l.n; ++j) free(detector_gpu.probs[j]);
free(detector_gpu.probs);
}
YOLODLL_API std::vector<bbox_t> Detector::detect(std::string image_filename, float thresh)
{
char *input = const_cast<char *>(image_filename.data());
image im = load_image_color(input, 0, 0);
image_t img;
img.c = im.c;
img.data = im.data;
img.h = im.h;
img.w = im.w;
return detect(img, thresh);
}
YOLODLL_API std::vector<bbox_t> Detector::detect(image_t img, float thresh)
{
detector_gpu_t &detector_gpu = *reinterpret_cast<detector_gpu_t *>(detector_gpu_ptr.get());
network &net = detector_gpu.net;
int old_gpu_index;
cudaGetDevice(&old_gpu_index);
cudaSetDevice(net.gpu_index);
//std::cout << "net.gpu_index = " << net.gpu_index << std::endl;
float nms = .4;
image im;
im.c = img.c;
im.data = img.data;
im.h = img.h;
im.w = img.w;
image sized = resize_image(im, net.w, net.h);
layer l = net.layers[net.n - 1];
//box *boxes = (box *)calloc(l.w*l.h*l.n, sizeof(box));
//float **probs = (float **)calloc(l.w*l.h*l.n, sizeof(float *));
// (int j = 0; j < l.w*l.h*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *));
float *X = sized.data;
network_predict(net, X);
get_region_boxes(l, 1, 1, thresh, detector_gpu.probs, detector_gpu.boxes, 0, 0);
if (nms) do_nms_sort(detector_gpu.boxes, detector_gpu.probs, l.w*l.h*l.n, l.classes, nms);
//draw_detections(im, l.w*l.h*l.n, thresh, boxes, probs, names, alphabet, l.classes);
std::vector<bbox_t> bbox_vec;
for (size_t i = 0; i < (l.w*l.h*l.n); ++i) {
box b = detector_gpu.boxes[i];
int const obj_id = max_index(detector_gpu.probs[i], l.classes);
float const prob = detector_gpu.probs[i][obj_id];
if (prob > thresh)
{
bbox_t bbox;
bbox.x = (b.x - b.w / 2.)*im.w;
bbox.y = (b.y - b.h / 2.)*im.h;
bbox.w = b.w*im.w;
bbox.h = b.h*im.h;
bbox.obj_id = obj_id;
bbox.prob = prob;
bbox_vec.push_back(bbox);
}
}
cudaSetDevice(old_gpu_index);
return bbox_vec;
}

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#pragma once
#include <memory>
#include <vector>
#ifdef OPENCV
#include <opencv2/opencv.hpp> // C++
#include "opencv2/highgui/highgui_c.h" // C
#include "opencv2/imgproc/imgproc_c.h" // C
#endif // OPENCV
//extern "C" {
//#include "image.h"
//}
#ifdef YOLODLL_EXPORTS
#define YOLODLL_API __declspec(dllexport)
#else
#define YOLODLL_API __declspec(dllimport)
#endif
struct bbox_t {
float x, y, w, h;
float prob;
unsigned int obj_id;
};
typedef struct {
int h;
int w;
int c;
float *data;
} image_t;
class Detector {
std::shared_ptr<void> detector_gpu_ptr;
public:
YOLODLL_API Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0);
YOLODLL_API ~Detector();
YOLODLL_API std::vector<bbox_t> Detector::detect(std::string image_filename, float thresh = 0.2);
YOLODLL_API std::vector<bbox_t> detect(image_t img, float thresh = 0.2);
#ifdef OPENCV
std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2) {
std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { free_image(*img); } );
*image_ptr = mat_to_image(mat);
return detect(*image_ptr, thresh);
}
private:
static image_t mat_to_image(cv::Mat img)
{
std::shared_ptr<IplImage> ipl_small = std::make_shared<IplImage>(img);
image_t im_small = ipl_to_image(ipl_small.get());
rgbgr_image(im_small);
return im_small;
}
static image_t ipl_to_image(IplImage* src)
{
unsigned char *data = (unsigned char *)src->imageData;
int h = src->height;
int w = src->width;
int c = src->nChannels;
int step = src->widthStep;
image_t out = make_image_custom(w, h, c);
int i, j, k, count = 0;;
for (k = 0; k < c; ++k) {
for (i = 0; i < h; ++i) {
for (j = 0; j < w; ++j) {
out.data[count++] = data[i*step + j*c + k] / 255.;
}
}
}
return out;
}
static image_t make_empty_image(int w, int h, int c)
{
image_t out;
out.data = 0;
out.h = h;
out.w = w;
out.c = c;
return out;
}
static image_t make_image_custom(int w, int h, int c)
{
image_t out = make_empty_image(w, h, c);
out.data = (float *)calloc(h*w*c, sizeof(float));
return out;
}
static void rgbgr_image(image_t im)
{
int i;
for (i = 0; i < im.w*im.h; ++i) {
float swap = im.data[i];
im.data[i] = im.data[i + im.w*im.h * 2];
im.data[i + im.w*im.h * 2] = swap;
}
}
static void free_image(image_t m)
{
if (m.data) {
free(m.data);
}
}
#endif // OPENCV
};