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

src/yolo_v2_class.cpp

@@ -0,0 +1,165 @@
+#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;
+}