diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp
index aad58760..7fdb4fda 100644
--- a/src/yolo_v2_class.cpp
+++ b/src/yolo_v2_class.cpp
@@ -22,24 +22,18 @@ extern "C" {
 
 #define FRAMES 3
 
-static Detector* detector;
-//static std::unique_ptr<Detector> detector;
+//static Detector* detector = NULL;
+static std::unique_ptr<Detector> detector;
 
-int init(const char *configurationFilename, const char *weightsFilename, int gpu) {
-    std::string configurationFilenameString;
-    configurationFilenameString = configurationFilename;
-    std::string weightsFilenameString;
-    weightsFilenameString = weightsFilename;
-
-    detector = new Detector(configurationFilenameString, weightsFilenameString, gpu);
+int init(const char *configurationFilename, const char *weightsFilename, int gpu)
+{
+    detector.reset(new Detector(configurationFilename, weightsFilename, gpu));
     return 1;
 }
 
-int detect_image(const char *filename, bbox_t_container &container) {
-    std::string filenameString;
-    filenameString = filename;
-
-    std::vector<bbox_t> detection = detector->detect(filenameString);
+int detect_image(const char *filename, bbox_t_container &container)
+{
+    std::vector<bbox_t> detection = detector->detect(filename);
     for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i)
         container.candidates[i] = detection[i];
     return detection.size();
@@ -60,305 +54,306 @@ int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &
 }
 
 int dispose() {
-    detector->~Detector();
-    //detector.reset();
+    //if (detector != NULL) delete detector;
+    //detector = NULL;
+    detector.reset();
     return 1;
 }
 
 #ifdef GPU
 void check_cuda(cudaError_t status) {
-	if (status != cudaSuccess) {
-		const char *s = cudaGetErrorString(status);
-		printf("CUDA Error Prev: %s\n", s);
-	}
+    if (status != cudaSuccess) {
+        const char *s = cudaGetErrorString(status);
+        printf("CUDA Error Prev: %s\n", s);
+    }
 }
 #endif
 
 struct detector_gpu_t {
-	network net;
-	image images[FRAMES];
-	float *avg;
-	float *predictions[FRAMES];
-	int demo_index;
-	unsigned int *track_id;
+    network net;
+    image images[FRAMES];
+    float *avg;
+    float *predictions[FRAMES];
+    int demo_index;
+    unsigned int *track_id;
 };
 
 YOLODLL_API Detector::Detector(std::string cfg_filename, std::string weight_filename, int gpu_id) : cur_gpu_id(gpu_id)
 {
-	wait_stream = 0;
-	int old_gpu_index;
+    wait_stream = 0;
+    int old_gpu_index;
 #ifdef GPU
-	check_cuda( cudaGetDevice(&old_gpu_index) );
+    check_cuda(cudaGetDevice(&old_gpu_index));
 #endif
 
-	detector_gpu_ptr = std::make_shared<detector_gpu_t>();
-	detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+    detector_gpu_ptr = std::make_shared<detector_gpu_t>();
+    detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
 
 #ifdef GPU
-	//check_cuda( cudaSetDevice(cur_gpu_id) );
-	cuda_set_device(cur_gpu_id);
-	printf(" Used GPU %d \n", cur_gpu_id);
+    //check_cuda( cudaSetDevice(cur_gpu_id) );
+    cuda_set_device(cur_gpu_id);
+    printf(" Used GPU %d \n", cur_gpu_id);
 #endif
-	network &net = detector_gpu.net;
-	net.gpu_index = cur_gpu_id;
-	//gpu_index = i;
-	
-	char *cfgfile = const_cast<char *>(cfg_filename.data());
-	char *weightfile = const_cast<char *>(weight_filename.data());
+    network &net = detector_gpu.net;
+    net.gpu_index = cur_gpu_id;
+    //gpu_index = i;
 
-	net = parse_network_cfg_custom(cfgfile, 1);
-	if (weightfile) {
-		load_weights(&net, weightfile);
-	}
-	set_batch_network(&net, 1);
-	net.gpu_index = cur_gpu_id;
-	fuse_conv_batchnorm(net);
+    char *cfgfile = const_cast<char *>(cfg_filename.data());
+    char *weightfile = const_cast<char *>(weight_filename.data());
 
-	layer l = net.layers[net.n - 1];
-	int j;
+    net = parse_network_cfg_custom(cfgfile, 1);
+    if (weightfile) {
+        load_weights(&net, weightfile);
+    }
+    set_batch_network(&net, 1);
+    net.gpu_index = cur_gpu_id;
+    fuse_conv_batchnorm(net);
 
-	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);
+    layer l = net.layers[net.n - 1];
+    int j;
 
-	detector_gpu.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int));
-	for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1;
+    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.track_id = (unsigned int *)calloc(l.classes, sizeof(unsigned int));
+    for (j = 0; j < l.classes; ++j) detector_gpu.track_id[j] = 1;
 
 #ifdef GPU
-	check_cuda( cudaSetDevice(old_gpu_index) );
+    check_cuda(cudaSetDevice(old_gpu_index));
 #endif
 }
 
 
-YOLODLL_API Detector::~Detector() 
+YOLODLL_API Detector::~Detector()
 {
-	detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
-	layer l = detector_gpu.net.layers[detector_gpu.net.n - 1];
+    detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+    layer l = detector_gpu.net.layers[detector_gpu.net.n - 1];
 
-	free(detector_gpu.track_id);
+    free(detector_gpu.track_id);
 
-	free(detector_gpu.avg);
-	for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]);
-	for (int j = 0; j < FRAMES; ++j) if(detector_gpu.images[j].data) free(detector_gpu.images[j].data);
+    free(detector_gpu.avg);
+    for (int j = 0; j < FRAMES; ++j) free(detector_gpu.predictions[j]);
+    for (int j = 0; j < FRAMES; ++j) if (detector_gpu.images[j].data) free(detector_gpu.images[j].data);
 
-	int old_gpu_index;
+    int old_gpu_index;
 #ifdef GPU
-	cudaGetDevice(&old_gpu_index);
-	cuda_set_device(detector_gpu.net.gpu_index);
+    cudaGetDevice(&old_gpu_index);
+    cuda_set_device(detector_gpu.net.gpu_index);
 #endif
 
-	free_network(detector_gpu.net);
+    free_network(detector_gpu.net);
 
 #ifdef GPU
-	cudaSetDevice(old_gpu_index);
+    cudaSetDevice(old_gpu_index);
 #endif
 }
 
 YOLODLL_API int Detector::get_net_width() const {
-	detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
-	return detector_gpu.net.w;
+    detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+    return detector_gpu.net.w;
 }
 YOLODLL_API int Detector::get_net_height() const {
-	detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
-	return detector_gpu.net.h;
+    detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+    return detector_gpu.net.h;
 }
 YOLODLL_API int Detector::get_net_color_depth() const {
-	detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
-	return detector_gpu.net.c;
+    detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+    return detector_gpu.net.c;
 }
 
 
 YOLODLL_API std::vector<bbox_t> Detector::detect(std::string image_filename, float thresh, bool use_mean)
 {
-	std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; });
-	*image_ptr = load_image(image_filename);
-	return detect(*image_ptr, thresh, use_mean);
+    std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { if (img->data) free(img->data); delete img; });
+    *image_ptr = load_image(image_filename);
+    return detect(*image_ptr, thresh, use_mean);
 }
 
 static image load_image_stb(char *filename, int channels)
 {
-	int w, h, c;
-	unsigned char *data = stbi_load(filename, &w, &h, &c, channels);
-	if (!data) 
-		throw std::runtime_error("file not found");
-	if (channels) c = channels;
-	int i, j, k;
-	image im = make_image(w, h, c);
-	for (k = 0; k < c; ++k) {
-		for (j = 0; j < h; ++j) {
-			for (i = 0; i < w; ++i) {
-				int dst_index = i + w*j + w*h*k;
-				int src_index = k + c*i + c*w*j;
-				im.data[dst_index] = (float)data[src_index] / 255.;
-			}
-		}
-	}
-	free(data);
-	return im;
+    int w, h, c;
+    unsigned char *data = stbi_load(filename, &w, &h, &c, channels);
+    if (!data)
+        throw std::runtime_error("file not found");
+    if (channels) c = channels;
+    int i, j, k;
+    image im = make_image(w, h, c);
+    for (k = 0; k < c; ++k) {
+        for (j = 0; j < h; ++j) {
+            for (i = 0; i < w; ++i) {
+                int dst_index = i + w * j + w * h*k;
+                int src_index = k + c * i + c * w*j;
+                im.data[dst_index] = (float)data[src_index] / 255.;
+            }
+        }
+    }
+    free(data);
+    return im;
 }
 
 YOLODLL_API image_t Detector::load_image(std::string image_filename)
 {
-	char *input = const_cast<char *>(image_filename.data());
-	image im = load_image_stb(input, 3);
+    char *input = const_cast<char *>(image_filename.data());
+    image im = load_image_stb(input, 3);
 
-	image_t img;
-	img.c = im.c;
-	img.data = im.data;
-	img.h = im.h;
-	img.w = im.w;
+    image_t img;
+    img.c = im.c;
+    img.data = im.data;
+    img.h = im.h;
+    img.w = im.w;
 
-	return img;
+    return img;
 }
 
 
 YOLODLL_API void Detector::free_image(image_t m)
 {
-	if (m.data) {
-		free(m.data);
-	}
+    if (m.data) {
+        free(m.data);
+    }
 }
 
 YOLODLL_API std::vector<bbox_t> Detector::detect(image_t img, float thresh, bool use_mean)
 {
-	detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
-	network &net = detector_gpu.net;
-	int old_gpu_index;
+    detector_gpu_t &detector_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+    network &net = detector_gpu.net;
+    int old_gpu_index;
 #ifdef GPU
-	cudaGetDevice(&old_gpu_index);
-	if(cur_gpu_id != old_gpu_index)
-		cudaSetDevice(net.gpu_index);
+    cudaGetDevice(&old_gpu_index);
+    if (cur_gpu_id != old_gpu_index)
+        cudaSetDevice(net.gpu_index);
 
-	net.wait_stream = wait_stream;	// 1 - wait CUDA-stream, 0 - not to wait
+    net.wait_stream = wait_stream;	// 1 - wait CUDA-stream, 0 - not to wait
 #endif
-	//std::cout << "net.gpu_index = " << net.gpu_index << std::endl;
+                                    //std::cout << "net.gpu_index = " << net.gpu_index << std::endl;
 
-	//float nms = .4;
+                                    //float nms = .4;
 
-	image im;
-	im.c = img.c;
-	im.data = img.data;
-	im.h = img.h;
-	im.w = img.w;
+    image im;
+    im.c = img.c;
+    im.data = img.data;
+    im.h = img.h;
+    im.w = img.w;
 
-	image sized;
-	
-	if (net.w == im.w && net.h == im.h) {
-		sized = make_image(im.w, im.h, im.c);
-		memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float));
-	}
-	else
-		sized = resize_image(im, net.w, net.h);
+    image sized;
 
-	layer l = net.layers[net.n - 1];
+    if (net.w == im.w && net.h == im.h) {
+        sized = make_image(im.w, im.h, im.c);
+        memcpy(sized.data, im.data, im.w*im.h*im.c * sizeof(float));
+    }
+    else
+        sized = resize_image(im, net.w, net.h);
 
-	float *X = sized.data;
+    layer l = net.layers[net.n - 1];
 
-	float *prediction = network_predict(net, X);
+    float *X = sized.data;
 
-	if (use_mean) {
-		memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float));
-		mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg);
-		l.output = detector_gpu.avg;
-		detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES;
-	}
-	//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);
+    float *prediction = network_predict(net, X);
 
-	int nboxes = 0;
-	int letterbox = 0;
-	float hier_thresh = 0.5;
-	detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
-	if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
+    if (use_mean) {
+        memcpy(detector_gpu.predictions[detector_gpu.demo_index], prediction, l.outputs * sizeof(float));
+        mean_arrays(detector_gpu.predictions, FRAMES, l.outputs, detector_gpu.avg);
+        l.output = detector_gpu.avg;
+        detector_gpu.demo_index = (detector_gpu.demo_index + 1) % FRAMES;
+    }
+    //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);
 
-	std::vector<bbox_t> bbox_vec;
+    int nboxes = 0;
+    int letterbox = 0;
+    float hier_thresh = 0.5;
+    detection *dets = get_network_boxes(&net, im.w, im.h, thresh, hier_thresh, 0, 1, &nboxes, letterbox);
+    if (nms) do_nms_sort(dets, nboxes, l.classes, nms);
 
-	for (size_t i = 0; i < nboxes; ++i) {
-		box b = dets[i].bbox;
-		int const obj_id = max_index(dets[i].prob, l.classes);
-		float const prob = dets[i].prob[obj_id];
-		
-		if (prob > thresh) 
-		{
-			bbox_t bbox;
-			bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w);
-			bbox.y = std::max((double)0, (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.track_id = 0;
+    std::vector<bbox_t> bbox_vec;
 
-			bbox_vec.push_back(bbox);
-		}
-	}
+    for (size_t i = 0; i < nboxes; ++i) {
+        box b = dets[i].bbox;
+        int const obj_id = max_index(dets[i].prob, l.classes);
+        float const prob = dets[i].prob[obj_id];
 
-	free_detections(dets, nboxes);
-	if(sized.data)
-		free(sized.data);
+        if (prob > thresh)
+        {
+            bbox_t bbox;
+            bbox.x = std::max((double)0, (b.x - b.w / 2.)*im.w);
+            bbox.y = std::max((double)0, (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.track_id = 0;
+
+            bbox_vec.push_back(bbox);
+        }
+    }
+
+    free_detections(dets, nboxes);
+    if (sized.data)
+        free(sized.data);
 
 #ifdef GPU
-	if (cur_gpu_id != old_gpu_index)
-		cudaSetDevice(old_gpu_index);
+    if (cur_gpu_id != old_gpu_index)
+        cudaSetDevice(old_gpu_index);
 #endif
 
-	return bbox_vec;
+    return bbox_vec;
 }
 
-YOLODLL_API std::vector<bbox_t> Detector::tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history, 
-	int const frames_story, int const max_dist)
+YOLODLL_API std::vector<bbox_t> Detector::tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history,
+    int const frames_story, int const max_dist)
 {
-	detector_gpu_t &det_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
+    detector_gpu_t &det_gpu = *static_cast<detector_gpu_t *>(detector_gpu_ptr.get());
 
-	bool prev_track_id_present = false;
-	for (auto &i : prev_bbox_vec_deque)
-		if (i.size() > 0) prev_track_id_present = true;
+    bool prev_track_id_present = false;
+    for (auto &i : prev_bbox_vec_deque)
+        if (i.size() > 0) prev_track_id_present = true;
 
-	if (!prev_track_id_present) {
-		for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
-			cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++;
-		prev_bbox_vec_deque.push_front(cur_bbox_vec);
-		if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back();
-		return cur_bbox_vec;
-	}
+    if (!prev_track_id_present) {
+        for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
+            cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++;
+        prev_bbox_vec_deque.push_front(cur_bbox_vec);
+        if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back();
+        return cur_bbox_vec;
+    }
 
-	std::vector<unsigned int> dist_vec(cur_bbox_vec.size(), std::numeric_limits<unsigned int>::max());
+    std::vector<unsigned int> dist_vec(cur_bbox_vec.size(), std::numeric_limits<unsigned int>::max());
 
-	for (auto &prev_bbox_vec : prev_bbox_vec_deque) {
-		for (auto &i : prev_bbox_vec) {
-			int cur_index = -1;
-			for (size_t m = 0; m < cur_bbox_vec.size(); ++m) {
-				bbox_t const& k = cur_bbox_vec[m];
-				if (i.obj_id == k.obj_id) {
-					float center_x_diff = (float)(i.x + i.w/2) - (float)(k.x + k.w/2);
-					float center_y_diff = (float)(i.y + i.h/2) - (float)(k.y + k.h/2);
-					unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff*center_y_diff);
-					if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) {
-						dist_vec[m] = cur_dist;
-						cur_index = m;
-					}
-				}
-			}
+    for (auto &prev_bbox_vec : prev_bbox_vec_deque) {
+        for (auto &i : prev_bbox_vec) {
+            int cur_index = -1;
+            for (size_t m = 0; m < cur_bbox_vec.size(); ++m) {
+                bbox_t const& k = cur_bbox_vec[m];
+                if (i.obj_id == k.obj_id) {
+                    float center_x_diff = (float)(i.x + i.w / 2) - (float)(k.x + k.w / 2);
+                    float center_y_diff = (float)(i.y + i.h / 2) - (float)(k.y + k.h / 2);
+                    unsigned int cur_dist = sqrt(center_x_diff*center_x_diff + center_y_diff * center_y_diff);
+                    if (cur_dist < max_dist && (k.track_id == 0 || dist_vec[m] > cur_dist)) {
+                        dist_vec[m] = cur_dist;
+                        cur_index = m;
+                    }
+                }
+            }
 
-			bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(), 
-				[&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; });
+            bool track_id_absent = !std::any_of(cur_bbox_vec.begin(), cur_bbox_vec.end(),
+                [&i](bbox_t const& b) { return b.track_id == i.track_id && b.obj_id == i.obj_id; });
 
-			if (cur_index >= 0 && track_id_absent){
-				cur_bbox_vec[cur_index].track_id = i.track_id;
-				cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2;
-				cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2;
-			}
-		}
-	}
+            if (cur_index >= 0 && track_id_absent) {
+                cur_bbox_vec[cur_index].track_id = i.track_id;
+                cur_bbox_vec[cur_index].w = (cur_bbox_vec[cur_index].w + i.w) / 2;
+                cur_bbox_vec[cur_index].h = (cur_bbox_vec[cur_index].h + i.h) / 2;
+            }
+        }
+    }
 
-	for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
-		if (cur_bbox_vec[i].track_id == 0)
-			cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++;
+    for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
+        if (cur_bbox_vec[i].track_id == 0)
+            cur_bbox_vec[i].track_id = det_gpu.track_id[cur_bbox_vec[i].obj_id]++;
 
-	if (change_history) {
-		prev_bbox_vec_deque.push_front(cur_bbox_vec);
-		if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back();
-	}
+    if (change_history) {
+        prev_bbox_vec_deque.push_front(cur_bbox_vec);
+        if (prev_bbox_vec_deque.size() > frames_story) prev_bbox_vec_deque.pop_back();
+    }
 
-	return cur_bbox_vec;
+    return cur_bbox_vec;
 }
\ No newline at end of file