From d6cb0fcabcece079c6a5b511159127f585897dba Mon Sep 17 00:00:00 2001
From: Tino Hager <tino.hager@nager.at>
Date: Wed, 27 Jun 2018 21:11:33 +0000
Subject: [PATCH] optimize max object definition
---
src/yolo_v2_class.cpp | 78 ++++++++++++++++++++++++++++++---------
1 files changed, 60 insertions(+), 18 deletions(-)
diff --git a/src/yolo_v2_class.cpp b/src/yolo_v2_class.cpp
index 076bab8..aad5876 100644
--- a/src/yolo_v2_class.cpp
+++ b/src/yolo_v2_class.cpp
@@ -22,6 +22,49 @@
#define FRAMES 3
+static Detector* detector;
+//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);
+ 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);
+ for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i)
+ container.candidates[i] = detection[i];
+ return detection.size();
+}
+
+int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container) {
+#ifdef OPENCV
+ std::vector<char> vdata(data, data + data_length);
+ cv::Mat image = imdecode(cv::Mat(vdata), 1);
+
+ std::vector<bbox_t> detection = detector->detect(image);
+ for (size_t i = 0; i < detection.size() && i < C_SHARP_MAX_OBJECTS; ++i)
+ container.candidates[i] = detection[i];
+ return detection.size();
+#else
+ return -1;
+#endif // OPENCV
+}
+
+int dispose() {
+ detector->~Detector();
+ //detector.reset();
+ return 1;
+}
+
#ifdef GPU
void check_cuda(cudaError_t status) {
if (status != cudaSuccess) {
@@ -32,8 +75,6 @@
#endif
struct detector_gpu_t {
- float **probs;
- box *boxes;
network net;
image images[FRAMES];
float *avg;
@@ -71,6 +112,7 @@
}
set_batch_network(&net, 1);
net.gpu_index = cur_gpu_id;
+ fuse_conv_batchnorm(net);
layer l = net.layers[net.n - 1];
int j;
@@ -79,10 +121,6 @@
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));
-
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;
@@ -103,14 +141,9 @@
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);
- for (int j = 0; j < l.w*l.h*l.n; ++j) free(detector_gpu.probs[j]);
- free(detector_gpu.boxes);
- free(detector_gpu.probs);
-
int old_gpu_index;
#ifdef GPU
cudaGetDevice(&old_gpu_index);
- //cudaSetDevice(detector_gpu.net.gpu_index);
cuda_set_device(detector_gpu.net.gpu_index);
#endif
@@ -129,6 +162,10 @@
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;
+}
YOLODLL_API std::vector<bbox_t> Detector::detect(std::string image_filename, float thresh, bool use_mean)
@@ -225,17 +262,21 @@
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);
- 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);
+ 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);
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];
+ 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)
{
@@ -252,6 +293,7 @@
}
}
+ free_detections(dets, nboxes);
if(sized.data)
free(sized.data);
--
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