**• Hardware Platform:Jetson• Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)
Is it possible to serialize/stringify a whole NvDsFrameMeta?osd_sink_pad_buffer_probe in /opt/nvidia/deepstream/deepstream-5.1/sources/apps/sample_apps/deepstream-test1 requires to read each of fields manually. However, most of time we don’t familiar to every field. Therefore, I want to know how to serialize/stringify a whole NvDsFrameMeta directly, so that I can got a complete inferring results quickly.
Base on your hint, I made a modification from the example :
static GstPadProbeReturn osd_sink_pad_buffer_probe(GstPad * pad, GstPadProbeInfo * info, gpointer u_data){
GstBuffer *buf =(GstBuffer *) info->data;
NvDsObjectMeta *obj_meta = NULL;
guint vehicle_count = 0;
guint person_count = 0;
guint lp_count = 0;
guint label_i = 0;
NvDsMetaList * l_frame = NULL;
NvDsMetaList * l_obj = NULL;
NvDsMetaList * l_class = NULL;
NvDsMetaList * l_label = NULL;
NvDsDisplayMeta *display_meta = NULL;
NvDsClassifierMeta * class_meta = NULL;
NvDsLabelInfo * label_info = NULL;
GstClockTime now;
perf_measure * perf =(perf_measure *)(u_data);
NvDsBatchMeta *batch_meta = gst_buffer_get_nvds_batch_meta(buf);
now = g_get_monotonic_time();
if(perf->pre_time == GST_CLOCK_TIME_NONE) {
perf->pre_time = now;
perf->total_time = GST_CLOCK_TIME_NONE;
} else {
if(perf->total_time == GST_CLOCK_TIME_NONE) {
perf->total_time =(now - perf->pre_time);
} else {
perf->total_time +=(now - perf->pre_time);
}
perf->pre_time = now;
perf->count++;
}
nlohmann::json js = nlohmann::json::object();
js["frame"] = nlohmann::json::object();
for(l_frame = batch_meta->frame_meta_list; l_frame != NULL; l_frame = l_frame->next) {
NvDsFrameMeta *frame_meta =(NvDsFrameMeta *)(l_frame->data);
int offset = 0;
if(!frame_meta){
continue;
}
js["frame"]["source_id"] = frame_meta->source_id;
js["frame"]["frame_num"] = frame_meta->frame_num;
js["frame"]["object"] = nlohmann::json::array();
for(l_obj = frame_meta->obj_meta_list; l_obj != NULL; l_obj = l_obj->next) {
obj_meta =(NvDsObjectMeta *)(l_obj->data);
nlohmann::json obj = nlohmann::json::object();
if(!obj_meta){
continue;
}
obj["rect_left"] = obj_meta->rect_params.left;
obj["rect_top"] = obj_meta->rect_params.top;
obj["rect_width"] = obj_meta->rect_params.width;
obj["rect_height"] = obj_meta->rect_params.height;
obj["rect_confidence"] = obj_meta->confidence;
obj["unique_component_id"] = obj_meta->unique_component_id;
obj["class_id"] = obj_meta->class_id;
obj["object_id"] = obj_meta->object_id;
// Check that the object has been detected by the primary detector and that the class id is that of vehicles/persons.
if(obj_meta->unique_component_id == PRIMARY_DETECTOR_UID) {
if(obj_meta->class_id == PGIE_CLASS_ID_VEHICLE){
vehicle_count++;
obj["class_string"] = "vehicle";
}
if(obj_meta->class_id == PGIE_CLASS_ID_PERSON){
person_count++;
obj["class_string"] = "person";
}
}
if(obj_meta->unique_component_id == SECONDARY_DETECTOR_UID) {
if(obj_meta->class_id == SGIE_CLASS_ID_LPD) {
lp_count++;
/* Print this info only when operating in secondary model. */
if(obj_meta->parent){
g_print("License plate found for parent object %p(type=%s)\n",
obj_meta->parent, pgie_classes_str[obj_meta->parent->class_id]);
obj["class_str"] = pgie_classes_str[obj_meta->parent->class_id];
}
obj_meta->text_params.set_bg_clr = 1;
obj_meta->text_params.text_bg_clr.red = 0.0;
obj_meta->text_params.text_bg_clr.green = 0.0;
obj_meta->text_params.text_bg_clr.blue = 0.0;
obj_meta->text_params.text_bg_clr.alpha = 0.0;
obj_meta->text_params.font_params.font_color.red = 1.0;
obj_meta->text_params.font_params.font_color.green = 1.0;
obj_meta->text_params.font_params.font_color.blue = 0.0;
obj_meta->text_params.font_params.font_color.alpha = 1.0;
obj_meta->text_params.font_params.font_size = 12;
}
}
for(l_class = obj_meta->classifier_meta_list; l_class != NULL; l_class = l_class->next) {
class_meta =(NvDsClassifierMeta *)(l_class->data);
if(!class_meta) continue;
if(class_meta->unique_component_id == SECONDARY_CLASSIFIER_UID) {
for( label_i = 0, l_label = class_meta->label_info_list;
label_i < class_meta->num_labels && l_label; label_i++,
l_label = l_label->next) {
label_info =(NvDsLabelInfo *)(l_label->data);
if(label_info) {
if(label_info->label_id == 0 && label_info->result_class_id == 1) {
g_print("Plate License %s\n",label_info->result_label);
obj["plate"] = label_info->result_label;
}
}
}
}
}
js["frame"]["object"].push_back(obj);
}
display_meta = nvds_acquire_display_meta_from_pool(batch_meta);
NvOSD_TextParams *txt_params = &display_meta->text_params[0];
display_meta->num_labels = 1;
txt_params->display_text =(char*) g_malloc0(MAX_DISPLAY_LEN);
offset = snprintf(txt_params->display_text, MAX_DISPLAY_LEN,
"Person = %d ", person_count);
offset += snprintf(txt_params->display_text + offset , MAX_DISPLAY_LEN,
"Vehicle = %d ", vehicle_count);
/* Now set the offsets where the string should appear */
txt_params->x_offset = 10;
txt_params->y_offset = 12;
/* Font , font-color and font-size */
char font_n[6];
snprintf(font_n, 6, "Serif");
txt_params->font_params.font_name = font_n;
txt_params->font_params.font_size = 10;
txt_params->font_params.font_color.red = 1.0;
txt_params->font_params.font_color.green = 1.0;
txt_params->font_params.font_color.blue = 1.0;
txt_params->font_params.font_color.alpha = 1.0;
/* Text background color */
txt_params->set_bg_clr = 1;
txt_params->text_bg_clr.red = 0.0;
txt_params->text_bg_clr.green = 0.0;
txt_params->text_bg_clr.blue = 0.0;
txt_params->text_bg_clr.alpha = 1.0;
nvds_add_display_meta_to_frame(frame_meta, display_meta);
}
std::ofstream fd;
fd.open("/tmp/js_out.txt", std::ios::out | std::ios::app );
fd << js.dump() << "\n";
fd.close();
g_print("Frame Number = %d Vehicle Count = %d Person Count = %d"
" License Plate Count = %d\n",
frame_number, vehicle_count, person_count,
lp_count);
frame_number++;
total_plate_number += lp_count;
return GST_PAD_PROBE_OK;
}
The codes fetch each required value manually, it’s very inconvenient. I mean what if the deepstream provides a method to output a complete inferring results directly. The output could be something like JSON, YAML, XML, et al.
Deepstream is just a SDK. How to output the inference result is the purpose of application but not for a SDK. You can customize your own output format and ways, there is no limitation from deepstream.
system
November 30, 2021, 2:52am
7
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