Please provide complete information as applicable to your setup.
**• Hardware Platform ------------> GPU
**• DeepStream Version -----------> 6.1.1
**• TensorRT Version ---------------> 8.5
**• NVIDIA GPU Driver Version ---------------> 535
My question is I want to access LPR output for my custom function But How I get the full output ?
for example If I want to access obj_meta , I will go frame_meta —> Obj_meta —> classifier_meta—>
Can You share like that code in python where I can access LPR output and use it properly for my use cases.
You can refer to the c language code and rewrite it into python
you can refer the following code in osd_sink_pad_buffer_probe function
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;
for (l_obj = frame_meta->obj_meta_list; l_obj != NULL;
l_obj = l_obj->next) {
obj_meta = (NvDsObjectMeta *) (l_obj->data);
if (!obj_meta)
continue;
/* 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++;
if (obj_meta->class_id == PGIE_CLASS_ID_PERSON)
person_count++;
}
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_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);
}
}
}
}
}
}