Still can’t access seg_meta.unique_id.
AttributeError: 'pyds.NvDsInferSegmentationMeta' object has no attribute 'unique_id'
Below are the modified files:
gstnvinfer_meta_utils.cpp:
nano /opt/nvidia/deepstream/deepstream-6.0/sources/gst-plugins/gst-nvinfer/gstnvinfer_meta_utils.cpp
/**
* Copyright (c) 2018-2021, NVIDIA CORPORATION. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual property
* and proprietary rights in and to this software, related documentation
* and any modifications thereto. Any use, reproduction, disclosure or
* distribution of this software and related documentation without an express
* license agreement from NVIDIA Corporation is strictly prohibited.
*
*/
#include <cmath>
#include <cstring>
#include "gstnvinfer_meta_utils.h"
#include <iostream>
static inline int
get_element_size (NvDsInferDataType data_type)
{
switch (data_type) {
case FLOAT:
return 4;
case HALF:
return 2;
case INT32:
return 4;
case INT8:
return 1;
default:
return 0;
}
}
/**
* Attach metadata for the detector. We will be adding a new metadata.
*/
void
attach_metadata_detector (GstNvInfer * nvinfer, GstMiniObject * tensor_out_object,
GstNvInferFrame & frame, NvDsInferDetectionOutput & detection_output, float segmentationThreshold)
{
static gchar font_name[] = "Serif";
NvDsObjectMeta *obj_meta = NULL;
NvDsObjectMeta *parent_obj_meta = frame.obj_meta; /* This will be NULL in case of primary detector */
NvDsFrameMeta *frame_meta = frame.frame_meta;
NvDsBatchMeta *batch_meta = frame_meta->base_meta.batch_meta;
nvds_acquire_meta_lock (batch_meta);
frame_meta->bInferDone = TRUE;
/* Iterate through the inference output for one frame and attach the detected
* bnounding boxes. */
for (guint i = 0; i < detection_output.numObjects; i++) {
NvDsInferObject & obj = detection_output.objects[i];
GstNvInferDetectionFilterParams & filter_params =
(*nvinfer->perClassDetectionFilterParams)[obj.classIndex];
/* Scale the bounding boxes proportionally based on how the object/frame was
* scaled during input. */
obj.left = (obj.left - frame.offset_left)/frame.scale_ratio_x + frame.roi_left;
obj.top = (obj.top - frame.offset_top)/frame.scale_ratio_y + frame.roi_top;
obj.width /= frame.scale_ratio_x;
obj.height /= frame.scale_ratio_y;
/* Check if the scaled box co-ordinates meet the detection filter criteria.
* Skip the box if it does not. */
if(nvinfer->filter_out_class_ids->find(obj.classIndex) != nvinfer->filter_out_class_ids->end())
continue;
if (obj.width < filter_params.detectionMinWidth)
continue;
if (obj.height < filter_params.detectionMinHeight)
continue;
if (filter_params.detectionMaxWidth > 0 &&
obj.width > filter_params.detectionMaxWidth)
continue;
if (filter_params.detectionMaxHeight > 0 &&
obj.height > filter_params.detectionMaxHeight)
continue;
if (obj.top < filter_params.roiTopOffset)
continue;
if (obj.top + obj.height >
(frame.input_surf_params->height - filter_params.roiBottomOffset))
continue;
obj_meta = nvds_acquire_obj_meta_from_pool (batch_meta);
obj_meta->unique_component_id = nvinfer->unique_id;
obj_meta->confidence = obj.confidence;
/* This is an untracked object. Set tracking_id to -1. */
obj_meta->object_id = UNTRACKED_OBJECT_ID;
obj_meta->class_id = obj.classIndex;
NvOSD_RectParams & rect_params = obj_meta->rect_params;
NvOSD_TextParams & text_params = obj_meta->text_params;
/* Assign bounding box coordinates. These can be overwritten if tracker
* component is present in the pipeline */
rect_params.left = obj.left;
rect_params.top = obj.top;
rect_params.width = obj.width;
rect_params.height = obj.height;
if(!nvinfer->process_full_frame) {
rect_params.left += parent_obj_meta->rect_params.left;
rect_params.top += parent_obj_meta->rect_params.top;
}
/* Preserve original positional bounding box coordinates of detector in the
* frame so that those can be accessed after tracker */
obj_meta->detector_bbox_info.org_bbox_coords.left = rect_params.left;
obj_meta->detector_bbox_info.org_bbox_coords.top = rect_params.top;
obj_meta->detector_bbox_info.org_bbox_coords.width = rect_params.width;
obj_meta->detector_bbox_info.org_bbox_coords.height = rect_params.height;
/* Border of width 3. */
rect_params.border_width = 3;
if (obj.classIndex > (gint) nvinfer->perClassColorParams->size()) {
rect_params.has_bg_color = 0;
rect_params.border_color = (NvOSD_ColorParams) {1, 0, 0, 1};
} else {
GstNvInferColorParams &color_params =
(*nvinfer->perClassColorParams)[obj.classIndex];
rect_params.has_bg_color = color_params.have_bg_color;
rect_params.bg_color = color_params.bg_color;
rect_params.border_color = color_params.border_color;
}
if (obj.label)
strncpy (obj_meta->obj_label, obj.label, MAX_LABEL_SIZE);
/* display_text requires heap allocated memory. */
text_params.display_text = g_strdup (obj.label);
/* Display text above the left top corner of the object. */
text_params.x_offset = rect_params.left;
text_params.y_offset = rect_params.top - 10;
/* Set black background for the text. */
text_params.set_bg_clr = 1;
text_params.text_bg_clr = (NvOSD_ColorParams) {
0, 0, 0, 1};
/* Font face, size and color. */
text_params.font_params.font_name = font_name;
text_params.font_params.font_size = 11;
text_params.font_params.font_color = (NvOSD_ColorParams) {
1, 1, 1, 1};
if (nvinfer->output_instance_mask && obj.mask) {
float *mask = (float *)g_malloc(obj.mask_size);
memcpy(mask, obj.mask, obj.mask_size);
obj_meta->mask_params.data = mask;
obj_meta->mask_params.size = obj.mask_size;
obj_meta->mask_params.threshold = segmentationThreshold;
obj_meta->mask_params.width = obj.mask_width;
obj_meta->mask_params.height = obj.mask_height;
}
nvds_add_obj_meta_to_frame (frame_meta, obj_meta, parent_obj_meta);
}
nvds_release_meta_lock (batch_meta);
}
/**
* Update string label in an existing object metadata. If processing on full
* frames, need to attach a new metadata. Assume only one label per object is generated.
*/
void
attach_metadata_classifier (GstNvInfer * nvinfer, GstMiniObject * tensor_out_object,
GstNvInferFrame & frame, GstNvInferObjectInfo & object_info)
{
NvDsObjectMeta *object_meta = frame.obj_meta;
NvDsBatchMeta *batch_meta = (nvinfer->process_full_frame) ?
frame.frame_meta->base_meta.batch_meta : object_meta->base_meta.batch_meta;
if (object_info.attributes.size () == 0 ||
object_info.label.length() == 0)
return;
nvds_acquire_meta_lock (batch_meta);
if (nvinfer->process_full_frame && !nvinfer->input_tensor_from_meta) {
/* Attach only one object in the meta since this is a full frame
* classification. */
object_meta = nvds_acquire_obj_meta_from_pool (batch_meta);
/* Font to be used for label text. */
static gchar font_name[] = "Serif";
NvOSD_RectParams & rect_params = object_meta->rect_params;
NvOSD_TextParams & text_params = object_meta->text_params;
//frame.object_meta = object_meta;
/* Assign bounding box coordinates. */
rect_params.left = 0;
rect_params.top = 0;
rect_params.width = frame.input_surf_params->width;
rect_params.height = frame.input_surf_params->height;
/* Semi-transparent yellow background. */
rect_params.has_bg_color = 0;
rect_params.bg_color = (NvOSD_ColorParams) {
1, 1, 0, 0.4};
/* Red border of width 6. */
rect_params.border_width = 6;
rect_params.border_color = (NvOSD_ColorParams) {
1, 0, 0, 1};
object_meta->object_id = UNTRACKED_OBJECT_ID;
object_meta->class_id = -1;
/* display_text requires heap allocated memory. Actual string formation
* is done later in the function. */
text_params.display_text = g_strdup ("");
/* Display text above the left top corner of the object. */
text_params.x_offset = rect_params.left;
text_params.y_offset = rect_params.top - 10;
/* Set black background for the text. */
text_params.set_bg_clr = 1;
text_params.text_bg_clr = (NvOSD_ColorParams) {
0, 0, 0, 1};
/* Font face, size and color. */
text_params.font_params.font_name = font_name;
text_params.font_params.font_size = 11;
text_params.font_params.font_color = (NvOSD_ColorParams) {
1, 1, 1, 1};
/* Attach the NvDsFrameMeta structure as NvDsMeta to the buffer. Pass the
* function to be called when freeing the meta_data. */
nvds_add_obj_meta_to_frame (frame.frame_meta, object_meta, NULL);
}
std::string string_label = object_info.label;
/* Fill the attribute info structure for the object. */
guint num_attrs = object_info.attributes.size ();
NvDsClassifierMeta *classifier_meta =
nvds_acquire_classifier_meta_from_pool (batch_meta);
classifier_meta->unique_component_id = nvinfer->unique_id;
classifier_meta->classifier_type = nvinfer->classifier_type;
for (unsigned int i = 0; i < num_attrs; i++) {
NvDsLabelInfo *label_info =
nvds_acquire_label_info_meta_from_pool (batch_meta);
NvDsInferAttribute &attr = object_info.attributes[i];
label_info->label_id = attr.attributeIndex;
label_info->result_class_id = attr.attributeValue;
label_info->result_prob = attr.attributeConfidence;
if (attr.attributeLabel) {
g_strlcpy (label_info->result_label, attr.attributeLabel, MAX_LABEL_SIZE);
if (object_info.label.length() == 0)
string_label.append (attr.attributeLabel).append(" ");
}
nvds_add_label_info_meta_to_classifier(classifier_meta, label_info);
}
if (string_label.length () > 0 && object_meta) {
gchar *temp = object_meta->text_params.display_text;
if(temp == nullptr) {
NvOSD_TextParams & text_params = object_meta->text_params;
NvOSD_RectParams & rect_params = object_meta->rect_params;
/* display_text requires heap allocated memory. Actual string formation
* is done later in the function. */
text_params.display_text = g_strdup ("");
/* Display text above the left top corner of the object. */
text_params.x_offset = rect_params.left;
text_params.y_offset = rect_params.top - 10;
/* Set black background for the text. */
text_params.set_bg_clr = 1;
text_params.text_bg_clr = (NvOSD_ColorParams) {
0, 0, 0, 1};
/* Font face, size and color. */
text_params.font_params.font_name = const_cast<gchar*>("Serif");
text_params.font_params.font_size = 11;
text_params.font_params.font_color = (NvOSD_ColorParams) {
1, 1, 1, 1};
}
temp = object_meta->text_params.display_text;
object_meta->text_params.display_text =
g_strconcat (temp, " ", string_label.c_str (), nullptr);
g_free (temp);
}
if (nvinfer->input_tensor_from_meta) {
nvds_add_classifier_meta_to_roi (frame.roi_meta, classifier_meta);
} else {
nvds_add_classifier_meta_to_object (object_meta, classifier_meta);
}
nvds_release_meta_lock (batch_meta);
}
/**
* Given an object's history, merge the new classification results with the
* previous cached results. This can be used to improve the results of
* classification when reinferencing over time. Currently, the function
* just uses the latest results.
*/
void
merge_classification_output (GstNvInferObjectHistory & history,
GstNvInferObjectInfo &new_result)
{
for (auto &attr : history.cached_info.attributes) {
free(attr.attributeLabel);
}
history.cached_info.attributes.assign (new_result.attributes.begin (),
new_result.attributes.end ());
for (auto &attr : history.cached_info.attributes) {
attr.attributeLabel =
attr.attributeLabel ? strdup (attr.attributeLabel) : nullptr;
}
history.cached_info.label.assign (new_result.label);
}
static void
release_segmentation_meta (gpointer data, gpointer user_data)
{
NvDsUserMeta *user_meta = (NvDsUserMeta *) data;
NvDsInferSegmentationMeta *meta = (NvDsInferSegmentationMeta *) user_meta->user_meta_data;
if (meta->priv_data) {
gst_mini_object_unref (GST_MINI_OBJECT (meta->priv_data));
} else {
g_free (meta->class_map);
g_free (meta->class_probabilities_map);
}
delete meta;
}
static gpointer
copy_segmentation_meta (gpointer data, gpointer user_data)
{
NvDsUserMeta *src_user_meta = (NvDsUserMeta *) data;
NvDsInferSegmentationMeta *src_meta = (NvDsInferSegmentationMeta *) src_user_meta->user_meta_data;
NvDsInferSegmentationMeta *meta = (NvDsInferSegmentationMeta *) g_malloc (sizeof (NvDsInferSegmentationMeta));
meta->classes = src_meta->classes;
meta->width = src_meta->width;
meta->height = src_meta->height;
meta->class_map = (gint *) g_memdup(src_meta->class_map, meta->width * meta->height * sizeof (gint));
meta->class_probabilities_map = (gfloat *) g_memdup(src_meta->class_probabilities_map, meta->classes * meta->width * meta->height * sizeof (gfloat));
meta->priv_data = NULL;
return meta;
}
void
attach_metadata_segmentation (GstNvInfer * nvinfer, GstMiniObject * tensor_out_object,
GstNvInferFrame & frame, NvDsInferSegmentationOutput & segmentation_output)
{
NvDsBatchMeta *batch_meta = (nvinfer->process_full_frame) ?
frame.frame_meta->base_meta.batch_meta : frame.obj_meta->base_meta.batch_meta;
NvDsUserMeta *user_meta = nvds_acquire_user_meta_from_pool (batch_meta);
NvDsInferSegmentationMeta *meta = (NvDsInferSegmentationMeta *) g_malloc (sizeof (NvDsInferSegmentationMeta));
meta->unique_id = nvinfer->unique_id;
// Both of the below lines are printing unique_id on screen.
// std::cout << "'unique_id' has been added to NvDsInferSegmentationMeta.";
// std::cout << "\n meta->unique_id :"<<meta->unique_id;
meta->classes = segmentation_output.classes;
meta->width = segmentation_output.width;
meta->height = segmentation_output.height;
meta->class_map = segmentation_output.class_map;
meta->class_probabilities_map = segmentation_output.class_probability_map;
meta->priv_data = gst_mini_object_ref (tensor_out_object);
user_meta->user_meta_data = meta;
user_meta->base_meta.meta_type = (NvDsMetaType) NVDSINFER_SEGMENTATION_META;
user_meta->base_meta.release_func = release_segmentation_meta;
user_meta->base_meta.copy_func = copy_segmentation_meta;
if (nvinfer->input_tensor_from_meta) {
nvds_add_user_meta_to_roi (frame.roi_meta, user_meta);
} else if (nvinfer->process_full_frame) {
nvds_add_user_meta_to_frame (frame.frame_meta, user_meta);
} else {
nvds_add_user_meta_to_obj (frame.obj_meta, user_meta);
}
}
/* Called when NvDsUserMeta for each frame/object is released. Reduce the
* refcount of the mini_object by 1 and free other memory. */
static void
release_tensor_output_meta (gpointer data, gpointer user_data)
{
NvDsUserMeta *user_meta = (NvDsUserMeta *) data;
NvDsInferTensorMeta *meta = (NvDsInferTensorMeta *) user_meta->user_meta_data;
if (meta->priv_data) {
gst_mini_object_unref (GST_MINI_OBJECT (meta->priv_data));
} else {
if (cudaSetDevice (meta->gpu_id) != cudaSuccess)
g_print ("Unable to set gpu device id during memory release.\n");
for (unsigned int i = 0; i < meta->num_output_layers; i++) {
if (meta->out_buf_ptrs_dev[i] &&
cudaFree (meta->out_buf_ptrs_dev[i]) != cudaSuccess)
g_print ("Unable to release device memory. \n");
if (meta->out_buf_ptrs_host[i] &&
cudaFreeHost (meta->out_buf_ptrs_host[i]) != cudaSuccess)
g_print ("Unable to release host memory. \n");
}
g_free (meta->output_layers_info);
}
delete[] meta->out_buf_ptrs_dev;
delete[] meta->out_buf_ptrs_host;
delete meta;
}
static gpointer
copy_tensor_output_meta (gpointer data, gpointer user_data)
{
NvDsUserMeta *src_user_meta = (NvDsUserMeta *) data;
NvDsInferTensorMeta *src_meta =
(NvDsInferTensorMeta *) src_user_meta->user_meta_data;
NvDsInferTensorMeta *tensor_output_meta = new NvDsInferTensorMeta;
tensor_output_meta->unique_id = src_meta->unique_id;
tensor_output_meta->num_output_layers = src_meta->num_output_layers;
tensor_output_meta->output_layers_info =
(NvDsInferLayerInfo *) g_memdup (src_meta->output_layers_info,
src_meta->num_output_layers * sizeof (NvDsInferLayerInfo));
tensor_output_meta->out_buf_ptrs_host =
new void *[src_meta->num_output_layers];
tensor_output_meta->out_buf_ptrs_dev =
new void *[src_meta->num_output_layers];
size_t layer_size = 0;
if (cudaSetDevice (src_meta->gpu_id) != cudaSuccess)
g_print ("Unable to set gpu device id.\n");
for (unsigned int i = 0; i < src_meta->num_output_layers; i++) {
NvDsInferLayerInfo *info = &src_meta->output_layers_info[i];
info->buffer = src_meta->out_buf_ptrs_host[i];
layer_size =
get_element_size (info->dataType) * info->inferDims.numElements;
if (src_meta->out_buf_ptrs_host[i]) {
if (cudaMallocHost ((void **) &tensor_output_meta->out_buf_ptrs_host[i],
layer_size) != cudaSuccess)
g_print ("Unable to allocate host memory. \n");
if (cudaMemcpy (tensor_output_meta->out_buf_ptrs_host[i],
src_meta->out_buf_ptrs_host[i], layer_size,
cudaMemcpyHostToHost) != cudaSuccess)
g_print ("Unable to copy between two host memories. \n");
} else {
tensor_output_meta->out_buf_ptrs_host[i] = NULL;
}
if (src_meta->out_buf_ptrs_dev[i]) {
if (cudaMalloc ((void **) &tensor_output_meta->out_buf_ptrs_dev[i],
layer_size) != cudaSuccess)
g_print ("Unable to allocate device memory. \n");
if (cudaMemcpy (tensor_output_meta->out_buf_ptrs_dev[i],
src_meta->out_buf_ptrs_dev[i], layer_size,
cudaMemcpyDeviceToDevice) != cudaSuccess)
g_print ("Unable to copy between two device memories. \n");
} else {
tensor_output_meta->out_buf_ptrs_dev[i] = NULL;
}
}
tensor_output_meta->gpu_id = src_meta->gpu_id;
tensor_output_meta->priv_data = NULL;
return tensor_output_meta;
}
/* Attaches the raw tensor output to the GstBuffer as metadata. */
void
attach_tensor_output_meta (GstNvInfer *nvinfer, GstMiniObject * tensor_out_object,
GstNvInferBatch *batch, NvDsInferContextBatchOutput *batch_output)
{
NvDsBatchMeta *batch_meta = (nvinfer->process_full_frame
|| nvinfer->input_tensor_from_meta) ? batch->frames[0].
frame_meta->base_meta.batch_meta : batch->frames[0].obj_meta->base_meta.
batch_meta;
/* Create and attach NvDsInferTensorMeta for each frame/object. Also
* increment the refcount of GstNvInferTensorOutputObject. */
for (size_t j = 0; j < batch->frames.size (); j++) {
GstNvInferFrame & frame = batch->frames[j];
/* Processing on ROIs (not frames or objects) skip attaching tensor output
* to frames or objects. */
NvDsInferTensorMeta *meta = new NvDsInferTensorMeta;
meta->unique_id = nvinfer->unique_id;
meta->num_output_layers = nvinfer->output_layers_info->size ();
meta->output_layers_info = nvinfer->output_layers_info->data ();
meta->out_buf_ptrs_host = new void *[meta->num_output_layers];
meta->out_buf_ptrs_dev = new void *[meta->num_output_layers];
meta->gpu_id = nvinfer->gpu_id;
meta->priv_data = gst_mini_object_ref (tensor_out_object);
meta->network_info = nvinfer->network_info;
for (unsigned int i = 0; i < meta->num_output_layers; i++) {
NvDsInferLayerInfo & info = meta->output_layers_info[i];
meta->out_buf_ptrs_dev[i] =
(uint8_t *) batch_output->outputDeviceBuffers[i] +
info.inferDims.numElements * get_element_size (info.dataType) * j;
meta->out_buf_ptrs_host[i] =
(uint8_t *) batch_output->hostBuffers[info.bindingIndex] +
info.inferDims.numElements * get_element_size (info.dataType) * j;
}
NvDsUserMeta *user_meta = nvds_acquire_user_meta_from_pool (batch_meta);
user_meta->user_meta_data = meta;
user_meta->base_meta.meta_type =
(NvDsMetaType) NVDSINFER_TENSOR_OUTPUT_META;
user_meta->base_meta.release_func = release_tensor_output_meta;
user_meta->base_meta.copy_func = copy_tensor_output_meta;
user_meta->base_meta.batch_meta = batch_meta;
if (nvinfer->input_tensor_from_meta) {
nvds_add_user_meta_to_roi (frame.roi_meta, user_meta);
} else if (nvinfer->process_full_frame) {
nvds_add_user_meta_to_frame (frame.frame_meta, user_meta);
} else {
nvds_add_user_meta_to_obj (frame.obj_meta, user_meta);
}
}
/* NvInfer is receiving input from tensor meta, also attach output tensor meta
* at batch level. */
if (nvinfer->input_tensor_from_meta) {
NvDsInferTensorMeta *meta = new NvDsInferTensorMeta;
meta->unique_id = nvinfer->unique_id;
meta->num_output_layers = nvinfer->output_layers_info->size ();
meta->output_layers_info = nvinfer->output_layers_info->data ();
meta->out_buf_ptrs_host = new void *[meta->num_output_layers];
meta->out_buf_ptrs_dev = new void *[meta->num_output_layers];
meta->gpu_id = nvinfer->gpu_id;
meta->priv_data = gst_mini_object_ref (tensor_out_object);
meta->network_info = nvinfer->network_info;
for (unsigned int i = 0; i < meta->num_output_layers; i++) {
NvDsInferLayerInfo & info = meta->output_layers_info[i];
meta->out_buf_ptrs_dev[i] =
(uint8_t *) batch_output->outputDeviceBuffers[i];
meta->out_buf_ptrs_host[i] =
(uint8_t *) batch_output->hostBuffers[info.bindingIndex];
}
NvDsUserMeta *user_meta = nvds_acquire_user_meta_from_pool (batch_meta);
user_meta->user_meta_data = meta;
user_meta->base_meta.meta_type =
(NvDsMetaType) NVDSINFER_TENSOR_OUTPUT_META;
user_meta->base_meta.release_func = release_tensor_output_meta;
user_meta->base_meta.copy_func = copy_tensor_output_meta;
user_meta->base_meta.batch_meta = batch_meta;
nvds_add_user_meta_to_batch (batch_meta, user_meta);
}
}
gstnvdsinfer.h :
nano /opt/nvidia/deepstream/deepstream-6.0/sources/includes/gstnvdsinfer.h
/*
* Copyright (c) 2018-2020, NVIDIA CORPORATION. All rights reserved.
*
* NVIDIA Corporation and its licensors retain all intellectual property
* and proprietary rights in and to this software, related documentation
* and any modifications thereto. Any use, reproduction, disclosure or
* distribution of this software and related documentation without an express
* license agreement from NVIDIA Corporation is strictly prohibited.
*/
/**
* @file gstnvdsinfer.h
* <b>NVIDIA DeepStream GStreamer NvInfer API Specification </b>
*
* @b Description: This file specifies the APIs and function definitions for
* the DeepStream GStreamer NvInfer Plugin.
*/
/**
* @defgroup gstreamer_nvinfer_api NvInfer Plugin
* Defines an API for the GStreamer NvInfer plugin.
* @ingroup custom_gstreamer
* @{
*/
G_BEGIN_DECLS
#include "nvdsinfer.h"
/**
* Function definition for the inference raw output generated callback of
* Gst-NvInfer plugin.
*
* The callback function can be registered by setting "raw-output-generated-callback"
* property on an "nvinfer" element instance. Additionally, a pointer to
* user data can be set through the "raw-output-generated-userdata" property.
* This pointer will be passed to the raw output generated callback function
* through the userdata parameter.
*
* Refer to the reference deepstream-app sources for a sample implementation
* of the callback.
*
* @param[in] buf Pointer to the GstBuffer on whose contents inference has been
* executed. The implementation should assume the buffer to be
* read-only and should not modify the buffer in any way.
* @param[in] network_info Network information for the model specified for the
* nvinfer element instance.
* @param[in] layers_info Pointer to the array containing information for all
* bound layers for the inference engine.
* @param[in] num_layers Number of layers bound for the inference engine i.e.
* number of elements in the layers_info array.
* @param[in] batch_size Number of valid input frames in the batch.
* @param[in] user_data Pointer to the user data set through the
* "raw-output-generated-userdata" property.
*/
typedef void (* gst_nvinfer_raw_output_generated_callback) (GstBuffer *buf,
NvDsInferNetworkInfo *network_info, NvDsInferLayerInfo *layers_info,
guint num_layers, guint batch_size, gpointer user_data);
/**
* Holds the raw tensor output information for one frame / one object.
*
* The "nvinfer" plugins adds this meta when the "output-tensor-meta" property
* of the element instance is set to TRUE.
*
* This meta data is added as NvDsUserMeta to the frame_user_meta_list of the
* corresponding frame_meta or object_user_meta_list of the corresponding object
* with the meta_type set to NVDSINFER_TENSOR_OUTPUT_META.
*/
typedef struct
{
/** Unique ID of the gst-nvinfer instance which attached this meta. */
guint unique_id;
/** Number of bound output layers. */
guint num_output_layers;
/** Pointer to the array containing information for the bound output layers.
* Size of the array will be equal to num_output_layers. Pointers inside
* the NvDsInferLayerInfo structure are not valid for this array. */
NvDsInferLayerInfo *output_layers_info;
/** Array of pointers to the output host buffers for the batch / frame / object. */
void **out_buf_ptrs_host;
/** Array of pointers to the output device buffers for the batch / frame / object. */
void **out_buf_ptrs_dev;
/** GPU device ID on which the device buffers have been allocated. */
gint gpu_id;
/** Private data used for the meta producer's internal memory management. */
void *priv_data;
/** Network information for the model specified for the nvinfer element instance. */
NvDsInferNetworkInfo network_info;
} NvDsInferTensorMeta;
/**
* Holds the segmentation model output information for one frame / one object.
*
* The "nvinfer" plugins adds this meta for segmentation models.
*
* This meta data is added as NvDsUserMeta to the frame_user_meta_list of the
* corresponding frame_meta or object_user_meta_list of the corresponding object
* with the meta_type set to NVDSINFER_SEGMENTATION_META.
*/
typedef struct
{
/** Unique ID of the gst-nvinfer instance which attached this meta. */
guint unique_id;
/** Number of classes in the segmentation output. */
guint classes;
/** Width of the segmentation output class map. */
guint width;
/** Height of the segmentation output class map. */
guint height;
/** Pointer to the array for 2D pixel class map. The output for pixel (x,y)
* will be at index (y * width + x). */
gint* class_map;
/** Pointer to the raw array containing the probabilities. The probability for
* class c and pixel (x,y) will be at index (c * width *height + y * width + x). */
gfloat *class_probabilities_map;
/** Private data used for the meta producer's internal memory management. */
void *priv_data;
} NvDsInferSegmentationMeta;
G_END_DECLS
/** @} */
Below is the command I used to copy file:
cp /opt/nvidia/deepstream/deepstream-6.0/sources/gst-plugins/gst-nvinfer/libnvdsgst_infer.so /opt/nvidia/deepstream/deepstream-6.0/lib/gst-plugins/libnvdsgst_infer.so
Can you figure out the issue?
