def meta_copy_func(data, user_data):
logging.debug("meta_copy_func: BEGIN")
# Cast data to pyds.NvDsUserMeta
user_meta = pyds.NvDsUserMeta.cast(data)
src_meta_data = user_meta.user_meta_data
# Cast src_meta_data to pyds.NvDsEventMsgMeta
srcmeta = pyds.NvDsEventMsgMeta.cast(src_meta_data)
# Duplicate the memory contents of srcmeta to dstmeta
# First use pyds.get_ptr() to get the C address of srcmeta, then
# use pyds.memdup() to allocate dstmeta and copy srcmeta into it.
# pyds.memdup returns C address of the allocated duplicate.
dstmeta_ptr = pyds.memdup(pyds.get_ptr(srcmeta),
sys.getsizeof(pyds.NvDsEventMsgMeta))
# Cast the duplicated memory to pyds.NvDsEventMsgMeta
dstmeta = pyds.NvDsEventMsgMeta.cast(dstmeta_ptr)
# Duplicate contents of ts field. Note that reading srcmeta.ts
# returns its C address. This allows to memory operations to be
# performed on it.
dstmeta.ts = pyds.strdup(srcmeta.ts)
# Copy the sensorStr. This field is a string property.
# The getter (read) returns its C address. The setter (write)
# takes string as input, allocates a string buffer and copies
# the input string into it.
# pyds.get_string() takes C address of a string and returns
# the reference to a string object and the assignment inside the binder copies content.
dstmeta.sensorStr = pyds.get_string(srcmeta.sensorStr)
if srcmeta.objSignature.size > 0:
dstmeta.objSignature.signature = pyds.memdup(
srcmeta.objSignature.signature, srcMeta.objSignature.size)
dstmeta.objSignature.size = srcmeta.objSignature.size
if srcmeta.extMsgSize > 0:
if srcmeta.objType == pyds.NvDsObjectType.NVDS_OBJECT_TYPE_VEHICLE:
srcobj = pyds.NvDsVehicleObject.cast(srcmeta.extMsg)
obj = pyds.alloc_nvds_vehicle_object()
obj.type = pyds.get_string(srcobj.type)
obj.make = pyds.get_string(srcobj.make)
obj.model = pyds.get_string(srcobj.model)
obj.color = pyds.get_string(srcobj.color)
obj.license = pyds.get_string(srcobj.license)
obj.region = pyds.get_string(srcobj.region)
dstmeta.extMsg = obj
dstmeta.extMsgSize = sys.getsizeof(pyds.NvDsVehicleObject)
elif srcmeta.objType == pyds.NvDsObjectType.NVDS_OBJECT_TYPE_PERSON:
srcobj = pyds.NvDsPersonObject.cast(srcmeta.extMsg)
obj = pyds.alloc_nvds_person_object()
obj.age = srcobj.age
obj.gender = pyds.get_string(srcobj.gender)
obj.cap = pyds.get_string(srcobj.cap)
obj.hair = pyds.get_string(srcobj.hair)
obj.apparel = pyds.get_string(srcobj.apparel)
dstmeta.extMsg = obj
dstmeta.extMsgSize = sys.getsizeof(pyds.NvDsVehicleObject)
logging.debug("meta_copy_func: END")
return dstmeta
def pgie_src_pad_buffer_probe(pad, info, u_data):
frame_number = 0
gst_buffer = info.get_buffer()
if not gst_buffer:
print("Unable to get GstBuffer ")
return
# Retrieve batch metadata from the gst_buffer
# Note that pyds.gst_buffer_get_nvds_batch_meta() expects the
# C address of gst_buffer as input, which is obtained with hash(gst_buffer)
batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
l_frame = batch_meta.frame_meta_list
while l_frame is not None:
try:
# Note that l_frame.data needs a cast to pyds.NvDsFrameMeta
# The casting is done by pyds.NvDsFrameMeta.cast()
# The casting also keeps ownership of the underlying memory
# in the C code, so the Python garbage collector will leave
# it alone.
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
except StopIteration:
break
frame_number = frame_meta.frame_num
l_user = frame_meta.frame_user_meta_list
print(l_user)
while l_user is not None:
try:
# Note that l_user.data needs a cast to pyds.NvDsUserMeta
# The casting also keeps ownership of the underlying memory
# in the C code, so the Python garbage collector will leave
# it alone.
user_meta = pyds.NvDsUserMeta.cast(l_user.data)
except StopIteration:
break
if (user_meta.base_meta.meta_type !=
pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META):
continue
tensor_meta = pyds.NvDsInferTensorMeta.cast(
user_meta.user_meta_data)
layers_info = []
layer = pyds.get_nvds_LayerInfo(tensor_meta, 0)
if layer.buffer:
ptr = ctypes.cast(pyds.get_ptr(layer.buffer),
ctypes.POINTER(ctypes.c_float))
v = np.ctypeslib.as_array(ptr, shape=(5, ))
prediction = int(np.argmax(np.array(v).squeeze(), axis=0))
classification = CLASSES[prediction]
classifications[frame_number] = classification
try:
l_user = l_user.next
except StopIteration:
break
fps_streams["stream{0}".format(frame_meta.pad_index)].get_fps()
try:
l_frame = l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
def sgie_sink_pad_buffer_probe(pad, info, u_data):
frame_number = 0
num_rects = 0
gst_buffer = info.get_buffer()
if not gst_buffer:
print("Unable to get GstBuffer ")
return
# Retrieve batch metadata from the gst_buffer
# Note that pyds.gst_buffer_get_nvds_batch_meta() expects the
# C address of gst_buffer as input, which is obtained with hash(gst_buffer)
batch_meta = pyds.gst_buffer_get_nvds_batch_meta(hash(gst_buffer))
l_frame = batch_meta.frame_meta_list
while l_frame is not None:
try:
# Note that l_frame.data needs a cast to pyds.NvDsFrameMeta
# The casting is done by pyds.NvDsFrameMeta.cast()
# The casting also keeps ownership of the underlying memory
# in the C code, so the Python garbage collector will leave
# it alone.
frame_meta = pyds.NvDsFrameMeta.cast(l_frame.data)
except StopIteration:
break
frame_number = frame_meta.frame_num
num_rects = frame_meta.num_obj_meta
l_obj = frame_meta.obj_meta_list
while l_obj is not None:
try:
# Casting l_obj.data to pyds.NvDsObjectMeta
obj_meta = pyds.NvDsObjectMeta.cast(l_obj.data)
except StopIteration:
break
l_user = obj_meta.obj_user_meta_list
# if obj_meta.class_id == SGIE_CLASS_ID_FACE:
# print(f'obj_meta.obj_user_meta_list {l_user}')
while l_user is not None:
try:
# Casting l_user.data to pyds.NvDsUserMeta
user_meta = pyds.NvDsUserMeta.cast(l_user.data)
except StopIteration:
break
if (user_meta.base_meta.meta_type !=
pyds.NvDsMetaType.NVDSINFER_TENSOR_OUTPUT_META):
continue
# Converting to tensor metadata
# Casting user_meta.user_meta_data to NvDsInferTensorMeta
tensor_meta = pyds.NvDsInferTensorMeta.cast(
user_meta.user_meta_data)
# Get output layer as NvDsInferLayerInfo
layer = pyds.get_nvds_LayerInfo(tensor_meta, 0)
# Convert NvDsInferLayerInfo buffer to numpy array
ptr = ctypes.cast(pyds.get_ptr(layer.buffer),
ctypes.POINTER(ctypes.c_float))
v = np.ctypeslib.as_array(ptr, shape=(128, ))
# Pridict face neme
yhat = v.reshape((1, -1))
face_to_predict_embedding = normalize_vectors(yhat)
result = predict_using_classifier(faces_embeddings, labels,
face_to_predict_embedding)
result = (str(result).title())
# print('Predicted name: %s' % result)
# Generate classifer metadata and attach to obj_meta
# Get NvDsClassifierMeta object
classifier_meta = pyds.nvds_acquire_classifier_meta_from_pool(
batch_meta)
# Pobulate classifier_meta data with pridction result
classifier_meta.unique_component_id = tensor_meta.unique_id
label_info = pyds.nvds_acquire_label_info_meta_from_pool(
batch_meta)
label_info.result_prob = 0
label_info.result_class_id = 0
pyds.nvds_add_label_info_meta_to_classifier(
classifier_meta, label_info)
pyds.nvds_add_classifier_meta_to_object(
obj_meta, classifier_meta)
display_text = pyds.get_string(
obj_meta.text_params.display_text)
obj_meta.text_params.display_text = f'{display_text} {result}'
try:
l_user = l_user.next
except StopIteration:
break
try:
l_obj = l_obj.next
except StopIteration:
break
try:
l_frame = l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
