Hi @AastaLLL ,
Thanks for your reply.
Here is my pipeline:
pgie = make_elm_or_print_err("nvinferserver", "primary-inference", "Nvinferserver")
nvvidconv1 = Gst.ElementFactory.make("nvvideoconvert", "convertor1")
if not nvvidconv1:
sys.stderr.write(" Unable to create nvvidconv1 \n")
print("Creating filter1 \n ")
caps1 = Gst.Caps.from_string("video/x-raw(memory:NVMM), format=RGBA")
filter1 = Gst.ElementFactory.make("capsfilter", "filter1")
if not filter1:
sys.stderr.write(" Unable to get the caps filter1 \n")
filter1.set_property("caps", caps1)
pgie.link(nvvidconv1)
nvvidconv1.link(filter1)
filtersrcpad = filter1.get_static_pad("src")
if not filtersrcpad:
sys.stderr.write(" Unable to get src pad of primary infer \n")
filtersrcpad.add_probe(Gst.PadProbeType.BUFFER, filter_src_pad_buffer_probe, 0)
And this is my filter_src_pad_buffer_probe function:
def filter_src_pad_buffer_probe(pad, info, u_data):
gst_buffer = info.get_buffer()
if not gst_buffer:
print("Unable to get GstBuffer ")
return
global count
global start_date
if count == 30:
end_date = time.time()
print(f"fps: {count / (end_date - start_date)}")
count = 0
start_date = end_date
else:
count += 1
# 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
detection_params = DetectionParam(CLASS_NB, ACCURACY_ALL_CLASS)
box_size_param = BoxSizeParam(IMAGE_HEIGHT, IMAGE_WIDTH,
MIN_BOX_WIDTH, MIN_BOX_HEIGHT)
nms_param = NmsParam(TOP_K, IOU_THRESHOLD)
while l_frame is not None:
try:
# Note that l_frame.data needs a cast to pyds.NvDsFrameMeta
# 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
n_frame = pyds.get_nvds_buf_surface(hash(gst_buffer), frame_meta.batch_id)
# convert python array into numy array format.
frame_image = np.array(n_frame, copy=True, order='C')
# covert the array into cv2 default color format
frame_image = cv2.cvtColor(frame_image, cv2.COLOR_RGBA2BGRA)
print(frame_image.shape)
all_poses = []
l_user = frame_meta.frame_user_meta_list
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)
# Boxes in the tensor meta should be in network resolution which is
# found in tensor_meta.network_info. Use this info to scale boxes to
# the input frame resolution.
layers_info = []
for i in range(tensor_meta.num_output_layers):
layer = pyds.get_nvds_LayerInfo(tensor_meta, i)
layers_info.append(layer)
frame_object_list = nvds_infer_parse_custom_tf_ssd(
layers_info, detection_params, box_size_param, nms_param
)
try:
l_user = l_user.next
except StopIteration:
break
all_poses += frame_object_list
add_obj_meta_to_frame(frame_image, all_poses, batch_meta, frame_meta)
try:
l_frame = l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
Another weird thing is, I can’t get any of my layer outputs inside layers_info if I attach the probe to filter. If I attach this function to pgie src pad, I can get layer outputs.
Thanks again!
