Please provide complete information as applicable to your setup.
**• Hardware Platform (Jetson / GPU)**Jetson Xavier AGX
• DeepStream Version5.1.0
**• JetPack Version (valid for Jetson only)**4.5-b129
• TensorRT Version7.1.3-1
**• Issue Type( questions, new requirements, bugs)**questions
I trained a Maskrcnn model with my own data on TLT by following this link:
https://developer.nvidia.com/blog/training-instance-segmentation-models-using-maskrcnn-on-the-transfer-learning-toolkit/
The model has been deployed with deepstream-app sucessfully by following the example deepstream_app_source1_mrcnn.txt and config_infer_primary_mrcnn.txt.
My reqirement is to get a output video or image sequences with the inference masks on it, but without the bounding boxs and other information. Also I need to change the color and opacity of the masks to sth like white. So I refer to the deepstream_python_apps/apps/deepstream-ssd-parser at master · NVIDIA-AI-IOT/deepstream_python_apps · GitHub to register a pgie srcpad probe function to get the tensor meta and transfer it to numpy. But I don’t know how to save them as images as I required. Would you please give me some support?
Here’s my pgie_src_pad_buffer_probe:
def pgie_src_pad_buffer_probe(pad, info, u_data):
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
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)
label_names = get_label_names_from_file("mrcnn_labels.txt")
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
l_user = frame_meta.frame_user_meta_list
frame_number = frame_meta.frame_num
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)
# INFO: [Implicit Engine Info]: layers num: 3
# 0 INPUT kFLOAT Input 3x832x1344
# 1 OUTPUT kFLOAT generate_detections 100x6 pyds.get_nvds_LayerInfo(tensor_meta, 0)
# 2 OUTPUT kFLOAT mask_head/mask_fcn_logits/BiasAdd 100x14x28x28
frame_outputs = []
for i in range(tensor_meta.num_output_layers):
layer = pyds.get_nvds_LayerInfo(tensor_meta, i)
# Convert NvDsInferLayerInfo buffer to numpy array
ptr = ctypes.cast(pyds.get_ptr(layer.buffer), ctypes.POINTER(ctypes.c_float))
v = np.array(np.ctypeslib.as_array(ptr, shape=(layer.dims.numElements,)), copy=True)
#print(v)
frame_outputs.append(v)
# # 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
# for frame_object in frame_object_list:
# add_obj_meta_to_frame(frame_object, batch_meta, frame_meta, label_names)
try:
l_frame = l_frame.next
except StopIteration:
break
return Gst.PadProbeReturn.OK
