Output Masks And Detection on Inference

Description

I have successfully tested segmentation using usb cam and masks overlay and detection seems working fine on output frames but I am unable to get the masks and detection as return which I want to use it for post processing.

I have checked the API reference link for segnet as well and it says all the functions such as
net.Overlay(img_overlay, width, height, opt.filter_mode)
net.Mask(img_mask, width/2, height/2, opt.filter_mode)
etc
https://rawgit.com/dusty-nv/jetson-inference/pytorch/docs/html/python/jetson.inference.html#segNet

Almost all function’s having None as returned type. So, please any help to get the detection boxes and masks points as return type ?

I have seen there’s option in object detection but there is so far couldn’t find any option in segmentation.

I will be very thank you.

Environment

**TensorRT **:
GPU Type:
Nvidia Driver:
CUDA 10:
CUDNN 7:
Jetson Nano:
Python3:
TensorFlow 1.15:
PyTorch:

Relevant Files

Steps To Reproduce

Hi,
Mask and Overlay outputs the result as a CUDA memory capsule.

Mask(...)
Produce a colorized RGBA segmentation mask of the output.
 
Parameters:
  image  (capsule) -- output CUDA memory capsule
  width  (int) -- width of the image (in pixels)
  height (int) -- height of the image (in pixels)
  filter_mode (string) -- optional string indicating the filter mode, 'point' or 'linear' (default: 'linear')
Returns:  (none)

Overlay(...)
Produce the segmentation overlay alpha blended on top of the original image.
 
Parameters:
  image  (capsule) -- output CUDA memory capsule
  width  (int) -- width of the image (in pixels)
  height (int) -- height of the image (in pixels)
  filter_mode (string) -- optional string indicating the filter mode, 'point' or 'linear' (default: 'linear')
Returns:  (none)

You can refer to render function to check the code to rescale the image for display:

I will recommend for any issue or enhancement request related to Segnet, please file an issue on https://github.com/dusty-nv/jetson-inference/issues

Thanks