Holoscan video output via AJA capture card

Hello, I am developing an implementation on Holoscan where I am using an AJA capture card to feed the input video to the Holoscan and now I want to pass the output of the Holoscan application through AJA. I am using an integrated AJASourceOp Operator for video input, is there any way to redirect the Holoscan output via AJA?


Hi there! We have a reference on holohub/applications/endoscopy_tool_tracking at main · nvidia-holoscan/holohub · GitHub and are now in the process of testing whether this works in SDK v2.0, please stay tuned! Will update here when we find out. Thank you.

For AJA cards that support Hardware Keying, the pipeline can also be configured to overlay the segmentation results on the input video on the AJA card FPGA, instead of on the GPU: when is_aja_overlay_enabled is True, the overlay layer is sent from Holoviz back to the AJA Source operator which handles the alpha blending and outputs it to a port of the the AJA card. The blended image is also sent back to the Holoviz operator (instead of the input video only) for rendering the same image buffer. The overlay configuration does not work with Holoscan SDK v0.6. As Holoscan supports only Directed Acyclic Graph (DAG) for a fragment, the cycle formed by the edge between the Holoviz and AJA Source operators is incompatible. The overlay feature will again be available in a future release of Holoscan SDK.

Hello, it’s confirmed that the overlay feature is again available in the latest release SDK v2.0, but please note that with dGPU it currently only works with the RDMA enabled, and also works with iGPU mode. Please let us know how it goes with the overlay in your application. Thank you.

Hi, I am facing an issue. Following the endoscopy_tool_tracking i changed my code to do the overlay buffer flow between AJA source and visualizer but i am getting an error related to the buffering loop between AJA and Holoviz.

[info] [gxf_executor.cpp:210] Creating context
DEBUGGING: is_aja_overlay_enabled is True
[info] [gxf_executor.cpp:1595] Loading extensions from configs...
[error] [gxf_executor.cpp:1245] Worklist is empty, but not all nodes have been visited. There is a cycle.
[error] [gxf_executor.cpp:1247] Application is being aborted.
[error] [gxf_executor.cpp:1621] Failed to initialize fragment
[error] [gxf_executor.cpp:292] Failed to initialize GXF graph

Bellow attached my workflow aswell as my Yaml file configuration

        if is_aja: # If the source is AJA
            #self.add_flow(source, viz, {("video_buffer_output", "receivers")})
            self.add_flow(source, drop_alpha_channel, {("video_buffer_output", "")})
            #self.add_flow(drop_alpha_channel, preprocessor)
            if do_preprocessing:
                self.add_flow(drop_alpha_channel, ImageProcessing, {("", "input_tensor")})
                self.add_flow(ImageProcessing, preprocessor, {("output_tensor", "source_video")})
            #self.add_flow(drop_alpha_channel, ImageProcessing, {("", "input_tensor")}
            #self.add_flow(ImageProcessing, viz, {("output_tensor", "receivers")})
            #self.add_flow(ImageProcessing, preprocessor, {("output_tensor", "source_video")})
        else: # If the source is not AJA (video file)
            if do_preprocessing: # If the preprocessing is done
                self.add_flow(source, ImageProcessing, {("output", "input_tensor")})
                self.add_flow(source, viz, {("video_buffer_output" if is_aja else "output", "receivers")})

                #self.add_flow(ImageProcessing, Debugging, {("output_tensor", "input_tensor")})
                #self.add_flow(Debugging, preprocessor, {("output_tensor", "source_video")})
                self.add_flow(ImageProcessing, preprocessor, {("output_tensor", "source_video")})

                #self.add_flow(ImageProcessing, MyPreprocessor, {("output_tensor", "input_tensor")})
            else: # If the preprocessing is not done
                self.add_flow(source, preprocessor, {("output", "source_video")})

        #self.add_flow(preprocessor, DebuggingPreprocessor, {("tensor", "input_tensor")})
        #self.add_flow(DebuggingPreprocessor, inference, {("output_tensor", "receivers")})
        self.add_flow(preprocessor, inference, {("tensor", "receivers")})

        #self.add_flow(MyPreprocessor, inference, {("output_tensor", "receivers")})
        #self.add_flow(inference, DebuggingInference, {("transmitter", "input_tensor")})
        #self.add_flow(DebuggingInference, postprocessor, {("output_tensor", "in_tensor")})
        self.add_flow(inference, postprocessor, {("transmitter", "in_tensor")})
        self.add_flow(postprocessor, PostImageProcessing, {("out_tensor", "input_tensor")})
        self.add_flow(PostImageProcessing, viz, {("out_tensor", "receivers")})

        if is_aja_overlay_enabled:
            print('DEBUGGING: is_aja_overlay_enabled is True')
            self.add_flow(source, viz, {("overlay_buffer_output", "render_buffer_input")})
            self.add_flow(viz, source, {("render_buffer_output", "overlay_buffer_input")})

        if do_record_output: 
            self.add_flow(viz, recorder_format_converter, {("render_buffer_output", "source_video")})
            self.add_flow(recorder_format_converter, recorder)
%YAML 1.2


is_record_segmentation: False # True if the segmentation is recorded
is_record_output: True # True if the output is recorded
is_preprocessing:  False # True if the preprocessing is done

replayer:  # VideoStreamReplayer
  basename: "arthroscopic_segmentation"  
  frame_rate: 60 # as specified in timestamps
  repeat: False # default: false
  realtime: True # default: true
  count: 0 # default: 0 (no frame count restriction)

aja:  # AJASourceOp
  width: 1920
  height: 1080
  rdma: true
  enable_overlay: True
  overlay_rdma: True

drop_alpha_channel:  # FormatConverter
  in_dtype: "rgba8888"
  in_tensor_name: source_video
  out_dtype: "rgb888"

  in_tensor_name: input_tensor
  out_tensor_name: output_tensor

  in_tensor_name: input_tensor
  out_tensor_name: output_tensor

  in_tensor_name: input_tensor
  out_tensor_name: output_tensor

  in_tensor_name: input_tensor
  out_tensor_name: output_tensor

MyPreprocessor:  # FormatConverter
  in_tensor_name: input_tensor
  out_tensor_name: output_tensor

preprocessor:  # FormatConverter
  out_tensor_name: source_video
  out_dtype: "float32"
  #scale_min: 0.0 
  #scale_max: 255.0
  #if not is_preprocessing:
  #  resize_width: 1024
  #  resize_height: 1024
  # out_channel_order: [2,1,0]  # BGR to RGB

inference:  # Inference
  backend: "trt"
    "AJA_arthrosegmentation": ["source_video"]
    "AJA_arthrosegmentation": ["output"]
  # dla_core: 0  
  # Para usar o DLA - Holoscan 0.4.0 encontrei informaçao SDK e suporta - Holoscan 1.0.3 não suporta?

postprocessor:  # SegmentationPostprocessor
  in_tensor_name: output
  network_output_type: softmax
  data_format: nchw

  in_dtype: "rgba8888"
  out_dtype: "rgb888"
  scale_min: 0.0
  scale_max: 255.0
  out_tensor_name: out_video

  in_tensor_name: input_tensor
  out_tensor_name: output_tensor

  in_tensor_name: input_tensor
  out_tensor_name: output_tensor

  in_tensor_name: source_video
  directory: "/tmp"
  basename: "tensor"
viz:  # Holoviz
  width: 1920
  height: 1080
  color_lut: [
    [0, 0, 0, 0],
    #[1, 1, 1, 1]
    [0.2, 0.63, 0.17, 0.32]
  #use_exclusive_display: False # default: true
  #display_name: "DP-0" # default: "DP-2"
  fullscreen: False
  enable_render_buffer_output: True
  enable_render_buffer_input: True ```

This error is about supporting cycle in the SDK. Could you let us know the SDK version you’re using, and the hardware (if IGX, dGPU or iGPU)? Thank you

Sure, my SDK version is 1.0.3 and i am using an jetson Orin DevKit so it’s an iGPU. I havent migrated yet to the new version Holoscan 2.0 because i am using Ubuntu 20.04. Thanks

Oh I see, the SDK version 1.0.3 is likely the issue then. Unfortunately it does need a SDK update.