Application structure

Sensor Bridge v1.0.0

Holoscan applications are built by specifying sequences of operators. Connecting the output of one operator to the input of another operator (via the add_flow API) configures Holoscan’s pipeline and specifies when individual operators can run.

Holoscan sensor bridge leverages this framework by providing operators and objects that send and receive data in Holoscan applications. There are additional operators for converting application specific data (e.g. CSI-2 formatted video data) into formats that are acceptable inputs for other standard Holoscan operators. To see how a sensor bridge application works, we’ll step through the example IMX274 player.

The application in examples/ configures the following pipeline. When a loop through the pipeline finishes, execution restarts at the top, where new data is acquired and processed.

%%{init: {"theme": "base", "themeVariables": { }} }%% graph r[RoceReceiverOperator] --> c[CsiToBayerOp] c --> i[ImageProcessorOp] i --> d[BayerDemosaicOp] d --> g[GammaCorrectionOp] g --> v[HolovizOp]

Fig. 1 IMX274 Player

  • RoceReceiverOperator blocks until an end-of-frame UDP message is received. On return, the received frame data is available in GPU memory, along with metadata which is published to the application layer. Holoscan sensor bridge uses RoCE v2 to transmit data plane traffic over UDP; this is why the receiver is called RoceReceiverOperator.

  • CsiToBayerOp is aware that the received data is a CSI-2 RAW10 image, which it translates into a bayer video frame. Each pixel color component in this image is decoded and stored as a uint16 value. For more information about RAW10, see the MIPI CSI-2 specification.

  • ImageProcessorOp adjusts the received bayer image color and brightness to make it acceptable for display.

  • BayerDemosaicOp converts the bayer image data into RGBA.

  • GammaCorrectionOp adjusts the luminance of the RGBA image to improve human perception.

  • HolovizOp displays the RGBA image on the GUI.

For each step in the pipeline, the image data is stored in a buffer in GPU memory. Pointers to that data are passed between each element in the pipeline, avoiding expensive memory copies between host and GPU memory. GPU acceleration is used to perform each operator’s function, resulting in very low latency operation. initializes the sensor bridge device, camera, and pipeline in this way. To enhance readability, some details are skipped–be sure and check the actual example code for more details.


import hololink as hololink_module def main(): # Get handles to GPU cuda.cuInit(0) cu_device_ordinal = 0 cu_device = cuda.cuDeviceGet(cu_device_ordinal) cu_context = cuda.cuCtxCreate(0, cu_device) # Look for sensor bridge enumeration messages; return only the one we're looking for channel_metadata = hololink_module.HololinkEnumerator.find_channel(channel_ip="") # Use that enumeration data to instantiate a data receiver object hololink_channel = hololink_module.HololinkDataChannel(channel_metadata) # Now that we can communicate, create the camera controller camera = hololink_module.sensors.imx274.dual_imx274.Imx274Cam(hololink_channel, ...) # Set up our Holoscan pipeline application = HoloscanApplication(cu_context, cu_device_ordinal, camera, hololink_channel, ...) application.config(...) # Connect and initialize the sensor bridge device hololink = hololink_channel.hololink() hololink.start() # Establish a connection to the sensor bridge device hololink.reset() # Drive the sensor bridge to a known state # Configure the camera for 4k at 60 frames per second camera_mode = imx274_mode.Imx274_Mode.IMX274_MODE_3840X2160_60FPS camera.setup_clock() camera.configure(camera_mode) # Run our Holoscan pipeline # we don't usually return from this call. hololink.stop()

Important details:

  • HololinkEnumerator.find_channel blocks the caller until an enumeration message that matches the given criteria is found. If no matching device is found, this method will time out (default 20 seconds) and raise an exception. Holoscan sensor bridge enumeration messages are sent once per second.

  • Holoscan sensor bridge devices transmit enumeration messages for each data plane controller, which currently correspond directly with each sensor bridge Ethernet interface. If both interfaces on a device are connected to a host, the host will receive a pair of distinct enumeration messages, one for each data port, from the same sensor bridge device.

  • Enumeration messages are sent to the local broadcast address, and routers are not allowed to forward these local broadcast messages to other networks. You must have a local connection between the host and the sensor bridge device in order to enumerate it.

  • HololinkEnumerator.find_channel returns a dictionary of name/value pairs containing identifying information about the data port being discovered, including MAC ID, IP address, versions of all the programmable components within the device, device serial number, and which specific instance this data port controller is within the device. While the IP address may change, the MAC ID, serial number, and data plane controller instance are constant. The host does not need to request any of the data included in this dictionary; its all broadcast by the sensor bridge device.

  • HololinkDataChannel is the local controller for a data plane on the sensor bridge device. It contains the APIs for configuring the target addresses for packets transmitted on that data plane– this is used by the receiver operator, described below.

  • In this example, the camera object provides most of the APIs that the application layer would access. When the application configures the camera, the camera object knows how to work with the various sensor bridge controller objects to properly configure HololinkDataChannel.

  • Usually there are multiple HololinkDataChannel instances on a single Hololink sensor bridge device, and many APIs on the Hololink device will affect all the HololinkDataChannel objects on that same device. In this example, calling hololink.reset will reset all the data channels on this device; and in the stereo IMX274 configuration, calling camera.setup_clock sets the clock that is shared between both cameras. For this reason, it’s important that the application is careful about calling camera.setup_clock–resetting the clock while a camera is running can lead to undefined states.

Holoscan, on the call to, invokes the application’s compose method, which includes this:


class HoloscanApplication(holoscan.core.Application): def __init__(self, ..., camera, hololink_channel, ...): ... self._camera = camera self._hololink_channel = hololink_channel ... def compose(self): ... # Create the CSI to bayer converter. csi_to_bayer_operator = hololink_module.operators.CsiToBayerOp(...) # The call to camera.configure(...) earlier set our image dimensions # and bytes per pixel. This call asks the camera to configure the # converter accordingly. self._camera.configure_converter(csi_to_bayer_operator) # csi_to_bayer_operator now knows the image dimensions and bytes per pixel, # and can compute the overall size of the received image data. frame_size = csi_to_bayer_operator.get_csi_length() # Create a receiver object that fills out our frame buffer. The receiver # operator knows how to configure hololink_channel to send its data # to us and to provide an end-of-frame indication at the right time. receiver_operator = hololink_module.operators.RoceReceiverOperator( hololink_channel, frame_size, ...) ... # Use add_flow to connect the operators together: ... # receiver_operator.compute() will be followed by csi_to_bayer_operator.compute() self.add_flow(receiver_operator, csi_to_bayer_operator, {("output", "input")}) ...

Some key points:

  • receiver_operator has no idea it is dealing with video data. It’s just informed of the memory region(s) to fill and the size of a block of data. When a complete block of data is received, the CPU will be notified so that pipeline processing can continue.

  • Applications can pass in a frame buffer device memory pointer to the constructor for receiver_opearator, or the receiver will allocate one for you. When it allocates a buffer, it can take into account special requirements for various configurations of GPU and RDMA controller.

  • csi_to_bayer_operator is aware of memory layout for CSI-2 formatted image data. Our call to camera.configure_converter allows the camera to communicate the image dimensions and pixel depth; with that knowledge, the call to csi_to_bayer_operator.get_csi_length can return the size of the memory block necessary to manage these images. This memory size includes not only the image data itself, but CSI-2 metadata, and GPU memory alignment requirements. Because CsiToBayerOp is a GPU accelerated function, it may have special memory requirements that the camera sensor object is not aware of.

  • receiver_operator coordinates with holoscan_channel to configure the sensor bridge data plane. Configuration automatically handles setting the sensor bridge device with our host Ethernet and IP addresses, destination memory addresses, security keys, and frame size information.

  • To support RDMA, the receiver operator is given a single block of memory (instead of a memory pool to allocate from). The peripheral component is granted access to this region only, and that region does not change throughout the life of the sensor bridge application.

  • the sensor bridge device, following configuration by the holoscan_channel object, will start forwarding all received sensor data to the configured receiver. We haven’t instructed the camera to start streaming data yet, but at this point, we’re ready to receive it.

  • receiver_operator keeps track of a device parameter, which in this application is our camera. When receiver_operator.start is called, it will call device.start, which in our IMX274 implementation, will instruct the camera to begin streaming data.

In this example, receiver_operator is a RoceReceiverOperator instance, which takes advantage of the RDMA acceleration features present in the ConnectX firmware. With RoceReceiverOperator, the CPU only sees an interrupt when the last packet for the frame is received–all frame data sent before that is written to GPU memory in the background. In systems without ConnectX devices, LinuxReceiverOperator provides the same functionality but uses the host CPU and Linux kernel to receive the ingress UDP requests; and the CPU writes that payload data to GPU memory. This provides the same functionality as RoceReceiverOperator but at considerably lower performance.

A demonstration application where inference is used to generate a video overlay is included in examples/ The Tao PeopleNet is used to determine the locations of persons, bags, and faces in the live video stream. This example program draws bounding boxes on an overlay illustrating where those objects are detected in the video frame.

Pipeline structure:

%%{init: {"theme": "base", "themeVariables": { }} }%% graph r[RoceReceiverOperator] --> c[CsiToBayerOp] c --> i[ImageProcessorOp] i --> d[BayerDemosaicOp] d --> g[GammaCorrectionOp] g --> s[ImageShiftToUint8Operator] s --> p[FormatConverterOp] p --> fi[FormatInferenceInputOp] fi --> in[InferenceOp] in --> pf[PostprocessorOp] s -- live video --> v[HolovizOp] pf -- overlay --> v

Fig. 2 IMX274 Player with Inference

Adding inference to the video pipeline is easy: just add the appropriate operators and data flows. In our case, we use the video mixer built in to HolovizOp to display the overlay generated by inference. RoceReceiverOperator is specified to always provide the most recently received video frame, so if a pipeline takes more than one frame time to complete, the next iteration through the loop will always work on the most recently received video frame.

The Body Pose Estimation application takes input from a live video, performs inference using YOLOv8 pose model, and then shows keypoints overlayed onto the original video. The keypoints are:

[nose, left eye, right eye, left ear, right ear, left shoulder, right shoulder, left elbow, right elbow, left wrist, right wrist, left hip, right hip, left knee, right knee, left ankle, right ankle]

This application’s pipeline is the same as the People Detection application:

%%{init: {"theme": "base", "themeVariables": { }} }%% graph r[RoceReceiverOperator] --> c[CsiToBayerOp] c --> i[ImageProcessorOp] i --> d[BayerDemosaicOp] d --> g[GammaCorrectionOp] g --> s[ImageShiftToUint8Operator] s --> p[FormatConverterOp] p --> fi[FormatInferenceInputOp] fi --> in[InferenceOp] in --> pf[PostprocessorOp] s -- live video --> v[HolovizOp] pf -- overlay --> v

Fig. 3 Body Pose Estimation

The difference is that InferenceOp uses the YOLOv8 pose model and the post processor operator has logic that performs postprocessing specific to the YOLOv8 model. Specifically, it takes the output of Inference and filters out detections that have low scores and applies non-max suppression (nms) before sending the output to HolovizOp.

Multiple receiver operators can be instantiated to support data feeds from multiple cameras. In examples/, the same pipeline for live video feed is presented, except that it is instantiated twice, once for each camera on the IMX274 stereo camera board. In this case, Holoscan cycles between each pipeline, providing two separate windows (one for each visualizer) on the display. Each receiver_operator instance is independent and runs simultaneously.

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