Tutorial 7:Develop Applications with Webcam API

MMPose Webcam API is a toolkit to develop pose-empowered applications. This tutorial introduces the features and usage of Webcam API. More technical details can be found at API Reference.


Figure 1. Overview of Webcam API

Webcam API is composed of the following main modules (Shown in Fig. 1):

  1. WebcamExecutor (See The interface to build and launch the application program, and perform video capturing and displaying. Besides, WebcamExecutor builds a certain number of functional modules according to the config to perform different basic functions like model inference, data processing, logical decision, and image drawing. when launched, the WebcamExecutor continually reads video frames, controls the data flow among all function modules, and finally displays the processed results. And below are concepts related to WebcamExecutor:

    1. Config : The configuration file contains the parameters of the WebcamExecutor and all function modules. Webcam API uses python files as configs, following the common practice of OpenMMLab;

    2. Launcher (e.g. A script to load the config file, build WebcamExecutor and invoke its run() method to start the application program;

  2. Node (See The interface of function module. One node usually implements a basic function. For example, DetectorNode performs object detection from the frame; ObjectVisualizerNode draws the bbox and keypoints of objects; RecorderNode write the frames into a local video file. Users can also add custom nodes by inheriting the Node interface.

  3. Utils: Utility modules and functions including:

    1. Message (See The data interface of the WebcamExecutor and Node. Message instances may contain images, model inference results, text information, or arbitrary custom data;

    2. Buffer (See The container of Message instances for asynchronous communication between nodes. A node fetches the input from its input buffers once it’s ready, and put the output into its output buffer;

    3. Event (See The event manager supports event communication within the program. Different from the data message that follows a route defined by the config, an event can be set or responded by the executor or nodes immediately. For example, when the user presses a key on the keyboard, an event will be broadcasted to all nodes. This mechanism is useful in user interaction functions.

An Example of Webcam Applications

In this section, we will introduce how to build an application by Webcam API via a simple example.

Run the demo

Before we dive into technical details, you can try running this demo first with the following command. What it does is read the video stream from the webcam, display it on the screen and save it to a local file.

# python demo/ --config CONFIG_PATH [--debug]
python demo/ --config demo/webcam_cfg/


Now let’s look at the config used in this demo:

executor_cfg = dict(
    name='Test Webcam', # name of the application
    camera_id=0,  # camera ID (optionally, it can be a path of an input video file)
    camera_max_fps=30,  # maximum FPS to read the video
        # `MonitorNode` shows the system and application information
            type='MonitorNode',  # node type
            name='monitor',  # node name
            enable_key='m',  # hot key to switch on/off
            enable=False,  # init status of on/off
            input_buffer='_frame_',  # input buffer
            output_buffer='display'),  # output buffer
        # `RecorderNode` saves output to a local file
            type='RecorderNode',  # node name
            name='recorder',  # node type
            out_video_file='webcam_output.mp4',  # path to save output
            input_buffer='display',  # input buffer
            output_buffer='_display_') # output buffer

As shown above, the content of the config file is a dict named executor_cfg, which contains basic parameters (e.g. name, camera_id, et al. See the document for details) and node configs (nodes). The node configs are stored in a list, of which each element is a dict that contains parameters of one node. There are 2 nodes in the demo, namely a DetectorNode and a RecorderNode. See the document of node for more information.

Buffer configurations

From the demo config, you may have noticed that nodes usually have a special type of parameters: input and output buffers. As noted previously, a buffer is a data container to hold the input and output of nodes. And in the config, we can specify the input and output buffer of each node by buffer names. In the demo config, for example, MonitorNode fetches input from a buffer named "_frame"_, and puts output to a buffer named "display"; and RecorderNode fetches input from the buffer "display", and outputs to another buffer "_display_".

In the config, you can assign arbitrary buffer names, and the executor will build buffers accordingly and connect them with the nodes. It’s important to note that the following 3 names are reserved for special buffers to exchange data between the executor and nodes:

  • "_input_": The buffer to store frames read by the executor for model inference;

  • "_frame_": The buffer to store frames read by the executor (same as "_input_") for visualization functions. We use separate inputs for model inference and visualization so they can run asynchronously.

  • "_display_": The buffer to store output that has been processed by nodes. The executor will load from this buffer to display.

In an application, the executor will build a BufferManager instance to hold all buffers (See BufferManager document for details).

Hot-key configurations

Some nodes support switch state control by hot-keys. These nodes have the following parameters:

  • enable_key (str): Specify the hot-key for switch state control;

  • enable (bool): Set the initial switch state.

The hot-key response is supported by the event mechanism. The executor has a EvenetManager (See EventManager document) instance to manage all user-defined events in the application. A node can register events at initialization. Registered events can be set, waited, or cleared at run time.

Architecture of a webcam application

Now we have introduced the concept of WebcamExecutor, Node, Buffer, and Event. The architecture of a webcam application can be illustrated as shown in Fig. 2.

Figure 2. Architecture of a webcam application

Extending Webcam API with Custom Nodes

Webcam API provides a simple and efficient interface to extend by defining new nodes. In this section, we will show you how to do this via examples.

Custom nodes for general functions

We first introduce the general steps to define new nodes. Here we take DetectorNode as an example.

Inherit from Node class

All node classes should inherit from the base class Node (See and be registered to the registry NODES. So the node instances can be built from configs.

from import Node, NODES

class DetectorNode(Node):

Implement __init__() method

The __init__() method of DetectorNode is impolemented as below:

    def __init__(self,
                 name: str,
                 model_config: str,
                 model_checkpoint: str,
                 input_buffer: str,
                 output_buffer: Union[str, List[str]],
                 enable_key: Optional[Union[str, int]] = None,
                 enable: bool = True,
                 device: str = 'cuda:0',
                 bbox_thr: float = 0.5):

        # Initialize the base class
        super().__init__(name=name, enable_key=enable_key, enable=enable)

        # Initialize parameters
        self.model_config = get_config_path(model_config, 'mmdet')
        self.model_checkpoint = model_checkpoint
        self.device = device.lower()
        self.bbox_thr = bbox_thr

        self.model = init_detector(
            self.model_config, self.model_checkpoint, device=self.device)

        # Register input/output buffers
        self.register_input_buffer(input_buffer, 'input', trigger=True)  # Set trigger

The __init__() method usually does the following steps:

  1. Initialize the base class: Call super().__init__() with parameters like name, enable_key and enable;

  2. Initialize node parameters: In this example, we initializes the parameters like model_config, device, bbox_thr in the node, and load the model with MMDetection APIs.

  3. Register buffers: A node needs to register its input and output buffers during initialization:

    1. Register each input buffer by register_input_buffer() method. This method maps the buffer name (i.e. input_buffer from the config) to an indicator (i.e. "input" in the example). At runtime, the node can access the data from the registered buffers by indicators (See Implement process() method).

    2. Register the output buffers by register_output_buffer() method. At runtime, the node output will be stored in every registered output buffer (each buffer will store a deep copy of the node output).

Implement process() method

The process() method defines the behavior of a node. In the DetectorNode example, we implement detection model inference in the process() method:

    def process(self, input_msgs):

        # Get the input message from the buffer by the indicator 'input'
        input_msg = input_msgs['input']

        # Get image data from the input message
        img = input_msg.get_image()

        # Process model inference using MMDetection API
        preds = inference_detector(self.model, img)
        objects = self._post_process(preds)

        # Assign the detection results into the message

        # Return the message
        return input_msg

The process() method usually does the following steps:

  1. Get input data: The argument input_msgs contains data fetched from all registered input buffers. Data from a specific buffer can be obtained by the indicator (e.g. "input");

  2. Parse input data: The input data are usually FrameMessage instances (See the document for details). The node can extract the image data and model inference results from the message;

  3. Process: In this example, we use MMDetection APIs to detect objects from the input image, and post-process the result format;

  4. Return results: The detection results are assigned to the input_msg by the update_objects() method. Then the message is returned by process() and will be stored in all registered output buffers to serve as the input of downstream nodes.

Implement bypass() method

If a node supports switch state control by hot-keys, its bypass() method should be implemented to define the node behavior when turned off. The bypass() method has the same function signature as the process() method. DetectorNode simply outputs the input message in the bypass() method as the following:

    def bypass(self, input_msgs):
        return input_msgs['input']

Custom nodes for visualization

Visualizer Node is a special category of nodes for visualization functions. Here we will introduce a simpler interface to extend this kind of nodes. We take NoticeBoardNode as an example, whose function is to show text information in the output frames.

Inherit from BaseVisualizerNode class

BaseVisualizerNode is a subclass of Node that partially implements the process() method and exposes the draw() method as an image editing interface. Visualizer nodes should inherit from BaseVisualizerNode and be registered to the registry NODES.

from import BaseVisualizerNode, NODES

class NoticeBoardNode(BaseVisualizerNode):

The implementation of __init__() in visualizer nodes is similar to it in general nodes. Please refer to Implement __init__() method. Note that a visualizer node should register one and only one input buffer with the name "input".

Implement draw() method

The draw() method has one argument input_msg, which is the data fetched from the buffer indicated by "input". The return value of draw() is an image in np.ndarray type, which will be used to update the image data in input_msg. And the updated input_msg will be the node output.

We implement the draw() method of NoticeBoardNode as the following:

    def draw(self, input_msg: FrameMessage) -> np.ndarray:
        # Get frame image data
        img = input_msg.get_image()

        # Create a canvas
        canvas = np.full(img.shape, self.background_color, dtype=img.dtype)

        # Put the text on the canvas image
        x = self.x_offset
        y = self.y_offset
        max_len = max([len(line) for line in self.content_lines])

        def _put_line(line=''):
            nonlocal y
            cv2.putText(canvas, line, (x, y), cv2.FONT_HERSHEY_DUPLEX,
                        self.text_scale, self.text_color, 1)
            y += self.y_delta

        for line in self.content_lines:

        # Copy and paste the valid region of the canvas to the frame image
        x1 = max(0, self.x_offset)
        x2 = min(img.shape[1], int(x + max_len * self.text_scale * 20))
        y1 = max(0, self.y_offset - self.y_delta)
        y2 = min(img.shape[0], y)

        src1 = canvas[y1:y2, x1:x2]
        src2 = img[y1:y2, x1:x2]
        img[y1:y2, x1:x2] = cv2.addWeighted(src1, 0.5, src2, 0.5, 0)

        # Return the processed image
        return img
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