rbflurry-node-red-contrib-post-object-detection v0.0.2

node-red-contrib-post-object-detection

This is a custom Node-RED node that handles the prediction results of an Object Detection model.

Installation

Prerequisite

This module requires @tensorflow/tfjs-node as a peer dependency. You need to install it within Node-RED manually. TensorFlow.js on Node.js (@tensorflow/tfjs-node or @tensorflow/tfjs-node-gpu), depend on the TensorFlow shared libraries. Putting TensorFlow.js as the dependency of a custom Node-RED node may cause the situation where multiple custom nodes each install their own tfjs-node module as a dependency. This causes an attempt at loading multiple TensorFlow shared libraries in the same process, which subsequently causes the process to abort with a protobuf assertion error.

Therefore, this module puts @tensorflow/tfjs-node as a peer dependency. You need to install it with Node-RED manully.

Install @tensorflow/tfjs-node:

npm install @tensorflow/tfjs-node

This custom Node-RED node leverages the node-canvas npm package to draw the bounding boxes on a image. Please make sure the platform running Node-RED fulfills the prerequisites listed here. For example, while using Ubuntu, you need to run the following command to install the dependencies:

sudo apt-get install build-essential libcairo2-dev libpango1.0-dev libjpeg-dev libgif-dev librsvg2-dev

Install this module:

Once you install the peer dependency and the prerequisites, you can install this module:

npm install node-red-contrib-post-object-detection

Usage

There are two custom Node-RED nodes in this package:

• post-object-detection: This is used to process the output of an Object Detection model.
• bbox-image: This is used to annotate an input original image with bounding boxes.

post-object-detection node

The input for this node should be an array of tf.Tensor objects with a length of 2. The first tensor in this array corresponds to the detected objects with a [1, number of box detectors, number of classes] shape where 1 is the batch size. The second tensor is the bounding boxes with a [1, number of box detectors, 1, 4] shape where 4 is the four coordinates of the box. This node also requires class information through the use of the Class URL property. The file specified here should be a JSON file containing the id and className for each class. For example:

{
    "0": "person",
    "1": "cup",
    ...
    ...
}

The following node properties can also be altered from the defaults:

• IoU: The intersection over union threshold for determining whether boxes overlap too much with respect to IOU during non-max suppression. Must be between 0, 1. Defaults to 0.5 (50% box overlap).
• Min Score: Minimum score needed for a box to be accepted during non-max suppression. Defaults to 0.5.

The node then calculates the object detection results and returns the detected objects as an Object[]. Each object contains bbox, className and score properties.

• bbox: The coordinates of the box, width and height: [x, y, w, h]. These values are float number between 0.0 and 1.0.
• className: The name of the class.
• score: The confidence value between 0.0 and 1.0.

bbox-image node

The msg.payload passed to this node should be an object containing these two properties:

• image: The image data in Buffer data type
• objects: An object array containing a list of detected objects. Each object has the following information:

  {
    bbox: [x, y, w, h],
    className: string,
    score: number
  }

This node annotates the image by drawing the bounding boxes of detected objects onto it. This annotated image is then output as a Buffer for the next node.