Update the existing posenet model with pose-detection model

[shivaylamba]

Posenet model has been deprecated.

Replace the existing model with one of the new pose-detection models:

1. Movenet
2. Blazepost
3. PoseNet

Compare the performance between the 3 and check how you can solve this.

[dosubot]

🤖

Hey @shivaylamba, good to see you again! Hope you're doing well and ready to delve into some more ML magic.

To replace the existing posenet model with one of the new pose-detection models (Movenet, Blazepost, or PoseNet), you will need to update the init function in the index.js file where the model is loaded.

Here is a general idea of how you can modify the function to load a new model:

async function init(modelName) {
    var modelURL;
    var metadataURL;

    switch(modelName) {
        case 'Movenet':
            modelURL = 'path/to/movenet/model.json';
            metadataURL = 'path/to/movenet/metadata.json';
            break;
        case 'Blazepost':
            modelURL = 'path/to/blazepost/model.json';
            metadataURL = 'path/to/blazepost/metadata.json';
            break;
        case 'PoseNet':
            modelURL = 'path/to/posenet/model.json';
            metadataURL = 'path/to/posenet/metadata.json';
            break;
        default:
            console.log('Invalid model name');
            return;
    }

    // load the model and metadata
    model = await tmPose.load(modelURL, metadataURL);
    maxPredictions = model.getTotalClasses();

    // ... rest of the function
}

In this code, modelName is used to decide which model to load. Depending on the value of modelName, the URLs for the model and metadata are set accordingly.

Please note that you will need to replace 'path/to/movenet/model.json', 'path/to/blazepost/model.json', and 'path/to/posenet/model.json' with the actual URLs or paths to the models and metadata files.

As for the dependencies, the TensorFlow.js Pose Detection API supports the Movenet and Blazepose models, so you will need to include the TensorFlow.js library in your project if it's not already included. You can do this by adding the following line to your HTML file:

<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>

And then you can load the models using the tf.loadGraphModel function:

const model = await tf.loadGraphModel('path/to/model.json');

For more information on how to use the TensorFlow.js Pose Detection API, you can refer to the official documentation.

As for comparing the performance between the three models, you can use the model.estimatePoses function to get the poses from the model and then measure the time it takes to get the poses. You can do this for each model and then compare the times.

Please note that the performance may vary depending on the specific use case and the hardware that the models are run on. Therefore, it's recommended to test the models on the actual devices that will be used by the end users.

I hope this helps! If you have any other questions, feel free to ask.

-Dosu

Sources

• index.js

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