Custom processing blocks are a way to extend the capabilities of Edge Impulse beyond the processing blocks built into the platform. If none of the existing blocks created by Edge Impulse fit your needs, you can create custom processing blocks to implement your own feature generation algorithms for unique project requirements. Ready to dive in and start building? Jump to the examples!
Hosting custom processing blocks in Edge Impulse is only available on the Enterprise planHosting a custom processing block in the Edge Impulse infrastructure, and making it available to everyone in your organization, is only available on the Enterprise plan. Other developers can host their custom processing block themselves and expose it to projects. See Testing the block locally in this document.

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Block structure

The processing block structure is shown below. A key difference for processing blocks versus other types of blocks is that they implement an HTTP server within the application. Please see the custom blocks overview page for more details.

Custom processing block structure

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Block interface

Processing blocks are expected to implement an HTTP server to handle requests. The sections below define the required and optional inputs (requests) and the expected outputs (responses) for custom processing blocks.

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Inputs

Information will be provided to your custom processing block through the request headers and body.

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Requests

MethodPathDescription
GET/Requesting general information about the processing block.
GET/parametersRequesting the parameters.json file for the block.
POST/runRequesting features be generated for a single data sample.
POST/batchRequesting features be generated for multiple data samples.

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Request headers

HeaderPassedDescription
x-ei-project-idConditionalProvided with GET /run or GET /batch requests. The ID of the project.
x-ei-sample-idConditionalProvided with GET /run request. The ID of the sample to be processed.
x-ei-sample-idsConditionalProvided with GET /batch request. A list of IDs of data samples to be processed.

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Request body

The request body adheres to the following interfaces for the POST methods. GET methods do not have a request body. 
interface DSPRequestBody {
    features: number[];
    axes: string[];
    sampling_freq: number;
    draw_graphs: boolean;
    project_id: number;
    implementation_version: number;
    params: { [k: string]: string | number | boolean | number[] | string[] | null };
    calculate_performance: boolean;
    named_axes: { [k: string]: string | false } | false | undefined;
}
The data samples that need to be processed by your script to generate features are provided as arrays in the features property. The axes property provides the names of the signals for the data sample. For example, if this was an accelerometer data sample, the axes could be [ 'accX', 'accY', 'accZ' ]. These names could be mapped to other names in the named_axes property. The parameters defined in your parameters.json file will be passed to your block in the params property. If your parameter names contain dashes, these are replaced with underscores before being added to the request body. For example, a processing block parameter named custom-processing-param is passed as custom_processing_param. Please refer to the parameters.json documentation for further details about creating this file, parameter options available, and examples.

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Outputs

The expected response from the HTTP server in your custom processing block varies depending on the type of request.

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GET methods

GET /: A plain text response with some information about the block. For example, the response could be the block name and author. 
Custom processing block by Awesome Developer.
GET /parameters: The parameters file returned as a JSON object.

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POST methods

The POST response bodies are expected to adhere to the following interfaces. 
{
    success: boolean,
    error?: string
} & DSPRunResponse

interface DSPRunResponse {
    features: number[];
    graphs: DSPRunGraph[];
    labels: string[] | undefined;
    fft_used: number[] | undefined;
    performance: {
        error: string | undefined | null,
        result: {
            time_ms: number
            memory: number
        }
    } | undefined;
    benchmark_fw_hash: string;
    output_config: DSPFeatureMetadataOutput;
    state_string: string | undefined;
}

interface DSPRunGraph {
    name: string;
    image?: string;
    imageMimeType?: string;
    X?: { [k: string]: number[] };
    y?: number[];
    suggestedYMin: number | undefined;
    suggestedYMax: number | undefined;
    suggestedXMin: number | undefined;
    suggestedXMax: number | undefined;
    type: string;
    lineWidth: number | undefined;
    smoothing: boolean;
    axisLabels?: { X: string; y: string; };
    highlights?: { [k: string]: number[] };
}

type DSPFeatureMetadataOutput = {
    type: 'image',
    shape: { width: number, height: number, channels: number, frames?: number },
    axes?: number
} | {
    type: 'spectrogram',
    shape: { width: number, height: number },
    axes?: number
} | {
    type: 'flat',
    shape: { width: number },
    axes?: number
};
The features property is where you return the features that were generated by processing the data sample(s). The labels property can be used return the names of the features you generated. For example, if you calculated the average, maximum, and minimum values of the signal, the labels could be [ 'Average', 'Maximum', 'Minimum' ]. These labels will be used for the feature explorer.

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Adding visualizations

The results of generating features can be shown in Studio through graphs and the feature explorer.

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Graphs

When configuring parameters for a processing block in Studio, a preview of the feature generation results for a single sample is shown. This preview can include displaying graphs. These are the graphs that you define and return in the graphs property of the response body for the POST /run method. Graphs should be created in your feature generation script conditionally based on the draw_graphs property in the request body. See the interface for a graphs object in the Outputs section above. Graphs can be of different types: linear, logarithmic, or an image. The type of graph is controlled by the type property of a graph object.
GraphType value
Linearlinear
Logarithmiclogarithmic
Imageimage

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Feature explorer

The results of generating features on all samples can be shown in the feature explorer. If you output high-dimensional data, you can enable dimensionality reduction for the feature explorer. This will run UMAP over the data to compress the features into two dimensions. To do so, you can set the visualization property in your parameters.json file to dimensionalityReduction.

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Initializing the block

When you are finished developing your block locally, you will want to initialize it. The procedure to initialize your block is described in the custom blocks overview page. Please refer to that documentation for details.

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Testing the block locally

The most convenient way to test your custom processing block before pushing it to Edge Impulse is to host it locally and then expose it to the internet so that it can be accessed by Studio. There are two ways to achieve this. You will need to have Docker and ngrok installed on your machine for either approach.

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With blocks runner

For the first method, you can use the CLI edge-impulse-blocks runner tool. See Block runner for additional details. The port you publish for your Docker container can be configured in the parameters.json file. The blocks runner will also look for the EXPOSE instruction in your Dockerfile and publish that port for you if you wish to override the default port. 
 edge-impulse-blocks runner
Note down the public URL that is returned in the terminal. You can use this URL to add the block to an impulse in a Studio project.

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With ngrok and Docker

For the second method, you can run Docker and ngrok directly instead of using the CLI blocks runner tool. First, you can build the Docker image and run the container. 
docker build -t custom-processing-block .
docker run -p 4446:4446 -it --rm custom-processing-block
Then, after signing up for and installing ngrok, you can use their CLI to create a public forwarding URL. Note down the https:// forwarding address in the response. You can use this URL to add the block to an impulse in a Studio project. 
ngrok http 4446

Session Status                online
Account                       Edge Impulse (Plan: Free)
Version                       2.3.35
Region                        United States (us)
Web Interface                 http://127.0.0.1:4040
Forwarding                    http://4d48dca5.ngrok.io -> http://localhost:4446
Forwarding                    https://4d48dca5.ngrok.io -> http://localhost:4446

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Viewing in Studio

With a public URL for your custom processing block, you can go into your project and add a processing block to your impulse. When the processing block selection modal pops up, go to the bottom left corner and click the Add custom block button. In the next modal that pops up, enter your forwarding URL from above and save. The block can now be used in your project and you will be able to view the processing results, including any visualizations you have created.

Adding a custom processing block using an ngrok forwarding URL

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Pushing the block to Edge Impulse

When you have initialized and finished testing your block locally, you will want to push it to Edge Impulse. The procedure to push your block to Edge Impulse is described in the custom blocks overview page. Please refer to that documentation for details.

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Using the block in a project

After you have pushed your block to Edge Impulse, it can be used in the same way as any other built-in block.

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Running on device

One caveat for custom processing blocks is that Edge Impulse cannot automatically generate optimized code to run on-device as is done with processing blocks built into the platform. This code will need to be written by you. To help you get started, the structure is provided for you. After exporting the C++ library from the Deployment page in Studio, you can see that a forward declaration for your custom processing block will have been created for you in the model-parameters/model_variables.h file. 
int extract_my_preprocessing_features(signal_t *signal, matrix_t *output_matrix, void *config_ptr, const float frequency);
The name for the function comes from the cppType property in your parameters.json file. 
extract_{cppType}_features(...)
You will need to implement this function in the main.cpp file of the C++ library. Example implementations for the processing blocks built into Edge Impulse can be found in the C++ inferencing SDK.

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Examples

Edge Impulse has developed several processing blocks that are built into the platform. The code for these blocks can be found in a public repository under the Edge Impulse GitHub account. See below. Additional examples can also be found in the Edge Impulse account. These repository names typically follow the convention of example-custom-processing-block-<description>. As such, they can be found by searching the repositories for example-custom-processing. Below are direct links to some examples:

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Troubleshooting

No common issues have been identified thus far. If you encounter an issue, please reach out on the forum or, if you are on the Enterprise plan, through your support channels.

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Additional resources