Edge Impulse Docs

Edge Impulse Documentation

Welcome to the Edge Impulse documentation. You'll find comprehensive guides and documentation to help you start working with Edge Impulse as quickly as possible, as well as support if you get stuck. Let's jump right in!

Running your impulse locally

Impulses can be deployed as a C++ library. This packages all your signal processing blocks, configuration and learning blocks up into a single package. You can include this package in your own application to run the impulse locally. The library does not have any external dependencies and can be built with any C++11 compiler.

We have end-to-end tutorials for:

📘

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You can build binaries for supported development boards straight from the studio. These will include your full impulse.

Input to the run_classifier function

The input to the run_classifier function is always a signal_t structure with raw sensor values. This structure has two properties:

  • total_length - the total number of values. This should be equal to EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE (from model_metadata.h). E.g. if you have 3 sensor axes, 100Hz sensor data, and 2 seconds of data this should be 600.
  • get_data - a function that retrieves slices of data required by the DSP process. This is used in some DSP algorithms (like all audio-based ones) to page in the required data, and thus saves memory. Using this function you can store (f.e.) the raw data in flash or external RAM, and page it in when required.

F.e. this is how you would page in data from flash:

// this is placed in flash
const float features[300] = { 0 };

// function that pages the data in
int raw_feature_get_data(size_t offset, size_t length, float *out_ptr) {
    memcpy(out_ptr, features + offset, length * sizeof(float));
    return 0;
}

int main() {
    // construct the signal
    signal_t signal;
    signal.total_length = 300;
    signal.get_data = &raw_feature_get_data;
    // ... rest of the application

If you have your data already in RAM you can use the signal_from_buffer function to construct the signal:

float features[30] = { 0 };
signal_t signal;
numpy::signal_from_buffer(features, 30, &signal);
// ... rest of the application

The get_data function expects floats to be returned, but you can use the int8_to_float and int16_to_float helper functions if your own buffers are int8_t or int16_t (useful to save memory). E.g.:

const int16_t features[300] = { 0 };

int raw_feature_get_data(size_t offset, size_t length, float *out_ptr) {
    return numpy::int16_to_float(features + offset, out_ptr, length);
}

int main() {
    signal_t signal;
    signal.total_length = 300;
    signal.get_data = &raw_feature_get_data;
    // ... rest of the application

Signal layout for time-series data

Signals are always a flat buffer, so if you have multiple sensor data you'll need to flatten it. E.g. for sensor data with three axes:

Input data:
Axis 1:  9.8,  9.7,  9.6
Axis 2:  0.3,  0.4,  0.5
Axis 3: -4.5, -4.6, -4.8

Signal: 9.8, 0.3, -4.5, 9.7, 0.4, -4.6, 9.6, 0.5, -4.8

Signal layout for image data

The signal for image data is also flattened, starting with row 1, then row 2 etc. And every pixel is a single value in HEX format (RRGGBB). E.g.:

Input data (3x2 pixel image):
BLACK RED  RED
GREEN BLUE WHITE

Signal: 0x000000, 0xFF0000, 0xFF0000, 0x00FF00, 0x0000FF, 0xFFFFFF

We do have an end-to-end example on constructing a signal from a frame buffer in RGB565 format, which is easily adaptable to other image formats, see: example-signal-from-rgb565-frame-buffer.

Directly quantize image data

If you're doing image classification you can quantize your input layer directly (f.e. when you retrieve the frame buffer from a camera) to avoid allocating the input layer as floats - this saves memory - with the EI_CLASSIFIER_TFLITE_INPUT_SCALE and EI_CLASSIFIER_TFLITE_INPUT_ZEROPOINT macros. See this example for a quick demonstration.

Updated about a month ago

Running your impulse locally


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