Public-facing functions for running inference using the Edge Impulse C++ library.
Source: classifier/ei_run_classifier.h
Brief: Initialize static variables for running preprocessing and inference continuously.
Description:
Initializes and clears any internal static variables needed by run_classifier_continuous(). This includes the moving average filter (MAF). This function should be called prior to calling run_classifier_continuous().
Blocking: yes
Example: nano_ble33_sense_microphone_continuous.ino
Brief: Initialize static variables for running preprocessing and inference continuously.
Description:
Initializes and clears any internal static variables needed by run_classifier_continuous(). This includes the moving average filter (MAF). This function should be called prior to calling run_classifier_continuous().
Blocking: yes
Example: nano_ble33_sense_microphone_continuous.ino
handle struct with information about model and DSP
Brief: Deletes static variables when running preprocessing and inference continuously.
Description:
Deletes internal static variables used by run_classifier_continuous(), which includes the moving average filter (MAF). This function should be called when you are done running continuous classification.
Blocking: yes
Example: ei_run_audio_impulse.cpp
Brief: Run preprocessing (DSP) on new slice of raw features. Add output features to rolling matrix and run inference on full sample.
Description:
Accepts a new slice of features give by the callback defined in the signal parameter. It performs preprocessing (DSP) on this new slice of features and appends the output to a sliding window of pre-processed features (stored in a static features matrix). The matrix stores the new slice and as many old slices as necessary to make up one full sample for performing inference.
run_classifier_init() must be called before making any calls to run_classifier_continuous().
For example, if you are doing keyword spotting on 1-second slices of audio and you want to perform inference 4 times per second (given by EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW), you would collect 0.25 seconds of audio and call run_classifier_continuous(). The function would compute the Mel-Frequency Cepstral Coefficients (MFCCs) for that 0.25 second slice of audio, drop the oldest 0.25 seconds' worth of MFCCs from its internal matrix, and append the newest slice of MFCCs. This process allows the library to keep track of the pre-processed features (e.g. MFCCs) in the window instead of the entire set of raw features (e.g. raw audio data), which can potentially save a lot of space in RAM. After updating the static matrix, inference is performed using the whole matrix, which acts as a sliding window of pre-processed features.
Additionally, a moving average filter (MAF) can be enabled for run_classifier_continuous(), which averages (arithmetic mean) the last n inference results for each class. n is EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW / 2. In our example above, if we enabled the MAF, the values in result would contain predictions averaged from the previous 2 inferences.
To learn more about run_classifier_continuous(), see this guide on continuous audio sampling. While the guide is written for audio signals, the concepts of continuous sampling and inference can be extrapolated to any time-series data.
Blocking: yes
Example: nano_ble33_sense_microphone_continuous.ino
signal Pointer to a signal_t struct that contains the number of elements in the slice of raw features (e.g. EI_CLASSIFIER_SLICE_SIZE) and a pointer to a callback that reads in the slice of raw features.
result Pointer to an ei_impulse_result_t struct that contains the various output results from inference after run_classifier() returns.
debug Print internal preprocessing and inference debugging information via ei_printf().
enable_maf Enable the moving average filter (MAF) for the classifier.
Error code as defined by EI_IMPULSE_ERROR enum. Will be EI_IMPULSE_OK if inference completed successfully.
Brief: Run preprocessing (DSP) on new slice of raw features. Add output features to rolling matrix and run inference on full sample.
Description:
Accepts a new slice of features give by the callback defined in the signal parameter. It performs preprocessing (DSP) on this new slice of features and appends the output to a sliding window of pre-processed features (stored in a static features matrix). The matrix stores the new slice and as many old slices as necessary to make up one full sample for performing inference.
run_classifier_init() must be called before making any calls to run_classifier_continuous().
For example, if you are doing keyword spotting on 1-second slices of audio and you want to perform inference 4 times per second (given by EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW), you would collect 0.25 seconds of audio and call run_classifier_continuous(). The function would compute the Mel-Frequency Cepstral Coefficients (MFCCs) for that 0.25 second slice of audio, drop the oldest 0.25 seconds' worth of MFCCs from its internal matrix, and append the newest slice of MFCCs. This process allows the library to keep track of the pre-processed features (e.g. MFCCs) in the window instead of the entire set of raw features (e.g. raw audio data), which can potentially save a lot of space in RAM. After updating the static matrix, inference is performed using the whole matrix, which acts as a sliding window of pre-processed features.
Additionally, a moving average filter (MAF) can be enabled for run_classifier_continuous(), which averages (arithmetic mean) the last n inference results for each class. n is EI_CLASSIFIER_SLICES_PER_MODEL_WINDOW / 2. In our example above, if we enabled the MAF, the values in result would contain predictions averaged from the previous 2 inferences.
To learn more about run_classifier_continuous(), see this guide on continuous audio sampling. While the guide is written for audio signals, the concepts of continuous sampling and inference can be extrapolated to any time-series data.
Blocking: yes
Example: nano_ble33_sense_microphone_continuous.ino
impulse ei_impulse_handle_t struct with information about preprocessing and model.
signal Pointer to a signal_t struct that contains the number of elements in the slice of raw features (e.g. EI_CLASSIFIER_SLICE_SIZE) and a pointer to a callback that reads in the slice of raw features.
result Pointer to an ei_impulse_result_t struct that contains the various output results from inference after run_classifier() returns.
debug Print internal preprocessing and inference debugging information via ei_printf().
enable_maf Enable the moving average filter (MAF) for the classifier.
Error code as defined by EI_IMPULSE_ERROR enum. Will be EI_IMPULSE_OK if inference completed successfully.
Brief: Run the classifier over a raw features array.
Description: Overloaded function run_classifier() that defaults to the single impulse.
Blocking: yes
signal Pointer to a signal_t struct that contains the total length of the raw feature array, which must match EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, and a pointer to a callback that reads in the raw features.
result Pointer to an ei_impulse_result_t struct that will contain the various output results from inference after run_classifier() returns.
debug Print internal preprocessing and inference debugging information via ei_printf().
Error code as defined by EI_IMPULSE_ERROR enum. Will be EI_IMPULSE_OK if inference completed successfully.
Brief: Run the classifier over a raw features array.
Description:
Accepts a signal_t input struct pointing to a callback that reads in pages of raw features. run_classifier() performs any necessary preprocessing on the raw features (e.g. DSP, cropping of images, etc.) before performing inference. Results from inference are stored in an ei_impulse_result_t struct.
Blocking: yes
Example: standalone inferencing main.cpp
impulse Pointer to an ei_impulse_handle_t struct that contains the model and preprocessing information.
signal Pointer to a signal_t struct that contains the total length of the raw feature array, which must match EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, and a pointer to a callback that reads in the raw features.
result Pointer to an ei_impulse_result_t struct that will contain the various output results from inference after run_classifier() returns.
debug Print internal preprocessing and inference debugging information via ei_printf().
Error code as defined by EI_IMPULSE_ERROR enum. Will be EI_IMPULSE_OK if inference completed successfully.