Increasing the number of features for raw data

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#1

Hi,
I am currently trying to increase the number of raw data in features so I am able to classify more data instead of a single set each time. Any advice or solution to doing so? Thank you!

Features
Features1054×160 46.9 KB

1 Like
#2

Hi @FYPGabriel

You are doing this in Zephyr right ? Whooo!

You’ll want to work with run_classifier_continuous()

  • run_classifier_continuous() - Retains a sliding window of features so that inference may be performed on a continuous stream of data. We will not explore this option in this tutorial.

Out of curiosity what board are you working with?

Best

Eoin

#3

Hey Eoin,

I am doing it on Zephyr as youve said! Working on the NRD52840-DK board. Thanks for the suggestion, I will give the run_classifer_continuous() a look through

Best Regards,
Gabriel

#4

What if my input data does not depend on any time series, how do i go about classifying raw data 1, raw data 2… raw data x?

#5

Hi @FYPGabriel

Ah right in that case just use the original run_inference function and customise to batch process an array of features:

https://unstop.com/blog/2d-array-in-cpp

I recommend following Shawns tutorial on C++

Best

Eoin

#6

Hi @Eoin,
May I ask what is the impulse parameter and what should be included for the run_inference function?

infer
infer976×387 85 KB

Best Regards,
Gabriel

#7

This is the main.cpp that i am currently using, hope it helps!

// Zpehyr 3.1.x and newer uses different include scheme
#include <version.h>
#if (KERNEL_VERSION_MAJOR > 3) || ((KERNEL_VERSION_MAJOR == 3) && (KERNEL_VERSION_MINOR >= 1))
#include <zephyr/kernel.h>
#else
#include <zephyr.h>
#endif
#include "edge-impulse-sdk/classifier/ei_run_classifier.h"
#include "edge-impulse-sdk/dsp/numpy.hpp"
#include <nrfx_clock.h>

 static const float features[] = {
	6.8047, 15.0982, 22.1960, 1.2526, -3.2098, -4.2553
    // copy raw features here (for example from the 'Live classification' page)
    // see https://docs.edgeimpulse.com/docs/running-your-impulse-locally-zephyr
};

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() {
    // This is needed so that output of printf is output immediately without buffering
    setvbuf(stdout, NULL, _IONBF, 0);

#ifdef CONFIG_SOC_NRF5340_CPUAPP
    // Switch CPU core clock to 128 MHz
    nrfx_clock_divider_set(NRF_CLOCK_DOMAIN_HFCLK, NRF_CLOCK_HFCLK_DIV_1);
#endif

    printk("Edge Impulse standalone inferencing (Zephyr)\n");

    if (sizeof(features) / sizeof(float) != EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE) {
        printk("The size of your 'features' array is not correct. Expected %d items, but had %u\n",
            EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, sizeof(features) / sizeof(float));
        return 1;
    }

    ei_impulse_result_t result = { 0 };

    while (1) {
        // the features are stored into flash, and we don't want to load everything into RAM
        signal_t features_signal;
        features_signal.total_length = sizeof(features) / sizeof(features[0]);
        features_signal.get_data = &raw_feature_get_data;

        // invoke the impulse
	EI_IMPULSE_ERROR res = run_classifier(&features_signal, &result, true);
        printk("run_classifier returned: %d\n", res);

        if (res != 0) return 1;

        printk("Predictions (DSP: %d ms., Classification: %d ms., Anomaly: %d ms.): \n",
                result.timing.dsp, result.timing.classification, result.timing.anomaly);
#if EI_CLASSIFIER_OBJECT_DETECTION == 1
        bool bb_found = result.bounding_boxes[0].value > 0;
        for (size_t ix = 0; ix < result.bounding_boxes_count; ix++) {
            auto bb = result.bounding_boxes[ix];
            if (bb.value == 0) {
                continue;
            }
            printk("    %s (%f) [ x: %u, y: %u, width: %u, height: %u ]\n", bb.label, bb.value, bb.x, bb.y, bb.width, bb.height);
        }
        if (!bb_found) {
            printk("    No objects found\n");
        }
#else
        for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
            printk("    %s: %.5f\n", result.classification[ix].label,
                                    result.classification[ix].value);
        }
#if EI_CLASSIFIER_HAS_ANOMALY == 1
        printk("    anomaly score: %.3f\n", result.anomaly);
#endif
#endif
        k_msleep(2000);
    }
}