ei_main.cpp

/* Edge Impulse ingestion SDK
 * Copyright (c) 2021 EdgeImpulse Inc.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */

/* Include ----------------------------------------------------------------- */
#include "ei_run_classifier.h"
#include "ei_classifier_porting.h"
#include "numpy.hpp"

static const float features[] = {
    // copy raw features here (for example from the 'Live classification' page)
    // see https://docs.edgeimpulse.com/docs/running-your-impulse-ti-launchxl
};


int raw_feature_get_data(size_t offset, size_t length, float *out_ptr) {
    memcpy(out_ptr, features + offset, length * sizeof(float));
    return 0;
}

extern "C" int ei_main() {
    ei_printf("Edge Impulse standalone inferencing (TI LaunchXL)\n");

    if (sizeof(features) / sizeof(float) != EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE) {
        ei_printf("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);


        ei_printf("run_classifier returned: %d\n", res);

        if (res != 0) return 1;

        ei_printf("Predictions (DSP: %d ms., Classification: %d ms., Anomaly: %d ms.): \n",
            result.timing.dsp, result.timing.classification, result.timing.anomaly);

        // print the predictions
        ei_printf("[");
        for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
            ei_printf("%.5f", result.classification[ix].value);
#if EI_CLASSIFIER_HAS_ANOMALY == 1
            ei_printf(", ");
#else
            if (ix != EI_CLASSIFIER_LABEL_COUNT - 1) {
                ei_printf(", ");
            }
#endif
        }
#if EI_CLASSIFIER_HAS_ANOMALY == 1
        ei_printf("%.3f", result.anomaly);
#endif
        ei_printf("]\n");

        ei_sleep(2000);
    }
}