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AIWintermuteAIAIWintermuteAI
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added proper RSP and RSP Continious examples
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‎examples/Wio_Terminal_EI_Rock_Paper_Scissors/Wio_Terminal_EI_Rock_Paper_Scissors.ino‎

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/*
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An example sketch for Edge Impulse trained model inference for Rock Paper Scissors classification with single Light sensor
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Copyright (c) 2021 Seeed technology co., ltd.
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Author : Dmitry Maslov
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Create Time : February 2021
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Change Log :
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The MIT License (MIT)
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in
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all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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THE SOFTWARE.
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*/
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/* Includes ---------------------------------------------------------------- */
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#include <video_tinyml_raw_inference.h>
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uint8_t axis_num = 1;
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/* Private variables ------------------------------------------------------- */
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static bool debug_nn = false; // Set this to true to see e.g. features generated from the raw signal
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/**
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* @brief Arduino setup function
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*/
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void setup()
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{
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// put your setup code here, to run once:
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Serial.begin(115200);
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Serial.println("Edge Impulse Inferencing Demo");
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if (EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME != axis_num) {
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ei_printf("ERR: EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME should be equal to (%d) (the (%d) sensor axes)\n", axis_num, axis_num);
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return;
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}
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}
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/**
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* @brief Printf function uses vsnprintf and output using Arduino Serial
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*
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* @param[in] format Variable argument list
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*/
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void ei_printf(const char *format, ...) {
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static char print_buf[1024] = { 0 };
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va_list args;
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va_start(args, format);
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int r = vsnprintf(print_buf, sizeof(print_buf), format, args);
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va_end(args);
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if (r > 0) {
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Serial.write(print_buf);
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}
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}
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/**
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* @brief Get data and run inferencing
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*
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* @param[in] debug Get debug info if true
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*/
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void loop()
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{
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ei_printf("Sampling...\n");
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// Allocate a buffer here for the values we'll read from the IMU
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float buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE] = {0};
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for (size_t ix = 0; ix < EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE; ix += axis_num) {
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// Determine the next tick (and then sleep later)
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uint64_t next_tick = micros() + (EI_CLASSIFIER_INTERVAL_MS * 1000);
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buffer[ix + 0] = analogRead(WIO_LIGHT);
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delayMicroseconds(next_tick - micros());
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}
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// Turn the raw buffer in a signal which we can the classify
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signal_t signal;
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int err = numpy::signal_from_buffer(buffer, EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, &signal);
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if (err != 0) {
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ei_printf("Failed to create signal from buffer (%d)\n", err);
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return;
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}
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// Run the classifier
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ei_impulse_result_t result = { 0 };
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err = run_classifier(&signal, &result, debug_nn);
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if (err != EI_IMPULSE_OK) {
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ei_printf("ERR: Failed to run classifier (%d)\n", err);
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return;
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}
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// print the predictions
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ei_printf("Predictions ");
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ei_printf("(DSP: %d ms., Classification: %d ms., Anomaly: %d ms.)",
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result.timing.dsp, result.timing.classification, result.timing.anomaly);
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ei_printf(": \n");
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for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
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ei_printf(" %s: %.5f\n", result.classification[ix].label, result.classification[ix].value);
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}
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#if EI_CLASSIFIER_HAS_ANOMALY == 1
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ei_printf(" anomaly score: %.3f\n", result.anomaly);
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#endif
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}

‎examples/Wio_Terminal_EI_Rock_Paper_Scissors_Continious/Wio_Terminal_EI_Rock_Paper_Scissors_Continious.ino‎

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/*
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An example sketch for Edge Impulse trained model inference for Rock Paper Scissors classification with single Light sensor
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(continious inference)
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Copyright (c) 2021 Seeed technology co., ltd.
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Author : Dmitry Maslov
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Create Time : February 2021
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Change Log :
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The MIT License (MIT)
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
14+
in the Software without restriction, including without limitation the rights
15+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
16+
copies of the Software, and to permit persons to whom the Software is
17+
furnished to do so, subject to the following conditions:
18+
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The above copyright notice and this permission notice shall be included in
20+
all copies or substantial portions of the Software.
21+
22+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
23+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
24+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
25+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
26+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
27+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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THE SOFTWARE.
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*/
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/* Includes ---------------------------------------------------------------- */
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#include <video_tinyml_raw_inference.h>
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#include <Seeed_Arduino_FreeRTOS.h>
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#define ERROR_LED_LIGHTUP_STATE HIGH
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/* Private variables ------------------------------------------------------- */
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static bool debug_nn = false; // Set this to true to see e.g. features generated from the raw signal
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static uint32_t run_inference_every_ms = 250;
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static float buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE] = {0};
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static float inference_buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE];
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uint8_t axis_num = 1;
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TaskHandle_t Handle_aTask;
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TaskHandle_t Handle_bTask;
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/**
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* @brief Arduino setup function
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*/
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void setup()
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{
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// put your setup code here, to run once:
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Serial.begin(115200);
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Serial.println("Edge Impulse Inferencing Demo");
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if (EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME != axis_num) {
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ei_printf("ERR: EI_CLASSIFIER_RAW_SAMPLES_PER_FRAME should be equal to (%d) (the (%d) sensor axes)\n", axis_num, axis_num);
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return;
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}
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vSetErrorLed(LED_BUILTIN, ERROR_LED_LIGHTUP_STATE);
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// Create the threads that will be managed by the rtos
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// Sets the stack size and priority of each task
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// Also initializes a handler pointer to each task, which are important to communicate with and retrieve info from tasks
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xTaskCreate(run_inference_background,"Inference", 512, NULL, tskIDLE_PRIORITY + 2, &Handle_aTask);
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xTaskCreate(read_data, "Data collection", 256, NULL, tskIDLE_PRIORITY + 1, &Handle_bTask);
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// Start the RTOS, this function will never return and will schedule the tasks.
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vTaskStartScheduler();
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}
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/**
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* @brief Printf function uses vsnprintf and output using Arduino Serial
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*
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* @param[in] format Variable argument list
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*/
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void ei_printf(const char *format, ...) {
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static char print_buf[1024] = { 0 };
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va_list args;
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va_start(args, format);
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int r = vsnprintf(print_buf, sizeof(print_buf), format, args);
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va_end(args);
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if (r > 0) {
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Serial.write(print_buf);
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}
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}
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/**
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* @brief Run inferencing in the background.
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*/
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static void run_inference_background(void* pvParameters)
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{
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// wait until we have a full buffer
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delay((EI_CLASSIFIER_INTERVAL_MS * EI_CLASSIFIER_RAW_SAMPLE_COUNT) + 100);
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// This is a structure that smoothens the output result
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// With the default settings 70% of readings should be the same before classifying.
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ei_classifier_smooth_t smooth;
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ei_classifier_smooth_init(&smooth, 10 /* no. of readings */, 7 /* min. readings the same */, 0.8 /* min. confidence */, 0.3 /* max anomaly */);
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while (1) {
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// copy the buffer
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memcpy(inference_buffer, buffer, EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE * sizeof(float));
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// Turn the raw buffer in a signal which we can the classify
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signal_t signal;
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int err = numpy::signal_from_buffer(inference_buffer, EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, &signal);
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if (err != 0) {
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ei_printf("Failed to create signal from buffer (%d)\n", err);
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return;
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}
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// Run the classifier
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ei_impulse_result_t result = { 0 };
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err = run_classifier(&signal, &result, debug_nn);
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if (err != EI_IMPULSE_OK) {
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ei_printf("ERR: Failed to run classifier (%d)\n", err);
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return;
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}
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// print the predictions
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ei_printf("Predictions ");
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ei_printf("(DSP: %d ms., Classification: %d ms., Anomaly: %d ms.)",
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result.timing.dsp, result.timing.classification, result.timing.anomaly);
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ei_printf(": ");
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// ei_classifier_smooth_update yields the predicted label
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const char *prediction = ei_classifier_smooth_update(&smooth, &result);
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ei_printf("%s ", prediction);
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// print the cumulative results
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ei_printf(" [ ");
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for (size_t ix = 0; ix < smooth.count_size; ix++) {
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ei_printf("%u", smooth.count[ix]);
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if (ix != smooth.count_size + 1) {
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ei_printf(", ");
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}
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else {
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ei_printf(" ");
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}
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}
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ei_printf("]\n");
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delay(run_inference_every_ms);
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}
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ei_classifier_smooth_free(&smooth);
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}
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/**
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* @brief Get data and run inferencing
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*
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* @param[in] debug Get debug info if true
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*/
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static void read_data(void* pvParameters)
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{
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while (1) {
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// Determine the next tick (and then sleep later)
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uint64_t next_tick = micros() + (EI_CLASSIFIER_INTERVAL_MS * 1000);
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// roll the buffer -3 points so we can overwrite the last one
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numpy::roll(buffer, EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE, -1);
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buffer[EI_CLASSIFIER_DSP_INPUT_FRAME_SIZE - 1] = analogRead(WIO_LIGHT);
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// and wait for next tick
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uint64_t time_to_wait = next_tick - micros();
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delay((int)floor((float)time_to_wait / 1000.0f));
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delayMicroseconds(time_to_wait % 1000);
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}
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}
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void loop()
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{
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//nothing, all the work is done in two threads
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}

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