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/*
* SPDX-FileCopyrightText: Copyright 2021-2022 Arm Limited and/or its affiliates
* <open-source-office@arm.com> SPDX-License-Identifier: Apache-2.0
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "UseCaseHandler.hpp"
#include "AdMelSpectrogram.hpp"
#include "AdModel.hpp"
#include "AdProcessing.hpp"
#include "AudioUtils.hpp"
#include "Classifier.hpp"
#include "ImageUtils.hpp"
#include "InputFiles.hpp"
#include "UseCaseCommonUtils.hpp"
#include "hal.h"
#include "log_macros.h"
namespace arm {
namespace app {
/**
* @brief Presents inference results using the data presentation
* object.
* @param[in] result average sum of classification results
* @param[in] threshold if larger than this value we have an anomaly
* @return true if successful, false otherwise
**/
static bool PresentInferenceResult(float result, float threshold);
/** @brief Given a wav file name return AD model output index.
* @param[in] wavFileName Audio WAV filename.
* File name should be in format anything_goes_XX_here.wav
* where XX is the machine ID e.g. 00, 02, 04 or 06
* @return AD model output index as 8 bit integer.
**/
static int8_t OutputIndexFromFileName(std::string wavFileName);
/* Anomaly Detection inference handler */
bool ClassifyVibrationHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
{
constexpr uint32_t dataPsnTxtInfStartX = 20;
constexpr uint32_t dataPsnTxtInfStartY = 40;
auto& model = ctx.Get<Model&>("model");
/* If the request has a valid size, set the audio index */
if (clipIndex < NUMBER_OF_FILES) {
if (!SetAppCtxIfmIdx(ctx, clipIndex, "clipIndex")) {
return false;
}
}
if (!model.IsInited()) {
printf_err("Model is not initialised! Terminating processing.\n");
return false;
}
auto& profiler = ctx.Get<Profiler&>("profiler");
const auto melSpecFrameLength = ctx.Get<uint32_t>("frameLength");
const auto melSpecFrameStride = ctx.Get<uint32_t>("frameStride");
const auto scoreThreshold = ctx.Get<float>("scoreThreshold");
const auto trainingMean = ctx.Get<float>("trainingMean");
auto startClipIdx = ctx.Get<uint32_t>("clipIndex");
TfLiteTensor* outputTensor = model.GetOutputTensor(0);
TfLiteTensor* inputTensor = model.GetInputTensor(0);
if (!inputTensor->dims) {
printf_err("Invalid input tensor dims\n");
return false;
}
AdPreProcess preProcess{inputTensor, melSpecFrameLength, melSpecFrameStride, trainingMean};
AdPostProcess postProcess{outputTensor};
do {
hal_lcd_clear(COLOR_BLACK);
auto currentIndex = ctx.Get<uint32_t>("clipIndex");
/* Get the output index to look at based on id in the filename. */
int8_t machineOutputIndex = OutputIndexFromFileName(GetFilename(currentIndex));
if (machineOutputIndex == -1) {
return false;
}
/* Creating a sliding window through the whole audio clip. */
auto audioDataSlider =
audio::SlidingWindow<const int16_t>(GetAudioArray(currentIndex),
GetAudioArraySize(currentIndex),
preProcess.GetAudioWindowSize(),
preProcess.GetAudioDataStride());
/* Result is an averaged sum over inferences. */
float result = 0;
/* Display message on the LCD - inference running. */
std::string str_inf{"Running inference... "};
hal_lcd_display_text(
str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
info("Running inference on audio clip %" PRIu32 " => %s\n",
currentIndex,
GetFilename(currentIndex));
/* Start sliding through audio clip. */
while (audioDataSlider.HasNext()) {
const int16_t* inferenceWindow = audioDataSlider.Next();
preProcess.SetAudioWindowIndex(audioDataSlider.Index());
preProcess.DoPreProcess(inferenceWindow, preProcess.GetAudioWindowSize());
info("Inference %zu/%zu\n",
audioDataSlider.Index() + 1,
audioDataSlider.TotalStrides() + 1);
/* Run inference over this audio clip sliding window */
if (!RunInference(model, profiler)) {
return false;
}
postProcess.DoPostProcess();
result += 0 - postProcess.GetOutputValue(machineOutputIndex);
#if VERIFY_TEST_OUTPUT
DumpTensor(outputTensor);
#endif /* VERIFY_TEST_OUTPUT */
} /* while (audioDataSlider.HasNext()) */
/* Use average over whole clip as final score. */
result /= (audioDataSlider.TotalStrides() + 1);
/* Erase. */
str_inf = std::string(str_inf.size(), ' ');
hal_lcd_display_text(
str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
ctx.Set<float>("result", result);
if (!PresentInferenceResult(result, scoreThreshold)) {
return false;
}
profiler.PrintProfilingResult();
IncrementAppCtxIfmIdx(ctx, "clipIndex");
} while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
return true;
}
static bool PresentInferenceResult(float result, float threshold)
{
constexpr uint32_t dataPsnTxtStartX1 = 20;
constexpr uint32_t dataPsnTxtStartY1 = 30;
constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment */
hal_lcd_set_text_color(COLOR_GREEN);
/* Display each result */
uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
std::string anomalyScore =
std::string{"Average anomaly score is: "} + std::to_string(result);
std::string anomalyThreshold =
std::string("Anomaly threshold is: ") + std::to_string(threshold);
std::string anomalyResult;
if (result > threshold) {
anomalyResult += std::string("Anomaly detected!");
} else {
anomalyResult += std::string("Everything fine, no anomaly detected!");
}
hal_lcd_display_text(
anomalyScore.c_str(), anomalyScore.size(), dataPsnTxtStartX1, rowIdx1, false);
info("%s\n", anomalyScore.c_str());
info("%s\n", anomalyThreshold.c_str());
info("%s\n", anomalyResult.c_str());
return true;
}
static int8_t OutputIndexFromFileName(std::string wavFileName)
{
/* Filename is assumed in the form machine_id_00.wav */
std::string delimiter = "_"; /* First character used to split the file name up. */
size_t delimiterStart;
std::string subString;
size_t machineIdxInString =
3; /* Which part of the file name the machine id should be at. */
for (size_t i = 0; i < machineIdxInString; ++i) {
delimiterStart = wavFileName.find(delimiter);
subString = wavFileName.substr(0, delimiterStart);
wavFileName.erase(0, delimiterStart + delimiter.length());
}
/* At this point substring should be 00.wav */
delimiter = "."; /* Second character used to split the file name up. */
delimiterStart = subString.find(delimiter);
subString =
(delimiterStart != std::string::npos) ? subString.substr(0, delimiterStart) : subString;
auto is_number = [](const std::string& str) -> bool {
std::string::const_iterator it = str.begin();
while (it != str.end() && std::isdigit(*it))
++it;
return !str.empty() && it == str.end();
};
const int8_t machineIdx = is_number(subString) ? std::stoi(subString) : -1;
/* Return corresponding index in the output vector. */
if (machineIdx == 0) {
return 0;
} else if (machineIdx == 2) {
return 1;
} else if (machineIdx == 4) {
return 2;
} else if (machineIdx == 6) {
return 3;
} else {
printf_err("%d is an invalid machine index \n", machineIdx);
return -1;
}
}
} /* namespace app */
} /* namespace arm */
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