MLECO-3075: Add KWS use case API

Removed some of the templates for feature calculation that we are unlikely to ever use.
We might be able to refactor the feature caching and feature calculator code in the future
to better integrate it with with PreProcess API.

Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: Ic0c0c581c71e2553d41ff72cd1ed3b3efa64fa92

3 files changed, 39 insertions, 9 deletions

diff --git a/source/use_case/img_class/include/ImgClassProcessing.hpp b/source/use_case/img_class/include/ImgClassProcessing.hpp
index 5a59b5f..59db4a5 100644
--- a/source/use_case/img_class/include/ImgClassProcessing.hpp
+++ b/source/use_case/img_class/include/ImgClassProcessing.hpp
@@ -32,8 +32,19 @@ namespace app {
     class ImgClassPreProcess : public BasePreProcess {
 
     public:
+        /**
+         * @brief       Constructor
+         * @param[in]   model   Pointer to the the Image classification Model object.
+         **/
         explicit ImgClassPreProcess(Model* model);
 
+        /**
+         * @brief       Should perform pre-processing of 'raw' input image data and load it into
+         *              TFLite Micro input tensors ready for inference
+         * @param[in]   input      Pointer to the data that pre-processing will work on.
+         * @param[in]   inputSize  Size of the input data.
+         * @return      true if successful, false otherwise.
+         **/
         bool DoPreProcess(const void* input, size_t inputSize) override;
     };
 
@@ -50,10 +61,22 @@ namespace app {
         std::vector<ClassificationResult>& m_results;
 
     public:
+        /**
+         * @brief       Constructor
+         * @param[in]   classifier   Classifier object used to get top N results from classification.
+         * @param[in]   model        Pointer to the the Image classification Model object.
+         * @param[in]   labels       Vector of string labels to identify each output of the model.
+         * @param[in]   results      Vector of classification results to store decoded outputs.
+         **/
         ImgClassPostProcess(Classifier& classifier, Model* model,
                             const std::vector<std::string>& labels,
                             std::vector<ClassificationResult>& results);
 
+        /**
+         * @brief       Should perform post-processing of the result of inference then populate
+         *              populate classification result data for any later use.
+         * @return      true if successful, false otherwise.
+         **/
         bool DoPostProcess() override;
     };
 
diff --git a/source/use_case/img_class/src/ImgClassProcessing.cc b/source/use_case/img_class/src/ImgClassProcessing.cc
index e33e3c1..6ba88ad 100644
--- a/source/use_case/img_class/src/ImgClassProcessing.cc
+++ b/source/use_case/img_class/src/ImgClassProcessing.cc
@@ -23,6 +23,9 @@ namespace app {
 
     ImgClassPreProcess::ImgClassPreProcess(Model* model)
     {
+        if (!model->IsInited()) {
+            printf_err("Model is not initialised!.\n");
+        }
         this->m_model = model;
     }
 
@@ -35,7 +38,7 @@ namespace app {
         auto input = static_cast<const uint8_t*>(data);
         TfLiteTensor* inputTensor = this->m_model->GetInputTensor(0);
 
-        memcpy(inputTensor->data.data, input, inputSize);
+        std::memcpy(inputTensor->data.data, input, inputSize);
         debug("Input tensor populated \n");
 
         if (this->m_model->IsDataSigned()) {
@@ -52,6 +55,9 @@ namespace app {
              m_labels{labels},
              m_results{results}
     {
+        if (!model->IsInited()) {
+            printf_err("Model is not initialised!.\n");
+        }
         this->m_model = model;
     }
 
diff --git a/source/use_case/img_class/src/UseCaseHandler.cc b/source/use_case/img_class/src/UseCaseHandler.cc
index 98e2b59..11a1aa8 100644
--- a/source/use_case/img_class/src/UseCaseHandler.cc
+++ b/source/use_case/img_class/src/UseCaseHandler.cc
@@ -37,6 +37,12 @@ namespace app {
     {
         auto& profiler = ctx.Get<Profiler&>("profiler");
         auto& model = ctx.Get<Model&>("model");
+        /* If the request has a valid size, set the image index as it might not be set. */
+        if (imgIndex < NUMBER_OF_FILES) {
+            if (!SetAppCtxIfmIdx(ctx, imgIndex, "imgIndex")) {
+                return false;
+            }
+        }
         auto initialImIdx = ctx.Get<uint32_t>("imgIndex");
 
         constexpr uint32_t dataPsnImgDownscaleFactor = 2;
@@ -46,12 +52,7 @@ namespace app {
         constexpr uint32_t dataPsnTxtInfStartX = 150;
         constexpr uint32_t dataPsnTxtInfStartY = 40;
 
-        /* If the request has a valid size, set the image index. */
-        if (imgIndex < NUMBER_OF_FILES) {
-            if (!SetAppCtxIfmIdx(ctx, imgIndex, "imgIndex")) {
-                return false;
-            }
-        }
+
         if (!model.IsInited()) {
             printf_err("Model is not initialised! Terminating processing.\n");
             return false;
@@ -102,7 +103,7 @@ namespace app {
 
             /* Display message on the LCD - inference running. */
             hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
-                                    dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+                    dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
 
             /* Select the image to run inference with. */
             info("Running inference on image %" PRIu32 " => %s\n", ctx.Get<uint32_t>("imgIndex"),
@@ -129,7 +130,7 @@ namespace app {
             /* Erase. */
             str_inf = std::string(str_inf.size(), ' ');
             hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
-                                    dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+                    dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
 
             /* Add results to context for access outside handler. */
             ctx.Set<std::vector<ClassificationResult>>("results", results);