MLECO-3078: Add VWW use case API
We now expect that img_class and vww use cases model inputs should have 4 dims as dictated by
use case logic
Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: I67a57a3a28a7ff2e09c917c40e9fc2c08384a45c
diff --git a/source/use_case/vww/src/VisualWakeWordProcessing.cc b/source/use_case/vww/src/VisualWakeWordProcessing.cc
new file mode 100644
index 0000000..94eae28
--- /dev/null
+++ b/source/use_case/vww/src/VisualWakeWordProcessing.cc
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2022 Arm Limited. All rights reserved.
+ * 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 "VisualWakeWordProcessing.hpp"
+#include "ImageUtils.hpp"
+#include "VisualWakeWordModel.hpp"
+#include "log_macros.h"
+
+namespace arm {
+namespace app {
+
+ VisualWakeWordPreProcess::VisualWakeWordPreProcess(Model* model)
+ {
+ if (!model->IsInited()) {
+ printf_err("Model is not initialised!.\n");
+ }
+ this->m_model = model;
+ }
+
+ bool VisualWakeWordPreProcess::DoPreProcess(const void* data, size_t inputSize)
+ {
+ if (data == nullptr) {
+ printf_err("Data pointer is null");
+ }
+
+ auto input = static_cast<const uint8_t*>(data);
+ TfLiteTensor* inputTensor = this->m_model->GetInputTensor(0);
+
+ auto unsignedDstPtr = static_cast<uint8_t*>(inputTensor->data.data);
+
+ /* VWW model has one channel input => Convert image to grayscale here.
+ * We expect images to always be RGB. */
+ image::RgbToGrayscale(input, unsignedDstPtr, inputSize);
+
+ /* VWW model pre-processing is image conversion from uint8 to [0,1] float values,
+ * then quantize them with input quantization info. */
+ QuantParams inQuantParams = GetTensorQuantParams(inputTensor);
+
+ auto signedDstPtr = static_cast<int8_t*>(inputTensor->data.data);
+ for (size_t i = 0; i < inputTensor->bytes; i++) {
+ auto i_data_int8 = static_cast<int8_t>(
+ ((static_cast<float>(unsignedDstPtr[i]) / 255.0f) / inQuantParams.scale) + inQuantParams.offset
+ );
+ signedDstPtr[i] = std::min<int8_t>(INT8_MAX, std::max<int8_t>(i_data_int8, INT8_MIN));
+ }
+
+ debug("Input tensor populated \n");
+
+ return true;
+ }
+
+ VisualWakeWordPostProcess::VisualWakeWordPostProcess(Classifier& classifier, Model* model,
+ const std::vector<std::string>& labels, std::vector<ClassificationResult>& results)
+ :m_vwwClassifier{classifier},
+ m_labels{labels},
+ m_results{results}
+ {
+ if (!model->IsInited()) {
+ printf_err("Model is not initialised!.\n");
+ }
+ this->m_model = model;
+ }
+
+ bool VisualWakeWordPostProcess::DoPostProcess()
+ {
+ return this->m_vwwClassifier.GetClassificationResults(
+ this->m_model->GetOutputTensor(0), this->m_results,
+ this->m_labels, 1, true);
+ }
+
+} /* namespace app */
+} /* namespace arm */
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