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Diffstat (limited to 'source/use_case/vww/src/VisualWakeWordProcessing.cc')
| -rw-r--r-- | source/use_case/vww/src/VisualWakeWordProcessing.cc | 85 |
1 files changed, 85 insertions, 0 deletions
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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