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/*
// Copyright (C) 2020-2022 Intel Corporation
//
// 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 "models/segmentation_model.h"
#include <stddef.h>
#include <stdint.h>
#include <fstream>
#include <stdexcept>
#include <string>
#include <vector>
#include <opencv2/core.hpp>
#include <opencv2/imgproc.hpp>
#include <openvino/openvino.hpp>
#include "models/internal_model_data.h"
#include "models/results.h"
SegmentationModel::SegmentationModel(const std::string& modelFileName, bool useAutoResize, const std::string& layout)
: ImageModel(modelFileName, useAutoResize, layout) {}
std::vector<std::string> SegmentationModel::loadLabels(const std::string& labelFilename) {
std::vector<std::string> labelsList;
/* Read labels (if any) */
if (!labelFilename.empty()) {
std::ifstream inputFile(labelFilename);
if (!inputFile.is_open())
throw std::runtime_error("Can't open the labels file: " + labelFilename);
std::string label;
while (std::getline(inputFile, label)) {
labelsList.push_back(label);
}
if (labelsList.empty())
throw std::logic_error("File is empty: " + labelFilename);
}
return labelsList;
}
void SegmentationModel::prepareInputsOutputs(std::shared_ptr<ov::Model>& model) {
// --------------------------- Configure input & output ---------------------------------------------
// --------------------------- Prepare input -----------------------------------------------------
if (model->inputs().size() != 1) {
throw std::logic_error("Segmentation model wrapper supports topologies with only 1 input");
}
const auto& input = model->input();
inputsNames.push_back(input.get_any_name());
const ov::Layout& inputLayout = getInputLayout(input);
const ov::Shape& inputShape = input.get_shape();
if (inputShape.size() != 4 || inputShape[ov::layout::channels_idx(inputLayout)] != 3) {
throw std::logic_error("3-channel 4-dimensional model's input is expected");
}
ov::preprocess::PrePostProcessor ppp(model);
ppp.input().tensor().set_element_type(ov::element::u8).set_layout({"NHWC"});
if (useAutoResize) {
ppp.input().tensor().set_spatial_dynamic_shape();
ppp.input()
.preprocess()
.convert_element_type(ov::element::f32)
.resize(ov::preprocess::ResizeAlgorithm::RESIZE_LINEAR);
}
ppp.input().model().set_layout(inputLayout);
model = ppp.build();
// --------------------------- Prepare output -----------------------------------------------------
if (model->outputs().size() != 1) {
throw std::logic_error("Segmentation model wrapper supports topologies with only 1 output");
}
const auto& output = model->output();
outputsNames.push_back(output.get_any_name());
const ov::Shape& outputShape = output.get_shape();
ov::Layout outputLayout("");
switch (outputShape.size()) {
case 3:
outputLayout = "CHW";
outChannels = 1;
outHeight = static_cast<int>(outputShape[ov::layout::height_idx(outputLayout)]);
outWidth = static_cast<int>(outputShape[ov::layout::width_idx(outputLayout)]);
break;
case 4:
outputLayout = "NCHW";
outChannels = static_cast<int>(outputShape[ov::layout::channels_idx(outputLayout)]);
outHeight = static_cast<int>(outputShape[ov::layout::height_idx(outputLayout)]);
outWidth = static_cast<int>(outputShape[ov::layout::width_idx(outputLayout)]);
break;
default:
throw std::logic_error("Unexpected output tensor shape. Only 4D and 3D outputs are supported.");
}
}
std::unique_ptr<ResultBase> SegmentationModel::postprocess(InferenceResult& infResult) {
ImageResult* result = new ImageResult(infResult.frameId, infResult.metaData);
const auto& inputImgSize = infResult.internalModelData->asRef<InternalImageModelData>();
const auto& outTensor = infResult.getFirstOutputTensor();
result->resultImage = cv::Mat(outHeight, outWidth, CV_8UC1);
if (outChannels == 1 && outTensor.get_element_type() == ov::element::i32) {
cv::Mat predictions(outHeight, outWidth, CV_32SC1, outTensor.data<int32_t>());
predictions.convertTo(result->resultImage, CV_8UC1);
} else if (outChannels == 1 && outTensor.get_element_type() == ov::element::i64) {
cv::Mat predictions(outHeight, outWidth, CV_32SC1);
const auto data = outTensor.data<int64_t>();
for (size_t i = 0; i < predictions.total(); ++i) {
reinterpret_cast<int32_t*>(predictions.data)[i] = int32_t(data[i]);
}
predictions.convertTo(result->resultImage, CV_8UC1);
} else if (outTensor.get_element_type() == ov::element::f32) {
const float* data = outTensor.data<float>();
for (int rowId = 0; rowId < outHeight; ++rowId) {
for (int colId = 0; colId < outWidth; ++colId) {
int classId = 0;
float maxProb = -1.0f;
for (int chId = 0; chId < outChannels; ++chId) {
float prob = data[chId * outHeight * outWidth + rowId * outWidth + colId];
if (prob > maxProb) {
classId = chId;
maxProb = prob;
}
} // nChannels
result->resultImage.at<uint8_t>(rowId, colId) = classId;
} // width
} // height
}
cv::resize(result->resultImage,
result->resultImage,
cv::Size(inputImgSize.inputImgWidth, inputImgSize.inputImgHeight),
0,
0,
cv::INTER_NEAREST);
return std::unique_ptr<ResultBase>(result);
}
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