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/*
// Copyright (C) 2021-2022 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/deblurring_model.h"
#include <algorithm>
#include <stdexcept>
#include <string>
#include <vector>
#include <opencv2/imgproc.hpp>
#include <openvino/openvino.hpp>
#include <utils/ocv_common.hpp>
#include <utils/slog.hpp>
#include "models/input_data.h"
#include "models/internal_model_data.h"
#include "models/results.h"
DeblurringModel::DeblurringModel(const std::string& modelFileName,
const cv::Size& inputImgSize,
const std::string& layout)
: ImageModel(modelFileName, false, layout) {
netInputHeight = inputImgSize.height;
netInputWidth = inputImgSize.width;
}
void DeblurringModel::prepareInputsOutputs(std::shared_ptr<ov::Model>& model) {
// --------------------------- Configure input & output -------------------------------------------------
// --------------------------- Prepare input ------------------------------------------------------
if (model->inputs().size() != 1) {
throw std::logic_error("Deblurring model wrapper supports topologies with only 1 input");
}
inputsNames.push_back(model->input().get_any_name());
const ov::Shape& inputShape = model->input().get_shape();
const ov::Layout& inputLayout = getInputLayout(model->input());
if (inputShape.size() != 4 || inputShape[ov::layout::batch_idx(inputLayout)] != 1 ||
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");
ppp.input().model().set_layout(inputLayout);
// --------------------------- Prepare output -----------------------------------------------------
if (model->outputs().size() != 1) {
throw std::logic_error("Deblurring model wrapper supports topologies with only 1 output");
}
outputsNames.push_back(model->output().get_any_name());
const ov::Shape& outputShape = model->output().get_shape();
const ov::Layout outputLayout("NCHW");
if (outputShape.size() != 4 || outputShape[ov::layout::batch_idx(outputLayout)] != 1 ||
outputShape[ov::layout::channels_idx(outputLayout)] != 3) {
throw std::logic_error("3-channel 4-dimensional model's output is expected");
}
ppp.output().tensor().set_element_type(ov::element::f32);
model = ppp.build();
changeInputSize(model);
}
void DeblurringModel::changeInputSize(std::shared_ptr<ov::Model>& model) {
const ov::Layout& layout = ov::layout::get_layout(model->input());
ov::Shape inputShape = model->input().get_shape();
const auto batchId = ov::layout::batch_idx(layout);
const auto heightId = ov::layout::height_idx(layout);
const auto widthId = ov::layout::width_idx(layout);
if (inputShape[heightId] % stride || inputShape[widthId] % stride) {
throw std::logic_error("Model input shape HxW = " + std::to_string(inputShape[heightId]) + "x" +
std::to_string(inputShape[widthId]) + "must be divisible by stride " +
std::to_string(stride));
}
netInputHeight = static_cast<int>((netInputHeight + stride - 1) / stride) * stride;
netInputWidth = static_cast<int>((netInputWidth + stride - 1) / stride) * stride;
inputShape[batchId] = 1;
inputShape[heightId] = netInputHeight;
inputShape[widthId] = netInputWidth;
model->reshape(inputShape);
}
std::shared_ptr<InternalModelData> DeblurringModel::preprocess(const InputData& inputData, ov::InferRequest& request) {
auto& image = inputData.asRef<ImageInputData>().inputImage;
size_t h = image.rows;
size_t w = image.cols;
cv::Mat resizedImage;
if (netInputHeight - stride < h && h <= netInputHeight && netInputWidth - stride < w && w <= netInputWidth) {
int bottom = netInputHeight - h;
int right = netInputWidth - w;
cv::copyMakeBorder(image, resizedImage, 0, bottom, 0, right, cv::BORDER_CONSTANT, 0);
} else {
slog::warn << "\tChosen model aspect ratio doesn't match image aspect ratio" << slog::endl;
cv::resize(image, resizedImage, cv::Size(netInputWidth, netInputHeight));
}
request.set_input_tensor(wrapMat2Tensor(resizedImage));
return std::make_shared<InternalImageModelData>(image.cols, image.rows);
}
std::unique_ptr<ResultBase> DeblurringModel::postprocess(InferenceResult& infResult) {
ImageResult* result = new ImageResult;
*static_cast<ResultBase*>(result) = static_cast<ResultBase&>(infResult);
const auto& inputImgSize = infResult.internalModelData->asRef<InternalImageModelData>();
const auto outputData = infResult.getFirstOutputTensor().data<float>();
std::vector<cv::Mat> imgPlanes;
const ov::Shape& outputShape = infResult.getFirstOutputTensor().get_shape();
const ov::Layout outputLayout("NCHW");
size_t outHeight = static_cast<int>((outputShape[ov::layout::height_idx(outputLayout)]));
size_t outWidth = static_cast<int>((outputShape[ov::layout::width_idx(outputLayout)]));
size_t numOfPixels = outWidth * outHeight;
imgPlanes = std::vector<cv::Mat>{cv::Mat(outHeight, outWidth, CV_32FC1, &(outputData[0])),
cv::Mat(outHeight, outWidth, CV_32FC1, &(outputData[numOfPixels])),
cv::Mat(outHeight, outWidth, CV_32FC1, &(outputData[numOfPixels * 2]))};
cv::Mat resultImg;
cv::merge(imgPlanes, resultImg);
if (netInputHeight - stride < static_cast<size_t>(inputImgSize.inputImgHeight) &&
static_cast<size_t>(inputImgSize.inputImgHeight) <= netInputHeight &&
netInputWidth - stride < static_cast<size_t>(inputImgSize.inputImgWidth) &&
static_cast<size_t>(inputImgSize.inputImgWidth) <= netInputWidth) {
result->resultImage = resultImg(cv::Rect(0, 0, inputImgSize.inputImgWidth, inputImgSize.inputImgHeight));
} else {
cv::resize(resultImg, result->resultImage, cv::Size(inputImgSize.inputImgWidth, inputImgSize.inputImgHeight));
}
result->resultImage.convertTo(result->resultImage, CV_8UC3, 255);
return std::unique_ptr<ResultBase>(result);
}
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