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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/image_model.h"
#include <stdexcept>
#include <vector>
#include <opencv2/core.hpp>
#include <openvino/openvino.hpp>
#include <utils/image_utils.h>
#include <utils/ocv_common.hpp>
#include "models/input_data.h"
#include "models/internal_model_data.h"
ImageModel::ImageModel(const std::string& modelFileName, bool useAutoResize, const std::string& layout)
: ModelBase(modelFileName, layout),
useAutoResize(useAutoResize) {}
std::shared_ptr<InternalModelData> ImageModel::preprocess(const InputData& inputData, ov::InferRequest& request) {
const auto& origImg = inputData.asRef<ImageInputData>().inputImage;
auto img = inputTransform(origImg);
if (!useAutoResize) {
// /* Resize and copy data from the image to the input tensor */
const ov::Tensor& frameTensor = request.get_tensor(inputsNames[0]); // first input should be image
const ov::Shape& tensorShape = frameTensor.get_shape();
const ov::Layout layout("NHWC");
const size_t width = tensorShape[ov::layout::width_idx(layout)];
const size_t height = tensorShape[ov::layout::height_idx(layout)];
const size_t channels = tensorShape[ov::layout::channels_idx(layout)];
if (static_cast<size_t>(img.channels()) != channels) {
throw std::runtime_error("The number of channels for model input and image must match");
}
if (channels != 1 && channels != 3) {
throw std::runtime_error("Unsupported number of channels");
}
img = resizeImageExt(img, width, height, resizeMode, interpolationMode);
}
request.set_tensor(inputsNames[0], wrapMat2Tensor(img));
return std::make_shared<InternalImageModelData>(origImg.cols, origImg.rows);
}
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