63 lines
2.5 KiB
C++
63 lines
2.5 KiB
C++
/*
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// Copyright (C) 2021-2024 Intel Corporation
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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*/
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#include "models/image_model.h"
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#include <stdexcept>
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#include <vector>
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#include <opencv2/core.hpp>
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#include <openvino/openvino.hpp>
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#include <utils/image_utils.h>
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#include <utils/ocv_common.hpp>
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#include "models/input_data.h"
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#include "models/internal_model_data.h"
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ImageModel::ImageModel() {
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}
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ImageModel::ImageModel(const std::string& modelFileName, bool useAutoResize, const std::string& layout)
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: ModelBase(modelFileName, layout),
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useAutoResize(useAutoResize) {}
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std::shared_ptr<InternalModelData> ImageModel::preprocess(const InputData& inputData, ov::InferRequest& request) {
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const auto& origImg = inputData.asRef<ImageInputData>().inputImage;
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auto img = inputTransform(origImg);
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if (!useAutoResize) {
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// /* Resize and copy data from the image to the input tensor */
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const ov::Tensor& frameTensor = request.get_tensor(inputsNames[0]); // first input should be image
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const ov::Shape& tensorShape = frameTensor.get_shape();
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const ov::Layout layout("NHWC");
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const size_t width = tensorShape[ov::layout::width_idx(layout)];
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const size_t height = tensorShape[ov::layout::height_idx(layout)];
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const size_t channels = tensorShape[ov::layout::channels_idx(layout)];
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if (static_cast<size_t>(img.channels()) != channels) {
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throw std::runtime_error(std::string("The number of channels for model input: ") +
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std::to_string(channels) + " and image: " +
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std::to_string(img.channels()) + " - must match");
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}
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if (channels != 1 && channels != 3) {
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throw std::runtime_error("Unsupported number of channels");
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}
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img = resizeImageExt(img, width, height, resizeMode, interpolationMode);
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}
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request.set_tensor(inputsNames[0], wrapMat2Tensor(img));
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return std::make_shared<InternalImageModelData>(origImg.cols, origImg.rows);
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}
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