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* check in cmake * move backend_ops to csrc/backend_ops * check in preprocess, model, some codebase and their c-apis * check in CMakeLists.txt * check in parts of test_csrc * commit everything else * add readme * update core's BUILD_INTERFACE directory * skip codespell on third_party * update trt_net and ort_net's CMakeLists * ignore clion's build directory * check in pybind11 * add onnx.proto. Remove MMDeploy's dependency on ncnn's source code * export MMDeployTargets only when MMDEPLOY_BUILD_SDK is ON * remove useless message * target include directory is wrong * change target name from mmdeploy_ppl_net to mmdeploy_pplnn_net * skip install directory * update project's cmake * remove useless code * set CMAKE_BUILD_TYPE to Release by force if it isn't set by user * update custom ops CMakeLists * pass object target's source lists * fix lint end-of-file * fix lint: trailing whitespace * fix codespell hook * remove bicubic_interpolate to csrc/backend_ops/ * set MMDEPLOY_BUILD_SDK OFF * change custom ops build command * add spdlog installation command * update docs on how to checkout pybind11 * move bicubic_interpolate to backend_ops/tensorrt directory * remove useless code * correct cmake * fix typo * fix typo * fix install directory * correct sdk's readme * set cub dir when cuda version < 11.0 * change directory where clang-format will apply to * fix build command * add .clang-format * change clang-format style from google to file * reformat csrc/backend_ops * format sdk's code * turn off clang-format for some files * add -Xcompiler=-fno-gnu-unique * fix trt topk initialize * check in config for sdk demo * update cmake script and csrc's readme * correct config's path * add cuda include directory, otherwise compile failed in case of tensorrt8.2 * clang-format onnx2ncnn.cpp Co-authored-by: zhangli <lzhang329@gmail.com> Co-authored-by: grimoire <yaoqian@sensetime.com>
82 lines
2.2 KiB
C++
82 lines
2.2 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include "load.h"
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#include "archive/json_archive.h"
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namespace mmdeploy {
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PrepareImageImpl::PrepareImageImpl(const Value& args) : TransformImpl(args) {
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arg_.to_float32 = args.value("to_float32", false);
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arg_.color_type = args.value("color_type", std::string("color"));
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}
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/**
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* Input:
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{
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"ori_img": cv::Mat,
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"attribute": {
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}
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}
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* Output:
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{
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"ori_img": cv::Mat,
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"img": Tensor,
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"img_shape": [],
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"ori_shape": [],
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"img_fields": ["img"],
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"attribute": {
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}
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}
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*/
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Result<Value> PrepareImageImpl::Process(const Value& input) {
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INFO("input: {}", to_json(input).dump(2));
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assert(input.contains("ori_img"));
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// copy input data, and update its properties later
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Value output = input;
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Mat src_mat = input["ori_img"].get<Mat>();
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auto res = (arg_.color_type == "color" ? ConvertToBGR(src_mat) : ConvertToGray(src_mat));
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OUTCOME_TRY(auto tensor, std::move(res));
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output["img"] = tensor;
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for (auto v : tensor.desc().shape) {
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output["img_shape"].push_back(v);
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}
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output["ori_shape"] = {1, src_mat.height(), src_mat.width(), src_mat.channel()};
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output["img_fields"].push_back("img");
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INFO("output: {}", to_json(output).dump(2));
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return output;
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}
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PrepareImage::PrepareImage(const Value& args, int version) : Transform(args) {
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auto impl_creator = Registry<PrepareImageImpl>::Get().GetCreator(specified_platform_, version);
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if (nullptr == impl_creator) {
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ERROR("'PrepareImage' is not supported on '{}' platform", specified_platform_);
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throw std::domain_error("'PrepareImage' is not supported on specified platform");
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}
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impl_ = impl_creator->Create(args);
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}
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class PrepareImageCreator : public Creator<Transform> {
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public:
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PrepareImageCreator() = default;
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~PrepareImageCreator() = default;
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const char* GetName() const override { return "LoadImageFromFile"; }
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int GetVersion() const override { return version_; }
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std::unique_ptr<Transform> Create(const Value& value) override {
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return std::make_unique<PrepareImage>(value, version_);
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}
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private:
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int version_{1};
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};
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REGISTER_MODULE(Transform, PrepareImageCreator);
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} // namespace mmdeploy
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