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https://github.com/open-mmlab/mmdeploy.git
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* minor changes * support windows * fix GCC build * fix lint * reformat * fix Windows build * fix GCC build * search backend ops for onnxruntime * fix lint * fix lint * code clean-up * code clean-up * fix clang build * fix trt support * fix cmake for ncnn * fix cmake for openvino * fix SDK Python API * handle ops for other backends (ncnn, trt) * handle SDK Python API library location * robustify linkage * fix cuda * minor fix for openvino & ncnn * use CMAKE_CUDA_ARCHITECTURES if set * fix cuda preprocessor * fix misc * fix pplnn & pplcv, drop support for pplcv<0.6.0 * robustify cmake * update build.md (#2) * build dynamic modules as module library & fix demo (partially) * fix candidate path for mmdeploy_python * move "enable CUDA" to cmake config for demo * refine demo cmake * add comment * fix ubuntu build * revert docs/en/build.md * fix C API * fix lint * Windows build doc (#3) * check in docs related to mmdeploy build on windows * update build guide on windows platform * update build guide on windows platform * make path of thirdparty libraries consistent * make path consistency * correct build command for custom ops * correct build command for sdk * update sdk build instructions * update doc * correct build command * fix lint * correct build command and fix lint Co-authored-by: lvhan <lvhan@pjlab.org> * trailing whitespace (#4) * minor fix * fix sr sdk model * fix type deduction * fix cudaFree after driver shutting down * update ppl.cv installation warning (#5) * fix device allocator threshold & fix lint * update doc (#6) * update ppl.cv installation warning * missing 'git clone' Co-authored-by: chenxin <chenxin2@sensetime.com> Co-authored-by: zhangli <zhangli@sensetime.com> Co-authored-by: lvhan028 <lvhan_028@163.com> Co-authored-by: lvhan <lvhan@pjlab.org>
87 lines
2.3 KiB
C++
87 lines
2.3 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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MMDEPLOY_DEBUG("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" || arg_.color_type == "color_ignore_orientation"
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? ConvertToBGR(src_mat)
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: 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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MMDEPLOY_DEBUG("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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MMDEPLOY_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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MMDEPLOY_DEFINE_REGISTRY(PrepareImageImpl);
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} // namespace mmdeploy
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