mirror of
https://github.com/open-mmlab/mmdeploy.git
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a494a6f6ff
* sync SDK changes according to performance benchmarks * fix end-of-file lint * fix clang-format issue * fix clang-format by adding 'clang-format off' * remove useless casts * remove 'data' argument of 'operator()' * change 'Tensor2Img' to 'TensorToImg' according to spec * correct tensor's name according spec Co-authored-by: lvhan028 <lvhan_028@163.com>
91 lines
3.1 KiB
C++
91 lines
3.1 KiB
C++
// Copyright (c) OpenMMLab. All rights reserved.
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#include "crop.h"
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#include "archive/json_archive.h"
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using namespace std;
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namespace mmdeploy {
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CenterCropImpl::CenterCropImpl(const Value& args) : TransformImpl(args) {
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if (!args.contains(("crop_size"))) {
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throw std::invalid_argument("'crop_size' is expected");
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}
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if (args["crop_size"].is_number_integer()) {
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int crop_size = args["crop_size"].get<int>();
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arg_.crop_size[0] = arg_.crop_size[1] = crop_size;
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} else if (args["crop_size"].is_array() && args["crop_size"].size() == 2) {
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arg_.crop_size[0] = args["crop_size"][0].get<int>();
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arg_.crop_size[1] = args["crop_size"][1].get<int>();
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} else {
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throw std::invalid_argument("'crop_size' should be integer or an int array of size 2");
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}
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}
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Result<Value> CenterCropImpl::Process(const Value& input) {
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DEBUG("input: {}", to_json(input).dump(2));
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auto img_fields = GetImageFields(input);
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// copy input data, and update its properties
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Value output = input;
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for (auto& key : img_fields) {
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auto tensor = input[key].get<Tensor>();
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auto desc = tensor.desc();
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int h = desc.shape[1];
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int w = desc.shape[2];
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int crop_height = arg_.crop_size[0];
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int crop_width = arg_.crop_size[1];
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int y1 = std::max(0, int(std::round((h - crop_height) / 2.0)));
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int x1 = std::max(0, int(std::round((w - crop_width) / 2.0)));
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int y2 = std::min(h, y1 + crop_height) - 1;
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int x2 = std::min(w, x1 + crop_width) - 1;
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OUTCOME_TRY(auto dst_tensor, CropImage(tensor, y1, x1, y2, x2));
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auto& shape = dst_tensor.desc().shape;
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output[key] = dst_tensor;
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output["img_shape"] = {shape[0], shape[1], shape[2], shape[3]};
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if (input.contains("scale_factor")) {
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// image has been processed by `Resize` transform before.
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// Compute cropped image's offset against the original image
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assert(input["scale_factor"].is_array() && input["scale_factor"].size() >= 2);
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float w_scale = input["scale_factor"][0].get<float>();
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float h_scale = input["scale_factor"][1].get<float>();
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output["offset"].push_back(x1 / w_scale);
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output["offset"].push_back(y1 / h_scale);
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} else {
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output["offset"].push_back(x1);
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output["offset"].push_back(y1);
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}
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}
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DEBUG("output: {}", to_json(output).dump(2));
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return output;
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}
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CenterCrop::CenterCrop(const Value& args, int version) : Transform(args) {
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auto impl_creator = Registry<CenterCropImpl>::Get().GetCreator(specified_platform_, version);
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if (nullptr == impl_creator) {
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ERROR("'CenterCrop' is not supported on '{}' platform", specified_platform_);
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throw std::domain_error("'Resize' 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 CenterCropCreator : public Creator<Transform> {
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public:
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const char* GetName(void) const override { return "CenterCrop"; }
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int GetVersion(void) const override { return version_; }
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ReturnType Create(const Value& args) override { return make_unique<CenterCrop>(args, version_); }
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private:
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int version_{1};
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};
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REGISTER_MODULE(Transform, CenterCropCreator);
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} // namespace mmdeploy
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