Modify PPMatting backend and docs #182
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,78 @@ | ||
| // Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // 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. | ||
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| #include "fastdeploy/vision/common/processors/resize_by_long.h" | ||
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| namespace fastdeploy { | ||
| namespace vision { | ||
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| bool ResizeByLong::CpuRun(Mat* mat) { | ||
| cv::Mat* im = mat->GetCpuMat(); | ||
| int origin_w = im->cols; | ||
| int origin_h = im->rows; | ||
| double scale = GenerateScale(origin_w, origin_h); | ||
| if (use_scale_) { | ||
| cv::resize(*im, *im, cv::Size(), scale, scale, interp_); | ||
| } else { | ||
| int width = static_cast<int>(round(scale * im->cols)); | ||
| int height = static_cast<int>(round(scale * im->rows)); | ||
| cv::resize(*im, *im, cv::Size(width, height), 0, 0, interp_); | ||
| } | ||
| mat->SetWidth(im->cols); | ||
| mat->SetHeight(im->rows); | ||
| return true; | ||
| } | ||
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| #ifdef ENABLE_OPENCV_CUDA | ||
| bool ResizeByLong::GpuRun(Mat* mat) { | ||
| cv::cuda::GpuMat* im = mat->GetGpuMat(); | ||
| int origin_w = im->cols; | ||
| int origin_h = im->rows; | ||
| double scale = GenerateScale(origin_w, origin_h); | ||
| im->convertTo(*im, CV_32FC(im->channels())); | ||
| if (use_scale_) { | ||
| cv::cuda::resize(*im, *im, cv::Size(), scale, scale, interp_); | ||
| } else { | ||
| int width = static_cast<int>(round(scale * im->cols)); | ||
| int height = static_cast<int>(round(scale * im->rows)); | ||
| cv::cuda::resize(*im, *im, cv::Size(width, height), 0, 0, interp_); | ||
| } | ||
| mat->SetWidth(im->cols); | ||
| mat->SetHeight(im->rows); | ||
| return true; | ||
| } | ||
| #endif | ||
|
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| double ResizeByLong::GenerateScale(const int origin_w, const int origin_h) { | ||
| int im_size_max = std::max(origin_w, origin_h); | ||
| int im_size_min = std::min(origin_w, origin_h); | ||
| double scale = 1.0f; | ||
| if (target_size_ == -1) { | ||
| if (im_size_max > max_size_) { | ||
| scale = static_cast<double>(max_size_) / static_cast<double>(im_size_max); | ||
| } | ||
| } else { | ||
| scale = | ||
| static_cast<double>(target_size_) / static_cast<double>(im_size_max); | ||
| } | ||
| return scale; | ||
| } | ||
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| bool ResizeByLong::Run(Mat* mat, int target_size, int interp, bool use_scale, | ||
| int max_size, ProcLib lib) { | ||
| auto r = ResizeByLong(target_size, interp, use_scale, max_size); | ||
| return r(mat, lib); | ||
| } | ||
| } // namespace vision | ||
| } // namespace fastdeploy |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,49 @@ | ||
| // Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
| // | ||
| // 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. | ||
|
|
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| #pragma once | ||
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| #include "fastdeploy/vision/common/processors/base.h" | ||
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| namespace fastdeploy { | ||
| namespace vision { | ||
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| class ResizeByLong : public Processor { | ||
| public: | ||
| ResizeByLong(int target_size, int interp = 1, bool use_scale = true, | ||
| int max_size = -1) { | ||
| target_size_ = target_size; | ||
| max_size_ = max_size; | ||
| interp_ = interp; | ||
| use_scale_ = use_scale; | ||
| } | ||
| bool CpuRun(Mat* mat); | ||
| #ifdef ENABLE_OPENCV_CUDA | ||
| bool GpuRun(Mat* mat); | ||
| #endif | ||
| std::string Name() { return "ResizeByLong"; } | ||
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| static bool Run(Mat* mat, int target_size, int interp = 1, | ||
| bool use_scale = true, int max_size = -1, | ||
| ProcLib lib = ProcLib::OPENCV_CPU); | ||
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| private: | ||
| double GenerateScale(const int origin_w, const int origin_h); | ||
| int target_size_; | ||
| int max_size_; | ||
| int interp_; | ||
| bool use_scale_; | ||
| }; | ||
| } // namespace vision | ||
| } // namespace fastdeploy | ||
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naive 值传递 不需要 const限制