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https://github.com/PaddlePaddle/FastDeploy.git
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a51e5a6e55
* [Android] Add Android build docs and demo (#26) * [Backend] Add override flag to lite backend * [Docs] Add Android C++ SDK build docs * [Doc] fix android_build_docs typos * Update CMakeLists.txt * Update android.md * [Doc] Add PicoDet Android demo docs * [Doc] Update PicoDet Andorid demo docs * [Doc] Update PaddleClasModel Android demo docs * [Doc] Update fastdeploy android jni docs * [Doc] Update fastdeploy android jni usage docs * [Android] init fastdeploy android jar package * [Backend] support int8 option for lite backend * [Model] add Backend::Lite to paddle model * [Backend] use CopyFromCpu for lite backend. * [Android] package jni srcs and java api into aar * Update infer.cc * Update infer.cc * [Android] Update package build.gradle * [Android] Update android app examples * [Android] update android detection app
85 lines
2.9 KiB
C++
85 lines
2.9 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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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#include "fastdeploy/vision/detection/ppdet/rcnn.h"
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namespace fastdeploy {
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namespace vision {
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namespace detection {
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FasterRCNN::FasterRCNN(const std::string& model_file,
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const std::string& params_file,
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const std::string& config_file,
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const RuntimeOption& custom_option,
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const ModelFormat& model_format) {
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config_file_ = config_file;
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valid_cpu_backends = {Backend::PDINFER, Backend::LITE};
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valid_gpu_backends = {Backend::PDINFER};
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has_nms_ = true;
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runtime_option = custom_option;
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runtime_option.model_format = model_format;
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runtime_option.model_file = model_file;
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runtime_option.params_file = params_file;
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initialized = Initialize();
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}
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bool FasterRCNN::Initialize() {
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if (!BuildPreprocessPipelineFromConfig()) {
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FDERROR << "Failed to build preprocess pipeline from configuration file."
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<< std::endl;
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return false;
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}
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if (!InitRuntime()) {
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FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
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return false;
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}
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return true;
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}
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bool FasterRCNN::Preprocess(Mat* mat, std::vector<FDTensor>* outputs) {
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int origin_w = mat->Width();
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int origin_h = mat->Height();
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float scale[2] = {1.0, 1.0};
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for (size_t i = 0; i < processors_.size(); ++i) {
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if (!(*(processors_[i].get()))(mat)) {
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FDERROR << "Failed to process image data in " << processors_[i]->Name()
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<< "." << std::endl;
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return false;
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}
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if (processors_[i]->Name().find("Resize") != std::string::npos) {
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scale[0] = mat->Height() * 1.0 / origin_h;
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scale[1] = mat->Width() * 1.0 / origin_w;
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}
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}
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outputs->resize(3);
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(*outputs)[0].Allocate({1, 2}, FDDataType::FP32, "im_shape");
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(*outputs)[2].Allocate({1, 2}, FDDataType::FP32, "scale_factor");
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float* ptr0 = static_cast<float*>((*outputs)[0].MutableData());
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ptr0[0] = mat->Height();
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ptr0[1] = mat->Width();
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float* ptr2 = static_cast<float*>((*outputs)[2].MutableData());
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ptr2[0] = scale[0];
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ptr2[1] = scale[1];
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(*outputs)[1].name = "image";
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mat->ShareWithTensor(&((*outputs)[1]));
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// reshape to [1, c, h, w]
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(*outputs)[1].shape.insert((*outputs)[1].shape.begin(), 1);
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return true;
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}
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} // namespace detection
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} // namespace vision
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} // namespace fastdeploy
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