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https://github.com/PaddlePaddle/FastDeploy.git
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Refine code structure (#89)
* refine code structure * refine code structure
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Jason
14
examples/vision/detection/yolov7/cpp/CMakeLists.txt
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examples/vision/detection/yolov7/cpp/CMakeLists.txt
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.12)
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# 指定下载解压后的fastdeploy库路径
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# 添加FastDeploy依赖头文件
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include_directories(${FASTDEPLOY_INCS})
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
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# 添加FastDeploy库依赖
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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77
examples/vision/detection/yolov7/cpp/README.md
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examples/vision/detection/yolov7/cpp/README.md
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# YOLOv7 C++部署示例
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本目录下提供`infer.cc`快速完成YOLOv7在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/quick_start/requirements.md)
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- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/compile/prebuild_libraries.md)
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以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试
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```
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mkdir build
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cd build
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wget https://xxx.tgz
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tar xvf fastdeploy-linux-x64-0.2.0.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.2.0
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make -j
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#下载官方转换好的yolov7模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000087038.jpg
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# CPU推理
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./infer_demo yolov7.onnx 000000087038.jpg 0
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# GPU推理
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./infer_demo yolov7.onnx 000000087038.jpg 1
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# GPU上TensorRT推理
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./infer_demo yolov7.onnx 000000087038.jpg 2
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```
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## YOLOv7 C++接口
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### YOLOv7类
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```
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fastdeploy::vision::detection::YOLOv7(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const Frontend& model_format = Frontend::ONNX)
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```
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YOLOv7模型加载和初始化，其中model_file为导出的ONNX模型格式。
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径，当模型格式为ONNX时，此参数传入空字符串即可
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(Frontend): 模型格式，默认为ONNX格式
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#### Predict函数
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> ```
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> YOLOv7::Predict(cv::Mat* im, DetectionResult* result,
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> float conf_threshold = 0.25,
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> float nms_iou_threshold = 0.5)
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> ```
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>
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> 模型预测接口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **im**: 输入图像，注意需为HWC，BGR格式
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> > * **result**: 检测结果，包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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> > * **conf_threshold**: 检测框置信度过滤阈值
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> > * **nms_iou_threshold**: NMS处理过程中iou阈值
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### 类成员变量
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> > * **size**(vector<int>): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[640, 640]
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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../docs/api/vision_results/)
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105
examples/vision/detection/yolov7/cpp/infer.cc
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examples/vision/detection/yolov7/cpp/infer.cc
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// 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.h"
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void CpuInfer(const std::string& model_file, const std::string& image_file) {
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auto model = fastdeploy::vision::detection::YOLOv7(model_file);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::DetectionResult res;
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, res);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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void GpuInfer(const std::string& model_file, const std::string& image_file) {
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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auto model = fastdeploy::vision::detection::YOLOv7(model_file, "", option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::DetectionResult res;
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, res);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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void TrtInfer(const std::string& model_file, const std::string& image_file) {
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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option.UseTrtBackend();
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option.SetTrtInputShape("images", {1, 3, 640, 640});
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auto model = fastdeploy::vision::detection::YOLOv7(model_file, "", option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::DetectionResult res;
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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auto vis_im = fastdeploy::vision::Visualize::VisDetection(im_bak, res);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
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}
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int main(int argc, char* argv[]) {
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if (argc < 4) {
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std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
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"e.g ./infer_model ./yolov7.onnx ./test.jpeg 0"
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<< std::endl;
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std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
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"with gpu; 2: run with gpu and use tensorrt backend."
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<< std::endl;
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return -1;
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}
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if (std::atoi(argv[3]) == 0) {
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CpuInfer(argv[1], argv[2]);
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} else if (std::atoi(argv[3]) == 1) {
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GpuInfer(argv[1], argv[2]);
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} else if (std::atoi(argv[3]) == 2) {
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TrtInfer(argv[1], argv[2]);
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}
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return 0;
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}
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