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cpp shitu code format

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This commit is contained in:
dongshuilong 2021-11-15 12:19:21 +08:00
parent a96305c225
commit 29f8fb830a
19 changed files with 1314 additions and 1291 deletions
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19
deploy/cpp_shitu/include/feature_extracter.h
View File

@ -35,8 +35,8 @@ using namespace paddle_infer;
namespace Feature {
class FeatureExtracter {
public:
class FeatureExtracter {
public:
explicit FeatureExtracter(const YAML::Node &config_file) {
this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
if (config_file["Global"]["gpu_id"].IsDefined())
@ -59,15 +59,13 @@ public:
this->resize_size_ =
config_file["RecPreProcess"]["transform_ops"][0]["ResizeImage"]["size"]
.as<int>();
this->scale_ = config_file["RecPreProcess"]["transform_ops"][1]
["NormalizeImage"]["scale"]
.as<float>();
this->scale_ = config_file["RecPreProcess"]["transform_ops"][1]["NormalizeImage"]["scale"].as<float>();
this->mean_ = config_file["RecPreProcess"]["transform_ops"][1]
["NormalizeImage"]["mean"]
.as<std::vector<float>>();
.as < std::vector < float >> ();
this->std_ = config_file["RecPreProcess"]["transform_ops"][1]
["NormalizeImage"]["std"]
.as<std::vector<float>>();
.as < std::vector < float >> ();
if (config_file["Global"]["rec_feature_normlize"].IsDefined())
this->feature_norm =
config_file["Global"]["rec_feature_normlize"].as<bool>();
@ -81,11 +79,12 @@ public:
// Run predictor
void Run(cv::Mat &img, std::vector<float> &out_data,
std::vector<double> &times);
void FeatureNorm(std::vector<float> &feature);
std::shared_ptr<Predictor> predictor_;
std::shared_ptr <Predictor> predictor_;
private:
private:
bool use_gpu_ = false;
int gpu_id_ = 0;
int gpu_mem_ = 4000;
@ -106,6 +105,6 @@ private:
ResizeImg resize_op_;
Normalize normalize_op_;
Permute permute_op_;
};
};
} // namespace Feature

12
deploy/cpp_shitu/include/nms.h
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@ -17,7 +17,7 @@
#include <algorithm>
#include <include/object_detector.h>
template <typename T>
template<typename T>
static inline bool SortScorePairDescend(const std::pair<float, T> &pair1,
const std::pair<float, T> &pair2) {
return pair1.first > pair2.first;
@ -40,9 +40,9 @@ float RectOverlap(const Detection::ObjectResult &a,
// top_k: if -1, keep all; otherwise, keep at most top_k.
// score_index_vec: store the sorted (score, index) pair.
inline void
GetMaxScoreIndex(const std::vector<Detection::ObjectResult> &det_result,
GetMaxScoreIndex(const std::vector <Detection::ObjectResult> &det_result,
const float threshold,
std::vector<std::pair<float, int>> &score_index_vec) {
std::vector <std::pair<float, int>> &score_index_vec) {
// Generate index score pairs.
for (size_t i = 0; i < det_result.size(); ++i) {
if (det_result[i].confidence > threshold) {
@ -61,12 +61,12 @@ GetMaxScoreIndex(const std::vector<Detection::ObjectResult> &det_result,
// }
}
void NMSBoxes(const std::vector<Detection::ObjectResult> det_result,
void NMSBoxes(const std::vector <Detection::ObjectResult> det_result,
const float score_threshold, const float nms_threshold,
std::vector<int> &indices) {
int a = 1;
// Get top_k scores (with corresponding indices).
std::vector<std::pair<float, int>> score_index_vec;
std::vector <std::pair<float, int>> score_index_vec;
GetMaxScoreIndex(det_result, score_threshold, score_index_vec);
// Do nms
@ -74,7 +74,7 @@ void NMSBoxes(const std::vector<Detection::ObjectResult> det_result,
for (size_t i = 0; i < score_index_vec.size(); ++i) {
const int idx = score_index_vec[i].second;
bool keep = true;
for (int k = 0; k < (int)indices.size() && keep; ++k) {
for (int k = 0; k < (int) indices.size() && keep; ++k) {
const int kept_idx = indices[k];
float overlap = RectOverlap(det_result[idx], det_result[kept_idx]);
keep = overlap <= nms_threshold;

41
deploy/cpp_shitu/include/object_detector.h
View File

@ -33,26 +33,26 @@ using namespace paddle_infer;
namespace Detection {
// Object Detection Result
struct ObjectResult {
struct ObjectResult {
// Rectangle coordinates of detected object: left, right, top, down
std::vector<int> rect;
// Class id of detected object
int class_id;
// Confidence of detected object
float confidence;
};
};
// Generate visualization colormap for each class
std::vector<int> GenerateColorMap(int num_class);
std::vector<int> GenerateColorMap(int num_class);
// Visualiztion Detection Result
cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector<ObjectResult> &results,
const std::vector<std::string> &lables,
cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector <ObjectResult> &results,
const std::vector <std::string> &lables,
const std::vector<int> &colormap, const bool is_rbox);
class ObjectDetector {
public:
class ObjectDetector {
public:
explicit ObjectDetector(const YAML::Node &config_file) {
this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
if (config_file["Global"]["gpu_id"].IsDefined())
@ -68,9 +68,9 @@ public:
this->threshold_ = config_file["Global"]["threshold"].as<float>();
this->max_det_results_ = config_file["Global"]["max_det_results"].as<int>();
this->image_shape_ =
config_file["Global"]["image_shape"].as<std::vector<int>>();
config_file["Global"]["image_shape"].as < std::vector < int >> ();
this->label_list_ =
config_file["Global"]["labe_list"].as<std::vector<std::string>>();
config_file["Global"]["labe_list"].as < std::vector < std::string >> ();
this->ir_optim_ = config_file["Global"]["ir_optim"].as<bool>();
this->batch_size_ = config_file["Global"]["batch_size"].as<int>();
@ -83,17 +83,19 @@ public:
const std::string &run_mode = "fluid");
// Run predictor
void Predict(const std::vector<cv::Mat> imgs, const int warmup = 0,
void Predict(const std::vector <cv::Mat> imgs, const int warmup = 0,
const int repeats = 1,
std::vector<ObjectResult> *result = nullptr,
std::vector <ObjectResult> *result = nullptr,
std::vector<int> *bbox_num = nullptr,
std::vector<double> *times = nullptr);
const std::vector<std::string> &GetLabelList() const {
const std::vector <std::string> &GetLabelList() const {
return this->label_list_;
}
const float &GetThreshold() const { return this->threshold_; }
private:
private:
bool use_gpu_ = true;
int gpu_id_ = 0;
int gpu_mem_ = 800;
@ -107,7 +109,7 @@ private:
float threshold_ = 0.5;
float max_det_results_ = 5;
std::vector<int> image_shape_ = {3, 640, 640};
std::vector<std::string> label_list_;
std::vector <std::string> label_list_;
bool ir_optim_ = true;
bool det_permute_ = true;
bool det_postprocess_ = true;
@ -120,16 +122,17 @@ private:
// Preprocess image and copy data to input buffer
void Preprocess(const cv::Mat &image_mat);
// Postprocess result
void Postprocess(const std::vector<cv::Mat> mats,
std::vector<ObjectResult> *result, std::vector<int> bbox_num,
void Postprocess(const std::vector <cv::Mat> mats,
std::vector <ObjectResult> *result, std::vector<int> bbox_num,
bool is_rbox);
std::shared_ptr<Predictor> predictor_;
std::shared_ptr <Predictor> predictor_;
Preprocessor preprocessor_;
ImageBlob inputs_;
std::vector<float> output_data_;
std::vector<int> out_bbox_num_data_;
};
};
} // namespace Detection

24
deploy/cpp_shitu/include/preprocess_op.h
View File

@ -31,27 +31,27 @@ using namespace std;
namespace Feature {
class Normalize {
public:
class Normalize {
public:
virtual void Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale);
};
};
// RGB -> CHW
class Permute {
public:
class Permute {
public:
virtual void Run(const cv::Mat *im, float *data);
};
};
class CenterCropImg {
public:
class CenterCropImg {
public:
virtual void Run(cv::Mat &im, const int crop_size = 224);
};
};
class ResizeImg {
public:
class ResizeImg {
public:
virtual void Run(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,
int size = 0);
};
};
} // namespace Feature

75
deploy/cpp_shitu/include/preprocess_op_det.h
View File

@ -31,8 +31,8 @@
namespace Detection {
// Object for storing all preprocessed data
class ImageBlob {
public:
class ImageBlob {
public:
// image width and height
std::vector<float> im_shape_;
// Buffer for image data after preprocessing
@ -43,51 +43,54 @@ public:
// std::vector<float> eval_im_size_f_;
// Scale factor for image size to origin image size
std::vector<float> scale_factor_;
};
};
// Abstraction of preprocessing opration class
class PreprocessOp {
public:
class PreprocessOp {
public:
virtual void Init(const YAML::Node &item) = 0;
virtual void Run(cv::Mat *im, ImageBlob *data) = 0;
};
};
class InitInfo : public PreprocessOp {
public:
class InitInfo : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {}
virtual void Run(cv::Mat *im, ImageBlob *data);
};
class NormalizeImage : public PreprocessOp {
public:
virtual void Run(cv::Mat *im, ImageBlob *data);
};
class NormalizeImage : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {
mean_ = item["mean"].as<std::vector<float>>();
scale_ = item["std"].as<std::vector<float>>();
mean_ = item["mean"].as < std::vector < float >> ();
scale_ = item["std"].as < std::vector < float >> ();
is_scale_ = item["is_scale"].as<bool>();
}
virtual void Run(cv::Mat *im, ImageBlob *data);
private:
private:
// CHW or HWC
std::vector<float> mean_;
std::vector<float> scale_;
bool is_scale_;
};
};
class Permute : public PreprocessOp {
public:
class Permute : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {}
virtual void Run(cv::Mat *im, ImageBlob *data);
};
class Resize : public PreprocessOp {
public:
virtual void Run(cv::Mat *im, ImageBlob *data);
};
class Resize : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {
interp_ = item["interp"].as<int>();
// max_size_ = item["target_size"].as<int>();
keep_ratio_ = item["keep_ratio"].as<bool>();
target_size_ = item["target_size"].as<std::vector<int>>();
target_size_ = item["target_size"].as < std::vector < int >> ();
}
// Compute best resize scale for x-dimension, y-dimension
@ -95,28 +98,28 @@ public:
virtual void Run(cv::Mat *im, ImageBlob *data);
private:
private:
int interp_ = 2;
bool keep_ratio_;
std::vector<int> target_size_;
std::vector<int> in_net_shape_;
};
};
// Models with FPN need input shape % stride == 0
class PadStride : public PreprocessOp {
public:
class PadStride : public PreprocessOp {
public:
virtual void Init(const YAML::Node &item) {
stride_ = item["stride"].as<int>();
}
virtual void Run(cv::Mat *im, ImageBlob *data);
private:
private:
int stride_;
};
};
class Preprocessor {
public:
class Preprocessor {
public:
void Init(const YAML::Node &config_node) {
// initialize image info at first
ops_["InitInfo"] = std::make_shared<InitInfo>();
@ -145,11 +148,11 @@ public:
void Run(cv::Mat *im, ImageBlob *data);
public:
static const std::vector<std::string> RUN_ORDER;
public:
static const std::vector <std::string> RUN_ORDER;
private:
std::unordered_map<std::string, std::shared_ptr<PreprocessOp>> ops_;
};
private:
std::unordered_map <std::string, std::shared_ptr<PreprocessOp>> ops_;
};
} // namespace Detection

9
deploy/cpp_shitu/include/vector_search.h
View File

@ -26,7 +26,7 @@
#include <map>
struct SearchResult {
std::vector<faiss::Index::idx_t> I;
std::vector <faiss::Index::idx_t> I;
std::vector<float> D;
int return_k;
};
@ -46,10 +46,15 @@ public:
this->I.resize(this->return_k * this->max_query_number);
this->D.resize(this->return_k * this->max_query_number);
};
void LoadIdMap();
void LoadIndexFile();
const SearchResult &Search(float *feature, int query_number);
const std::string &GetLabel(faiss::Index::idx_t ind);
const float &GetThreshold() { return this->score_thres; }
private:
@ -60,6 +65,6 @@ private:
faiss::Index *index;
int max_query_number = 6;
std::vector<float> D;
std::vector<faiss::Index::idx_t> I;
std::vector <faiss::Index::idx_t> I;
SearchResult sr;
};

7
deploy/cpp_shitu/include/yaml_config.h
View File

@ -45,9 +45,14 @@ public:
explicit YamlConfig(const std::string &path) {
config_file = ReadYamlConfig(path);
}
static std::vector<std::string> ReadDict(const std::string &path);
static std::vector <std::string> ReadDict(const std::string &path);
static std::map<int, std::string> ReadIndexId(const std::string &path);
static YAML::Node ReadYamlConfig(const std::string &path);
void PrintConfigInfo();
YAML::Node config_file;
};

134
deploy/cpp_shitu/readme.md
View File

@ -6,10 +6,7 @@
## 1. 准备环境
### 运行准备
- Linux环境，推荐使用docker。
- Windows环境，目前支持基于`Visual Studio 2019 Community`进行编译；此外，如果您希望通过生成`sln解决方案`的方式进行编译，可以参考该文档：[https://zhuanlan.zhihu.com/p/145446681](https://zhuanlan.zhihu.com/p/145446681)
* 该文档主要介绍基于Linux环境下的PaddleClas C++预测流程，如果需要在Windows环境下使用预测库进行C++预测，具体编译方法请参考[Windows下编译教程](./docs/windows_vs2019_build.md)。
- Linux环境，推荐使用ubuntu docker。
### 1.1 编译opencv库
@ -103,7 +100,7 @@ make -j
make inference_lib_dist
```
更多编译参数选项可以参考Paddle C++预测库官网：[https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)。
更多编译参数选项可以参考[Paddle C++预测库官网](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16)。
* 编译完成之后，可以在`build/paddle_inference_install_dir/`文件下看到生成了以下文件及文件夹。
@ -137,6 +134,7 @@ tar -xvf paddle_inference.tgz
### 1.3 安装faiss库
```shell
# 下载 faiss
git clone https://github.com/facebookresearch/faiss.git
cd faiss
cmake -B build . -DFAISS_ENABLE_PYTHON=OFF -DCMAKE_INSTALL_PREFIX=${faiss_install_path}
@ -144,22 +142,19 @@ tar -xvf paddle_inference.tgz
make -C build install
```
## 2 开始运行
### 2.1 将模型导出为inference model
* 可以参考[模型导出](../../tools/export_model.py)，导出`inference model`，用于模型预测。得到预测模型后，假设模型文件放在`inference`目录下，则目录结构如下。
在安装`faiss`前，请安装`openblas`，`ubuntu`系统中安装命令如下：
```shell
apt-get install libopenblas-dev
```
inference/
|--cls_infer.pdmodel
|--cls_infer.pdiparams
```
**注意**：上述文件中，`cls_infer.pdmodel`文件存储了模型结构信息，`cls_infer.pdiparams`文件存储了模型参数信息。注意两个文件的路径需要与配置文件`tools/config.txt`中的`cls_model_path`和`cls_params_path`参数对应一致。
注意本教程以安装faiss cpu版本为例，安装时请参考[faiss](https://github.com/facebookresearch/faiss)官网文档，根据需求自行安装。
## 2 代码编译
### 2.2 编译PaddleClas C++预测demo
* 编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。
编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。同时，编译过程中需要下载编译`yaml-cpp`等C++库，请保持联网环境。
```shell
@ -169,11 +164,12 @@ sh tools/build.sh
具体地，`tools/build.sh`中内容如下。
```shell
OPENCV_DIR=your_opencv_dir
LIB_DIR=your_paddle_inference_dir
CUDA_LIB_DIR=your_cuda_lib_dir
CUDNN_LIB_DIR=your_cudnn_lib_dir
TENSORRT_DIR=your_tensorrt_lib_dir
OPENCV_DIR=${opencv_install_dir}
LIB_DIR=${paddle_inference_dir}
CUDA_LIB_DIR=/usr/local/cuda/lib64
CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
FAISS_DIR=${faiss_install_dir}
FAISS_WITH_MKL=OFF
BUILD_DIR=build
rm -rf ${BUILD_DIR}
@ -182,14 +178,14 @@ cd ${BUILD_DIR}
cmake .. \
-DPADDLE_LIB=${LIB_DIR} \
-DWITH_MKL=ON \
-DDEMO_NAME=clas_system \
-DWITH_GPU=OFF \
-DWITH_STATIC_LIB=OFF \
-DWITH_TENSORRT=OFF \
-DTENSORRT_DIR=${TENSORRT_DIR} \
-DUSE_TENSORRT=OFF \
-DOPENCV_DIR=${OPENCV_DIR} \
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
-DCUDA_LIB=${CUDA_LIB_DIR} \
-DFAISS_DIR=${FAISS_DIR} \
-DFAISS_WITH_MKL=${FAISS_WITH_MKL}
make -j
```
@ -197,47 +193,75 @@ make -j
上述命令中，
* `OPENCV_DIR`为opencv编译安装的地址（本例中为`opencv-3.4.7/opencv3`文件夹的路径）；
* `LIB_DIR`为下载的Paddle预测库（`paddle_inference`文件夹），或编译生成的Paddle预测库（`build/paddle_inference_install_dir`文件夹）的路径；
* `CUDA_LIB_DIR`为cuda库文件地址，在docker中为`/usr/local/cuda/lib64`；
* `CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。
* `TENSORRT_DIR`是tensorrt库文件地址，在dokcer中为`/usr/local/TensorRT6-cuda10.0-cudnn7/`，TensorRT需要结合GPU使用。
在执行上述命令，编译完成之后，会在当前路径下生成`build`文件夹，其中生成一个名为`clas_system`的可执行文件。
* `FAISS_DIR`是faiss的安装地址
* `FAISS_WITH_MKL`是指在编译faiss的过程中，是否使用了mkldnn，本文档中编译faiss，没有使用，而使用了openblas，故设置为`OFF`，若使用了mkldnn，则为`ON`.
### 运行demo
* 首先修改`tools/config.txt`中对应字段：
* use_gpu：是否使用GPU；
* gpu_id：使用的GPU卡号；
* gpu_mem：显存；
* cpu_math_library_num_threads：底层科学计算库所用线程的数量；
* use_mkldnn：是否使用MKLDNN加速；
* use_tensorrt: 是否使用tensorRT进行加速；
* use_fp16：是否使用半精度浮点数进行计算，该选项仅在use_tensorrt为true时有效；
* cls_model_path：预测模型结构文件路径；
* cls_params_path：预测模型参数文件路径；
* resize_short_size：预处理时图像缩放大小；
* crop_size：预处理时图像裁剪后的大小。
在执行上述命令，编译完成之后，会在当前路径下生成`build`文件夹，其中生成一个名为`pp_shitu`的可执行文件。
* 然后修改`tools/run.sh`：
* `./build/clas_system ./tools/config.txt ./docs/imgs/ILSVRC2012_val_00000666.JPEG`
* 上述命令中分别为：编译得到的可执行文件`clas_system`；运行时的配置文件`config.txt`；待预测的图像。
## 3 运行demo
* 最后执行以下命令，完成对一幅图像的分类。
- 请参考[识别快速开始文档](../../docs/zh_CN/quick_start/quick_start_recognition.md)，下载好相应的 轻量级通用主体检测模型、轻量级通用识别模型及瓶装饮料测试数据并解压。
```shell
sh tools/run.sh
```
```shell
mkdir models
cd models
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
tar -xf picodet_PPLCNet_x2_5_mainbody_lite_v1.0_infer.tar
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
tar -xf general_PPLCNet_x2_5_lite_v1.0_infer.tar
cd ..
* 最终屏幕上会输出结果，如下图所示。
mkdir data
cd data
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
tar -xf drink_dataset_v1.0.tar
cd ..
```
<div align="center">
<img src="./docs/imgs/cpp_infer_result.png" width="600">
</div>
- 将相应的yaml文件拷到`test`文件夹下
```shell
cp ../configs/inference_drink.yaml .
```
其中`class id`表示置信度最高的类别对应的id，score表示图片属于该类别的概率。
- 将`inference_drink.yaml`中的相对路径，改成基于本目录的路径或者绝对路径。涉及到的参数有
- Global.infer_imgs ：此参数可以是具体的图像地址，也可以是图像集所在的目录
- Global.det_inference_model_dir ： 检测模型存储目录
- Global.rec_inference_model_dir ： 识别模型存储目录
- IndexProcess.index_dir ： 检索库的存储目录，在示例中，检索库在下载的demo数据中。
- 字典转换
由于python的检索库的字典，使用`pickle`进行的序列化存储，导致C++不方便读取，因此进行转换
```shell
python tools/transform_id_map.py -c inference_drink.yaml
```
转换成功后，在`IndexProcess.index_dir`目录下生成`id_map.txt`，方便c++ 读取。
- 执行程序
```shell
./build/pp_shitu -c inference_drink.yaml
# or
./build/pp_shitu -config inference_drink.yaml
```
若对图像集进行检索，则可能得到，如下结果。注意，此结果只做展示，具体以实际运行结果为准。
同时，需注意的是，由于opencv 版本问题，会导致图像在预处理的过程中，resize产生细微差别，导致python 和c++结果，轻微不同，如bbox相差几个像素，检索结果小数点后3位diff等。但不会改变最终检索label。
![](../../docs/images/quick_start/shitu_c++_result.png)
## 4 使用自己模型
使用自己训练的模型，可以参考[模型导出](../../docs/zh_CN/inference_deployment/export_model.md)，导出`inference model`，用于模型预测。
同时注意修改`yaml`文件中具体参数。

12
deploy/cpp_shitu/src/feature_extracter.cpp
View File

@ -18,7 +18,7 @@
namespace Feature {
void FeatureExtracter::LoadModel(const std::string &model_path,
void FeatureExtracter::LoadModel(const std::string &model_path,
const std::string &params_path) {
paddle_infer::Config config;
config.SetModel(model_path, params_path);
@ -52,9 +52,9 @@ void FeatureExtracter::LoadModel(const std::string &model_path,
config.DisableGlogInfo();
this->predictor_ = CreatePredictor(config);
}
}
void FeatureExtracter::Run(cv::Mat &img, std::vector<float> &out_data,
void FeatureExtracter::Run(cv::Mat &img, std::vector<float> &out_data,
std::vector<double> &times) {
cv::Mat resize_img;
std::vector<double> time;
@ -108,12 +108,12 @@ void FeatureExtracter::Run(cv::Mat &img, std::vector<float> &out_data,
times[0] += time[0];
times[1] += time[1];
times[2] += time[2];
}
}
void FeatureExtracter::FeatureNorm(std::vector<float> &featuer) {
void FeatureExtracter::FeatureNorm(std::vector<float> &featuer) {
float featuer_sqrt = std::sqrt(std::inner_product(
featuer.begin(), featuer.end(), featuer.begin(), 0.0f));
for (int i = 0; i < featuer.size(); ++i)
featuer[i] /= featuer_sqrt;
}
}
} // namespace Feature

26
deploy/cpp_shitu/src/main.cpp
View File

@ -37,13 +37,15 @@
using namespace std;
using namespace cv;
DEFINE_string(config, "", "Path of yaml file");
DEFINE_string(c, "", "Path of yaml file");
DEFINE_string(config,
"", "Path of yaml file");
DEFINE_string(c,
"", "Path of yaml file");
void DetPredictImage(const std::vector<cv::Mat> &batch_imgs,
const std::vector<std::string> &all_img_paths,
void DetPredictImage(const std::vector <cv::Mat> &batch_imgs,
const std::vector <std::string> &all_img_paths,
const int batch_size, Detection::ObjectDetector *det,
std::vector<Detection::ObjectResult> &im_result,
std::vector <Detection::ObjectResult> &im_result,
std::vector<int> &im_bbox_num, std::vector<double> &det_t,
const bool visual_det = false,
const bool run_benchmark = false,
@ -63,7 +65,7 @@ void DetPredictImage(const std::vector<cv::Mat> &batch_imgs,
// }
// Store all detected result
std::vector<Detection::ObjectResult> result;
std::vector <Detection::ObjectResult> result;
std::vector<int> bbox_num;
std::vector<double> det_times;
bool is_rbox = false;
@ -134,7 +136,7 @@ void DetPredictImage(const std::vector<cv::Mat> &batch_imgs,
}
void PrintResult(std::string &img_path,
std::vector<Detection::ObjectResult> &det_result,
std::vector <Detection::ObjectResult> &det_result,
std::vector<int> &indeices, VectorSearch &vector_search,
SearchResult &search_result) {
printf("%s:\n", img_path.c_str());
@ -194,9 +196,9 @@ int main(int argc, char **argv) {
// load image_file_path
std::string path =
config.config_file["Global"]["infer_imgs"].as<std::string>();
std::vector<std::string> img_files_list;
std::vector <std::string> img_files_list;
if (cv::utils::fs::isDirectory(path)) {
std::vector<cv::String> filenames;
std::vector <cv::String> filenames;
cv::glob(path, filenames);
for (auto f : filenames) {
img_files_list.push_back(f);
@ -209,10 +211,10 @@ int main(int argc, char **argv) {
std::vector<double> cls_times = {0, 0, 0};
std::vector<double> det_times = {0, 0, 0};
// for read images
std::vector<cv::Mat> batch_imgs;
std::vector<std::string> img_paths;
std::vector <cv::Mat> batch_imgs;
std::vector <std::string> img_paths;
// for detection
std::vector<Detection::ObjectResult> det_result;
std::vector <Detection::ObjectResult> det_result;
std::vector<int> det_bbox_num;
// for vector search
std::vector<float> features;

38
deploy/cpp_shitu/src/object_detector.cpp
View File

@ -22,7 +22,7 @@ using namespace paddle_infer;
namespace Detection {
// Load Model and create model predictor
void ObjectDetector::LoadModel(const std::string &model_dir,
void ObjectDetector::LoadModel(const std::string &model_dir,
const int batch_size,
const std::string &run_mode) {
paddle_infer::Config config;
@ -64,11 +64,11 @@ void ObjectDetector::LoadModel(const std::string &model_dir,
this->trt_max_shape_};
const std::vector<int> opt_input_shape = {1, 3, this->trt_opt_shape_,
this->trt_opt_shape_};
const std::map<std::string, std::vector<int>> map_min_input_shape = {
const std::map <std::string, std::vector<int>> map_min_input_shape = {
{"image", min_input_shape}};
const std::map<std::string, std::vector<int>> map_max_input_shape = {
const std::map <std::string, std::vector<int>> map_max_input_shape = {
{"image", max_input_shape}};
const std::map<std::string, std::vector<int>> map_opt_input_shape = {
const std::map <std::string, std::vector<int>> map_opt_input_shape = {
{"image", opt_input_shape}};
config.SetTRTDynamicShapeInfo(map_min_input_shape, map_max_input_shape,
@ -94,12 +94,12 @@ void ObjectDetector::LoadModel(const std::string &model_dir,
// Memory optimization
config.EnableMemoryOptim();
predictor_ = std::move(CreatePredictor(config));
}
}
// Visualiztion MaskDetector results
cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector<ObjectResult> &results,
const std::vector<std::string> &lables,
cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector <ObjectResult> &results,
const std::vector <std::string> &lables,
const std::vector<int> &colormap,
const bool is_rbox = false) {
cv::Mat vis_img = img.clone();
@ -151,17 +151,17 @@ cv::Mat VisualizeResult(const cv::Mat &img,
cv::Scalar(255, 255, 255), thickness);
}
return vis_img;
}
}
void ObjectDetector::Preprocess(const cv::Mat &ori_im) {
void ObjectDetector::Preprocess(const cv::Mat &ori_im) {
// Clone the image : keep the original mat for postprocess
cv::Mat im = ori_im.clone();
cv::cvtColor(im, im, cv::COLOR_BGR2RGB);
preprocessor_.Run(&im, &inputs_);
}
}
void ObjectDetector::Postprocess(const std::vector<cv::Mat> mats,
std::vector<ObjectResult> *result,
void ObjectDetector::Postprocess(const std::vector <cv::Mat> mats,
std::vector <ObjectResult> *result,
std::vector<int> bbox_num,
bool is_rbox = false) {
result->clear();
@ -215,11 +215,11 @@ void ObjectDetector::Postprocess(const std::vector<cv::Mat> mats,
}
start_idx += bbox_num[im_id];
}
}
}
void ObjectDetector::Predict(const std::vector<cv::Mat> imgs, const int warmup,
void ObjectDetector::Predict(const std::vector <cv::Mat> imgs, const int warmup,
const int repeats,
std::vector<ObjectResult> *result,
std::vector <ObjectResult> *result,
std::vector<int> *bbox_num,
std::vector<double> *times) {
auto preprocess_start = std::chrono::steady_clock::now();
@ -344,9 +344,9 @@ void ObjectDetector::Predict(const std::vector<cv::Mat> imgs, const int warmup,
std::chrono::duration<float> postprocess_diff =
postprocess_end - postprocess_start;
times->push_back(double(postprocess_diff.count() * 1000));
}
}
std::vector<int> GenerateColorMap(int num_class) {
std::vector<int> GenerateColorMap(int num_class) {
auto colormap = std::vector<int>(3 * num_class, 0);
for (int i = 0; i < num_class; ++i) {
int j = 0;
@ -360,6 +360,6 @@ std::vector<int> GenerateColorMap(int num_class) {
}
}
return colormap;
}
}
} // namespace Detection

16
deploy/cpp_shitu/src/preprocess_op.cpp
View File

@ -32,16 +32,16 @@
namespace Feature {
void Permute::Run(const cv::Mat *im, float *data) {
void Permute::Run(const cv::Mat *im, float *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
}
}
}
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale) {
(*im).convertTo(*im, CV_32FC3, scale);
for (int h = 0; h < im->rows; h++) {
@ -54,18 +54,18 @@ void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2];
}
}
}
}
void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
void CenterCropImg::Run(cv::Mat &img, const int crop_size) {
int resize_w = img.cols;
int resize_h = img.rows;
int w_start = int((resize_w - crop_size) / 2);
int h_start = int((resize_h - crop_size) / 2);
cv::Rect rect(w_start, h_start, crop_size, crop_size);
img = img(rect);
}
}
void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
int resize_short_size, int size) {
int resize_h = 0;
int resize_w = 0;
@ -86,6 +86,6 @@ void ResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
resize_w = round(float(w) * ratio);
}
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
}
} // namespace Feature

30
deploy/cpp_shitu/src/preprocess_op_det.cpp
View File

@ -19,15 +19,15 @@
namespace Detection {
void InitInfo::Run(cv::Mat *im, ImageBlob *data) {
void InitInfo::Run(cv::Mat *im, ImageBlob *data) {
data->im_shape_ = {static_cast<float>(im->rows),
static_cast<float>(im->cols)};
data->scale_factor_ = {1., 1.};
data->in_net_shape_ = {static_cast<float>(im->rows),
static_cast<float>(im->cols)};
}
}
void NormalizeImage::Run(cv::Mat *im, ImageBlob *data) {
void NormalizeImage::Run(cv::Mat *im, ImageBlob *data) {
double e = 1.0;
if (is_scale_) {
e /= 255.0;
@ -43,9 +43,9 @@ void NormalizeImage::Run(cv::Mat *im, ImageBlob *data) {
(im->at<cv::Vec3f>(h, w)[2] - mean_[2]) / scale_[2];
}
}
}
}
void Permute::Run(cv::Mat *im, ImageBlob *data) {
void Permute::Run(cv::Mat *im, ImageBlob *data) {
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
@ -54,9 +54,9 @@ void Permute::Run(cv::Mat *im, ImageBlob *data) {
for (int i = 0; i < rc; ++i) {
cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, base + i * rh * rw), i);
}
}
}
void Resize::Run(cv::Mat *im, ImageBlob *data) {
void Resize::Run(cv::Mat *im, ImageBlob *data) {
auto resize_scale = GenerateScale(*im);
data->im_shape_ = {static_cast<float>(im->cols * resize_scale.first),
static_cast<float>(im->rows * resize_scale.second)};
@ -70,9 +70,9 @@ void Resize::Run(cv::Mat *im, ImageBlob *data) {
data->scale_factor_ = {
resize_scale.second, resize_scale.first,
};
}
}
std::pair<double, double> Resize::GenerateScale(const cv::Mat &im) {
std::pair<double, double> Resize::GenerateScale(const cv::Mat &im) {
std::pair<double, double> resize_scale;
int origin_w = im.cols;
int origin_h = im.rows;
@ -97,9 +97,9 @@ std::pair<double, double> Resize::GenerateScale(const cv::Mat &im) {
static_cast<double>(target_size_[0]) / static_cast<double>(origin_h);
}
return resize_scale;
}
}
void PadStride::Run(cv::Mat *im, ImageBlob *data) {
void PadStride::Run(cv::Mat *im, ImageBlob *data) {
if (stride_ <= 0) {
return;
}
@ -113,18 +113,18 @@ void PadStride::Run(cv::Mat *im, ImageBlob *data) {
data->in_net_shape_ = {
static_cast<float>(im->rows), static_cast<float>(im->cols),
};
}
}
// Preprocessor op running order
const std::vector<std::string> Preprocessor::RUN_ORDER = {
const std::vector <std::string> Preprocessor::RUN_ORDER = {
"InitInfo", "Resize", "NormalizeImage", "PadStride", "Permute"};
void Preprocessor::Run(cv::Mat *im, ImageBlob *data) {
void Preprocessor::Run(cv::Mat *im, ImageBlob *data) {
for (const auto &name : RUN_ORDER) {
if (ops_.find(name) != ops_.end()) {
ops_[name]->Run(im, data);
}
}
}
}
} // namespace Detection

4
deploy/cpp_shitu/src/vector_search.cpp
View File

@ -29,11 +29,11 @@ void VectorSearch::LoadIdMap() {
std::string file_path = this->index_dir + OS_PATH_SEP + "id_map.txt";
std::ifstream in(file_path);
std::string line;
std::vector<std::string> m_vec;
std::vector <std::string> m_vec;
if (in) {
while (getline(in, line)) {
std::regex ws_re("\\s+");
std::vector<std::string> v(
std::vector <std::string> v(
std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1),
std::sregex_token_iterator());
if (v.size() != 2) {

6
deploy/cpp_shitu/src/yaml_config.cpp
View File

@ -19,10 +19,10 @@
#include <include/yaml_config.h>
std::vector<std::string> YamlConfig::ReadDict(const std::string &path) {
std::vector <std::string> YamlConfig::ReadDict(const std::string &path) {
std::ifstream in(path);
std::string line;
std::vector<std::string> m_vec;
std::vector <std::string> m_vec;
if (in) {
while (getline(in, line)) {
m_vec.push_back(line);
@ -42,7 +42,7 @@ std::map<int, std::string> YamlConfig::ReadIndexId(const std::string &path) {
if (in) {
while (getline(in, line)) {
std::regex ws_re("\\s+");
std::vector<std::string> v(
std::vector <std::string> v(
std::sregex_token_iterator(line.begin(), line.end(), ws_re, -1),
std::sregex_token_iterator());
if (v.size() != 3) {

6
deploy/cpp_shitu/tools/build.sh
View File

@ -1,8 +1,8 @@
OPENCV_DIR=/work/project/project/cpp_infer/opencv-3.4.7/opencv3
LIB_DIR=/work/project/project/cpp_infer/paddle_inference/
OPENCV_DIR=${opencv_install_dir}
LIB_DIR=${paddle_inference_dir}
CUDA_LIB_DIR=/usr/local/cuda/lib64
CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
FAISS_DIR=/work/project/project/cpp_infer/faiss/faiss_install
FAISS_DIR=${faiss_install_dir}
FAISS_WITH_MKL=OFF
BUILD_DIR=build

17
deploy/cpp_shitu/tools/config.txt
View File

@ -1,17 +0,0 @@
# model load config
use_gpu 0
gpu_id 0
gpu_mem 4000
cpu_threads 10
use_mkldnn 1
use_tensorrt 0
use_fp16 0
# cls config
cls_model_path /PaddleClas/inference/cls_infer.pdmodel
cls_params_path /PaddleClas/inference/cls_infer.pdiparams
resize_short_size 256
crop_size 224
# for log env info
benchmark 0

1
deploy/cpp_shitu/tools/run.sh
View File

@ -1 +0,0 @@
./build/clas_system ../configs/inference_rec.yaml

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