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Merge pull request #2146 from RainFrost1/release/2.4 

[cherry-pick]修复lite_shitu bug
pull/2180/head
Walter 2022-07-13 15:10:59 +08:00 committed by GitHub
parent 3a28ee2900 c839a87cff
commit e1502ac353
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18 changed files with 376 additions and 270 deletions
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73
deploy/lite_shitu/README.md
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@ -92,9 +92,9 @@ PaddleClas 提供了转换并优化后的推理模型，可以直接参考下方
```shell
# 进入lite_ppshitu目录
cd $PaddleClas/deploy/lite_shitu
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/lite/ppshitu_lite_models_v1.1.tar
tar -xf ppshitu_lite_models_v1.1.tar
rm -f ppshitu_lite_models_v1.1.tar
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/lite/ppshitu_lite_models_v1.2.tar
tar -xf ppshitu_lite_models_v1.2.tar
rm -f ppshitu_lite_models_v1.2.tar
```
#### 2.1.2 使用其他模型
@ -162,7 +162,7 @@ git clone https://github.com/PaddlePaddle/PaddleDetection.git
# 进入PaddleDetection根目录
cd PaddleDetection
# 将预训练模型导出为inference模型
python tools/export_model.py -c configs/picodet/application/mainbody_detection/picodet_lcnet_x2_5_640_mainbody.yml -o weights=https://paddledet.bj.bcebos.com/models/picodet_lcnet_x2_5_640_mainbody.pdparams --output_dir=inference
python tools/export_model.py -c configs/picodet/application/mainbody_detection/picodet_lcnet_x2_5_640_mainbody.yml -o weights=https://paddledet.bj.bcebos.com/models/picodet_lcnet_x2_5_640_mainbody.pdparams export_post_process=False --output_dir=inference
# 将inference模型转化为Paddle-Lite优化模型
paddle_lite_opt --model_file=inference/picodet_lcnet_x2_5_640_mainbody/model.pdmodel --param_file=inference/picodet_lcnet_x2_5_640_mainbody/model.pdiparams --optimize_out=inference/picodet_lcnet_x2_5_640_mainbody/mainbody_det
# 将转好的模型复制到lite_shitu目录下
@ -183,24 +183,56 @@ cd deploy/lite_shitu
**注意**：`--optimize_out` 参数为优化后模型的保存路径，无需加后缀`.nb`；`--model_file` 参数为模型结构信息文件的路径，`--param_file` 参数为模型权重信息文件的路径，请注意文件名。
### 2.2 将yaml文件转换成json文件
```shell
# 如果测试单张图像
python generate_json_config.py --det_model_path ppshitu_lite_models_v1.1/mainbody_PPLCNet_x2_5_640_quant_v1.1_lite.nb --rec_model_path ppshitu_lite_models_v1.1/general_PPLCNet_x2_5_lite_v1.1_infer.nb --img_path images/demo.jpg
# or
# 如果测试多张图像
python generate_json_config.py --det_model_path ppshitu_lite_models_v1.1/mainbody_PPLCNet_x2_5_640_quant_v1.1_lite.nb --rec_model_path ppshitu_lite_models_v1.1/general_PPLCNet_x2_5_lite_v1.1_infer.nb --img_dir images
# 执行完成后，会在lit_shitu下生成shitu_config.json配置文件
```
### 2.3 index字典转换
由于python的检索库字典，使用`pickle`进行的序列化存储，导致C++不方便读取，因此需要进行转换
### 2.2 生成新的检索库
由于lite 版本的检索库用的是`faiss1.5.3`版本，与新版本不兼容，因此需要重新生成index库
#### 2.2.1 数据及环境配置
```shell
# 进入上级目录
cd ..
# 下载瓶装饮料数据集
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar && tar -xf drink_dataset_v1.0.tar
rm -rf drink_dataset_v1.0.tar
rm -rf drink_dataset_v1.0/index
# 安装1.5.3版本的faiss
pip install faiss-cpu==1.5.3
# 下载通用识别模型，可替换成自己的inference model
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
rm -rf general_PPLCNet_x2_5_lite_v1.0_infer.tar
```
#### 2.2.2 生成新的index文件
```shell
# 生成新的index库，注意指定好识别模型的路径，同时将index_mothod修改成Flat，HNSW32和IVF在此版本中可能存在bug，请慎重使用。
# 如果使用自己的识别模型，对应的修改inference model的目录
python python/build_gallery.py -c configs/inference_drink.yaml -o Global.rec_inference_model_dir=general_PPLCNet_x2_5_lite_v1.0_infer -o IndexProcess.index_method=Flat
# 进入到lite_shitu目录
cd lite_shitu
mv ../drink_dataset_v1.0 .
```
### 2.3 将yaml文件转换成json文件
```shell
# 如果测试单张图像
python generate_json_config.py --det_model_path ppshitu_lite_models_v1.2/mainbody_PPLCNet_x2_5_640_v1.2_lite.nb --rec_model_path ppshitu_lite_models_v1.2/general_PPLCNet_x2_5_lite_v1.2_infer.nb --img_path images/demo.jpeg
# or
# 如果测试多张图像
python generate_json_config.py --det_model_path ppshitu_lite_models_v1.2/mainbody_PPLCNet_x2_5_640_v1.2_lite.nb --rec_model_path ppshitu_lite_models_v1.2/general_PPLCNet_x2_5_lite_v1.2_infer.nb --img_dir images
# 执行完成后，会在lit_shitu下生成shitu_config.json配置文件
```
### 2.4 index字典转换
由于python的检索库字典，使用`pickle`进行的序列化存储，导致C++不方便读取，因此需要进行转换
```shell
# 转化id_map.pkl为id_map.txt
python transform_id_map.py -c ../configs/inference_drink.yaml
@ -208,7 +240,7 @@ python transform_id_map.py -c ../configs/inference_drink.yaml
转换成功后，会在`IndexProcess.index_dir`目录下生成`id_map.txt`。
### 2.4 与手机联调
### 2.5 与手机联调
首先需要进行一些准备工作。
1. 准备一台arm8的安卓手机，如果编译的预测库是armv7，则需要arm7的手机，并修改Makefile中`ARM_ABI=arm7`。
@ -308,8 +340,9 @@ chmod 777 pp_shitu
运行效果如下：
```
images/demo.jpg:
result0: bbox[253, 275, 1146, 872], score: 0.974196, label: 伊藤园_果蔬汁
images/demo.jpeg:
result0: bbox[344, 98, 527, 593], score: 0.811656, label: 红牛-强化型
result1: bbox[0, 0, 600, 600], score: 0.729664, label: 红牛-强化型
```
## FAQ

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8
deploy/lite_shitu/include/config_parser.h
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@ -29,16 +29,16 @@
namespace PPShiTu {
void load_jsonf(std::string jsonfile, Json::Value& jsondata);
void load_jsonf(std::string jsonfile, Json::Value &jsondata);
// Inference model configuration parser
class ConfigPaser {
public:
public:
ConfigPaser() {}
~ConfigPaser() {}
bool load_config(const Json::Value& config) {
bool load_config(const Json::Value &config) {
// Get model arch : YOLO, SSD, RetinaNet, RCNN, Face
if (config["Global"].isMember("det_arch")) {
@ -89,4 +89,4 @@ class ConfigPaser {
std::vector<int> fpn_stride_;
};
} // namespace PPShiTu
} // namespace PPShiTu

26
deploy/lite_shitu/include/feature_extractor.h
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@ -18,6 +18,7 @@
#include <arm_neon.h>
#include <chrono>
#include <fstream>
#include <include/preprocess_op.h>
#include <iostream>
#include <math.h>
#include <opencv2/opencv.hpp>
@ -48,10 +49,6 @@ public:
config_file["Global"]["rec_model_path"].as<std::string>());
this->predictor = CreatePaddlePredictor<MobileConfig>(config);
if (config_file["Global"]["rec_label_path"].as<std::string>().empty()) {
std::cout << "Please set [rec_label_path] in config file" << std::endl;
exit(-1);
}
SetPreProcessParam(config_file["RecPreProcess"]["transform_ops"]);
printf("feature extract model create!\n");
}
@ -68,24 +65,29 @@ public:
this->mean.emplace_back(tmp.as<float>());
}
for (auto tmp : item["std"]) {
this->std.emplace_back(1 / tmp.as<float>());
this->std.emplace_back(tmp.as<float>());
}
this->scale = item["scale"].as<double>();
}
}
}
void RunRecModel(const cv::Mat &img, double &cost_time, std::vector<float> &feature);
//void PostProcess(std::vector<float> &feature);
cv::Mat ResizeImage(const cv::Mat &img);
void NeonMeanScale(const float *din, float *dout, int size);
void RunRecModel(const cv::Mat &img, double &cost_time,
std::vector<float> &feature);
// void PostProcess(std::vector<float> &feature);
void FeatureNorm(std::vector<float> &featuer);
private:
std::shared_ptr<PaddlePredictor> predictor;
//std::vector<std::string> label_list;
// std::vector<std::string> label_list;
std::vector<float> mean = {0.485f, 0.456f, 0.406f};
std::vector<float> std = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
std::vector<float> std = {0.229f, 0.224f, 0.225f};
double scale = 0.00392157;
float size = 224;
int size = 224;
// pre-process
Resize resize_op_;
NormalizeImage normalize_op_;
Permute permute_op_;
};
} // namespace PPShiTu

61
deploy/lite_shitu/include/object_detector.h
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@ -16,24 +16,24 @@
#include <ctime>
#include <memory>
#include <stdlib.h>
#include <string>
#include <utility>
#include <vector>
#include <stdlib.h>
#include "json/json.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "json/json.h"
#include "paddle_api.h" // NOLINT
#include "paddle_api.h" // NOLINT
#include "include/config_parser.h"
#include "include/picodet_postprocess.h"
#include "include/preprocess_op.h"
#include "include/utils.h"
#include "include/picodet_postprocess.h"
using namespace paddle::lite_api; // NOLINT
using namespace paddle::lite_api; // NOLINT
namespace PPShiTu {
@ -41,53 +41,51 @@ namespace PPShiTu {
std::vector<int> GenerateColorMap(int num_class);
// Visualiztion Detection Result
cv::Mat VisualizeResult(const cv::Mat& img,
const std::vector<PPShiTu::ObjectResult>& results,
const std::vector<std::string>& lables,
const std::vector<int>& colormap,
const bool is_rbox);
cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector<PPShiTu::ObjectResult> &results,
const std::vector<std::string> &lables,
const std::vector<int> &colormap, const bool is_rbox);
class ObjectDetector {
public:
explicit ObjectDetector(const Json::Value& config,
const std::string& model_dir,
int cpu_threads = 1,
public:
explicit ObjectDetector(const Json::Value &config,
const std::string &model_dir, int cpu_threads = 1,
const int batch_size = 1) {
config_.load_config(config);
printf("config created\n");
preprocessor_.Init(config_.preprocess_info_);
printf("before object detector\n");
if(config["Global"]["det_model_path"].as<std::string>().empty()){
std::cout << "Please set [det_model_path] in config file" << std::endl;
exit(-1);
if (config["Global"]["det_model_path"].as<std::string>().empty()) {
std::cout << "Please set [det_model_path] in config file" << std::endl;
exit(-1);
}
LoadModel(config["Global"]["det_model_path"].as<std::string>(), cpu_threads);
printf("create object detector\n"); }
LoadModel(config["Global"]["det_model_path"].as<std::string>(),
cpu_threads);
printf("create object detector\n");
}
// Load Paddle inference model
void LoadModel(std::string model_file, int num_theads);
// 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<PPShiTu::ObjectResult>* result = nullptr,
std::vector<int>* bbox_num = nullptr,
std::vector<double>* times = nullptr);
std::vector<PPShiTu::ObjectResult> *result = nullptr,
std::vector<int> *bbox_num = nullptr,
std::vector<double> *times = nullptr);
// Get Model Label list
const std::vector<std::string>& GetLabelList() const {
const std::vector<std::string> &GetLabelList() const {
return config_.label_list_;
}
private:
private:
// Preprocess image and copy data to input buffer
void Preprocess(const cv::Mat& image_mat);
void Preprocess(const cv::Mat &image_mat);
// Postprocess result
void Postprocess(const std::vector<cv::Mat> mats,
std::vector<PPShiTu::ObjectResult>* result,
std::vector<int> bbox_num,
bool is_rbox);
std::vector<PPShiTu::ObjectResult> *result,
std::vector<int> bbox_num, bool is_rbox);
std::shared_ptr<PaddlePredictor> predictor_;
Preprocessor preprocessor_;
@ -96,7 +94,6 @@ class ObjectDetector {
std::vector<int> out_bbox_num_data_;
float threshold_;
ConfigPaser config_;
};
} // namespace PPShiTu
} // namespace PPShiTu

26
deploy/lite_shitu/include/picodet_postprocess.h
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@ -14,25 +14,23 @@
#pragma once
#include <string>
#include <vector>
#include <memory>
#include <utility>
#include <ctime>
#include <memory>
#include <numeric>
#include <string>
#include <utility>
#include <vector>
#include "include/utils.h"
namespace PPShiTu {
void PicoDetPostProcess(std::vector<PPShiTu::ObjectResult>* results,
std::vector<const float *> outs,
std::vector<int> fpn_stride,
std::vector<float> im_shape,
std::vector<float> scale_factor,
float score_threshold = 0.3,
float nms_threshold = 0.5,
int num_class = 80,
int reg_max = 7);
void PicoDetPostProcess(std::vector<PPShiTu::ObjectResult> *results,
std::vector<const float *> outs,
std::vector<int> fpn_stride,
std::vector<float> im_shape,
std::vector<float> scale_factor,
float score_threshold = 0.3, float nms_threshold = 0.5,
int num_class = 80, int reg_max = 7);
} // namespace PPShiTu
} // namespace PPShiTu

84
deploy/lite_shitu/include/preprocess_op.h
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@ -21,16 +21,16 @@
#include <utility>
#include <vector>
#include "json/json.h"
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include "json/json.h"
namespace PPShiTu {
// Object for storing all preprocessed data
class ImageBlob {
public:
public:
// image width and height
std::vector<float> im_shape_;
// Buffer for image data after preprocessing
@ -45,20 +45,20 @@ class ImageBlob {
// Abstraction of preprocessing opration class
class PreprocessOp {
public:
virtual void Init(const Json::Value& item) = 0;
virtual void Run(cv::Mat* im, ImageBlob* data) = 0;
public:
virtual void Init(const Json::Value &item) = 0;
virtual void Run(cv::Mat *im, ImageBlob *data) = 0;
};
class InitInfo : public PreprocessOp {
public:
virtual void Init(const Json::Value& item) {}
virtual void Run(cv::Mat* im, ImageBlob* data);
public:
virtual void Init(const Json::Value &item) {}
virtual void Run(cv::Mat *im, ImageBlob *data);
};
class NormalizeImage : public PreprocessOp {
public:
virtual void Init(const Json::Value& item) {
public:
virtual void Init(const Json::Value &item) {
mean_.clear();
scale_.clear();
for (auto tmp : item["mean"]) {
@ -70,9 +70,11 @@ class NormalizeImage : public PreprocessOp {
is_scale_ = item["is_scale"].as<bool>();
}
virtual void Run(cv::Mat* im, ImageBlob* data);
virtual void Run(cv::Mat *im, ImageBlob *data);
void Run_feature(cv::Mat *im, const std::vector<float> &mean,
const std::vector<float> &std, float scale);
private:
private:
// CHW or HWC
std::vector<float> mean_;
std::vector<float> scale_;
@ -80,14 +82,15 @@ class NormalizeImage : public PreprocessOp {
};
class Permute : public PreprocessOp {
public:
virtual void Init(const Json::Value& item) {}
virtual void Run(cv::Mat* im, ImageBlob* data);
public:
virtual void Init(const Json::Value &item) {}
virtual void Run(cv::Mat *im, ImageBlob *data);
void Run_feature(const cv::Mat *im, float *data);
};
class Resize : public PreprocessOp {
public:
virtual void Init(const Json::Value& item) {
public:
virtual void Init(const Json::Value &item) {
interp_ = item["interp"].as<int>();
// max_size_ = item["target_size"].as<int>();
keep_ratio_ = item["keep_ratio"].as<bool>();
@ -98,11 +101,13 @@ class Resize : public PreprocessOp {
}
// Compute best resize scale for x-dimension, y-dimension
std::pair<float, float> GenerateScale(const cv::Mat& im);
std::pair<float, float> GenerateScale(const cv::Mat &im);
virtual void Run(cv::Mat* im, ImageBlob* data);
virtual void Run(cv::Mat *im, ImageBlob *data);
void Run_feature(const cv::Mat &img, cv::Mat &resize_img, int max_size_len,
int size = 0);
private:
private:
int interp_;
bool keep_ratio_;
std::vector<int> target_size_;
@ -111,46 +116,43 @@ class Resize : public PreprocessOp {
// Models with FPN need input shape % stride == 0
class PadStride : public PreprocessOp {
public:
virtual void Init(const Json::Value& item) {
public:
virtual void Init(const Json::Value &item) {
stride_ = item["stride"].as<int>();
}
virtual void Run(cv::Mat* im, ImageBlob* data);
virtual void Run(cv::Mat *im, ImageBlob *data);
private:
private:
int stride_;
};
class TopDownEvalAffine : public PreprocessOp {
public:
virtual void Init(const Json::Value& item) {
public:
virtual void Init(const Json::Value &item) {
trainsize_.clear();
for (auto tmp : item["trainsize"]) {
trainsize_.emplace_back(tmp.as<int>());
}
}
virtual void Run(cv::Mat* im, ImageBlob* data);
virtual void Run(cv::Mat *im, ImageBlob *data);
private:
private:
int interp_ = 1;
std::vector<int> trainsize_;
};
void CropImg(cv::Mat& img,
cv::Mat& crop_img,
std::vector<int>& area,
std::vector<float>& center,
std::vector<float>& scale,
void CropImg(cv::Mat &img, cv::Mat &crop_img, std::vector<int> &area,
std::vector<float> &center, std::vector<float> &scale,
float expandratio = 0.15);
class Preprocessor {
public:
void Init(const Json::Value& config_node) {
public:
void Init(const Json::Value &config_node) {
// initialize image info at first
ops_["InitInfo"] = std::make_shared<InitInfo>();
for (const auto& item : config_node) {
for (const auto &item : config_node) {
auto op_name = item["type"].as<std::string>();
ops_[op_name] = CreateOp(op_name);
@ -158,7 +160,7 @@ class Preprocessor {
}
}
std::shared_ptr<PreprocessOp> CreateOp(const std::string& name) {
std::shared_ptr<PreprocessOp> CreateOp(const std::string &name) {
if (name == "DetResize") {
return std::make_shared<Resize>();
} else if (name == "DetPermute") {
@ -176,13 +178,13 @@ class Preprocessor {
return nullptr;
}
void Run(cv::Mat* im, ImageBlob* data);
void Run(cv::Mat *im, ImageBlob *data);
public:
public:
static const std::vector<std::string> RUN_ORDER;
private:
private:
std::unordered_map<std::string, std::shared_ptr<PreprocessOp>> ops_;
};
} // namespace PPShiTu
} // namespace PPShiTu

19
deploy/lite_shitu/include/utils.h
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@ -38,6 +38,23 @@ struct ObjectResult {
std::vector<RESULT> rec_result;
};
void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold, bool rec_nms=false);
void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold,
bool rec_nms = false);
template <typename T>
static inline bool SortScorePairDescend(const std::pair<float, T> &pair1,
const std::pair<float, T> &pair2) {
return pair1.first > pair2.first;
}
float RectOverlap(const ObjectResult &a, const ObjectResult &b);
inline void
GetMaxScoreIndex(const std::vector<ObjectResult> &det_result,
const float threshold,
std::vector<std::pair<float, int>> &score_index_vec);
void NMSBoxes(const std::vector<ObjectResult> det_result,
const float score_threshold, const float nms_threshold,
std::vector<int> &indices);
} // namespace PPShiTu

2
deploy/lite_shitu/include/vector_search.h
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@ -70,4 +70,4 @@ private:
std::vector<faiss::Index::idx_t> I;
SearchResult sr;
};
}
} // namespace PPShiTu

2
deploy/lite_shitu/src/config_parser.cc
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@ -29,4 +29,4 @@ void load_jsonf(std::string jsonfile, Json::Value &jsondata) {
}
}
} // namespace PPShiTu
} // namespace PPShiTu

89
deploy/lite_shitu/src/feature_extractor.cc
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@ -13,24 +13,29 @@
// limitations under the License.
#include "include/feature_extractor.h"
#include <cmath>
#include <numeric>
namespace PPShiTu {
void FeatureExtract::RunRecModel(const cv::Mat &img,
double &cost_time,
void FeatureExtract::RunRecModel(const cv::Mat &img, double &cost_time,
std::vector<float> &feature) {
// Read img
cv::Mat resize_image = ResizeImage(img);
cv::Mat img_fp;
resize_image.convertTo(img_fp, CV_32FC3, scale);
this->resize_op_.Run_feature(img, img_fp, this->size, this->size);
this->normalize_op_.Run_feature(&img_fp, this->mean, this->std, this->scale);
std::vector<float> input(1 * 3 * img_fp.rows * img_fp.cols, 0.0f);
this->permute_op_.Run_feature(&img_fp, input.data());
// Prepare input data from image
std::unique_ptr<Tensor> input_tensor(std::move(this->predictor->GetInput(0)));
input_tensor->Resize({1, 3, img_fp.rows, img_fp.cols});
input_tensor->Resize({1, 3, this->size, this->size});
auto *data0 = input_tensor->mutable_data<float>();
const float *dimg = reinterpret_cast<const float *>(img_fp.data);
NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols);
// const float *dimg = reinterpret_cast<const float *>(img_fp.data);
// NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols);
for (int i = 0; i < input.size(); ++i) {
data0[i] = input[i];
}
auto start = std::chrono::system_clock::now();
// Run predictor
@ -38,7 +43,7 @@ void FeatureExtract::RunRecModel(const cv::Mat &img,
// Get output and post process
std::unique_ptr<const Tensor> output_tensor(
std::move(this->predictor->GetOutput(0))); //only one output
std::move(this->predictor->GetOutput(0))); // only one output
auto end = std::chrono::system_clock::now();
auto duration =
std::chrono::duration_cast<std::chrono::microseconds>(end - start);
@ -46,7 +51,7 @@ void FeatureExtract::RunRecModel(const cv::Mat &img,
std::chrono::microseconds::period::num /
std::chrono::microseconds::period::den;
//do postprocess
// do postprocess
int output_size = 1;
for (auto dim : output_tensor->shape()) {
output_size *= dim;
@ -54,63 +59,15 @@ void FeatureExtract::RunRecModel(const cv::Mat &img,
feature.resize(output_size);
output_tensor->CopyToCpu(feature.data());
//postprocess include sqrt or binarize.
//PostProcess(feature);
// postprocess include sqrt or binarize.
FeatureNorm(feature);
return;
}
// void FeatureExtract::PostProcess(std::vector<float> &feature){
// float feature_sqrt = std::sqrt(std::inner_product(
// feature.begin(), feature.end(), feature.begin(), 0.0f));
// for (int i = 0; i < feature.size(); ++i)
// feature[i] /= feature_sqrt;
// }
void FeatureExtract::NeonMeanScale(const float *din, float *dout, int size) {
if (this->mean.size() != 3 || this->std.size() != 3) {
std::cerr << "[ERROR] mean or scale size must equal to 3\n";
exit(1);
}
float32x4_t vmean0 = vdupq_n_f32(mean[0]);
float32x4_t vmean1 = vdupq_n_f32(mean[1]);
float32x4_t vmean2 = vdupq_n_f32(mean[2]);
float32x4_t vscale0 = vdupq_n_f32(std[0]);
float32x4_t vscale1 = vdupq_n_f32(std[1]);
float32x4_t vscale2 = vdupq_n_f32(std[2]);
float *dout_c0 = dout;
float *dout_c1 = dout + size;
float *dout_c2 = dout + size * 2;
int i = 0;
for (; i < size - 3; i += 4) {
float32x4x3_t vin3 = vld3q_f32(din);
float32x4_t vsub0 = vsubq_f32(vin3.val[0], vmean0);
float32x4_t vsub1 = vsubq_f32(vin3.val[1], vmean1);
float32x4_t vsub2 = vsubq_f32(vin3.val[2], vmean2);
float32x4_t vs0 = vmulq_f32(vsub0, vscale0);
float32x4_t vs1 = vmulq_f32(vsub1, vscale1);
float32x4_t vs2 = vmulq_f32(vsub2, vscale2);
vst1q_f32(dout_c0, vs0);
vst1q_f32(dout_c1, vs1);
vst1q_f32(dout_c2, vs2);
din += 12;
dout_c0 += 4;
dout_c1 += 4;
dout_c2 += 4;
}
for (; i < size; i++) {
*(dout_c0++) = (*(din++) - this->mean[0]) * this->std[0];
*(dout_c1++) = (*(din++) - this->mean[1]) * this->std[1];
*(dout_c2++) = (*(din++) - this->mean[2]) * this->std[2];
}
}
cv::Mat FeatureExtract::ResizeImage(const cv::Mat &img) {
cv::Mat resize_img;
cv::resize(img, resize_img, cv::Size(this->size, this->size));
return resize_img;
}
void FeatureExtract::FeatureNorm(std::vector<float> &feature) {
float feature_sqrt = std::sqrt(std::inner_product(
feature.begin(), feature.end(), feature.begin(), 0.0f));
for (int i = 0; i < feature.size(); ++i)
feature[i] /= feature_sqrt;
}
} // namespace PPShiTu

21
deploy/lite_shitu/src/main.cc
View File

@ -27,6 +27,7 @@
#include "include/feature_extractor.h"
#include "include/object_detector.h"
#include "include/preprocess_op.h"
#include "include/utils.h"
#include "include/vector_search.h"
#include "json/json.h"
@ -158,6 +159,11 @@ int main(int argc, char **argv) {
<< " [image_dir]>" << std::endl;
return -1;
}
float rec_nms_threshold = 0.05;
if (RT_Config["Global"]["rec_nms_thresold"].isDouble())
rec_nms_threshold = RT_Config["Global"]["rec_nms_thresold"].as<float>();
// Load model and create a object detector
PPShiTu::ObjectDetector det(
RT_Config, RT_Config["Global"]["det_model_path"].as<std::string>(),
@ -174,6 +180,7 @@ int main(int argc, char **argv) {
// for vector search
std::vector<float> feature;
std::vector<float> features;
std::vector<int> indeices;
double rec_time;
if (!RT_Config["Global"]["infer_imgs"].as<std::string>().empty() ||
!img_dir.empty()) {
@ -208,9 +215,9 @@ int main(int argc, char **argv) {
RT_Config["Global"]["max_det_results"].as<int>(), false, &det);
// add the whole image for recognition to improve recall
// PPShiTu::ObjectResult result_whole_img = {
// {0, 0, srcimg.cols, srcimg.rows}, 0, 1.0};
// det_result.push_back(result_whole_img);
PPShiTu::ObjectResult result_whole_img = {
{0, 0, srcimg.cols, srcimg.rows}, 0, 1.0};
det_result.push_back(result_whole_img);
// get rec result
PPShiTu::SearchResult search_result;
@ -225,10 +232,18 @@ int main(int argc, char **argv) {
// do vectore search
search_result = searcher.Search(features.data(), det_result.size());
for (int i = 0; i < det_result.size(); ++i) {
det_result[i].confidence = search_result.D[search_result.return_k * i];
}
NMSBoxes(det_result, searcher.GetThreshold(), rec_nms_threshold,
indeices);
PrintResult(img_path, det_result, searcher, search_result);
batch_imgs.clear();
det_result.clear();
features.clear();
feature.clear();
indeices.clear();
}
}
return 0;

69
deploy/lite_shitu/src/object_detector.cc
View File

@ -13,9 +13,9 @@
// limitations under the License.
#include <sstream>
// for setprecision
#include "include/object_detector.h"
#include <chrono>
#include <iomanip>
#include "include/object_detector.h"
namespace PPShiTu {
@ -30,10 +30,10 @@ void ObjectDetector::LoadModel(std::string model_file, int num_theads) {
}
// Visualiztion MaskDetector results
cv::Mat VisualizeResult(const cv::Mat& img,
const std::vector<PPShiTu::ObjectResult>& results,
const std::vector<std::string>& lables,
const std::vector<int>& colormap,
cv::Mat VisualizeResult(const cv::Mat &img,
const std::vector<PPShiTu::ObjectResult> &results,
const std::vector<std::string> &lables,
const std::vector<int> &colormap,
const bool is_rbox = false) {
cv::Mat vis_img = img.clone();
for (int i = 0; i < results.size(); ++i) {
@ -75,24 +75,18 @@ cv::Mat VisualizeResult(const cv::Mat& img,
origin.y = results[i].rect[1];
// Configure text background
cv::Rect text_back = cv::Rect(results[i].rect[0],
results[i].rect[1] - text_size.height,
text_size.width,
text_size.height);
cv::Rect text_back =
cv::Rect(results[i].rect[0], results[i].rect[1] - text_size.height,
text_size.width, text_size.height);
// Draw text, and background
cv::rectangle(vis_img, text_back, roi_color, -1);
cv::putText(vis_img,
text,
origin,
font_face,
font_scale,
cv::Scalar(255, 255, 255),
thickness);
cv::putText(vis_img, text, origin, font_face, font_scale,
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);
@ -100,7 +94,7 @@ void ObjectDetector::Preprocess(const cv::Mat& ori_im) {
}
void ObjectDetector::Postprocess(const std::vector<cv::Mat> mats,
std::vector<PPShiTu::ObjectResult>* result,
std::vector<PPShiTu::ObjectResult> *result,
std::vector<int> bbox_num,
bool is_rbox = false) {
result->clear();
@ -156,12 +150,11 @@ void ObjectDetector::Postprocess(const std::vector<cv::Mat> mats,
}
}
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<PPShiTu::ObjectResult>* result,
std::vector<int>* bbox_num,
std::vector<double>* times) {
std::vector<PPShiTu::ObjectResult> *result,
std::vector<int> *bbox_num,
std::vector<double> *times) {
auto preprocess_start = std::chrono::steady_clock::now();
int batch_size = imgs.size();
@ -180,29 +173,29 @@ void ObjectDetector::Predict(const std::vector<cv::Mat>& imgs,
scale_factor_all[bs_idx * 2 + 1] = inputs_.scale_factor_[1];
// TODO: reduce cost time
in_data_all.insert(
in_data_all.end(), inputs_.im_data_.begin(), inputs_.im_data_.end());
in_data_all.insert(in_data_all.end(), inputs_.im_data_.begin(),
inputs_.im_data_.end());
}
auto preprocess_end = std::chrono::steady_clock::now();
std::vector<const float *> output_data_list_;
// Prepare input tensor
auto input_names = predictor_->GetInputNames();
for (const auto& tensor_name : input_names) {
for (const auto &tensor_name : input_names) {
auto in_tensor = predictor_->GetInputByName(tensor_name);
if (tensor_name == "image") {
int rh = inputs_.in_net_shape_[0];
int rw = inputs_.in_net_shape_[1];
in_tensor->Resize({batch_size, 3, rh, rw});
auto* inptr = in_tensor->mutable_data<float>();
auto *inptr = in_tensor->mutable_data<float>();
std::copy_n(in_data_all.data(), in_data_all.size(), inptr);
} else if (tensor_name == "im_shape") {
in_tensor->Resize({batch_size, 2});
auto* inptr = in_tensor->mutable_data<float>();
auto *inptr = in_tensor->mutable_data<float>();
std::copy_n(im_shape_all.data(), im_shape_all.size(), inptr);
} else if (tensor_name == "scale_factor") {
in_tensor->Resize({batch_size, 2});
auto* inptr = in_tensor->mutable_data<float>();
auto *inptr = in_tensor->mutable_data<float>();
std::copy_n(scale_factor_all.data(), scale_factor_all.size(), inptr);
}
}
@ -216,7 +209,7 @@ void ObjectDetector::Predict(const std::vector<cv::Mat>& imgs,
if (config_.arch_ == "PicoDet") {
for (int j = 0; j < output_names.size(); j++) {
auto output_tensor = predictor_->GetTensor(output_names[j]);
const float* outptr = output_tensor->data<float>();
const float *outptr = output_tensor->data<float>();
std::vector<int64_t> output_shape = output_tensor->shape();
output_data_list_.push_back(outptr);
}
@ -242,7 +235,7 @@ void ObjectDetector::Predict(const std::vector<cv::Mat>& imgs,
if (config_.arch_ == "PicoDet") {
for (int i = 0; i < output_names.size(); i++) {
auto output_tensor = predictor_->GetTensor(output_names[i]);
const float* outptr = output_tensor->data<float>();
const float *outptr = output_tensor->data<float>();
std::vector<int64_t> output_shape = output_tensor->shape();
if (i == 0) {
num_class = output_shape[2];
@ -268,16 +261,15 @@ void ObjectDetector::Predict(const std::vector<cv::Mat>& imgs,
std::cerr << "[WARNING] No object detected." << std::endl;
}
output_data_.resize(output_size);
std::copy_n(
output_tensor->mutable_data<float>(), output_size, output_data_.data());
std::copy_n(output_tensor->mutable_data<float>(), output_size,
output_data_.data());
int out_bbox_num_size = 1;
for (int j = 0; j < out_bbox_num_shape.size(); ++j) {
out_bbox_num_size *= out_bbox_num_shape[j];
}
out_bbox_num_data_.resize(out_bbox_num_size);
std::copy_n(out_bbox_num->mutable_data<int>(),
out_bbox_num_size,
std::copy_n(out_bbox_num->mutable_data<int>(), out_bbox_num_size,
out_bbox_num_data_.data());
}
// Postprocessing result
@ -285,9 +277,8 @@ void ObjectDetector::Predict(const std::vector<cv::Mat>& imgs,
result->clear();
if (config_.arch_ == "PicoDet") {
PPShiTu::PicoDetPostProcess(
result, output_data_list_, config_.fpn_stride_,
inputs_.im_shape_, inputs_.scale_factor_,
config_.nms_info_["score_threshold"].as<float>(),
result, output_data_list_, config_.fpn_stride_, inputs_.im_shape_,
inputs_.scale_factor_, config_.nms_info_["score_threshold"].as<float>(),
config_.nms_info_["nms_threshold"].as<float>(), num_class, reg_max);
bbox_num->push_back(result->size());
} else {
@ -326,4 +317,4 @@ std::vector<int> GenerateColorMap(int num_class) {
return colormap;
}
} // namespace PPShiTu
} // namespace PPShiTu

6
deploy/lite_shitu/src/picodet_postprocess.cc
View File

@ -47,9 +47,9 @@ int activation_function_softmax(const _Tp *src, _Tp *dst, int length) {
}
// PicoDet decode
PPShiTu::ObjectResult
disPred2Bbox(const float *&dfl_det, int label, float score, int x, int y,
int stride, std::vector<float> im_shape, int reg_max) {
PPShiTu::ObjectResult disPred2Bbox(const float *&dfl_det, int label,
float score, int x, int y, int stride,
std::vector<float> im_shape, int reg_max) {
float ct_x = (x + 0.5) * stride;
float ct_y = (y + 0.5) * stride;
std::vector<float> dis_pred;

109
deploy/lite_shitu/src/preprocess_op.cc
View File

@ -20,7 +20,7 @@
namespace PPShiTu {
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.};
@ -28,10 +28,10 @@ void InitInfo::Run(cv::Mat* im, ImageBlob* data) {
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 *= 1./255.0;
e *= 1. / 255.0;
}
(*im).convertTo(*im, CV_32FC3, e);
for (int h = 0; h < im->rows; h++) {
@ -46,35 +46,61 @@ void NormalizeImage::Run(cv::Mat* im, ImageBlob* data) {
}
}
void Permute::Run(cv::Mat* im, ImageBlob* data) {
void NormalizeImage::Run_feature(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++) {
for (int w = 0; w < im->cols; w++) {
im->at<cv::Vec3f>(h, w)[0] =
(im->at<cv::Vec3f>(h, w)[0] - mean[0]) / std[0];
im->at<cv::Vec3f>(h, w)[1] =
(im->at<cv::Vec3f>(h, w)[1] - mean[1]) / std[1];
im->at<cv::Vec3f>(h, w)[2] =
(im->at<cv::Vec3f>(h, w)[2] - mean[2]) / std[2];
}
}
}
void Permute::Run(cv::Mat *im, ImageBlob *data) {
(*im).convertTo(*im, CV_32FC3);
int rh = im->rows;
int rw = im->cols;
int rc = im->channels();
(data->im_data_).resize(rc * rh * rw);
float* base = (data->im_data_).data();
float *base = (data->im_data_).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 Permute::Run_feature(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 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)};
data->in_net_shape_ = {static_cast<float>(im->cols * resize_scale.first),
static_cast<float>(im->rows * resize_scale.second)};
cv::resize(
*im, *im, cv::Size(), resize_scale.first, resize_scale.second, interp_);
cv::resize(*im, *im, cv::Size(), resize_scale.first, resize_scale.second,
interp_);
data->im_shape_ = {
static_cast<float>(im->rows), static_cast<float>(im->cols),
static_cast<float>(im->rows),
static_cast<float>(im->cols),
};
data->scale_factor_ = {
resize_scale.second, resize_scale.first,
resize_scale.second,
resize_scale.first,
};
}
std::pair<float, float> Resize::GenerateScale(const cv::Mat& im) {
std::pair<float, float> Resize::GenerateScale(const cv::Mat &im) {
std::pair<float, float> resize_scale;
int origin_w = im.cols;
int origin_h = im.rows;
@ -101,7 +127,30 @@ std::pair<float, float> Resize::GenerateScale(const cv::Mat& im) {
return resize_scale;
}
void PadStride::Run(cv::Mat* im, ImageBlob* data) {
void Resize::Run_feature(const cv::Mat &img, cv::Mat &resize_img,
int resize_short_size, int size) {
int resize_h = 0;
int resize_w = 0;
if (size > 0) {
resize_h = size;
resize_w = size;
} else {
int w = img.cols;
int h = img.rows;
float ratio = 1.f;
if (h < w) {
ratio = float(resize_short_size) / float(h);
} else {
ratio = float(resize_short_size) / float(w);
}
resize_h = round(float(h) * ratio);
resize_w = round(float(w) * ratio);
}
cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}
void PadStride::Run(cv::Mat *im, ImageBlob *data) {
if (stride_ <= 0) {
return;
}
@ -110,48 +159,44 @@ void PadStride::Run(cv::Mat* im, ImageBlob* data) {
int rw = im->cols;
int nh = (rh / stride_) * stride_ + (rh % stride_ != 0) * stride_;
int nw = (rw / stride_) * stride_ + (rw % stride_ != 0) * stride_;
cv::copyMakeBorder(
*im, *im, 0, nh - rh, 0, nw - rw, cv::BORDER_CONSTANT, cv::Scalar(0));
cv::copyMakeBorder(*im, *im, 0, nh - rh, 0, nw - rw, cv::BORDER_CONSTANT,
cv::Scalar(0));
data->in_net_shape_ = {
static_cast<float>(im->rows), static_cast<float>(im->cols),
static_cast<float>(im->rows),
static_cast<float>(im->cols),
};
}
void TopDownEvalAffine::Run(cv::Mat* im, ImageBlob* data) {
void TopDownEvalAffine::Run(cv::Mat *im, ImageBlob *data) {
cv::resize(*im, *im, cv::Size(trainsize_[0], trainsize_[1]), 0, 0, interp_);
// todo: Simd::ResizeBilinear();
data->in_net_shape_ = {
static_cast<float>(trainsize_[1]), static_cast<float>(trainsize_[0]),
static_cast<float>(trainsize_[1]),
static_cast<float>(trainsize_[0]),
};
}
// Preprocessor op running order
const std::vector<std::string> Preprocessor::RUN_ORDER = {"InitInfo",
"DetTopDownEvalAffine",
"DetResize",
"DetNormalizeImage",
"DetPadStride",
"DetPermute"};
const std::vector<std::string> Preprocessor::RUN_ORDER = {
"InitInfo", "DetTopDownEvalAffine", "DetResize",
"DetNormalizeImage", "DetPadStride", "DetPermute"};
void Preprocessor::Run(cv::Mat* im, ImageBlob* data) {
for (const auto& name : RUN_ORDER) {
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);
}
}
}
void CropImg(cv::Mat& img,
cv::Mat& crop_img,
std::vector<int>& area,
std::vector<float>& center,
std::vector<float>& scale,
void CropImg(cv::Mat &img, cv::Mat &crop_img, std::vector<int> &area,
std::vector<float> &center, std::vector<float> &scale,
float expandratio) {
int crop_x1 = std::max(0, area[0]);
int crop_y1 = std::max(0, area[1]);
int crop_x2 = std::min(img.cols - 1, area[2]);
int crop_y2 = std::min(img.rows - 1, area[3]);
int center_x = (crop_x1 + crop_x2) / 2.;
int center_y = (crop_y1 + crop_y2) / 2.;
int half_h = (crop_y2 - crop_y1) / 2.;
@ -182,4 +227,4 @@ void CropImg(cv::Mat& img,
scale.emplace_back((crop_y2 - crop_y1));
}
} // namespace PPShiTu
} // namespace PPShiTu

49
deploy/lite_shitu/src/utils.cc
View File

@ -54,4 +54,53 @@ void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold,
}
}
float RectOverlap(const ObjectResult &a, const ObjectResult &b) {
float Aa = (a.rect[2] - a.rect[0] + 1) * (a.rect[3] - a.rect[1] + 1);
float Ab = (b.rect[2] - b.rect[0] + 1) * (b.rect[3] - b.rect[1] + 1);
int iou_w = max(min(a.rect[2], b.rect[2]) - max(a.rect[0], b.rect[0]) + 1, 0);
int iou_h = max(min(a.rect[3], b.rect[3]) - max(a.rect[1], b.rect[1]) + 1, 0);
float Aab = iou_w * iou_h;
return Aab / (Aa + Ab - Aab);
}
inline void
GetMaxScoreIndex(const std::vector<ObjectResult> &det_result,
const float threshold,
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) {
score_index_vec.push_back(std::make_pair(det_result[i].confidence, i));
}
}
// Sort the score pair according to the scores in descending order
std::stable_sort(score_index_vec.begin(), score_index_vec.end(),
SortScorePairDescend<int>);
}
void NMSBoxes(const std::vector<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;
GetMaxScoreIndex(det_result, score_threshold, score_index_vec);
// Do nms
indices.clear();
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) {
const int kept_idx = indices[k];
float overlap = RectOverlap(det_result[idx], det_result[kept_idx]);
keep = overlap <= nms_threshold;
}
if (keep)
indices.push_back(idx);
}
}
} // namespace PPShiTu

2
deploy/lite_shitu/src/vector_search.cc
View File

@ -64,4 +64,4 @@ const SearchResult &VectorSearch::Search(float *feature, int query_number) {
const std::string &VectorSearch::GetLabel(faiss::Index::idx_t ind) {
return this->id_map.at(ind);
}
}
} // namespace PPShiTu