code format for lite_shitu

deploy/lite_shitu/include/config_parser.h

@@ -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

deploy/lite_shitu/include/feature_extractor.h

@@ -18,13 +18,13 @@
 #include <arm_neon.h>
 #include <chrono>
 #include <fstream>
+#include <include/preprocess_op.h>
 #include <iostream>
 #include <math.h>
 #include <opencv2/opencv.hpp>
 #include <stdlib.h>
 #include <sys/time.h>
 #include <vector>
-#include <include/preprocess_op.h>
 
 using namespace paddle::lite_api; // NOLINT
 using namespace std;
@@ -72,13 +72,14 @@ public:
     }
   }
 
-  void RunRecModel(const cv::Mat &img, double &cost_time, std::vector<float> &feature);
-  //void PostProcess(std::vector<float> &feature);
+  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 = {0.229f, 0.224f, 0.225f};
   double scale = 0.00392157;

deploy/lite_shitu/include/object_detector.h

@@ -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

deploy/lite_shitu/include/picodet_postprocess.h

@@ -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

deploy/lite_shitu/include/preprocess_op.h

@@ -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"]) {
@@ -70,11 +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);
+                   const std::vector<float> &std, float scale);
 
- private:
+private:
   // CHW or HWC
   std::vector<float> mean_;
   std::vector<float> scale_;
@@ -82,15 +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>();
@@ -101,12 +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);
-  void Run_feature(const cv::Mat &img, cv::Mat &resize_img, int max_size_len, int size=0);
+  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_;
@@ -115,46 +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);
@@ -162,7 +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") {
@@ -180,13 +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

deploy/lite_shitu/include/utils.h

@@ -43,12 +43,11 @@ void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold,
 
 template <typename T>
 static inline bool SortScorePairDescend(const std::pair<float, T> &pair1,
-		const std::pair<float, T> &pair2){
+                                        const std::pair<float, T> &pair2) {
   return pair1.first > pair2.first;
 }
 
-float RectOverlap(const ObjectResult &a,
-                  const ObjectResult &b);
+float RectOverlap(const ObjectResult &a, const ObjectResult &b);
 
 inline void
 GetMaxScoreIndex(const std::vector<ObjectResult> &det_result,

deploy/lite_shitu/include/vector_search.h

@@ -70,4 +70,4 @@ private:
   std::vector<faiss::Index::idx_t> I;
   SearchResult sr;
 };
-}
+} // namespace PPShiTu

deploy/lite_shitu/src/config_parser.cc

@@ -29,4 +29,4 @@ void load_jsonf(std::string jsonfile, Json::Value &jsondata) {
   }
 }
 
-}  // namespace PPShiTu
+} // namespace PPShiTu

deploy/lite_shitu/src/feature_extractor.cc

@@ -17,8 +17,7 @@
 #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 img_fp;
@@ -34,7 +33,7 @@ void FeatureExtract::RunRecModel(const cv::Mat &img,
 
   // 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){
+  for (int i = 0; i < input.size(); ++i) {
     data0[i] = input[i];
   }
 
@@ -44,7 +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);
@@ -52,7 +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;
@@ -60,15 +59,15 @@ void FeatureExtract::RunRecModel(const cv::Mat &img,
   feature.resize(output_size);
   output_tensor->CopyToCpu(feature.data());
 
-  //postprocess include sqrt or binarize.
+  // postprocess include sqrt or binarize.
   FeatureNorm(feature);
   return;
 }
 
-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;
-}
+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

deploy/lite_shitu/src/main.cc

@@ -27,8 +27,8 @@
 #include "include/feature_extractor.h"
 #include "include/object_detector.h"
 #include "include/preprocess_op.h"
-#include "include/vector_search.h"
 #include "include/utils.h"
+#include "include/vector_search.h"
 #include "json/json.h"
 
 Json::Value RT_Config;
@@ -216,7 +216,7 @@ int main(int argc, char **argv) {
 
       // add the whole image for recognition to improve recall
       PPShiTu::ObjectResult result_whole_img = {
-	 {0, 0, srcimg.cols, srcimg.rows}, 0, 1.0};
+          {0, 0, srcimg.cols, srcimg.rows}, 0, 1.0};
       det_result.push_back(result_whole_img);
 
       // get rec result
@@ -233,9 +233,10 @@ 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];
+        det_result[i].confidence = search_result.D[search_result.return_k * i];
       }
-      NMSBoxes(det_result, searcher.GetThreshold(), rec_nms_threshold, indeices);
+      NMSBoxes(det_result, searcher.GetThreshold(), rec_nms_threshold,
+               indeices);
       PrintResult(img_path, det_result, searcher, search_result);
 
       batch_imgs.clear();
@@ -243,7 +244,6 @@ int main(int argc, char **argv) {
       features.clear();
       feature.clear();
       indeices.clear();
-
     }
   }
   return 0;

deploy/lite_shitu/src/object_detector.cc

@@ -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

deploy/lite_shitu/src/picodet_postprocess.cc

@@ -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;

deploy/lite_shitu/src/preprocess_op.cc

@@ -47,7 +47,7 @@ void NormalizeImage::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) {
+                                 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++) {
@@ -127,8 +127,8 @@ std::pair<float, float> Resize::GenerateScale(const cv::Mat &im) {
   return resize_scale;
 }
 
-void Resize::Run_feature(const cv::Mat &img, cv::Mat &resize_img, int resize_short_size,
-                 int size) {
+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) {

deploy/lite_shitu/src/utils.cc

@@ -54,9 +54,7 @@ void nms(std::vector<ObjectResult> &input_boxes, float nms_threshold,
   }
 }
 
-
-float RectOverlap(const ObjectResult &a,
-                  const ObjectResult &b) {
+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);
 

deploy/lite_shitu/src/vector_search.cc

@@ -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