change labe_list to label_list

deploy/configs/inference_cartoon.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   use_gpu: True

deploy/configs/inference_det.yaml

@@ -5,7 +5,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 1
-  labe_list:
+  label_list:
   - foreground
 
   # inference engine config

deploy/configs/inference_drink.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   use_gpu: True

deploy/configs/inference_general.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   use_gpu: True

deploy/configs/inference_general_binary.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   use_gpu: True

deploy/configs/inference_logo.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   use_gpu: True

deploy/configs/inference_product.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   # inference engine config

deploy/configs/inference_vehicle.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   use_gpu: True

deploy/cpp_shitu/include/object_detector.h

@@ -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 @@ namespace Detection {
     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"]["label_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>();
 
@@ -83,19 +83,19 @@ namespace Detection {
                  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;
@@ -109,7 +109,7 @@ namespace Detection {
   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;
@@ -124,15 +124,15 @@ namespace Detection {
   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

deploy/lite_shitu/generate_json_config.py

@@ -95,7 +95,7 @@ def main():
     config_json["Global"]["det_model_path"] = args.det_model_path
     config_json["Global"]["rec_model_path"] = args.rec_model_path
     config_json["Global"]["rec_label_path"] = args.rec_label_path
-    config_json["Global"]["label_list"] = config_yaml["Global"]["labe_list"]
+    config_json["Global"]["label_list"] = config_yaml["Global"]["label_list"]
     config_json["Global"]["rec_nms_thresold"] = config_yaml["Global"][
         "rec_nms_thresold"]
     config_json["Global"]["max_det_results"] = config_yaml["Global"][

deploy/python/predict_det.py

@@ -134,7 +134,7 @@ class DetPredictor(Predictor):
 
         results = self.parse_det_results(results,
                                          self.config["Global"]["threshold"],
-                                         self.config["Global"]["labe_list"])
+                                         self.config["Global"]["label_list"])
         return results