Keep the parameter names the same as python

deploy/cpp_infer/include/ocr_det.h

@@ -45,7 +45,7 @@ public:
                       const double &det_db_thresh,
                       const double &det_db_box_thresh,
                       const double &det_db_unclip_ratio,
-                      const bool &use_polygon_score, const bool &use_dilation,
+                      const bool &det_db_score_mode, const bool &use_dilation,
                       const bool &use_tensorrt, const std::string &precision) {
     this->use_gpu_ = use_gpu;
     this->gpu_id_ = gpu_id;
@@ -58,7 +58,7 @@ public:
     this->det_db_thresh_ = det_db_thresh;
     this->det_db_box_thresh_ = det_db_box_thresh;
     this->det_db_unclip_ratio_ = det_db_unclip_ratio;
-    this->use_polygon_score_ = use_polygon_score;
+    this->det_db_score_mode_ = det_db_score_mode;
     this->use_dilation_ = use_dilation;
 
     this->use_tensorrt_ = use_tensorrt;
@@ -88,7 +88,7 @@ private:
   double det_db_thresh_ = 0.3;
   double det_db_box_thresh_ = 0.5;
   double det_db_unclip_ratio_ = 2.0;
-  bool use_polygon_score_ = false;
+  std::string det_db_score_mode_ = "slow";
   bool use_dilation_ = false;
 
   bool visualize_ = true;

deploy/cpp_infer/include/postprocess_op.h

@@ -56,7 +56,7 @@ public:
   std::vector<std::vector<std::vector<int>>>
   BoxesFromBitmap(const cv::Mat pred, const cv::Mat bitmap,
                   const float &box_thresh, const float &det_db_unclip_ratio,
-                  const bool &use_polygon_score);
+                  const std::string &det_db_score_mode);
 
   std::vector<std::vector<std::vector<int>>>
   FilterTagDetRes(std::vector<std::vector<std::vector<int>>> boxes,

deploy/cpp_infer/src/main.cpp

@@ -36,25 +36,26 @@
 #include "auto_log/autolog.h"
 #include <gflags/gflags.h>
 
+// common args
 DEFINE_bool(use_gpu, false, "Infering with GPU or CPU.");
+DEFINE_bool(use_tensorrt, false, "Whether use tensorrt.");
 DEFINE_int32(gpu_id, 0, "Device id of GPU to execute.");
 DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU.");
 DEFINE_int32(cpu_threads, 10, "Num of threads with CPU.");
 DEFINE_bool(enable_mkldnn, false, "Whether use mkldnn with CPU.");
-DEFINE_bool(use_tensorrt, false, "Whether use tensorrt.");
 DEFINE_string(precision, "fp32", "Precision be one of fp32/fp16/int8");
 DEFINE_bool(benchmark, false, "Whether use benchmark.");
 DEFINE_string(output, "./output/", "Save benchmark log path.");
-// detection related
 DEFINE_string(image_dir, "", "Dir of input image.");
+DEFINE_bool(visualize, true, "Whether show the detection results.");
+// detection related
 DEFINE_string(det_model_dir, "", "Path of det inference model.");
 DEFINE_int32(max_side_len, 960, "max_side_len of input image.");
 DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh.");
 DEFINE_double(det_db_box_thresh, 0.6, "Threshold of det_db_box_thresh.");
 DEFINE_double(det_db_unclip_ratio, 1.5, "Threshold of det_db_unclip_ratio.");
-DEFINE_bool(use_polygon_score, false, "Whether use polygon score.");
 DEFINE_bool(use_dilation, false, "Whether use the dilation on output map.");
-DEFINE_bool(visualize, true, "Whether show the detection results.");
+DEFINE_bool(det_db_score_mode, false, "Whether use polygon score.");
 // classification related
 DEFINE_bool(use_angle_cls, false, "Whether use use_angle_cls.");
 DEFINE_string(cls_model_dir, "", "Path of cls inference model.");
@@ -85,7 +86,7 @@ int main_det(std::vector<cv::String> cv_all_img_names) {
                  FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
                  FLAGS_max_side_len, FLAGS_det_db_thresh,
                  FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
-                 FLAGS_use_polygon_score, FLAGS_use_dilation,
+                 FLAGS_det_db_score_mode, FLAGS_use_dilation,
                  FLAGS_use_tensorrt, FLAGS_precision);
 
   if (!PathExists(FLAGS_output)) {
@@ -117,13 +118,21 @@ int main_det(std::vector<cv::String> cv_all_img_names) {
     time_info[2] += det_times[2];
 
     if (FLAGS_benchmark) {
-      cout << cv_all_img_names[i] << '\t';
+      cout << cv_all_img_names[i] << "\t[";
       for (int n = 0; n < boxes.size(); n++) {
+        cout << '[';
         for (int m = 0; m < boxes[n].size(); m++) {
-          cout << boxes[n][m][0] << ' ' << boxes[n][m][1] << ' ';
+          cout << '[' << boxes[n][m][0] << ',' << boxes[n][m][1] << "]";
+          if (m != boxes[n].size() - 1) {
+            cout << ',';
+          }
+        }
+        cout << ']';
+        if (n != boxes.size() - 1) {
+          cout << ',';
         }
       }
-      cout << endl;
+      cout << ']' << endl;
     }
   }
 
@@ -140,8 +149,6 @@ int main_rec(std::vector<cv::String> cv_all_img_names) {
   std::vector<double> time_info = {0, 0, 0};
 
   std::string rec_char_dict_path = FLAGS_rec_char_dict_path;
-  if (FLAGS_benchmark)
-    rec_char_dict_path = FLAGS_rec_char_dict_path.substr(6);
   cout << "label file: " << rec_char_dict_path << endl;
 
   CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
@@ -194,7 +201,7 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
                  FLAGS_gpu_mem, FLAGS_cpu_threads, FLAGS_enable_mkldnn,
                  FLAGS_max_side_len, FLAGS_det_db_thresh,
                  FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
-                 FLAGS_use_polygon_score, FLAGS_use_dilation,
+                 FLAGS_det_db_score_mode, FLAGS_use_dilation,
                  FLAGS_use_tensorrt, FLAGS_precision);
 
   Classifier *cls = nullptr;
@@ -205,8 +212,6 @@ int main_system(std::vector<cv::String> cv_all_img_names) {
   }
 
   std::string rec_char_dict_path = FLAGS_rec_char_dict_path;
-  if (FLAGS_benchmark)
-    rec_char_dict_path = FLAGS_rec_char_dict_path.substr(6);
   cout << "label file: " << rec_char_dict_path << endl;
 
   CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,

deploy/cpp_infer/src/ocr_det.cpp

@@ -161,7 +161,7 @@ void DBDetector::Run(cv::Mat &img,
 
   boxes = post_processor_.BoxesFromBitmap(
       pred_map, bit_map, this->det_db_box_thresh_, this->det_db_unclip_ratio_,
-      this->use_polygon_score_);
+      this->det_db_score_mode_);
 
   boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
   auto postprocess_end = std::chrono::steady_clock::now();

deploy/cpp_infer/src/postprocess_op.cpp

@@ -12,8 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include <include/clipper.h>
 #include <include/postprocess_op.h>
-#include <include/clipper.cpp>
 
 namespace PaddleOCR {
 
@@ -187,23 +187,22 @@ float PostProcessor::PolygonScoreAcc(std::vector<cv::Point> contour,
   cv::Mat mask;
   mask = cv::Mat::zeros(ymax - ymin + 1, xmax - xmin + 1, CV_8UC1);
 
+  cv::Point *rook_point = new cv::Point[contour.size()];
 
-  cv::Point* rook_point = new cv::Point[contour.size()];
-   
   for (int i = 0; i < contour.size(); ++i) {
     rook_point[i] = cv::Point(int(box_x[i]) - xmin, int(box_y[i]) - ymin);
   }
   const cv::Point *ppt[1] = {rook_point};
   int npt[] = {int(contour.size())};
 
-
   cv::fillPoly(mask, ppt, npt, 1, cv::Scalar(1));
 
   cv::Mat croppedImg;
-  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1)).copyTo(croppedImg);
+  pred(cv::Rect(xmin, ymin, xmax - xmin + 1, ymax - ymin + 1))
+      .copyTo(croppedImg);
   float score = cv::mean(croppedImg, mask)[0];
 
-  delete []rook_point;
+  delete[] rook_point;
   return score;
 }
 
@@ -247,7 +246,7 @@ float PostProcessor::BoxScoreFast(std::vector<std::vector<float>> box_array,
 
 std::vector<std::vector<std::vector<int>>> PostProcessor::BoxesFromBitmap(
     const cv::Mat pred, const cv::Mat bitmap, const float &box_thresh,
-    const float &det_db_unclip_ratio, const bool &use_polygon_score) {
+    const float &det_db_unclip_ratio, const std::string &det_db_score_mode) {
   const int min_size = 3;
   const int max_candidates = 1000;
 
@@ -281,7 +280,7 @@ std::vector<std::vector<std::vector<int>>> PostProcessor::BoxesFromBitmap(
     }
 
     float score;
-    if (use_polygon_score)
+    if (det_db_score_mode == "slow")
       /* compute using polygon*/
       score = PolygonScoreAcc(contours[_i], pred);
     else