Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into re1

configs/rec/PP-OCRv3/ch_PP-OCRv3_rec.yml

@@ -88,6 +88,7 @@ Train:
         prob: 0.5
         ext_data_num: 2
         image_shape: [48, 320, 3]
+        max_text_length: *max_text_length
     - RecAug:
     - MultiLabelEncode:
     - RecResizeImg:

configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml

@@ -162,6 +162,7 @@ Train:
         prob: 0.5
         ext_data_num: 2
         image_shape: [48, 320, 3]
+        max_text_length: *max_text_length
     - RecAug:
     - MultiLabelEncode:
     - RecResizeImg:

configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml

@@ -88,6 +88,7 @@ Train:
         prob: 0.5
         ext_data_num: 2
         image_shape: [48, 320, 3]
+        max_text_length: *max_text_length
     - RecAug:
     - MultiLabelEncode:
     - RecResizeImg:

deploy/cpp_infer/include/args.h

@@ -49,6 +49,11 @@ DECLARE_int32(rec_batch_num);
 DECLARE_string(rec_char_dict_path);
 DECLARE_int32(rec_img_h);
 DECLARE_int32(rec_img_w);
+// layout model related
+DECLARE_string(layout_model_dir);
+DECLARE_string(layout_dict_path);
+DECLARE_double(layout_score_threshold);
+DECLARE_double(layout_nms_threshold);
 // structure model related
 DECLARE_string(table_model_dir);
 DECLARE_int32(table_max_len);
@@ -59,4 +64,5 @@ DECLARE_bool(merge_no_span_structure);
 DECLARE_bool(det);
 DECLARE_bool(rec);
 DECLARE_bool(cls);
-DECLARE_bool(table);
\ No newline at end of file
+DECLARE_bool(table);
+DECLARE_bool(layout);
\ No newline at end of file

deploy/cpp_infer/include/ocr_cls.h

@@ -14,26 +14,12 @@
 
 #pragma once
 
-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
 #include "paddle_api.h"
 #include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
 
 #include <include/preprocess_op.h>
 #include <include/utility.h>
 
-using namespace paddle_infer;
-
 namespace PaddleOCR {
 
 class Classifier {
@@ -66,7 +52,7 @@ public:
            std::vector<float> &cls_scores, std::vector<double> &times);
 
 private:
-  std::shared_ptr<Predictor> predictor_;
+  std::shared_ptr<paddle_infer::Predictor> predictor_;
 
   bool use_gpu_ = false;
   int gpu_id_ = 0;

deploy/cpp_infer/include/ocr_det.h

@@ -14,26 +14,12 @@
 
 #pragma once
 
-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
 #include "paddle_api.h"
 #include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
 
 #include <include/postprocess_op.h>
 #include <include/preprocess_op.h>
 
-using namespace paddle_infer;
-
 namespace PaddleOCR {
 
 class DBDetector {
@@ -41,7 +27,7 @@ public:
   explicit DBDetector(const std::string &model_dir, const bool &use_gpu,
                       const int &gpu_id, const int &gpu_mem,
                       const int &cpu_math_library_num_threads,
-                      const bool &use_mkldnn, const string &limit_type,
+                      const bool &use_mkldnn, const std::string &limit_type,
                       const int &limit_side_len, const double &det_db_thresh,
                       const double &det_db_box_thresh,
                       const double &det_db_unclip_ratio,
@@ -77,7 +63,7 @@ public:
            std::vector<double> &times);
 
 private:
-  std::shared_ptr<Predictor> predictor_;
+  std::shared_ptr<paddle_infer::Predictor> predictor_;
 
   bool use_gpu_ = false;
   int gpu_id_ = 0;
@@ -85,7 +71,7 @@ private:
   int cpu_math_library_num_threads_ = 4;
   bool use_mkldnn_ = false;
 
-  string limit_type_ = "max";
+  std::string limit_type_ = "max";
   int limit_side_len_ = 960;
 
   double det_db_thresh_ = 0.3;

deploy/cpp_infer/include/ocr_rec.h

@@ -14,27 +14,12 @@
 
 #pragma once
 
-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
 #include "paddle_api.h"
 #include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
 
 #include <include/ocr_cls.h>
-#include <include/preprocess_op.h>
 #include <include/utility.h>
 
-using namespace paddle_infer;
-
 namespace PaddleOCR {
 
 class CRNNRecognizer {
@@ -42,7 +27,7 @@ public:
   explicit CRNNRecognizer(const std::string &model_dir, const bool &use_gpu,
                           const int &gpu_id, const int &gpu_mem,
                           const int &cpu_math_library_num_threads,
-                          const bool &use_mkldnn, const string &label_path,
+                          const bool &use_mkldnn, const std::string &label_path,
                           const bool &use_tensorrt,
                           const std::string &precision,
                           const int &rec_batch_num, const int &rec_img_h,
@@ -75,7 +60,7 @@ public:
            std::vector<float> &rec_text_scores, std::vector<double> &times);
 
 private:
-  std::shared_ptr<Predictor> predictor_;
+  std::shared_ptr<paddle_infer::Predictor> predictor_;
 
   bool use_gpu_ = false;
   int gpu_id_ = 0;

deploy/cpp_infer/include/paddleocr.h

@@ -14,28 +14,9 @@
 
 #pragma once
 
-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
-#include "paddle_api.h"
-#include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
-
 #include <include/ocr_cls.h>
 #include <include/ocr_det.h>
 #include <include/ocr_rec.h>
-#include <include/preprocess_op.h>
-#include <include/utility.h>
-
-using namespace paddle_infer;
 
 namespace PaddleOCR {
 
@@ -43,21 +24,27 @@ class PPOCR {
 public:
   explicit PPOCR();
   ~PPOCR();
-  std::vector<std::vector<OCRPredictResult>>
-  ocr(std::vector<cv::String> cv_all_img_names, bool det = true,
-      bool rec = true, bool cls = true);
+
+  std::vector<std::vector<OCRPredictResult>> ocr(std::vector<cv::Mat> img_list,
+                                                 bool det = true,
+                                                 bool rec = true,
+                                                 bool cls = true);
+  std::vector<OCRPredictResult> ocr(cv::Mat img, bool det = true,
+                                    bool rec = true, bool cls = true);
+
+  void reset_timer();
+  void benchmark_log(int img_num);
 
 protected:
-  void det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results,
-           std::vector<double> &times);
+  std::vector<double> time_info_det = {0, 0, 0};
+  std::vector<double> time_info_rec = {0, 0, 0};
+  std::vector<double> time_info_cls = {0, 0, 0};
+
+  void det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results);
   void rec(std::vector<cv::Mat> img_list,
-           std::vector<OCRPredictResult> &ocr_results,
-           std::vector<double> &times);
+           std::vector<OCRPredictResult> &ocr_results);
   void cls(std::vector<cv::Mat> img_list,
-           std::vector<OCRPredictResult> &ocr_results,
-           std::vector<double> &times);
-  void log(std::vector<double> &det_times, std::vector<double> &rec_times,
-           std::vector<double> &cls_times, int img_num);
+           std::vector<OCRPredictResult> &ocr_results);
 
 private:
   DBDetector *detector_ = nullptr;

deploy/cpp_infer/include/paddlestructure.h

@@ -14,27 +14,9 @@
 
 #pragma once
 
-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
-#include "paddle_api.h"
-#include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
-
 #include <include/paddleocr.h>
-#include <include/preprocess_op.h>
+#include <include/structure_layout.h>
 #include <include/structure_table.h>
-#include <include/utility.h>
-
-using namespace paddle_infer;
 
 namespace PaddleOCR {
 
@@ -42,23 +24,31 @@ class PaddleStructure : public PPOCR {
 public:
   explicit PaddleStructure();
   ~PaddleStructure();
-  std::vector<std::vector<StructurePredictResult>>
-  structure(std::vector<cv::String> cv_all_img_names, bool layout = false,
-            bool table = true);
+
+  std::vector<StructurePredictResult> structure(cv::Mat img,
+                                                bool layout = false,
+                                                bool table = true,
+                                                bool ocr = false);
+
+  void reset_timer();
+  void benchmark_log(int img_num);
 
 private:
-  StructureTableRecognizer *recognizer_ = nullptr;
+  std::vector<double> time_info_table = {0, 0, 0};
+  std::vector<double> time_info_layout = {0, 0, 0};
+
+  StructureTableRecognizer *table_model_ = nullptr;
+  StructureLayoutRecognizer *layout_model_ = nullptr;
+
+  void layout(cv::Mat img,
+              std::vector<StructurePredictResult> &structure_result);
+
+  void table(cv::Mat img, StructurePredictResult &structure_result);
 
-  void table(cv::Mat img, StructurePredictResult &structure_result,
-             std::vector<double> &time_info_table,
-             std::vector<double> &time_info_det,
-             std::vector<double> &time_info_rec,
-             std::vector<double> &time_info_cls);
   std::string rebuild_table(std::vector<std::string> rec_html_tags,
                             std::vector<std::vector<int>> rec_boxes,
                             std::vector<OCRPredictResult> &ocr_result);
 
-  float iou(std::vector<int> &box1, std::vector<int> &box2);
   float dis(std::vector<int> &box1, std::vector<int> &box2);
 
   static bool comparison_dis(const std::vector<float> &dis1,

deploy/cpp_infer/include/postprocess_op.h

@@ -14,24 +14,9 @@
 
 #pragma once
 
-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
-
 #include "include/clipper.h"
 #include "include/utility.h"
 
-using namespace std;
-
 namespace PaddleOCR {
 
 class DBPostProcessor {
@@ -106,4 +91,27 @@ private:
   std::string beg = "sos";
 };
 
+class PicodetPostProcessor {
+public:
+  void init(std::string label_path, const double score_threshold = 0.4,
+            const double nms_threshold = 0.5,
+            const std::vector<int> &fpn_stride = {8, 16, 32, 64});
+  void Run(std::vector<StructurePredictResult> &results,
+           std::vector<std::vector<float>> outs, std::vector<int> ori_shape,
+           std::vector<int> resize_shape, int eg_max);
+  std::vector<int> fpn_stride_ = {8, 16, 32, 64};
+
+private:
+  StructurePredictResult disPred2Bbox(std::vector<float> bbox_pred, int label,
+                                      float score, int x, int y, int stride,
+                                      std::vector<int> im_shape, int reg_max);
+  void nms(std::vector<StructurePredictResult> &input_boxes,
+           float nms_threshold);
+
+  std::vector<std::string> label_list_;
+  double score_threshold_ = 0.4;
+  double nms_threshold_ = 0.5;
+  int num_class_ = 5;
+};
+
 } // namespace PaddleOCR

deploy/cpp_infer/include/preprocess_op.h

@@ -14,21 +14,12 @@
 
 #pragma once
 
-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
-#include <chrono>
-#include <iomanip>
 #include <iostream>
-#include <ostream>
 #include <vector>
 
-#include <cstring>
-#include <fstream>
-#include <numeric>
-
-using namespace std;
-using namespace paddle;
+#include "opencv2/core.hpp"
+#include "opencv2/imgcodecs.hpp"
+#include "opencv2/imgproc.hpp"
 
 namespace PaddleOCR {
 
@@ -51,9 +42,9 @@ public:
 
 class ResizeImgType0 {
 public:
-  virtual void Run(const cv::Mat &img, cv::Mat &resize_img, string limit_type,
-                   int limit_side_len, float &ratio_h, float &ratio_w,
-                   bool use_tensorrt);
+  virtual void Run(const cv::Mat &img, cv::Mat &resize_img,
+                   std::string limit_type, int limit_side_len, float &ratio_h,
+                   float &ratio_w, bool use_tensorrt);
 };
 
 class CrnnResizeImg {
@@ -82,4 +73,10 @@ public:
                    const int max_len = 488);
 };
 
+class Resize {
+public:
+  virtual void Run(const cv::Mat &img, cv::Mat &resize_img, const int h,
+                   const int w);
+};
+
 } // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/include/structure_layout.h

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "paddle_api.h"
+#include "paddle_inference_api.h"
+
+#include <include/postprocess_op.h>
+#include <include/preprocess_op.h>
+
+namespace PaddleOCR {
+
+class StructureLayoutRecognizer {
+public:
+  explicit StructureLayoutRecognizer(
+      const std::string &model_dir, const bool &use_gpu, const int &gpu_id,
+      const int &gpu_mem, const int &cpu_math_library_num_threads,
+      const bool &use_mkldnn, const std::string &label_path,
+      const bool &use_tensorrt, const std::string &precision,
+      const double &layout_score_threshold,
+      const double &layout_nms_threshold) {
+    this->use_gpu_ = use_gpu;
+    this->gpu_id_ = gpu_id;
+    this->gpu_mem_ = gpu_mem;
+    this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
+    this->use_mkldnn_ = use_mkldnn;
+    this->use_tensorrt_ = use_tensorrt;
+    this->precision_ = precision;
+
+    this->post_processor_.init(label_path, layout_score_threshold,
+                               layout_nms_threshold);
+    LoadModel(model_dir);
+  }
+
+  // Load Paddle inference model
+  void LoadModel(const std::string &model_dir);
+
+  void Run(cv::Mat img, std::vector<StructurePredictResult> &result,
+           std::vector<double> &times);
+
+private:
+  std::shared_ptr<paddle_infer::Predictor> predictor_;
+
+  bool use_gpu_ = false;
+  int gpu_id_ = 0;
+  int gpu_mem_ = 4000;
+  int cpu_math_library_num_threads_ = 4;
+  bool use_mkldnn_ = false;
+
+  std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
+  std::vector<float> scale_ = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
+  bool is_scale_ = true;
+
+  bool use_tensorrt_ = false;
+  std::string precision_ = "fp32";
+
+  // pre-process
+  Resize resize_op_;
+  Normalize normalize_op_;
+  Permute permute_op_;
+
+  // post-process
+  PicodetPostProcessor post_processor_;
+};
+
+} // namespace PaddleOCR
\ No newline at end of file

deploy/cpp_infer/include/structure_table.h

@@ -14,26 +14,11 @@
 
 #pragma once
 
-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
 #include "paddle_api.h"
 #include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
 
 #include <include/postprocess_op.h>
 #include <include/preprocess_op.h>
-#include <include/utility.h>
-
-using namespace paddle_infer;
 
 namespace PaddleOCR {
 
@@ -42,7 +27,7 @@ public:
   explicit StructureTableRecognizer(
       const std::string &model_dir, const bool &use_gpu, const int &gpu_id,
       const int &gpu_mem, const int &cpu_math_library_num_threads,
-      const bool &use_mkldnn, const string &label_path,
+      const bool &use_mkldnn, const std::string &label_path,
       const bool &use_tensorrt, const std::string &precision,
       const int &table_batch_num, const int &table_max_len,
       const bool &merge_no_span_structure) {
@@ -70,7 +55,7 @@ public:
            std::vector<double> &times);
 
 private:
-  std::shared_ptr<Predictor> predictor_;
+  std::shared_ptr<paddle_infer::Predictor> predictor_;
 
   bool use_gpu_ = false;
   int gpu_id_ = 0;

deploy/cpp_infer/include/utility.h

@@ -41,12 +41,13 @@ struct OCRPredictResult {
 };
 
 struct StructurePredictResult {
-  std::vector<int> box;
+  std::vector<float> box;
   std::vector<std::vector<int>> cell_box;
   std::string type;
   std::vector<OCRPredictResult> text_res;
   std::string html;
   float html_score = -1;
+  float confidence;
 };
 
 class Utility {
@@ -82,13 +83,20 @@ public:
 
   static void print_result(const std::vector<OCRPredictResult> &ocr_result);
 
-  static cv::Mat crop_image(cv::Mat &img, std::vector<int> &area);
+  static cv::Mat crop_image(cv::Mat &img, const std::vector<int> &area);
+  static cv::Mat crop_image(cv::Mat &img, const std::vector<float> &area);
 
   static void sorted_boxes(std::vector<OCRPredictResult> &ocr_result);
 
   static std::vector<int> xyxyxyxy2xyxy(std::vector<std::vector<int>> &box);
   static std::vector<int> xyxyxyxy2xyxy(std::vector<int> &box);
 
+  static float fast_exp(float x);
+  static std::vector<float>
+  activation_function_softmax(std::vector<float> &src);
+  static float iou(std::vector<int> &box1, std::vector<int> &box2);
+  static float iou(std::vector<float> &box1, std::vector<float> &box2);
+
 private:
   static bool comparison_box(const OCRPredictResult &result1,
                              const OCRPredictResult &result2) {

deploy/cpp_infer/readme.md

@@ -174,6 +174,9 @@ inference/
 |-- table
 |   |--inference.pdiparams
 |   |--inference.pdmodel
+|-- layout
+|   |--inference.pdiparams
+|   |--inference.pdmodel
 ```
 
 
@@ -278,8 +281,30 @@ Specifically,
     --cls=true \
 ```
 
+##### 7. layout+table
+```shell
+./build/ppocr --det_model_dir=inference/det_db \
+    --rec_model_dir=inference/rec_rcnn \
+    --table_model_dir=inference/table \
+    --image_dir=../../ppstructure/docs/table/table.jpg \
+    --layout_model_dir=inference/layout \
+    --type=structure \
+    --table=true \
+    --layout=true
+```
+
+##### 8. layout
+```shell
+./build/ppocr --layout_model_dir=inference/layout \
+    --image_dir=../../ppstructure/docs/table/1.png \
+    --type=structure \
+    --table=false \
+    --layout=true \
+    --det=false \
+    --rec=false
+```
 
-##### 7. table
+##### 9. table
 ```shell
 ./build/ppocr --det_model_dir=inference/det_db \
     --rec_model_dir=inference/rec_rcnn \
@@ -343,6 +368,16 @@ More parameters are as follows,
 |rec_img_h|int|48|image height of recognition|
 |rec_img_w|int|320|image width of recognition|
 
+- Layout related parameters
+
+|parameter|data type|default|meaning|
+| :---: | :---: | :---: | :---: |
+|layout_model_dir|string|-| Address of layout inference model|
+|layout_dict_path|string|../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt|dictionary file|
+|layout_score_threshold|float|0.5|Threshold of score.|
+|layout_nms_threshold|float|0.5|Threshold of nms.|
+
+
 - Table recognition related parameters
 
 |parameter|data type|default|meaning|
@@ -368,11 +403,51 @@ predict img: ../../doc/imgs/12.jpg
 The detection visualized image saved in ./output//12.jpg
 ```
 
-- table
+- layout+table
 
 ```bash
-predict img: ../../ppstructure/docs/table/table.jpg
-0       type: table, region: [0,0,371,293], res: <html><body><table><thead><tr><td>Methods</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>SegLink [26]</td><td>70.0</td><td>86.0</td><td>77.0</td><td>8.9</td></tr><tr><td>PixelLink [4]</td><td>73.2</td><td>83.0</td><td>77.8</td><td>-</td></tr><tr><td>TextSnake [18]</td><td>73.9</td><td>83.2</td><td>78.3</td><td>1.1</td></tr><tr><td>TextField [37]</td><td>75.9</td><td>87.4</td><td>81.3</td><td>5.2 </td></tr><tr><td>MSR[38]</td><td>76.7</td><td>87.4</td><td>81.7</td><td>-</td></tr><tr><td>FTSN [3]</td><td>77.1</td><td>87.6</td><td>82.0</td><td>-</td></tr><tr><td>LSE[30]</td><td>81.7</td><td>84.2</td><td>82.9</td><td>-</td></tr><tr><td>CRAFT [2]</td><td>78.2</td><td>88.2</td><td>82.9</td><td>8.6</td></tr><tr><td>MCN [16]</td><td>79</td><td>88</td><td>83</td><td>-</td></tr><tr><td>ATRR[35]</td><td>82.1</td><td>85.2</td><td>83.6</td><td>-</td></tr><tr><td>PAN [34]</td><td>83.8</td><td>84.4</td><td>84.1</td><td>30.2</td></tr><tr><td>DB[12]</td><td>79.2</td><td>91.5</td><td>84.9</td><td>32.0</td></tr><tr><td>DRRG [41]</td><td>82.30</td><td>88.05</td><td>85.08</td><td>-</td></tr><tr><td>Ours (SynText)</td><td>80.68</td><td>85.40</td><td>82.97</td><td>12.68</td></tr><tr><td>Ours (MLT-17)</td><td>84.54</td><td>86.62</td><td>85.57</td><td>12.31</td></tr></tbody></table></body></html>
+predict img: ../../ppstructure/docs/table/1.png
+0       type: text, region: [12,729,410,848], score: 0.781044, res: count of ocr result is : 7
+********** print ocr result **********
+0       det boxes: [[4,1],[79,1],[79,12],[4,12]] rec text: CTW1500. rec score: 0.769472
+...
+6       det boxes: [[4,99],[391,99],[391,112],[4,112]] rec text: sate-of-the-artmethods[12.34.36l.ourapproachachieves rec score: 0.90414
+********** end print ocr result **********
+1       type: text, region: [69,342,342,359], score: 0.703666, res: count of ocr result is : 1
+********** print ocr result **********
+0       det boxes: [[8,2],[269,2],[269,13],[8,13]] rec text: Table6.Experimentalresults on CTW-1500 rec score: 0.890454
+********** end print ocr result **********
+2       type: text, region: [70,316,706,332], score: 0.659738, res: count of ocr result is : 2
+********** print ocr result **********
+0       det boxes: [[373,2],[630,2],[630,11],[373,11]] rec text: oroposals.andthegreencontoursarefinal rec score: 0.919729
+1       det boxes: [[8,3],[357,3],[357,11],[8,11]] rec text: Visualexperimentalresultshebluecontoursareboundar rec score: 0.915963
+********** end print ocr result **********
+3       type: text, region: [489,342,789,359], score: 0.630538, res: count of ocr result is : 1
+********** print ocr result **********
+0       det boxes: [[8,2],[294,2],[294,14],[8,14]] rec text: Table7.Experimentalresults onMSRA-TD500 rec score: 0.942251
+********** end print ocr result **********
+4       type: text, region: [444,751,841,848], score: 0.607345, res: count of ocr result is : 5
+********** print ocr result **********
+0       det boxes: [[19,3],[389,3],[389,17],[19,17]] rec text: Inthispaper,weproposeanovel adaptivebound rec score: 0.941031
+1       det boxes: [[4,22],[390,22],[390,36],[4,36]] rec text: aryproposalnetworkforarbitraryshapetextdetection rec score: 0.960172
+2       det boxes: [[4,42],[392,42],[392,56],[4,56]] rec text: whichadoptanboundaryproposalmodeltogeneratecoarse rec score: 0.934647
+3       det boxes: [[4,61],[389,61],[389,75],[4,75]] rec text: ooundaryproposals,andthenadoptanadaptiveboundary rec score: 0.946296
+4       det boxes: [[5,80],[387,80],[387,93],[5,93]] rec text: leformationmodelcombinedwithGCNandRNNtoper rec score: 0.952401
+********** end print ocr result **********
+5       type: title, region: [444,705,564,724], score: 0.785429, res: count of ocr result is : 1
+********** print ocr result **********
+0       det boxes: [[6,2],[113,2],[113,14],[6,14]] rec text: 5.Conclusion rec score: 0.856903
+********** end print ocr result **********
+6       type: table, region: [14,360,402,711], score: 0.963643, res: <html><body><table><thead><tr><td>Methods</td><td>Ext</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>TextSnake [18]</td><td>Syn</td><td>85.3</td><td>67.9</td><td>75.6</td><td></td></tr><tr><td>CSE [17]</td><td>MiLT</td><td>76.1</td><td>78.7</td><td>77.4</td><td>0.38</td></tr><tr><td>LOMO[40]</td><td>Syn</td><td>76.5</td><td>85.7</td><td>80.8</td><td>4.4</td></tr><tr><td>ATRR[35]</td><td>Sy-</td><td>80.2</td><td>80.1</td><td>80.1</td><td>-</td></tr><tr><td>SegLink++ [28]</td><td>Syn</td><td>79.8</td><td>82.8</td><td>81.3</td><td>-</td></tr><tr><td>TextField [37]</td><td>Syn</td><td>79.8</td><td>83.0</td><td>81.4</td><td>6.0</td></tr><tr><td>MSR[38]</td><td>Syn</td><td>79.0</td><td>84.1</td><td>81.5</td><td>4.3</td></tr><tr><td>PSENet-1s [33]</td><td>MLT</td><td>79.7</td><td>84.8</td><td>82.2</td><td>3.9</td></tr><tr><td>DB [12]</td><td>Syn</td><td>80.2</td><td>86.9</td><td>83.4</td><td>22.0</td></tr><tr><td>CRAFT [2]</td><td>Syn</td><td>81.1</td><td>86.0</td><td>83.5</td><td>-</td></tr><tr><td>TextDragon [5]</td><td>MLT+</td><td>82.8</td><td>84.5</td><td>83.6</td><td></td></tr><tr><td>PAN [34]</td><td>Syn</td><td>81.2</td><td>86.4</td><td>83.7</td><td>39.8</td></tr><tr><td>ContourNet [36]</td><td></td><td>84.1</td><td>83.7</td><td>83.9</td><td>4.5</td></tr><tr><td>DRRG [41]</td><td>MLT</td><td>83.02</td><td>85.93</td><td>84.45</td><td>-</td></tr><tr><td>TextPerception[23]</td><td>Syn</td><td>81.9</td><td>87.5</td><td>84.6</td><td></td></tr><tr><td>Ours</td><td> Syn</td><td>80.57</td><td>87.66</td><td>83.97</td><td>12.08</td></tr><tr><td>Ours</td><td></td><td>81.45</td><td>87.81</td><td>84.51</td><td>12.15</td></tr><tr><td>Ours</td><td>MLT</td><td>83.60</td><td>86.45</td><td>85.00</td><td>12.21</td></tr></tbody></table></body></html>
+The table visualized image saved in ./output//6_1.png
+7       type: table, region: [462,359,820,657], score: 0.953917, res: <html><body><table><thead><tr><td>Methods</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>SegLink [26]</td><td>70.0</td><td>86.0</td><td>77.0</td><td>8.9</td></tr><tr><td>PixelLink [4]</td><td>73.2</td><td>83.0</td><td>77.8</td><td>-</td></tr><tr><td>TextSnake [18]</td><td>73.9</td><td>83.2</td><td>78.3</td><td>1.1</td></tr><tr><td>TextField [37]</td><td>75.9</td><td>87.4</td><td>81.3</td><td>5.2 </td></tr><tr><td>MSR[38]</td><td>76.7</td><td>87.4</td><td>81.7</td><td>-</td></tr><tr><td>FTSN[3]</td><td>77.1</td><td>87.6</td><td>82.0</td><td>:</td></tr><tr><td>LSE[30]</td><td>81.7</td><td>84.2</td><td>82.9</td><td></td></tr><tr><td>CRAFT [2]</td><td>78.2</td><td>88.2</td><td>82.9</td><td>8.6</td></tr><tr><td>MCN [16]</td><td>79</td><td>88</td><td>83</td><td>-</td></tr><tr><td>ATRR[35]</td><td>82.1</td><td>85.2</td><td>83.6</td><td>-</td></tr><tr><td>PAN [34]</td><td>83.8</td><td>84.4</td><td>84.1</td><td>30.2</td></tr><tr><td>DB[12]</td><td>79.2</td><td>91.5</td><td>84.9</td><td>32.0</td></tr><tr><td>DRRG [41]</td><td>82.30</td><td>88.05</td><td>85.08</td><td>-</td></tr><tr><td>Ours (SynText)</td><td>80.68</td><td>85.40</td><td>82.97</td><td>12.68</td></tr><tr><td>Ours (MLT-17)</td><td>84.54</td><td>86.62</td><td>85.57</td><td>12.31</td></tr></tbody></table></body></html>
+The table visualized image saved in ./output//7_1.png
+8       type: figure, region: [14,3,836,310], score: 0.969443, res: count of ocr result is : 26
+********** print ocr result **********
+0       det boxes: [[506,14],[539,15],[539,22],[506,21]] rec text: E rec score: 0.318073
+...
+25      det boxes: [[680,290],[759,288],[759,303],[680,305]] rec text: (d) CTW1500 rec score: 0.95911
+********** end print ocr result **********
 ```
 
 <a name="3"></a>

deploy/cpp_infer/readme_ch.md

@@ -184,6 +184,9 @@ inference/
 |-- table
 |   |--inference.pdiparams
 |   |--inference.pdmodel
+|-- layout
+|   |--inference.pdiparams
+|   |--inference.pdmodel
 ```
 
 <a name="22"></a>
@@ -288,7 +291,30 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
     --cls=true \
 ```
 
-##### 7. 表格识别
+##### 7. 版面分析+表格识别
+```shell
+./build/ppocr --det_model_dir=inference/det_db \
+    --rec_model_dir=inference/rec_rcnn \
+    --table_model_dir=inference/table \
+    --image_dir=../../ppstructure/docs/table/table.jpg \
+    --layout_model_dir=inference/layout \
+    --type=structure \
+    --table=true \
+    --layout=true
+```
+
+##### 8. 版面分析
+```shell
+./build/ppocr --layout_model_dir=inference/layout \
+    --image_dir=../../ppstructure/docs/table/1.png \
+    --type=structure \
+    --table=false \
+    --layout=true \
+    --det=false \
+    --rec=false
+```
+
+##### 9. 表格识别
 ```shell
 ./build/ppocr --det_model_dir=inference/det_db \
     --rec_model_dir=inference/rec_rcnn \
@@ -352,12 +378,22 @@ CUDNN_LIB_DIR=/your_cudnn_lib_dir
 |rec_img_w|int|320|文字识别模型输入图像宽度|
 
 
+- 版面分析模型相关
+
+|参数名称|类型|默认参数|意义|
+| :---: | :---: | :---: | :---: |
+|layout_model_dir|string|-|版面分析模型inference model地址|
+|layout_dict_path|string|../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt|字典文件|
+|layout_score_threshold|float|0.5|检测框的分数阈值|
+|layout_nms_threshold|float|0.5|nms的阈值|
+
+
 - 表格识别模型相关
 
 |参数名称|类型|默认参数|意义|
 | :---: | :---: | :---: | :---: |
 |table_model_dir|string|-|表格识别模型inference model地址|
-|table_char_dict_path|string|../../ppocr/utils/dict/table_structure_dict.txt|字典文件|
+|table_char_dict_path|string|../../ppocr/utils/dict/table_structure_dict_ch.txt|字典文件|
 |table_max_len|int|488|表格识别模型输入图像长边大小，最终网络输入图像大小为（table_max_len，table_max_len）|
 |merge_no_span_structure|bool|true|是否合并<td> 和 </td> 为<td></td>|
 
@@ -378,11 +414,51 @@ predict img: ../../doc/imgs/12.jpg
 The detection visualized image saved in ./output//12.jpg
 ```
 
-- table
+- layout+table
 
 ```bash
-predict img: ../../ppstructure/docs/table/table.jpg
-0       type: table, region: [0,0,371,293], res: <html><body><table><thead><tr><td>Methods</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>SegLink [26]</td><td>70.0</td><td>86.0</td><td>77.0</td><td>8.9</td></tr><tr><td>PixelLink [4]</td><td>73.2</td><td>83.0</td><td>77.8</td><td>-</td></tr><tr><td>TextSnake [18]</td><td>73.9</td><td>83.2</td><td>78.3</td><td>1.1</td></tr><tr><td>TextField [37]</td><td>75.9</td><td>87.4</td><td>81.3</td><td>5.2 </td></tr><tr><td>MSR[38]</td><td>76.7</td><td>87.4</td><td>81.7</td><td>-</td></tr><tr><td>FTSN [3]</td><td>77.1</td><td>87.6</td><td>82.0</td><td>-</td></tr><tr><td>LSE[30]</td><td>81.7</td><td>84.2</td><td>82.9</td><td>-</td></tr><tr><td>CRAFT [2]</td><td>78.2</td><td>88.2</td><td>82.9</td><td>8.6</td></tr><tr><td>MCN [16]</td><td>79</td><td>88</td><td>83</td><td>-</td></tr><tr><td>ATRR[35]</td><td>82.1</td><td>85.2</td><td>83.6</td><td>-</td></tr><tr><td>PAN [34]</td><td>83.8</td><td>84.4</td><td>84.1</td><td>30.2</td></tr><tr><td>DB[12]</td><td>79.2</td><td>91.5</td><td>84.9</td><td>32.0</td></tr><tr><td>DRRG [41]</td><td>82.30</td><td>88.05</td><td>85.08</td><td>-</td></tr><tr><td>Ours (SynText)</td><td>80.68</td><td>85.40</td><td>82.97</td><td>12.68</td></tr><tr><td>Ours (MLT-17)</td><td>84.54</td><td>86.62</td><td>85.57</td><td>12.31</td></tr></tbody></table></body></html>
+predict img: ../../ppstructure/docs/table/1.png
+0       type: text, region: [12,729,410,848], score: 0.781044, res: count of ocr result is : 7
+********** print ocr result **********
+0       det boxes: [[4,1],[79,1],[79,12],[4,12]] rec text: CTW1500. rec score: 0.769472
+...
+6       det boxes: [[4,99],[391,99],[391,112],[4,112]] rec text: sate-of-the-artmethods[12.34.36l.ourapproachachieves rec score: 0.90414
+********** end print ocr result **********
+1       type: text, region: [69,342,342,359], score: 0.703666, res: count of ocr result is : 1
+********** print ocr result **********
+0       det boxes: [[8,2],[269,2],[269,13],[8,13]] rec text: Table6.Experimentalresults on CTW-1500 rec score: 0.890454
+********** end print ocr result **********
+2       type: text, region: [70,316,706,332], score: 0.659738, res: count of ocr result is : 2
+********** print ocr result **********
+0       det boxes: [[373,2],[630,2],[630,11],[373,11]] rec text: oroposals.andthegreencontoursarefinal rec score: 0.919729
+1       det boxes: [[8,3],[357,3],[357,11],[8,11]] rec text: Visualexperimentalresultshebluecontoursareboundar rec score: 0.915963
+********** end print ocr result **********
+3       type: text, region: [489,342,789,359], score: 0.630538, res: count of ocr result is : 1
+********** print ocr result **********
+0       det boxes: [[8,2],[294,2],[294,14],[8,14]] rec text: Table7.Experimentalresults onMSRA-TD500 rec score: 0.942251
+********** end print ocr result **********
+4       type: text, region: [444,751,841,848], score: 0.607345, res: count of ocr result is : 5
+********** print ocr result **********
+0       det boxes: [[19,3],[389,3],[389,17],[19,17]] rec text: Inthispaper,weproposeanovel adaptivebound rec score: 0.941031
+1       det boxes: [[4,22],[390,22],[390,36],[4,36]] rec text: aryproposalnetworkforarbitraryshapetextdetection rec score: 0.960172
+2       det boxes: [[4,42],[392,42],[392,56],[4,56]] rec text: whichadoptanboundaryproposalmodeltogeneratecoarse rec score: 0.934647
+3       det boxes: [[4,61],[389,61],[389,75],[4,75]] rec text: ooundaryproposals,andthenadoptanadaptiveboundary rec score: 0.946296
+4       det boxes: [[5,80],[387,80],[387,93],[5,93]] rec text: leformationmodelcombinedwithGCNandRNNtoper rec score: 0.952401
+********** end print ocr result **********
+5       type: title, region: [444,705,564,724], score: 0.785429, res: count of ocr result is : 1
+********** print ocr result **********
+0       det boxes: [[6,2],[113,2],[113,14],[6,14]] rec text: 5.Conclusion rec score: 0.856903
+********** end print ocr result **********
+6       type: table, region: [14,360,402,711], score: 0.963643, res: <html><body><table><thead><tr><td>Methods</td><td>Ext</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>TextSnake [18]</td><td>Syn</td><td>85.3</td><td>67.9</td><td>75.6</td><td></td></tr><tr><td>CSE [17]</td><td>MiLT</td><td>76.1</td><td>78.7</td><td>77.4</td><td>0.38</td></tr><tr><td>LOMO[40]</td><td>Syn</td><td>76.5</td><td>85.7</td><td>80.8</td><td>4.4</td></tr><tr><td>ATRR[35]</td><td>Sy-</td><td>80.2</td><td>80.1</td><td>80.1</td><td>-</td></tr><tr><td>SegLink++ [28]</td><td>Syn</td><td>79.8</td><td>82.8</td><td>81.3</td><td>-</td></tr><tr><td>TextField [37]</td><td>Syn</td><td>79.8</td><td>83.0</td><td>81.4</td><td>6.0</td></tr><tr><td>MSR[38]</td><td>Syn</td><td>79.0</td><td>84.1</td><td>81.5</td><td>4.3</td></tr><tr><td>PSENet-1s [33]</td><td>MLT</td><td>79.7</td><td>84.8</td><td>82.2</td><td>3.9</td></tr><tr><td>DB [12]</td><td>Syn</td><td>80.2</td><td>86.9</td><td>83.4</td><td>22.0</td></tr><tr><td>CRAFT [2]</td><td>Syn</td><td>81.1</td><td>86.0</td><td>83.5</td><td>-</td></tr><tr><td>TextDragon [5]</td><td>MLT+</td><td>82.8</td><td>84.5</td><td>83.6</td><td></td></tr><tr><td>PAN [34]</td><td>Syn</td><td>81.2</td><td>86.4</td><td>83.7</td><td>39.8</td></tr><tr><td>ContourNet [36]</td><td></td><td>84.1</td><td>83.7</td><td>83.9</td><td>4.5</td></tr><tr><td>DRRG [41]</td><td>MLT</td><td>83.02</td><td>85.93</td><td>84.45</td><td>-</td></tr><tr><td>TextPerception[23]</td><td>Syn</td><td>81.9</td><td>87.5</td><td>84.6</td><td></td></tr><tr><td>Ours</td><td> Syn</td><td>80.57</td><td>87.66</td><td>83.97</td><td>12.08</td></tr><tr><td>Ours</td><td></td><td>81.45</td><td>87.81</td><td>84.51</td><td>12.15</td></tr><tr><td>Ours</td><td>MLT</td><td>83.60</td><td>86.45</td><td>85.00</td><td>12.21</td></tr></tbody></table></body></html>
+The table visualized image saved in ./output//6_1.png
+7       type: table, region: [462,359,820,657], score: 0.953917, res: <html><body><table><thead><tr><td>Methods</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>SegLink [26]</td><td>70.0</td><td>86.0</td><td>77.0</td><td>8.9</td></tr><tr><td>PixelLink [4]</td><td>73.2</td><td>83.0</td><td>77.8</td><td>-</td></tr><tr><td>TextSnake [18]</td><td>73.9</td><td>83.2</td><td>78.3</td><td>1.1</td></tr><tr><td>TextField [37]</td><td>75.9</td><td>87.4</td><td>81.3</td><td>5.2 </td></tr><tr><td>MSR[38]</td><td>76.7</td><td>87.4</td><td>81.7</td><td>-</td></tr><tr><td>FTSN[3]</td><td>77.1</td><td>87.6</td><td>82.0</td><td>:</td></tr><tr><td>LSE[30]</td><td>81.7</td><td>84.2</td><td>82.9</td><td></td></tr><tr><td>CRAFT [2]</td><td>78.2</td><td>88.2</td><td>82.9</td><td>8.6</td></tr><tr><td>MCN [16]</td><td>79</td><td>88</td><td>83</td><td>-</td></tr><tr><td>ATRR[35]</td><td>82.1</td><td>85.2</td><td>83.6</td><td>-</td></tr><tr><td>PAN [34]</td><td>83.8</td><td>84.4</td><td>84.1</td><td>30.2</td></tr><tr><td>DB[12]</td><td>79.2</td><td>91.5</td><td>84.9</td><td>32.0</td></tr><tr><td>DRRG [41]</td><td>82.30</td><td>88.05</td><td>85.08</td><td>-</td></tr><tr><td>Ours (SynText)</td><td>80.68</td><td>85.40</td><td>82.97</td><td>12.68</td></tr><tr><td>Ours (MLT-17)</td><td>84.54</td><td>86.62</td><td>85.57</td><td>12.31</td></tr></tbody></table></body></html>
+The table visualized image saved in ./output//7_1.png
+8       type: figure, region: [14,3,836,310], score: 0.969443, res: count of ocr result is : 26
+********** print ocr result **********
+0       det boxes: [[506,14],[539,15],[539,22],[506,21]] rec text: E rec score: 0.318073
+...
+25      det boxes: [[680,290],[759,288],[759,303],[680,305]] rec text: (d) CTW1500 rec score: 0.95911
+********** end print ocr result **********
 ```
 
 <a name="3"></a>

deploy/cpp_infer/src/args.cpp

@@ -51,6 +51,13 @@ DEFINE_string(rec_char_dict_path, "../../ppocr/utils/ppocr_keys_v1.txt",
 DEFINE_int32(rec_img_h, 48, "rec image height");
 DEFINE_int32(rec_img_w, 320, "rec image width");
 
+// layout model related
+DEFINE_string(layout_model_dir, "", "Path of table layout inference model.");
+DEFINE_string(layout_dict_path,
+              "../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt",
+              "Path of dictionary.");
+DEFINE_double(layout_score_threshold, 0.5, "Threshold of score.");
+DEFINE_double(layout_nms_threshold, 0.5, "Threshold of nms.");
 // structure model related
 DEFINE_string(table_model_dir, "", "Path of table struture inference model.");
 DEFINE_int32(table_max_len, 488, "max len size of input image.");
@@ -65,4 +72,5 @@ DEFINE_string(table_char_dict_path,
 DEFINE_bool(det, true, "Whether use det in forward.");
 DEFINE_bool(rec, true, "Whether use rec in forward.");
 DEFINE_bool(cls, false, "Whether use cls in forward.");
-DEFINE_bool(table, false, "Whether use table structure in forward.");
\ No newline at end of file
+DEFINE_bool(table, false, "Whether use table structure in forward.");
+DEFINE_bool(layout, false, "Whether use layout analysis in forward.");
\ No newline at end of file

deploy/cpp_infer/src/main.cpp

@@ -65,9 +65,18 @@ void check_params() {
       exit(1);
     }
   }
+  if (FLAGS_layout) {
+    if (FLAGS_layout_model_dir.empty() || FLAGS_image_dir.empty()) {
+      std::cout << "Usage[layout]: ./ppocr "
+                << "--layout_model_dir=/PATH/TO/LAYOUT_INFERENCE_MODEL/ "
+                << "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
+      exit(1);
+    }
+  }
   if (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" &&
       FLAGS_precision != "int8") {
-    cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. " << endl;
+    std::cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. "
+              << std::endl;
     exit(1);
   }
 }
@@ -75,71 +84,94 @@ void check_params() {
 void ocr(std::vector<cv::String> &cv_all_img_names) {
   PPOCR ocr = PPOCR();
 
-  std::vector<std::vector<OCRPredictResult>> ocr_results =
-      ocr.ocr(cv_all_img_names, FLAGS_det, FLAGS_rec, FLAGS_cls);
+  if (FLAGS_benchmark) {
+    ocr.reset_timer();
+  }
 
+  std::vector<cv::Mat> img_list;
+  std::vector<cv::String> img_names;
   for (int i = 0; i < cv_all_img_names.size(); ++i) {
-    if (FLAGS_benchmark) {
-      cout << cv_all_img_names[i] << '\t';
-      if (FLAGS_rec && FLAGS_det) {
-        Utility::print_result(ocr_results[i]);
-      } else if (FLAGS_det) {
-        for (int n = 0; n < ocr_results[i].size(); n++) {
-          for (int m = 0; m < ocr_results[i][n].box.size(); m++) {
-            cout << ocr_results[i][n].box[m][0] << ' '
-                 << ocr_results[i][n].box[m][1] << ' ';
-          }
-        }
-        cout << endl;
-      } else {
-        Utility::print_result(ocr_results[i]);
-      }
-    } else {
-      cout << cv_all_img_names[i] << "\n";
-      Utility::print_result(ocr_results[i]);
-      if (FLAGS_visualize && FLAGS_det) {
-        cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
-        if (!srcimg.data) {
-          std::cerr << "[ERROR] image read failed! image path: "
-                    << cv_all_img_names[i] << endl;
-          exit(1);
-        }
-        std::string file_name = Utility::basename(cv_all_img_names[i]);
+    cv::Mat img = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
+    if (!img.data) {
+      std::cerr << "[ERROR] image read failed! image path: "
+                << cv_all_img_names[i] << std::endl;
+      continue;
+    }
+    img_list.push_back(img);
+    img_names.push_back(cv_all_img_names[i]);
+  }
 
-        Utility::VisualizeBboxes(srcimg, ocr_results[i],
-                                 FLAGS_output + "/" + file_name);
-      }
-      cout << "***************************" << endl;
+  std::vector<std::vector<OCRPredictResult>> ocr_results =
+      ocr.ocr(img_list, FLAGS_det, FLAGS_rec, FLAGS_cls);
+
+  for (int i = 0; i < img_names.size(); ++i) {
+    std::cout << "predict img: " << cv_all_img_names[i] << std::endl;
+    Utility::print_result(ocr_results[i]);
+    if (FLAGS_visualize && FLAGS_det) {
+      std::string file_name = Utility::basename(img_names[i]);
+      cv::Mat srcimg = img_list[i];
+      Utility::VisualizeBboxes(srcimg, ocr_results[i],
+                               FLAGS_output + "/" + file_name);
     }
   }
+  if (FLAGS_benchmark) {
+    ocr.benchmark_log(cv_all_img_names.size());
+  }
 }
 
 void structure(std::vector<cv::String> &cv_all_img_names) {
   PaddleOCR::PaddleStructure engine = PaddleOCR::PaddleStructure();
-  std::vector<std::vector<StructurePredictResult>> structure_results =
-      engine.structure(cv_all_img_names, false, FLAGS_table);
+
+  if (FLAGS_benchmark) {
+    engine.reset_timer();
+  }
+
   for (int i = 0; i < cv_all_img_names.size(); i++) {
-    cout << "predict img: " << cv_all_img_names[i] << endl;
-    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
-    for (int j = 0; j < structure_results[i].size(); j++) {
-      std::cout << j << "\ttype: " << structure_results[i][j].type
+    std::cout << "predict img: " << cv_all_img_names[i] << std::endl;
+    cv::Mat img = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
+    if (!img.data) {
+      std::cerr << "[ERROR] image read failed! image path: "
+                << cv_all_img_names[i] << std::endl;
+      continue;
+    }
+
+    std::vector<StructurePredictResult> structure_results = engine.structure(
+        img, FLAGS_layout, FLAGS_table, FLAGS_det && FLAGS_rec);
+
+    for (int j = 0; j < structure_results.size(); j++) {
+      std::cout << j << "\ttype: " << structure_results[j].type
                 << ", region: [";
-      std::cout << structure_results[i][j].box[0] << ","
-                << structure_results[i][j].box[1] << ","
-                << structure_results[i][j].box[2] << ","
-                << structure_results[i][j].box[3] << "], res: ";
-      if (structure_results[i][j].type == "table") {
-        std::cout << structure_results[i][j].html << std::endl;
-        std::string file_name = Utility::basename(cv_all_img_names[i]);
-
-        Utility::VisualizeBboxes(srcimg, structure_results[i][j],
-                                 FLAGS_output + "/" + std::to_string(j) + "_" +
-                                     file_name);
+      std::cout << structure_results[j].box[0] << ","
+                << structure_results[j].box[1] << ","
+                << structure_results[j].box[2] << ","
+                << structure_results[j].box[3] << "], score: ";
+      std::cout << structure_results[j].confidence << ", res: ";
+
+      if (structure_results[j].type == "table") {
+        std::cout << structure_results[j].html << std::endl;
+        if (structure_results[j].cell_box.size() > 0 && FLAGS_visualize) {
+          std::string file_name = Utility::basename(cv_all_img_names[i]);
+
+          Utility::VisualizeBboxes(img, structure_results[j],
+                                   FLAGS_output + "/" + std::to_string(j) +
+                                       "_" + file_name);
+        }
       } else {
-        Utility::print_result(structure_results[i][j].text_res);
+        std::cout << "count of ocr result is : "
+                  << structure_results[j].text_res.size() << std::endl;
+        if (structure_results[j].text_res.size() > 0) {
+          std::cout << "********** print ocr result "
+                    << "**********" << std::endl;
+          Utility::print_result(structure_results[j].text_res);
+          std::cout << "********** end print ocr result "
+                    << "**********" << std::endl;
+        }
       }
     }
   }
+  if (FLAGS_benchmark) {
+    engine.benchmark_log(cv_all_img_names.size());
+  }
 }
 
 int main(int argc, char **argv) {
@@ -149,19 +181,22 @@ int main(int argc, char **argv) {
 
   if (!Utility::PathExists(FLAGS_image_dir)) {
     std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir
-              << endl;
+              << std::endl;
     exit(1);
   }
 
   std::vector<cv::String> cv_all_img_names;
   cv::glob(FLAGS_image_dir, cv_all_img_names);
-  std::cout << "total images num: " << cv_all_img_names.size() << endl;
+  std::cout << "total images num: " << cv_all_img_names.size() << std::endl;
 
+  if (!Utility::PathExists(FLAGS_output)) {
+    Utility::CreateDir(FLAGS_output);
+  }
   if (FLAGS_type == "ocr") {
     ocr(cv_all_img_names);
   } else if (FLAGS_type == "structure") {
     structure(cv_all_img_names);
   } else {
-    std::cout << "only value in ['ocr','structure'] is supported" << endl;
+    std::cout << "only value in ['ocr','structure'] is supported" << std::endl;
   }
 }

deploy/cpp_infer/src/ocr_cls.cpp

@@ -32,7 +32,7 @@ void Classifier::Run(std::vector<cv::Mat> img_list,
   for (int beg_img_no = 0; beg_img_no < img_num;
        beg_img_no += this->cls_batch_num_) {
     auto preprocess_start = std::chrono::steady_clock::now();
-    int end_img_no = min(img_num, beg_img_no + this->cls_batch_num_);
+    int end_img_no = std::min(img_num, beg_img_no + this->cls_batch_num_);
     int batch_num = end_img_no - beg_img_no;
     // preprocess
     std::vector<cv::Mat> norm_img_batch;
@@ -97,7 +97,7 @@ void Classifier::Run(std::vector<cv::Mat> img_list,
 }
 
 void Classifier::LoadModel(const std::string &model_dir) {
-  AnalysisConfig config;
+  paddle_infer::Config config;
   config.SetModel(model_dir + "/inference.pdmodel",
                   model_dir + "/inference.pdiparams");
 
@@ -112,9 +112,9 @@ void Classifier::LoadModel(const std::string &model_dir) {
         precision = paddle_infer::Config::Precision::kInt8;
       }
       config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
-      if (!Utility::PathExists("./trt_cls_shape.txt")){
+      if (!Utility::PathExists("./trt_cls_shape.txt")) {
         config.CollectShapeRangeInfo("./trt_cls_shape.txt");
-      } else { 
+      } else {
         config.EnableTunedTensorRtDynamicShape("./trt_cls_shape.txt", true);
       }
     }
@@ -136,6 +136,6 @@ void Classifier::LoadModel(const std::string &model_dir) {
   config.EnableMemoryOptim();
   config.DisableGlogInfo();
 
-  this->predictor_ = CreatePredictor(config);
+  this->predictor_ = paddle_infer::CreatePredictor(config);
 }
 } // namespace PaddleOCR

deploy/cpp_infer/src/ocr_det.cpp

@@ -33,12 +33,11 @@ void DBDetector::LoadModel(const std::string &model_dir) {
         precision = paddle_infer::Config::Precision::kInt8;
       }
       config.EnableTensorRtEngine(1 << 30, 1, 20, precision, false, false);
-      if (!Utility::PathExists("./trt_det_shape.txt")){
+      if (!Utility::PathExists("./trt_det_shape.txt")) {
         config.CollectShapeRangeInfo("./trt_det_shape.txt");
-      } else { 
+      } else {
         config.EnableTunedTensorRtDynamicShape("./trt_det_shape.txt", true);
       }
-      
     }
   } else {
     config.DisableGpu();
@@ -59,7 +58,7 @@ void DBDetector::LoadModel(const std::string &model_dir) {
   config.EnableMemoryOptim();
   // config.DisableGlogInfo();
 
-  this->predictor_ = CreatePredictor(config);
+  this->predictor_ = paddle_infer::CreatePredictor(config);
 }
 
 void DBDetector::Run(cv::Mat &img,

deploy/cpp_infer/src/ocr_rec.cpp

@@ -37,7 +37,7 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
   for (int beg_img_no = 0; beg_img_no < img_num;
        beg_img_no += this->rec_batch_num_) {
     auto preprocess_start = std::chrono::steady_clock::now();
-    int end_img_no = min(img_num, beg_img_no + this->rec_batch_num_);
+    int end_img_no = std::min(img_num, beg_img_no + this->rec_batch_num_);
     int batch_num = end_img_no - beg_img_no;
     int imgH = this->rec_image_shape_[1];
     int imgW = this->rec_image_shape_[2];
@@ -46,7 +46,7 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
       int h = img_list[indices[ino]].rows;
       int w = img_list[indices[ino]].cols;
       float wh_ratio = w * 1.0 / h;
-      max_wh_ratio = max(max_wh_ratio, wh_ratio);
+      max_wh_ratio = std::max(max_wh_ratio, wh_ratio);
     }
 
     int batch_width = imgW;
@@ -60,7 +60,7 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
       this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
                               this->is_scale_);
       norm_img_batch.push_back(resize_img);
-      batch_width = max(resize_img.cols, batch_width);
+      batch_width = std::max(resize_img.cols, batch_width);
     }
 
     std::vector<float> input(batch_num * 3 * imgH * batch_width, 0.0f);
@@ -115,7 +115,7 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
         last_index = argmax_idx;
       }
       score /= count;
-      if (isnan(score)) {
+      if (std::isnan(score)) {
         continue;
       }
       rec_texts[indices[beg_img_no + m]] = str_res;
@@ -130,7 +130,6 @@ void CRNNRecognizer::Run(std::vector<cv::Mat> img_list,
 }
 
 void CRNNRecognizer::LoadModel(const std::string &model_dir) {
-  //   AnalysisConfig config;
   paddle_infer::Config config;
   config.SetModel(model_dir + "/inference.pdmodel",
                   model_dir + "/inference.pdiparams");
@@ -147,12 +146,11 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
       if (this->precision_ == "int8") {
         precision = paddle_infer::Config::Precision::kInt8;
       }
-      if (!Utility::PathExists("./trt_rec_shape.txt")){
+      if (!Utility::PathExists("./trt_rec_shape.txt")) {
         config.CollectShapeRangeInfo("./trt_rec_shape.txt");
-      } else { 
+      } else {
         config.EnableTunedTensorRtDynamicShape("./trt_rec_shape.txt", true);
       }
-      
     }
   } else {
     config.DisableGpu();
@@ -177,7 +175,7 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
   config.EnableMemoryOptim();
   //   config.DisableGlogInfo();
 
-  this->predictor_ = CreatePredictor(config);
+  this->predictor_ = paddle_infer::CreatePredictor(config);
 }
 
 } // namespace PaddleOCR

deploy/cpp_infer/src/paddleocr.cpp

@@ -16,7 +16,7 @@
 #include <include/paddleocr.h>
 
 #include "auto_log/autolog.h"
-#include <numeric>
+
 namespace PaddleOCR {
 
 PPOCR::PPOCR() {
@@ -44,8 +44,71 @@ PPOCR::PPOCR() {
   }
 };
 
-void PPOCR::det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results,
-                std::vector<double> &times) {
+std::vector<std::vector<OCRPredictResult>>
+PPOCR::ocr(std::vector<cv::Mat> img_list, bool det, bool rec, bool cls) {
+  std::vector<std::vector<OCRPredictResult>> ocr_results;
+
+  if (!det) {
+    std::vector<OCRPredictResult> ocr_result;
+    ocr_result.resize(img_list.size());
+    if (cls && this->classifier_ != nullptr) {
+      this->cls(img_list, ocr_result);
+      for (int i = 0; i < img_list.size(); i++) {
+        if (ocr_result[i].cls_label % 2 == 1 &&
+            ocr_result[i].cls_score > this->classifier_->cls_thresh) {
+          cv::rotate(img_list[i], img_list[i], 1);
+        }
+      }
+    }
+    if (rec) {
+      this->rec(img_list, ocr_result);
+    }
+    for (int i = 0; i < ocr_result.size(); ++i) {
+      std::vector<OCRPredictResult> ocr_result_tmp;
+      ocr_result_tmp.push_back(ocr_result[i]);
+      ocr_results.push_back(ocr_result_tmp);
+    }
+  } else {
+    for (int i = 0; i < img_list.size(); ++i) {
+      std::vector<OCRPredictResult> ocr_result =
+          this->ocr(img_list[i], true, rec, cls);
+      ocr_results.push_back(ocr_result);
+    }
+  }
+  return ocr_results;
+}
+
+std::vector<OCRPredictResult> PPOCR::ocr(cv::Mat img, bool det, bool rec,
+                                         bool cls) {
+
+  std::vector<OCRPredictResult> ocr_result;
+  // det
+  this->det(img, ocr_result);
+  // crop image
+  std::vector<cv::Mat> img_list;
+  for (int j = 0; j < ocr_result.size(); j++) {
+    cv::Mat crop_img;
+    crop_img = Utility::GetRotateCropImage(img, ocr_result[j].box);
+    img_list.push_back(crop_img);
+  }
+  // cls
+  if (cls && this->classifier_ != nullptr) {
+    this->cls(img_list, ocr_result);
+    for (int i = 0; i < img_list.size(); i++) {
+      if (ocr_result[i].cls_label % 2 == 1 &&
+          ocr_result[i].cls_score > this->classifier_->cls_thresh) {
+        cv::rotate(img_list[i], img_list[i], 1);
+      }
+    }
+  }
+  // rec
+  if (rec) {
+    this->rec(img_list, ocr_result);
+  }
+  return ocr_result;
+}
+
+void PPOCR::det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results) {
   std::vector<std::vector<std::vector<int>>> boxes;
   std::vector<double> det_times;
 
@@ -58,14 +121,13 @@ void PPOCR::det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results,
   }
   // sort boex from top to bottom, from left to right
   Utility::sorted_boxes(ocr_results);
-  times[0] += det_times[0];
-  times[1] += det_times[1];
-  times[2] += det_times[2];
+  this->time_info_det[0] += det_times[0];
+  this->time_info_det[1] += det_times[1];
+  this->time_info_det[2] += det_times[2];
 }
 
 void PPOCR::rec(std::vector<cv::Mat> img_list,
-                std::vector<OCRPredictResult> &ocr_results,
-                std::vector<double> &times) {
+                std::vector<OCRPredictResult> &ocr_results) {
   std::vector<std::string> rec_texts(img_list.size(), "");
   std::vector<float> rec_text_scores(img_list.size(), 0);
   std::vector<double> rec_times;
@@ -75,14 +137,13 @@ void PPOCR::rec(std::vector<cv::Mat> img_list,
     ocr_results[i].text = rec_texts[i];
     ocr_results[i].score = rec_text_scores[i];
   }
-  times[0] += rec_times[0];
-  times[1] += rec_times[1];
-  times[2] += rec_times[2];
+  this->time_info_rec[0] += rec_times[0];
+  this->time_info_rec[1] += rec_times[1];
+  this->time_info_rec[2] += rec_times[2];
 }
 
 void PPOCR::cls(std::vector<cv::Mat> img_list,
-                std::vector<OCRPredictResult> &ocr_results,
-                std::vector<double> &times) {
+                std::vector<OCRPredictResult> &ocr_results) {
   std::vector<int> cls_labels(img_list.size(), 0);
   std::vector<float> cls_scores(img_list.size(), 0);
   std::vector<double> cls_times;
@@ -92,125 +153,43 @@ void PPOCR::cls(std::vector<cv::Mat> img_list,
     ocr_results[i].cls_label = cls_labels[i];
     ocr_results[i].cls_score = cls_scores[i];
   }
-  times[0] += cls_times[0];
-  times[1] += cls_times[1];
-  times[2] += cls_times[2];
+  this->time_info_cls[0] += cls_times[0];
+  this->time_info_cls[1] += cls_times[1];
+  this->time_info_cls[2] += cls_times[2];
 }
 
-std::vector<std::vector<OCRPredictResult>>
-PPOCR::ocr(std::vector<cv::String> cv_all_img_names, bool det, bool rec,
-           bool cls) {
-  std::vector<double> time_info_det = {0, 0, 0};
-  std::vector<double> time_info_rec = {0, 0, 0};
-  std::vector<double> time_info_cls = {0, 0, 0};
-  std::vector<std::vector<OCRPredictResult>> ocr_results;
-
-  if (!det) {
-    std::vector<OCRPredictResult> ocr_result;
-    // read image
-    std::vector<cv::Mat> img_list;
-    for (int i = 0; i < cv_all_img_names.size(); ++i) {
-      cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
-      if (!srcimg.data) {
-        std::cerr << "[ERROR] image read failed! image path: "
-                  << cv_all_img_names[i] << endl;
-        exit(1);
-      }
-      img_list.push_back(srcimg);
-      OCRPredictResult res;
-      ocr_result.push_back(res);
-    }
-    if (cls && this->classifier_ != nullptr) {
-      this->cls(img_list, ocr_result, time_info_cls);
-      for (int i = 0; i < img_list.size(); i++) {
-        if (ocr_result[i].cls_label % 2 == 1 &&
-            ocr_result[i].cls_score > this->classifier_->cls_thresh) {
-          cv::rotate(img_list[i], img_list[i], 1);
-        }
-      }
-    }
-    if (rec) {
-      this->rec(img_list, ocr_result, time_info_rec);
-    }
-    for (int i = 0; i < cv_all_img_names.size(); ++i) {
-      std::vector<OCRPredictResult> ocr_result_tmp;
-      ocr_result_tmp.push_back(ocr_result[i]);
-      ocr_results.push_back(ocr_result_tmp);
-    }
-  } else {
-    if (!Utility::PathExists(FLAGS_output) && FLAGS_det) {
-      Utility::CreateDir(FLAGS_output);
-    }
-
-    for (int i = 0; i < cv_all_img_names.size(); ++i) {
-      std::vector<OCRPredictResult> ocr_result;
-      if (!FLAGS_benchmark) {
-        cout << "predict img: " << cv_all_img_names[i] << endl;
-      }
-
-      cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
-      if (!srcimg.data) {
-        std::cerr << "[ERROR] image read failed! image path: "
-                  << cv_all_img_names[i] << endl;
-        exit(1);
-      }
-      // det
-      this->det(srcimg, ocr_result, time_info_det);
-      // crop image
-      std::vector<cv::Mat> img_list;
-      for (int j = 0; j < ocr_result.size(); j++) {
-        cv::Mat crop_img;
-        crop_img = Utility::GetRotateCropImage(srcimg, ocr_result[j].box);
-        img_list.push_back(crop_img);
-      }
-
-      // cls
-      if (cls && this->classifier_ != nullptr) {
-        this->cls(img_list, ocr_result, time_info_cls);
-        for (int i = 0; i < img_list.size(); i++) {
-          if (ocr_result[i].cls_label % 2 == 1 &&
-              ocr_result[i].cls_score > this->classifier_->cls_thresh) {
-            cv::rotate(img_list[i], img_list[i], 1);
-          }
-        }
-      }
-      // rec
-      if (rec) {
-        this->rec(img_list, ocr_result, time_info_rec);
-      }
-      ocr_results.push_back(ocr_result);
-    }
-  }
-  if (FLAGS_benchmark) {
-    this->log(time_info_det, time_info_rec, time_info_cls,
-              cv_all_img_names.size());
-  }
-  return ocr_results;
-} // namespace PaddleOCR
+void PPOCR::reset_timer() {
+  this->time_info_det = {0, 0, 0};
+  this->time_info_rec = {0, 0, 0};
+  this->time_info_cls = {0, 0, 0};
+}
 
-void PPOCR::log(std::vector<double> &det_times, std::vector<double> &rec_times,
-                std::vector<double> &cls_times, int img_num) {
-  if (det_times[0] + det_times[1] + det_times[2] > 0) {
+void PPOCR::benchmark_log(int img_num) {
+  if (this->time_info_det[0] + this->time_info_det[1] + this->time_info_det[2] >
+      0) {
     AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
                            FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
-                           FLAGS_precision, det_times, img_num);
+                           FLAGS_precision, this->time_info_det, img_num);
     autolog_det.report();
   }
-  if (rec_times[0] + rec_times[1] + rec_times[2] > 0) {
+  if (this->time_info_rec[0] + this->time_info_rec[1] + this->time_info_rec[2] >
+      0) {
     AutoLogger autolog_rec("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
                            FLAGS_enable_mkldnn, FLAGS_cpu_threads,
                            FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
-                           rec_times, img_num);
+                           this->time_info_rec, img_num);
     autolog_rec.report();
   }
-  if (cls_times[0] + cls_times[1] + cls_times[2] > 0) {
+  if (this->time_info_cls[0] + this->time_info_cls[1] + this->time_info_cls[2] >
+      0) {
     AutoLogger autolog_cls("ocr_cls", FLAGS_use_gpu, FLAGS_use_tensorrt,
                            FLAGS_enable_mkldnn, FLAGS_cpu_threads,
                            FLAGS_cls_batch_num, "dynamic", FLAGS_precision,
-                           cls_times, img_num);
+                           this->time_info_cls, img_num);
     autolog_cls.report();
   }
 }
+
 PPOCR::~PPOCR() {
   if (this->detector_ != nullptr) {
     delete this->detector_;

deploy/cpp_infer/src/paddlestructure.cpp

@@ -16,14 +16,19 @@
 #include <include/paddlestructure.h>
 
 #include "auto_log/autolog.h"
-#include <numeric>
-#include <sys/stat.h>
 
 namespace PaddleOCR {
 
 PaddleStructure::PaddleStructure() {
+  if (FLAGS_layout) {
+    this->layout_model_ = new StructureLayoutRecognizer(
+        FLAGS_layout_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem,
+        FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_layout_dict_path,
+        FLAGS_use_tensorrt, FLAGS_precision, FLAGS_layout_score_threshold,
+        FLAGS_layout_nms_threshold);
+  }
   if (FLAGS_table) {
-    this->recognizer_ = new StructureTableRecognizer(
+    this->table_model_ = new StructureTableRecognizer(
         FLAGS_table_model_dir, FLAGS_use_gpu, FLAGS_gpu_id, FLAGS_gpu_mem,
         FLAGS_cpu_threads, FLAGS_enable_mkldnn, FLAGS_table_char_dict_path,
         FLAGS_use_tensorrt, FLAGS_precision, FLAGS_table_batch_num,
@@ -31,68 +36,63 @@ PaddleStructure::PaddleStructure() {
   }
 };
 
-std::vector<std::vector<StructurePredictResult>>
-PaddleStructure::structure(std::vector<cv::String> cv_all_img_names,
-                           bool layout, bool table) {
-  std::vector<double> time_info_det = {0, 0, 0};
-  std::vector<double> time_info_rec = {0, 0, 0};
-  std::vector<double> time_info_cls = {0, 0, 0};
-  std::vector<double> time_info_table = {0, 0, 0};
+std::vector<StructurePredictResult>
+PaddleStructure::structure(cv::Mat srcimg, bool layout, bool table, bool ocr) {
+  cv::Mat img;
+  srcimg.copyTo(img);
 
-  std::vector<std::vector<StructurePredictResult>> structure_results;
+  std::vector<StructurePredictResult> structure_results;
 
-  if (!Utility::PathExists(FLAGS_output) && FLAGS_det) {
-    Utility::CreateDir(FLAGS_output);
+  if (layout) {
+    this->layout(img, structure_results);
+  } else {
+    StructurePredictResult res;
+    res.type = "table";
+    res.box = std::vector<float>(4, 0.0);
+    res.box[2] = img.cols;
+    res.box[3] = img.rows;
+    structure_results.push_back(res);
   }
-  for (int i = 0; i < cv_all_img_names.size(); ++i) {
-    std::vector<StructurePredictResult> structure_result;
-    cv::Mat srcimg = cv::imread(cv_all_img_names[i], cv::IMREAD_COLOR);
-    if (!srcimg.data) {
-      std::cerr << "[ERROR] image read failed! image path: "
-                << cv_all_img_names[i] << endl;
-      exit(1);
-    }
-    if (layout) {
-    } else {
-      StructurePredictResult res;
-      res.type = "table";
-      res.box = std::vector<int>(4, 0);
-      res.box[2] = srcimg.cols;
-      res.box[3] = srcimg.rows;
-      structure_result.push_back(res);
-    }
-    cv::Mat roi_img;
-    for (int i = 0; i < structure_result.size(); i++) {
-      // crop image
-      roi_img = Utility::crop_image(srcimg, structure_result[i].box);
-      if (structure_result[i].type == "table") {
-        this->table(roi_img, structure_result[i], time_info_table,
-                    time_info_det, time_info_rec, time_info_cls);
-      }
+  cv::Mat roi_img;
+  for (int i = 0; i < structure_results.size(); i++) {
+    // crop image
+    roi_img = Utility::crop_image(img, structure_results[i].box);
+    if (structure_results[i].type == "table" && table) {
+      this->table(roi_img, structure_results[i]);
+    } else if (ocr) {
+      structure_results[i].text_res = this->ocr(roi_img, true, true, false);
     }
-    structure_results.push_back(structure_result);
   }
+
   return structure_results;
 };
 
+void PaddleStructure::layout(
+    cv::Mat img, std::vector<StructurePredictResult> &structure_result) {
+  std::vector<double> layout_times;
+  this->layout_model_->Run(img, structure_result, layout_times);
+
+  this->time_info_layout[0] += layout_times[0];
+  this->time_info_layout[1] += layout_times[1];
+  this->time_info_layout[2] += layout_times[2];
+}
+
 void PaddleStructure::table(cv::Mat img,
-                            StructurePredictResult &structure_result,
-                            std::vector<double> &time_info_table,
-                            std::vector<double> &time_info_det,
-                            std::vector<double> &time_info_rec,
-                            std::vector<double> &time_info_cls) {
+                            StructurePredictResult &structure_result) {
   // predict structure
   std::vector<std::vector<std::string>> structure_html_tags;
   std::vector<float> structure_scores(1, 0);
   std::vector<std::vector<std::vector<int>>> structure_boxes;
-  std::vector<double> structure_imes;
+  std::vector<double> structure_times;
   std::vector<cv::Mat> img_list;
   img_list.push_back(img);
-  this->recognizer_->Run(img_list, structure_html_tags, structure_scores,
-                         structure_boxes, structure_imes);
-  time_info_table[0] += structure_imes[0];
-  time_info_table[1] += structure_imes[1];
-  time_info_table[2] += structure_imes[2];
+
+  this->table_model_->Run(img_list, structure_html_tags, structure_scores,
+                          structure_boxes, structure_times);
+
+  this->time_info_table[0] += structure_times[0];
+  this->time_info_table[1] += structure_times[1];
+  this->time_info_table[2] += structure_times[2];
 
   std::vector<OCRPredictResult> ocr_result;
   std::string html;
@@ -100,22 +100,22 @@ void PaddleStructure::table(cv::Mat img,
 
   for (int i = 0; i < img_list.size(); i++) {
     // det
-    this->det(img_list[i], ocr_result, time_info_det);
+    this->det(img_list[i], ocr_result);
     // crop image
     std::vector<cv::Mat> rec_img_list;
     std::vector<int> ocr_box;
     for (int j = 0; j < ocr_result.size(); j++) {
       ocr_box = Utility::xyxyxyxy2xyxy(ocr_result[j].box);
-      ocr_box[0] = max(0, ocr_box[0] - expand_pixel);
-      ocr_box[1] = max(0, ocr_box[1] - expand_pixel),
-      ocr_box[2] = min(img_list[i].cols, ocr_box[2] + expand_pixel);
-      ocr_box[3] = min(img_list[i].rows, ocr_box[3] + expand_pixel);
+      ocr_box[0] = std::max(0, ocr_box[0] - expand_pixel);
+      ocr_box[1] = std::max(0, ocr_box[1] - expand_pixel),
+      ocr_box[2] = std::min(img_list[i].cols, ocr_box[2] + expand_pixel);
+      ocr_box[3] = std::min(img_list[i].rows, ocr_box[3] + expand_pixel);
 
       cv::Mat crop_img = Utility::crop_image(img_list[i], ocr_box);
       rec_img_list.push_back(crop_img);
     }
     // rec
-    this->rec(rec_img_list, ocr_result, time_info_rec);
+    this->rec(rec_img_list, ocr_result);
     // rebuild table
     html = this->rebuild_table(structure_html_tags[i], structure_boxes[i],
                                ocr_result);
@@ -130,8 +130,8 @@ PaddleStructure::rebuild_table(std::vector<std::string> structure_html_tags,
                                std::vector<std::vector<int>> structure_boxes,
                                std::vector<OCRPredictResult> &ocr_result) {
   // match text in same cell
-  std::vector<std::vector<string>> matched(structure_boxes.size(),
-                                           std::vector<std::string>());
+  std::vector<std::vector<std::string>> matched(structure_boxes.size(),
+                                                std::vector<std::string>());
 
   std::vector<int> ocr_box;
   std::vector<int> structure_box;
@@ -150,7 +150,7 @@ PaddleStructure::rebuild_table(std::vector<std::string> structure_html_tags,
         structure_box = structure_boxes[j];
       }
       dis_list[j][0] = this->dis(ocr_box, structure_box);
-      dis_list[j][1] = 1 - this->iou(ocr_box, structure_box);
+      dis_list[j][1] = 1 - Utility::iou(ocr_box, structure_box);
       dis_list[j][2] = j;
     }
     // find min dis idx
@@ -216,28 +216,6 @@ PaddleStructure::rebuild_table(std::vector<std::string> structure_html_tags,
   return html_str;
 }
 
-float PaddleStructure::iou(std::vector<int> &box1, std::vector<int> &box2) {
-  int area1 = max(0, box1[2] - box1[0]) * max(0, box1[3] - box1[1]);
-  int area2 = max(0, box2[2] - box2[0]) * max(0, box2[3] - box2[1]);
-
-  // computing the sum_area
-  int sum_area = area1 + area2;
-
-  // find the each point of intersect rectangle
-  int x1 = max(box1[0], box2[0]);
-  int y1 = max(box1[1], box2[1]);
-  int x2 = min(box1[2], box2[2]);
-  int y2 = min(box1[3], box2[3]);
-
-  // judge if there is an intersect
-  if (y1 >= y2 || x1 >= x2) {
-    return 0.0;
-  } else {
-    int intersect = (x2 - x1) * (y2 - y1);
-    return intersect / (sum_area - intersect + 0.00000001);
-  }
-}
-
 float PaddleStructure::dis(std::vector<int> &box1, std::vector<int> &box2) {
   int x1_1 = box1[0];
   int y1_1 = box1[1];
@@ -253,12 +231,64 @@ float PaddleStructure::dis(std::vector<int> &box1, std::vector<int> &box2) {
       abs(x1_2 - x1_1) + abs(y1_2 - y1_1) + abs(x2_2 - x2_1) + abs(y2_2 - y2_1);
   float dis_2 = abs(x1_2 - x1_1) + abs(y1_2 - y1_1);
   float dis_3 = abs(x2_2 - x2_1) + abs(y2_2 - y2_1);
-  return dis + min(dis_2, dis_3);
+  return dis + std::min(dis_2, dis_3);
+}
+
+void PaddleStructure::reset_timer() {
+  this->time_info_det = {0, 0, 0};
+  this->time_info_rec = {0, 0, 0};
+  this->time_info_cls = {0, 0, 0};
+  this->time_info_table = {0, 0, 0};
+  this->time_info_layout = {0, 0, 0};
+}
+
+void PaddleStructure::benchmark_log(int img_num) {
+  if (this->time_info_det[0] + this->time_info_det[1] + this->time_info_det[2] >
+      0) {
+    AutoLogger autolog_det("ocr_det", FLAGS_use_gpu, FLAGS_use_tensorrt,
+                           FLAGS_enable_mkldnn, FLAGS_cpu_threads, 1, "dynamic",
+                           FLAGS_precision, this->time_info_det, img_num);
+    autolog_det.report();
+  }
+  if (this->time_info_rec[0] + this->time_info_rec[1] + this->time_info_rec[2] >
+      0) {
+    AutoLogger autolog_rec("ocr_rec", FLAGS_use_gpu, FLAGS_use_tensorrt,
+                           FLAGS_enable_mkldnn, FLAGS_cpu_threads,
+                           FLAGS_rec_batch_num, "dynamic", FLAGS_precision,
+                           this->time_info_rec, img_num);
+    autolog_rec.report();
+  }
+  if (this->time_info_cls[0] + this->time_info_cls[1] + this->time_info_cls[2] >
+      0) {
+    AutoLogger autolog_cls("ocr_cls", FLAGS_use_gpu, FLAGS_use_tensorrt,
+                           FLAGS_enable_mkldnn, FLAGS_cpu_threads,
+                           FLAGS_cls_batch_num, "dynamic", FLAGS_precision,
+                           this->time_info_cls, img_num);
+    autolog_cls.report();
+  }
+  if (this->time_info_table[0] + this->time_info_table[1] +
+          this->time_info_table[2] >
+      0) {
+    AutoLogger autolog_table("table", FLAGS_use_gpu, FLAGS_use_tensorrt,
+                             FLAGS_enable_mkldnn, FLAGS_cpu_threads,
+                             FLAGS_cls_batch_num, "dynamic", FLAGS_precision,
+                             this->time_info_table, img_num);
+    autolog_table.report();
+  }
+  if (this->time_info_layout[0] + this->time_info_layout[1] +
+          this->time_info_layout[2] >
+      0) {
+    AutoLogger autolog_layout("layout", FLAGS_use_gpu, FLAGS_use_tensorrt,
+                              FLAGS_enable_mkldnn, FLAGS_cpu_threads,
+                              FLAGS_cls_batch_num, "dynamic", FLAGS_precision,
+                              this->time_info_layout, img_num);
+    autolog_layout.report();
+  }
 }
 
 PaddleStructure::~PaddleStructure() {
-  if (this->recognizer_ != nullptr) {
-    delete this->recognizer_;
+  if (this->table_model_ != nullptr) {
+    delete this->table_model_;
   }
 };
 

deploy/cpp_infer/src/postprocess_op.cpp

@@ -12,7 +12,6 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <include/clipper.h>
 #include <include/postprocess_op.h>
 
 namespace PaddleOCR {
@@ -431,7 +430,7 @@ void TablePostProcessor::Run(
       }
     }
     score /= count;
-    if (isnan(score) || rec_boxes.size() == 0) {
+    if (std::isnan(score) || rec_boxes.size() == 0) {
       score = -1;
     }
     rec_scores.push_back(score);
@@ -440,4 +439,137 @@ void TablePostProcessor::Run(
   }
 }
 
+void PicodetPostProcessor::init(std::string label_path,
+                                const double score_threshold,
+                                const double nms_threshold,
+                                const std::vector<int> &fpn_stride) {
+  this->label_list_ = Utility::ReadDict(label_path);
+  this->score_threshold_ = score_threshold;
+  this->nms_threshold_ = nms_threshold;
+  this->num_class_ = label_list_.size();
+  this->fpn_stride_ = fpn_stride;
+}
+
+void PicodetPostProcessor::Run(std::vector<StructurePredictResult> &results,
+                               std::vector<std::vector<float>> outs,
+                               std::vector<int> ori_shape,
+                               std::vector<int> resize_shape, int reg_max) {
+  int in_h = resize_shape[0];
+  int in_w = resize_shape[1];
+  float scale_factor_h = resize_shape[0] / float(ori_shape[0]);
+  float scale_factor_w = resize_shape[1] / float(ori_shape[1]);
+
+  std::vector<std::vector<StructurePredictResult>> bbox_results;
+  bbox_results.resize(this->num_class_);
+  for (int i = 0; i < this->fpn_stride_.size(); ++i) {
+    int feature_h = std::ceil((float)in_h / this->fpn_stride_[i]);
+    int feature_w = std::ceil((float)in_w / this->fpn_stride_[i]);
+    for (int idx = 0; idx < feature_h * feature_w; idx++) {
+      // score and label
+      float score = 0;
+      int cur_label = 0;
+      for (int label = 0; label < this->num_class_; label++) {
+        if (outs[i][idx * this->num_class_ + label] > score) {
+          score = outs[i][idx * this->num_class_ + label];
+          cur_label = label;
+        }
+      }
+      // bbox
+      if (score > this->score_threshold_) {
+        int row = idx / feature_w;
+        int col = idx % feature_w;
+        std::vector<float> bbox_pred(
+            outs[i + this->fpn_stride_.size()].begin() + idx * 4 * reg_max,
+            outs[i + this->fpn_stride_.size()].begin() +
+                (idx + 1) * 4 * reg_max);
+        bbox_results[cur_label].push_back(
+            this->disPred2Bbox(bbox_pred, cur_label, score, col, row,
+                               this->fpn_stride_[i], resize_shape, reg_max));
+      }
+    }
+  }
+  for (int i = 0; i < bbox_results.size(); i++) {
+    bool flag = bbox_results[i].size() <= 0;
+  }
+  for (int i = 0; i < bbox_results.size(); i++) {
+    bool flag = bbox_results[i].size() <= 0;
+    if (bbox_results[i].size() <= 0) {
+      continue;
+    }
+    this->nms(bbox_results[i], this->nms_threshold_);
+    for (auto box : bbox_results[i]) {
+      box.box[0] = box.box[0] / scale_factor_w;
+      box.box[2] = box.box[2] / scale_factor_w;
+      box.box[1] = box.box[1] / scale_factor_h;
+      box.box[3] = box.box[3] / scale_factor_h;
+      results.push_back(box);
+    }
+  }
+}
+
+StructurePredictResult
+PicodetPostProcessor::disPred2Bbox(std::vector<float> bbox_pred, int label,
+                                   float score, int x, int y, int stride,
+                                   std::vector<int> im_shape, int reg_max) {
+  float ct_x = (x + 0.5) * stride;
+  float ct_y = (y + 0.5) * stride;
+  std::vector<float> dis_pred;
+  dis_pred.resize(4);
+  for (int i = 0; i < 4; i++) {
+    float dis = 0;
+    std::vector<float> bbox_pred_i(bbox_pred.begin() + i * reg_max,
+                                   bbox_pred.begin() + (i + 1) * reg_max);
+    std::vector<float> dis_after_sm =
+        Utility::activation_function_softmax(bbox_pred_i);
+    for (int j = 0; j < reg_max; j++) {
+      dis += j * dis_after_sm[j];
+    }
+    dis *= stride;
+    dis_pred[i] = dis;
+  }
+
+  float xmin = (std::max)(ct_x - dis_pred[0], .0f);
+  float ymin = (std::max)(ct_y - dis_pred[1], .0f);
+  float xmax = (std::min)(ct_x + dis_pred[2], (float)im_shape[1]);
+  float ymax = (std::min)(ct_y + dis_pred[3], (float)im_shape[0]);
+
+  StructurePredictResult result_item;
+  result_item.box = {xmin, ymin, xmax, ymax};
+  result_item.type = this->label_list_[label];
+  result_item.confidence = score;
+
+  return result_item;
+}
+
+void PicodetPostProcessor::nms(std::vector<StructurePredictResult> &input_boxes,
+                               float nms_threshold) {
+  std::sort(input_boxes.begin(), input_boxes.end(),
+            [](StructurePredictResult a, StructurePredictResult b) {
+              return a.confidence > b.confidence;
+            });
+  std::vector<int> picked(input_boxes.size(), 1);
+
+  for (int i = 0; i < input_boxes.size(); ++i) {
+    if (picked[i] == 0) {
+      continue;
+    }
+    for (int j = i + 1; j < input_boxes.size(); ++j) {
+      if (picked[j] == 0) {
+        continue;
+      }
+      float iou = Utility::iou(input_boxes[i].box, input_boxes[j].box);
+      if (iou > nms_threshold) {
+        picked[j] = 0;
+      }
+    }
+  }
+  std::vector<StructurePredictResult> input_boxes_nms;
+  for (int i = 0; i < input_boxes.size(); ++i) {
+    if (picked[i] == 1) {
+      input_boxes_nms.push_back(input_boxes[i]);
+    }
+  }
+  input_boxes = input_boxes_nms;
+}
+
 } // namespace PaddleOCR

deploy/cpp_infer/src/preprocess_op.cpp

@@ -12,21 +12,6 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "opencv2/core.hpp"
-#include "opencv2/imgcodecs.hpp"
-#include "opencv2/imgproc.hpp"
-#include "paddle_api.h"
-#include "paddle_inference_api.h"
-#include <chrono>
-#include <iomanip>
-#include <iostream>
-#include <ostream>
-#include <vector>
-
-#include <cstring>
-#include <fstream>
-#include <numeric>
-
 #include <include/preprocess_op.h>
 
 namespace PaddleOCR {
@@ -69,13 +54,13 @@ void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
 }
 
 void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
-                         string limit_type, int limit_side_len, float &ratio_h,
-                         float &ratio_w, bool use_tensorrt) {
+                         std::string limit_type, int limit_side_len,
+                         float &ratio_h, float &ratio_w, bool use_tensorrt) {
   int w = img.cols;
   int h = img.rows;
   float ratio = 1.f;
   if (limit_type == "min") {
-    int min_wh = min(h, w);
+    int min_wh = std::min(h, w);
     if (min_wh < limit_side_len) {
       if (h < w) {
         ratio = float(limit_side_len) / float(h);
@@ -84,7 +69,7 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
       }
     }
   } else {
-    int max_wh = max(h, w);
+    int max_wh = std::max(h, w);
     if (max_wh > limit_side_len) {
       if (h > w) {
         ratio = float(limit_side_len) / float(h);
@@ -97,8 +82,8 @@ void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
   int resize_h = int(float(h) * ratio);
   int resize_w = int(float(w) * ratio);
 
-  resize_h = max(int(round(float(resize_h) / 32) * 32), 32);
-  resize_w = max(int(round(float(resize_w) / 32) * 32), 32);
+  resize_h = std::max(int(round(float(resize_h) / 32) * 32), 32);
+  resize_w = std::max(int(round(float(resize_w) / 32) * 32), 32);
 
   cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
   ratio_h = float(resize_h) / float(h);
@@ -175,4 +160,9 @@ void TablePadImg::Run(const cv::Mat &img, cv::Mat &resize_img,
                      cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
 }
 
+void Resize::Run(const cv::Mat &img, cv::Mat &resize_img, const int h,
+                 const int w) {
+  cv::resize(img, resize_img, cv::Size(w, h));
+}
+
 } // namespace PaddleOCR

deploy/cpp_infer/src/structure_layout.cpp

+// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <include/structure_layout.h>
+
+namespace PaddleOCR {
+
+void StructureLayoutRecognizer::Run(cv::Mat img,
+                                    std::vector<StructurePredictResult> &result,
+                                    std::vector<double> &times) {
+  std::chrono::duration<float> preprocess_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> inference_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+  std::chrono::duration<float> postprocess_diff =
+      std::chrono::steady_clock::now() - std::chrono::steady_clock::now();
+
+  // preprocess
+  auto preprocess_start = std::chrono::steady_clock::now();
+
+  cv::Mat srcimg;
+  img.copyTo(srcimg);
+  cv::Mat resize_img;
+  this->resize_op_.Run(srcimg, resize_img, 800, 608);
+  this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
+                          this->is_scale_);
+
+  std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
+  this->permute_op_.Run(&resize_img, input.data());
+  auto preprocess_end = std::chrono::steady_clock::now();
+  preprocess_diff += preprocess_end - preprocess_start;
+
+  // inference.
+  auto input_names = this->predictor_->GetInputNames();
+  auto input_t = this->predictor_->GetInputHandle(input_names[0]);
+  input_t->Reshape({1, 3, resize_img.rows, resize_img.cols});
+  auto inference_start = std::chrono::steady_clock::now();
+  input_t->CopyFromCpu(input.data());
+
+  this->predictor_->Run();
+
+  // Get output tensor
+  std::vector<std::vector<float>> out_tensor_list;
+  std::vector<std::vector<int>> output_shape_list;
+  auto output_names = this->predictor_->GetOutputNames();
+  for (int j = 0; j < output_names.size(); j++) {
+    auto output_tensor = this->predictor_->GetOutputHandle(output_names[j]);
+    std::vector<int> output_shape = output_tensor->shape();
+    int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
+                                  std::multiplies<int>());
+    output_shape_list.push_back(output_shape);
+
+    std::vector<float> out_data;
+    out_data.resize(out_num);
+    output_tensor->CopyToCpu(out_data.data());
+    out_tensor_list.push_back(out_data);
+  }
+  auto inference_end = std::chrono::steady_clock::now();
+  inference_diff += inference_end - inference_start;
+
+  // postprocess
+  auto postprocess_start = std::chrono::steady_clock::now();
+
+  std::vector<int> bbox_num;
+  int reg_max = 0;
+  for (int i = 0; i < out_tensor_list.size(); i++) {
+    if (i == this->post_processor_.fpn_stride_.size()) {
+      reg_max = output_shape_list[i][2] / 4;
+      break;
+    }
+  }
+  std::vector<int> ori_shape = {srcimg.rows, srcimg.cols};
+  std::vector<int> resize_shape = {resize_img.rows, resize_img.cols};
+  this->post_processor_.Run(result, out_tensor_list, ori_shape, resize_shape,
+                            reg_max);
+  bbox_num.push_back(result.size());
+
+  auto postprocess_end = std::chrono::steady_clock::now();
+  postprocess_diff += postprocess_end - postprocess_start;
+  times.push_back(double(preprocess_diff.count() * 1000));
+  times.push_back(double(inference_diff.count() * 1000));
+  times.push_back(double(postprocess_diff.count() * 1000));
+}
+
+void StructureLayoutRecognizer::LoadModel(const std::string &model_dir) {
+  paddle_infer::Config config;
+  if (Utility::PathExists(model_dir + "/inference.pdmodel") &&
+      Utility::PathExists(model_dir + "/inference.pdiparams")) {
+    config.SetModel(model_dir + "/inference.pdmodel",
+                    model_dir + "/inference.pdiparams");
+  } else if (Utility::PathExists(model_dir + "/model.pdmodel") &&
+             Utility::PathExists(model_dir + "/model.pdiparams")) {
+    config.SetModel(model_dir + "/model.pdmodel",
+                    model_dir + "/model.pdiparams");
+  } else {
+    std::cerr << "[ERROR] not find model.pdiparams or inference.pdiparams in "
+              << model_dir << std::endl;
+    exit(1);
+  }
+
+  if (this->use_gpu_) {
+    config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
+    if (this->use_tensorrt_) {
+      auto precision = paddle_infer::Config::Precision::kFloat32;
+      if (this->precision_ == "fp16") {
+        precision = paddle_infer::Config::Precision::kHalf;
+      }
+      if (this->precision_ == "int8") {
+        precision = paddle_infer::Config::Precision::kInt8;
+      }
+      config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
+      if (!Utility::PathExists("./trt_layout_shape.txt")) {
+        config.CollectShapeRangeInfo("./trt_layout_shape.txt");
+      } else {
+        config.EnableTunedTensorRtDynamicShape("./trt_layout_shape.txt", true);
+      }
+    }
+  } else {
+    config.DisableGpu();
+    if (this->use_mkldnn_) {
+      config.EnableMKLDNN();
+    }
+    config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
+  }
+
+  // false for zero copy tensor
+  config.SwitchUseFeedFetchOps(false);
+  // true for multiple input
+  config.SwitchSpecifyInputNames(true);
+
+  config.SwitchIrOptim(true);
+
+  config.EnableMemoryOptim();
+  config.DisableGlogInfo();
+
+  this->predictor_ = paddle_infer::CreatePredictor(config);
+}
+} // namespace PaddleOCR

deploy/cpp_infer/src/structure_table.cpp

@@ -34,7 +34,7 @@ void StructureTableRecognizer::Run(
        beg_img_no += this->table_batch_num_) {
     // preprocess
     auto preprocess_start = std::chrono::steady_clock::now();
-    int end_img_no = min(img_num, beg_img_no + this->table_batch_num_);
+    int end_img_no = std::min(img_num, beg_img_no + this->table_batch_num_);
     int batch_num = end_img_no - beg_img_no;
     std::vector<cv::Mat> norm_img_batch;
     std::vector<int> width_list;
@@ -118,7 +118,7 @@ void StructureTableRecognizer::Run(
 }
 
 void StructureTableRecognizer::LoadModel(const std::string &model_dir) {
-  AnalysisConfig config;
+  paddle_infer::Config config;
   config.SetModel(model_dir + "/inference.pdmodel",
                   model_dir + "/inference.pdiparams");
 
@@ -133,6 +133,11 @@ void StructureTableRecognizer::LoadModel(const std::string &model_dir) {
         precision = paddle_infer::Config::Precision::kInt8;
       }
       config.EnableTensorRtEngine(1 << 20, 10, 3, precision, false, false);
+      if (!Utility::PathExists("./trt_table_shape.txt")) {
+        config.CollectShapeRangeInfo("./trt_table_shape.txt");
+      } else {
+        config.EnableTunedTensorRtDynamicShape("./trt_table_shape.txt", true);
+      }
     }
   } else {
     config.DisableGpu();
@@ -152,6 +157,6 @@ void StructureTableRecognizer::LoadModel(const std::string &model_dir) {
   config.EnableMemoryOptim();
   config.DisableGlogInfo();
 
-  this->predictor_ = CreatePredictor(config);
+  this->predictor_ = paddle_infer::CreatePredictor(config);
 }
 } // namespace PaddleOCR

deploy/cpp_infer/src/utility.cpp

@@ -70,6 +70,7 @@ void Utility::VisualizeBboxes(const cv::Mat &srcimg,
                               const std::string &save_path) {
   cv::Mat img_vis;
   srcimg.copyTo(img_vis);
+  img_vis = crop_image(img_vis, structure_result.box);
   for (int n = 0; n < structure_result.cell_box.size(); n++) {
     if (structure_result.cell_box[n].size() == 8) {
       cv::Point rook_points[4];
@@ -280,23 +281,29 @@ void Utility::print_result(const std::vector<OCRPredictResult> &ocr_result) {
   }
 }
 
-cv::Mat Utility::crop_image(cv::Mat &img, std::vector<int> &area) {
+cv::Mat Utility::crop_image(cv::Mat &img, const std::vector<int> &box) {
   cv::Mat crop_im;
-  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] - 1);
-  int crop_y2 = std::min(img.rows - 1, area[3] - 1);
+  int crop_x1 = std::max(0, box[0]);
+  int crop_y1 = std::max(0, box[1]);
+  int crop_x2 = std::min(img.cols - 1, box[2] - 1);
+  int crop_y2 = std::min(img.rows - 1, box[3] - 1);
 
-  crop_im = cv::Mat::zeros(area[3] - area[1], area[2] - area[0], 16);
+  crop_im = cv::Mat::zeros(box[3] - box[1], box[2] - box[0], 16);
   cv::Mat crop_im_window =
-      crop_im(cv::Range(crop_y1 - area[1], crop_y2 + 1 - area[1]),
-              cv::Range(crop_x1 - area[0], crop_x2 + 1 - area[0]));
+      crop_im(cv::Range(crop_y1 - box[1], crop_y2 + 1 - box[1]),
+              cv::Range(crop_x1 - box[0], crop_x2 + 1 - box[0]));
   cv::Mat roi_img =
       img(cv::Range(crop_y1, crop_y2 + 1), cv::Range(crop_x1, crop_x2 + 1));
   crop_im_window += roi_img;
   return crop_im;
 }
 
+cv::Mat Utility::crop_image(cv::Mat &img, const std::vector<float> &box) {
+  std::vector<int> box_int = {(int)box[0], (int)box[1], (int)box[2],
+                              (int)box[3]};
+  return crop_image(img, box_int);
+}
+
 void Utility::sorted_boxes(std::vector<OCRPredictResult> &ocr_result) {
   std::sort(ocr_result.begin(), ocr_result.end(), Utility::comparison_box);
   if (ocr_result.size() > 0) {
@@ -341,4 +348,78 @@ std::vector<int> Utility::xyxyxyxy2xyxy(std::vector<int> &box) {
   return box1;
 }
 
+float Utility::fast_exp(float x) {
+  union {
+    uint32_t i;
+    float f;
+  } v{};
+  v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
+  return v.f;
+}
+
+std::vector<float>
+Utility::activation_function_softmax(std::vector<float> &src) {
+  int length = src.size();
+  std::vector<float> dst;
+  dst.resize(length);
+  const float alpha = float(*std::max_element(&src[0], &src[0 + length]));
+  float denominator{0};
+
+  for (int i = 0; i < length; ++i) {
+    dst[i] = fast_exp(src[i] - alpha);
+    denominator += dst[i];
+  }
+
+  for (int i = 0; i < length; ++i) {
+    dst[i] /= denominator;
+  }
+  return dst;
+}
+
+float Utility::iou(std::vector<int> &box1, std::vector<int> &box2) {
+  int area1 = std::max(0, box1[2] - box1[0]) * std::max(0, box1[3] - box1[1]);
+  int area2 = std::max(0, box2[2] - box2[0]) * std::max(0, box2[3] - box2[1]);
+
+  // computing the sum_area
+  int sum_area = area1 + area2;
+
+  // find the each point of intersect rectangle
+  int x1 = std::max(box1[0], box2[0]);
+  int y1 = std::max(box1[1], box2[1]);
+  int x2 = std::min(box1[2], box2[2]);
+  int y2 = std::min(box1[3], box2[3]);
+
+  // judge if there is an intersect
+  if (y1 >= y2 || x1 >= x2) {
+    return 0.0;
+  } else {
+    int intersect = (x2 - x1) * (y2 - y1);
+    return intersect / (sum_area - intersect + 0.00000001);
+  }
+}
+
+float Utility::iou(std::vector<float> &box1, std::vector<float> &box2) {
+  float area1 = std::max((float)0.0, box1[2] - box1[0]) *
+                std::max((float)0.0, box1[3] - box1[1]);
+  float area2 = std::max((float)0.0, box2[2] - box2[0]) *
+                std::max((float)0.0, box2[3] - box2[1]);
+
+  // computing the sum_area
+  float sum_area = area1 + area2;
+
+  // find the each point of intersect rectangle
+  float x1 = std::max(box1[0], box2[0]);
+  float y1 = std::max(box1[1], box2[1]);
+  float x2 = std::min(box1[2], box2[2]);
+  float y2 = std::min(box1[3], box2[3]);
+
+  // judge if there is an intersect
+  if (y1 >= y2 || x1 >= x2) {
+    return 0.0;
+  } else {
+    float intersect = (x2 - x1) * (y2 - y1);
+    return intersect / (sum_area - intersect + 0.00000001);
+  }
+}
+
 } // namespace PaddleOCR
\ No newline at end of file

ppstructure/layout/README.md

@@ -23,7 +23,7 @@ English | [简体中文](README_ch.md)
 
 ## 1. Introduction
 
-Layout analysis refers to the regional division of documents in the form of pictures and the positioning of key areas, such as text, title, table, picture, etc. The layout analysis algorithm is based on the lightweight model PP-picodet of [PaddleDetection]( https://github.com/PaddlePaddle/PaddleDetection )
+Layout analysis refers to the regional division of documents in the form of pictures and the positioning of key areas, such as text, title, table, picture, etc. The layout analysis algorithm is based on the lightweight model PP-picodet of [PaddleDetection]( https://github.com/PaddlePaddle/PaddleDetection ), including English layout analysis, Chinese layout analysis and table layout analysis models.  English layout analysis models can detect document layout elements such as text, title, table, figure, list. Chinese layout analysis models can detect document layout elements such as text, figure, figure caption, table, table caption, header, footer, reference, and equation. Table layout analysis models can detect table regions.
 
 <div align="center">
     <img src="../docs/layout/layout.png" width="800">
@@ -152,7 +152,7 @@ We provide CDLA(Chinese layout analysis), TableBank(Table layout analysis)etc. d
 | [cTDaR2019_cTDaR](https://cndplab-founder.github.io/cTDaR2019/) | For form detection (TRACKA) and form identification (TRACKB).Image types include historical data sets (beginning with cTDaR_t0, such as CTDAR_T00872.jpg) and modern data sets (beginning with cTDaR_t1, CTDAR_T10482.jpg). |
 | [IIIT-AR-13K](http://cvit.iiit.ac.in/usodi/iiitar13k.php)    | Data sets constructed by manually annotating figures or pages from publicly available annual reports, containing 5 categories:table, figure, natural image, logo, and signature. |
 | [TableBank](https://github.com/doc-analysis/TableBank)       | For table detection and recognition of large datasets, including Word and Latex document formats |
-| [CDLA](https://github.com/buptlihang/CDLA)                   | Chinese document layout analysis data set, for Chinese literature (paper) scenarios, including 10 categories:Table, Figure, Figure caption, Table, Table caption, Header, Footer, Reference, Equation |
+| [CDLA](https://github.com/buptlihang/CDLA)                   | Chinese document layout analysis data set, for Chinese literature (paper) scenarios, including 10 categories:Text, Title, Figure, Figure caption, Table, Table caption, Header, Footer, Reference, Equation |
 | [DocBank](https://github.com/doc-analysis/DocBank)           | Large-scale dataset (500K document pages) constructed using weakly supervised methods for document layout analysis, containing 12 categories:Author, Caption, Date, Equation, Figure, Footer, List, Paragraph, Reference, Section, Table, Title |
 
 
@@ -175,7 +175,7 @@ If the test image is Chinese, the pre-trained model of Chinese CDLA dataset can
 
 ### 5.1. Train
 
-Train:
+Start training with the PaddleDetection [layout analysis profile](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/picodet/legacy_model/application/layout_analysis)
 
 * Modify Profile
 

ppstructure/layout/README_ch.md

@@ -22,7 +22,7 @@
 
 ## 1. 简介
 
-版面分析指的是对图片形式的文档进行区域划分，定位其中的关键区域，如文字、标题、表格、图片等。版面分析算法基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)的轻量模型PP-PicoDet进行开发。
+版面分析指的是对图片形式的文档进行区域划分，定位其中的关键区域，如文字、标题、表格、图片等。版面分析算法基于[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)的轻量模型PP-PicoDet进行开发，包含英文、中文、表格版面分析3类模型。其中，英文模型支持Text、Title、Tale、Figure、List5类区域的检测，中文模型支持Text、Title、Figure、Figure caption、Table、Table caption、Header、Footer、Reference、Equation10类区域的检测，表格版面分析支持Table区域的检测，版面分析效果如下图所示：
 
 <div align="center">
     <img src="../docs/layout/layout.png" width="800">
@@ -152,7 +152,7 @@ json文件包含所有图像的标注，数据以字典嵌套的方式存放，
 | ------------------------------------------------------------ | ------------------------------------------------------------ |
 | [cTDaR2019_cTDaR](https://cndplab-founder.github.io/cTDaR2019/) | 用于表格检测(TRACKA)和表格识别(TRACKB)。图片类型包含历史数据集(以cTDaR_t0开头，如cTDaR_t00872.jpg)和现代数据集(以cTDaR_t1开头，cTDaR_t10482.jpg)。 |
 | [IIIT-AR-13K](http://cvit.iiit.ac.in/usodi/iiitar13k.php)    | 手动注释公开的年度报告中的图形或页面而构建的数据集，包含5类：table, figure, natural image, logo, and signature |
-| [CDLA](https://github.com/buptlihang/CDLA)                   | 中文文档版面分析数据集，面向中文文献类（论文）场景，包含10类：Table、Figure、Figure caption、Table、Table caption、Header、Footer、Reference、Equation |
+| [CDLA](https://github.com/buptlihang/CDLA)                   | 中文文档版面分析数据集，面向中文文献类（论文）场景，包含10类：Text、Title、Figure、Figure caption、Table、Table caption、Header、Footer、Reference、Equation |
 | [TableBank](https://github.com/doc-analysis/TableBank)       | 用于表格检测和识别大型数据集，包含Word和Latex2种文档格式     |
 | [DocBank](https://github.com/doc-analysis/DocBank)           | 使用弱监督方法构建的大规模数据集(500K文档页面)，用于文档布局分析，包含12类：Author、Caption、Date、Equation、Figure、Footer、List、Paragraph、Reference、Section、Table、Title |
 
@@ -161,7 +161,7 @@ json文件包含所有图像的标注，数据以字典嵌套的方式存放，
 
 提供了训练脚本、评估脚本和预测脚本，本节将以PubLayNet预训练模型为例进行讲解。
 
-如果不希望训练，直接体验后面的模型评估、预测、动转静、推理的流程，可以下载提供的预训练模型(PubLayNet数据集)，并跳过本部分。
+如果不希望训练，直接体验后面的模型评估、预测、动转静、推理的流程，可以下载提供的预训练模型(PubLayNet数据集)，并跳过5.1和5.2。
 
 ```
 mkdir pretrained_model
@@ -176,7 +176,7 @@ wget https://paddleocr.bj.bcebos.com/ppstructure/models/layout/picodet_lcnet_x1_
 
 ### 5.1. 启动训练
 
-开始训练:
+使用PaddleDetection[版面分析配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.5/configs/picodet/legacy_model/application/layout_analysis)启动训练
 
 * 修改配置文件
 

ppstructure/predict_system.py

@@ -255,8 +255,7 @@ def main(args):
 
         if args.recovery and all_res != []:
             try:
-                convert_info_docx(img, all_res, save_folder, img_name,
-                                  args.save_pdf)
+                convert_info_docx(img, all_res, save_folder, img_name)
             except Exception as ex:
                 logger.error("error in layout recovery image:{}, err msg: {}".
                              format(image_file, ex))

ppstructure/recovery/README.md

@@ -82,8 +82,11 @@ Through layout analysis, we divided the image/PDF documents into regions, locate
 
 We can restore the test picture through the layout information, OCR detection and recognition structure, table information, and saved pictures.
 
-The whl package is also provided  for quick use, see [quickstart](../docs/quickstart_en.md) for details.
+The whl package is also provided  for quick use, follow the above code, for more infomation please refer to [quickstart](../docs/quickstart_en.md) for details.
 
+```bash
+paddleocr --image_dir=ppstructure/docs/table/1.png --type=structure --recovery=true --lang='en'
+```
 
 <a name="3.1"></a>
 ### 3.1 Download models

ppstructure/recovery/README_ch.md

@@ -83,7 +83,16 @@ python3 -m pip install -r ppstructure/recovery/requirements.txt
 
 我们通过版面信息、OCR检测和识别结构、表格信息、保存的图片，对测试图片进行恢复即可。
 
-提供如下代码实现版面恢复，也提供了whl包的形式方便快速使用，详见 [quickstart](../docs/quickstart.md)。
+提供如下代码实现版面恢复，也提供了whl包的形式方便快速使用，代码如下，更多信息详见 [quickstart](../docs/quickstart.md)。
+
+```bash
+# 中文测试图
+paddleocr --image_dir=ppstructure/docs/table/1.png --type=structure --recovery=true
+# 英文测试图
+paddleocr --image_dir=ppstructure/docs/table/1.png --type=structure --recovery=true --lang='en'
+# pdf测试文件
+paddleocr --image_dir=ppstructure/recovery/UnrealText.pdf --type=structure --recovery=true --lang='en'
+```
 
 <a name="3.1"></a>
 

ppstructure/recovery/recovery_to_doc.py

@@ -28,7 +28,7 @@ from ppocr.utils.logging import get_logger
 logger = get_logger()
 
 
-def convert_info_docx(img, res, save_folder, img_name, save_pdf=False):
+def convert_info_docx(img, res, save_folder, img_name):
     doc = Document()
     doc.styles['Normal'].font.name = 'Times New Roman'
     doc.styles['Normal']._element.rPr.rFonts.set(qn('w:eastAsia'), u'宋体')
@@ -60,14 +60,9 @@ def convert_info_docx(img, res, save_folder, img_name, save_pdf=False):
         elif region['type'].lower() == 'title':
             doc.add_heading(region['res'][0]['text'])
         elif region['type'].lower() == 'table':
-            paragraph = doc.add_paragraph()
-            new_parser = HtmlToDocx()
-            new_parser.table_style = 'TableGrid'
-            table = new_parser.handle_table(html=region['res']['html'])
-            new_table = deepcopy(table)
-            new_table.alignment = WD_TABLE_ALIGNMENT.CENTER
-            paragraph.add_run().element.addnext(new_table._tbl)
-
+            parser = HtmlToDocx()
+            parser.table_style = 'TableGrid'
+            parser.handle_table(region['res']['html'], doc)
         else:
             paragraph = doc.add_paragraph()
             paragraph_format = paragraph.paragraph_format
@@ -82,13 +77,6 @@ def convert_info_docx(img, res, save_folder, img_name, save_pdf=False):
     doc.save(docx_path)
     logger.info('docx save to {}'.format(docx_path))
 
-    # save to pdf
-    if save_pdf:
-        pdf_path = os.path.join(save_folder, '{}.pdf'.format(img_name))
-        from docx2pdf import convert
-        convert(docx_path, pdf_path)
-        logger.info('pdf save to {}'.format(pdf_path))
-
 
 def sorted_layout_boxes(res, w):
     """

ppstructure/recovery/requirements.txt

 python-docx
-docx2pdf
 PyMuPDF
 beautifulsoup4
\ No newline at end of file

ppstructure/utility.py

@@ -92,11 +92,6 @@ def init_args():
         type=str2bool,
         default=False,
         help='Whether to enable layout of recovery')
-    parser.add_argument(
-        "--save_pdf",
-        type=str2bool,
-        default=False,
-        help='Whether to save pdf file')
 
     return parser
 
@@ -110,7 +105,38 @@ def draw_structure_result(image, result, font_path):
     if isinstance(image, np.ndarray):
         image = Image.fromarray(image)
     boxes, txts, scores = [], [], []
+
+    img_layout = image.copy()
+    draw_layout = ImageDraw.Draw(img_layout)
+    text_color = (255, 255, 255)
+    text_background_color = (80, 127, 255)
+    catid2color = {}
+    font_size = 15
+    font = ImageFont.truetype(font_path, font_size, encoding="utf-8")
+
     for region in result:
+        if region['type'] not in catid2color:
+            box_color = (random.randint(0, 255), random.randint(0, 255),
+                         random.randint(0, 255))
+            catid2color[region['type']] = box_color
+        else:
+            box_color = catid2color[region['type']]
+        box_layout = region['bbox']
+        draw_layout.rectangle(
+            [(box_layout[0], box_layout[1]), (box_layout[2], box_layout[3])],
+            outline=box_color,
+            width=3)
+        text_w, text_h = font.getsize(region['type'])
+        draw_layout.rectangle(
+            [(box_layout[0], box_layout[1]),
+             (box_layout[0] + text_w, box_layout[1] + text_h)],
+            fill=text_background_color)
+        draw_layout.text(
+            (box_layout[0], box_layout[1]),
+            region['type'],
+            fill=text_color,
+            font=font)
+
         if region['type'] == 'table':
             pass
         else:
@@ -118,6 +144,7 @@ def draw_structure_result(image, result, font_path):
                 boxes.append(np.array(text_result['text_region']))
                 txts.append(text_result['text'])
                 scores.append(text_result['confidence'])
+
     im_show = draw_ocr_box_txt(
-        image, boxes, txts, scores, font_path=font_path, drop_score=0)
+        img_layout, boxes, txts, scores, font_path=font_path, drop_score=0)
     return im_show