Fix Diff & Improve OCR readme (#184)

* Add PaddleOCR Support

* Add PaddleOCR Support

* Add PaddleOCRv3 Support

* Add PaddleOCRv3 Support

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Add PaddleOCRv3 Support

* Add PaddleOCRv3 Supports

* Add PaddleOCRv3 Suport

* Fix Rec diff

* Remove useless functions

* Remove useless comments

* Add PaddleOCRv2 Support

* Add PaddleOCRv3 & PaddleOCRv2 Support

* remove useless parameters

* Add utils of sorting det boxes

* Fix code naming convention

* Fix code naming convention

* Fix code naming convention

* Fix bug in the Classify process

* Imporve OCR Readme

* Fix diff in Cls model

* Update Model Download Link in Readme

csrc/fastdeploy/vision/ocr/ppocr/classifier.cc

@@ -46,7 +46,7 @@ bool Classifier::Initialize() {
   cls_thresh = 0.9;
   cls_image_shape = {3, 48, 192};
   cls_batch_num = 1;
-  mean = {0.485f, 0.456f, 0.406f};
+  mean = {0.5f, 0.5f, 0.5f};
   scale = {0.5f, 0.5f, 0.5f};
   is_scale = true;
 

csrc/fastdeploy/vision/ocr/ppocr/dbdetector.cc

@@ -144,10 +144,11 @@ bool DBDetector::Postprocess(
 
   std::vector<std::vector<std::vector<int>>> boxes;
 
-  post_processor_.BoxesFromBitmap(pred_map, &boxes, bit_map, det_db_box_thresh,
+  boxes =
-                                  det_db_unclip_ratio, det_db_score_mode);
+      post_processor_.BoxesFromBitmap(pred_map, bit_map, det_db_box_thresh,
+                                      det_db_unclip_ratio, det_db_score_mode);
 
-  post_processor_.FilterTagDetRes(&boxes, ratio_h, ratio_w, im_info);
+  boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, im_info);
 
   // boxes to boxes_result
   for (int i = 0; i < boxes.size(); i++) {

csrc/fastdeploy/vision/ocr/ppocr/ppocr_system_v2.cc

@@ -36,7 +36,7 @@ void PPOCRSystemv2::Detect(cv::Mat* img,
 void PPOCRSystemv2::Recognize(cv::Mat* img,
                               fastdeploy::vision::OCRResult* result) {
   std::tuple<std::string, float> rec_result;
-
+  this->recognizer->rec_image_shape[1] = 32;
   this->recognizer->Predict(img, &rec_result);
 
   result->text.push_back(std::get<0>(rec_result));

csrc/fastdeploy/vision/ocr/ppocr/ppocr_system_v3.cc

@@ -94,7 +94,6 @@ bool PPOCRSystemv3::Predict(cv::Mat* img,
       for (int i = 0; i < img_list.size(); i++) {
         if ((result->cls_labels)[i] % 2 == 1 &&
             (result->cls_scores)[i] > this->classifier->cls_thresh) {
-          std::cout << "Rotate this image " << std::endl;
           cv::rotate(img_list[i], img_list[i], 1);
         }
       }

csrc/fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.cc

@@ -250,9 +250,8 @@ float PostProcessor::BoxScoreFast(std::vector<std::vector<float>> box_array,
 
 //这个应该是DB（差分二值化）相关的内容，方法从 Bitmap 图中获取检测框
 //涉及到box_thresh（低于这个阈值的boxs不予显示）和det_db_unclip_ratio（文本框扩张的系数，关系到文本框的大小）
-void PostProcessor::BoxesFromBitmap(
+std::vector<std::vector<std::vector<int>>> PostProcessor::BoxesFromBitmap(
-    const cv::Mat pred, std::vector<std::vector<std::vector<int>>> *boxes,
+    const cv::Mat pred, const cv::Mat bitmap, const float &box_thresh,
-    const cv::Mat bitmap, const float &box_thresh,
     const float &det_db_unclip_ratio, const std::string &det_db_score_mode) {
   const int min_size = 3;
   const int max_candidates = 1000;
@@ -269,6 +268,8 @@ void PostProcessor::BoxesFromBitmap(
   int num_contours =
       contours.size() >= max_candidates ? max_candidates : contours.size();
 
+  std::vector<std::vector<std::vector<int>>> boxes;
+
   for (int _i = 0; _i < num_contours; _i++) {
     if (contours[_i].size() <= 2) {
       continue;
@@ -318,45 +319,42 @@ void PostProcessor::BoxesFromBitmap(
               0, float(dest_height)))};
       intcliparray.push_back(a);
     }
-    boxes->push_back(intcliparray);
+    boxes.push_back(intcliparray);
 
   }  // end for
-  // return true;
+  return boxes;
 }
 
-//方法根据识别结果获取目标框位置
+std::vector<std::vector<std::vector<int>>> PostProcessor::FilterTagDetRes(
-void PostProcessor::FilterTagDetRes(
+    std::vector<std::vector<std::vector<int>>> boxes, float ratio_h,
-    std::vector<std::vector<std::vector<int>>> *boxes, const float ratio_h,
+    float ratio_w, const std::map<std::string, std::array<float, 2>> &im_info) {
-    const float ratio_w,
-    const std::map<std::string, std::array<float, 2>> &im_info) {
   int oriimg_h = im_info.at("input_shape")[0];
   int oriimg_w = im_info.at("input_shape")[1];
 
-  for (int n = 0; n < boxes->size(); n++) {
+  std::vector<std::vector<std::vector<int>>> root_points;
-    (*boxes)[n] = OrderPointsClockwise((*boxes)[n]);
+  for (int n = 0; n < boxes.size(); n++) {
-    for (int m = 0; m < (*boxes)[0].size(); m++) {
+    boxes[n] = OrderPointsClockwise(boxes[n]);
-      (*boxes)[n][m][0] /= ratio_w;
+    for (int m = 0; m < boxes[0].size(); m++) {
-      (*boxes)[n][m][1] /= ratio_h;
+      boxes[n][m][0] /= ratio_w;
+      boxes[n][m][1] /= ratio_h;
 
-      (*boxes)[n][m][0] = int(_min(_max((*boxes)[n][m][0], 0), oriimg_w - 1));
+      boxes[n][m][0] = int(_min(_max(boxes[n][m][0], 0), oriimg_w - 1));
-      (*boxes)[n][m][1] = int(_min(_max((*boxes)[n][m][1], 0), oriimg_h - 1));
+      boxes[n][m][1] = int(_min(_max(boxes[n][m][1], 0), oriimg_h - 1));
     }
   }
 
-  //此时已经拿到所有的点. 再进行下面的筛选
+  for (int n = 0; n < boxes.size(); n++) {
-  for (int n = (*boxes).size() - 1; n >= 0; n--) {
     int rect_width, rect_height;
-    rect_width = int(sqrt(pow((*boxes)[n][0][0] - (*boxes)[n][1][0], 2) +
+    rect_width = int(sqrt(pow(boxes[n][0][0] - boxes[n][1][0], 2) +
-                          pow((*boxes)[n][0][1] - (*boxes)[n][1][1], 2)));
+                          pow(boxes[n][0][1] - boxes[n][1][1], 2)));
-    rect_height = int(sqrt(pow((*boxes)[n][0][0] - (*boxes)[n][3][0], 2) +
+    rect_height = int(sqrt(pow(boxes[n][0][0] - boxes[n][3][0], 2) +
-                           pow((*boxes)[n][0][1] - (*boxes)[n][3][1], 2)));
+                           pow(boxes[n][0][1] - boxes[n][3][1], 2)));
-
+    if (rect_width <= 4 || rect_height <= 4) continue;
-    //小于4的删除掉. erase配合逆序遍历.
+    root_points.push_back(boxes[n]);
-    if (rect_width <= 4 || rect_height <= 4) {
-      boxes->erase(boxes->begin() + n);
-    }
   }
+  return root_points;
 }
+
 }  // namespace ocr
 }  // namespace vision
 }  // namespace fastdeploy

csrc/fastdeploy/vision/ocr/ppocr/utils/ocr_postprocess_op.h

@@ -52,15 +52,13 @@ class PostProcessor {
   float BoxScoreFast(std::vector<std::vector<float>> box_array, cv::Mat pred);
   float PolygonScoreAcc(std::vector<cv::Point> contour, cv::Mat pred);
 
-  void BoxesFromBitmap(const cv::Mat pred,
+  std::vector<std::vector<std::vector<int>>> BoxesFromBitmap(
-                       std::vector<std::vector<std::vector<int>>> *boxes,
+      const cv::Mat pred, const cv::Mat bitmap, const float &box_thresh,
-                       const cv::Mat bitmap, const float &box_thresh,
+      const float &det_db_unclip_ratio, const std::string &det_db_score_mode);
-                       const float &det_db_unclip_ratio,
-                       const std::string &det_db_score_mode);
 
-  void FilterTagDetRes(
+  std::vector<std::vector<std::vector<int>>> FilterTagDetRes(
-      std::vector<std::vector<std::vector<int>>> *boxes, const float ratio_h,
+      std::vector<std::vector<std::vector<int>>> boxes, float ratio_h,
-      const float ratio_w,
+      float ratio_w,
       const std::map<std::string, std::array<float, 2>> &im_info);
 
  private:

examples/vision/ocr/PPOCRSystemv2/cpp/infer.cc

@@ -65,8 +65,6 @@ void CpuInfer(const std::string& det_model_dir,
   if (!rec_model_dir.empty()) {
     auto rec_option = fastdeploy::RuntimeOption();
     rec_option.UseCpu();
-    rec_option.UsePaddleBackend();  // OCRv2的rec模型暂不支持ORT后端
-
     rec_model = fastdeploy::vision::ocr::Recognizer(
         rec_model_file, rec_params_file, rec_label, rec_option);
 

examples/vision/ocr/PPOCRSystemv3/python/infer.py

@@ -65,7 +65,6 @@ def build_det_option(args):
 
 def build_cls_option(args):
     option = fd.RuntimeOption()
-    option.use_paddle_backend()
 
     if args.device.lower() == "gpu":
         option.use_gpu()

examples/vision/ocr/README.md

@@ -1,15 +1,15 @@
 # PaddleOCR 模型部署
 
-## 模型版本说明
+## PaddleOCR系列预测模型下载
 
-- [PaddleOCR Release/2.5](https://github.com/PaddlePaddle/PaddleOCR/tree/release/2.5)
+| 模型                                                               | 参数文件大小 | 说明 | 模型格式 | 版本 |
+|:---------------------------------------------------------------- |:----- |:----- | :------- | :--- |
+| [ch_PP-OCRv3_det](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) | 3.8MB | OCRv3的检测模型 | PADDLE | [Release/2.6](https://github.com/PaddlePaddle/PaddleOCR) |
+| [ch_PP-OCRv3_rec](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar)  | 12.4MB  | OCRv3的识别模型 | PADDLE | [Release/2.6](https://github.com/PaddlePaddle/PaddleOCR) |
+| [ch_ppocr_mobile_v2.0_cls](https://bj.bcebos.com/paddlehub/fastdeploy/ch_ppocr_mobile_v2.0_cls_infer.tar.gz) | 1.38MB | OCRv2和v3通用的分类模型 | PADDLE | [Release/2.6](https://github.com/PaddlePaddle/PaddleOCR) |
+| [ch_PP-OCRv2_det](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar)| 3MB | OCRv2的检测模型 |  PADDLE | [Release/2.6](https://github.com/PaddlePaddle/PaddleOCR) |
+| [ch_PP-OCRv2_rec](https://bj.bcebos.com/paddlehub/fastdeploy/ch_PP-OCRv2_rec_infer) | 8.5MB | OCRv2的识别模型 |  PADDLE | [Release/2.6](https://github.com/PaddlePaddle/PaddleOCR) |
 
-目前FastDeploy支持如下模型的部署
-
-- [PaddleOCRv3系列模型](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/doc/doc_ch/models_list.md)
-
-## 准备PaddleOCRv3部署模型
-用户在[PP-OCR系列模型列表](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.5/doc/doc_ch/models_list.md)下载相应的的OCRv3系列推理模型即可.
 
 ## 详细部署文档