[Hackthon_4th 177] Support PP-YOLOE-R with BM1684 (#1809)

* first draft * add robx iou * add benchmark for ppyoloe_r * remove trash code * fix bugs * add pybind nms rotated option * add missing head file * fix bug * fix bug2 * fix shape bug --------- Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
2025-10-04 16:22:57 +08:00 · 2023-04-21 10:48:05 +08:00
parent f3d44785c4
commit 51be3fea78
31 changed files with 1389 additions and 6 deletions
--- a/benchmark/cpp/CMakeLists.txt
+++ b/benchmark/cpp/CMakeLists.txt
@@ -22,6 +22,8 @@ add_executable(benchmark_ppmatting ${PROJECT_SOURCE_DIR}/benchmark_ppmatting.cc)
 add_executable(benchmark_ppocr_det ${PROJECT_SOURCE_DIR}/benchmark_ppocr_det.cc)
 add_executable(benchmark_ppocr_cls ${PROJECT_SOURCE_DIR}/benchmark_ppocr_cls.cc)
 add_executable(benchmark_ppocr_rec ${PROJECT_SOURCE_DIR}/benchmark_ppocr_rec.cc)
+add_executable(benchmark_ppyoloe_r ${PROJECT_SOURCE_DIR}/benchmark_ppyoloe_r.cc)
+add_executable(benchmark_ppyoloe_r_sophgo ${PROJECT_SOURCE_DIR}/benchmark_ppyoloe_r_sophgo.cc)
 add_executable(benchmark_ppyolo ${PROJECT_SOURCE_DIR}/benchmark_ppyolo.cc)
 add_executable(benchmark_yolov3 ${PROJECT_SOURCE_DIR}/benchmark_yolov3.cc)
 add_executable(benchmark_fasterrcnn ${PROJECT_SOURCE_DIR}/benchmark_fasterrcnn.cc)
@@ -44,6 +46,8 @@ if(UNIX AND (NOT APPLE) AND (NOT ANDROID))
  target_link_libraries(benchmark_ppyolov8 ${FASTDEPLOY_LIBS} gflags pthread)
  target_link_libraries(benchmark_ppyolox ${FASTDEPLOY_LIBS} gflags pthread)
  target_link_libraries(benchmark_ppyoloe ${FASTDEPLOY_LIBS} gflags pthread)
+  target_link_libraries(benchmark_ppyoloe_r ${FASTDEPLOY_LIBS} gflags pthread)
+  target_link_libraries(benchmark_ppyoloe_r_sophgo ${FASTDEPLOY_LIBS} gflags pthread)
  target_link_libraries(benchmark_picodet ${FASTDEPLOY_LIBS} gflags pthread)
  target_link_libraries(benchmark_ppcls ${FASTDEPLOY_LIBS} gflags pthread)
  target_link_libraries(benchmark_ppseg ${FASTDEPLOY_LIBS} gflags pthread)
@@ -72,6 +76,8 @@ else()
  target_link_libraries(benchmark_ppyolov8 ${FASTDEPLOY_LIBS} gflags)
  target_link_libraries(benchmark_ppyolox ${FASTDEPLOY_LIBS} gflags)
  target_link_libraries(benchmark_ppyoloe ${FASTDEPLOY_LIBS} gflags)
+  target_link_libraries(benchmark_ppyoloe_r ${FASTDEPLOY_LIBS} gflags)
+  target_link_libraries(benchmark_ppyoloe_r_sophgo ${FASTDEPLOY_LIBS} gflags)
  target_link_libraries(benchmark_picodet ${FASTDEPLOY_LIBS} gflags)
  target_link_libraries(benchmark_ppcls ${FASTDEPLOY_LIBS} gflags)
  target_link_libraries(benchmark_ppseg ${FASTDEPLOY_LIBS} gflags)
--- a/benchmark/cpp/benchmark_ppyoloe_r.cc
+++ b/benchmark/cpp/benchmark_ppyoloe_r.cc
@@ -0,0 +1,60 @@
+// Copyright (c) 2023 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 <fstream>
+
+#include "flags.h"
+#include "macros.h"
+#include "option.h"
+
+namespace vision = fastdeploy::vision;
+namespace benchmark = fastdeploy::benchmark;
+
+DEFINE_bool(no_nms, false, "Whether the model contains nms.");
+
+int main(int argc, char* argv[]) {
+#if defined(ENABLE_BENCHMARK) && defined(ENABLE_VISION)
+  // Initialization
+  auto option = fastdeploy::RuntimeOption();
+  if (!CreateRuntimeOption(&option, argc, argv, true)) {
+    return -1;
+  }
+  auto im = cv::imread(FLAGS_image);
+  std::unordered_map<std::string, std::string> config_info;
+  benchmark::ResultManager::LoadBenchmarkConfig(FLAGS_config_path,
+                                                &config_info);
+  std::string model_name, params_name, config_name;
+  auto model_format = fastdeploy::ModelFormat::PADDLE;
+  if (!UpdateModelResourceName(&model_name, &params_name, &config_name,
+                               &model_format, config_info)) {
+    return -1;
+  }
+  auto model_file = FLAGS_model + sep + model_name;
+  auto params_file = FLAGS_model + sep + params_name;
+  auto config_file = FLAGS_model + sep + config_name;
+
+  auto model_ppyoloe_r = vision::detection::PPYOLOER(
+      model_file, params_file, config_file, option, model_format);
+
+  vision::DetectionResult res;
+
+  // Run profiling
+  BENCHMARK_MODEL(model_ppyoloe_r, model_ppyoloe_r.Predict(im, &res))
+
+  auto vis_im = vision::VisDetection(im, res);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+#endif
+  return 0;
+}
--- a/benchmark/cpp/benchmark_ppyoloe_r_sophgo.cc
+++ b/benchmark/cpp/benchmark_ppyoloe_r_sophgo.cc
@@ -0,0 +1,61 @@
+// Copyright (c) 2023 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 <fstream>
+
+#include "flags.h"
+#include "macros.h"
+#include "option.h"
+
+namespace vision = fastdeploy::vision;
+namespace benchmark = fastdeploy::benchmark;
+
+DEFINE_bool(no_nms, false, "Whether the model contains nms.");
+
+int main(int argc, char* argv[]) {
+#if defined(ENABLE_BENCHMARK) && defined(ENABLE_VISION)
+  // Initialization
+  auto option = fastdeploy::RuntimeOption();
+  if (!CreateRuntimeOption(&option, argc, argv, true)) {
+    return -1;
+  }
+  auto im = cv::imread(FLAGS_image);
+  std::unordered_map<std::string, std::string> config_info;
+  benchmark::ResultManager::LoadBenchmarkConfig(FLAGS_config_path,
+                                                &config_info);
+  std::string model_name, params_name, config_name;
+  auto model_format = fastdeploy::ModelFormat::SOPHGO;
+  if (!UpdateModelResourceName(&model_name, &params_name, &config_name,
+                               &model_format, config_info)) {
+    return -1;
+  }
+
+  auto model_file = FLAGS_model + sep + model_name;
+  auto params_file = FLAGS_model + sep + params_name;
+  auto config_file = FLAGS_model + sep + config_name;
+
+  auto model_ppyoloe_r = vision::detection::PPYOLOER(
+      model_file, params_file, config_file, option, model_format);
+
+  vision::DetectionResult res;
+
+  // Run profiling
+  BENCHMARK_MODEL(model_ppyoloe_r, model_ppyoloe_r.Predict(im, &res))
+
+  auto vis_im = vision::VisDetection(im, res);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+#endif
+  return 0;
+}
--- a/benchmark/cpp/flags.h
+++ b/benchmark/cpp/flags.h
@@ -158,5 +158,14 @@ static bool UpdateModelResourceName(
      return false;
    }
  }
+
+  if (config_info["backend"] == "sophgo") {
+    *model_format = fastdeploy::ModelFormat::SOPHGO;
+    if (!GetModelResoucesNameFromDir(FLAGS_model, model_name, "bmodel")) {
+      std::cout << "Can not find sophgo model resources." << std::endl;
+      return false;
+    }
+  }
+
  return true;
 }
--- a/benchmark/cpp/option.h
+++ b/benchmark/cpp/option.h
@@ -103,6 +103,9 @@ static bool CreateRuntimeOption(fastdeploy::RuntimeOption* option,
      if (config_info["use_fp16"] == "true") {
        option->paddle_lite_option.enable_fp16 = true;
      }
+    } else if (config_info["backend"] == "sophgo") {
+      option->UseSophgo();
+      option->UseSophgoBackend();
    } else if (config_info["backend"] == "default") {
      PrintBenchmarkInfo(config_info);
      return true;
--- a/c_api/fastdeploy_capi/vision/result.h
+++ b/c_api/fastdeploy_capi/vision/result.h
@@ -50,6 +50,7 @@ typedef struct FD_C_OneDimMask {

 typedef struct FD_C_DetectionResult {
  FD_C_TwoDimArrayFloat boxes;
+  FD_C_TwoDimArrayFloat rotated_boxes;
  FD_C_OneDimArrayFloat scores;
  FD_C_OneDimArrayInt32 label_ids;
  FD_C_OneDimMask masks;
--- a/csharp/fastdeploy/types_internal_c.cs
+++ b/csharp/fastdeploy/types_internal_c.cs
@@ -135,6 +135,7 @@ public struct FD_OneDimMask {
 [StructLayout(LayoutKind.Sequential)]
 public struct FD_DetectionResult {
  public FD_TwoDimArrayFloat boxes;
+  public FD_TwoDimArrayFloat rotated_boxes;
  public FD_OneDimArrayFloat scores;
  public FD_OneDimArrayInt32 label_ids;
  public FD_OneDimMask masks;
--- a/examples/vision/detection/paddledetection/README.md
+++ b/examples/vision/detection/paddledetection/README.md
@@ -10,6 +10,7 @@ English | [简体中文](README_CN.md)
 Now FastDeploy supports the deployment of the following models

 - [PP-YOLOE(including PP-YOLOE+) models](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe)
+- [PP-YOLOE-R models](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/rotate/ppyoloe_r)
 - [PicoDet models](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet)
 - [PP-YOLO models(including v2)](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo)
 - [YOLOv3 models](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3)
@@ -43,7 +44,7 @@ Before deployment, PaddleDetection needs to be exported into the deployment mode

 ## Download Pre-trained Model

-For developers' testing, models exported by PaddleDetection are provided below. Developers can download them directly. 
+For developers' testing, models exported by PaddleDetection are provided below. Developers can download them directly.

 The accuracy metric is from model descriptions in PaddleDetection. Refer to them for details.

--- a/examples/vision/detection/paddledetection/README_CN.md
+++ b/examples/vision/detection/paddledetection/README_CN.md
@@ -10,6 +10,7 @@
 目前FastDeploy支持如下模型的部署

 - [PP-YOLOE(含PP-YOLOE+)系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe)
+- [PP-YOLOE-R系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/rotate/ppyoloe_r)
 - [PicoDet系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet)
 - [PP-YOLO系列模型(含v2)](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyolo)
 - [YOLOv3系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/yolov3)
--- a/examples/vision/detection/paddledetection/cpp/CMakeLists.txt
+++ b/examples/vision/detection/paddledetection/cpp/CMakeLists.txt
@@ -15,6 +15,9 @@ target_link_libraries(infer_faster_rcnn_demo ${FASTDEPLOY_LIBS})
 add_executable(infer_ppyoloe_demo ${PROJECT_SOURCE_DIR}/infer_ppyoloe.cc)
 target_link_libraries(infer_ppyoloe_demo ${FASTDEPLOY_LIBS})

+add_executable(infer_ppyoloe_r_demo ${PROJECT_SOURCE_DIR}/infer_ppyoloe_r.cc)
+target_link_libraries(infer_ppyoloe_r_demo ${FASTDEPLOY_LIBS})
+
 add_executable(infer_picodet_demo ${PROJECT_SOURCE_DIR}/infer_picodet.cc)
 target_link_libraries(infer_picodet_demo ${FASTDEPLOY_LIBS})

--- a/examples/vision/detection/paddledetection/cpp/infer_ppyoloe_r.cc
+++ b/examples/vision/detection/paddledetection/cpp/infer_ppyoloe_r.cc
@@ -0,0 +1,98 @@
+// Copyright (c) 2022 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 "fastdeploy/vision.h"
+
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void CpuInfer(const std::string& model_dir, const std::string& image_file) {
+  auto model_file = model_dir + sep + "model.pdmodel";
+  auto params_file = model_dir + sep + "model.pdiparams";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+  auto option = fastdeploy::RuntimeOption();
+  option.UseCpu();
+  auto model = fastdeploy::vision::detection::PPYOLOER(model_file, params_file,
+                                                        config_file, option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+  std::cout << im.cols << " vs " << im.rows << std::endl;
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  std::cout << res.Str() << std::endl;
+  auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+}
+
+void GpuInfer(const std::string& model_dir, const std::string& image_file) {
+  auto model_file = model_dir + sep + "model.pdmodel";
+  auto params_file = model_dir + sep + "model.pdiparams";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  auto model = fastdeploy::vision::detection::PPYOLOER(model_file, params_file,
+                                                        config_file, option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  const cv::Mat im = cv::imread(image_file);
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  std::cout << res.Str() << std::endl;
+  auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.1);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+}
+
+int main(int argc, char* argv[]) {
+  if (argc < 4) {
+    std::cout
+        << "Usage: infer_ppyoloe_r path/to/model_dir path/to/image run_option, "
+           "e.g ./infer_ppyoloe_r ./ppyoloe_model_dir ./test.jpeg 0"
+        << std::endl;
+    std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run "
+                 "with kunlunxin."
+              << std::endl;
+    return -1;
+  }
+
+  if (std::atoi(argv[3]) == 0) {
+    CpuInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 1) {
+    GpuInfer(argv[1], argv[2]);
+  }
+
+  return 0;
+}
--- a/examples/vision/detection/paddledetection/python/infer_ppyoloe_r.py
+++ b/examples/vision/detection/paddledetection/python/infer_ppyoloe_r.py
@@ -0,0 +1,78 @@
+import cv2
+import os
+
+import fastdeploy as fd
+print(111)
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_dir",
+        default=None,
+        help="Path of PaddleDetection model directory")
+    parser.add_argument(
+        "--image", default=None, help="Path of test image file.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
+    parser.add_argument(
+        "--use_trt",
+        type=ast.literal_eval,
+        default=False,
+        help="Wether to use tensorrt.")
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+
+    if args.device.lower() == "kunlunxin":
+        option.use_kunlunxin()
+
+    if args.device.lower() == "ascend":
+        option.use_ascend()
+
+    if args.device.lower() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+    return option
+
+
+args = parse_arguments()
+
+if args.model_dir is None:
+    model_dir = fd.download_model(name='ppyoloe_crn_l_300e_coco')
+else:
+    model_dir = args.model_dir
+
+model_file = os.path.join(model_dir, "model.pdmodel")
+params_file = os.path.join(model_dir, "model.pdiparams")
+config_file = os.path.join(model_dir, "infer_cfg.yml")
+
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+print(args)
+model = fd.vision.detection.PPYOLOER(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+print(2222)
+
+# 预测图片检测结果
+if args.image is None:
+    image = fd.utils.get_detection_test_image()
+else:
+    image = args.image
+im = cv2.imread(image)
+result = model.predict(im)
+print(result)
+
+# 预测结果可视化
+vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
+cv2.imwrite("visualized_result.jpg", vis_im)
+print("Visualized result save in ./visualized_result.jpg")
--- a/examples/vision/detection/paddledetection/sophgo/README.md
+++ b/examples/vision/detection/paddledetection/sophgo/README.md
@@ -6,6 +6,7 @@
 - [PP-YOLOE系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/ppyoloe)
 - [PicoDet系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/configs/picodet)
 - [YOLOV8系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4)
+- [PP-YOLOE-R系列模型](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.6/configs/rotate/ppyoloe_r)

 ## 准备PP-YOLOE YOLOV8或者PicoDet部署模型以及转换模型

--- a/examples/vision/detection/paddledetection/sophgo/cpp/CMakeLists.txt
+++ b/examples/vision/detection/paddledetection/sophgo/cpp/CMakeLists.txt
@@ -13,9 +13,11 @@ include_directories(${FASTDEPLOY_INCS})
 include_directories(${FastDeploy_INCLUDE_DIRS})

 add_executable(infer_ppyoloe ${PROJECT_SOURCE_DIR}/infer_ppyoloe.cc)
+add_executable(infer_ppyoloe_r ${PROJECT_SOURCE_DIR}/infer_ppyoloe_r.cc)
 add_executable(infer_picodet ${PROJECT_SOURCE_DIR}/infer_picodet.cc)
 add_executable(infer_yolov8 ${PROJECT_SOURCE_DIR}/infer_yolov8.cc)
 # 添加FastDeploy库依赖
 target_link_libraries(infer_ppyoloe ${FASTDEPLOY_LIBS})
+target_link_libraries(infer_ppyoloe_r ${FASTDEPLOY_LIBS})
 target_link_libraries(infer_picodet ${FASTDEPLOY_LIBS})
 target_link_libraries(infer_yolov8 ${FASTDEPLOY_LIBS})
--- a/examples/vision/detection/paddledetection/sophgo/cpp/README.md
+++ b/examples/vision/detection/paddledetection/sophgo/cpp/README.md
@@ -1,6 +1,6 @@
 # PaddleDetection C++部署示例

-本目录下提供`infer_ppyoloe.cc`,`infer_yolov8.cc`和`infer_picodet.cc`快速完成PP-YOLOE模型,YOLOV8模型和PicoDet模型在SOPHGO BM1684x板子上加速部署的示例。
+本目录下提供`infer_ppyoloe.cc`,`infer_ppyoloe_r.cc`,`infer_yolov8.cc`和`infer_picodet.cc`快速完成PP-YOLOE模型,PP-YOLOE-R模型,YOLOV8模型和PicoDet模型在SOPHGO BM1684x板子上加速部署的示例。

 在部署前，需确认以下两个步骤:

@@ -18,6 +18,7 @@
 ├── build  # 编译文件夹
 ├── image  # 存放图片的文件夹
 ├── infer_ppyoloe.cc
+├── infer_ppyoloe_r.cc
 ├── infer_picodet.cc
 ├── infer_yolov8.cc
 └── model  # 存放模型文件的文件夹
@@ -37,6 +38,9 @@
 ```bash
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
 cp 000000014439.jpg ./images
+
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/P0861__1.0__1154___824.png
+cp P0861__1.0__1154___824.png ./images
 ```

 ### 编译example
@@ -53,6 +57,9 @@ make
 #ppyoloe推理示例
 ./infer_ppyoloe model images/000000014439.jpg

+#ppyoloe_r推理示例
+./infer_ppyoloe_r model images/P0861__1.0__1154___824.png
+
 #picodet推理示例
 ./infer_picodet model images/000000014439.jpg
 ```
--- a/examples/vision/detection/paddledetection/sophgo/cpp/infer_ppyoloe_r.cc
+++ b/examples/vision/detection/paddledetection/sophgo/cpp/infer_ppyoloe_r.cc
@@ -0,0 +1,58 @@
+// Copyright (c) 2022 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 <sys/time.h>
+
+#include <iostream>
+#include <string>
+
+#include "fastdeploy/vision.h"
+
+void SophgoInfer(const std::string& model_dir, const std::string& image_file) {
+  auto model_file = model_dir + "/ppyoloe_r_crn_s_3x_dota_1684x_f32.bmodel";
+  auto params_file = "";
+  auto config_file = model_dir + "/infer_cfg.yml";
+
+  auto option = fastdeploy::RuntimeOption();
+  option.UseSophgo();
+  option.UseSophgoBackend();
+
+  auto format = fastdeploy::ModelFormat::SOPHGO;
+  auto model = fastdeploy::vision::detection::PPYOLOER(
+      model_file, params_file, config_file, option, format);
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  std::cout << res.Str() << std::endl;
+  auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.1);
+  cv::imwrite("infer_sophgo.jpg", vis_im);
+  std::cout << "Visualized result saved in ./infer_sophgo.jpg" << std::endl;
+}
+
+int main(int argc, char* argv[]) {
+  if (argc < 3) {
+    std::cout
+        << "Usage: infer_demo path/to/model_dir path/to/image run_option, "
+           "e.g ./infer_model ./model_dir ./test.jpeg"
+        << std::endl;
+    return -1;
+  }
+  SophgoInfer(argv[1], argv[2]);
+  return 0;
+}
--- a/examples/vision/detection/paddledetection/sophgo/python/README.md
+++ b/examples/vision/detection/paddledetection/sophgo/python/README.md
@@ -13,6 +13,7 @@ cd FastDeploy/examples/vision/detection/paddledetection/sophgo/python

 # 下载图片
 wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/P0861__1.0__1154___824.png

 # 推理
 # ppyoloe推理示例
@@ -33,12 +34,19 @@ python3 infer_picodet.py --auto False --pp_detect_path '' --model_file model/ppy
 python3 infer_yolov8.py --model_file model/yolov8s_s_300e_coco_1684x_f32.bmodel --config_file model/infer_cfg.yml --image ./000000014439.jpg
 # 运行完成后返回结果如下所示
 可视化结果存储在sophgo_result.jpg中
+
+# ppyoloe_r推理示例
+# 指定--auto True，自动完成模型准备、转换和推理，需要指定PaddleDetection路径
+python3 infer_ppyoloe_r.py --model_file model/ppyoloe_r_crn_s_3x_dota_1684x_f32.bmodel --image P0861__1.0__1154___824.png --config_file model/infer_cfg.yml
+可视化结果存储在sophgo_result_ppyoloe_r.jpg中
 ```

 ## 其它文档
 - [PP-YOLOE C++部署](../cpp)
 - [PicoDet C++部署](../cpp)
 - [YOLOV8 C++部署](../cpp)
+- [PP-YOLOE-R C++部署](../cpp)
 - [转换PicoDet SOPHGO模型文档](../README.md)
 - [转换PP-YOLOE SOPHGO模型文档](../README.md)
 - [转换YOLOV8 SOPHGO模型文档](../README.md)
+- [转换PP-YOLOE-R SOPHGO模型文档](../README.md)
--- a/examples/vision/detection/paddledetection/sophgo/python/infer_ppyoloe_r.py
+++ b/examples/vision/detection/paddledetection/sophgo/python/infer_ppyoloe_r.py
@@ -0,0 +1,159 @@
+# Copyright (c) 2022 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.
+import fastdeploy as fd
+import cv2
+import os
+from subprocess import run
+from prepare_npz import prepare
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--auto",
+        action="store_true",
+        help="Auto download, convert, compile and infer if True")
+    parser.add_argument(
+        "--pp_detect_path",
+        default='/workspace/PaddleDetection',
+        help="Path of PaddleDetection folder")
+    parser.add_argument(
+        "--model_file", required=True, help="Path of sophgo model.")
+    parser.add_argument("--config_file", required=True, help="Path of config.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    return parser.parse_args()
+
+
+def export_model(args):
+    PPDetection_path = args.pp_detect_path
+    export_str = 'python3 tools/export_model.py \
+                -c configs/rotate/ppyoloe_r/ppyoloe_r_crn_s_3x_dota.yml \
+                -output_dir=output_inference \
+                -o weights=https://paddledet.bj.bcebos.com/models/ppyoloe_r_crn_s_3x_dota.pdparams'
+
+    cur_path = os.getcwd()
+    os.chdir(PPDetection_path)
+    print(export_str)
+    run(export_str, shell=True)
+    cp_str = 'cp -r ./output_inference/ppyoloe_crn_s_300e_coco ' + cur_path
+    print(cp_str)
+    run(cp_str, shell=True)
+    os.chdir(cur_path)
+
+
+def paddle2onnx():
+    convert_str = 'paddle2onnx --model_dir ppyoloe_r_crn_s_3x_dota \
+                --model_filename model.pdmodel \
+                --params_filename model.pdiparams \
+                --save_file ppyoloe_r_crn_s_3x_dota.onnx \
+                --enable_dev_version True'
+
+    print(convert_str)
+    run(convert_str, shell=True)
+
+
+def mlir_prepare():
+    mlir_path = os.getenv("MODEL_ZOO_PATH")
+    mlir_path = mlir_path[:-13]
+    regression_path = os.path.join(mlir_path, 'regression')
+    mv_str_list = [
+        'mkdir ppyoloe_r', 'cp -rf ' + os.path.join(
+            regression_path, 'dataset/COCO2017/') + ' ./ppyoloe_r',
+        'cp -rf ' + os.path.join(regression_path, 'image/') + ' ./ppyoloe_r',
+        'cp ppyoloe_r_crn_s_3x_dota.onnx ./ppyoloe_r',
+        'mkdir ./ppyoloe_r/workspace'
+    ]
+    for str in mv_str_list:
+        print(str)
+        run(str, shell=True)
+
+
+def image_prepare():
+    img_str = 'wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/P0861__1.0__1154___824.png'
+    if not os.path.exists('P0861__1.0__1154___824.png'):
+        print(img_str)
+        run(img_str, shell=True)
+    prepare('P0861__1.0__1154___824.png', [640, 640])
+    cp_npz_str = 'cp ./inputs.npz ./ppyoloe_r'
+    print(cp_npz_str)
+    run(cp_npz_str, shell=True)
+
+
+def onnx2mlir():
+    transform_str = 'model_transform.py \
+            --model_name ppyoloe_r_crn_s_3x_dota \
+            --model_def ../ppyoloe_r_crn_s_3x_dota.onnx \
+            --input_shapes [[1,3,1024,1024],[1,2]] \
+            --keep_aspect_ratio \
+            --pixel_format rgb \
+            --mlir ppyoloe_r_crn_s_3x_dota.mlir'
+
+    os.chdir('./ppyoloe_r/workspace')
+    print(transform_str)
+    run(transform_str, shell=True)
+    os.chdir('../../')
+
+
+def mlir2bmodel():
+    deploy_str = 'model_deploy.py \
+            --mlir ppyoloe_r_crn_s_3x_dota.mlir \
+            --quantize F32 \
+            --chip bm1684x \
+            --model ppyoloe_r_crn_s_3x_dota_1684x_f32.bmodel'
+
+    os.chdir('./ppyoloe_r/workspace')
+    print(deploy_str)
+    run(deploy_str, shell=True)
+    os.chdir('../../')
+
+
+if __name__ == "__main__":
+    args = parse_arguments()
+
+    if args.auto:
+        export_model(args)
+        paddle2onnx()
+        mlir_prepare()
+        image_prepare()
+        onnx2mlir()
+        mlir2bmodel()
+
+    model_file = './ppyoloe/workspace/ppyoloe_crn_s_300e_coco_1684x_f32.bmodel' if args.auto else args.model_file
+    params_file = ""
+    config_file = './ppyoloe_r_crn_s_3x_dota/infer_cfg.yml' if args.auto else args.config_file
+    image_file = './P0861__1.0__1154___824.png' if args.auto else args.image
+
+    # 配置runtime，加载模型
+    runtime_option = fd.RuntimeOption()
+    runtime_option.use_sophgo()
+
+    model = fd.vision.detection.PPYOLOER(
+        model_file,
+        params_file,
+        config_file,
+        runtime_option=runtime_option,
+        model_format=fd.ModelFormat.SOPHGO)
+
+    # 预测图片分割结果
+    im = cv2.imread(image_file)
+    result = model.predict(im)
+    print(result)
+
+    # 可视化结果
+    vis_im = fd.vision.vis_detection(im, result, score_threshold=0.1)
+    cv2.imwrite("sophgo_result_ppyoloe_r.jpg", vis_im)
+    print("Visualized result save in ./sophgo_result_ppyoloe_r.jpg")
--- a/fastdeploy/runtime/backends/sophgo/sophgo_backend.cc
+++ b/fastdeploy/runtime/backends/sophgo/sophgo_backend.cc
@@ -176,6 +176,7 @@ bool SophgoBackend::Infer(std::vector<FDTensor>& inputs,
  bm_tensor_t input_tensors[input_size];
  bm_status_t status = BM_SUCCESS;

+  RUNTIME_PROFILE_LOOP_H2D_D2H_BEGIN
  bm_data_type_t* input_dtypes = net_info_->input_dtypes;
  for (int i = 0; i < input_size; i++) {
    status = bm_malloc_device_byte(handle_, &input_tensors[i].device_mem,
@@ -198,12 +199,14 @@ bool SophgoBackend::Infer(std::vector<FDTensor>& inputs,
    assert(BM_SUCCESS == status);
  }

+  RUNTIME_PROFILE_LOOP_BEGIN(1)
  bool launch_status = bmrt_launch_tensor_ex(
      p_bmrt_, net_name_.c_str(), input_tensors, net_info_->input_num,
      output_tensors, net_info_->output_num, true, false);
  assert(launch_status);
  status = bm_thread_sync(handle_);
  assert(status == BM_SUCCESS);
+  RUNTIME_PROFILE_LOOP_END

  outputs->resize(outputs_desc_.size());
  bm_data_type_t* output_dtypes = net_info_->output_dtypes;
@@ -231,6 +234,7 @@ bool SophgoBackend::Infer(std::vector<FDTensor>& inputs,
  for (int i = 0; i < output_size; i++) {
    bm_free_device(handle_, output_tensors[i].device_mem);
  }
+   RUNTIME_PROFILE_LOOP_H2D_D2H_END

  return true;
 }
--- a/fastdeploy/vision/common/result.cc
+++ b/fastdeploy/vision/common/result.cc
@@ -83,6 +83,7 @@ std::string Mask::Str() {

 DetectionResult::DetectionResult(const DetectionResult& res) {
  boxes.assign(res.boxes.begin(), res.boxes.end());
+  rotated_boxes.assign(res.rotated_boxes.begin(), res.rotated_boxes.end());
  scores.assign(res.scores.begin(), res.scores.end());
  label_ids.assign(res.label_ids.begin(), res.label_ids.end());
  contain_masks = res.contain_masks;
@@ -98,6 +99,7 @@ DetectionResult::DetectionResult(const DetectionResult& res) {
 DetectionResult& DetectionResult::operator=(DetectionResult&& other) {
  if (&other != this) {
    boxes = std::move(other.boxes);
+    rotated_boxes = std::move(other.rotated_boxes);
    scores = std::move(other.scores);
    label_ids = std::move(other.label_ids);
    contain_masks = std::move(other.contain_masks);
@@ -111,6 +113,7 @@ DetectionResult& DetectionResult::operator=(DetectionResult&& other) {

 void DetectionResult::Free() {
  std::vector<std::array<float, 4>>().swap(boxes);
+  std::vector<std::array<float, 8>>().swap(rotated_boxes);
  std::vector<float>().swap(scores);
  std::vector<int32_t>().swap(label_ids);
  std::vector<Mask>().swap(masks);
@@ -119,6 +122,7 @@ void DetectionResult::Free() {

 void DetectionResult::Clear() {
  boxes.clear();
+  rotated_boxes.clear();
  scores.clear();
  label_ids.clear();
  masks.clear();
@@ -127,6 +131,7 @@ void DetectionResult::Clear() {

 void DetectionResult::Reserve(int size) {
  boxes.reserve(size);
+  rotated_boxes.reserve(size);
  scores.reserve(size);
  label_ids.reserve(size);
  if (contain_masks) {
@@ -136,6 +141,7 @@ void DetectionResult::Reserve(int size) {

 void DetectionResult::Resize(int size) {
  boxes.resize(size);
+  rotated_boxes.resize(size);
  scores.resize(size);
  label_ids.resize(size);
  if (contain_masks) {
@@ -163,6 +169,19 @@ std::string DetectionResult::Str() {
      out += ", " + masks[i].Str();
    }
  }
+
+  for (size_t i = 0; i < rotated_boxes.size(); ++i) {
+    out = out + std::to_string(rotated_boxes[i][0]) + "," +
+          std::to_string(rotated_boxes[i][1]) + ", " +
+          std::to_string(rotated_boxes[i][2]) + ", " +
+          std::to_string(rotated_boxes[i][3]) + ", " +
+          std::to_string(rotated_boxes[i][4]) + "," +
+          std::to_string(rotated_boxes[i][5]) + ", " +
+          std::to_string(rotated_boxes[i][6]) + ", " +
+          std::to_string(rotated_boxes[i][7]) + ", " +
+          std::to_string(scores[i]) + ", " + std::to_string(label_ids[i]);
+    out += "\n";
+  }
  return out;
 }

--- a/fastdeploy/vision/common/result.h
+++ b/fastdeploy/vision/common/result.h
@@ -108,6 +108,9 @@ struct FASTDEPLOY_DECL DetectionResult : public BaseResult {
  /** \brief All the detected object boxes for an input image, the size of `boxes` is the number of detected objects, and the element of `boxes` is a array of 4 float values, means [xmin, ymin, xmax, ymax]
   */
  std::vector<std::array<float, 4>> boxes;
+  /** \brief All the detected rotated object boxes for an input image, the size of `boxes` is the number of detected objects, and the element of `rotated_boxes` is an array of 8 float values, means [x1, y1, x2, y2, x3, y3, x4, y4]
+   */
+  std::vector<std::array<float, 8>> rotated_boxes;
  /** \brief The confidence for all the detected objects
   */
  std::vector<float> scores;
--- a/fastdeploy/vision/detection/ppdet/model.h
+++ b/fastdeploy/vision/detection/ppdet/model.h
@@ -15,6 +15,7 @@
 #pragma once
 #include "fastdeploy/vision/detection/ppdet/base.h"
 #include "fastdeploy/vision/detection/ppdet/multiclass_nms.h"
+#include "fastdeploy/vision/detection/ppdet/multiclass_nms_rotated.h"

 namespace fastdeploy {
 namespace vision {
@@ -439,6 +440,23 @@ class FASTDEPLOY_DECL GFL : public PPDetBase {
  virtual std::string ModelName() const { return "PaddleDetection/GFL"; }
 };

+class FASTDEPLOY_DECL PPYOLOER : public PPDetBase {
+ public:
+  PPYOLOER(const std::string& model_file, const std::string& params_file,
+          const std::string& config_file,
+          const RuntimeOption& custom_option = RuntimeOption(),
+          const ModelFormat& model_format = ModelFormat::PADDLE)
+      : PPDetBase(model_file, params_file, config_file, custom_option,
+                  model_format) {
+    valid_cpu_backends = { Backend::PDINFER};
+    valid_gpu_backends = {Backend::PDINFER};
+    valid_sophgonpu_backends = {Backend::SOPHGOTPU};
+    initialized = Initialize();
+  }
+
+  virtual std::string ModelName() const { return "PPYOLOER"; }
+};
+
 }  // namespace detection
 }  // namespace vision
 }  // namespace fastdeploy
--- a/fastdeploy/vision/detection/ppdet/multiclass_nms.h
+++ b/fastdeploy/vision/detection/ppdet/multiclass_nms.h
@@ -61,7 +61,7 @@ struct PaddleMultiClassNMS {
               const std::vector<int64_t>& boxes_dim,
               const std::vector<int64_t>& scores_dim);

-  void SetNMSOption(const struct NMSOption &nms_option){
+  void SetNMSOption(const struct NMSOption &nms_option) {
    background_label = nms_option.background_label;
    keep_top_k = nms_option.keep_top_k;
    nms_eta = nms_option.nms_eta;
--- a/fastdeploy/vision/detection/ppdet/multiclass_nms_rotated.cc
+++ b/fastdeploy/vision/detection/ppdet/multiclass_nms_rotated.cc
@@ -0,0 +1,477 @@
+// Copyright (c) 2022 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 "fastdeploy/vision/detection/ppdet/multiclass_nms_rotated.h"
+
+#include <algorithm>
+#include <cmath>
+#include <opencv2/opencv.hpp>
+#include <vector>
+
+#include "fastdeploy/core/fd_tensor.h"
+#include "fastdeploy/utils/utils.h"
+#include "fastdeploy/vision/detection/ppdet/multiclass_nms.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace detection {
+
+template <typename T>
+struct RotatedBox {
+  T x_ctr, y_ctr, w, h, a;
+};
+
+template <typename T>
+struct Point {
+  T x, y;
+  Point(const T& px = 0, const T& py = 0) : x(px), y(py) {}
+  Point operator+(const Point& p) const { return Point(x + p.x, y + p.y); }
+  Point& operator+=(const Point& p) {
+    x += p.x;
+    y += p.y;
+    return *this;
+  }
+  Point operator-(const Point& p) const { return Point(x - p.x, y - p.y); }
+  Point operator*(const T coeff) const { return Point(x * coeff, y * coeff); }
+};
+
+template <typename T>
+T Dot2D(const Point<T>& A, const Point<T>& B) {
+  return A.x * B.x + A.y * B.y;
+}
+
+template <typename T>
+T Cross2D(const Point<T>& A, const Point<T>& B) {
+  return A.x * B.y - B.x * A.y;
+}
+
+template <typename T>
+int GetIntersectionPoints(const Point<T> (&pts1)[4],
+                            const Point<T> (&pts2)[4],
+                            Point<T> (&intersections)[24]) {
+  // Line vector
+  // A line from p1 to p2 is: p1 + (p2-p1)*t, t=[0,1]
+  Point<T> vec1[4], vec2[4];
+  for (int i = 0; i < 4; i++) {
+    vec1[i] = pts1[(i + 1) % 4] - pts1[i];
+    vec2[i] = pts2[(i + 1) % 4] - pts2[i];
+  }
+
+  // Line test - test all line combos for intersection
+  int num = 0;  // number of intersections
+  for (int i = 0; i < 4; i++) {
+    for (int j = 0; j < 4; j++) {
+      // Solve for 2x2 Ax=b
+      T det = Cross2D<T>(vec2[j], vec1[i]);
+
+      // This takes care of parallel lines
+      if (fabs(det) <= 1e-14) {
+        continue;
+      }
+
+      auto vec12 = pts2[j] - pts1[i];
+
+      T t1 = Cross2D<T>(vec2[j], vec12) / det;
+      T t2 = Cross2D<T>(vec1[i], vec12) / det;
+
+      if (t1 >= 0.0f && t1 <= 1.0f && t2 >= 0.0f && t2 <= 1.0f) {
+        intersections[num++] = pts1[i] + vec1[i] * t1;
+      }
+    }
+  }
+
+  // Check for vertices of rect1 inside rect2
+  {
+    const auto& AB = vec2[0];
+    const auto& DA = vec2[3];
+    auto ABdotAB = Dot2D<T>(AB, AB);
+    auto ADdotAD = Dot2D<T>(DA, DA);
+    for (int i = 0; i < 4; i++) {
+      // assume ABCD is the rectangle, and P is the point to be judged
+      // P is inside ABCD iff. P's projection on AB lies within AB
+      // and P's projection on AD lies within AD
+
+      auto AP = pts1[i] - pts2[0];
+
+      auto APdotAB = Dot2D<T>(AP, AB);
+      auto APdotAD = -Dot2D<T>(AP, DA);
+
+      if ((APdotAB >= 0) && (APdotAD >= 0) && (APdotAB <= ABdotAB) &&
+          (APdotAD <= ADdotAD)) {
+        intersections[num++] = pts1[i];
+      }
+    }
+  }
+
+  // Reverse the check - check for vertices of rect2 inside rect1
+  {
+    const auto& AB = vec1[0];
+    const auto& DA = vec1[3];
+    auto ABdotAB = Dot2D<T>(AB, AB);
+    auto ADdotAD = Dot2D<T>(DA, DA);
+    for (int i = 0; i < 4; i++) {
+      auto AP = pts2[i] - pts1[0];
+
+      auto APdotAB = Dot2D<T>(AP, AB);
+      auto APdotAD = -Dot2D<T>(AP, DA);
+
+      if ((APdotAB >= 0) && (APdotAD >= 0) && (APdotAB <= ABdotAB) &&
+          (APdotAD <= ADdotAD)) {
+        intersections[num++] = pts2[i];
+      }
+    }
+  }
+
+  return num;
+}
+
+template <typename T>
+int ConvexHullGraham(const Point<T> (&p)[24], const int& num_in,
+                       Point<T> (&q)[24], bool shift_to_zero = false) {
+  assert(num_in >= 2);
+
+  // Step 1:
+  // Find point with minimum y
+  // if more than 1 points have the same minimum y,
+  // pick the one with the minimum x.
+  int t = 0;
+  for (int i = 1; i < num_in; i++) {
+    if (p[i].y < p[t].y || (p[i].y == p[t].y && p[i].x < p[t].x)) {
+      t = i;
+    }
+  }
+  auto& start = p[t];  // starting point
+
+  // Step 2:
+  // Subtract starting point from every points (for sorting in the next step)
+  for (int i = 0; i < num_in; i++) {
+    q[i] = p[i] - start;
+  }
+
+  // Swap the starting point to position 0
+  auto tmp = q[0];
+  q[0] = q[t];
+  q[t] = tmp;
+
+  // Step 3:
+  // Sort point 1 ~ num_in according to their relative cross-product values
+  // (essentially sorting according to angles)
+  // If the angles are the same, sort according to their distance to origin
+  T dist[24];
+  for (int i = 0; i < num_in; i++) {
+    dist[i] = Dot2D<T>(q[i], q[i]);
+  }
+
+  // CPU version
+  std::sort(q + 1, q + num_in,
+            [](const Point<T>& A, const Point<T>& B) -> bool {
+              T temp = Cross2D<T>(A, B);
+              if (fabs(temp) < 1e-6) {
+                return Dot2D<T>(A, A) < Dot2D<T>(B, B);
+              } else {
+                return temp > 0;
+              }
+            });
+
+  // Step 4:
+  // Make sure there are at least 2 points (that don't overlap with each other)
+  // in the stack
+  int k;  // index of the non-overlapped second point
+  for (k = 1; k < num_in; k++) {
+    if (dist[k] > 1e-8) {
+      break;
+    }
+  }
+  if (k == num_in) {
+    // We reach the end, which means the convex hull is just one point
+    q[0] = p[t];
+    return 1;
+  }
+  q[1] = q[k];
+  int m = 2;  // 2 points in the stack
+  // Step 5:
+  // Finally we can start the scanning process.
+  // When a non-convex relationship between the 3 points is found
+  // (either concave shape or duplicated points),
+  // we pop the previous point from the stack
+  // until the 3-point relationship is convex again, or
+  // until the stack only contains two points
+  for (int i = k + 1; i < num_in; i++) {
+    while (m > 1 && Cross2D<T>(q[i] - q[m - 2], q[m - 1] - q[m - 2]) >= 0) {
+      m--;
+    }
+    q[m++] = q[i];
+  }
+
+  // Step 6 (Optional):
+  // In general sense we need the original coordinates, so we
+  // need to shift the points back (reverting Step 2)
+  // But if we're only interested in getting the area/perimeter of the shape
+  // We can simply return.
+  if (!shift_to_zero) {
+    for (int i = 0; i < m; i++) {
+      q[i] += start;
+    }
+  }
+
+  return m;
+}
+
+template <typename T>
+T PolygonArea(const Point<T> (&q)[24], const int& m) {
+  if (m <= 2) {
+    return 0;
+  }
+
+  T area = 0;
+  for (int i = 1; i < m - 1; i++) {
+    area += fabs(Cross2D<T>(q[i] - q[0], q[i + 1] - q[0]));
+  }
+
+  return area / 2.0;
+}
+
+template <typename T>
+T RboxesIntersection(T const* const poly1_raw, T const* const poly2_raw) {
+  // There are up to 4 x 4 + 4 + 4 = 24 intersections (including dups) returned
+  // from rotated_rect_intersection_pts
+  Point<T> intersectPts[24], orderedPts[24];
+
+  Point<T> pts1[4];
+
+  Point<T> pts2[4];
+  for (int i = 0; i < 4; i++) {
+    pts1[i] = Point<T>(poly1_raw[2 * i], poly1_raw[2 * i + 1]);
+    pts2[i] = Point<T>(poly2_raw[2 * i], poly2_raw[2 * i + 1]);
+  }
+
+  int num = GetIntersectionPoints<T>(pts1, pts2, intersectPts);
+  if (num <= 2) {
+    return 0.0;
+  }
+
+  // Convex Hull to order the intersection points in clockwise order and find
+  // the contour area.
+  int num_convex = ConvexHullGraham<T>(intersectPts, num, orderedPts, true);
+  return PolygonArea<T>(orderedPts, num_convex);
+}
+
+template <typename T>
+T PolyArea(T const* const poly_raw) {
+  T area = 0.0;
+  int j = 3;
+  for (int i = 0; i < 4; i++) {
+    // area += (x[j] + x[i]) * (y[j] - y[i]);
+    area += (poly_raw[2 * j] + poly_raw[2 * i]) *
+            (poly_raw[2 * j + 1] - poly_raw[2 * i + 1]);
+    j = i;
+  }
+  // return static_cast<T>(abs(static_cast<float>(area) / 2.0));
+  return std::abs(area / 2.0);
+}
+
+template <typename T>
+void Poly2Rbox(T const* const poly_raw, RotatedBox<T>& box) {
+  std::vector<cv::Point2f> contour_poly{
+      cv::Point2f(poly_raw[0], poly_raw[1]),
+      cv::Point2f(poly_raw[2], poly_raw[3]),
+      cv::Point2f(poly_raw[4], poly_raw[5]),
+      cv::Point2f(poly_raw[6], poly_raw[7]),
+  };
+  cv::RotatedRect rotate_rect = cv::minAreaRect(contour_poly);
+  box.x_ctr = rotate_rect.center.x;
+  box.y_ctr = rotate_rect.center.y;
+  box.w = rotate_rect.size.width;
+  box.h = rotate_rect.size.height;
+  box.a = rotate_rect.angle;
+}
+
+template <typename T>
+T RboxIouSingle(T const* const poly1_raw, T const* const poly2_raw) {
+  const T area1 = PolyArea(poly1_raw);
+  const T area2 = PolyArea(poly2_raw);
+
+  const T intersection = RboxesIntersection<T>(poly1_raw, poly2_raw);
+  const T iou = intersection / (area1 + area2 - intersection);
+  return iou;
+}
+
+template <typename T>
+bool SortScorePairDescendRotated(const std::pair<float, T>& pair1,
+                                 const std::pair<float, T>& pair2) {
+  return pair1.first > pair2.first;
+}
+
+void GetMaxScoreIndexRotated(
+    const float* scores, const int& score_size, const float& threshold,
+    const int& top_k, std::vector<std::pair<float, int>>* sorted_indices) {
+  for (size_t i = 0; i < score_size; ++i) {
+    if (scores[i] > threshold) {
+      sorted_indices->push_back(std::make_pair(scores[i], i));
+    }
+  }
+  // Sort the score pair according to the scores in descending order
+  std::stable_sort(sorted_indices->begin(), sorted_indices->end(),
+                   SortScorePairDescendRotated<int>);
+  // Keep top_k scores if needed.
+  if (top_k > -1 && top_k < static_cast<int>(sorted_indices->size())) {
+    sorted_indices->resize(top_k);
+  }
+}
+
+void PaddleMultiClassNMSRotated::FastNMSRotated(
+    const float* boxes, const float* scores, const int& num_boxes,
+    std::vector<int>* keep_indices) {
+  std::vector<std::pair<float, int>> sorted_indices;
+  GetMaxScoreIndexRotated(scores, num_boxes, score_threshold, nms_top_k,
+                          &sorted_indices);
+  // printf("nms thrd: %f, sort dim: %d\n", nms_threshold,
+  // int(sorted_indices.size()));
+  float adaptive_threshold = nms_threshold;
+  while (sorted_indices.size() != 0) {
+    const int idx = sorted_indices.front().second;
+    bool keep = true;
+    for (size_t k = 0; k < keep_indices->size(); ++k) {
+      if (!keep) {
+        break;
+      }
+      const int kept_idx = (*keep_indices)[k];
+      float overlap =
+          RboxIouSingle<float>(boxes + idx * 8, boxes + kept_idx * 8);
+
+      keep = overlap <= adaptive_threshold;
+    }
+    if (keep) {
+      keep_indices->push_back(idx);
+    }
+    sorted_indices.erase(sorted_indices.begin());
+    if (keep && nms_eta<1.0 & adaptive_threshold> 0.5) {
+      adaptive_threshold *= nms_eta;
+    }
+  }
+}
+
+int PaddleMultiClassNMSRotated::NMSRotatedForEachSample(
+    const float* boxes, const float* scores, int num_boxes, int num_classes,
+    std::map<int, std::vector<int>>* keep_indices) {
+  for (int i = 0; i < num_classes; ++i) {
+    if (i == background_label) {
+      continue;
+    }
+    const float* score_for_class_i = scores + i * num_boxes;
+    FastNMSRotated(boxes, score_for_class_i, num_boxes, &((*keep_indices)[i]));
+  }
+  int num_det = 0;
+  for (auto iter = keep_indices->begin(); iter != keep_indices->end(); ++iter) {
+    num_det += iter->second.size();
+  }
+
+  if (keep_top_k > -1 && num_det > keep_top_k) {
+    std::vector<std::pair<float, std::pair<int, int>>> score_index_pairs;
+    for (const auto& it : *keep_indices) {
+      int label = it.first;
+      const float* current_score = scores + label * num_boxes;
+      auto& label_indices = it.second;
+      for (size_t j = 0; j < label_indices.size(); ++j) {
+        int idx = label_indices[j];
+        score_index_pairs.push_back(
+            std::make_pair(current_score[idx], std::make_pair(label, idx)));
+      }
+    }
+
+    std::stable_sort(score_index_pairs.begin(), score_index_pairs.end(),
+                     SortScorePairDescendRotated<std::pair<int, int>>);
+    score_index_pairs.resize(keep_top_k);
+
+    std::map<int, std::vector<int>> new_indices;
+    for (size_t j = 0; j < score_index_pairs.size(); ++j) {
+      int label = score_index_pairs[j].second.first;
+      int idx = score_index_pairs[j].second.second;
+      new_indices[label].push_back(idx);
+    }
+    new_indices.swap(*keep_indices);
+    num_det = keep_top_k;
+  }
+  return num_det;
+}
+
+void PaddleMultiClassNMSRotated::Compute(
+    const float* boxes_data, const float* scores_data,
+    const std::vector<int64_t>& boxes_dim,
+    const std::vector<int64_t>& scores_dim) {
+  int score_size = scores_dim.size();
+
+  int64_t batch_size = scores_dim[0];
+  int64_t box_dim = boxes_dim[2];
+  int64_t out_dim = box_dim + 2;
+
+  int num_nmsed_out = 0;
+  FDASSERT(score_size == 3,
+           "Require rank of input scores be 3, but now it's %d.", score_size);
+  FDASSERT(boxes_dim[2] == 8,
+           "Require the 3-dimension of input boxes be 8, but now it's %lld.",
+           box_dim);
+  out_num_rois_data.resize(batch_size);
+
+  std::vector<std::map<int, std::vector<int>>> all_indices;
+  for (size_t i = 0; i < batch_size; ++i) {
+    std::map<int, std::vector<int>> indices;  // indices kept for each class
+    const float* current_boxes_ptr =
+        boxes_data + i * boxes_dim[1] * boxes_dim[2];
+    const float* current_scores_ptr =
+        scores_data + i * scores_dim[1] * scores_dim[2];
+    int num = NMSRotatedForEachSample(current_boxes_ptr, current_scores_ptr,
+                                      boxes_dim[1], scores_dim[1], &indices);
+    num_nmsed_out += num;
+    out_num_rois_data[i] = num;
+    all_indices.emplace_back(indices);
+  }
+  std::vector<int64_t> out_box_dims = {num_nmsed_out, 10};
+  std::vector<int64_t> out_index_dims = {num_nmsed_out, 1};
+  if (num_nmsed_out == 0) {
+    for (size_t i = 0; i < batch_size; ++i) {
+      out_num_rois_data[i] = 0;
+    }
+    return;
+  }
+  out_box_data.resize(num_nmsed_out * 10);
+  out_index_data.resize(num_nmsed_out);
+
+  int count = 0;
+  for (size_t i = 0; i < batch_size; ++i) {
+    const float* current_boxes_ptr =
+        boxes_data + i * boxes_dim[1] * boxes_dim[2];
+    const float* current_scores_ptr =
+        scores_data + i * scores_dim[1] * scores_dim[2];
+    for (const auto& it : all_indices[i]) {
+      int label = it.first;
+      const auto& indices = it.second;
+      const float* current_scores_class_ptr =
+          current_scores_ptr + label * scores_dim[2];
+      for (size_t j = 0; j < indices.size(); ++j) {
+        int start = count * 10;
+        out_box_data[start] = label;
+        out_box_data[start + 1] = current_scores_class_ptr[indices[j]];
+        for (int k = 0; k < 8; k++) {
+          out_box_data[start + 2 + k] = current_boxes_ptr[indices[j] * 8 + k];
+        }
+        out_index_data[count] = i * boxes_dim[1] + indices[j];
+        count += 1;
+      }
+    }
+  }
+}
+}  // namespace detection
+}  // namespace vision
+}  // namespace fastdeploy
--- a/fastdeploy/vision/detection/ppdet/multiclass_nms_rotated.h
+++ b/fastdeploy/vision/detection/ppdet/multiclass_nms_rotated.h
@@ -0,0 +1,76 @@
+// Copyright (c) 2022 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 <map>
+#include <string>
+#include <vector>
+
+namespace fastdeploy {
+namespace vision {
+namespace detection {
+/** \brief Config for PaddleMultiClassNMSRotated
+   * \param[in] background_label the value of background label
+   * \param[in] keep_top_k the value of keep_top_k
+   * \param[in] nms_eta the value of nms_eta
+   * \param[in] nms_threshold a dict that contains the arguments of nms operations
+   * \param[in] nms_top_k if there are more than max_num bboxes after NMS, only top max_num will be kept.
+   * \param[in] normalized Determine whether normalized is required
+   * \param[in] score_threshold bbox threshold, bboxes with scores lower than it will not be considered.
+   */
+struct NMSRotatedOption{
+  NMSRotatedOption() = default;
+  int64_t background_label = -1;
+  int64_t keep_top_k = -1;
+  float nms_eta = 1.0;
+  float nms_threshold = 0.1;
+  int64_t nms_top_k = 2000;
+  bool normalized = false;
+  float score_threshold = 0.1;
+};
+
+struct PaddleMultiClassNMSRotated {
+  int64_t background_label = -1;
+  int64_t keep_top_k = -1;
+  float nms_eta;
+  float nms_threshold = 0.1;
+  int64_t nms_top_k;
+  bool normalized;
+  float score_threshold;
+
+  std::vector<int32_t> out_num_rois_data;
+  std::vector<int32_t> out_index_data;
+  std::vector<float> out_box_data;
+  void FastNMSRotated(const float* boxes, const float* scores, const int& num_boxes,
+               std::vector<int>* keep_indices);
+  int NMSRotatedForEachSample(const float* boxes, const float* scores, int num_boxes,
+                       int num_classes,
+                       std::map<int, std::vector<int>>* keep_indices);
+  void Compute(const float* ploy_boxes, const float* scores,
+               const std::vector<int64_t>& boxes_dim,
+               const std::vector<int64_t>& scores_dim);
+
+  void SetNMSRotatedOption(const struct NMSRotatedOption &nms_rotated_option) {
+    background_label = nms_rotated_option.background_label;
+    keep_top_k = nms_rotated_option.keep_top_k;
+    nms_eta = nms_rotated_option.nms_eta;
+    nms_threshold = nms_rotated_option.nms_threshold;
+    nms_top_k = nms_rotated_option.nms_top_k;
+    normalized = nms_rotated_option.normalized;
+    score_threshold = nms_rotated_option.score_threshold;
+  }
+};
+}  // namespace detection
+}  // namespace vision
+}  // namespace fastdeploy
--- a/fastdeploy/vision/detection/ppdet/postprocessor.cc
+++ b/fastdeploy/vision/detection/ppdet/postprocessor.cc
@@ -264,6 +264,57 @@ bool PaddleDetPostprocessor::ProcessSolov2(
  return true;
 }

+bool PaddleDetPostprocessor::ProcessPPYOLOER(
+    const std::vector<FDTensor>& tensors,
+    std::vector<DetectionResult>* results) {
+  if (tensors.size() != 2) {
+    FDERROR << "The size of tensors for PPYOLOER must be 2." << std::endl;
+    return false;
+  }
+
+  int boxes_index = 0;
+  int scores_index = 1;
+  multi_class_nms_rotated_.Compute(
+      static_cast<const float*>(tensors[boxes_index].Data()),
+      static_cast<const float*>(tensors[scores_index].Data()),
+      tensors[boxes_index].shape, tensors[scores_index].shape);
+  auto num_boxes = multi_class_nms_rotated_.out_num_rois_data;
+  auto box_data =
+      static_cast<const float*>(multi_class_nms_rotated_.out_box_data.data());
+
+  // Get boxes for each input image
+  results->resize(num_boxes.size());
+  int offset = 0;
+  for (size_t i = 0; i < num_boxes.size(); ++i) {
+    const float* ptr = box_data + offset;
+    (*results)[i].Reserve(num_boxes[i]);
+    for (size_t j = 0; j < num_boxes[i]; ++j) {
+      (*results)[i].label_ids.push_back(
+          static_cast<int32_t>(round(ptr[j * 10])));
+      (*results)[i].scores.push_back(ptr[j * 10 + 1]);
+      (*results)[i].rotated_boxes.push_back(std::array<float, 8>(
+          {ptr[j * 10 + 2], ptr[j * 10 + 3], ptr[j * 10 + 4], ptr[j * 10 + 5],
+           ptr[j * 10 + 6], ptr[j * 10 + 7], ptr[j * 10 + 8],
+           ptr[j * 10 + 9]}));
+    }
+    offset += (num_boxes[i] * 10);
+  }
+
+  // do scale
+  if (GetScaleFactor()[0] != 0) {
+    for (auto& result : *results) {
+      for (int i = 0; i < result.rotated_boxes.size(); i++) {
+        for (int j = 0; j < 8; j++) {
+          auto scale = i % 2 == 0 ? GetScaleFactor()[1] : GetScaleFactor()[0];
+          result.rotated_boxes[i][j] /= float(scale);
+        }
+      }
+    }
+  }
+
+  return true;
+}
+
 bool PaddleDetPostprocessor::Run(const std::vector<FDTensor>& tensors,
                                 std::vector<DetectionResult>* results) {
  if (arch_ == "SOLOv2") {
@@ -272,6 +323,10 @@ bool PaddleDetPostprocessor::Run(const std::vector<FDTensor>& tensors,
    // The fourth output of solov2 is mask
    return ProcessMask(tensors[3], results);
  } else {
+    if (tensors[0].Shape().size() == 3 && tensors[0].Shape()[2] == 8) {  // PPYOLOER
+      return ProcessPPYOLOER(tensors, results);
+    }
+
    // Do process according to whether NMS exists.
    if (with_nms_) {
      if (!ProcessWithNMS(tensors, results)) {
--- a/fastdeploy/vision/detection/ppdet/postprocessor.h
+++ b/fastdeploy/vision/detection/ppdet/postprocessor.h
@@ -16,6 +16,7 @@
 #include "fastdeploy/vision/common/processors/transform.h"
 #include "fastdeploy/vision/common/result.h"
 #include "fastdeploy/vision/detection/ppdet/multiclass_nms.h"
+#include "fastdeploy/vision/detection/ppdet/multiclass_nms_rotated.h"

 namespace fastdeploy {
 namespace vision {
@@ -28,6 +29,7 @@ class FASTDEPLOY_DECL PaddleDetPostprocessor {
    // There may be no NMS config in the yaml file,
    // so we need to give a initial value to multi_class_nms_.
    multi_class_nms_.SetNMSOption(NMSOption());
+    multi_class_nms_rotated_.SetNMSRotatedOption(NMSRotatedOption());
  }

  /** \brief Create a preprocessor instance for PaddleDet serials model
@@ -40,6 +42,7 @@ class FASTDEPLOY_DECL PaddleDetPostprocessor {
    // There may be no NMS config in the yaml file,
    // so we need to give a initial value to multi_class_nms_.
    multi_class_nms_.SetNMSOption(NMSOption());
+    multi_class_nms_rotated_.SetNMSRotatedOption(NMSRotatedOption());
  }

  /** \brief Process the result of runtime and fill to ClassifyResult structure
@@ -55,6 +58,12 @@ class FASTDEPLOY_DECL PaddleDetPostprocessor {
  /// only available for those model exported without box decoding and nms.
  void ApplyNMS() { with_nms_ = false; }

+  /// If you do not want to modify the Yaml configuration file,
+  /// you can use this function to set rotated NMS parameters.
+  void SetNMSRotatedOption(const NMSRotatedOption& option) {
+    multi_class_nms_rotated_.SetNMSRotatedOption(option);
+  }
+
  /// If you do not want to modify the Yaml configuration file,
  /// you can use this function to set NMS parameters.
  void SetNMSOption(const NMSOption& option) {
@@ -79,6 +88,8 @@ class FASTDEPLOY_DECL PaddleDetPostprocessor {

  PaddleMultiClassNMS multi_class_nms_{};

+  PaddleMultiClassNMSRotated multi_class_nms_rotated_{};
+
  // Process for General tensor without nms.
  bool ProcessWithoutNMS(const std::vector<FDTensor>& tensors,
                         std::vector<DetectionResult>* results);
@@ -91,6 +102,10 @@ class FASTDEPLOY_DECL PaddleDetPostprocessor {
  bool ProcessSolov2(const std::vector<FDTensor>& tensors,
                     std::vector<DetectionResult>* results);

+  // Process PPYOLOER
+  bool ProcessPPYOLOER(const std::vector<FDTensor>& tensors,
+                        std::vector<DetectionResult>* results);
+
  // Process mask tensor for MaskRCNN
  bool ProcessMask(const FDTensor& tensor,
                   std::vector<DetectionResult>* results);
--- a/fastdeploy/vision/detection/ppdet/ppdet_pybind.cc
+++ b/fastdeploy/vision/detection/ppdet/ppdet_pybind.cc
@@ -78,6 +78,11 @@ void BindPPDet(pybind11::module& m) {
              vision::detection::NMSOption option) {
             self.SetNMSOption(option);
           })
+      .def("set_nms_rotated_option",
+           [](vision::detection::PaddleDetPostprocessor& self,
+              vision::detection::NMSRotatedOption option) {
+             self.SetNMSRotatedOption(option);
+           })
      .def("apply_nms",
           [](vision::detection::PaddleDetPostprocessor& self) {
             self.ApplyNMS();
@@ -233,5 +238,26 @@ void BindPPDet(pybind11::module& m) {
      m, "SOLOv2")
      .def(pybind11::init<std::string, std::string, std::string, RuntimeOption,
                          ModelFormat>());
+
+  pybind11::class_<vision::detection::PPYOLOER, vision::detection::PPDetBase>(
+      m, "PPYOLOER")
+      .def(pybind11::init<std::string, std::string, std::string, RuntimeOption,
+                          ModelFormat>());
+
+  pybind11::class_<vision::detection::NMSRotatedOption>(m, "NMSRotatedOption")
+      .def(pybind11::init())
+      .def_readwrite("background_label",
+                     &vision::detection::NMSRotatedOption::background_label)
+      .def_readwrite("keep_top_k",
+                     &vision::detection::NMSRotatedOption::keep_top_k)
+      .def_readwrite("nms_eta", &vision::detection::NMSRotatedOption::nms_eta)
+      .def_readwrite("nms_threshold",
+                     &vision::detection::NMSRotatedOption::nms_threshold)
+      .def_readwrite("nms_top_k",
+                     &vision::detection::NMSRotatedOption::nms_top_k)
+      .def_readwrite("normalized",
+                     &vision::detection::NMSRotatedOption::normalized)
+      .def_readwrite("score_threshold",
+                     &vision::detection::NMSRotatedOption::score_threshold);
 }
 }  // namespace fastdeploy
--- a/fastdeploy/vision/vision_pybind.cc
+++ b/fastdeploy/vision/vision_pybind.cc
@@ -84,13 +84,14 @@ void BindVision(pybind11::module& m) {
      .def(pybind11::init())
      .def_readwrite("boxes", &vision::DetectionResult::boxes)
      .def_readwrite("scores", &vision::DetectionResult::scores)
+      .def_readwrite("rotated_boxes", &vision::DetectionResult::rotated_boxes)
      .def_readwrite("label_ids", &vision::DetectionResult::label_ids)
      .def_readwrite("masks", &vision::DetectionResult::masks)
      .def_readwrite("contain_masks", &vision::DetectionResult::contain_masks)
      .def(pybind11::pickle(
          [](const vision::DetectionResult& d) {
-            return pybind11::make_tuple(d.boxes, d.scores, d.label_ids, d.masks,
-                                        d.contain_masks);
+            return pybind11::make_tuple(d.boxes, d.scores, d.rotated_boxes,
+                                        d.label_ids, d.masks, d.contain_masks);
          },
          [](pybind11::tuple t) {
            if (t.size() != 5)
@@ -99,6 +100,7 @@ void BindVision(pybind11::module& m) {

            vision::DetectionResult d;
            d.boxes = t[0].cast<std::vector<std::array<float, 4>>>();
+            d.rotated_boxes = t[0].cast<std::vector<std::array<float, 8>>>();
            d.scores = t[1].cast<std::vector<float>>();
            d.label_ids = t[2].cast<std::vector<int32_t>>();
            d.masks = t[3].cast<std::vector<vision::Mask>>();
--- a/fastdeploy/vision/visualize/detection.cc
+++ b/fastdeploy/vision/visualize/detection.cc
@@ -22,7 +22,7 @@ namespace vision {

 cv::Mat VisDetection(const cv::Mat& im, const DetectionResult& result,
                     float score_threshold, int line_size, float font_size) {
-  if (result.boxes.empty()) {
+  if (result.boxes.empty() && result.rotated_boxes.empty()) {
    return im;
  }
  if (result.contain_masks) {
@@ -38,6 +38,45 @@ cv::Mat VisDetection(const cv::Mat& im, const DetectionResult& result,
  int h = im.rows;
  int w = im.cols;
  auto vis_im = im.clone();
+  for (size_t i = 0; i < result.rotated_boxes.size(); ++i) {
+    if (result.scores[i] < score_threshold) {
+      continue;
+    }
+
+    int c0 = color_map[3 * result.label_ids[i] + 0];
+    int c1 = color_map[3 * result.label_ids[i] + 1];
+    int c2 = color_map[3 * result.label_ids[i] + 2];
+    cv::Scalar rect_color = cv::Scalar(c0, c1, c2);
+    std::string id = std::to_string(result.label_ids[i]);
+    std::string score = std::to_string(result.scores[i]);
+    if (score.size() > 4) {
+      score = score.substr(0, 4);
+    }
+    std::string text = id + ", " + score;
+    int font = cv::FONT_HERSHEY_SIMPLEX;
+    cv::Size text_size = cv::getTextSize(text, font, font_size, 1, nullptr);
+
+    for (int j = 0; j < 4; j++) {
+      auto start = cv::Point(
+          static_cast<int>(round(result.rotated_boxes[i][2 * j])),
+          static_cast<int>(round(result.rotated_boxes[i][2 * j + 1])));
+
+      cv::Point end;
+      if (j != 3) {
+        end = cv::Point(
+            static_cast<int>(round(result.rotated_boxes[i][2 * (j + 1)])),
+            static_cast<int>(round(result.rotated_boxes[i][2 * (j + 1) + 1])));
+      } else {
+        end = cv::Point(static_cast<int>(round(result.rotated_boxes[i][0])),
+                        static_cast<int>(round(result.rotated_boxes[i][1])));
+        cv::putText(vis_im, text, end, font, font_size,
+                    cv::Scalar(255, 255, 255), 1);
+      }
+      cv::line(vis_im, start, end, cv::Scalar(255, 255, 255), 3, cv::LINE_AA,
+               0);
+    }
+  }
+
  for (size_t i = 0; i < result.boxes.size(); ++i) {
    if (result.scores[i] < score_threshold) {
      continue;
@@ -125,6 +164,44 @@ cv::Mat VisDetection(const cv::Mat& im, const DetectionResult& result,
  int h = im.rows;
  int w = im.cols;
  auto vis_im = im.clone();
+  for (size_t i = 0; i < result.rotated_boxes.size(); ++i) {
+    if (result.scores[i] < score_threshold) {
+      continue;
+    }
+
+    int c0 = color_map[3 * result.label_ids[i] + 0];
+    int c1 = color_map[3 * result.label_ids[i] + 1];
+    int c2 = color_map[3 * result.label_ids[i] + 2];
+    cv::Scalar rect_color = cv::Scalar(c0, c1, c2);
+    std::string id = std::to_string(result.label_ids[i]);
+    std::string score = std::to_string(result.scores[i]);
+    if (score.size() > 4) {
+      score = score.substr(0, 4);
+    }
+    std::string text = id + ", " + score;
+    int font = cv::FONT_HERSHEY_SIMPLEX;
+    cv::Size text_size = cv::getTextSize(text, font, font_size, 1, nullptr);
+
+    for (int j = 0; j < 4; j++) {
+      auto start = cv::Point(
+          static_cast<int>(round(result.rotated_boxes[i][2 * j])),
+          static_cast<int>(round(result.rotated_boxes[i][2 * j + 1])));
+
+      cv::Point end;
+      if (j == 3) {
+        end = cv::Point(
+            static_cast<int>(round(result.rotated_boxes[i][2 * j])),
+            static_cast<int>(round(result.rotated_boxes[i][2 * j + 1])));
+      } else {
+        end = cv::Point(static_cast<int>(round(result.rotated_boxes[i][0])),
+                        static_cast<int>(round(result.rotated_boxes[i][1])));
+        cv::putText(vis_im, text, end, font, font_size,
+                    cv::Scalar(255, 255, 255), 1);
+      }
+      cv::line(vis_im, start, end, cv::Scalar(255, 255, 255), 3, cv::LINE_AA,
+               0);
+    }
+  }
  for (size_t i = 0; i < result.boxes.size(); ++i) {
    if (result.scores[i] < score_threshold) {
      continue;
--- a/python/fastdeploy/vision/detection/ppdet/init.py
+++ b/python/fastdeploy/vision/detection/ppdet/init.py
@@ -50,6 +50,15 @@ class NMSOption:
        return self.nms_option.background_label


+class NMSRotatedOption:
+    def __init__(self):
+        self.nms_rotated_option = C.vision.detection.NMSRotatedOption()
+
+    @property
+    def background_label(self):
+        return self.nms_rotated_option.background_label
+
+
 class PaddleDetPostprocessor:
    def __init__(self):
        """Create a postprocessor for PaddleDetection Model
@@ -75,6 +84,14 @@ class PaddleDetPostprocessor:
            nms_option = NMSOption()
        self._postprocessor.set_nms_option(self, nms_option.nms_option)

+    def set_nms_rotated_option(self, nms_rotated_option=None):
+        """This function will enable decode and rotated nms in postprocess step.
+        """
+        if nms_rotated_option is None:
+            nms_rotated_option = NMSRotatedOption()
+        self._postprocessor.set_nms_rotated_option(
+            self, nms_rotated_option.nms_rotated_option)
+

 class PPYOLOE(FastDeployModel):
    def __init__(self,
@@ -781,3 +798,40 @@ class GFL(PPYOLOE):
                                             config_file, self._runtime_option,
                                             model_format)
        assert self.initialized, "GFL model initialize failed."
+
+
+class PPYOLOER(PPYOLOE):
+    def __init__(self,
+                 model_file,
+                 params_file,
+                 config_file,
+                 runtime_option=None,
+                 model_format=ModelFormat.PADDLE):
+        """Load a PPYOLOER model exported by PaddleDetection.
+
+        :param model_file: (str)Path of model file, e.g ppyoloe_r/model.pdmodel
+        :param params_file: (str)Path of parameters file, e.g ppyoloe_r/model.pdiparams, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param config_file: (str)Path of configuration file for deployment, e.g ppyoloe_r/infer_cfg.yml
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model
+        """
+
+        super(PPYOLOE, self).__init__(runtime_option)
+
+        self._model = C.vision.detection.PPYOLOER(
+            model_file, params_file, config_file, self._runtime_option,
+            model_format)
+        assert self.initialized, "PicoDet model initialize failed."
+
+    def clone(self):
+        """Clone PPYOLOER object
+
+        :return: a new PPYOLOER object
+        """
+
+        class PPYOLOERClone(PPYOLOER):
+            def __init__(self, model):
+                self._model = model
+
+        clone_model = PPYOLOERClone(self._model.clone())
+        return clone_model