[LaneSeg] Fix a bug in cpp project

contrib/LaneSeg/README.md

@@ -227,6 +227,6 @@ python deploy/python/infer.py --help
 ```
 
 #### Paddle Inference（C++）
-reference [Paddle Inference tutorial](../../deploy/cpp/)
+reference [Paddle Inference tutorial](./deploy/cpp/README.md)
 
 the C++ sources files of the project is in LaneSeg/deploy/cpp

contrib/LaneSeg/README_CN.md

@@ -230,6 +230,6 @@ python deploy/python/infer.py --help
 ```
 
 #### Paddle Inference部署（C++）
-参见[Paddle Inference部署教程](../../deploy/cpp/)
+参见[Paddle Inference部署教程](./deploy/cpp/README_cn.md)
 
 本项目使用的C++源文件在LaneSeg/deploy/cpp目录下

contrib/LaneSeg/deploy/cpp/CMakeLists.txt

@@ -1,16 +1,12 @@
 cmake_minimum_required(VERSION 3.0)
 project(cpp_inference_demo CXX C)
 
-option(WITH_MKL        "Compile demo with MKL/OpenBlas support, default use MKL."       OFF)
+option(WITH_MKL        "Compile demo with MKL/OpenBlas support, default use MKL."       ON)
 option(WITH_GPU        "Compile demo with GPU/CPU, default use CPU."                    OFF)
 option(WITH_STATIC_LIB "Compile demo with static/shared library, default use static."   ON)
 option(USE_TENSORRT "Compile demo with TensorRT."   OFF)
 option(WITH_ROCM "Compile demo with rocm." OFF)
 
-
-set(PADDLE_LIB ${CMAKE_SOURCE_DIR}/paddle)
-set(DEMO_NAME test_seg)
-
 if(NOT WITH_STATIC_LIB)
   add_definitions("-DPADDLE_WITH_SHARED_LIB")
 else()
@@ -150,7 +146,7 @@ else()
 endif()
 
 if (NOT WIN32)
-  set(EXTERNAL_LIB "-ldl -lpthread")
+  set(EXTERNAL_LIB "-lrt -ldl -lpthread")
   set(DEPS ${DEPS}
       ${MATH_LIB} ${MKLDNN_LIB}
       glog gflags protobuf xxhash cryptopp

contrib/LaneSeg/deploy/cpp/README.md

@@ -0,0 +1,46 @@
+English | [简体中文](README_CN.md)
+
+## Deploy the PaddleSeg model using Paddle Inference C++
+
+
+### 1、Install
+
+- Paddle Inference C++
+
+- OpenCV
+
+- Yaml
+
+ More install informations，please refer to [Tutorial](../../../../docs/deployment/inference/cpp_inference.md)。
+
+### 2、Models and Pictures
+
+ - Downdload model
+
+   Enter to `LaneSeg/` directory, and execute commands as follows:
+```shell
+   mkdir output # if not exists
+   wget -P output https://paddleseg.bj.bcebos.com/lane_seg/bisenet/model.pdparams
+```
+ - Export Model
+
+```shell
+   python export.py \
+    --config configs/bisenetV2_tusimple_640x368_300k.yml \
+    --model_path output/model.pdparams \
+    --save_dir output/export
+```  
+
+ - Using the image `data/test_images/3.jpg`
+
+### 3、Compile and execute
+
+Enter to the `LaneSeg/deploy/cpp`
+
+Execute `sh run_seg_cpu.sh`, it will compile and then perform prediction on X86 CPU.
+
+Execute `sh run_seg_gpu.sh`, it will compile and then perform prediction on Nvidia GPU.
+
+The result will be saved in the`out_img_seg.jpg` and `out_image_points.jpg` images
+
+- Note：For the path of the model and image, you can change the files `run_seg_cpu.sh` and `run_seg_gpu.sh` as needed

contrib/LaneSeg/deploy/cpp/README_CN.md

@@ -0,0 +1,44 @@
+简体中文 | [English](README.md)
+
+## 使用Paddle Inference C++部署PaddleSeg模型
+
+### 1、安装
+
+- Paddle Inference C++
+
+- OpenCV
+
+- Yaml
+
+ 更多的安装信息，请参考[教程](../../../../docs/deployment/inference/cpp_inference_cn.md)。
+
+### 2、模型和图片
+
+ - 下载模型
+
+ 进入`LaneSeg/`目录下，执行如下命令:
+```shell
+   mkdir output # if not exists
+   wget -P output https://paddleseg.bj.bcebos.com/lane_seg/bisenet/model.pdparams
+```
+ - 导出模型
+```shell
+   python export.py \
+    --config configs/bisenetV2_tusimple_640x368_300k.yml \
+    --model_path output/model.pdparams \
+    --save_dir output/export
+```  
+
+ - 图片使用 `data/test_images/3.jpg`
+
+### 3、编译、执行
+
+进入目录`LaneSeg/deploy/cpp`
+
+执行`sh run_seg_cpu.sh`，会进行编译，然后在X86 CPU上执行预测。
+
+执行`sh run_seg_gpu.sh`，会进行编译，然后在Nvidia GPU上执行预测。
+
+结果会保存在当前目录的`out_img_seg.jpg`和`out_image_points.jpg`图片。
+
+- 注意：对于模型和图片的路径，可以按需要对文件`run_seg_cpu.sh`和`run_seg_gpu.sh`进行修改。

contrib/LaneSeg/deploy/cpp/run_seg_cpu.sh

@@ -0,0 +1,35 @@
+#!/bin/bash
+set +x
+set -e
+
+WITH_MKL=ON
+WITH_GPU=OFF
+USE_TENSORRT=OFF
+DEMO_NAME=test_seg
+
+work_path=$(dirname $(readlink -f $0))
+LIB_DIR="${work_path}/paddle_inference"
+
+# compile
+mkdir -p build
+cd build
+rm -rf *
+
+cmake .. \
+  -DDEMO_NAME=${DEMO_NAME} \
+  -DWITH_MKL=${WITH_MKL} \
+  -DWITH_GPU=${WITH_GPU} \
+  -DUSE_TENSORRT=${USE_TENSORRT} \
+  -DWITH_STATIC_LIB=OFF \
+  -DPADDLE_LIB=${LIB_DIR}
+
+make -j
+
+# run
+cd ..
+# change model_dir and img_path according to your needs
+./build/test_seg \
+    --model_dir=../../output/export/ \
+    --img_path=../../data/test_images/3.jpg \
+    --use_cpu=true \
+    --use_mkldnn=true

contrib/LaneSeg/deploy/cpp/run_seg_gpu.sh

@@ -0,0 +1,34 @@
+#!/bin/bash
+set +x
+set -e
+
+WITH_MKL=ON
+WITH_GPU=ON
+USE_TENSORRT=OFF
+DEMO_NAME=test_seg
+
+work_path=$(dirname $(readlink -f $0))
+LIB_DIR="${work_path}/paddle_inference"
+
+# compile
+mkdir -p build
+cd build
+rm -rf *
+
+cmake .. \
+  -DDEMO_NAME=${DEMO_NAME} \
+  -DWITH_MKL=${WITH_MKL} \
+  -DWITH_GPU=${WITH_GPU} \
+  -DUSE_TENSORRT=${USE_TENSORRT} \
+  -DWITH_STATIC_LIB=OFF \
+  -DPADDLE_LIB=${LIB_DIR}
+
+make -j
+
+# run
+cd ..
+# change model_dir and img_path according to your needs
+./build/test_seg \
+    --model_dir=../../output/export/ \
+    --img_path=../../data/test_images/3.jpg \
+    --use_cpu=false

contrib/LaneSeg/deploy/cpp/src/test_seg.cc

@@ -35,7 +35,8 @@ DEFINE_string(model_dir, "", "Directory of the inference model. "
                              "It constains deploy.yaml and infer models");
 DEFINE_string(img_path, "", "Path of the test image.");
 DEFINE_bool(use_cpu, false, "Wether use CPU. Default: use GPU.");
-
+DEFINE_bool(use_trt, false, "Wether enable TensorRT when use GPU. Defualt: false.");
+DEFINE_bool(use_mkldnn, false, "Wether enable MKLDNN when use CPU. Defualt: false.");
 DEFINE_string(save_dir, "", "Directory of the output image.");
 
 typedef struct YamlConfig {
@@ -77,6 +78,22 @@ std::shared_ptr<paddle_infer::Predictor> create_predictor(
                   model_dir + "/" + yaml_config.params_file);
   infer_config.EnableMemoryOptim();
 
+  if (FLAGS_use_cpu) {
+    LOG(INFO) << "Use CPU";
+    if (FLAGS_use_mkldnn) {
+      // TODO(jc): fix the bug
+      //infer_config.EnableMKLDNN();
+      infer_config.SetCpuMathLibraryNumThreads(5);
+    }
+  } else {
+    LOG(INFO) << "Use GPU";
+    infer_config.EnableUseGpu(100, 0);
+    if (FLAGS_use_trt) {
+      infer_config.EnableTensorRtEngine(1 << 20, 1, 3,
+        paddle_infer::PrecisionType::kFloat32, false, false);
+    }
+  }
+
   auto predictor = paddle_infer::CreatePredictor(infer_config);
   return predictor;
 }
@@ -100,12 +117,13 @@ void process_image(const YamlConfig& yaml_config, cv::Mat& img) {
     }
 }
 
+
 int main(int argc, char *argv[]) {
     google::ParseCommandLineFlags(&argc, &argv, true);
     if (FLAGS_model_dir == "") {
-    LOG(FATAL) << "The model_dir should not be empty.";
+      LOG(FATAL) << "The model_dir should not be empty.";
     }
-    
+
     // Load yaml
     std::string yaml_path = FLAGS_model_dir + "/deploy.yaml";
     YamlConfig yaml_config = load_yaml(yaml_path);
@@ -144,13 +162,13 @@ int main(int argc, char *argv[]) {
     auto output_t = predictor->GetOutputHandle(output_names[0]);
     std::vector<int> output_shape = output_t->shape();
     int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
-                                std::multiplies<int>());
+                                  std::multiplies<int>());
     std::vector<float> out_data(out_num);
     output_t->CopyToCpu(out_data.data());
-    
+
     cv::Size size = cv::Size(cols, rows);
     int skip_index = size.height * size.width;
-    
+
     const int num_classes = 7;
     LanePostProcess* laneNet = new LanePostProcess(input_height, input_width, rows, cols, num_classes);
     auto lane_coords = laneNet->lane_process(out_data, cut_height);
@@ -170,10 +188,9 @@ int main(int argc, char *argv[]) {
         }
         lane_id++;
     }
-
-    cv::imshow("image lane", image_ori);
-    cv::waitKey();
-
+
+    cv::imwrite("out_image_points.jpg", image_ori);
+
     cv::Mat seg_planes[num_classes];
     for(int i = 0; i < num_classes; i++) {
         seg_planes[i].create(size, CV_32FC(1));
@@ -200,7 +217,7 @@ int main(int argc, char *argv[]) {
     // Get pseudo image
     cv::Mat out_eq_img;
     cv::equalizeHist(binary_image, out_eq_img);
-    cv::imwrite("out_img.jpg", binary_image*255);
+    cv::imwrite("out_img_seg.jpg", binary_image*255);
 
     LOG(INFO) << "Finish";
 }