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update cpp_infer readme

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LDOUBLEV 2020-12-22 10:09:51 +08:00
parent c94428a880
commit dbd27878cb
2 changed files with 17 additions and 17 deletions
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15
deploy/cpp_infer/readme.md
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@ -122,10 +122,10 @@ build/paddle_inference_install_dir/
* 下载之后使用下面的方法解压。
```
tar -xf fluid_inference.tgz
tar -xf paddle_inference.tgz
```
最终会在当前的文件夹中生成`fluid_inference/`的子文件夹。
最终会在当前的文件夹中生成`paddle_inference/`的子文件夹。
## 2 开始运行
@ -137,11 +137,11 @@ tar -xf fluid_inference.tgz
```
inference/
|-- det_db
| |--model
| |--params
| |--inference.pdparams
| |--inference.pdimodel
|-- rec_rcnn
| |--model
| |--params
| |--inference.pdparams
| |--inference.pdparams
```
@ -180,7 +180,7 @@ cmake .. \
make -j
```
`OPENCV_DIR`为opencv编译安装的地址；`LIB_DIR`为下载(`fluid_inference`文件夹)或者编译生成的Paddle预测库地址(`build/fluid_inference_install_dir`文件夹)；`CUDA_LIB_DIR`为cuda库文件地址，在docker中；为`/usr/local/cuda/lib64`；`CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。
`OPENCV_DIR`为opencv编译安装的地址；`LIB_DIR`为下载(`paddle_inference`文件夹)或者编译生成的Paddle预测库地址(`build/paddle_inference_install_dir`文件夹)；`CUDA_LIB_DIR`为cuda库文件地址，在docker中；为`/usr/local/cuda/lib64`；`CUDNN_LIB_DIR`为cudnn库文件地址，在docker中为`/usr/lib/x86_64-linux-gnu/`。
* 编译完成之后，会在`build`文件夹下生成一个名为`ocr_system`的可执行文件。
@ -202,7 +202,6 @@ gpu_id 0 # GPU id，使用GPU时有效
gpu_mem 4000 # 申请的GPU内存
cpu_math_library_num_threads 10 # CPU预测时的线程数，在机器核数充足的情况下，该值越大，预测速度越快
use_mkldnn 1 # 是否使用mkldnn库
use_zero_copy_run 1 # 是否使用use_zero_copy_run进行预测
# det config
max_side_len 960 # 输入图像长宽大于960时，等比例缩放图像，使得图像最长边为960

19
deploy/cpp_infer/readme_en.md
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@ -130,10 +130,10 @@ Among them, `paddle` is the Paddle library required for C++ prediction later, an
* After downloading, use the following method to uncompress.
```
tar -xf fluid_inference.tgz
tar -xf paddle_inference.tgz
```
Finally you can see the following files in the folder of `fluid_inference/`.
Finally you can see the following files in the folder of `paddle_inference/`.
## 2. Compile and run the demo
@ -145,11 +145,11 @@ Finally you can see the following files in the folder of `fluid_inference/`.
```
inference/
|-- det_db
| |--model
| |--params
| |--inference.pdparams
| |--inference.pdimodel
|-- rec_rcnn
| |--model
| |--params
| |--inference.pdparams
| |--inference.pdparams
```
@ -188,7 +188,9 @@ cmake .. \
make -j
```
`OPENCV_DIR` is the opencv installation path; `LIB_DIR` is the download (`fluid_inference` folder) or the generated Paddle inference library path (`build/fluid_inference_install_dir` folder); `CUDA_LIB_DIR` is the cuda library file path, in docker; it is `/usr/local/cuda/lib64`; `CUDNN_LIB_DIR` is the cudnn library file path, in docker it is `/usr/lib/x86_64-linux-gnu/`.
`OPENCV_DIR` is the opencv installation path; `LIB_DIR` is the download (`paddle_inference` folder)
or the generated Paddle inference library path (`build/paddle_inference_install_dir` folder);
`CUDA_LIB_DIR` is the cuda library file path, in docker; it is `/usr/local/cuda/lib64`; `CUDNN_LIB_DIR` is the cudnn library file path, in docker it is `/usr/lib/x86_64-linux-gnu/`.
* After the compilation is completed, an executable file named `ocr_system` will be generated in the `build` folder.
@ -211,7 +213,6 @@ gpu_id 0 # GPU id when use_gpu is 1
gpu_mem 4000 # GPU memory requested
cpu_math_library_num_threads 10 # Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed
use_mkldnn 1 # Whether to use mkdlnn library
use_zero_copy_run 1 # Whether to use use_zero_copy_run for inference
max_side_len 960 # Limit the maximum image height and width to 960
det_db_thresh 0.3 # Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result
@ -244,4 +245,4 @@ The detection results will be shown on the screen, which is as follows.
### 2.3 Notes
* Paddle2.0.0-beta0 inference model library is recommanded for this tuturial.
* Paddle2.0.0-beta0 inference model library is recommended for this toturial.