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Merge pull request #3630 from MissPenguin/dygraph 

split cpp inference
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MissPenguin 2021-08-12 13:52:25 +08:00 committed by GitHub
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5
deploy/cpp_infer/CMakeLists.txt
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@ -1,4 +1,4 @@
project(ocr_system CXX C) project(ppocr CXX C)
option(WITH_MKL "Compile demo with MKL/OpenBlas support, default use MKL." ON) 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_GPU "Compile demo with GPU/CPU, default use CPU." OFF)
@ -11,7 +11,8 @@ SET(CUDA_LIB "" CACHE PATH "Location of libraries")
SET(CUDNN_LIB "" CACHE PATH "Location of libraries") SET(CUDNN_LIB "" CACHE PATH "Location of libraries")
SET(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT") SET(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT")
set(DEMO_NAME "ocr_system") set(DEMO_NAME "ppocr")
macro(safe_set_static_flag) macro(safe_set_static_flag)
foreach(flag_var foreach(flag_var

0
deploy/cpp_infer/src/clipper.cpp → deploy/cpp_infer/include/clipper.cpp
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2
deploy/cpp_infer/include/clipper.h
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@ -31,6 +31,8 @@
* * * *
*******************************************************************************/ *******************************************************************************/
#pragma once
#ifndef clipper_hpp #ifndef clipper_hpp
#define clipper_hpp #define clipper_hpp

123
deploy/cpp_infer/include/config.h
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@ -1,123 +0,0 @@
// 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 <iomanip>
#include <iostream>
#include <map>
#include <ostream>
#include <string>
#include <vector>
#include "include/utility.h"
namespace PaddleOCR {
class OCRConfig {
public:
explicit OCRConfig(const std::string &config_file) {
config_map_ = LoadConfig(config_file);
this->use_gpu = bool(stoi(config_map_["use_gpu"]));
this->gpu_id = stoi(config_map_["gpu_id"]);
this->gpu_mem = stoi(config_map_["gpu_mem"]);
this->cpu_math_library_num_threads =
stoi(config_map_["cpu_math_library_num_threads"]);
this->use_mkldnn = bool(stoi(config_map_["use_mkldnn"]));
this->max_side_len = stoi(config_map_["max_side_len"]);
this->det_db_thresh = stod(config_map_["det_db_thresh"]);
this->det_db_box_thresh = stod(config_map_["det_db_box_thresh"]);
this->det_db_unclip_ratio = stod(config_map_["det_db_unclip_ratio"]);
this->use_polygon_score = bool(stoi(config_map_["use_polygon_score"]));
this->det_model_dir.assign(config_map_["det_model_dir"]);
this->rec_model_dir.assign(config_map_["rec_model_dir"]);
this->char_list_file.assign(config_map_["char_list_file"]);
this->use_angle_cls = bool(stoi(config_map_["use_angle_cls"]));
this->cls_model_dir.assign(config_map_["cls_model_dir"]);
this->cls_thresh = stod(config_map_["cls_thresh"]);
this->visualize = bool(stoi(config_map_["visualize"]));
this->use_tensorrt = bool(stoi(config_map_["use_tensorrt"]));
this->use_fp16 = bool(stod(config_map_["use_fp16"]));
}
bool use_gpu = false;
int gpu_id = 0;
int gpu_mem = 4000;
int cpu_math_library_num_threads = 1;
bool use_mkldnn = false;
int max_side_len = 960;
double det_db_thresh = 0.3;
double det_db_box_thresh = 0.5;
double det_db_unclip_ratio = 2.0;
bool use_polygon_score = false;
std::string det_model_dir;
std::string rec_model_dir;
bool use_angle_cls;
std::string char_list_file;
std::string cls_model_dir;
double cls_thresh;
bool visualize = true;
bool use_tensorrt = false;
bool use_fp16 = false;
void PrintConfigInfo();
private:
// Load configuration
std::map<std::string, std::string> LoadConfig(const std::string &config_file);
std::vector<std::string> split(const std::string &str,
const std::string &delim);
std::map<std::string, std::string> config_map_;
};
} // namespace PaddleOCR

2
deploy/cpp_infer/include/ocr_cls.h
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@ -12,6 +12,8 @@
// See the License for the specific language governing permissions and // See the License for the specific language governing permissions and
// limitations under the License. // limitations under the License.
#pragma once
#include "opencv2/core.hpp" #include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp" #include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp" #include "opencv2/imgproc.hpp"

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deploy/cpp_infer/include/ocr_rec.h
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@ -12,6 +12,8 @@
// See the License for the specific language governing permissions and // See the License for the specific language governing permissions and
// limitations under the License. // limitations under the License.
#pragma once
#include "opencv2/core.hpp" #include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp" #include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp" #include "opencv2/imgproc.hpp"
@ -62,8 +64,7 @@ public:
// Load Paddle inference model // Load Paddle inference model
void LoadModel(const std::string &model_dir); void LoadModel(const std::string &model_dir);
void Run(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat &img, void Run(cv::Mat &img);
Classifier *cls);
private: private:
std::shared_ptr<Predictor> predictor_; std::shared_ptr<Predictor> predictor_;

118
deploy/cpp_infer/readme.md
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@ -154,82 +154,102 @@ inference/
* 编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。 * 编译命令如下，其中Paddle C++预测库、opencv等其他依赖库的地址需要换成自己机器上的实际地址。
```shell ```shell
sh tools/build.sh sh tools/build.sh
``` ```
具体地，`tools/build.sh`中内容如下。 * 具体的，需要修改`tools/build.sh`中环境路径，相关内容如下：
```shell ```shell
OPENCV_DIR=your_opencv_dir OPENCV_DIR=your_opencv_dir
LIB_DIR=your_paddle_inference_dir LIB_DIR=your_paddle_inference_dir
CUDA_LIB_DIR=your_cuda_lib_dir CUDA_LIB_DIR=your_cuda_lib_dir
CUDNN_LIB_DIR=/your_cudnn_lib_dir CUDNN_LIB_DIR=/your_cudnn_lib_dir
BUILD_DIR=build
rm -rf ${BUILD_DIR}
mkdir ${BUILD_DIR}
cd ${BUILD_DIR}
cmake .. \
-DPADDLE_LIB=${LIB_DIR} \
-DWITH_MKL=ON \
-DDEMO_NAME=ocr_system \
-DWITH_GPU=OFF \
-DWITH_STATIC_LIB=OFF \
-DUSE_TENSORRT=OFF \
-DOPENCV_DIR=${OPENCV_DIR} \
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
-DCUDA_LIB=${CUDA_LIB_DIR} \
make -j
``` ```
`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/`。**注意**：以上路径都写绝对路径，不要写相对路径。 其中，`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/`。**注意：以上路径都写绝对路径，不要写相对路径。**
* 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。 * 编译完成之后，会在`build`文件夹下生成一个名为`ppocr`的可执行文件。
### 运行demo ### 运行demo
* 执行以下命令，完成对一幅图像的OCR识别与检测。
运行方式：
```shell ```shell
sh tools/run.sh ./build/ppocr <mode> [--param1] [--param2] [...]
```
其中，`mode`为必选参数，表示选择的功能，取值范围['det', 'rec', 'system']，分别表示调用检测、识别、检测识别串联（包括方向分类器）。具体命令如下：
##### 1. 只调用检测：
```shell
./build/ppocr det \
--det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \
--image_dir=../../doc/imgs/12.jpg
```
##### 2. 只调用识别：
```shell
./build/ppocr rec \
--rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \
--image_dir=../../doc/imgs_words/ch/
```
##### 3. 调用串联：
```shell
# 不使用方向分类器
./build/ppocr system \
--det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \
--rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \
--image_dir=../../doc/imgs/12.jpg
# 使用方向分类器
./build/ppocr system \
--det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \
--use_angle_cls=true \
--cls_model_dir=inference/ch_ppocr_mobile_v2.0_cls_infer \
--rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \
--image_dir=../../doc/imgs/12.jpg
``` ```
* 若需要使用方向分类器，则需要将`tools/config.txt`中的`use_angle_cls`参数修改为1，表示开启方向分类器的预测。 更多参数如下：
* 更多地，tools/config.txt中的参数及解释如下。
``` - 通用参数
use_gpu 0 # 是否使用GPU，1表示使用，0表示不使用
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
# det config |参数名称|类型|默认参数|意义|
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 | --- | --- | --- | --- |
det_db_thresh 0.3 # 用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显 |use_gpu|bool|false|是否使用GPU|
det_db_box_thresh 0.5 # DB后处理过滤box的阈值，如果检测存在漏框情况，可酌情减小 |gpu_id|int|0|GPU id，使用GPU时有效|
det_db_unclip_ratio 1.6 # 表示文本框的紧致程度，越小则文本框更靠近文本 |gpu_mem|int|4000|申请的GPU内存|
use_polygon_score 1 # 是否使用多边形框计算bbox score，0表示使用矩形框计算。矩形框计算速度更快，多边形框对弯曲文本区域计算更准确。 |cpu_math_library_num_threads|int|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|
det_model_dir ./inference/det_db # 检测模型inference model地址 |use_mkldnn|bool|true|是否使用mkldnn库|
# cls config - 检测模型相关
use_angle_cls 0 # 是否使用方向分类器，0表示不使用，1表示使用
cls_model_dir ./inference/cls # 方向分类器inference model地址
cls_thresh 0.9 # 方向分类器的得分阈值
# rec config |参数名称|类型|默认参数|意义|
rec_model_dir ./inference/rec_crnn # 识别模型inference model地址 | --- | --- | --- | --- |
char_list_file ../../ppocr/utils/ppocr_keys_v1.txt # 字典文件 |det_model_dir|string|-|检测模型inference model地址|
|max_side_len|int|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|
|det_db_thresh|float|0.3|用于过滤DB预测的二值化图像，设置为0.-0.3对结果影响不明显|
|det_db_box_thresh|float|0.5|DB后处理过滤box的阈值，如果检测存在漏框情况，可酌情减小|
|det_db_unclip_ratio|float|1.6|表示文本框的紧致程度，越小则文本框更靠近文本|
|use_polygon_score|bool|false|是否使用多边形框计算bbox score，false表示使用矩形框计算。矩形框计算速度更快，多边形框对弯曲文本区域计算更准确。|
|visualize|bool|true|是否对结果进行可视化，为1时，会在当前文件夹下保存文件名为`ocr_vis.png`的预测结果。|
# show the detection results - 方向分类器相关
visualize 1 # 是否对结果进行可视化，为1时，会在当前文件夹下保存文件名为`ocr_vis.png`的预测结果。
```
* PaddleOCR也支持多语言的预测，更多支持的语言和模型可以参考[识别文档](../../doc/doc_ch/recognition.md)中的多语言字典与模型部分，如果希望进行多语言预测，只需将修改`tools/config.txt`中的`char_list_file`（字典文件路径）以及`rec_model_dir`（inference模型路径）字段即可。 |参数名称|类型|默认参数|意义|
| --- | --- | --- | --- |
|use_angle_cls|bool|false|是否使用方向分类器|
|cls_model_dir|string|-|方向分类器inference model地址|
|cls_thresh|float|0.9|方向分类器的得分阈值|
- 识别模型相关
|参数名称|类型|默认参数|意义|
| --- | --- | --- | --- |
|rec_model_dir|string|-|识别模型inference model地址|
|char_list_file|string|../../ppocr/utils/ppocr_keys_v1.txt|字典文件|
* PaddleOCR也支持多语言的预测，更多支持的语言和模型可以参考[识别文档](../../doc/doc_ch/recognition.md)中的多语言字典与模型部分，如果希望进行多语言预测，只需将修改`char_list_file`（字典文件路径）以及`rec_model_dir`（inference模型路径）字段即可。
最终屏幕上会输出检测结果如下。 最终屏幕上会输出检测结果如下。

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deploy/cpp_infer/readme_en.md
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@ -162,30 +162,13 @@ inference/
sh tools/build.sh sh tools/build.sh
``` ```
Specifically, the content in `tools/build.sh` is as follows. Specifically, you should modify the paths in `tools/build.sh`. The related content is as follows.
```shell ```shell
OPENCV_DIR=your_opencv_dir OPENCV_DIR=your_opencv_dir
LIB_DIR=your_paddle_inference_dir LIB_DIR=your_paddle_inference_dir
CUDA_LIB_DIR=your_cuda_lib_dir CUDA_LIB_DIR=your_cuda_lib_dir
CUDNN_LIB_DIR=your_cudnn_lib_dir CUDNN_LIB_DIR=your_cudnn_lib_dir
BUILD_DIR=build
rm -rf ${BUILD_DIR}
mkdir ${BUILD_DIR}
cd ${BUILD_DIR}
cmake .. \
-DPADDLE_LIB=${LIB_DIR} \
-DWITH_MKL=ON \
-DDEMO_NAME=ocr_system \
-DWITH_GPU=OFF \
-DWITH_STATIC_LIB=OFF \
-DUSE_TENSORRT=OFF \
-DOPENCV_DIR=${OPENCV_DIR} \
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
-DCUDA_LIB=${CUDA_LIB_DIR} \
make -j
``` ```
`OPENCV_DIR` is the opencv installation path; `LIB_DIR` is the download (`paddle_inference` folder) `OPENCV_DIR` is the opencv installation path; `LIB_DIR` is the download (`paddle_inference` folder)
@ -193,48 +176,84 @@ or the generated Paddle inference library path (`build/paddle_inference_install_
`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/`. `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. * After the compilation is completed, an executable file named `ppocr` will be generated in the `build` folder.
### Run the demo ### Run the demo
* Execute the following command to complete the OCR recognition and detection of an image. Execute the built executable file:
```shell ```shell
sh tools/run.sh ./build/ppocr <mode> [--param1] [--param2] [...]
```
Here, `mode` is a required parameter，and the value range is ['det', 'rec', 'system'], representing using detection only, using recognition only and using the end-to-end system respectively. Specifically,
##### 1. run det demo:
```shell
./build/ppocr det \
--det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \
--image_dir=../../doc/imgs/12.jpg
```
##### 2. run rec demo:
```shell
./build/ppocr rec \
--rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \
--image_dir=../../doc/imgs_words/ch/
```
##### 3. run system demo:
```shell
# without text direction classifier
./build/ppocr system \
--det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \
--rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \
--image_dir=../../doc/imgs/12.jpg
# with text direction classifier
./build/ppocr system \
--det_model_dir=inference/ch_ppocr_mobile_v2.0_det_infer \
--use_angle_cls=true \
--cls_model_dir=inference/ch_ppocr_mobile_v2.0_cls_infer \
--rec_model_dir=inference/ch_ppocr_mobile_v2.0_rec_infer \
--image_dir=../../doc/imgs/12.jpg
``` ```
* If you want to orientation classifier to correct the detected boxes, you can set `use_angle_cls` in the file `tools/config.txt` as 1 to enable the function. More parameters are as follows,
* What's more, Parameters and their meanings in `tools/config.txt` are as follows.
- common parameters
``` |parameter|data type|default|meaning|
use_gpu 0 # Whether to use GPU, 0 means not to use, 1 means to use | --- | --- | --- | --- |
gpu_id 0 # GPU id when use_gpu is 1 |use_gpu|bool|false|Whether to use GPU|
gpu_mem 4000 # GPU memory requested |gpu_id|int|0|GPU id when use_gpu is true|
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 |gpu_mem|int|4000|GPU memory requested|
use_mkldnn 1 # Whether to use mkdlnn library |cpu_math_library_num_threads|int|10|Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed|
|use_mkldnn|bool|true|Whether to use mkdlnn library|
max_side_len 960 # Limit the maximum image height and width to 960 - detection related parameters
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
det_db_box_thresh 0.5 # DDB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate
det_db_unclip_ratio 1.6 # Indicates the compactness of the text box, the smaller the value, the closer the text box to the text
use_polygon_score 1 # Whether to use polygon box to calculate bbox score, 0 means to use rectangle box to calculate. Use rectangular box to calculate faster, and polygonal box more accurate for curved text area.
det_model_dir ./inference/det_db # Address of detection inference model
# cls config |parameter|data type|default|meaning|
use_angle_cls 0 # Whether to use the direction classifier, 0 means not to use, 1 means to use | --- | --- | --- | --- |
cls_model_dir ./inference/cls # Address of direction classifier inference model |det_model_dir|string|-|Address of detection inference model|
cls_thresh 0.9 # Score threshold of the direction classifier |max_side_len|int|960|Limit the maximum image height and width to 960|
|det_db_thresh|float|0.3|Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result|
|det_db_box_thresh|float|0.5|DB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate|
|det_db_unclip_ratio|float|1.6|Indicates the compactness of the text box, the smaller the value, the closer the text box to the text|
|use_polygon_score|bool|false|Whether to use polygon box to calculate bbox score, false means to use rectangle box to calculate. Use rectangular box to calculate faster, and polygonal box more accurate for curved text area.|
|visualize|bool|true|Whether to visualize the results，when it is set as true, The prediction result will be save in the image file `./ocr_vis.png`.|
# rec config - classifier related parameters
rec_model_dir ./inference/rec_crnn # Address of recognition inference model
char_list_file ../../ppocr/utils/ppocr_keys_v1.txt # dictionary file
# show the detection results |parameter|data type|default|meaning|
visualize 1 # Whether to visualize the results，when it is set as 1, The prediction result will be save in the image file `./ocr_vis.png`. | --- | --- | --- | --- |
``` |use_angle_cls|bool|false|Whether to use the direction classifier|
|cls_model_dir|string|-|Address of direction classifier inference model|
|cls_thresh|float|0.9|Score threshold of the direction classifier|
* Multi-language inference is also supported in PaddleOCR, you can refer to [recognition tutorial](../../doc/doc_en/recognition_en.md) for more supported languages and models in PaddleOCR. Specifically, if you want to infer using multi-language models, you just need to modify values of `char_list_file` and `rec_model_dir` in file `tools/config.txt`. - recogniton related parameters
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
|rec_model_dir|string|-|Address of recognition inference model|
|char_list_file|string|../../ppocr/utils/ppocr_keys_v1.txt|dictionary file|
* Multi-language inference is also supported in PaddleOCR, you can refer to [recognition tutorial](../../doc/doc_en/recognition_en.md) for more supported languages and models in PaddleOCR. Specifically, if you want to infer using multi-language models, you just need to modify values of `char_list_file` and `rec_model_dir`.
The detection results will be shown on the screen, which is as follows. The detection results will be shown on the screen, which is as follows.

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deploy/cpp_infer/src/config.cpp
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@ -1,70 +0,0 @@
// 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/config.h>
namespace PaddleOCR {
std::vector<std::string> OCRConfig::split(const std::string &str,
const std::string &delim) {
std::vector<std::string> res;
if ("" == str)
return res;
int strlen = str.length() + 1;
char *strs = new char[strlen];
std::strcpy(strs, str.c_str());
int delimlen = delim.length() + 1;
char *d = new char[delimlen];
std::strcpy(d, delim.c_str());
char *p = std::strtok(strs, d);
while (p) {
std::string s = p;
res.push_back(s);
p = std::strtok(NULL, d);
}
delete[] strs;
delete[] d;
return res;
}
std::map<std::string, std::string>
OCRConfig::LoadConfig(const std::string &config_path) {
auto config = Utility::ReadDict(config_path);
std::map<std::string, std::string> dict;
for (int i = 0; i < config.size(); i++) {
// pass for empty line or comment
if (config[i].size() <= 1 || config[i][0] == '#') {
continue;
}
std::vector<std::string> res = split(config[i], " ");
dict[res[0]] = res[1];
}
return dict;
}
void OCRConfig::PrintConfigInfo() {
std::cout << "=======Paddle OCR inference config======" << std::endl;
for (auto iter = config_map_.begin(); iter != config_map_.end(); iter++) {
std::cout << iter->first << " : " << iter->second << std::endl;
}
std::cout << "=======End of Paddle OCR inference config======" << std::endl;
}
} // namespace PaddleOCR

359
deploy/cpp_infer/src/main.cpp
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@ -28,76 +28,309 @@
#include <numeric> #include <numeric>
#include <glog/logging.h> #include <glog/logging.h>
#include <include/config.h>
#include <include/ocr_det.h> #include <include/ocr_det.h>
#include <include/ocr_cls.h>
#include <include/ocr_rec.h> #include <include/ocr_rec.h>
#include <include/utility.h>
#include <sys/stat.h> #include <sys/stat.h>
#include <gflags/gflags.h>
DEFINE_bool(use_gpu, false, "Infering with GPU or CPU.");
DEFINE_int32(gpu_id, 0, "Device id of GPU to execute.");
DEFINE_int32(gpu_mem, 4000, "GPU id when infering with GPU.");
DEFINE_int32(cpu_math_library_num_threads, 10, "Num of threads with CPU.");
DEFINE_bool(use_mkldnn, false, "Whether use mkldnn with CPU.");
DEFINE_bool(use_tensorrt, false, "Whether use tensorrt.");
DEFINE_bool(use_fp16, false, "Whether use fp16 when use tensorrt.");
// detection related
DEFINE_string(image_dir, "", "Dir of input image.");
DEFINE_string(det_model_dir, "", "Path of det inference model.");
DEFINE_int32(max_side_len, 960, "max_side_len of input image.");
DEFINE_double(det_db_thresh, 0.3, "Threshold of det_db_thresh.");
DEFINE_double(det_db_box_thresh, 0.5, "Threshold of det_db_box_thresh.");
DEFINE_double(det_db_unclip_ratio, 1.6, "Threshold of det_db_unclip_ratio.");
DEFINE_bool(use_polygon_score, false, "Whether use polygon score.");
DEFINE_bool(visualize, true, "Whether show the detection results.");
// classification related
DEFINE_bool(use_angle_cls, false, "Whether use use_angle_cls.");
DEFINE_string(cls_model_dir, "", "Path of cls inference model.");
DEFINE_double(cls_thresh, 0.9, "Threshold of cls_thresh.");
// recognition related
DEFINE_string(rec_model_dir, "", "Path of rec inference model.");
DEFINE_string(char_list_file, "../../ppocr/utils/ppocr_keys_v1.txt", "Path of dictionary.");
using namespace std; using namespace std;
using namespace cv; using namespace cv;
using namespace PaddleOCR; using namespace PaddleOCR;
int main(int argc, char **argv) {
if (argc < 3) {
std::cerr << "[ERROR] usage: " << argv[0]
<< " configure_filepath image_path\n";
exit(1);
}
OCRConfig config(argv[1]); static bool PathExists(const std::string& path){
#ifdef _WIN32
config.PrintConfigInfo(); struct _stat buffer;
return (_stat(path.c_str(), &buffer) == 0);
std::string img_path(argv[2]); #else
std::vector<std::string> all_img_names; struct stat buffer;
Utility::GetAllFiles((char *)img_path.c_str(), all_img_names); return (stat(path.c_str(), &buffer) == 0);
#endif // !_WIN32
DBDetector det(config.det_model_dir, config.use_gpu, config.gpu_id, }
config.gpu_mem, config.cpu_math_library_num_threads,
config.use_mkldnn, config.max_side_len, config.det_db_thresh,
config.det_db_box_thresh, config.det_db_unclip_ratio, cv::Mat GetRotateCropImage(const cv::Mat &srcimage,
config.use_polygon_score, config.visualize, std::vector<std::vector<int>> box) {
config.use_tensorrt, config.use_fp16); cv::Mat image;
srcimage.copyTo(image);
Classifier *cls = nullptr; std::vector<std::vector<int>> points = box;
if (config.use_angle_cls == true) {
cls = new Classifier(config.cls_model_dir, config.use_gpu, config.gpu_id, int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
config.gpu_mem, config.cpu_math_library_num_threads, int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
config.use_mkldnn, config.cls_thresh, int left = int(*std::min_element(x_collect, x_collect + 4));
config.use_tensorrt, config.use_fp16); int right = int(*std::max_element(x_collect, x_collect + 4));
} int top = int(*std::min_element(y_collect, y_collect + 4));
int bottom = int(*std::max_element(y_collect, y_collect + 4));
CRNNRecognizer rec(config.rec_model_dir, config.use_gpu, config.gpu_id,
config.gpu_mem, config.cpu_math_library_num_threads, cv::Mat img_crop;
config.use_mkldnn, config.char_list_file, image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
config.use_tensorrt, config.use_fp16);
for (int i = 0; i < points.size(); i++) {
auto start = std::chrono::system_clock::now(); points[i][0] -= left;
points[i][1] -= top;
for (auto img_dir : all_img_names) { }
LOG(INFO) << "The predict img: " << img_dir;
int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
cv::Mat srcimg = cv::imread(img_dir, cv::IMREAD_COLOR); pow(points[0][1] - points[1][1], 2)));
if (!srcimg.data) { int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
std::cerr << "[ERROR] image read failed! image path: " << img_path pow(points[0][1] - points[3][1], 2)));
<< "\n";
exit(1); cv::Point2f pts_std[4];
} pts_std[0] = cv::Point2f(0., 0.);
std::vector<std::vector<std::vector<int>>> boxes; pts_std[1] = cv::Point2f(img_crop_width, 0.);
pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
det.Run(srcimg, boxes); pts_std[3] = cv::Point2f(0.f, img_crop_height);
rec.Run(boxes, srcimg, cls); cv::Point2f pointsf[4];
auto end = std::chrono::system_clock::now(); pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
auto duration = pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
std::chrono::duration_cast<std::chrono::microseconds>(end - start); pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
std::cout << "Cost " pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
<< double(duration.count()) *
std::chrono::microseconds::period::num / cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
std::chrono::microseconds::period::den
<< "s" << std::endl; cv::Mat dst_img;
} cv::warpPerspective(img_crop, dst_img, M,
cv::Size(img_crop_width, img_crop_height),
return 0; cv::BORDER_REPLICATE);
if (float(dst_img.rows) >= float(dst_img.cols) * 1.5) {
cv::Mat srcCopy = cv::Mat(dst_img.rows, dst_img.cols, dst_img.depth());
cv::transpose(dst_img, srcCopy);
cv::flip(srcCopy, srcCopy, 0);
return srcCopy;
} else {
return dst_img;
}
}
int main_det(int argc, char **argv) {
// Parsing command-line
google::ParseCommandLineFlags(&argc, &argv, true);
if (FLAGS_det_model_dir.empty() || FLAGS_image_dir.empty()) {
std::cout << "Usage[det]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
exit(1);
}
if (!PathExists(FLAGS_image_dir)) {
std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir << 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;
DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_math_library_num_threads,
FLAGS_use_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh,
FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
FLAGS_use_polygon_score, FLAGS_visualize,
FLAGS_use_tensorrt, FLAGS_use_fp16);
auto start = std::chrono::system_clock::now();
for (int i = 0; i < cv_all_img_names.size(); ++i) {
LOG(INFO) << "The predict img: " << cv_all_img_names[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);
}
std::vector<std::vector<std::vector<int>>> boxes;
det.Run(srcimg, boxes);
auto end = std::chrono::system_clock::now();
auto duration =
std::chrono::duration_cast<std::chrono::microseconds>(end - start);
std::cout << "Cost "
<< double(duration.count()) *
std::chrono::microseconds::period::num /
std::chrono::microseconds::period::den
<< "s" << std::endl;
}
return 0;
}
int main_rec(int argc, char **argv) {
// Parsing command-line
google::ParseCommandLineFlags(&argc, &argv, true);
if (FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) {
std::cout << "Usage[rec]: ./ppocr --rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
exit(1);
}
if (!PathExists(FLAGS_image_dir)) {
std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir << 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;
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_math_library_num_threads,
FLAGS_use_mkldnn, FLAGS_char_list_file,
FLAGS_use_tensorrt, FLAGS_use_fp16);
auto start = std::chrono::system_clock::now();
for (int i = 0; i < cv_all_img_names.size(); ++i) {
LOG(INFO) << "The predict img: " << cv_all_img_names[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);
}
rec.Run(srcimg);
auto end = std::chrono::system_clock::now();
auto duration =
std::chrono::duration_cast<std::chrono::microseconds>(end - start);
std::cout << "Cost "
<< double(duration.count()) *
std::chrono::microseconds::period::num /
std::chrono::microseconds::period::den
<< "s" << std::endl;
}
return 0;
}
int main_system(int argc, char **argv) {
// Parsing command-line
google::ParseCommandLineFlags(&argc, &argv, true);
if ((FLAGS_det_model_dir.empty() || FLAGS_rec_model_dir.empty() || FLAGS_image_dir.empty()) ||
(FLAGS_use_angle_cls && FLAGS_cls_model_dir.empty())) {
std::cout << "Usage[system without angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<< "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
std::cout << "Usage[system with angle cls]: ./ppocr --det_model_dir=/PATH/TO/DET_INFERENCE_MODEL/ "
<< "--use_angle_cls=true "
<< "--cls_model_dir=/PATH/TO/CLS_INFERENCE_MODEL/ "
<< "--rec_model_dir=/PATH/TO/REC_INFERENCE_MODEL/ "
<< "--image_dir=/PATH/TO/INPUT/IMAGE/" << std::endl;
exit(1);
}
if (!PathExists(FLAGS_image_dir)) {
std::cerr << "[ERROR] image path not exist! image_dir: " << FLAGS_image_dir << 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;
DBDetector det(FLAGS_det_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_math_library_num_threads,
FLAGS_use_mkldnn, FLAGS_max_side_len, FLAGS_det_db_thresh,
FLAGS_det_db_box_thresh, FLAGS_det_db_unclip_ratio,
FLAGS_use_polygon_score, FLAGS_visualize,
FLAGS_use_tensorrt, FLAGS_use_fp16);
Classifier *cls = nullptr;
if (FLAGS_use_angle_cls) {
cls = new Classifier(FLAGS_cls_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_math_library_num_threads,
FLAGS_use_mkldnn, FLAGS_cls_thresh,
FLAGS_use_tensorrt, FLAGS_use_fp16);
}
CRNNRecognizer rec(FLAGS_rec_model_dir, FLAGS_use_gpu, FLAGS_gpu_id,
FLAGS_gpu_mem, FLAGS_cpu_math_library_num_threads,
FLAGS_use_mkldnn, FLAGS_char_list_file,
FLAGS_use_tensorrt, FLAGS_use_fp16);
auto start = std::chrono::system_clock::now();
for (int i = 0; i < cv_all_img_names.size(); ++i) {
LOG(INFO) << "The predict img: " << cv_all_img_names[i];
cv::Mat srcimg = cv::imread(FLAGS_image_dir, cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << cv_all_img_names[i] << endl;
exit(1);
}
std::vector<std::vector<std::vector<int>>> boxes;
det.Run(srcimg, boxes);
cv::Mat crop_img;
for (int j = 0; j < boxes.size(); j++) {
crop_img = GetRotateCropImage(srcimg, boxes[j]);
if (cls != nullptr) {
crop_img = cls->Run(crop_img);
}
rec.Run(crop_img);
}
auto end = std::chrono::system_clock::now();
auto duration =
std::chrono::duration_cast<std::chrono::microseconds>(end - start);
std::cout << "Cost "
<< double(duration.count()) *
std::chrono::microseconds::period::num /
std::chrono::microseconds::period::den
<< "s" << std::endl;
}
return 0;
}
int main(int argc, char **argv) {
if (strcmp(argv[1], "det")!=0 && strcmp(argv[1], "rec")!=0 && strcmp(argv[1], "system")!=0) {
std::cout << "Please choose one mode of [det, rec, system] !" << std::endl;
return -1;
}
std::cout << "mode: " << argv[1] << endl;
if (strcmp(argv[1], "det")==0) {
return main_det(argc, argv);
}
if (strcmp(argv[1], "rec")==0) {
return main_rec(argc, argv);
}
if (strcmp(argv[1], "system")==0) {
return main_system(argc, argv);
}
// return 0;
} }

4
deploy/cpp_infer/src/ocr_det.cpp
View File

@ -14,6 +14,7 @@
#include <include/ocr_det.h> #include <include/ocr_det.h>
namespace PaddleOCR { namespace PaddleOCR {
void DBDetector::LoadModel(const std::string &model_dir) { void DBDetector::LoadModel(const std::string &model_dir) {
@ -150,7 +151,8 @@ void DBDetector::Run(cv::Mat &img,
this->det_db_unclip_ratio_, this->use_polygon_score_); this->det_db_unclip_ratio_, this->use_polygon_score_);
boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg); boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
std::cout << "Detected boxes num: " << boxes.size() << endl;
//// visualization //// visualization
if (this->visualize_) { if (this->visualize_) {
Utility::VisualizeBboxes(srcimg, boxes); Utility::VisualizeBboxes(srcimg, boxes);

122
deploy/cpp_infer/src/ocr_rec.cpp
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@ -16,80 +16,68 @@
namespace PaddleOCR { namespace PaddleOCR {
void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes, void CRNNRecognizer::Run(cv::Mat &img) {
cv::Mat &img, Classifier *cls) {
cv::Mat srcimg; cv::Mat srcimg;
img.copyTo(srcimg); img.copyTo(srcimg);
cv::Mat crop_img;
cv::Mat resize_img; cv::Mat resize_img;
std::cout << "The predicted text is :" << std::endl; float wh_ratio = float(srcimg.cols) / float(srcimg.rows);
int index = 0;
for (int i = 0; i < boxes.size(); i++) {
crop_img = GetRotateCropImage(srcimg, boxes[i]);
if (cls != nullptr) { this->resize_op_.Run(srcimg, resize_img, wh_ratio, this->use_tensorrt_);
crop_img = cls->Run(crop_img);
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());
// 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});
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
std::vector<float> predict_batch;
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
auto predict_shape = output_t->shape();
int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
std::multiplies<int>());
predict_batch.resize(out_num);
output_t->CopyToCpu(predict_batch.data());
// ctc decode
std::vector<std::string> str_res;
int argmax_idx;
int last_index = 0;
float score = 0.f;
int count = 0;
float max_value = 0.0f;
for (int n = 0; n < predict_shape[1]; n++) {
argmax_idx =
int(Utility::argmax(&predict_batch[n * predict_shape[2]],
&predict_batch[(n + 1) * predict_shape[2]]));
max_value =
float(*std::max_element(&predict_batch[n * predict_shape[2]],
&predict_batch[(n + 1) * predict_shape[2]]));
if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
score += max_value;
count += 1;
str_res.push_back(label_list_[argmax_idx]);
} }
last_index = argmax_idx;
float wh_ratio = float(crop_img.cols) / float(crop_img.rows);
this->resize_op_.Run(crop_img, resize_img, wh_ratio, this->use_tensorrt_);
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());
// 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});
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
std::vector<float> predict_batch;
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
auto predict_shape = output_t->shape();
int out_num = std::accumulate(predict_shape.begin(), predict_shape.end(), 1,
std::multiplies<int>());
predict_batch.resize(out_num);
output_t->CopyToCpu(predict_batch.data());
// ctc decode
std::vector<std::string> str_res;
int argmax_idx;
int last_index = 0;
float score = 0.f;
int count = 0;
float max_value = 0.0f;
for (int n = 0; n < predict_shape[1]; n++) {
argmax_idx =
int(Utility::argmax(&predict_batch[n * predict_shape[2]],
&predict_batch[(n + 1) * predict_shape[2]]));
max_value =
float(*std::max_element(&predict_batch[n * predict_shape[2]],
&predict_batch[(n + 1) * predict_shape[2]]));
if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
score += max_value;
count += 1;
str_res.push_back(label_list_[argmax_idx]);
}
last_index = argmax_idx;
}
score /= count;
for (int i = 0; i < str_res.size(); i++) {
std::cout << str_res[i];
}
std::cout << "\tscore: " << score << std::endl;
} }
score /= count;
for (int i = 0; i < str_res.size(); i++) {
std::cout << str_res[i];
}
std::cout << "\tscore: " << score << std::endl;
} }
void CRNNRecognizer::LoadModel(const std::string &model_dir) { void CRNNRecognizer::LoadModel(const std::string &model_dir) {

1
deploy/cpp_infer/src/postprocess_op.cpp
View File

@ -13,6 +13,7 @@
// limitations under the License. // limitations under the License.
#include <include/postprocess_op.h> #include <include/postprocess_op.h>
#include <include/clipper.cpp>
namespace PaddleOCR { namespace PaddleOCR {

31
deploy/cpp_infer/tools/config.txt
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@ -1,31 +0,0 @@
# model load config
use_gpu 0
gpu_id 0
gpu_mem 4000
cpu_math_library_num_threads 10
use_mkldnn 0
# det config
max_side_len 960
det_db_thresh 0.3
det_db_box_thresh 0.5
det_db_unclip_ratio 1.6
use_polygon_score 1
det_model_dir ./inference/ch_ppocr_mobile_v2.0_det_infer/
# cls config
use_angle_cls 0
cls_model_dir ./inference/ch_ppocr_mobile_v2.0_cls_infer/
cls_thresh 0.9
# rec config
rec_model_dir ./inference/ch_ppocr_mobile_v2.0_rec_infer/
char_list_file ../../ppocr/utils/ppocr_keys_v1.txt
# show the detection results
visualize 0
# use_tensorrt
use_tensorrt 0
use_fp16 0

2
deploy/cpp_infer/tools/run.sh
View File

@ -1,2 +0,0 @@
./build/ocr_system ./tools/config.txt ../../doc/imgs/12.jpg