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Merge remote-tracking branch 'origin/dygraph' into dygraph

This commit is contained in:
Leif 2021-08-18 11:36:29 +08:00
parent ef383cf19b 7c43db1466
commit 3305d9fdab
25 changed files with 666 additions and 536 deletions
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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_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(TENSORRT_DIR "" CACHE PATH "Compile demo with TensorRT")
set(DEMO_NAME "ocr_system")
set(DEMO_NAME "ppocr")
macro(safe_set_static_flag)
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
#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

8
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
// limitations under the License.
#pragma once
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
@ -40,7 +42,7 @@ public:
const int &gpu_id, const int &gpu_mem,
const int &cpu_math_library_num_threads,
const bool &use_mkldnn, const double &cls_thresh,
const bool &use_tensorrt, const bool &use_fp16) {
const bool &use_tensorrt, const std::string &precision) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
@ -49,7 +51,7 @@ public:
this->cls_thresh = cls_thresh;
this->use_tensorrt_ = use_tensorrt;
this->use_fp16_ = use_fp16;
this->precision_ = precision;
LoadModel(model_dir);
}
@ -73,7 +75,7 @@ private:
std::vector<float> scale_ = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
bool is_scale_ = true;
bool use_tensorrt_ = false;
bool use_fp16_ = false;
std::string precision_ = "fp32";
// pre-process
ClsResizeImg resize_op_;
Normalize normalize_op_;

8
deploy/cpp_infer/include/ocr_det.h
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@ -46,7 +46,7 @@ public:
const double &det_db_box_thresh,
const double &det_db_unclip_ratio,
const bool &use_polygon_score, const bool &visualize,
const bool &use_tensorrt, const bool &use_fp16) {
const bool &use_tensorrt, const std::string &precision) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
@ -62,7 +62,7 @@ public:
this->visualize_ = visualize;
this->use_tensorrt_ = use_tensorrt;
this->use_fp16_ = use_fp16;
this->precision_ = precision;
LoadModel(model_dir);
}
@ -71,7 +71,7 @@ public:
void LoadModel(const std::string &model_dir);
// Run predictor
void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes);
void Run(cv::Mat &img, std::vector<std::vector<std::vector<int>>> &boxes, std::vector<double> *times);
private:
std::shared_ptr<Predictor> predictor_;
@ -91,7 +91,7 @@ private:
bool visualize_ = true;
bool use_tensorrt_ = false;
bool use_fp16_ = false;
std::string precision_ = "fp32";
std::vector<float> mean_ = {0.485f, 0.456f, 0.406f};
std::vector<float> scale_ = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};

14
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
// limitations under the License.
#pragma once
#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
@ -42,14 +44,14 @@ public:
const int &gpu_id, const int &gpu_mem,
const int &cpu_math_library_num_threads,
const bool &use_mkldnn, const string &label_path,
const bool &use_tensorrt, const bool &use_fp16) {
const bool &use_tensorrt, const std::string &precision) {
this->use_gpu_ = use_gpu;
this->gpu_id_ = gpu_id;
this->gpu_mem_ = gpu_mem;
this->cpu_math_library_num_threads_ = cpu_math_library_num_threads;
this->use_mkldnn_ = use_mkldnn;
this->use_tensorrt_ = use_tensorrt;
this->use_fp16_ = use_fp16;
this->precision_ = precision;
this->label_list_ = Utility::ReadDict(label_path);
this->label_list_.insert(this->label_list_.begin(),
@ -62,8 +64,7 @@ public:
// Load Paddle inference model
void LoadModel(const std::string &model_dir);
void Run(std::vector<std::vector<std::vector<int>>> boxes, cv::Mat &img,
Classifier *cls);
void Run(cv::Mat &img, std::vector<double> *times);
private:
std::shared_ptr<Predictor> predictor_;
@ -80,7 +81,7 @@ private:
std::vector<float> scale_ = {1 / 0.5f, 1 / 0.5f, 1 / 0.5f};
bool is_scale_ = true;
bool use_tensorrt_ = false;
bool use_fp16_ = false;
std::string precision_ = "fp32";
// pre-process
CrnnResizeImg resize_op_;
Normalize normalize_op_;
@ -89,9 +90,6 @@ private:
// post-process
PostProcessor post_processor_;
cv::Mat GetRotateCropImage(const cv::Mat &srcimage,
std::vector<std::vector<int>> box);
}; // class CrnnRecognizer
} // namespace PaddleOCR

3
deploy/cpp_infer/include/utility.h
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@ -47,6 +47,9 @@ public:
static void GetAllFiles(const char *dir_name,
std::vector<std::string> &all_inputs);
static cv::Mat GetRotateCropImage(const cv::Mat &srcimage,
std::vector<std::vector<int>> box);
};
} // namespace PaddleOCR

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。
```shell
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
OPENCV_DIR=your_opencv_dir
LIB_DIR=your_paddle_inference_dir
CUDA_LIB_DIR=your_cuda_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
* 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
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。
* 更多地，tools/config.txt中的参数及解释如下。
更多参数如下：
```
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
- 通用参数
# 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
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
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
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
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。
det_model_dir ./inference/det_db # 检测模型inference model地址
|参数名称|类型|默认参数|意义|
| --- | --- | --- | --- |
|use_gpu|bool|false|是否使用GPU|
|gpu_id|int|0|GPU id，使用GPU时有效|
|gpu_mem|int|4000|申请的GPU内存|
|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|
|use_mkldnn|bool|true|是否使用mkldnn库|
# cls config
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
- 检测模型相关
# rec config
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
|参数名称|类型|默认参数|意义|
| --- | --- | --- | --- |
|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
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。
```
- 方向分类器相关
* 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。

113
deploy/cpp_infer/readme_en.md
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@ -162,30 +162,13 @@ inference/
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
OPENCV_DIR=your_opencv_dir
LIB_DIR=your_paddle_inference_dir
CUDA_LIB_DIR=your_cuda_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)
@ -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/`.
* 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
* Execute the following command to complete the OCR recognition and detection of an image.
Execute the built executable file:
```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.
* What's more, Parameters and their meanings in `tools/config.txt` are as follows.
More parameters are as follows,
- common parameters
```
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
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
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
|use_gpu|bool|false|Whether to use GPU|
|gpu_id|int|0|GPU id when use_gpu is true|
|gpu_mem|int|4000|GPU memory requested|
|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
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
- detection related parameters
# cls config
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
cls_thresh 0.9 # Score threshold of the direction classifier
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
|det_model_dir|string|-|Address of detection inference model|
|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
rec_model_dir ./inference/rec_crnn # Address of recognition inference model
char_list_file ../../ppocr/utils/ppocr_keys_v1.txt # dictionary file
- classifier related parameters
# show the detection results
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`.
```
|parameter|data type|default|meaning|
| --- | --- | --- | --- |
|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.

70
deploy/cpp_infer/src/config.cpp
View File

@ -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;
chars *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());
delete[] strs;
delete[] d;
char *p = std::strtok(strs, d);
while (p) {
std::string s = p;
res.push_back(s);
p = std::strtok(NULL, 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

324
deploy/cpp_infer/src/main.cpp
View File

@ -28,76 +28,276 @@
#include <numeric>
#include <glog/logging.h>
#include <include/config.h>
#include <include/ocr_det.h>
#include <include/ocr_cls.h>
#include <include/ocr_rec.h>
#include <include/utility.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_string(precision, "fp32", "Precision be one of fp32/fp16/int8");
DEFINE_bool(benchmark, true, "Whether use benchmark.");
DEFINE_string(save_log_path, "./log_output/", "Save benchmark log path.");
// 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 cv;
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]);
config.PrintConfigInfo();
std::string img_path(argv[2]);
std::vector<std::string> all_img_names;
Utility::GetAllFiles((char *)img_path.c_str(), all_img_names);
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,
config.use_polygon_score, config.visualize,
config.use_tensorrt, config.use_fp16);
Classifier *cls = nullptr;
if (config.use_angle_cls == true) {
cls = new Classifier(config.cls_model_dir, config.use_gpu, config.gpu_id,
config.gpu_mem, config.cpu_math_library_num_threads,
config.use_mkldnn, config.cls_thresh,
config.use_tensorrt, config.use_fp16);
}
CRNNRecognizer rec(config.rec_model_dir, config.use_gpu, config.gpu_id,
config.gpu_mem, config.cpu_math_library_num_threads,
config.use_mkldnn, config.char_list_file,
config.use_tensorrt, config.use_fp16);
auto start = std::chrono::system_clock::now();
for (auto img_dir : all_img_names) {
LOG(INFO) << "The predict img: " << img_dir;
cv::Mat srcimg = cv::imread(img_dir, cv::IMREAD_COLOR);
if (!srcimg.data) {
std::cerr << "[ERROR] image read failed! image path: " << img_path
<< "\n";
exit(1);
}
std::vector<std::vector<std::vector<int>>> boxes;
det.Run(srcimg, boxes);
rec.Run(boxes, srcimg, cls);
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;
void PrintBenchmarkLog(std::string model_name,
int batch_size,
std::string input_shape,
std::vector<double> time_info,
int img_num){
LOG(INFO) << "----------------------- Config info -----------------------";
LOG(INFO) << "runtime_device: " << (FLAGS_use_gpu ? "gpu" : "cpu");
LOG(INFO) << "ir_optim: " << "True";
LOG(INFO) << "enable_memory_optim: " << "True";
LOG(INFO) << "enable_tensorrt: " << FLAGS_use_tensorrt;
LOG(INFO) << "enable_mkldnn: " << (FLAGS_use_mkldnn ? "True" : "False");
LOG(INFO) << "cpu_math_library_num_threads: " << FLAGS_cpu_math_library_num_threads;
LOG(INFO) << "----------------------- Data info -----------------------";
LOG(INFO) << "batch_size: " << batch_size;
LOG(INFO) << "input_shape: " << input_shape;
LOG(INFO) << "data_num: " << img_num;
LOG(INFO) << "----------------------- Model info -----------------------";
LOG(INFO) << "model_name: " << model_name;
LOG(INFO) << "precision: " << FLAGS_precision;
LOG(INFO) << "----------------------- Perf info ------------------------";
LOG(INFO) << "Total time spent(ms): "
<< std::accumulate(time_info.begin(), time_info.end(), 0);
LOG(INFO) << "preprocess_time(ms): " << time_info[0] / img_num
<< ", inference_time(ms): " << time_info[1] / img_num
<< ", postprocess_time(ms): " << time_info[2] / img_num;
}
static bool PathExists(const std::string& path){
#ifdef _WIN32
struct _stat buffer;
return (_stat(path.c_str(), &buffer) == 0);
#else
struct stat buffer;
return (stat(path.c_str(), &buffer) == 0);
#endif // !_WIN32
}
int main_det(std::vector<cv::String> cv_all_img_names) {
std::vector<double> time_info = {0, 0, 0};
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_precision);
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;
std::vector<double> det_times;
det.Run(srcimg, boxes, &det_times);
time_info[0] += det_times[0];
time_info[1] += det_times[1];
time_info[2] += det_times[2];
}
if (FLAGS_benchmark) {
PrintBenchmarkLog("det", 1, "dynamic", time_info, cv_all_img_names.size());
}
return 0;
}
int main_rec(std::vector<cv::String> cv_all_img_names) {
std::vector<double> time_info = {0, 0, 0};
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_precision);
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<double> rec_times;
rec.Run(srcimg, &rec_times);
time_info[0] += rec_times[0];
time_info[1] += rec_times[1];
time_info[2] += rec_times[2];
}
if (FLAGS_benchmark) {
PrintBenchmarkLog("rec", 1, "dynamic", time_info, cv_all_img_names.size());
}
return 0;
}
int main_system(std::vector<cv::String> cv_all_img_names) {
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_precision);
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_precision);
}
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_precision);
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;
std::vector<double> det_times;
std::vector<double> rec_times;
det.Run(srcimg, boxes, &det_times);
cv::Mat crop_img;
for (int j = 0; j < boxes.size(); j++) {
crop_img = Utility::GetRotateCropImage(srcimg, boxes[j]);
if (cls != nullptr) {
crop_img = cls->Run(crop_img);
}
rec.Run(crop_img, &rec_times);
}
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;
}
void check_params(char* mode) {
if (strcmp(mode, "det")==0) {
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 (strcmp(mode, "rec")==0) {
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 (strcmp(mode, "system")==0) {
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 (FLAGS_precision != "fp32" && FLAGS_precision != "fp16" && FLAGS_precision != "int8") {
cout << "precison should be 'fp32'(default), 'fp16' or 'int8'. " << endl;
exit(1);
}
}
int main(int argc, char **argv) {
if (argc<=1 || (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;
// Parsing command-line
google::ParseCommandLineFlags(&argc, &argv, true);
check_params(argv[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;
if (strcmp(argv[1], "det")==0) {
return main_det(cv_all_img_names);
}
if (strcmp(argv[1], "rec")==0) {
return main_rec(cv_all_img_names);
}
if (strcmp(argv[1], "system")==0) {
return main_system(cv_all_img_names);
}
}

10
deploy/cpp_infer/src/ocr_cls.cpp
View File

@ -77,10 +77,16 @@ void Classifier::LoadModel(const std::string &model_dir) {
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) {
auto precision = paddle_infer::Config::Precision::kFloat32;
if (this->precision_ == "fp16") {
precision = paddle_infer::Config::Precision::kHalf;
}
if (this->precision_ == "int8") {
precision = paddle_infer::Config::Precision::kInt8;
}
config.EnableTensorRtEngine(
1 << 20, 10, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
precision,
false, false);
}
} else {

36
deploy/cpp_infer/src/ocr_det.cpp
View File

@ -14,6 +14,7 @@
#include <include/ocr_det.h>
namespace PaddleOCR {
void DBDetector::LoadModel(const std::string &model_dir) {
@ -25,10 +26,16 @@ void DBDetector::LoadModel(const std::string &model_dir) {
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) {
auto precision = paddle_infer::Config::Precision::kFloat32;
if (this->precision_ == "fp16") {
precision = paddle_infer::Config::Precision::kHalf;
}
if (this->precision_ == "int8") {
precision = paddle_infer::Config::Precision::kInt8;
}
config.EnableTensorRtEngine(
1 << 20, 10, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
precision,
false, false);
std::map<std::string, std::vector<int>> min_input_shape = {
{"x", {1, 3, 50, 50}},
@ -90,13 +97,16 @@ void DBDetector::LoadModel(const std::string &model_dir) {
}
void DBDetector::Run(cv::Mat &img,
std::vector<std::vector<std::vector<int>>> &boxes) {
std::vector<std::vector<std::vector<int>>> &boxes,
std::vector<double> *times) {
float ratio_h{};
float ratio_w{};
cv::Mat srcimg;
cv::Mat resize_img;
img.copyTo(srcimg);
auto preprocess_start = std::chrono::steady_clock::now();
this->resize_op_.Run(img, resize_img, this->max_side_len_, ratio_h, ratio_w,
this->use_tensorrt_);
@ -105,14 +115,17 @@ void DBDetector::Run(cv::Mat &img,
std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
this->permute_op_.Run(&resize_img, input.data());
auto preprocess_end = std::chrono::steady_clock::now();
// 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});
auto inference_start = std::chrono::steady_clock::now();
input_t->CopyFromCpu(input.data());
this->predictor_->Run();
std::vector<float> out_data;
auto output_names = this->predictor_->GetOutputNames();
auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
@ -122,7 +135,9 @@ void DBDetector::Run(cv::Mat &img,
out_data.resize(out_num);
output_t->CopyToCpu(out_data.data());
auto inference_end = std::chrono::steady_clock::now();
auto postprocess_start = std::chrono::steady_clock::now();
int n2 = output_shape[2];
int n3 = output_shape[3];
int n = n2 * n3;
@ -150,7 +165,16 @@ void DBDetector::Run(cv::Mat &img,
this->det_db_unclip_ratio_, this->use_polygon_score_);
boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
auto postprocess_end = std::chrono::steady_clock::now();
std::cout << "Detected boxes num: " << boxes.size() << endl;
std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
times->push_back(double(preprocess_diff.count() * 1000));
std::chrono::duration<float> inference_diff = inference_end - inference_start;
times->push_back(double(inference_diff.count() * 1000));
std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
times->push_back(double(postprocess_diff.count() * 1000));
//// visualization
if (this->visualize_) {
Utility::VisualizeBboxes(srcimg, boxes);

199
deploy/cpp_infer/src/ocr_rec.cpp
View File

@ -16,80 +16,80 @@
namespace PaddleOCR {
void CRNNRecognizer::Run(std::vector<std::vector<std::vector<int>>> boxes,
cv::Mat &img, Classifier *cls) {
void CRNNRecognizer::Run(cv::Mat &img, std::vector<double> *times) {
cv::Mat srcimg;
img.copyTo(srcimg);
cv::Mat crop_img;
cv::Mat resize_img;
std::cout << "The predicted text is :" << std::endl;
int index = 0;
for (int i = 0; i < boxes.size(); i++) {
crop_img = GetRotateCropImage(srcimg, boxes[i]);
float wh_ratio = float(srcimg.cols) / float(srcimg.rows);
auto preprocess_start = std::chrono::steady_clock::now();
this->resize_op_.Run(srcimg, resize_img, wh_ratio, this->use_tensorrt_);
if (cls != nullptr) {
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());
auto preprocess_end = std::chrono::steady_clock::now();
// 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});
auto inference_start = std::chrono::steady_clock::now();
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());
auto inference_end = std::chrono::steady_clock::now();
// ctc decode
auto postprocess_start = std::chrono::steady_clock::now();
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]);
}
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;
last_index = argmax_idx;
}
auto postprocess_end = std::chrono::steady_clock::now();
score /= count;
for (int i = 0; i < str_res.size(); i++) {
std::cout << str_res[i];
}
std::cout << "\tscore: " << score << std::endl;
std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
times->push_back(double(preprocess_diff.count() * 1000));
std::chrono::duration<float> inference_diff = inference_end - inference_start;
times->push_back(double(inference_diff.count() * 1000));
std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
times->push_back(double(postprocess_diff.count() * 1000));
}
void CRNNRecognizer::LoadModel(const std::string &model_dir) {
@ -101,10 +101,16 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
if (this->use_gpu_) {
config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
if (this->use_tensorrt_) {
auto precision = paddle_infer::Config::Precision::kFloat32;
if (this->precision_ == "fp16") {
precision = paddle_infer::Config::Precision::kHalf;
}
if (this->precision_ == "int8") {
precision = paddle_infer::Config::Precision::kInt8;
}
config.EnableTensorRtEngine(
1 << 20, 10, 3,
this->use_fp16_ ? paddle_infer::Config::Precision::kHalf
: paddle_infer::Config::Precision::kFloat32,
precision,
false, false);
std::map<std::string, std::vector<int>> min_input_shape = {
{"x", {1, 3, 32, 10}}};
@ -138,59 +144,4 @@ void CRNNRecognizer::LoadModel(const std::string &model_dir) {
this->predictor_ = CreatePredictor(config);
}
cv::Mat CRNNRecognizer::GetRotateCropImage(const cv::Mat &srcimage,
std::vector<std::vector<int>> box) {
cv::Mat image;
srcimage.copyTo(image);
std::vector<std::vector<int>> points = box;
int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
int left = int(*std::min_element(x_collect, x_collect + 4));
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));
cv::Mat img_crop;
image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
for (int i = 0; i < points.size(); i++) {
points[i][0] -= left;
points[i][1] -= top;
}
int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
pow(points[0][1] - points[1][1], 2)));
int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
pow(points[0][1] - points[3][1], 2)));
cv::Point2f pts_std[4];
pts_std[0] = cv::Point2f(0., 0.);
pts_std[1] = cv::Point2f(img_crop_width, 0.);
pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
pts_std[3] = cv::Point2f(0.f, img_crop_height);
cv::Point2f pointsf[4];
pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
cv::Mat dst_img;
cv::warpPerspective(img_crop, dst_img, M,
cv::Size(img_crop_width, img_crop_height),
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;
}
}
} // namespace PaddleOCR

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

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

55
deploy/cpp_infer/src/utility.cpp
View File

@ -92,4 +92,59 @@ void Utility::GetAllFiles(const char *dir_name,
}
}
cv::Mat Utility::GetRotateCropImage(const cv::Mat &srcimage,
std::vector<std::vector<int>> box) {
cv::Mat image;
srcimage.copyTo(image);
std::vector<std::vector<int>> points = box;
int x_collect[4] = {box[0][0], box[1][0], box[2][0], box[3][0]};
int y_collect[4] = {box[0][1], box[1][1], box[2][1], box[3][1]};
int left = int(*std::min_element(x_collect, x_collect + 4));
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));
cv::Mat img_crop;
image(cv::Rect(left, top, right - left, bottom - top)).copyTo(img_crop);
for (int i = 0; i < points.size(); i++) {
points[i][0] -= left;
points[i][1] -= top;
}
int img_crop_width = int(sqrt(pow(points[0][0] - points[1][0], 2) +
pow(points[0][1] - points[1][1], 2)));
int img_crop_height = int(sqrt(pow(points[0][0] - points[3][0], 2) +
pow(points[0][1] - points[3][1], 2)));
cv::Point2f pts_std[4];
pts_std[0] = cv::Point2f(0., 0.);
pts_std[1] = cv::Point2f(img_crop_width, 0.);
pts_std[2] = cv::Point2f(img_crop_width, img_crop_height);
pts_std[3] = cv::Point2f(0.f, img_crop_height);
cv::Point2f pointsf[4];
pointsf[0] = cv::Point2f(points[0][0], points[0][1]);
pointsf[1] = cv::Point2f(points[1][0], points[1][1]);
pointsf[2] = cv::Point2f(points[2][0], points[2][1]);
pointsf[3] = cv::Point2f(points[3][0], points[3][1]);
cv::Mat M = cv::getPerspectiveTransform(pointsf, pts_std);
cv::Mat dst_img;
cv::warpPerspective(img_crop, dst_img, M,
cv::Size(img_crop_width, img_crop_height),
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;
}
}
} // namespace PaddleOCR

31
deploy/cpp_infer/tools/config.txt
View File

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

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2
tests/prepare.sh
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@ -62,7 +62,7 @@ else
if [ ${model_name} = "ocr_det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
cd ./inference && tar xf ${eval_model_name}.tar && tar xf ch_det_data_50.tar && cd ../
else

58
tests/readme.md Normal file
View File

@ -0,0 +1,58 @@
# 介绍
test.sh和params.txt文件配合使用，完成OCR轻量检测和识别模型从训练到预测的流程测试。
# 安装依赖
- 安装PaddlePaddle >= 2.0
- 安装PaddleOCR依赖
```
pip3 install -r ../requirements.txt
```
- 安装autolog
```
git clone https://github.com/LDOUBLEV/AutoLog
cd AutoLog
pip3 install -r requirements.txt
python3 setup.py bdist_wheel
pip3 install ./dist/auto_log-1.0.0-py3-none-any.whl
cd ../
```
# 目录介绍
```bash
tests/
├── ocr_det_params.txt # 测试OCR检测模型的参数配置文件
├── ocr_rec_params.txt # 测试OCR识别模型的参数配置文件
└── prepare.sh # 完成test.sh运行所需要的数据和模型下载
└── test.sh # 根据
```
# 使用方法
test.sh包含四种运行模式，每种模式的运行数据不同，分别用于测试速度和精度，分别是：
- 模式1 lite_train_infer，使用少量数据训练，用于快速验证训练到预测的走通流程，不验证精度和速度；
```
bash test/prepare.sh ./tests/ocr_det_params.txt 'lite_train_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'lite_train_infer'
```
- 模式2 whole_infer，使用少量数据训练，一定量数据预测，用于验证训练后的模型执行预测，预测速度是否合理；
```
bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'whole_infer'
```
- 模式3 infer 不训练，全量数据预测，走通开源模型评估、动转静，检查inference model预测时间和精度;
```
bash tests/prepare.sh ./tests/ocr_det_params.txt 'infer'
用法1:
bash tests/test.sh ./tests/ocr_det_params.txt 'infer'
用法2: 指定GPU卡预测，第三个传入参数为GPU卡号
bash tests/test.sh ./tests/ocr_det_params.txt 'infer' '1'
```
模式4: whole_train_infer , CE： 全量数据训练，全量数据预测，验证模型训练精度，预测精度，预测速度
```
bash tests/prepare.sh ./tests/ocr_det_params.txt 'whole_train_infer'
bash tests/test.sh ./tests/ocr_det_params.txt 'whole_train_infer'
```

3
tools/infer/predict_det.py
View File

@ -101,6 +101,7 @@ class TextDetector(object):
if args.benchmark:
import auto_log
pid = os.getpid()
gpu_id = utility.get_infer_gpuid()
self.autolog = auto_log.AutoLogger(
model_name="det",
model_precision=args.precision,
@ -110,7 +111,7 @@ class TextDetector(object):
inference_config=self.config,
pids=pid,
process_name=None,
gpu_ids=0,
gpu_ids=gpu_id if args.use_gpu else None,
time_keys=[
'preprocess_time', 'inference_time', 'postprocess_time'
],

3
tools/infer/predict_rec.py
View File

@ -68,6 +68,7 @@ class TextRecognizer(object):
if args.benchmark:
import auto_log
pid = os.getpid()
gpu_id = utility.get_infer_gpuid()
self.autolog = auto_log.AutoLogger(
model_name="rec",
model_precision=args.precision,
@ -77,7 +78,7 @@ class TextRecognizer(object):
inference_config=self.config,
pids=pid,
process_name=None,
gpu_ids=0 if args.use_gpu else None,
gpu_ids=gpu_id if args.use_gpu else None,
time_keys=[
'preprocess_time', 'inference_time', 'postprocess_time'
],

14
tools/infer/utility.py
View File

@ -280,6 +280,20 @@ def create_predictor(args, mode, logger):
return predictor, input_tensor, output_tensors, config
def get_infer_gpuid():
cmd = "nvidia-smi"
res = os.popen(cmd).readlines()
if len(res) == 0:
return None
cmd = "env | grep CUDA_VISIBLE_DEVICES"
env_cuda = os.popen(cmd).readlines()
if len(env_cuda) == 0:
return 0
else:
gpu_id = env_cuda[0].strip().split("=")[1]
return int(gpu_id[0])
def draw_e2e_res(dt_boxes, strs, img_path):
src_im = cv2.imread(img_path)
for box, str in zip(dt_boxes, strs):