xiajingrui/id_ocr
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

init

Browse Source
This commit is contained in:
xiajingrui
2024-01-17 15:18:16 +08:00
commit 1bd4060e68
1514 changed files with 193581 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
deploy/fastdeploy/kunlunxin/README.md
+32
View File
@@ -0,0 +1,32 @@
[English](README.md) | 简体中文
# PaddleOCR 在昆仑芯上部署方案-FastDeploy
## 1. 说明
PaddleOCR支持利用FastDeploy在昆仑芯片上部署模型.
支持如下芯片的部署
- 昆仑 818-100（推理芯片）
- 昆仑 818-300（训练芯片）
支持如下芯片的设备
- K100/K200 昆仑 AI 加速卡
- R200 昆仑芯 AI 加速卡
## 2. 支持的PaddleOCR推理模型
下表中的推理模型为FastDeploy测试过的模型, 下载链接由PaddleOCR模型库提供,
更多的模型, 详见[PP-OCR系列模型列表](https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/doc/doc_ch/models_list.md), 欢迎用户尝试.
| PaddleOCR版本 | 文本框检测 | 方向分类模型 | 文字识别 |字典文件| 说明 |
|:----|:----|:----|:----|:----|:--------|
| ch_PP-OCRv3[推荐] |[ch_PP-OCRv3_det](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) | [ch_ppocr_mobile_v2.0_cls](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) | [ch_PP-OCRv3_rec](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) | [ppocr_keys_v1.txt](https://bj.bcebos.com/paddlehub/fastdeploy/ppocr_keys_v1.txt) | OCRv3系列原始超轻量模型，支持中英文、多语种文本检测 |
| en_PP-OCRv3[推荐] |[en_PP-OCRv3_det](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) | [ch_ppocr_mobile_v2.0_cls](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) | [en_PP-OCRv3_rec](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) | [en_dict.txt](https://bj.bcebos.com/paddlehub/fastdeploy/en_dict.txt) | OCRv3系列原始超轻量模型，支持英文与数字识别，除检测模型和识别模型的训练数据与中文模型不同以外，无其他区别 |
| ch_PP-OCRv2 |[ch_PP-OCRv2_det](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) | [ch_ppocr_mobile_v2.0_cls](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) | [ch_PP-OCRv2_rec](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) | [ppocr_keys_v1.txt](https://bj.bcebos.com/paddlehub/fastdeploy/ppocr_keys_v1.txt) | OCRv2系列原始超轻量模型，支持中英文、多语种文本检测 |
| ch_PP-OCRv2_mobile |[ch_ppocr_mobile_v2.0_det](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) | [ch_ppocr_mobile_v2.0_cls](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) | [ch_ppocr_mobile_v2.0_rec](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) | [ppocr_keys_v1.txt](https://bj.bcebos.com/paddlehub/fastdeploy/ppocr_keys_v1.txt) | OCRv2系列原始超轻量模型，支持中英文、多语种文本检测,比PPOCRv2更加轻量 |
| ch_PP-OCRv2_server |[ch_ppocr_server_v2.0_det](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) | [ch_ppocr_mobile_v2.0_cls](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) | [ch_ppocr_server_v2.0_rec](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) |[ppocr_keys_v1.txt](https://bj.bcebos.com/paddlehub/fastdeploy/ppocr_keys_v1.txt) | OCRv2服务器系列模型, 支持中英文、多语种文本检测，比超轻量模型更大，但效果更好|
## 3. 详细部署的部署示例
- [Python部署](python)
- [C++部署](cpp)
deploy/fastdeploy/kunlunxin/cpp/CMakeLists.txt
+14
View File
@@ -0,0 +1,14 @@
PROJECT(infer_demo C CXX)
CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
# 指定下载解压后的fastdeploy库路径
option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
# 添加FastDeploy依赖头文件
include_directories(${FASTDEPLOY_INCS})
add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
# 添加FastDeploy库依赖
target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
deploy/fastdeploy/kunlunxin/cpp/README.md
+58
View File
@@ -0,0 +1,58 @@
[English](README.md) | 简体中文
# PP-OCRv3 昆仑芯XPU C++部署示例
本目录下提供`infer.cc`, 供用户完成PP-OCRv3在昆仑芯XPU上的部署.
## 1. 部署环境准备
在部署前，需自行编译基于昆仑芯XPU的预测库，参考文档[昆仑芯XPU部署环境编译安装](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
## 2.部署模型准备
在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleOCR模型列表](../README.md)中下载所需模型.
## 3.运行部署示例
```
# 下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/ocr/PP-OCR/kunlunxin/cpp
# 如果您希望从PaddleOCR下载示例代码，请运行
git clone https://github.com/PaddlePaddle/PaddleOCR.git
# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到dygraph分支
git checkout dygraph
cd PaddleOCR/deploy/fastdeploy/kunlunxin/cpp
mkdir build
cd build
# 使用编译完成的FastDeploy库编译infer_demo
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-kunlunxin
make -j
# 下载PP-OCRv3文字检测模型
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
tar -xvf ch_PP-OCRv3_det_infer.tar
# 下载文字方向分类器模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar
# 下载PP-OCRv3文字识别模型
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
tar -xvf ch_PP-OCRv3_rec_infer.tar
# 下载预测图片与字典文件
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
./infer_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg
```
运行完成可视化结果如下图所示
<div align="center">
<img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">
</div>
## 4. 更多指南
- [PP-OCR系列 C++ API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/cpp/html/namespacefastdeploy_1_1vision_1_1ocr.html)
- [FastDeploy部署PaddleOCR模型概览](../../)
- [PP-OCRv3 Python部署](../python)
- 如果用户想要调整前后处理超参数、单独使用文字检测识别模型、使用其他模型等，更多详细文档与说明请参考[PP-OCR系列在CPU/GPU上的部署](../../cpu-gpu/cpp/README.md)
deploy/fastdeploy/kunlunxin/cpp/infer.cc
+115
View File
@@ -0,0 +1,115 @@
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "fastdeploy/vision.h"
#ifdef WIN32
const char sep = '\\';
#else
const char sep = '/';
#endif
void KunlunXinInfer(const std::string &det_model_dir,
const std::string &cls_model_dir,
const std::string &rec_model_dir,
const std::string &rec_label_file,
const std::string &image_file) {
auto det_model_file = det_model_dir + sep + "inference.pdmodel";
auto det_params_file = det_model_dir + sep + "inference.pdiparams";
auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
auto option = fastdeploy::RuntimeOption();
option.UseKunlunXin();
auto det_option = option;
auto cls_option = option;
auto rec_option = option;
// The cls and rec model can inference a batch of images now.
// User could initialize the inference batch size and set them after create
// PP-OCR model.
int cls_batch_size = 1;
int rec_batch_size = 6;
auto det_model = fastdeploy::vision::ocr::DBDetector(
det_model_file, det_params_file, det_option);
auto cls_model = fastdeploy::vision::ocr::Classifier(
cls_model_file, cls_params_file, cls_option);
auto rec_model = fastdeploy::vision::ocr::Recognizer(
rec_model_file, rec_params_file, rec_label_file, rec_option);
assert(det_model.Initialized());
assert(cls_model.Initialized());
assert(rec_model.Initialized());
// The classification model is optional, so the PP-OCR can also be connected
// in series as follows
// auto ppocr_v3 = fastdeploy::pipeline::PPOCRv3(&det_model, &rec_model);
auto ppocr_v3 =
fastdeploy::pipeline::PPOCRv3(&det_model, &cls_model, &rec_model);
// Set inference batch size for cls model and rec model, the value could be -1
// and 1 to positive infinity.
// When inference batch size is set to -1, it means that the inference batch
// size
// of the cls and rec models will be the same as the number of boxes detected
// by the det model.
ppocr_v3.SetClsBatchSize(cls_batch_size);
ppocr_v3.SetRecBatchSize(rec_batch_size);
if (!ppocr_v3.Initialized()) {
std::cerr << "Failed to initialize PP-OCR." << std::endl;
return;
}
auto im = cv::imread(image_file);
auto im_bak = im.clone();
fastdeploy::vision::OCRResult result;
if (!ppocr_v3.Predict(&im, &result)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
std::cout << result.Str() << std::endl;
auto vis_im = fastdeploy::vision::VisOcr(im_bak, result);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
int main(int argc, char *argv[]) {
if (argc < 6) {
std::cout << "Usage: infer_demo path/to/det_model path/to/cls_model "
"path/to/rec_model path/to/rec_label_file path/to/image "
"e.g ./infer_demo ./ch_PP-OCRv3_det_infer "
"./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer "
"./ppocr_keys_v1.txt ./12.jpg"
<< std::endl;
return -1;
}
std::string det_model_dir = argv[1];
std::string cls_model_dir = argv[2];
std::string rec_model_dir = argv[3];
std::string rec_label_file = argv[4];
std::string test_image = argv[5];
KunlunXinInfer(det_model_dir, cls_model_dir, rec_model_dir, rec_label_file,
test_image);
return 0;
}
deploy/fastdeploy/kunlunxin/python/README.md
+54
View File
@@ -0,0 +1,54 @@
[English](README.md) | 简体中文
# PP-OCRv3 昆仑芯XPU Python部署示例
本目录下提供`infer.py`, 供用户完成PP-OCRv3在昆仑芯XPU上的部署.
## 1. 部署环境准备
在部署前，需自行编译基于昆仑XPU的FastDeploy python wheel包并安装，参考文档[昆仑芯XPU部署环境](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
## 2.部署模型准备
在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleOCR模型列表](../README.md)中下载所需模型.
## 3.运行部署示例
```
# 下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/ocr/PP-OCR/kunlunxin/python
# 如果您希望从PaddleOCR下载示例代码，请运行
git clone https://github.com/PaddlePaddle/PaddleOCR.git
# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到dygraph分支
git checkout dygraph
cd PaddleOCR/deploy/fastdeploy/kunlunxin/python
# 下载PP-OCRv3文字检测模型
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
tar -xvf ch_PP-OCRv3_det_infer.tar
# 下载文字方向分类器模型
wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar
# 下载PP-OCRv3文字识别模型
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
tar -xvf ch_PP-OCRv3_rec_infer.tar
# 下载预测图片与字典文件
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
python infer.py --det_model ch_PP-OCRv3_det_infer --cls_model ch_ppocr_mobile_v2.0_cls_infer --rec_model ch_PP-OCRv3_rec_infer --rec_label_file ppocr_keys_v1.txt --image 12.jpg
```
运行完成可视化结果如下图所示
<div align="center">
<img width="640" src="https://user-images.githubusercontent.com/109218879/185826024-f7593a0c-1bd2-4a60-b76c-15588484fa08.jpg">
</div>
## 4. 更多指南
- [PP-OCR系列 Python API查阅](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/ocr.html)
- [FastDeploy部署PaddleOCR模型概览](../../)
- [PP-OCRv3 C++部署](../cpp)
- 如果用户想要调整前后处理超参数、单独使用文字检测识别模型、使用其他模型等，更多详细文档与说明请参考[PP-OCR系列在CPU/GPU上的部署](../../cpu-gpu/python/README.md)
## 5. 常见问题
- [如何将视觉模型预测结果转为numpy格式](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/vision_result_related_problems.md)
deploy/fastdeploy/kunlunxin/python/infer.py
+111
View File
@@ -0,0 +1,111 @@
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import fastdeploy as fd
import cv2
import os
def parse_arguments():
import argparse
import ast
parser = argparse.ArgumentParser()
parser.add_argument(
"--det_model", required=True, help="Path of Detection model of PPOCR.")
parser.add_argument(
"--cls_model",
required=True,
help="Path of Classification model of PPOCR.")
parser.add_argument(
"--rec_model",
required=True,
help="Path of Recognization model of PPOCR.")
parser.add_argument(
"--rec_label_file",
required=True,
help="Path of Recognization model of PPOCR.")
parser.add_argument(
"--image", type=str, required=True, help="Path of test image file.")
parser.add_argument(
"--cls_bs",
type=int,
default=1,
help="Classification model inference batch size.")
parser.add_argument(
"--rec_bs",
type=int,
default=6,
help="Recognition model inference batch size")
return parser.parse_args()
def build_option(args):
det_option = fd.RuntimeOption()
cls_option = fd.RuntimeOption()
rec_option = fd.RuntimeOption()
det_option.use_kunlunxin()
cls_option.use_kunlunxin()
rec_option.use_kunlunxin()
return det_option, cls_option, rec_option
args = parse_arguments()
det_model_file = os.path.join(args.det_model, "inference.pdmodel")
det_params_file = os.path.join(args.det_model, "inference.pdiparams")
cls_model_file = os.path.join(args.cls_model, "inference.pdmodel")
cls_params_file = os.path.join(args.cls_model, "inference.pdiparams")
rec_model_file = os.path.join(args.rec_model, "inference.pdmodel")
rec_params_file = os.path.join(args.rec_model, "inference.pdiparams")
rec_label_file = args.rec_label_file
det_option, cls_option, rec_option = build_option(args)
det_model = fd.vision.ocr.DBDetector(
det_model_file, det_params_file, runtime_option=det_option)
cls_model = fd.vision.ocr.Classifier(
cls_model_file, cls_params_file, runtime_option=cls_option)
rec_model = fd.vision.ocr.Recognizer(
rec_model_file, rec_params_file, rec_label_file, runtime_option=rec_option)
# Create PP-OCRv3, if cls_model is not needed,
# just set cls_model=None .
ppocr_v3 = fd.vision.ocr.PPOCRv3(
det_model=det_model, cls_model=cls_model, rec_model=rec_model)
# Set inference batch size for cls model and rec model, the value could be -1 and 1 to positive infinity.
# When inference batch size is set to -1, it means that the inference batch size
# of the cls and rec models will be the same as the number of boxes detected by the det model.
ppocr_v3.cls_batch_size = args.cls_bs
ppocr_v3.rec_batch_size = args.rec_bs
# Prepare image.
im = cv2.imread(args.image)
# Print the results.
result = ppocr_v3.predict(im)
print(result)
# Visuliaze the output.
vis_im = fd.vision.vis_ppocr(im, result)
cv2.imwrite("visualized_result.jpg", vis_im)
print("Visualized result save in ./visualized_result.jpg")