init
This commit is contained in:
@@ -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 ${FASTDEPLOY_LIBS})
|
||||
@@ -0,0 +1,58 @@
|
||||
[English](README.md) | 简体中文
|
||||
# PP-OCRv3 昆仑芯XPU C++部署示例
|
||||
|
||||
本目录下提供`infer.cc`, 供用户完成PP-OCRv3在昆仑芯XPU上的部署.
|
||||
|
||||
## 1. 部署环境准备
|
||||
在部署前，需自行编译基于昆仑芯XPU的预测库，参考文档[昆仑芯XPU部署环境编译安装](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#自行编译安装)
|
||||
|
||||
## 2.部署模型准备
|
||||
在部署前, 请准备好您所需要运行的推理模型, 您可以在[FastDeploy支持的PaddleOCR模型列表](../README.md)中下载所需模型.
|
||||
|
||||
## 3.运行部署示例
|
||||
```
|
||||
# 下载部署示例代码
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd FastDeploy/examples/vision/ocr/PP-OCR/kunlunxin/cpp
|
||||
|
||||
# 如果您希望从PaddleOCR下载示例代码，请运行
|
||||
git clone https://github.com/PaddlePaddle/PaddleOCR.git
|
||||
# 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到dygraph分支
|
||||
git checkout dygraph
|
||||
cd PaddleOCR/deploy/fastdeploy/kunlunxin/cpp
|
||||
|
||||
mkdir build
|
||||
cd build
|
||||
# 使用编译完成的FastDeploy库编译infer_demo
|
||||
cmake .. -DFASTDEPLOY_INSTALL_DIR=${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)
|
||||
@@ -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;
|
||||
}
|
||||
Reference in New Issue
Block a user