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Optimize ocr system code (#209)

Browse Source 
* Support PPYOLOE plus model

* Optimize ocr system code

* modify example code

* fix patchelf of openvino

* optimize demo code of ocr

* remove debug code

* update demo code of ocr

Co-authored-by: Jack Zhou <zhoushunjie@baidu.com>
This commit is contained in:
Jason
2022-09-14 09:46:03 +08:00
committed by GitHub
parent 1452275efe
commit 0dd9ecee65
39 changed files with 1041 additions and 909 deletions
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26
examples/vision/ocr/PPOCRSystemv3/cpp/README.md
View File

@@ -28,10 +28,9 @@ tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar.gz
wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
tar -xvf ch_PP-OCRv3_rec_infer.tar
wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/doc/imgs/12.jpg
wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/utils/ppocr_keys_v1.txt
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
# CPU推理
./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 0
@@ -39,8 +38,6 @@ wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/
./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 1
# GPU上TensorRT推理
./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 2
# OCR还支持det/cls/rec三个模型的组合使用，例如当我们不想使用cls模型的时候，只需要给cls模型路径的位置，传入一个空的字符串, 例子如下
./infer_demo ./ch_PP-OCRv3_det_infer "" ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 0
```
运行完成可视化结果如下图所示
@@ -53,12 +50,12 @@ wget https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/release/2.6/ppocr/
### PPOCRSystemv3类
```
fastdeploy::application::ocrsystem::PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* ocr_det = nullptr,
fastdeploy::vision::ocr::Classifier* ocr_cls = nullptr,
fastdeploy::vision::ocr::Recognizer* ocr_rec = nullptr);
fastdeploy::application::ocrsystem::PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* det_model,
fastdeploy::vision::ocr::Classifier* cls_model,
fastdeploy::vision::ocr::Recognizer* rec_model);
```
PPOCRSystemv3 的初始化，由检测，分类和识别模型串联构成
PPOCRSystemv2 的初始化，由检测，分类和识别模型串联构成
**参数**
@@ -66,6 +63,17 @@ PPOCRSystemv3 的初始化，由检测，分类和识别模型串联构成
> * **Classifier**(model): OCR中的分类模型
> * **Recognizer**(model): OCR中的识别模型
```
fastdeploy::application::ocrsystem::PPOCRSystemv3(fastdeploy::vision::ocr::DBDetector* det_model,
fastdeploy::vision::ocr::Recognizer* rec_model);
```
PPOCRSystemv2 的初始化，由检测，识别模型串联构成(无分类器)
**参数**
> * **DBDetector**(model): OCR中的检测模型
> * **Recognizer**(model): OCR中的识别模型
#### Predict函数
> ```

264
examples/vision/ocr/PPOCRSystemv3/cpp/infer.cc
View File

@@ -19,11 +19,7 @@ const char sep = '\\';
const char sep = '/';
#endif
void CpuInfer(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) {
void InitAndInfer(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, const fastdeploy::RuntimeOption& option) {
auto det_model_file = det_model_dir + sep + "inference.pdmodel";
auto det_params_file = det_model_dir + sep + "inference.pdiparams";
@@ -32,235 +28,32 @@ void CpuInfer(const std::string& det_model_dir,
auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
auto rec_label = rec_label_file;
fastdeploy::vision::ocr::DBDetector det_model;
fastdeploy::vision::ocr::Classifier cls_model;
fastdeploy::vision::ocr::Recognizer rec_model;
auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, option);
auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, option);
auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, option);
if (!det_model_dir.empty()) {
auto det_option = fastdeploy::RuntimeOption();
det_option.UseCpu();
det_model = fastdeploy::vision::ocr::DBDetector(
det_model_file, det_params_file, det_option);
assert(det_model.Initialized());
assert(cls_model.Initialized());
assert(rec_model.Initialized());
if (!det_model.Initialized()) {
std::cerr << "Failed to initialize det_model." << std::endl;
return;
}
}
if (!cls_model_dir.empty()) {
auto cls_option = fastdeploy::RuntimeOption();
cls_option.UseCpu();
cls_model = fastdeploy::vision::ocr::Classifier(
cls_model_file, cls_params_file, cls_option);
if (!cls_model.Initialized()) {
std::cerr << "Failed to initialize cls_model." << std::endl;
return;
}
}
if (!rec_model_dir.empty()) {
auto rec_option = fastdeploy::RuntimeOption();
rec_option.UseCpu();
rec_model = fastdeploy::vision::ocr::Recognizer(
rec_model_file, rec_params_file, rec_label, rec_option);
if (!rec_model.Initialized()) {
std::cerr << "Failed to initialize rec_model." << std::endl;
return;
}
}
auto ocrv3_app = fastdeploy::application::ocrsystem::PPOCRSystemv3(
&det_model, &cls_model, &rec_model);
// 其中分类模型可选，因此也可使用如下方式串联OCR系统
// auto ocr_system_v3 = fastdeploy::application::ocrsystem::PPOCRSystemv3(&det_model, &rec_model);
auto ocr_system_v3 = fastdeploy::application::ocrsystem::PPOCRSystemv3(&det_model, &cls_model, &rec_model);
auto im = cv::imread(image_file);
auto im_bak = im.clone();
fastdeploy::vision::OCRResult res;
//开始预测
if (!ocrv3_app.Predict(&im, &res)) {
fastdeploy::vision::OCRResult result;
if (!ocr_system_v3.Predict(&im, &result)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
//输出预测信息
std::cout << res.Str() << std::endl;
std::cout << result.Str() << std::endl;
//可视化
auto vis_img = fastdeploy::vision::Visualize::VisOcr(im_bak, res);
cv::imwrite("vis_result.jpg", vis_img);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
void GpuInfer(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 rec_label = rec_label_file;
fastdeploy::vision::ocr::DBDetector det_model;
fastdeploy::vision::ocr::Classifier cls_model;
fastdeploy::vision::ocr::Recognizer rec_model;
//准备模型
if (!det_model_dir.empty()) {
auto det_option = fastdeploy::RuntimeOption();
det_option.UseGpu();
det_model = fastdeploy::vision::ocr::DBDetector(
det_model_file, det_params_file, det_option);
if (!det_model.Initialized()) {
std::cerr << "Failed to initialize det_model." << std::endl;
return;
}
}
if (!cls_model_dir.empty()) {
auto cls_option = fastdeploy::RuntimeOption();
cls_option.UseGpu();
cls_model = fastdeploy::vision::ocr::Classifier(
cls_model_file, cls_params_file, cls_option);
if (!cls_model.Initialized()) {
std::cerr << "Failed to initialize cls_model." << std::endl;
return;
}
}
if (!rec_model_dir.empty()) {
auto rec_option = fastdeploy::RuntimeOption();
rec_option.UseGpu();
rec_model = fastdeploy::vision::ocr::Recognizer(
rec_model_file, rec_params_file, rec_label, rec_option);
if (!rec_model.Initialized()) {
std::cerr << "Failed to initialize rec_model." << std::endl;
return;
}
}
auto ocrv3_app = fastdeploy::application::ocrsystem::PPOCRSystemv3(
&det_model, &cls_model, &rec_model);
auto im = cv::imread(image_file);
auto im_bak = im.clone();
fastdeploy::vision::OCRResult res;
//开始预测
if (!ocrv3_app.Predict(&im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
//输出预测信息
std::cout << res.Str() << std::endl;
//可视化
auto vis_img = fastdeploy::vision::Visualize::VisOcr(im_bak, res);
cv::imwrite("vis_result.jpg", vis_img);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
void TrtInfer(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 rec_label = rec_label_file;
fastdeploy::vision::ocr::DBDetector det_model;
fastdeploy::vision::ocr::Classifier cls_model;
fastdeploy::vision::ocr::Recognizer rec_model;
//准备模型
if (!det_model_dir.empty()) {
auto det_option = fastdeploy::RuntimeOption();
det_option.UseGpu();
det_option.UseTrtBackend();
det_option.SetTrtInputShape("x", {1, 3, 50, 50}, {1, 3, 640, 640},
{1, 3, 960, 960});
det_model = fastdeploy::vision::ocr::DBDetector(
det_model_file, det_params_file, det_option);
if (!det_model.Initialized()) {
std::cerr << "Failed to initialize det_model." << std::endl;
return;
}
}
if (!cls_model_dir.empty()) {
auto cls_option = fastdeploy::RuntimeOption();
cls_option.UseGpu();
cls_option.UseTrtBackend();
cls_option.SetTrtInputShape("x", {1, 3, 48, 192});
cls_model = fastdeploy::vision::ocr::Classifier(
cls_model_file, cls_params_file, cls_option);
if (!cls_model.Initialized()) {
std::cerr << "Failed to initialize cls_model." << std::endl;
return;
}
}
if (!rec_model_dir.empty()) {
auto rec_option = fastdeploy::RuntimeOption();
rec_option.UseGpu();
rec_option.UseTrtBackend();
rec_option.SetTrtInputShape("x", {1, 3, 48, 10}, {1, 3, 48, 320},
{1, 3, 48, 2000});
rec_model = fastdeploy::vision::ocr::Recognizer(
rec_model_file, rec_params_file, rec_label, rec_option);
if (!rec_model.Initialized()) {
std::cerr << "Failed to initialize rec_model." << std::endl;
return;
}
}
auto ocrv3_app = fastdeploy::application::ocrsystem::PPOCRSystemv3(
&det_model, &cls_model, &rec_model);
auto im = cv::imread(image_file);
auto im_bak = im.clone();
fastdeploy::vision::OCRResult res;
//开始预测
if (!ocrv3_app.Predict(&im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
//输出预测信息
std::cout << res.Str() << std::endl;
//可视化
auto vis_img = fastdeploy::vision::Visualize::VisOcr(im_bak, res);
cv::imwrite("vis_result.jpg", vis_img);
auto vis_im = fastdeploy::vision::Visualize::VisOcr(im_bak, result);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
@@ -279,12 +72,23 @@ int main(int argc, char* argv[]) {
return -1;
}
if (std::atoi(argv[6]) == 0) {
CpuInfer(argv[1], argv[2], argv[3], argv[4], argv[5]);
} else if (std::atoi(argv[6]) == 1) {
GpuInfer(argv[1], argv[2], argv[3], argv[4], argv[5]);
} else if (std::atoi(argv[6]) == 2) {
TrtInfer(argv[1], argv[2], argv[3], argv[4], argv[5]);
fastdeploy::RuntimeOption option;
int flag = std::atoi(argv[6]);
if (flag == 0) {
option.UseCpu();
} else if (flag == 1) {
option.UseGpu();
} else if (flag == 2) {
option.UseGpu();
option.UseTrtBackend();
}
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];
InitAndInfer(det_model_dir, cls_model_dir, rec_model_dir, rec_label_file, test_image, option);
return 0;
}
}