mirror of
https://github.com/RapidAI/RapidOCR.git
synced 2026-11-13 06:20:49 +08:00
149 lines
6.4 KiB
Markdown
149 lines
6.4 KiB
Markdown
### 使用步骤
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1. 下载`python/onnxruntime_infer`目录到本地
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2. 下载相应模型和用于显示的字体文件
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- [百度网盘 提取码：30jv](https://pan.baidu.com/s/1qkqWK4wRdMjqGGbzR-FyWg) | [Google Drive](https://drive.google.com/drive/folders/1x_a9KpCo_1blxH1xFOfgKVkw1HYRVywY?usp=sharing)
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- 下载之后模型和相应字体文件放在`fonts`和`models`下，最终目录结构如下：
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```text
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models
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|-- ch_PP-OCRv2_det_infer.onnx
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|-- ch_ppocr_mobile_v2.0_cls_infer.onnx
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|-- ch_ppocr_mobile_v2.0_det_infer.onnx
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|-- ch_ppocr_server_v2.0_det_infer.onnx
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|-- ch_ppocr_server_v2.0_rec_infer.onnx
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|-- en_number_mobile_v2.0_rec_infer.onnx
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|-- korean_mobile_v2.0_rec_infer.onnx
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`-- japan_rec_crnn.onnx
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fonts
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|-- msyh.ttc
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`-- korean.ttf
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```
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3. 运行
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- 接口调用方式运行
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- 如果想要使用其他语言的识别模型，可以只对`rec_model_path`和`keys_path`作对应更改即可
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- **!!!各个不同语言的推理代码区别只在于模型和字典文件!!!**
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```python
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from rapid_ocr_api import TextSystem, visualize
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det_model_path = 'models/ch_ppocr_mobile_v2.0_det_infer.onnx'
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cls_model_path = 'models/ch_ppocr_mobile_v2.0_cls_infer.onnx'
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# 中英文识别
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rec_model_path = 'models/ch_ppocr_mobile_v2.0_rec_infer.onnx'
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keys_path = 'rec_dict/ppocr_keys_v1.txt'
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text_sys = TextSystem(det_model_path,
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rec_model_path,
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use_angle_cls=True,
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cls_model_path=cls_model_path,
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keys_path=keys_path)
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image_path = r'test_images/det_images/ch_en_num.jpg'
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dt_boxes, rec_res = text_sys(image_path)
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visualize(image_path, dt_boxes, rec_res)
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# 只有中英文和数字识别
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rec_model_path = 'models/en_number_mobile_v2.0_rec_infer.onnx'
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keys_path = 'rec_dict/en_dict.txt'
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text_sys = TextSystem(det_model_path,
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rec_model_path,
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use_angle_cls=True,
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cls_model_path=cls_model_path,
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keys_path=keys_path)
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image_path = r'test_images/det_images/en_num.png'
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dt_boxes, rec_res = text_sys(image_path)
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visualize(image_path, dt_boxes, rec_res)
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# 日语识别
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rec_model_path = 'models/japan_rec_crnn.onnx'
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keys_path = 'rec_dict/japan_dict.txt'
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text_sys = TextSystem(det_model_path,
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rec_model_path,
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use_angle_cls=True,
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cls_model_path=cls_model_path,
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keys_path=keys_path)
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image_path = r'test_images/det_images/japan.png'
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dt_boxes, rec_res = text_sys(image_path)
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visualize(image_path, dt_boxes, rec_res)
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# 韩语识别
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rec_model_path = 'models/korean_mobile_v2.0_rec_infer.onnx'
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keys_path = 'rec_dict/korean_dict.txt'
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font_path = 'fonts/korean.ttf'
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text_sys = TextSystem(det_model_path,
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rec_model_path,
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use_angle_cls=True,
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cls_model_path=cls_model_path,
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keys_path=keys_path)
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image_path = r'test_images/det_images/korean_1.jpg'
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dt_boxes, rec_res = text_sys(image_path)
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visualize(image_path, dt_boxes, rec_res, font_path)
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```
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- 命令行运行
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```shell
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$ bash test_demo.sh
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```
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### 相关调节参数
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|参数名称|作用|建议取值范围|默认值|代码位置|
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|:---:|:---:|:---:|:---:|:---:|
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|box_thresh|文本检测所得框是否保留的阈值，值越大，召回率越低|[0, 1]|0.5|[text_detect.py](https://github.com/RapidAI/RapidOCR/blob/6aa79aa390c9c9e8f41df0f0c35f3dca97e6dc93/python/ch_ppocr_mobile_v2_det/text_detect.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L55)|
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|unclip_ratio|控制文本检测框的大小，值越大，检测框整体越大|[1.6, 2.0]|1.6|[text_detect.py](https://github.com/RapidAI/RapidOCR/blob/6aa79aa390c9c9e8f41df0f0c35f3dca97e6dc93/python/ch_ppocr_mobile_v2_det/text_detect.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L57)|
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|text_score|文本识别结果置信度，值越大，把握越大|[0, 1]|0.5|[rapidOCR.py](https://github.com/RapidAI/RapidOCR/blob/6aa79aa390c9c9e8f41df0f0c35f3dca97e6dc93/python/rapidOCR.py?_pjax=%23js-repo-pjax-container%2C%20div%5Bitemtype%3D%22http%3A%2F%2Fschema.org%2FSoftwareSourceCode%22%5D%20main%2C%20%5Bdata-pjax-container%5D#L270)|
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### onnxruntime-gpu版推理配置
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1. onnxruntime-gpu需要严格按照与cuda、cudnn版本对应来安装，具体参考[文档](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements)，**这一步关乎后面是否可以成功调用GPU**
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2. 推理代码中，加载onnx模型部分，用以下对应语言代码替换即可,详细参见：[官方教程](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html)
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- python版本
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```python
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import onnxruntime as ort
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model_path = '<path to model>'
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providers = [
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('CUDAExecutionProvider', {
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'device_id': 0,
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'arena_extend_strategy': 'kNextPowerOfTwo',
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'gpu_mem_limit': 2 * 1024 * 1024 * 1024,
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'cudnn_conv_algo_search': 'EXHAUSTIVE',
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'do_copy_in_default_stream': True,
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}),
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'CPUExecutionProvider',
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]
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session = ort.InferenceSession(model_path, providers=providers)
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```
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- C/C++版本
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```c++
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OrtSessionOptions* session_options = /* ... */;
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OrtCUDAProviderOptions options;
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options.device_id = 0;
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options.arena_extend_strategy = 0;
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options.gpu_mem_limit = 2 * 1024 * 1024 * 1024;
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options.cudnn_conv_algo_search = OrtCudnnConvAlgoSearch::EXHAUSTIVE;
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options.do_copy_in_default_stream = 1;
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SessionOptionsAppendExecutionProvider_CUDA(session_options, &options);
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```
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3. 推理时间粗略对比(完整跑完一张图像)
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|推理方式|推理图像数目|耗费时间|
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|:---:|:---:|:---:|
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|CPU|46张图像|191 s|
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|GPU|46张图像|52.38 s|
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