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247 lines
11 KiB
Markdown
Executable File
247 lines
11 KiB
Markdown
Executable File
## RapidOCR Python
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<p>
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<a href=""><img src="https://img.shields.io/badge/Python->=3.7,<=3.10-aff.svg"></a>
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<a href=""><img src="https://img.shields.io/badge/OS-Linux%2C%20Win%2C%20Mac-pink.svg"></a>
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<a href="https://pepy.tech/project/rapidocr_onnxruntime"><img src="https://static.pepy.tech/personalized-badge/rapidocr_onnxruntime?period=total&units=abbreviation&left_color=grey&right_color=blue&left_text=Downloads%20Ort"></a>
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<a href="https://pepy.tech/project/rapidocr_openvino"><img src="https://static.pepy.tech/personalized-badge/rapidocr_openvino?period=total&units=abbreviation&left_color=grey&right_color=blue&left_text=Downloads%20Vino"></a>
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</p>
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<details open>
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<summary>目录</summary>
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- [RapidOCR Python](#rapidocr-python)
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- [简介和说明](#简介和说明)
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- [（推荐）pip安装快速使用](#推荐pip安装快速使用)
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- [源码使用步骤](#源码使用步骤)
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</details>
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### 简介和说明
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- **各个版本的ONNX模型下载地址：**[百度网盘](https://pan.baidu.com/s/1PTcgXG2zEgQU6A_A3kGJ3Q?pwd=jhai) | [Google Drive](https://drive.google.com/drive/folders/1x_a9KpCo_1blxH1xFOfgKVkw1HYRVywY?usp=sharing)
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- 所有常用的参数配置都在[`config.yaml`](https://github.com/RapidAI/RapidOCR/blob/main/python/rapidocr_onnxruntime/config.yaml)下，一目了然，更加便捷
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- **目前[`config.yaml`](https://github.com/RapidAI/RapidOCR/blob/main/python/rapidocr_onnxruntime/config.yaml)中配置为权衡速度和准确度的最优组合。**
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- 每个独立的模块下均有独立的`config.yaml`配置文件，可以单独使用
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- `det`部分：
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- `det`中`mobile`和`server`版，推理代码一致，直接更改配置文件中模型路径即可
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- `det`中`v2`和`v3`两个版本，推理代码一致。
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- `rec`部分：
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- `rec`中`mobile`和`server`版本，推理代码一致，直接更改配置文件中模型路径即可
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- `rec`中`v2`和`v3`两个版本，共用同一个推理代码。
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- 两版本差别仅在输入shape和模型。经过测试，采用`v3 rec模型`+`[3, 48, 320]`效果最好。
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- 目前配置文件`config.yaml`中（如下所示）已是最优组合。
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```yaml
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module_name: ch_ppocr_v3_rec
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class_name: TextRecognizer
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model_path: resources/models/ch_PP-OCRv3_rec_infer.onnx
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rec_img_shape: [3, 48, 320]
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rec_batch_num: 6
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```
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- 关于选择哪个推理引擎（onnxruntime 或者 openvino）?
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|推理引擎|推理速度更快|占用内存更少|
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|:---:|:---:|:---:|
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|onnxruntime||✓|
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|openvino|✓|存在内存不释放的问题，参见[wiki](https://github.com/RapidAI/RapidOCR/wiki/openvino%E5%85%A5%E9%97%A8)|
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### （推荐）pip安装快速使用
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1. 安装包（⚠️注意：两个包接口一致，只是推理引擎不同而已）
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|包名|版本|安装命令|
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|:---|:---:|:---|
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|`rapidocr_onnxruntime`|<a href="https://pypi.org/project/rapidocr-onnxruntime/"><img alt="PyPI" src="https://img.shields.io/pypi/v/rapidocr-onnxruntime?style=flat-square"></a>|`pip install rapidocr-onnxruntime`|
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|`rapidocr_openvino`|<a href="https://pypi.org/project/rapidocr-openvino/"><img alt="PyPI" src="https://img.shields.io/pypi/v/rapidocr-openvino?style=flat-square"></a>|`pip install rapidocr-openvino`|
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1. 推理使用
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- 脚本使用：
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- 📌初始化RapidOCR可不提供`config.yaml`，默认使用**rapidocr_onnxruntime**目录下的。如有自定义需求，可直接通过初始化参数传入。详情参数参考命令行部分，和`config.yaml`基本对应。
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- 输入：`Union[str, np.ndarray, bytes, Path]`
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- 输出：`[[文本框坐标], 文本内容, 置信度]`, 为空：`(None, None)`
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- 示例：
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```python
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import cv2
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from rapidocr_onnxruntime import RapidOCR
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# from rapidocr_openvino import RapidOCR
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# RapidOCR可传入参数参考下面的命令行部分
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rapid_ocr = RapidOCR()
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img_path = 'tests/test_files/ch_en_num.jpg'
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# 输入格式一：str
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result, elapse = rapid_ocr(img_path)
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# 输入格式二：np.ndarray
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img = cv2.imread('tests/test_files/ch_en_num.jpg')
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result, elapse = rapid_ocr(img)
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# 输入格式三：bytes
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with open(img_path, 'rb') as f:
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img = f.read()
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result, elapse = rapid_ocr(img)
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# 输入格式四：Path
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result, elapse = rapid_ocr(Path(img_path))
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print(result)
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# result: [[文本框坐标], 文本内容, 置信度]
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# 示例：[[左上, 右上, 右下, 左下], '小明', '0.99']
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# elapse: [det_elapse, cls_elapse, rec_elapse]
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# all_elapse = det_elapse + cls_elapse + rec_elapse
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# 如果没有有效文本，则result: (None, None)
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```
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- 命令行使用：
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```bash
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$ rapidocr_onnxruntime -h
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usage: rapidocr_onnxruntime [-h] -img IMG_PATH [-p] [--text_score TEXT_SCORE]
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[--use_angle_cls USE_ANGLE_CLS]
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[--use_text_det USE_TEXT_DET]
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[--print_verbose PRINT_VERBOSE]
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[--min_height MIN_HEIGHT]
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[--width_height_ratio WIDTH_HEIGHT_RATIO]
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[--det_model_path DET_MODEL_PATH]
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[--det_limit_side_len DET_LIMIT_SIDE_LEN]
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[--det_limit_type {max,min}]
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[--det_thresh DET_THRESH]
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[--det_box_thresh DET_BOX_THRESH]
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[--det_unclip_ratio DET_UNCLIP_RATIO]
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[--det_use_dilation DET_USE_DILATION]
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[--det_score_mode {slow,fast}]
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[--cls_model_path CLS_MODEL_PATH]
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[--cls_image_shape CLS_IMAGE_SHAPE]
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[--cls_label_list CLS_LABEL_LIST]
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[--cls_batch_num CLS_BATCH_NUM]
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[--cls_thresh CLS_THRESH]
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[--rec_model_path REC_MODEL_PATH]
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[--rec_img_shape REC_IMAGE_SHAPE]
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[--rec_batch_num REC_BATCH_NUM]
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optional arguments:
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-h, --help show this help message and exit
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-img IMG_PATH, --img_path IMG_PATH MUST
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-p, --print_cost
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Global:
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--text_score TEXT_SCORE
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--use_angle_cls USE_ANGLE_CLS
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--use_text_det USE_TEXT_DET
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--print_verbose PRINT_VERBOSE
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--min_height MIN_HEIGHT
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--width_height_ratio WIDTH_HEIGHT_RATIO
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Det:
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--det_model_path DET_MODEL_PATH
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--det_limit_side_len DET_LIMIT_SIDE_LEN
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--det_limit_type {max,min}
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--det_thresh DET_THRESH
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--det_box_thresh DET_BOX_THRESH
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--det_unclip_ratio DET_UNCLIP_RATIO
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--det_use_dilation DET_USE_DILATION
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--det_score_mode {slow,fast}
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Cls:
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--cls_model_path CLS_MODEL_PATH
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--cls_image_shape CLS_IMAGE_SHAPE
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--cls_label_list CLS_LABEL_LIST
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--cls_batch_num CLS_BATCH_NUM
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--cls_thresh CLS_THRESH
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Rec:
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--rec_model_path REC_MODEL_PATH
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--rec_img_shape REC_IMAGE_SHAPE
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--rec_batch_num REC_BATCH_NUM
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$ rapidocr_onnxruntime -img tests/test_files/ch_en_num.jpg
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```
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### 源码使用步骤
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1. 下载整个项目到本地
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```shell
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cd RapidOCR/python
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```
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2. 下载链接下的`resources`目录（包含模型和显示的字体文件）
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- 下载链接：[Github](https://github.com/RapidAI/RapidOCR/releases/download/v1.1.0/resources.zip) | [Gitee](https://gitee.com/RapidAI/RapidOCR/releases/download/v1.1.0/resources.zip) | [百度网盘](https://pan.baidu.com/s/1PTcgXG2zEgQU6A_A3kGJ3Q?pwd=jhai) | [Google Drive](https://drive.google.com/drive/folders/1x_a9KpCo_1blxH1xFOfgKVkw1HYRVywY?usp=sharing)
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- `resources/models`下模型搭配已经为最优组合（速度和精度平衡）
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```text
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ch_PP-OCRv3_det + ch_ppocr_mobile_v2.0_cls + ch_PP-OCRv3_rec
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```
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- 最终目录如下，自行比对:
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```text
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.
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├── README.md
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├── demo.py
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├── rapidocr_onnxruntime
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│ ├── __init__.py
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│ ├── ch_ppocr_v2_cls
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│ ├── ch_ppocr_v3_det
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│ ├── ch_ppocr_v3_rec
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│ ├── config.yaml
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│ ├── rapid_ocr_api.py
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│ └── models
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│ ├── ch_PP-OCRv3_det_infer.onnx
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│ ├── ch_ppocr_mobile_v2.0_cls_infer.onnx
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│ └── ch_PP-OCRv3_rec_infer.onnx
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├── rapidocr_openvino
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│ ├── __init__.py
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│ ├── README.md
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│ ├── ch_ppocr_v2_cls
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│ ├── ch_ppocr_v3_det
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│ ├── ch_ppocr_v3_rec
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│ ├── config.yaml
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│ ├── rapid_ocr_api.py
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│ └── models
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│ ├── ch_PP-OCRv3_det_infer.onnx
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│ ├── ch_ppocr_mobile_v2.0_cls_infer.onnx
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│ └── ch_PP-OCRv3_rec_infer.onnx
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├── requirements.txt
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└── resources
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└── fonts
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└── FZYTK.TTF
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```
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3. 安装运行环境
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- 基于onnxruntime推理所需环境安装：
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```bash
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pip install onnxruntime>=1.7.0
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pip install -r requirements.txt
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```
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- 基于openvino推理所需环境安装：
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```bash
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pip install openvino
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pip install -r requirements.txt
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```
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- ⚠️: 在Windows端，Shapely库自动安装可能会有问题，解决方案参见[Q15](https://github.com/RapidAI/RapidOCR/blob/main/docs/FAQ.md#q-windows系统下装完环境之后运行示例程序之后报错oserror-winerror-126-找不到指定的模組)
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4. 运行示例
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- 运行单元测试
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```bash
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pytest tests/test_*.py
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```
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- 接口调用
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```python
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import cv2
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# 基于onnxruntime引擎推理
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from rapidocr_onnxruntime import RapidOCR
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# 基于openvino引擎推理
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# from rapidocr_openvino import RapidOCR
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rapid_ocr = RapidOCR()
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image_path = r'test_images/det_images/ch_en_num.jpg'
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img = cv2.imread(image_path)
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result = rapid_ocr(img)
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print(result)
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# result: [[文本框坐标], 文本内容, 置信度]
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# 示例：[[左上, 右上, 右下, 左下], '小明', '0.99']
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```
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- 直接运行`demo.py`，可直接可视化查看结果。
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```bash
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python demo.py
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```
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