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120 lines
2.3 KiB
Markdown
120 lines
2.3 KiB
Markdown
# 接入Surya OCR文本检测
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## 源码部署
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### 1. 安装环境
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- Python 3.9+
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- CUDA 11.8
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- 科学上网环境
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### 2. 安装依赖
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```bash
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pip install -r requirements.txt
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```
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### 3. 下载模型
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代码首次运行时会自动从huggingface下载模型，可跳过以下步骤。
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也可以手动下载模型，在对应代码目录下clone模型
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```sh
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mkdir vikp && cd vikp
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git lfs install
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git clone https://huggingface.co/vikp/surya_det3
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# 镜像下载 https://hf-mirror.com/vikp/surya_det3
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git clone https://huggingface.co/vikp/surya_rec2
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# 镜像下载 https://hf-mirror.com/vikp/surya_rec2
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```
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最终手动下载的目录结构如下：
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```
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vikp/surya_det3
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vikp/surya_rec2
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app.py
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Dockerfile
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requirements.txt
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```
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### 4. 运行代码
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```bash
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python app.py
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```
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对应请求地址为
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`http://0.0.0.0:7230/v1/surya_ocr`
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### 5. 测试
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```python
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import requests
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import base64
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IMAGE_PATH = "your/path/to/image.png"
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ACCESS_TOKEN = "your_access_token"
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with open(IMAGE_PATH, 'rb') as img_file:
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encoded_string = base64.b64encode(img_file.read())
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encoded_image = encoded_string.decode('utf-8')
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data = {"images": [encoded_image], "sorted": True}
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {ACCESS_TOKEN}"
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}
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res = requests.post(url="http://0.0.0.0:7230/v1/surya_ocr",
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headers=headers,
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json=data)
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print(res.text)
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```
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## docker部署
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### 镜像获取
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**本地编译镜像：**
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```bash
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docker build -t surya_ocr:v0.1 .
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```
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**或拉取线上镜像：**
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Todo：待发布
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### docker-compose.yml示例
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```yaml
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version: '3'
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services:
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surya-ocr:
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image: surya_ocr:v0.1
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container_name: surya-ocr
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# GPU运行环境，如果宿主机未安装，将deploy配置隐藏即可
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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count: all
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capabilities: [gpu]
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ports:
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- 7230:7230
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environment:
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- BATCH_SIZE=32
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- ACCESS_TOKEN=YOUR_ACCESS_TOKEN
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- LANGS='["zh","en"]'
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```
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**环境变量：**
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```
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BATCH_SIZE：根据实际内存/显存情况配置，每个batch约占用40MB的VRAM，cpu默认32，mps默认64，cuda默认512
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ACCESS_TOKEN：服务的access_token
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LANGS：支持的语言列表，默认["zh","en"]
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```
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## 接入FastGPT
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Todo: 待补充 |