submit ocr module (#2815)

python/suryaocr/Dockerfile

+FROM pytorch/pytorch:2.4.0-cuda11.8-cudnn9-runtime
+
+# please download the model from https://huggingface.co/vikp/surya_det3 
+# and https://huggingface.co/vikp/surya_rec2, and put it in the directory vikp/
+COPY ./vikp ./vikp
+
+COPY requirements.txt .
+
+RUN python3 -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+RUN python3 -m pip uninstall opencv-python -y
+
+RUN python3 -m pip install opencv-python-headless -i https://pypi.tuna.tsinghua.edu.cn/simple
+
+COPY app.py Dockerfile ./
+
+ENTRYPOINT python3 app.py
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python/suryaocr/README.md

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

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+import base64
+import io
+import json
+import logging
+import os
+from typing import List, Optional
+
+import torch
+import uvicorn
+from fastapi import FastAPI, HTTPException, Security
+from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
+from PIL import Image, ImageFile
+from pydantic import BaseModel
+from surya.model.detection.model import load_model as load_det_model
+from surya.model.detection.model import load_processor as load_det_processor
+from surya.model.recognition.model import load_model as load_rec_model
+from surya.model.recognition.processor import load_processor as load_rec_processor
+from surya.ocr import run_ocr
+from surya.schema import OCRResult
+
+app = FastAPI()
+security = HTTPBearer()
+env_bearer_token = None
+
+
+# GPU显存回收
+def torch_gc():
+    if torch.cuda.is_available():  # 检查是否可用CUDA
+        torch.cuda.empty_cache()  # 清空CUDA缓存
+        torch.cuda.ipc_collect()  # 收集CUDA内存碎片
+
+
+class ImageReq(BaseModel):
+    images: List[str]
+    sorted: Optional[bool] = False
+
+
+class Singleton(type):
+
+    def __call__(cls, *args, **kwargs):
+        if not hasattr(cls, '_instance'):
+            cls._instance = super().__call__(*args, **kwargs)
+        return cls._instance
+
+
+class Surya(metaclass=Singleton):
+
+    def __init__(self):
+        self.langs = json.loads(os.getenv("LANGS", '["zh", "en"]'))
+        self.batch_size = os.getenv("BATCH_SIZE")
+        if self.batch_size is not None:
+            self.batch_size = int(self.batch_size)
+        self.det_processor, self.det_model = load_det_processor(
+        ), load_det_model()
+        self.rec_model, self.rec_processor = load_rec_model(
+        ), load_rec_processor()
+
+    def run(self, image: ImageFile.ImageFile) -> List[OCRResult]:
+        predictions = run_ocr([image], [self.langs], self.det_model,
+                              self.det_processor, self.rec_model,
+                              self.rec_processor, self.batch_size)
+        return predictions
+
+
+class Chat(object):
+
+    def __init__(self):
+        self.surya = Surya()
+
+    def base64_to_image(base64_string: str) -> ImageFile.ImageFile:
+        image_data = base64.b64decode(base64_string)
+        image_stream = io.BytesIO(image_data)
+        image = Image.open(image_stream)
+        return image
+
+    def sort_text_by_bbox(original_data: List[dict]) -> str:
+        # 根据bbox进行排序，从左到右，从上到下。返回排序后的按行的字符串。
+        # 排序
+        lines, line = [], []
+        original_data.sort(key=lambda item: item["bbox"][1])
+        for item in original_data:
+            mid_h = (item["bbox"][1] + item["bbox"][3]) / 2
+            if len(line) == 0 or (mid_h >= line[0]["bbox"][1]
+                                  and mid_h <= line[0]["bbox"][3]):
+                line.append(item)
+            else:
+                lines.append(line)
+                line = [item]
+        lines.append(line)
+        for line in lines:
+            line.sort(key=lambda item: item["bbox"][0])
+        # 构建行字符串
+        string_result = ""
+        for line in lines:
+            for item in line:
+                string_result += item["text"] + " "
+            string_result += "\n"
+        return string_result
+
+    def query_ocr(self, image_base64: str,
+                  sorted: bool) -> List[OCRResult] | str:
+        if image_base64 is None or len(image_base64) == 0:
+            return []
+        image = Chat.base64_to_image(image_base64)
+
+        ocr_result = self.surya.run(image)
+        result = []
+
+        for text_line in ocr_result[0].text_lines:
+            result.append({"text": text_line.text, "bbox": text_line.bbox})
+        if sorted:
+            result = Chat.sort_text_by_bbox(result)
+
+        torch_gc()
+        return result
+
+
+@app.post('/v1/surya_ocr')
+async def handle_post_request(
+    image_req: ImageReq,
+    credentials: HTTPAuthorizationCredentials = Security(security)):
+    token = credentials.credentials
+    if env_bearer_token is not None and token != env_bearer_token:
+        raise HTTPException(status_code=401, detail="Invalid token")
+    chat = Chat()
+    try:
+        results = []
+        for image_base64 in image_req.images:
+            results.append(chat.query_ocr(image_base64, image_req.sorted))
+        return {"error": "success", "results": results}
+    except Exception as e:
+        logging.error(f"识别报错：{e}")
+        return {"error": "识别出错"}
+
+
+if __name__ == "__main__":
+    env_bearer_token = os.getenv("ACCESS_TOKEN")
+    try:
+        uvicorn.run(app, host='0.0.0.0', port=7230)
+    except Exception as e:
+        logging.error(f"API启动失败！报错：{e}")

python/suryaocr/requirements.txt

+surya-ocr==0.5.0
+fastapi==0.104.1
+uvicorn==0.17.6
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