Merge pull request #2 from yjmm10/Docly

rename project to "Docly"

.github/workflows/main.yml

@@ -1,4 +1,4 @@
-name: Telos CI/CD
+name: Docly CI/CD
 
 on:
   push:
@@ -54,7 +54,7 @@ jobs:
         run: |
           git config --global credential.helper store
           echo "https://USER:[email protected]" > ~/.git-credentials
-          git clone https://huggingface.co/liferecords/Telos.git telos/models
+          git clone https://huggingface.co/liferecords/Telos.git docly/models
       - name: Run tests
         run: |
           tox -e py

.gitignore

@@ -180,6 +180,6 @@ venv.bak/
 **/*.local.yml
 **/.secrets.yml
 
-telos/models
+docly/models
 output-*
 tests/output

Dockerfile

@@ -1,8 +1,8 @@
 FROM python:3.10
 
-LABEL name="Telos"
-LABEL version="0.0.2"
-LABEL description="🛠️  Component design with module-based functionality, allowing for on-demand feature acquisition,🚀 easy to expand, and flexible to use, just like playing with building blocks! | 🛠️ 组件化设计，让功能模块化，实现按需获取，🚀 易于扩展，使用起来灵活方便，就像搭积木一样简单！"
+LABEL name="Docly"
+LABEL version="0.0.3"
+LABEL description="🛠️ Docly: A modular toolkit for intelligent document analysis and processing. Easy to expand and flexible to use, just like playing with building blocks!"
 
 WORKDIR /app
 

README.md

@@ -1,11 +1,11 @@
 English | [中文](README_zh.md) | [日语]()
-# Telos 🚀
+# Docly 🚀
 
 ## 1. Overview 🌟
 
 🛠️ Component design with module-based functionality, allowing for on-demand feature acquisition, 🚀 easy to expand, and flexible to use, just like playing with building blocks!
 
-Telos is a modular component-based toolkit for document analysis and processing. It's designed with flexibility and extensibility in mind, making it easy to expand and use various document processing functionalities as needed.
+Docly is a modular component-based toolkit for document analysis and processing. It's designed with flexibility and extensibility in mind, making it easy to expand and use various document processing functionalities as needed.
 
 ## 2. Features 🛠️
 
@@ -27,9 +27,9 @@ Telos is a modular component-based toolkit for document analysis and processing.
 
 1. Clone the repository:
    ```bash
-   git clone https://github.com/yjmm10/telos.git
-   cd telos
-   git clone https://huggingface.co/liferecords/Telos.git telos/models
+   git clone https://github.com/yjmm10/docly.git
+   cd docly
+   git clone https://huggingface.co/liferecords/Telos.git docly/models
    ```
 
 2. Install dependencies:
@@ -39,10 +39,10 @@ Telos is a modular component-based toolkit for document analysis and processing.
 
 ### 3.3 Usage
 
-Here's a quick example of how to use Telos for OCR:
+Here's a quick example of how to use Docly for OCR:
 
 ```python
-from telos import OCR
+from docly import OCR
 import cv2
 
 # Initialize the OCR model
@@ -57,7 +57,7 @@ result = ocr_model(image)
 print(result)
 ```
 
-Telos comes with a Streamlit-based web UI for easy demonstration of its capabilities:
+Docly comes with a Streamlit-based web UI for easy demonstration of its capabilities:
 
 1. Run the demo:
    ```bash
@@ -68,26 +68,26 @@ Telos comes with a Streamlit-based web UI for easy demonstration of its capabili
 
 3. Upload an image and select the model you want to use for processing
 
-Telos also provides a FastAPI-based API service for integration into other applications:
+Docly also provides a FastAPI-based API service for integration into other applications:
 
 1. Start the API server:
    ```bash
-   uvicorn api.telos_api:app --host 0.0.0.0 --port 8000
+   uvicorn api.docly_api:app --host 0.0.0.0 --port 8000
    ```
 
 2. The API documentation will be available at http://localhost:8000/docs
 
 ## 4. Development 🔬
 
-For detailed information on development, please refer to the [development guide](./docs/development.md). This guide will help you set up your IDE for working with Telos, including SRC Layout configuration.
+For detailed information on development, please refer to the [development guide](./docs/development.md). This guide will help you set up your IDE for working with Docly, including SRC Layout configuration.
 
 ## 5. Contributing 🤝
 
 We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for more details.
 
 ## 6. License 📄
 
-Telos is released under the MIT License. See the [LICENSE](LICENSE) file for more details.
+Docly is released under the MIT License. See the [LICENSE](LICENSE) file for more details.
 
 ## 7. Contact 📧
 

README_zh.md

@@ -1,12 +1,12 @@
 [English](README.md) | 中文 | [日语]()
 
-# Telos 🚀
+# Docly 🚀
 
 ## 1. 概述 🌟
 
 🛠️ 组件化设计，让功能模块化，实现按需获取，🚀 易于扩展，使用起来灵活方便，就像搭积木一样简单！
 
-Telos 是一个基于模块化组件的文档分析和处理工具包。它的设计理念注重灵活性和可扩展性，使得各种文档处理功能的扩展和使用变得简单易行，可以根据需要随时添加新功能。
+Docly 是一个基于模块化组件的文档分析和处理工具包。它的设计理念注重灵活性和可扩展性，使得各种文档处理功能的扩展和使用变得简单易行，可以根据需要随时添加新功能。
 
 ## 2. 功能特性 🛠️
 
@@ -28,9 +28,9 @@ Telos 是一个基于模块化组件的文档分析和处理工具包。它的
 
 1. 克隆仓库：
    ```bash
-   git clone https://github.com/yjmm10/telos.git
-   cd telos
-   git clone https://huggingface.co/liferecords/Telos.git telos/models
+   git clone https://github.com/yjmm10/docly.git
+   cd docly
+   git clone https://huggingface.co/liferecords/Telos.git docly/models
    ```
 
 2. 安装依赖：
@@ -44,7 +44,7 @@ Telos 是一个基于模块化组件的文档分析和处理工具包。它的
 
 ## 4. 开发 🔬
 
-有关开发的详细信息，请参阅[开发指南](./docs/development.md)。该指南将帮助您设置 IDE 以使用 Telos，包括 SRC Layout 配置。
+有关开发的详细信息，请参阅[开发指南](./docs/development.md)。该指南将帮助您设置 IDE 以使用 Docly，包括 SRC Layout 配置。
 
 ## 5. 贡献 🤝
 

Telos_test.py

@@ -1,16 +1,16 @@
-import telos
-from telos import YOLOv8
+import docly
+from docly import YOLOv8
 
-print(telos.__version__)
-# print(telos.check_source())
-# result = telos.MetaFile(file_path="demo_1.png")
+print(docly__version__)
+# print(doclycheck_source())
+# result = doclyMetaFile(file_path="demo_1.png")
 # print(result)
-# result = telos.MetaFile(file_path="test_img/PDF.pdf")
+# result = doclyMetaFile(file_path="test_img/PDF.pdf")
 # print(result)
-# result = telos.MetaFile(file_path="test_img/SCAN.pdf")
+# result = doclyMetaFile(file_path="test_img/SCAN.pdf")
 # print(result)
 
-# result = telos.CVModel("telos")
+# result = doclyCVModel("docly")
 # print(result)
 
 
@@ -49,14 +49,14 @@ def test_yolo():
 def test_layout():
     import cv2
 
-    from telos import Layout
+    from docly import Layout
 
     model = Layout(conf_thres=0.3, iou_thres=0.5)
     img = cv2.imread("test_img/page_p6.png")
 
     # Detect Objects
     result = model(img)
-    result_T = model._telos()
+    result_T = model._docly()
     # print(result_T)
 
     # Draw detections
@@ -67,15 +67,15 @@ def test_layout():
 def test_formula():
     import cv2
 
-    from telos import DetFormula
+    from docly import DetFormula
 
     model = DetFormula(conf_thres=0.3, iou_thres=0.5)
     img = cv2.imread("test_img/formula_page0.jpg")
 
     # Detect Objects
     result = model(img)
     # print(result)
-    result_T = model._telos()
+    result_T = model._docly()
     print(result_T)
 
     # Draw detections
@@ -86,10 +86,10 @@ def test_formula():
 def test_latexocr():
     import cv2
 
-    from telos import LatexOCR
+    from docly import LatexOCR
 
     engine = LatexOCR(
-        model_path="telos/models/recognition/rec_formula",
+        model_path="docly/models/recognition/rec_formula",
     )
     img = cv2.imread("/home/zyj/project/MOP/test_img/formula01.png")
     result = engine(img)
@@ -99,7 +99,7 @@ def test_latexocr():
 
     # # Detect Objects
     # result= model(img)
-    # result_T = model._telos()
+    # result_T = model._docly()
     # print(result_T)
 
     # # Draw detections
@@ -110,7 +110,7 @@ def test_latexocr():
 def test_dbnet():
     import cv2
 
-    from telos import DBNet
+    from docly import DBNet
 
     model_path = "detection/det_text.onnx"
 
@@ -128,9 +128,9 @@ def test_dbnet():
 def test_crnnnet():
     import cv2
 
-    from telos import CRNN
+    from docly import CRNN
 
-    model_path = "/home/zyj/project/MOP/telos/models/recognition/rec_text"
+    model_path = "/home/zyj/project/MOP/docly/models/recognition/rec_text"
 
     # Initialize YOLOv8 object detector
     model = CRNN(model_path="recognition/rec_text")
@@ -148,17 +148,17 @@ def test_crnnnet():
 def test_OCR():
     import cv2
 
-    from telos import OCR
+    from docly import OCR
 
     # Initialize YOLOv8 object detector
     model = OCR()
-    img = cv2.imread("/nas/projects/Github/Telos/tests/test_img/test_ocr.png")
+    img = cv2.imread("/nas/projects/Github/Docly/tests/test_img/test_ocr.png")
     result = model(img)
     print(result)
-    # res_telos = model._telos()
+    # res_docly = model._docly()
     # print(model._json())
     # print(result)
-    # print(res_telos)
+    # print(res_docly)
 
     # # Draw detections
     # combined_img = model.draw_detections(img,mask_alpha=0.2)
@@ -168,22 +168,22 @@ def test_OCR():
 def test_OCR_wo_det():
     import cv2
 
-    from telos import OCR
+    from docly import OCR
 
     # Initialize YOLOv8 object detector
     model = OCR()
-    img = cv2.imread("/nas/projects/Github/Telos/tests/test_img/test_crnnnet.png")
+    img = cv2.imread("/nas/projects/Github/Docly/tests/test_img/test_crnnnet.png")
     result = model(img, use_det=False)
 
     # TODO:可视化
-    res_telos = model._telos()
+    res_docly = model._docly()
     print(model._json())
 
 
 def test_reading_order():
     import cv2
 
-    from telos import OCR, DBNet, Layout, ReadingOrder
+    from docly import OCR, DBNet, Layout, ReadingOrder
 
     # Initialize YOLOv8 object detector
     det = Layout(conf_thres=0.3, iou_thres=0.5)
@@ -204,7 +204,7 @@ def test_reading_order():
     result = model(img, bboxs)
     # print(model._json())
     print(result)
-    # print(res_telos)
+    # print(res_docly)
 
     # # Draw detections
     # combined_img = model.draw_detections(img,mask_alpha=0.2)
@@ -214,17 +214,17 @@ def test_reading_order():
 def test_Table():
     import cv2
 
-    from telos import Table_TSR
+    from docly import Table_TSR
 
     # Initialize YOLOv8 object detector
     model = Table_TSR()
-    img = cv2.imread("/nas/projects/Github/Telos/tests/test_img/test_lore.jpg")
+    img = cv2.imread("/nas/projects/Github/Docly/tests/test_img/test_lore.jpg")
     result = model(img)
     print(result)
-    # res_telos = model._telos()
+    # res_docly = model._docly()
     # print(model._json())
     # print(result)
-    # print(res_telos)
+    # print(res_docly)
 
     # # Draw detections
     # combined_img = model.draw_detections(img,mask_alpha=0.2)

api/telos_api.py → api/docly_api.py

@@ -3,7 +3,7 @@
 from fastapi import FastAPI, File, UploadFile
 from fastapi.responses import FileResponse, JSONResponse
 
-from telos import (CRNN, OCR, DBNet, DetFormula, LatexOCR, Layout,
+from docly import (CRNN, OCR, DBNet, DetFormula, LatexOCR, Layout,
                    ReadingOrder, Table_TSR, YOLOv8)
 
 app = FastAPI()
@@ -47,7 +47,7 @@ async def layout_endpoint(file: UploadFile = File(...)):
 
     model = Layout(conf_thres=0.3, iou_thres=0.5)
     result = model(img)
-    result_T = model._telos()
+    result_T = model._docly()
 
     return JSONResponse(content={"result": result, "result_T": result_T})
 
@@ -60,7 +60,7 @@ async def formula_detection_endpoint(file: UploadFile = File(...)):
 
     model = DetFormula(conf_thres=0.3, iou_thres=0.5)
     result = model(img)
-    result_T = model._telos()
+    result_T = model._docly()
 
     return JSONResponse(content={"result": result, "result_T": result_T})
 
@@ -71,7 +71,7 @@ async def latex_ocr_endpoint(file: UploadFile = File(...)):
     nparr = np.fromstring(contents, np.uint8)
     img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
 
-    engine = LatexOCR(model_path="telos/models/recognition/rec_formula")
+    engine = LatexOCR(model_path="docly/models/recognition/rec_formula")
     result = engine(img)
 
     return JSONResponse(content={"result": result})
@@ -146,5 +146,5 @@ async def table_tsr_endpoint(file: UploadFile = File(...)):
 
     # 定义你的模型路径和字典路径
     uvicorn.run(
-        "telos_api:app", host="0.0.0.0", port=8000, reload=True, timeout_keep_alive=30
+        "docly_api:app", host="0.0.0.0", port=8000, reload=True, timeout_keep_alive=30
     )

demo.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import numpy as np
 
-from telos import *
+from docly import *
 
 
 def dla(img, conf, iou, model):