add build.yaml Dockerfile app.py document.png requirements.txt

.gitea/workflows/build.yaml

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+name: Build
+run-name: ${{ github.actor }} is upgrade release 🚀
+on: [push]
+env:
+  REPOSITORY: ${{ github.repository }}
+  COMMIT_ID: ${{ github.sha }}
+jobs:
+  Build-Deploy-Actions:
+    runs-on: ubuntu-latest
+    steps:
+      - run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
+      - run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by Gitea!"
+      - run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
+      - name: Check out repository code
+        uses: actions/checkout@v3
+      -
+        name: Setup Git LFS
+        run: |
+          git lfs install
+          git lfs fetch
+          git lfs checkout                    
+      - name: List files in the repository
+        run: |
+                                        ls ${{ github.workspace }}
+      -
+        name: Docker Image Info
+        id: image-info
+        run: |
+          echo "::set-output name=image_name::$(echo $REPOSITORY | tr '[:upper:]' '[:lower:]')"
+          echo "::set-output name=image_tag::${COMMIT_ID:0:10}"                    
+      -
+        name: Login to Docker Hub
+        uses: docker/login-action@v2
+        with:
+          registry: artifacts.iflytek.com
+          username: ${{ secrets.DOCKERHUB_USERNAME }}
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v2
+      -
+        name: Build and push
+        run: |
+          docker version
+          docker buildx build -t artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }} . --file ${{ github.workspace }}/Dockerfile --load
+          docker push artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
+          docker rmi artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}                    
+      - run: echo "🍏 This job's status is ${{ job.status }}."

Dockerfile

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+FROM python:3.8.13
+
+WORKDIR /app
+
+COPY . /app
+
+RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple 
+
+ENV PYHTONUNBUFFERED=1
+RUN apt-get update && apt-get -y install tesseract-ocr
+
+RUN pip install pyyaml==5.1
+
+RUN pip install -r requirements.txt
+
+RUN pip install 'git+https://ghproxy.com/https://github.com/facebookresearch/detectron2.git'
+
+CMD ["python", "app.py"]

app.py

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+import gradio as gr
+import numpy as np
+from transformers import LayoutLMv2Processor, LayoutLMv2ForTokenClassification
+from datasets import load_dataset
+from PIL import Image, ImageDraw, ImageFont
+from gradio.themes.utils import sizes
+
+
+theme = gr.themes.Default(radius_size=sizes.radius_none).set(
+    block_label_text_color = '#4D63FF',
+    block_title_text_color = '#4D63FF',
+    button_primary_text_color = '#4D63FF',
+    button_primary_background_fill='#FFFFFF',
+    button_primary_border_color='#4D63FF',
+    button_primary_background_fill_hover='#EDEFFF',
+)
+
+processor = LayoutLMv2Processor.from_pretrained("microsoft/layoutlmv2-base-uncased")
+model = LayoutLMv2ForTokenClassification.from_pretrained("nielsr/layoutlmv2-finetuned-funsd")
+
+labels = ['O', 'B-HEADER', 'I-HEADER', 'B-QUESTION', 'I-QUESTION', 'B-ANSWER', 'I-ANSWER']
+id2label = {0: 'O', 1: 'B-HEADER', 2: 'I-HEADER', 3: 'B-QUESTION', 4: 'I-QUESTION', 5: 'B-ANSWER', 6: 'I-ANSWER'}
+label2color = {'question':'blue', 'answer':'green', 'header':'orange', 'other':'violet'}
+
+def unnormalize_box(bbox, width, height):
+     return [
+         width * (bbox[0] / 1000),
+         height * (bbox[1] / 1000),
+         width * (bbox[2] / 1000),
+         height * (bbox[3] / 1000),
+     ]
+
+def iob_to_label(label):
+    label = label[2:]
+    if not label:
+      return 'other'
+    return label
+
+def process_image(image):
+    width, height = image.size
+
+    # encode
+    encoding = processor(image, truncation=True, return_offsets_mapping=True, return_tensors="pt")
+    offset_mapping = encoding.pop('offset_mapping')
+
+    # forward pass
+    outputs = model(**encoding)
+
+    # get predictions
+    predictions = outputs.logits.argmax(-1).squeeze().tolist()
+    token_boxes = encoding.bbox.squeeze().tolist()
+
+    # only keep non-subword predictions
+    is_subword = np.array(offset_mapping.squeeze().tolist())[:,0] != 0
+    true_predictions = [id2label[pred] for idx, pred in enumerate(predictions) if not is_subword[idx]]
+    true_boxes = [unnormalize_box(box, width, height) for idx, box in enumerate(token_boxes) if not is_subword[idx]]
+
+    # draw predictions over the image
+    draw = ImageDraw.Draw(image)
+    font = ImageFont.load_default()
+    for prediction, box in zip(true_predictions, true_boxes):
+        predicted_label = iob_to_label(prediction).lower()
+        draw.rectangle(box, outline=label2color[predicted_label])
+        draw.text((box[0]+10, box[1]-10), text=predicted_label, fill=label2color[predicted_label], font=font)
+
+    return image
+
+with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
+    gr.Markdown("""
+    <div align='center' ><font size='60'>文档版面分析</font></div>
+    """)
+    with gr.Row():
+        with gr.Column():
+            image_input =gr.inputs.Image(type="pil", label="图片")
+            with gr.Row():
+                button = gr.Button("提交", variant="primary")
+        image_output = gr.Image(label="图片")
+
+    button.click(fn=process_image, inputs=image_input, outputs=image_output)
+    examples = gr.Examples(examples=[['document.png']],  inputs=[image_input], label="例子")
+
+
+if __name__ == "__main__":
+    demo.queue(concurrency_count=3).launch(server_name = "0.0.0.0")

requirements.txt

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+pytesseract
+tesseract
+transformers
+Pillow
+torch
+gradio==3.27.0
+torchvision
+datasets