add

.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:3.7.4-slim
+
+WORKDIR /app
+
+COPY requirements.txt /app
+
+RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple/ 
+
+RUN apt-get update && \
+    apt-get upgrade -y && \
+    apt-get install -y git
+
+RUN apt-get install -y tk
+
+RUN python -m pip install git+https://ghproxy.com/https://github.com/cocodataset/panopticapi.git
+
+RUN pip3 install --trusted-host pypi.python.org -r requirements.txt 
+
+COPY . /app
+
+CMD ["python", "app.py"]

app.py

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+#全景分割
+from PIL import Image
+import io
+import matplotlib.pyplot as plt
+import torch
+import torchvision.transforms as T
+import numpy
+import gradio as gr
+import itertools
+import seaborn as sns
+from panopticapi.utils import rgb2id
+from gradio.themes.utils import sizes
+from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
+from torch import nn
+import numpy as np
+
+
+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',
+)
+
+
+feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-base-coco")
+model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-coco")
+
+
+def detect_objects(image_input):
+    inputs = feature_extractor(images=image_input, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    result = feature_extractor.post_process_panoptic_segmentation(outputs, target_sizes=[image_input.size[::-1]])[0]
+
+    #Visualize prediction
+    viz_img = visualize_prediction(result["segmentation"], image_input)
+    
+    return viz_img
+
+
+def visualize_prediction(result, image):
+    color_palette = [list(np.random.choice(range(256), size=3)) for _ in range(len(model.config.id2label))]
+    seg = result 
+    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
+    palette = np.array(color_palette)
+    for label, color in enumerate(palette):
+        color_seg[seg == label, :] = color
+    # Convert to BGR
+    color_seg = color_seg[..., ::-1]
+
+    # Show image + mask
+    img = np.array(image) * 0.5 + color_seg * 0.5
+    img = img.astype(np.uint8)
+
+    return img
+        
+
+with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
+    gr.Markdown("""
+    <div align='center' ><font size='60'>全景分割</font></div>
+    """)
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label="图片", type="pil")
+            with gr.Row():
+                button = gr.Button("提交", variant="primary")
+        box2 = gr.Image(label="图片")
+
+    button.click(fn=detect_objects, inputs=[image], outputs=box2)
+    examples = gr.Examples(examples=[['1.jpg'], ['2.jpg']],  inputs=[image], label="例子")
+
+
+if __name__ == '__main__':
+    demo.queue().launch(server_name = "0.0.0.0")

requirements.txt

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+gradio
+huggingface
+torch
+transformers
+seaborn
+matplotlib
+pillow
+requests
+torchvision
+numpy
+scipy
+timm