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 DetrFeatureExtractor, DetrForSegmentation
+
+
+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',
+)
+
+
+# These are the COCO classes
+CLASSES = [
+    'N/A', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A',
+    'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
+    'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack',
+    'umbrella', 'N/A', 'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',
+    'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
+    'skateboard', 'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass',
+    'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
+    'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
+    'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table', 'N/A',
+    'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
+    'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A',
+    'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier',
+    'toothbrush'
+]
+
+# Detectron2 uses a different numbering scheme, we build a conversion table
+coco2d2 = {}
+count = 0
+for i, c in enumerate(CLASSES):
+  if c != "N/A":
+    coco2d2[i] = count
+    count+=1
+
+
+# Draw the bounding boxes on image.
+def fig2img(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf)
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+
+
+# Draw the bounding boxes.
+def visualize_prediction(result):
+    palette = itertools.cycle(sns.color_palette())
+
+    # The segmentation is stored in a special-format png
+    panoptic_seg = Image.open(io.BytesIO(result['png_string']))
+    panoptic_seg = numpy.array(panoptic_seg, dtype=numpy.uint8).copy()
+    # We retrieve the ids corresponding to each mask
+    panoptic_seg_id = rgb2id(panoptic_seg)
+
+    # Finally we color each mask individually
+    panoptic_seg[:, :, :] = 0
+    for id in range(panoptic_seg_id.max() + 1):
+        panoptic_seg[panoptic_seg_id == id] = numpy.asarray(next(palette)) * 255
+    plt.figure(figsize=(15,15))
+    plt.imshow(panoptic_seg)
+    plt.axis('off')
+    return fig2img(plt.gcf())
+
+
+feature_extractor = DetrFeatureExtractor.from_pretrained("facebook/detr-resnet-50-panoptic")
+model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
+
+
+transform = T.Compose([
+    T.Resize(800),
+    T.ToTensor(),
+    T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+model.eval();
+
+
+def detect_objects(image_input):
+    inputs = feature_extractor(images=image_input, return_tensors="pt")
+    outputs = model(**inputs)
+
+    processed_sizes = torch.as_tensor(inputs["pixel_values"].shape[-2:]).unsqueeze(0)
+    result = feature_extractor.post_process_panoptic(outputs, processed_sizes)[0]
+
+    #Visualize prediction
+    viz_img = visualize_prediction(result)
+    
+    return viz_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