ok

.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 artifacts.iflytek.com/docker-private/atp/base_image_for_ailab:0.0.1
+
+RUN sed -i 's/deb.debian.org/mirrors.ustc.edu.cn/g' /etc/apt/sources.list
+RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
+
+WORKDIR /app
+
+COPY . /app
+
+
+RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple 
+RUN pip install -r requirements.txt
+
+CMD ["python", "app.py"]

app.py

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+import gradio as gr
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+import cv2
+import sys
+sys.path.append("..")
+from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor
+from PIL import Image
+import io
+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',
+)
+
+sam_checkpoint = "sam_vit_b_01ec64.pth"
+model_type = "vit_b"
+
+device = "cuda"
+
+sam = sam_model_registry[model_type](checkpoint=sam_checkpoint)
+sam.to(device=device)
+
+mask_generator = SamAutomaticMaskGenerator(sam)
+
+mask_generator_2 = SamAutomaticMaskGenerator(
+    model=sam,
+    points_per_side=32,
+    pred_iou_thresh=0.86,
+    stability_score_thresh=0.92,
+    crop_n_layers=1,
+    crop_n_points_downscale_factor=2,
+    min_mask_region_area=100,  # Requires open-cv to run post-processing
+)
+
+
+def fig2img(fig):
+    buf = io.BytesIO()
+    fig.savefig(buf)
+    buf.seek(0)
+    img = Image.open(buf)
+    return img
+
+def show_anns(anns):
+    if len(anns) == 0:
+        return
+    sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
+    ax = plt.gca()
+    ax.set_autoscale_on(False)
+    polygons = []
+    color = []
+    for ann in sorted_anns:
+        m = ann['segmentation']
+        img = np.ones((m.shape[0], m.shape[1], 3))
+        color_mask = np.random.random((1, 3)).tolist()[0]
+        for i in range(3):
+            img[:,:,i] = color_mask[i]
+        ax.imshow(np.dstack((img, m*0.35)))
+
+
+def segment_image(image):
+    image = image.astype('uint8')
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    #masks = mask_generator.generate(image)
+    masks2 = mask_generator_2.generate(image)
+
+    plt.figure(figsize=(20,20))
+    plt.imshow(image)
+    #show_anns(masks)
+    show_anns(masks2)
+    plt.axis('off')
+
+    return fig2img(plt.gcf())
+
+    
+demo = gr.Interface(fn=segment_image, 
+                    inputs=gr.Image(), 
+                    outputs=gr.Image(),
+                    title = "图像分割",
+                    theme = theme,
+                    examples = ['dog.jpg'])
+
+
+if __name__ == "__main__":
+    demo.queue(concurrency_count=10)
+    demo.launch(server_name = "0.0.0.0")

requirements.txt

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+numpy
+opencv-python
+matplotlib
+Pillow
+segment_anything
+torchvision
+