add
Build-Deploy-Actions
Details
Build-Deploy-Actions
Details
This commit is contained in:
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name: Build
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run-name: ${{ github.actor }} is upgrade release 🚀
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on: [push]
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env:
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REPOSITORY: ${{ github.repository }}
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COMMIT_ID: ${{ github.sha }}
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jobs:
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Build-Deploy-Actions:
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runs-on: ubuntu-latest
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steps:
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- run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
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- run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by Gitea!"
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- run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
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- name: Check out repository code
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uses: actions/checkout@v3
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-
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name: Setup Git LFS
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run: |
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git lfs install
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git lfs fetch
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git lfs checkout
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- name: List files in the repository
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run: |
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ls ${{ github.workspace }}
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-
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name: Docker Image Info
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id: image-info
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run: |
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echo "::set-output name=image_name::$(echo $REPOSITORY | tr '[:upper:]' '[:lower:]')"
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echo "::set-output name=image_tag::${COMMIT_ID:0:10}"
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-
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name: Login to Docker Hub
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uses: docker/login-action@v2
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with:
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registry: artifacts.iflytek.com
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username: ${{ secrets.DOCKERHUB_USERNAME }}
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password: ${{ secrets.DOCKERHUB_TOKEN }}
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v2
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-
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name: Build and push
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run: |
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docker version
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docker buildx build -t artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }} . --file ${{ github.workspace }}/Dockerfile --load
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docker push artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
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docker rmi artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
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- run: echo "🍏 This job's status is ${{ job.status }}."
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FROM python:3.7.4-slim
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WORKDIR /app
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COPY requirements.txt /app
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RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple/
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RUN pip3 install --trusted-host pypi.python.org -r requirements.txt
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COPY . /app
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CMD ["python", "app.py"]
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from transformers import DetrImageProcessor, DetrForObjectDetection
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import torch
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from PIL import Image
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import gradio as gr
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from gradio.themes.utils import sizes
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import matplotlib.pyplot as plt
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import io
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theme = gr.themes.Default(radius_size=sizes.radius_none).set(
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block_label_text_color = '#4D63FF',
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block_title_text_color = '#4D63FF',
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button_primary_text_color = '#4D63FF',
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button_primary_background_fill='#FFFFFF',
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button_primary_border_color='#4D63FF',
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button_primary_background_fill_hover='#EDEFFF',
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)
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processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-101")
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model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-101")
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# visualization
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COLORS = [
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[0.000, 0.447, 0.741],
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[0.850, 0.325, 0.098],
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[0.929, 0.694, 0.125],
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[0.494, 0.184, 0.556],
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[0.466, 0.674, 0.188],
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[0.301, 0.745, 0.933]
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]
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# Draw the bounding boxes on image.
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def fig2img(fig):
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buf = io.BytesIO()
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fig.savefig(buf)
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buf.seek(0)
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img = Image.open(buf)
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return img
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def visualize_prediction(pil_img, results, id2label=None):
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labels = results["labels"]
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labels = labels.tolist()
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if id2label is not None:
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labels = [id2label.get(x) for x in labels]
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plt.figure(figsize=(16, 10))
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plt.imshow(pil_img)
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ax = plt.gca()
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colors = COLORS * 100
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for score, (xmin, ymin, xmax, ymax), label, color in zip(results["scores"].tolist(), results["boxes"].tolist(), labels, colors):
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ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3))
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ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5))
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plt.axis("off")
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return fig2img(plt.gcf())
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def detect_object(image):
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inputs = processor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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print(image.size)
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target_sizes = torch.tensor([image.size[::-1]])
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results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.9)[0]
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return visualize_prediction(image, results, model.config.id2label)
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with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
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gr.Markdown("""
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<div align='center' ><font size='60'>目标检测</font></div>
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""")
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with gr.Row():
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with gr.Column():
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box1 = gr.Image(label="图片", type="pil")
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with gr.Row():
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button = gr.Button("提交", variant="primary")
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box2 = gr.Image(label="图片")
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button.click(fn=detect_object, inputs=box1, outputs=box2)
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examples = gr.Examples(examples=[['1.jpg']], inputs=[box1], label="例子")
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if __name__ == "__main__":
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demo.queue(concurrency_count=3).launch(server_name = "0.0.0.0")
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@ -0,0 +1,9 @@
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gradio
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huggingface_hub
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torch
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transformers
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timm
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matplotlib
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Pillow
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requests
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