diff --git a/.gitea/workflows/build.yaml b/.gitea/workflows/build.yaml new file mode 100644 index 0000000..1966567 --- /dev/null +++ b/.gitea/workflows/build.yaml @@ -0,0 +1,47 @@ +name: Build +run-name: ${{ github.actor }} is upgrade release ๐ +on: [push] +env: + REPOSITORY: ${{ github.repository }} + COMMIT_ID: ${{ github.sha }} +jobs: + Build-Deploy-Actions: + runs-on: ubuntu-latest + steps: + - run: echo "๐ The job was automatically triggered by a ${{ github.event_name }} event." + - run: echo "๐ง This job is now running on a ${{ runner.os }} server hosted by Gitea!" + - run: echo "๐ 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 "๐ This job's status is ${{ job.status }}." diff --git a/1.jpg b/1.jpg new file mode 100644 index 0000000..06e31fc Binary files /dev/null and b/1.jpg differ diff --git a/2.jpg b/2.jpg new file mode 100644 index 0000000..f92b336 Binary files /dev/null and b/2.jpg differ diff --git a/3.jpg b/3.jpg new file mode 100644 index 0000000..a3058b9 Binary files /dev/null and b/3.jpg differ diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..823de7a --- /dev/null +++ b/Dockerfile @@ -0,0 +1,13 @@ +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 pip3 install --trusted-host pypi.python.org -r requirements.txt + +COPY . /app + +CMD ["python", "app.py"] diff --git a/app.py b/app.py new file mode 100644 index 0000000..0c810bd --- /dev/null +++ b/app.py @@ -0,0 +1,99 @@ +#็ฎๆ ๆฃๆต +import io +import gradio as gr +import matplotlib.pyplot as plt +import torch +from PIL import Image +from transformers import AutoFeatureExtractor, DetrForObjectDetection +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', +) + + +def make_prediction(img, feature_extractor, model): + inputs = feature_extractor(img, return_tensors="pt") + outputs = model(**inputs) + img_size = torch.tensor([tuple(reversed(img.size))]) + processed_outputs = feature_extractor.post_process(outputs, img_size) + return processed_outputs[0] + +def detect_objects(image_input): + #Extract model and feature extractor + feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50") + model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") + + # if image comes from upload + if image_input: + image = image_input + + #Make prediction + processed_outputs = make_prediction(image, feature_extractor, model) + + #Visualize prediction + viz_img = visualize_prediction(image, processed_outputs, 0.7, model.config.id2label) + + return viz_img + +# visualization +COLORS = [ + [0.000, 0.447, 0.741], + [0.850, 0.325, 0.098], + [0.929, 0.694, 0.125], + [0.494, 0.184, 0.556], + [0.466, 0.674, 0.188], + [0.301, 0.745, 0.933] +] + +# 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(pil_img, output_dict, threshold=0.7, id2label=None): + keep = output_dict["scores"] > threshold + boxes = output_dict["boxes"][keep].tolist() + scores = output_dict["scores"][keep].tolist() + labels = output_dict["labels"][keep].tolist() + if id2label is not None: + labels = [id2label[x] for x in labels] + + plt.figure(figsize=(16, 10)) + plt.imshow(pil_img) + ax = plt.gca() + colors = COLORS * 100 + for score, (xmin, ymin, xmax, ymax), label, color in zip(scores, boxes, labels, colors): + ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, fill=False, color=color, linewidth=3)) + ax.text(xmin, ymin, f"{label}: {score:0.2f}", fontsize=15, bbox=dict(facecolor="yellow", alpha=0.5)) + plt.axis("off") + return fig2img(plt.gcf()) + + +with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo: + gr.Markdown(""" +