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''' |
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Author: Egrt |
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Date: 2022-03-19 10:23:48 |
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LastEditors: Egrt |
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LastEditTime: 2022-03-21 00:05:27 |
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FilePath: \Luuu\app.py |
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''' |
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import gradio as gr |
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from huggingface_hub import hf_hub_download |
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filepath = hf_hub_download(repo_id="Egrt/Luuuu", filename="GDAL-3.4.1-cp38-cp38-manylinux_2_5_x86_64.whl") |
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import os |
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os.system('pip install {}'.format(filepath)) |
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from zipfile import ZipFile |
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from gis import GIS |
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gis = GIS() |
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def inference(filepath): |
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filename, file_list = gis.detect_image(filepath) |
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with ZipFile("result.zip", "w") as zipObj: |
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zipObj.write(file_list[0], "{}.tif".format(filename+'mask')) |
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zipObj.write(file_list[1], "{}.tif".format(filename)) |
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zipObj.write(file_list[2], "{}.pdf".format(filename)) |
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zipObj.write(file_list[3], "{}.cpg".format(filename)) |
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zipObj.write(file_list[4], "{}.dbf".format(filename)) |
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zipObj.write(file_list[5], "{}.shx".format(filename)) |
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zipObj.write(file_list[6], "{}.shp".format(filename)) |
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zipObj.write(file_list[7], "{}.prj".format(filename)) |
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return "result.zip" |
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title = "基于帧场学习的多边形建筑提取" |
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description = "目前最先进图像分割模型通常以栅格形式输出分割，但地理信息系统中的应用通常需要矢量多边形。我们在遥感图像中提取建筑物的任务中，将帧场输出添加到深度分割模型中，将预测的帧场与地面实况轮廓对齐，帮助减少深度网络输出与下游任务中输出样式之间的差距。 @Luuuu🐋🐋" |
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2004.14875' target='_blank'>Polygonization-by-Frame-Field-Learning</a> | <a href='https://github.com/JingyunLiang/SwinIR' target='_blank'>Github Repo</a></p>" |
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example_img_dir = 'images' |
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example_img_name = os.listdir(example_img_dir) |
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examples=[[os.path.join(example_img_dir, image_path)] for image_path in example_img_name if image_path.endswith('.png')] |
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gr.Interface( |
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inference, |
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[gr.inputs.Image(type="filepath", label="待检测图片")], |
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gr.outputs.File(label="检测结果"), |
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title=title, |
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description=description, |
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article=article, |
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enable_queue=True, |
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examples=examples |
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).launch(debug=True) |
