Create app.py

app.py

@@ -0,0 +1,51 @@
+'''
+Author: Egrt
+Date: 2022-03-19 10:23:48
+LastEditors: Egrt
+LastEditTime: 2022-03-21 00:05:27
+FilePath: \Luuu\app.py
+'''
+
+from gis import GIS
+import gradio as gr
+from huggingface_hub import hf_hub_download
+filepath = hf_hub_download(repo_id="Egrt/Luuuu", filename="GDAL-3.4.1-cp38-cp38-manylinux_2_5_x86_64.whl")
+import os
+os.system('pip install {}'.format(filepath))
+os.system('pip install requirements.txt')
+from zipfile import ZipFile
+gis = GIS()
+
+
+# --------模型推理---------- #
+def inference(filepath):
+    filename, file_list = gis.detect_image(filepath)
+    with ZipFile("result.zip", "w") as zipObj:
+        zipObj.write(file_list[0], "{}.tif".format(filename+'mask'))
+        zipObj.write(file_list[1], "{}.tif".format(filename))
+        zipObj.write(file_list[2], "{}.pdf".format(filename))
+        zipObj.write(file_list[3], "{}.cpg".format(filename))
+        zipObj.write(file_list[4], "{}.dbf".format(filename))
+        zipObj.write(file_list[5], "{}.shx".format(filename))
+        zipObj.write(file_list[6], "{}.shp".format(filename))
+        zipObj.write(file_list[7], "{}.prj".format(filename))
+    return "result.zip"
+
+
+# --------网页信息---------- #  
+title = "基于帧场学习的多边形建筑提取"
+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🐋🐋"
+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>"
+example_img_dir  = 'images'
+example_img_name = os.listdir(example_img_dir)
+examples=[[os.path.join(example_img_dir, image_path)] for image_path in example_img_name if image_path.endswith('.png')]
+gr.Interface(
+    inference, 
+    [gr.inputs.Image(type="filepath", label="待检测图片")], 
+    gr.outputs.File(label="检测结果"),
+    title=title,
+    description=description,
+    article=article,
+    enable_queue=True,
+    examples=examples
+    ).launch(debug=True)