遥感图像实例分割
Zero-shot Instance Segmentation of Remote Sensing Images
本代码基于以下材料：
这段代码用来带领大家实现实例分割。以下代码首先采用Zero-shot的方式，即在不训练的情况下，利用现有的checkpoint，实现实例分割。
接下来，大家采用给定数据集，对模型进行训练，提高模型的效果
有问题联系助教刘泽平
研究准备
步骤1：下载代码并解压
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# 下载代码并解压
!pip install -U --no-cache-dir gdown --pre
!gdown --id 15VcT-ORrcnxgc4K0HSOHrV_2KAX8sect --output "code.zip"
!unzip code.zip
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warnings.warn(
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Archive: code.zip
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inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torch/nn/functionnal.py
creating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torch/utils/
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torch/utils/complex.py
creating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torch/utils/data/
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torch/utils/data/dataset.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torch/utils/data/makedirs.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torch/utils/data/__init__.py
creating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/
creating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/datasets/
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/datasets/config.luxcarta_dataset.json
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/datasets/inria_aerial.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/datasets/luxcarta_buildings.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/datasets/mapping_challenge.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/datasets/open_cities_competition.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/datasets/utils.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/datasets/xview2_dataset.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/datasets/__init__.py
creating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/angle_field_init.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/approximate_polygon.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/filter_empty_polygons.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/filter_poly_vertex_count.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/functional.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/keep_keys.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/map.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/rasterize.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/remove_doubles.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/remove_keys.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/sample_uniform.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/tensorpoly.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/tensorskeleton.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/to_patches.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/transforms.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/transform_by_key.py
inflating: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/transforms/__init__.py
extracting: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/torchvision/__init__.py
extracting: Polygonization-by-Frame-Field-Learning-master/torch_lydorn/__init__.py
步骤2：切换到代码目录
[ ]:
cd /content/Polygonization-by-Frame-Field-Learning-master
/content/Polygonization-by-Frame-Field-Learning-master
步骤3：配置环境
[ ]:
!pip install fastapi
!pip install kaleido
!pip install python-multipart
!pip install uvicorn
!pip install imagecodecs
!pip install -r requirements.txt
import torch
!pip install torch-scatter -f https://data.pyg.org/whl/torch-{torch.__version__}.html
!pip install torch-sparse -f https://data.pyg.org/whl/torch-{torch.__version__}.html
!pip install torch-cluster -f https://data.pyg.org/whl/torch-{torch.__version__}.html
!pip install git+https://github.com/pyg-team/pytorch_geometric.git
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!mv /content/Polygonization-by-Frame-Field-Learning-master/frame_field_learning/runs/inria_dataset_polygonized.unet_resnet101_pretrained.leaderboard\ _\ 2020-06-02\ 07_57_31 /content/Polygonization-by-Frame-Field-Learning-master/frame_field_learning/runs/inria_dataset_polygonized.unet_resnet101_pretrained.leaderboard\ \|\ 2020-06-02\ 07\:57\:31
进行实验
步骤4：运行代码
格式为 **!python main.py –run_name inria_dataset_polygonized.unet_resnet101_pretrained.leaderboard –in_filepath **
我们提供了实例图像，来自Inria数据集的Austin区域的影像
如果想修改模型的输入影像，修改即可
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!python main.py --run_name inria_dataset_polygonized.unet_resnet101_pretrained.leaderboard --in_filepath /content/Polygonization-by-Frame-Field-Learning-master/data/austin1_WGS84.tif
/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
warnings.warn(
/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet101_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet101_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/resnet101-63fe2227.pth" to models/hub/checkpoints/resnet101-63fe2227.pth
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/content/Polygonization-by-Frame-Field-Learning-master/frame_field_learning/polygonize_simple.py:103: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
polygons = list(polygons)
/content/Polygonization-by-Frame-Field-Learning-master/frame_field_learning/polygonize_simple.py:103: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.
polygons = list(polygons)
/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:156: UserWarning: The epoch parameter in `scheduler.step()` was not necessary and is being deprecated where possible. Please use `scheduler.step()` to step the scheduler. During the deprecation, if epoch is different from None, the closed form is used instead of the new chainable form, where available. Please open an issue if you are unable to replicate your use case: https://github.com/pytorch/pytorch/issues/new/choose.
warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
/content/Polygonization-by-Frame-Field-Learning-master/frame_field_learning/polygonize_acm.py:299: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.
polygons = list(polygons)
/content/Polygonization-by-Frame-Field-Learning-master/frame_field_learning/polygonize_acm.py:299: ShapelyDeprecationWarning: __len__ for multi-part geometries is deprecated and will be removed in Shapely 2.0. Check the length of the `geoms` property instead to get the number of parts of a multi-part geometry.
polygons = list(polygons)
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vertices = concatenate([
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看看结果怎么样
[ ]:
import rasterio
from rasterio.plot import show
import matplotlib.pyplot as plt
# 首先看一下输入模型的影像
rgb_tif_path = '/content/Polygonization-by-Frame-Field-Learning-master/data/austin1_WGS84.tif'
# 打开 RGB TIFF 文件
with rasterio.open(rgb_tif_path) as src:
# 读取 RGB TIFF 文件中的数据
rgb_data = src.read()
# 显示 RGB 图像
show(rgb_data)
[ ]:
import geopandas as gpd
import matplotlib.pyplot as plt
# 接下来看一下模型的输出(shapefile)
shapefile_path = '/content/Polygonization-by-Frame-Field-Learning-master/data/poly_shapefile.acm.tol_1/austin1_WGS84.shp'
gdf = gpd.read_file(shapefile_path)
# 可视化矢量文件
gdf.plot(aspect='equal')
plt.title('Shapefile Visualization')
plt.show()
[ ]:
import shutil
from google.colab import files
# 接下来，我们把航拍影像，和对应的矢量文件下载下来，可以用导入到arcmap中进行观察
def zip_and_download(folder_path):
shutil.make_archive('/content/downloaded_folder', 'zip', folder_path)
shutil.move('/content/downloaded_folder.zip', '/content/downloaded_folder.zip')
download_link = '/content/downloaded_folder.zip'
files.download(download_link)
zip_and_download("/content/Polygonization-by-Frame-Field-Learning-master/data/poly_shapefile.acm.tol_1")
files.download("/content/Polygonization-by-Frame-Field-Learning-master/data/austin1_WGS84.tif")
Austin其他区域建筑物识别
学习代码，并根据本次实习中提供的其他区域影像，尝试得到对应的识别结果
这个压缩包内提供了新的影像，可以直接使用
[ ]:
# 下载Austin其他区域影像
!gdown --id 1EoLJc-a7VlhnTYUybes8aRr_91p6g8MO --output "additional_data_new.zip" # 如果手动下载了压缩包并上传到文件里，请注释掉这行代码
!unzip additional_data_new.zip
[ ]:
# 你来完成
使用自己的遥感图像，并进行建筑物识别
相比于本次实习提供的影像的识别结果，你自己的遥感图像的识别结果是更好，还是更坏，请思考为什么
输入影像必须满足以下要求：
图像只有三个波段 (例如RGB波段)
图像宽高大于1024*1024
jpg,tif,png格式
带有坐标系(推荐)
运行结束后，会在自己上传的图像的相同路径下，看到poly_shapefile.acm.tol_1 文件夹，这个文件夹下是输入图像对应的识别结果(shapefile格式)
[ ]:
# 你来完成
有什么办法可以改进识别结果吗
如何改进识别结果？一种更好的办法是采用微调，或重新训练的方式，即收集一定的影像-标签对，来让模型对新数据进行参数优化
有关怎么对本文使用的模型进行微调，请阅读该模型的技术手册 https://github.com/Lydorn/Polygonization-by-Frame-Field-Learning
[ ]:
# 你来完成
你需要做什么
基本要求
弄明白这些代码都在干什么
中等要求
在Austin其他区域进行建筑物轮廓识别
在自己收集的影像上，进行建筑物轮廓识别
终极要求
通过对现有模型微调，或重新训练的方式，改进预测结果