本文主要是介绍【PythonRS】Pyrsgis库安装+基础函数使用教程，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
pyrsgis库是一个用于处理地理信息系统(GIS)数据的Python库。它提供了一组功能强大的工具，可以帮助开发人员使用Python语言创建、处理、分析和可视化GIS数据。通过使用pyrsgis库，开发人员可以更轻松地理解和利用地理信息。
pyrsgis库包含了许多常见的GIS操作和功能，例如读取和写入shapefile文件、转换坐标系、执行空间查询、计算地理特征属性等。它提供了许多方便使用的类和方法，例如GeoPandas、Shapely、Fiona、Rasterio、Pyproj和GDAL等，这些都可以帮助开发人员更高效地处理GIS数据。
一、Pyrsgis库安装
Pyrsgis可以直接通过pip install pyrsgis安装，同样也可以下载压缩包然后本地安装。PyPI中Pyrsgis包下载地址：pyrsgis · PyPI
二、导入库和函数
这些都是我后面代码需要使用到的函数，注意要导入，别到时候报错。
import os
from pyrsgis import raster, convert, ml三、基础操作代码展示
1）获取影像基本信息
def Get_data(filepath):# 获取影像基本信息ds, data_arr = raster.read(filepath) # 基础信息资源和数组ds_bands = ds.RasterCount # 波段数ds_width = ds.RasterXSize # 宽度ds_height = ds.RasterYSize # 高度ds_bounds = ds.bbox # 四至范围ds_geo = ds.GeoTransform # 仿射地理变换参数ds_prj = ds.Projection # 投影坐标系print("影像的宽度为：" + str(ds_width))print("影像的高度为：" + str(ds_height))print("仿射地理变换参数为：" + str(ds_geo))print("投影坐标系为：" + str(ds_prj))2）计算NDVI
这里给大家介绍一个经典案例，就是NDVI的计算。通过这个应该很容易就能理解Pyrsgis库的数据结构了。
def Get_NDVI(filepath):# 计算NDVIds, data_arr = raster.read(filepath)red_arr = data_arr[3, :, :]nir_arr = data_arr[4, :, :]result_arr = (nir_arr - red_arr) / (nir_arr + red_arr)# result_arr = (data_arr[4, :, :] - data_arr[3, :, :]) / (data_arr[4, :, :] + data_arr[3, :, :])output_file = r'E:/path_to_your_file/landsat8_result.tif'raster.export(result_arr, ds, output_file, dtype='float32', bands="all", nodata=0, compress="LZW")# 写入的数组，基础信息，路径，格式，波段，无效值，压缩方式3）空间位置裁剪
这里的裁剪主要是按照输入的空间矩形进行裁剪，并没有演示如何使用shp进行裁剪。这个可以应用于分幅裁剪、滑动裁剪等。空行分割的是实现这个功能的两种函数的使用方式。
def Clip_data(filepath):# 按掩膜提取ds, data_arr = raster.read(filepath)print('Original bounding box:', ds.bbox)print('Original shape of raster:', data_arr.shape)new_ds, clipped_arr = raster.clip(ds, data_arr, x_min=770000, x_max=790000, y_min=1420000, y_max=1440000)raster.export(clipped_arr, new_ds, r'E:/path_to_your_file/clipped_file.tif')infile = r'E:/path_to_your_file/your_file.tif'outfile = r'E:/path_to_your_file/clipped_file.tif'raster.clip_file(infile, x_min=770000, x_max=790000, y_min=1420000, y_max=1440000, outfile=outfile)4）移除无效值
这里的函数是移除无效值的，如-9999之类的，理论上应该也可以修改其他的DN值，但我自己没去试过，大家可以自行尝试。
def Modify_data(filepath):# 修改影像数组，如移除无效值ds, data_arr = raster.read(filepath)new_ds, new_arr = raster.trim(ds, data_arr, remove=-9999)print('Shape of the input array:', data_arr.shape)print('Shape of the trimmed array:', new_arr.shape)ds, data_arr = raster.read(filepath)new_arr = raster.trim_array(data_arr, remove=-9999)print('Shape of the input array:', data_arr.shape)print('Shape of the trimmed array:', new_arr.shape)infile = r'E:/path_to_your_file/your_file.tif'outfile = r'E:/path_to_your_file/trimmed_file.tif'raster.trim_file(infile, -9999, outfile)5）平移影像
按照x、y方向进行影像平移，可选像素和坐标进行平移。
def Shift_data(filepath):# 平移影像ds, data_arr = raster.read(filepath)new_ds = raster.shift(ds, x=10, y=10) # x,y方向偏移量。按栅格的投影单位移动数据源 或分别按细胞数print('Original bounding box:', ds.bbox)print('Modified bounding box:', new_ds.bbox)new_ds = raster.shift(ds, x=10, y=10, shift_type='cell') # shift_type='coordinate'print('Modified bounding box:', new_ds.GeoTransform)raster.export(data_arr, new_ds, r'E:/path_to_your_file/shifted_file.tif')infile = r'E:/path_to_your_file/your_file.tif'outfile = r'E:/path_to_your_file/shifted_file.tif'raster.shift_file(infile, x=10, y=10, outfile=outfile, shift_type='cell')6）数组、表、CSV互转（包含剔除值）
这里的函数是数组、表、CSV互转，在转换的同时可以通过参数移除某些DN值。
def Convert_data(filepath):# 数组转表、CSV，修改值input_file = r'E:/path_to_your_file/raster_file.tif'ds, data_arr = raster.read(input_file) # Shape of the input array: (6, 800, 400)data_table = convert.array_to_table(data_arr) # Shape of the reshaped array: (320000, 6)# 该函数将单波段或多波段栅格数组转换为表，其中 列表示输入波段，每行表示一个单元格。input_file = r'E:/path_to_your_file/raster_file.tif'ds, data_arr = raster.read(input_file)data_table = convert.array_to_table(data_arr)print('Shape of the input array:', data_arr.shape) # Shape of the input array: (6, 800, 400)print('Shape of the reshaped array:', data_table.shape) # Shape of the reshaped array: (320000, 6)input_file = r'E:/path_to_your_file/raster_file.tif'new_data_arr = convert.table_to_array(data_table, n_rows=ds.RasterYSize, n_cols=ds.RasterXSize)print('Shape of the array with newly added bands:', new_data_arr.shape)# Shape of the array with newly added bands: (8, 800, 400)new_data_arr = convert.table_to_array(data_table[:, -2:], n_rows=ds.RasterYSize, n_cols=ds.RasterXSize)print('Shape of the array with newly added bands:', new_data_arr.shape)# Shape of the array with newly added bands: (2, 800, 400)# 表转数组input_file = r'E:/path_to_your_file/raster_file.tif'output_file = r'E:/path_to_your_file/tabular_file.csv'convert.raster_to_csv(input_file, filename=output_file)input_dir = r'E:/path_to_your_file/'output_file = r'E:/path_to_your_file/tabular_file.csv'convert.raster_to_csv(input_dir, filename=output_file)convert.raster_to_csv(input_dir, filename=output_file, negative=False, remove=[10, 54, 127], badrows=False)# 数组转表，可剔除负值、目标值、坏波段input_file = r'E:/path_to_your_file/raster_file.tif'out_csvfile = input_file.replace('.tif', '.csv')convert.raster_to_csv(input_file, filename=out_csvfile, negative=False)new_csvfile = r'E:/path_to_your_file/predicted_file.tif'out_tiffile = new_csvfile.replace('.csv', '.tif')convert.csv_to_raster(new_csvfile, ref_raster=input_file, filename=out_tiffile, compress='DEFLATE')convert.csv_to_raster(new_csvfile, ref_raster=input_file, filename=out_tiffile,cols=['Blue', 'Green', 'KMeans', 'RF_Class'], compress='DEFLATE')# 数组将堆叠并导出为多光谱文件convert.csv_to_raster(new_csvfile, ref_raster=input_file, filename=out_tiffile,cols=['Blue', 'Green', 'KMeans', 'RF_Class'], stacked=False, compress='DEFLATE')# 将每列导出为单独的波段，请将参数设置为 。stacked=False7）制作深度学习标签
此函数根据单波段或多波段栅格阵列生成影像片。图像芯片可以用作深度学习模型的直接输入（例如。卷积神经网络），输出格式：(4198376, 7, 7, 6)
def Create_CNN(filepath):# 此函数根据单波段或多波段栅格阵列生成影像片。图像 芯片可以用作深度学习模型的直接输入（例如。卷积神经网络）# -----------------------------数组生成深度学习芯片-----------------------------infile = r'E:/path_to_your_file/your_file.tif'ds, data_arr = raster.read(infile)image_chips = ml.array_to_chips(data_arr, y_size=7, x_size=7)print('Shape of input array:', data_arr.shape) # Shape of input array: (6, 2054, 2044)print('Shape of generated image chips:', image_chips.shape) # Shape of generated image chips: (4198376, 7, 7, 6)infile = r'E:/path_to_your_file/your_file.tif'ds, data_arr = raster.read(infile)image_chips = ml.array2d_to_chips(data_arr, y_size=5, x_size=5)print('Shape of input array:', data_arr.shape) # Shape of input array: (2054, 2044)print('Shape of generated image chips:', image_chips.shape) # Shape of generated image chips: (4198376, 5, 5)# ----------------------------影像直接生成深度学习芯片----------------------------infile_2d = r'E:/path_to_your_file/your_2d_file.tif'image_chips = ml.raster_to_chips(infile_2d, y_size=7, x_size=7)print('Shape of single band generated image chips:', image_chips.shape)# Shape of single bandgenerated image chips: (4198376, 7, 7)infile_3d = r'E:/path_to_your_file/your_3d_file.tif'image_chips = ml.raster_to_chips(infile_3d, y_size=7, x_size=7)print('Shape of multiband generated image chips:', image_chips.shape)# Shape of multiband generated image chips: (4198376, 7, 7, 6)8）翻转影像
按照东西或南北方向翻转影像
def Reverse_Image(filepath):# 按照东西、南北方向反转影像# -------------------------------北向、东向翻转--------------------------------input_file = r'E:/path_to_your_file/your_file.tif'ds, data_arr = raster.read(input_file)north_arr, east_arr = raster.north_east(data_arr)print(north_arr.shape, east_arr.shape)north_arr, east_arr = raster.north_east(data_arr, flip_north=True, flip_east=True)north_arr = raster.north_east(data_arr, layer='north')from matplotlib import pyplot as pltplt.imshow(north_arr)plt.show()plt.imshow(east_arr)plt.show()input_file = r'E:/path_to_your_file/your_file.tif'ds, data_arr = raster.read(input_file)north_arr, east_arr = raster.north_east(data_arr)print(north_arr.shape, east_arr.shape)north_arr = raster.north_east(data_arr, layer='north')from matplotlib import pyplot as pltplt.imshow(north_arr)plt.show()plt.imshow(east_arr)plt.show()raster.export(north_arr, ds, r'E:/path_to_your_file/northing.tif', dtype='float32')raster.export(east_arr, ds, r'E:/path_to_your_file/easting.tif', dtype='float32')# -------------------------使用参考.tif文件生成北向栅格----------------------------reference_file = r'E:/path_to_your_file/your_file.tif'raster.northing(file1, r'E:/path_to_your_file/northing_number.tif', flip=False, value='number')raster.northing(file1, r'E:/path_to_your_file/northing_normalised.tif', value='normalised') # 输出栅格进行归一化raster.northing(file1, r'E:/path_to_your_file/northing_coordinates.tif', value='coordinates')raster.northing(file1, r'E:/path_to_your_file/northing_number_compressed.tif', compress='DEFLATE')reference_file = r'E:/path_to_your_file/your_file.tif'raster.easting(file1, r'E:/path_to_your_file/easting_number.tif', flip=False, value='number')raster.easting(file1, r'E:/path_to_your_file/easting_normalised.tif', value='normalised')raster.easting(file1, r'E:/path_to_your_file/easting_normalised.tif', value='normalised')raster.easting(file1, r'E:/path_to_your_file/easting_number_compressed.tif', compress='DEFLATE')四、总结
Pyrsgis库之前使用的时候是因为要进行卷积神经网络的深度学习，然后里面制作深度学习标签的函数还是不错的，可以用一行代码实现标签的制作。但是如果数据过大，内存就会溢出报错，这个是Pyrsgis库没有解决的，当然我也没解决=。=大家可以自己尝试一下，有解决办法可以和我分享一下。总的来说Pyrsgis和Rasterio这两个库都还不错，都在GDAL的基础上进行了二开，方便了很多操作。
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