Python计算坡度坡向并输出二维、三维图

概述在上次代码的基础上做了一点儿修改，将定义的函数单独放在一个模块里面，主函数去单独调用该模块。DEMslopeAspect模块fromosgeoimportgdal,ogr,osrimportnumpyasnpimportmathimportdatetime#Pythonmatplotlib模块代码示例https://vimsky.com/examples/detail/pyt

在上次代码的基础上做了一点儿修改，将定义的函数单独放在一个模块里面，主函数去单独调用该模块。

DEMslopeAspect模块

from osgeo import gdal,ogr,osrimport numpy as npimport mathimport datetime# Python matplotlib模块代码示例 https://vimsky.com/examples/detail/python-module-matplotlib.HTML# Axes3D是mpl_toolkits.mplot3d中的一个绘图函数，mpl_toolkits.mplot3d;是Matplotlib里面专门用来画三维图的工具包。from mpl_toolkits.mplot3d import Axes3Dfrom matplotlib import cbook, cmfrom matplotlib.colors import lightSourceimport matplotlib.pyplot as plt# 正则表达式(regular Expression)描述了一种字符串匹配的模式，可以用来检查一个串是否含有某种子串、将匹配的子串做替换或者从某个串中取出符合某个条件的子串等。import re# 读取TIFF遥感影像def read_img(filename):    # dataset = gdal.Open(r'D:\ProfessionalProfile\DEMdata\slopeAspectPython0322\test2_3-0326\test.tif')  # 打开文件    # dataset = gdal.Open(r'D:\ProfessionalProfile\DEMdata\slopeAspectPython0322\proUTM50python.tif')    dataset = gdal.Open(filename)    im_wIDth = dataset.RasterXSize  # 栅格矩阵的列数    im_height = dataset.RasterYSize  # 栅格矩阵的行数    im_bands = dataset.RasterCount  # 波段数    im_geotrans = dataset.GetGeotransform()  # 仿射矩阵，左上角像素的大地坐标和像素分辨率    im_proj = dataset.GetProjection()  # 地图投影信息，字符串表示    im_data = dataset.ReadAsArray(0, 0, im_wIDth, im_height)    datatype = im_data.dtype    del dataset  # 关闭对象dataset，释放内存    return   im_data, im_proj, im_geotrans, im_height,im_wIDth, im_bands, datatype# 为便于后续坡度计算，需要在原图像的周围添加一圈数值def AddRound(npgrID):    nx, ny = npgrID.shape[0], npgrID.shape[1]   # ny:行数，nx:列数;此处注意顺序    # np.zeros()返回来一个给定形状和类型的用0填充的数组；    zbc=np.zeros((nx+2,ny+2))    # 填充原数据数组    zbc[1:-1,1:-1]=npgrID    #四边填充数据    zbc[0,1:-1]=npgrID[0,:]  #上边；0行，所有列；    zbc[-1,1:-1]=npgrID[-1,:] #下边；最后一行，所有列；    zbc[1:-1,0]=npgrID[:,0]  #左边；所有行，0列。    zbc[1:-1,-1]=npgrID[:,-1] #右边；所有行，最后一列    #填充剩下四个角点值    zbc[0,0]=npgrID[0,0]    zbc[0,-1]=npgrID[0,-1]    zbc[-1,0]=npgrID[-1,0]    zbc[-1,-1]=npgrID[-1,0]    return zbc#####计算xy方向的梯度def Cacdxdy(npgrID,sizex,sizey):    nx, ny = npgrID.shape    s_dx = np.zeros((nx,ny))    s_dy = np.zeros((nx,ny))    a_dx = np.zeros((nx, ny))    a_dy = np.zeros((nx, ny))    # 忘记加range报错：object is not iterable    # 坡度、坡向变化率的计算：https://help.arcgis.com/zh-cn/arcgisdesktop/10.0/help/index.HTML#/na/009z000000vz000000/    for i in range(1,nx-1):        for j in range(1,ny-1):            s_dx[i,j] = ((npgrID[i-1,j+1]+2*npgrID[i,j+1]+npgrID[i+1,j+1])-(npgrID[i-1,j-1]+2*npgrID[i,j-1]+npgrID[i+1,j-1])) / (8 * sizex)            s_dy[i, j] = ((npgrID[i+1, j-1] + 2 * npgrID[i+1, j] + npgrID[i+1,j+1])-(npgrID[i-1,j-1]+2 * npgrID[i-1,j] + npgrID[i-1,j+1])) / (8 * sizey)    a_dx=s_dx*sizex    a_dy=s_dy*sizey    # 保留原数据区域的梯度值    s_dx = s_dx[1:-1,1:-1]    s_dy = s_dy[1:-1,1:-1]    a_dx = a_dx[1:-1, 1:-1]    a_dy = a_dy[1:-1, 1:-1]    # np.savetxt(r"D:\ProfessionalProfile\DEMdata\slopeAspectPython0322dxdy.csv",dx,delimiter=",")    return s_dx,s_dy,a_dx,a_dy####计算坡度/坡向def CacslopAsp(s_dx,s_dy,a_dx,a_dy):    # 坡度    slope=(np.arctan(np.sqrt(s_dx*s_dx+s_dy*s_dy)))*180/math.pi    #转换成°    #坡向    # #出错：TypeError: only size-1 arrays can be converted to Python scalars    # a2 = math.atan2(a_dy,-a_dx)*180/math.pi    a=np.zeros((a_dy.shape[0],a_dy.shape[1]))    for i in range(0,a_dx.shape[0]):        for j in range(0,a_dx.shape[1]):            a[i,j] = math.atan2(a_dy[i,j], -a_dx[i,j]) * 180 / math.pi    # 输出    aspect = a    # 坡向值将根据以下规则转换为罗盘方向值（0 到 360 度）：    # https://help.arcgis.com/zh-cn/arcgisdesktop/10.0/help/index.HTML#/na/009z000000vp000000/    x, y = a.shape[0],a.shape[1]    for m in range(0,x):        for n in range(0,y):            if a[m,n] < 0:                aspect[m,n] = 90-a[m,n]            elif a[m,n] > 90:                aspect[m,n] = 360.0 - a[m,n] + 90.0            else:                aspect[m,n] =  90.0 - a[m,n]    return slope,aspect# 遥感影像的存储，写GeoTiff文件def write_img(filename, tar_proj, im_geotrans, im_data, datatype):    # 判断栅格数据的数据类型    if 'int8' in im_data.dtype.name:        datatype = gdal.GDT_Byte    elif 'int16' in im_data.dtype.name:        datatype = gdal.GDT_UInt16    else:        datatype = gdal.GDT_float32    # 判读数组维数    if len(im_data.shape) == 3:        # 注意数据的存储波段顺序：im_bands, im_height, im_wIDth        im_bands, im_height, im_wIDth = im_data.shape    else:        im_bands, (im_height, im_wIDth) = 1, im_data.shape    # 创建文件时 driver = gdal.GetDriverByname("GTiff")，数据类型必须要指定，因为要计算需要多大内存空间。    driver = gdal.GetDriverByname("GTiff")    dataset = driver.Create(filename, im_wIDth, im_height, im_bands, datatype)    dataset.SetGeotransform(im_geotrans)  # 写入仿射变换参数    dataset.SetProjection(tar_proj)  # 写入投影    if im_bands == 1:        dataset.GetRasterBand(1).WriteArray(im_data)  # 写入数组数据    else:        for i in range(im_bands):            dataset.GetRasterBand(i + 1).WriteArray(im_data[i])    del dataset# 定义投影函数（此次运行没有用到）def SetPro(filename,tar_proj,outputfilename):    ds = gdal.Open(filename)    im_geotrans = ds.GetGeotransform()  # 仿射矩阵信息    im_proj = ds.GetProjection()  # 地图投影信息    im_wIDth = ds.RasterXSize  # 栅格矩阵的列数    im_height = ds.RasterYSize  # 栅格矩阵的行数    im_bands = ds.RasterCount    ds_array = ds.ReadAsArray(0, 0, im_wIDth, im_height)  # 获取原数据信息，包括数据类型int16，维度，数组等信息    # 设置数据类型(原图像有负值)    datatype = gdal.GDT_float32    # 目标投影    img_proj = tar_proj    # 输出影像路径及名称    name = outputfilename    driver = gdal.GetDriverByname("GTiff")  # 创建文件驱动    dataset = driver.Create(name, im_wIDth, im_height, im_bands, datatype)    dataset.SetGeotransform(im_geotrans)  # 写入原图像的仿射变换参数    dataset.SetProjection(img_proj)  # 写入目标投影    # 写入影像数据    dataset.GetRasterBand(1).WriteArray(ds_array)    del dataset####绘制平面栅格图def DrawgrID(judge,pre=[],A=[],strs=""):    if judge==0:        if strs == "":            plt.imshow(A, interpolation='nearest', cmap=plt.cm.hot, origin='lower')  # cmap='bone'  cmap=plt.cm.hot            # plt.imshow(A, interpolation='nearest', cmap=plt.cm.hot, origin='lower')  # cmap='bone'  cmap=plt.cm.hot            plt.colorbar(shrink=0.8)            plt.xticks(())            plt.yticks(())            plt.show()        else:            plt.imshow(A, interpolation='nearest', cmap=strs, origin='lower')  # cmap='bone'  cmap=plt.cm.hot            plt.colorbar(shrink=0.8)            # 影像范围（原始图像的im_geotrans六参数有）            X = np.arange(113.99986111111112, 6113.999861111111, 30)            Y = np.arange(35.00013888888889, 3035.000138888889, 30)            plt.xticks(())            plt.yticks(())            plt.show()    # judge==1绘制三维DEM    elif judge==1:        fig = plt.figure()        ax = Axes3D(fig)        X = np.arange(113.99986111111112,6113.999861111111, 30)        Y = np.arange(35.00013888888889, 3035.000138888889, 30)        # xt=range(114.79763889,853584.79763889 , 30)        # yt=range(38.21347222, 413348.21347222, 30)        X, Y = np.meshgrID(X, Y)        Z = pre        ax.plot_surface(X, Y, Z, rstrIDe=1, cstrIDe=1, cmap=plt.get_cmap('rainbow'))  # cmap=plt.get_cmap('rainbow')        ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.cm.hot)        ax.set_zlim(0, 200000)        plt.show()

主函数

import D1_DEMslopeAspect as demfrom D1_DEMslopeAspect import DrawgrIDimport datetime# 程序入口if __name__ == "__main__":    startime = datetime.datetime.Now() # 程序开始时间    # 读取ASTER GDEM遥感影像    demgrID, proj, geotrans, row, column, band, type =dem.read_img(r'D:\ProfessionalProfile\DEMdata\slopeAspectPython0322\test2_3-0326\test2.tif')    # geotrans = (114.79763889, 0.00027777777778, 0.0, 38.21347222, 0.0, -0.00027777777778)    # row = 13777    # col = 28449    demgrIData = demgrID    # 为计算梯度给影像添加周围一圈数据    demgrID = dem.AddRound(demgrID)    # 梯度计算    dx1,dy1,dx2,dy2 = dem.Cacdxdy(demgrID,30,30)    # 坡度、坡向计算    slope,aspect =dem.CacslopAsp(dx1,dy1,dx2,dy2)    # 设置要投影的投影信息，此处是wgs84-UTM-50N    tar_proj = '''PROJCS["WGS 84 / UTM zone 50N",      GEOGCS["WGS 84",          DATUM["WGS_1984",              SPHEROID["WGS 84",6378137,298.257223563,                  AUTHORITY["epsg","7030"]],              AUTHORITY["epsg","6326"]],          PRIMEM["Greenwich",0,              AUTHORITY["epsg","8901"]],          UNIT["degree",0.01745329251994328,              AUTHORITY["epsg","9122"]],          AUTHORITY["epsg","4326"]],      UNIT["metre",1,          AUTHORITY["epsg","9001"]],      PROJECTION["Transverse_Mercator"],      ParaMETER["latitude_of_origin",0],      ParaMETER["central_merIDian",117],      ParaMETER["scale_factor",0.9996],      ParaMETER["false_easting",500000],      ParaMETER["false_northing",0],      AUTHORITY["epsg","32650"],      AXIS["Easting",EAST],      AXIS["northing",norTH]]'''    # 输出TIFF格式遥感影像,并设置投影坐标    slopeT = dem.write_img(r'D:\ProfessionalProfile\DEMdata\slopeAspectPython0322\test2_3-0326\slopetest2.tif', tar_proj, geotrans, slope, type)    aspectT = dem.write_img(r'D:\ProfessionalProfile\DEMdata\slopeAspectPython0322\test2_3-0326\aspecttest2.tif', tar_proj, geotrans, aspect, type)    endtime = datetime.datetime.Now()  # 程序结束时间    runtime = endtime - startime  # 程序运行时间    print('运行时间为： %d 秒' % (runtime.seconds))    # 绘制三维DEM    DrawgrID(judge=1, pre=demgrIData)    # 绘制二维DEM    DrawgrID(judge=0, A=demgrIData, strs="bone")    # 绘制坡度图    DrawgrID(judge=0, A=slope, strs="rainbow")    # 绘制坡向图    DrawgrID(judge=0, A=aspect)

效果图

由于整个山东省的面积太大，故而选择了一小片区域（100*200）测试。

                                                      原始DEM图

                                                       TIF坡度图

                                                             TIF坡向图

                                                             三维DEM图

                                                                   二维DEM图

                                                                     坡度图

                                                                坡向图

参考

[1]坡度：https://help.arcgis.com/zh-cn/arcgisdesktop/10.0/help/index.HTML#/na/009z000000vz000000/

[2]坡向：https://help.arcgis.com/zh-cn/arcgisdesktop/10.0/help/index.HTML#/na/009z000000vp000000/

[3]博主锃光瓦亮的枕小路：https://blog.csdn.net/weixin_45561357/article/details/106677574

[4]https://blog.csdn.net/weixin_40501429/article/details/114894497

[5]博主箜_Kong：https://blog.csdn.net/liminlu0314/article/details/8498985?ops_request_misc=%257B%2522request%255FID%2522%253A%2522161657597316780266219174%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fblog.%2522%257D&request_ID=161657597316780266219174&biz_ID=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_v1~rank_blog_v1-1-8498985.pc_v1_rank_blog_v1&utm_term=%E5%9D%A1%E5%BA%A6%E5%9D%A1%E5%90%91

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