使用python从栅格获取纬度/经度？

我想知道是否有人在不使用ArcGIS的情况下从栅格中获取高程数据，而是以python list或dict？

我以元组列表的形式获取XY数据：

xy34 =[perp_obj[j].CalcPnts(float(i.dist), orientation) for j in range (len(perp_obj))]

我想遍历列表或将其传递给函数或类方法，以获取xy对的相应高度。

我对该主题进行了一些研究，gdal API听起来很有前途。谁能建议我如何处理事情，陷阱和示例代码？

不能选择GDAL，因为我无法在正在使用的计算机上编辑系统路径变量！

有人知道其他方法吗？

与@Aragon的答案相比，这是使用GDAL的更具编程性的方式。我尚未对其进行测试，但是过去对我有用的大部分都是样板代码。它依赖于Numpy和GDAL绑定，仅此而已。

import osgeo.gdal as gdal
import osgeo.osr as osr
import numpy as np
from numpy import ma

def maFromGDAL(filename):
    dataset = gdal.Open(filename, gdal.GA_ReadOnly)

    if dataset is None:
        raise Exception()

    # Get the georeferencing metadata.
    # We don't need to know the CRS unless we want to specify coordinates
    # in a different CRS.
    #projection = dataset.GetProjection()
    geotransform = dataset.GetGeoTransform()

    # We need to know the geographic bounds and resolution of our dataset.
    if geotransform is None:
        dataset = None
        raise Exception()

    # Get the first band.
    band = dataset.GetRasterBand(1)
    # We need to nodata value for our MaskedArray later.
    nodata = band.GetNoDataValue()
    # Load the entire dataset into one numpy array.
    image = band.ReadAsArray(0, 0, band.XSize, band.YSize)
    # Close the dataset.
    dataset = None

    # Create a numpy MaskedArray from our regular numpy array.
    # If we want to be really clever, we could subclass MaskedArray to hold
    # our georeference metadata as well.
    # see here: http://docs.scipy.org/doc/numpy/user/basics.subclassing.html
    # For details.
    masked_image = ma.masked_values(image, nodata, copy=False)
    masked_image.fill_value = nodata

    return masked_image, geotransform

def pixelToMap(gt, pos):
    return (gt[0] + pos[0] * gt[1] + pos[1] * gt[2],
            gt[3] + pos[0] * gt[4] + pos[1] * gt[5])

# Reverses the operation of pixelToMap(), according to:
# https://en.wikipedia.org/wiki/World_file because GDAL's Affine GeoTransform
# uses the same values in the same order as an ESRI world file.
# See: http://www.gdal.org/gdal_datamodel.html
def mapToPixel(gt, pos):
    s = gt[0] * gt[4] - gt[3] * gt[1]
    x = (gt[4] * pos[0] - gt[1] * pos[1] + gt[1] * gt[5] - gt[4] * gt[2]) / s
    y = (-gt[3] * pos[0] + gt[0] * pos[1] + gt[3] * gt[2] - gt[0] * gt[5]) / s
    return (x, y)

def valueAtMapPos(image, gt, pos):
    pp = mapToPixel(gt, pos)
    x = int(pp[0])
    y = int(pp[1])

    if x < 0 or y < 0 or x >= image.shape[1] or y >= image.shape[0]:
        raise Exception()

    # Note how we reference the y column first. This is the way numpy arrays
    # work by default. But GDAL assumes x first.
    return image[y, x]

try:
    image, geotransform = maFromGDAL('myimage.tif')
    val = valueAtMapPos(image, geotransform, (434323.0, 2984745.0))
    print val
except:
    print('Something went wrong.')

在这里查看我的答案...并在此处阅读一些信息。以下信息来自Geotips：

使用gdallocationinfo，我们可以查询某个点的海拔：

$ gdallocationinfo gmted/all075.vrt -geoloc 87360 19679

上面命令的输出格式为：

Report:
   Location: (87360P,19679L)
Band 1:
   Value: 1418

这意味着在提供的地理位置上的高程值为1418。

参见例如以下基于GDAL的代码（和Python，不需要numpy）：https : //github.com/geometalab/retrieve-height-service

提供的python代码基于给定的x，y坐标提取栅格像元的值数据。它是从此出色来源中摘录的一个示例的略微改动版本。它基于，GDAL而numpy不是标准python发行版的一部分。感谢@Mike Toews指出了适用于Python扩展软件包的非官方Windows二进制文件，以使安装和使用变得快速简便。

import os, sys, time, gdal
from gdalconst import *


# coordinates to get pixel values for
xValues = [122588.008]
yValues = [484475.146]

# set directory
os.chdir(r'D:\\temp\\AHN2_060')

# register all of the drivers
gdal.AllRegister()
# open the image
ds = gdal.Open('i25gn1_131.img', GA_ReadOnly)

if ds is None:
    print 'Could not open image'
    sys.exit(1)

# get image size
rows = ds.RasterYSize
cols = ds.RasterXSize
bands = ds.RasterCount

# get georeference info
transform = ds.GetGeoTransform()
xOrigin = transform[0]
yOrigin = transform[3]
pixelWidth = transform[1]
pixelHeight = transform[5]

# loop through the coordinates
for xValue,yValue in zip(xValues,yValues):
    # get x,y
    x = xValue
    y = yValue

    # compute pixel offset
    xOffset = int((x - xOrigin) / pixelWidth)
    yOffset = int((y - yOrigin) / pixelHeight)
    # create a string to print out
    s = "%s %s %s %s " % (x, y, xOffset, yOffset)

    # loop through the bands
    for i in xrange(1,bands):
        band = ds.GetRasterBand(i) # 1-based index
        # read data and add the value to the string
        data = band.ReadAsArray(xOffset, yOffset, 1, 1)
        value = data[0,0]
        s = "%s%s " % (s, value) 
    # print out the data string
    print s
    # figure out how long the script took to run