概述：

本文讲述如何在Python中用GDAL实现根据输入矢量边界对栅格数据的裁剪。

效果：

裁剪前

矢量边界

裁剪后

实现代码：

# -*- coding: utf-8 -*-"""@author lzugis@date 2017-06-02@brief 利用shp裁剪影像""" from osgeo import gdal, gdalnumeric, ogrfrom PIL import Image, ImageDrawimport osimport operator gdal.UseExceptions() # This function will convert the rasterized clipper shapefile# to a mask for use within GDAL.def imageToArray(i): """ Converts a Python Imaging Library array to a gdalnumeric image. """ a=gdalnumeric.fromstring(i.tobytes(),'b') a.shape=i.im.size[1], i.im.size[0] return a def arrayToImage(a): """ Converts a gdalnumeric array to a Python Imaging Library Image. """ i=Image.frombytes('L',(a.shape[1],a.shape[0]), (a.astype('b')).tobytes()) return i def world2Pixel(geoMatrix, x, y): """ Uses a gdal geomatrix (gdal.GetGeoTransform()) to calculate the pixel location of a geospatial coordinate """ ulX = geoMatrix[0] ulY = geoMatrix[3] xDist = geoMatrix[1] pixel = int((x - ulX) / xDist) line = int((ulY - y) / xDist) return (pixel, line) ## EDIT: this is basically an overloaded# version of the gdal_array.OpenArray passing in xoff, yoff explicitly# so we can pass these params off to CopyDatasetInfo#def OpenArray( array, prototype_ds = None, xoff=0, yoff=0 ): ds = gdal.Open( gdalnumeric.GetArrayFilename(array) ) if ds is not None and prototype_ds is not None: if type(prototype_ds).__name__ == 'str': prototype_ds = gdal.Open( prototype_ds ) if prototype_ds is not None: gdalnumeric.CopyDatasetInfo( prototype_ds, ds, xoff=xoff, yoff=yoff ) return ds def histogram(a, bins=range(0,256)): """ Histogram function for multi-dimensional array. a = array bins = range of numbers to match """ fa = a.flat n = gdalnumeric.searchsorted(gdalnumeric.sort(fa), bins) n = gdalnumeric.concatenate([n, [len(fa)]]) hist = n[1:]-n[:-1] return hist def stretch(a): """ Performs a histogram stretch on a gdalnumeric array image. """ hist = histogram(a) im = arrayToImage(a) lut = [] for b in range(0, len(hist), 256): # step size step = reduce(operator.add, hist[b:b+256]) / 255 # create equalization lookup table n = 0 for i in range(256): lut.append(n / step) n = n + hist[i+b] im = im.point(lut) return imageToArray(im) def main( shapefile_path, raster_path ): # Load the source data as a gdalnumeric array srcArray = gdalnumeric.LoadFile(raster_path) # Also load as a gdal image to get geotransform # (world file) info srcImage = gdal.Open(raster_path) geoTrans = srcImage.GetGeoTransform() # Create an OGR layer from a boundary shapefile shapef = ogr.Open(shapefile_path) lyr = shapef.GetLayer( os.path.split( os.path.splitext( shapefile_path )[0] )[1] ) poly = lyr.GetNextFeature() # Convert the layer extent to image pixel coordinates minX, maxX, minY, maxY = lyr.GetExtent() ulX, ulY = world2Pixel(geoTrans, minX, maxY) lrX, lrY = world2Pixel(geoTrans, maxX, minY) # Calculate the pixel size of the new image pxWidth = int(lrX - ulX) pxHeight = int(lrY - ulY) clip = srcArray[:, ulY:lrY, ulX:lrX] # # EDIT: create pixel offset to pass to new image Projection info # xoffset = ulX yoffset = ulY print "Xoffset, Yoffset = ( %f, %f )" % ( xoffset, yoffset ) # Create a new geomatrix for the image geoTrans = list(geoTrans) geoTrans[0] = minX geoTrans[3] = maxY # Map points to pixels for drawing the # boundary on a blank 8-bit, # black and white, mask image. points = [] pixels = [] geom = poly.GetGeometryRef() pts = geom.GetGeometryRef(0) for p in range(pts.GetPointCount()): points.append((pts.GetX(p), pts.GetY(p))) for p in points: pixels.append(world2Pixel(geoTrans, p[0], p[1])) rasterPoly = Image.new("L", (pxWidth, pxHeight), 1) rasterize = ImageDraw.Draw(rasterPoly) rasterize.polygon(pixels, 0) mask = imageToArray(rasterPoly) # Clip the image using the mask clip = gdalnumeric.choose(mask, \ (clip, 0)).astype(gdalnumeric.uint8) # This image has 3 bands so we stretch each one to make them # visually brighter for i in range(3): clip[i,:,:] = stretch(clip[i,:,:]) # Save new tiff # # EDIT: instead of SaveArray, let's break all the # SaveArray steps out more explicity so # we can overwrite the offset of the destination # raster # ### the old way using SaveArray # # gdalnumeric.SaveArray(clip, "OUTPUT.tif", format="GTiff", prototype=raster_path) # ### # gtiffDriver = gdal.GetDriverByName( 'GTiff' ) if gtiffDriver is None: raise ValueError("Can't find GeoTiff Driver") gtiffDriver.CreateCopy( "beijing.tif", OpenArray( clip, prototype_ds=raster_path, xoff=xoffset, yoff=yoffset ) ) # Save as an 8-bit jpeg for an easy, quick preview clip = clip.astype(gdalnumeric.uint8) gdalnumeric.SaveArray(clip, "beijing.jpg", format="JPEG") gdal.ErrorReset() if __name__ == '__main__': #shapefile_path, raster_path shapefile_path = 'beijing.shp' raster_path = 'world.tif' main( shapefile_path, raster_path )

补充知识：Python+GDAL | 读取矢量并写出txt

这篇文章主要描述了如何使用GDAL/OGR打开矢量文件、读取属性表，并将部分属性写出至txt。

代码

import ogr,sys,osimport numpy as npos.chdir(r'E:\')#设置driver,并打开矢量文件driver = ogr.GetDriverByName('ESRI Shapefile')ds = driver.Open('sites.shp', 0)if ds is None: print("Could not open", 'sites.shp') sys.exit(1)#获取图册layer = ds.GetLayer()#要素数量numFeatures = layer.GetFeatureCount()print("Feature count: "+str(numFeatures))#获取范围extent = layer.GetExtent()print("Extent:", extent)print("UL:", extent[0],extent[3])print("LR:", extent[1],extent[2])#获取要素feature = layer.GetNextFeature()ids = []xs = []ys = []covers = []#循环每个要素属性while feature: #获取字段“id”的属性 id = feature.GetField('id') #获取空间属性 geometry = feature.GetGeometryRef() x = geometry.GetX() y = geometry.GetY() cover = feature.GetField('cover') ids.append(id) xs.append(x) ys.append(y) covers.append(cover) feature = layer.GetNextFeature()data = [ids, xs, ys, covers]data = np.array(data)data = data.transpose()#写出致txtnp.savetxt('myfile.txt',data, fmt='%s %s %s %s')np.savetxt('myfile.csv',data, fmt='%s %s %s %s')#释放文件空间layer.ResetReading()feature.Destroy()ds.Destroy()

以上这篇在Python中用GDAL实现矢量对栅格的切割实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。