虚拟翘曲

栅格有一个 WarpedVRT class that abstracts many of the details of raster warping by using an in-memory Warped VRT . 一 WarpedVRT 对于大数据集的平铺来说，这是最简单的解决方案。

例如，实际上扭曲 RGB.byte.tif 测试数据集从其适当的epsg:32618坐标参考系统到epsg:3857（web mercator）并提取与其中心缩放9图块对应的像素，执行以下操作。

from affine import Affine
import mercantile

import rasterio
from rasterio.enums import Resampling
from rasterio.vrt import WarpedVRT

with rasterio.open('tests/data/RGB.byte.tif') as src:
    with WarpedVRT(src, crs='EPSG:3857',
                   resampling=Resampling.bilinear) as vrt:

        # Determine the destination tile and its mercator bounds using
        # functions from the mercantile module.
        dst_tile = mercantile.tile(*vrt.lnglat(), 9)
        left, bottom, right, top = mercantile.xy_bounds(*dst_tile)

        # Determine the window to use in reading from the dataset.
        dst_window = vrt.window(left, bottom, right, top)

        # Read into a 3 x 512 x 512 array. Our output tile will be
        # 512 wide x 512 tall.
        data = vrt.read(window=dst_window, out_shape=(3, 512, 512))

        # Use the source's profile as a template for our output file.
        profile = vrt.profile
        profile['width'] = 512
        profile['height'] = 512
        profile['driver'] = 'GTiff'

        # We need determine the appropriate affine transformation matrix
        # for the dataset read window and then scale it by the dimensions
        # of the output array.
        dst_transform = vrt.window_transform(dst_window)
        scaling = Affine.scale(dst_window.num_cols / 512,
                               dst_window.num_rows / 512)
        dst_transform *= scaling
        profile['transform'] = dst_transform

        # Write the image tile to disk.
        with rasterio.open('/tmp/test-tile.tif', 'w', **profile) as dst:
            dst.write(data)

将数据规范化为一致的网格

A WarpedVRT 可用于将具有不同投影、边界、单元格大小或尺寸的图像堆栈与定义的边界框中的常规网格进行规格化。

这个 tests/data/RGB.byte.tif file is in UTM zone 18, so another file in a different CRS is required for demonstration. This command will create a new image with drastically different dimensions and cell size, and reproject to WGS84. As of this writing $ rio warp implements only a subset of $ gdalwarp 的功能，所以 $ gdalwarp 必须用于实现所需的转换：

$ gdalwarp \
    -t_srs EPSG:4326 \
    -te_srs EPSG:32618 \
    -te 101985 2673031 339315 2801254 \
    -ts 200 250 \
    tests/data/RGB.byte.tif \
    tests/data/WGS84-RGB.byte.tif

因此，这两幅图像的属性大不相同：

$ rio info --shape tests/data/RGB.byte.tif
718 791
$ rio info --shape tests/data/WGS84-RGB.byte.tif
250 200
$ rio info --crs tests/data/RGB.byte.tif
EPSG:32618
$ rio info --crs tests/data/WGS84-RGB.byte.tif
EPSG:4326
$ rio bounds --bbox --geographic --precision 7 tests/data/RGB.byte.tif
[-78.95865, 23.5649912, -76.5749237, 25.5508738]
$ rio bounds --bbox --geographic --precision 7 tests/data/WGS84-RGB.byte.tif
[-78.9147773, 24.119606, -76.5963819, 25.3192311]

这段代码演示了如何在预定义的边界框内将数据规范化为一致的维度、CRS和单元格大小：

from __future__ import division

import os

import affine

import rasterio
from rasterio.crs import CRS
from rasterio.enums import Resampling
from rasterio import shutil as rio_shutil
from rasterio.vrt import WarpedVRT


input_files = (
    # This file is in EPSG:32618
    'tests/data/RGB.byte.tif',
    # This file is in EPSG:4326
    'tests/data/WGS84-RGB.byte.tif'
)

# Destination CRS is Web Mercator
dst_crs = CRS.from_epsg(3857)

# These coordiantes are in Web Mercator
dst_bounds = -8744355, 2768114, -8559167, 2908677

# Output image dimensions
dst_height = dst_width = 100

# Output image transform
left, bottom, right, top = dst_bounds
xres = (right - left) / dst_width
yres = (top - bottom) / dst_height
dst_transform = affine.Affine(xres, 0.0, left,
                              0.0, -yres, top)

vrt_options = {
    'resampling': Resampling.cubic,
    'crs': dst_crs,
    'transform': dst_transform,
    'height': dst_height,
    'width': dst_width,
}

for path in input_files:

    with rasterio.open(path) as src:

        with WarpedVRT(src, **vrt_options) as vrt:

            # At this point 'vrt' is a full dataset with dimensions,
            # CRS, and spatial extent matching 'vrt_options'.

            # Read all data into memory.
            data = vrt.read()

            # Process the dataset in chunks.  Likely not very efficient.
            for _, window in vrt.block_windows():
                data = vrt.read(window=window)

            # Dump the aligned data into a new file.  A VRT representing
            # this transformation can also be produced by switching
            # to the VRT driver.
            directory, name = os.path.split(path)
            outfile = os.path.join(directory, 'aligned-{}'.format(name))
            rio_shutil.copy(vrt, outfile, driver='GTiff')