나는 gdal 및 numpy 기반 솔루션을 고안했습니다. 래스터 행렬을 행과 열로 나누고 빈 행 / 열을 삭제합니다. 이 구현에서 "빈"은 1보다 작은 값이며 단일 대역 래스터 만 설명합니다.
(이 스캔 라인 접근 방식은 데이터가 "칼라"가없는 이미지에만 적합하다는 것을 알고 있습니다. 데이터가 영해의 섬인 경우 섬 사이의 공간도 줄어들어 모든 것을 하나로 묶고 지리 참조를 엉망으로 만듭니다. .)
사업 부분 (육체 필요, 그대로 작동하지 않음) :
#read raster into a numpy array
data = np.array(gdal.Open(src_raster).ReadAsArray())
#scan for data
non_empty_columns = np.where(data.max(axis=0)>0)[0]
non_empty_rows = np.where(data.max(axis=1)>0)[0]
# assumes data is any value greater than zero
crop_box = (min(non_empty_rows), max(non_empty_rows),
min(non_empty_columns), max(non_empty_columns))
# retrieve source geo reference info
georef = raster.GetGeoTransform()
xmin, ymax = georef[0], georef[3]
xcell, ycell = georef[1], georef[5]
# Calculate cropped geo referencing
new_xmin = xmin + (xcell * crop_box[0]) + xcell
new_ymax = ymax + (ycell * crop_box[2]) - ycell
cropped_transform = new_xmin, xcell, 0.0, new_ymax, 0.0, ycell
# crop
new_data = data[crop_box[0]:crop_box[1]+1, crop_box[2]:crop_box[3]+1]
# write to disk
band = out_raster.GetRasterBand(1)
band.WriteArray(new_data)
band.FlushCache()
out_raster = None
전체 스크립트에서 :
import os
import sys
import numpy as np
from osgeo import gdal
if len(sys.argv) < 2:
print '\n{} [infile] [outfile]'.format(os.path.basename(sys.argv[0]))
sys.exit(1)
src_raster = sys.argv[1]
out_raster = sys.argv[2]
def main(src_raster):
raster = gdal.Open(src_raster)
# Read georeferencing, oriented from top-left
# ref:GDAL Tutorial, Getting Dataset Information
georef = raster.GetGeoTransform()
print '\nSource raster (geo units):'
xmin, ymax = georef[0], georef[3]
xcell, ycell = georef[1], georef[5]
cols, rows = raster.RasterYSize, raster.RasterXSize
print ' Origin (top left): {:10}, {:10}'.format(xmin, ymax)
print ' Pixel size (x,-y): {:10}, {:10}'.format(xcell, ycell)
print ' Columns, rows : {:10}, {:10}'.format(cols, rows)
# Transfer to numpy and scan for data
# oriented from bottom-left
data = np.array(raster.ReadAsArray())
non_empty_columns = np.where(data.max(axis=0)>0)[0]
non_empty_rows = np.where(data.max(axis=1)>0)[0]
crop_box = (min(non_empty_rows), max(non_empty_rows),
min(non_empty_columns), max(non_empty_columns))
# Calculate cropped geo referencing
new_xmin = xmin + (xcell * crop_box[0]) + xcell
new_ymax = ymax + (ycell * crop_box[2]) - ycell
cropped_transform = new_xmin, xcell, 0.0, new_ymax, 0.0, ycell
# crop
new_data = data[crop_box[0]:crop_box[1]+1, crop_box[2]:crop_box[3]+1]
new_rows, new_cols = new_data.shape # note: inverted relative to geo units
#print cropped_transform
print '\nCrop box (pixel units):', crop_box
print ' Stripped columns : {:10}'.format(cols - new_cols)
print ' Stripped rows : {:10}'.format(rows - new_rows)
print '\nCropped raster (geo units):'
print ' Origin (top left): {:10}, {:10}'.format(new_xmin, new_ymax)
print ' Columns, rows : {:10}, {:10}'.format(new_cols, new_rows)
raster = None
return new_data, cropped_transform
def write_raster(template, array, transform, filename):
'''Create a new raster from an array.
template = raster dataset to copy projection info from
array = numpy array of a raster
transform = geo referencing (x,y origin and pixel dimensions)
filename = path to output image (will be overwritten)
'''
template = gdal.Open(template)
driver = template.GetDriver()
rows,cols = array.shape
out_raster = driver.Create(filename, cols, rows, gdal.GDT_Byte)
out_raster.SetGeoTransform(transform)
out_raster.SetProjection(template.GetProjection())
band = out_raster.GetRasterBand(1)
band.WriteArray(array)
band.FlushCache()
out_raster = None
template = None
if __name__ == '__main__':
cropped_raster, cropped_transform = main(src_raster)
write_raster(src_raster, cropped_raster, cropped_transform, out_raster)
링크가 조금씩 404로 이동하면 스크립트 가 Github의 코드 숨김 상태 입니다. 이 폴더는 일부 재구성에 적합합니다.
