一景遥感影像通常比较大，那么如何通过代码对遥感影像进行裁剪或切割呢？
在进行了相应的处理之后，又需要将切割影像进行拼接合并操作，怎么办呢？
自从接触遥感影像，发现gdal库的好用！
妈妈再也不用担心我不会处理遥感影像啦！
so easy!!!
今天就来隆重介绍下gdal这个主角怎么实现遥感影像切割与合并的。
二话不说，直接上代码！
from osgeo import osr, gdal
import numpy as np
import os
from PIL import Image
import time
from skimage import io
import gdal
def get_file_names(data_dir, file_type = ['tif','tiff']):
result_dir = []
result_name = []
for maindir, subdir, file_name_list in os.walk(data_dir):
for filename in file_name_list:
apath = maindir+'/'+filename
ext = apath.split('.')[-1]
if ext in file_type:
result_dir.append(apath)
result_name.append(filename)
else:
pass
return result_dir, result_name
def get_same_img(img_dir, img_name):
result = {}
for idx, name in enumerate(img_name):
temp_name = ''
for idx2, item in enumerate(name.split('_')[:-4]):
if idx2==0:
temp_name = temp_name+item
else:
temp_name = temp_name+'_'+item
if temp_name in result:
result[temp_name].append(img_dir[idx])
else:
result[temp_name] = []
result[temp_name].append(img_dir[idx])
return result
def assign_spatial_reference_byfile(src_path, dst_path):
src_ds = gdal.Open(src_path, gdal.GA_ReadOnly)
sr = osr.SpatialReference()
sr.ImportFromWkt(src_ds.GetProjectionRef())
geoTransform = src_ds.GetGeoTransform()
dst_ds = gdal.Open(dst_path, gdal.GA_Update)
dst_ds.SetProjection(sr.ExportToWkt())
dst_ds.SetGeoTransform(geoTransform)
dst_ds = None
src_ds = None
def cut(in_dir, out_dir, file_type = ['tif','tiff'], out_type = 'png',out_size = 1024):
if not os.path.exists(out_dir):
os.makedirs(out_dir)
data_dir_list,_ = get_file_names(in_dir, file_type)
count = 0
print('Cut begining for ', str(len(data_dir_list)), ' images.....' )
for each_dir in data_dir_list:
time_start = time.time()
#image = np.array(io.imread(each_dir))
image = np.array(Image.open(each_dir))
print(image.shape)
cut_factor_row = int(np.ceil(image.shape[0]/out_size))
cut_factor_clo = int(np.ceil(image.shape[1]/out_size))
for i in range(cut_factor_row):
for j in range(cut_factor_clo):
if i == cut_factor_row-1:
i = image.shape[0]/out_size-1
else:
pass
if j== cut_factor_clo-1:
j = image.shape[1]/out_size-1
else:
pass
start_x = int(np.rint(i*out_size))
start_y = int(np.rint(j*out_size))
end_x = int(np.rint((i+1)*out_size))
end_y = int(np.rint((j+1)*out_size))
temp_image = image[start_x:end_x,start_y:end_y,:]
print('temp_image:',temp_image.shape)
out_dir_images = out_dir+'/'+each_dir.split('/')[-1].split('.')[0] \
+'_'+str(start_x)+'_'+str(end_x)+'_'+str(start_y)+'_'+str(end_y)+'.'+out_type
out_image = Image.fromarray(temp_image)
out_image.save(out_dir_images)
src_path = 'F:/带地理坐标.tif' #带地理影像
assign_spatial_reference_byfile(src_path, out_dir_images)
count += 1
print('End of '+str(count)+'/'+str(len(data_dir_list))+'...')
time_end = time.time()
print('Time cost: ', time_end-time_start)
print('Cut Finsh!')
return 0
def combine(data_dir, w, h, c, out_dir, out_type='tif', file_type=['tif', 'tiff']):
if not os.path.exists(out_dir):
os.makedirs(out_dir)
img_dir, img_name = get_file_names(data_dir, file_type)
print('Combine begining for ', str(len(img_dir)), ' images.....' )
dir_dict = get_same_img(img_dir, img_name)
count = 0
for key in dir_dict.keys():
temp_label = np.zeros(shape=(w,h,c),dtype=np.uint8)
dir_list = dir_dict[key]
for item in dir_list:
name_split = item.split('_')
x_start = int(name_split[-4])
x_end = int(name_split[-3])
y_start = int(name_split[-2])
y_end = int(name_split[-1].split('.')[0])
img = Image.open(item)
img = np.array(img)
temp_label[x_start:x_end,y_start:y_end,:]=img
img_name=key+'.'+out_type
new_out_dir=out_dir+'/'+img_name
label=Image.fromarray(temp_label)
label.save(new_out_dir)
src_path = 'F:/带地理坐标.tif' #带地理坐标影像
assign_spatial_reference_byfile(src_path, new_out_dir)
count+=1
print('End of '+str(count)+'/'+str(len(dir_dict))+'...')
print('Combine Finsh!')
return 0
if __name__=='__main__':
##### cut
data_dir='F:/Level1'
out_dir='F:/Level1/cut_960'
file_type=['tif']
out_type='tif'
cut_size=960
cut(data_dir,out_dir,file_type,out_type,cut_size)
##### combine
# data_dir='F:/Level1/cut_960'
# h=3072
# w=1792
# c=3
# out_dir='F:/Level1'
# out_type='tif'
# file_type=['tif']
#
# combine(data_dir, w, h, c, out_dir, out_type, file_type)其中：cut函数为裁剪功能；combine为拼接功能
cut函数使用说明
data_dir:存放待裁剪的影像文件夹，不用指定到tif文件
out_dir:存放裁剪结果的影像文件夹
file_type:待裁剪的影像文件类型，不一定是tif，我的tif文件比较多，所以我都取成tif了
out_type:裁剪结果影像文件类型，也不一定是tif，看自己需要
cut_size:裁剪尺寸，裁剪为n*n的方形
combine函数使用说明
data_dir:存放待裁剪的影像文件夹，不用指定到tif文件
w，h，c：拼接影像的宽度，高度和波段数
out_dir:存放裁剪结果的影像文件夹
out_type:裁剪结果影像文件类型，也不一定是tif，看自己需要
file_type:待裁剪的影像文件类型，不一定是tif，我的tif文件比较多，所以我都取成tif了
注意：assign_spatial_reference_byfile函数是为影像数据添加地理坐标的，如果你的要拼接影像本身是带有地理坐标的，那你就可以把这个函数和这个函数的地方注释掉。并且，在代码中assign_spatial_reference_byfile函数中src_path的tif文件是带有地理坐标的影像，否则会报错！
如有问题，可以留言，我帮你debug啊！