python GDAL 影像处理系列之裁剪与合并影像(三)
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!
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