gdal python 图像金字塔,针对tif,tiff（生成图像金字塔，展示各层级图像）
创建图像金字塔
原创不易
导包
import os
from osgeo import gdal
import cv2
import shutil
import skimage.io as io
from PIL import Image
Image.MAX_IMAGE_PIXELS = None
判断文件夹是否存在,存在就删除清空，不存在保留
if os.path.exists(self.dir_path):
shutil.rmtree(self.dir_path)
os.makedirs(self.dir_path)
else:
os.makedirs(self.dir_path)
检查输入和输出路径
class Image_pyramid:
def __init__(self,inputfpath):
self.inputtfpath=inputfpath//输入文件路劲
# print(self.inputtfpath)
dir_list = self.inputtfpath.strip(' ').split('.')[0].split("/")[0:-1]
print(dir_list)
dir_list_pic=self.inputtfpath.strip(' ').split('.')[0].split("/")[-1]
dir_path2=""
for i in range(0,len(dir_list)):
if len(dir_list[i])==0:
continue
else:
dir_list2=str(os.path.join('/',str(dir_list[i])))
dir_path2=str(dir_path2)+dir_list2
self.dir_path=os.path.join(dir_path2,dir_list_pic)
print(self.dir_path)
if os.path.exists(self.dir_path):
shutil.rmtree(self.dir_path)
os.makedirs(self.dir_path)
else:
os.makedirs(self.dir_path)
print(self.dir_path)
self.outputtfpath = os.path.join(dir_path2,"10")
print(self.outputtfpath)
生成新的tif图像的名称10.tif
def gdal_pymaid2(self):
if os.path.exists(self.outputtfpath):
shutil.rmtree(self.outputtfpath)
os.makedirs(self.outputtfpath)
else:
os.makedirs(self.outputtfpath)
outputfilename = os.path.join(self.outputtfpath, "10.tif")
print(333333333333333)
print(outputfilename)
获取tif图像的宽度和高度
inputDataset = gdal.Open(self.inputtfpath)
print(inputDataset==None)
inputBand = inputDataset.GetRasterBand(1)
img_arraytif = inputDataset.ReadAsArray(0, 0,
inputDataset.RasterXSize, inputDataset.RasterYSize)
获取原始卫星图像获取仿射矩阵信息，获取投影信息，创建tiff图像驱动，获取波段数量
im_geotrans = inputDataset.GetGeoTransform() # 获取仿射矩阵信息
im_proj = inputDataset.GetProjection() # 获取投影信息
driver = gdal.GetDriverByName("GTiff")，#创建tiff图像驱动
im_bands=int(inputDataset.RasterCount)#获取波段数量
print(im_bands)
将原始卫星图像的矩阵信息宽度信息，高度信息，反射变化矩阵，投影信息，等写入到新的tif图像中
if im_bands == 1:
datasets = driver.Create(outputfilename,int((inputDataset.RasterXSize)),
int((inputDataset.RasterYSize)), 1, inputBand.DataType)
datasets.SetGeoTransform(im_geotrans) # 写入仿射变换参数
datasets.SetProjection(im_proj) # 写入投影
else:
datasets = driver.Create(outputfilename, int((inputDataset.RasterXSize)), int((inputDataset.RasterYSize)), 3, inputBand.DataType)
datasets.SetGeoTransform(im_geotrans) # 写入仿射变换参数
datasets.SetProjection(im_proj) # 写入投影
卫星图像可能有一个波段，3个波段，甚至7个波段的信息，将每个波段的信息写入到对应的tif图像中，在这之前需要区分RGB通道，可以利用均值直方图信息，这里将图像一次缩小4倍，16倍，64倍…
if im_bands >= 3:
# for i in range(3):
datasets.GetRasterBand(1).WriteArray(img_arraytif[2])
datasets.GetRasterBand(2).WriteArray(img_arraytif[1])
datasets.GetRasterBand(3).WriteArray(img_arraytif[0])
for i in range(1, 4):
datasets.GetRasterBand(i).ComputeStatistics(False)
datasets.BuildOverviews('average', [1,2, 4, 8, 16, 32, 64])
else:
datasets.GetRasterBand(1).WriteArray(img_arraytif)
datasets.GetRasterBand(1).ComputeStatistics(False)
datasets.BuildOverviews('average', [1,2, 4, 8, 16, 32,64])
del datasets
cv写入不同层数的图像金字塔所对应的图像
def show_pymaid(self):
self.gdal_pymaid2()
outputfilename = os.path.join(self.outputtfpath, "10.tif")
读取新创建的卫星图像基本信息，获取概率图深度，即金字塔层数和波段数量
dataset = gdal.Open(outputfilename)
# print(dataset)
band = dataset.GetRasterBand(1)
overviewNum = band.GetOverviewCount()
print(dataset.RasterCount)
print(overviewNum)
im_bands = int(dataset.RasterCount)
不同波段的卫星图像，图像金字塔所对应的原始图像大小
if im_bands>1:
rband = dataset.GetRasterBand(3)
roverviewBand = rband.GetOverview(6)
r = roverviewBand.ReadAsArray()
gband = dataset.GetRasterBand(2)
goverviewBand = gband.GetOverview(6)
g = goverviewBand.ReadAsArray()
bband = dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(6)
b = broverviewBand.ReadAsArray()
data = cv2.merge([r, g, b])#正解
# data = cv2.merge([b, g, r])
print(data.shape)
cv2.imwrite(f'{self.dir_path}/1.bmp', data)
rband=dataset.GetRasterBand(3)
roverviewBand = rband.GetOverview(5)
r = roverviewBand.ReadAsArray()
gband=dataset.GetRasterBand(2)
goverviewBand = gband.GetOverview(5)
g = goverviewBand.ReadAsArray()
bband=dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(5)
b = broverviewBand.ReadAsArray()
data=cv2.merge([r,g,b])
# data = cv2.merge([b, g, r])
print(data.shape)
cv2.imwrite(f'{self.dir_path}/1.bmp',data)
del data
rband=dataset.GetRasterBand(3)
roverviewBand = rband.GetOverview(4)
r = roverviewBand.ReadAsArray()
gband=dataset.GetRasterBand(2)
goverviewBand = gband.GetOverview(4)
g = goverviewBand.ReadAsArray()
bband=dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(4)
b = broverviewBand.ReadAsArray()
data=cv2.merge([r,g,b])
# data = cv2.merge([b, g, r])
print(data.shape)
cv2.imwrite(f'{self.dir_path}/2.bmp',data)
del data
rband=dataset.GetRasterBand(3)
roverviewBand = rband.GetOverview(3)
r = roverviewBand.ReadAsArray()
gband=dataset.GetRasterBand(2)
goverviewBand = gband.GetOverview(3)
g = goverviewBand.ReadAsArray()
bband=dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(3)
b = broverviewBand.ReadAsArray()
data=cv2.merge([r,g,b])
# data = cv2.merge([b, g, r])
print(data.shape)
cv2.imwrite(f'{self.dir_path}/3.bmp',data)
del data
#
rband=dataset.GetRasterBand(3)
roverviewBand = rband.GetOverview(2)
r = roverviewBand.ReadAsArray()
gband=dataset.GetRasterBand(2)
goverviewBand = gband.GetOverview(2)
g = goverviewBand.ReadAsArray()
bband=dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(2)
b = broverviewBand.ReadAsArray()
data=cv2.merge([r,g,b])
# data = cv2.merge([b, g, r])
cv2.imwrite(f'{self.dir_path}/4.bmp',data)
del data
#
#
rband=dataset.GetRasterBand(3)
roverviewBand = rband.GetOverview(1)
r = roverviewBand.ReadAsArray()
gband=dataset.GetRasterBand(2)
goverviewBand = gband.GetOverview(1)
g = goverviewBand.ReadAsArray()
bband=dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(1)
b = broverviewBand.ReadAsArray()
data=cv2.merge([r,g,b])
# data = cv2.merge([b, g, r])
print(data.shape)
cv2.imwrite(f'{self.dir_path}/5.bmp',data)
del data
rband=dataset.GetRasterBand(3)
roverviewBand = rband.GetOverview(0)
r = roverviewBand.ReadAsArray()
gband=dataset.GetRasterBand(2)
goverviewBand = gband.GetOverview(0)
g = goverviewBand.ReadAsArray()
bband=dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(0)
b = broverviewBand.ReadAsArray()
import numpy as np
data=cv2.merge([r,g,b])
# data = cv2.merge([b, g, r])
print(data.shape)
# io.imsave(f'{self.dir_path}/6.bmp',data)
cv2.imwrite(f'{self.dir_path}/6.bmp',data)
del data
else:
bband = dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(6)
b = broverviewBand.ReadAsArray()
data = cv2.merge([b, b, b])
cv2.imwrite(f'{self.dir_path}/1.bmp', data)
del data
bband = dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(5)
b = broverviewBand.ReadAsArray()
data = cv2.merge([b, b, b])
cv2.imwrite(f'{self.dir_path}/1.bmp',data)
del data
bband = dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(4)
b = broverviewBand.ReadAsArray()
data = cv2.merge([b, b, b])
print(data.shape)
cv2.imwrite(f'{self.dir_path}/2.bmp',data)
del data
bband = dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(3)
b = broverviewBand.ReadAsArray()
data = cv2.merge([b, b, b])
cv2.imwrite(f'{self.dir_path}/3.bmp',data)
del data
bband = dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(2)
b = broverviewBand.ReadAsArray()
data = cv2.merge([b, b, b])
cv2.imwrite(f'{self.dir_path}/4.bmp',data)
del data
bband = dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(1)
b = broverviewBand.ReadAsArray()
data = cv2.merge([b, b, b])
cv2.imwrite(f'{self.dir_path}/5.bmp',data)
del data
bband=dataset.GetRasterBand(1)
broverviewBand = bband.GetOverview(0)
b = broverviewBand.ReadAsArray()
data=cv2.merge([b,b,b])
print(data.shape)
cv2.imwrite(f'{self.dir_path}/6.bmp',data)
# del data