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image_crop.py
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image_crop.py
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import os
from osgeo import gdal
import sys
#import gdal
import numpy as np
# 读取tif数据集
def readTif(fileName):
dataset = gdal.Open(fileName)
if dataset == None:
print(fileName + "文件无法打开")
return dataset
# 保存tif文件函数
def writeTiff(im_data, im_geotrans, im_proj, path):
if 'int8' in im_data.dtype.name:
datatype = gdal.GDT_Byte
elif 'int16' in im_data.dtype.name:
datatype = gdal.GDT_UInt16
else:
datatype = gdal.GDT_Float32
if len(im_data.shape) == 3:
im_bands, im_height, im_width = im_data.shape
else:
im_bands, (im_height, im_width) = 1, im_data.shape
# im_data = np.array([im_data])
# im_bands, im_height, im_width = im_data.shape
# 创建文件
driver = gdal.GetDriverByName("GTiff")
dataset = driver.Create(path, int(im_width), int(im_height), int(im_bands), datatype)
if (dataset != None):
dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数
dataset.SetProjection(im_proj) # 写入投影
if im_bands == 1:
dataset.GetRasterBand(1).WriteArray(im_data)
else:
for i in range(im_bands):
dataset.GetRasterBand(i + 1).WriteArray(im_data[i])
del dataset
'''
滑动窗口裁剪函数
TifPath 影像路径
SavePath 裁剪后保存目录
CropSize 裁剪尺寸
RepetitionRate 重复率
'''
def TifCrop(TifPath, SavePath, CropSize, RepetitionRate):
dataset_img = readTif(TifPath)
width = dataset_img.RasterXSize
height = dataset_img.RasterYSize
proj = dataset_img.GetProjection()
geotrans = dataset_img.GetGeoTransform()
img = dataset_img.ReadAsArray(0, 0, width, height) # 获取数据
# 获取当前文件夹的文件个数len,并以len+1命名即将裁剪得到的图像
new_name = 0
# 裁剪图片,重复率为RepetitionRate
for i in range(int((height - CropSize * RepetitionRate) / (CropSize * (1 - RepetitionRate)))):
for j in range(int((width - CropSize * RepetitionRate) / (CropSize * (1 - RepetitionRate)))):
# 如果图像是单波段
if (len(img.shape) == 2):
cropped = img[
int(i * CropSize * (1 - RepetitionRate)): int(i * CropSize * (1 - RepetitionRate)) + CropSize,
int(j * CropSize * (1 - RepetitionRate)): int(j * CropSize * (1 - RepetitionRate)) + CropSize]
# 如果图像是多波段
else:
cropped = img[:,
int(i * CropSize * (1 - RepetitionRate)): int(i * CropSize * (1 - RepetitionRate)) + CropSize,
int(j * CropSize * (1 - RepetitionRate)): int(j * CropSize * (1 - RepetitionRate)) + CropSize]
# 写图像
writeTiff(cropped, geotrans, proj, SavePath + "/%d.tif" % new_name)
# 文件名 + 1
new_name = new_name + 1
# 向前裁剪最后一列
for i in range(int((height - CropSize * RepetitionRate) / (CropSize * (1 - RepetitionRate)))):
if (len(img.shape) == 2):
cropped = img[int(i * CropSize * (1 - RepetitionRate)): int(i * CropSize * (1 - RepetitionRate)) + CropSize,
(width - CropSize): width]
else:
cropped = img[:,
int(i * CropSize * (1 - RepetitionRate)): int(i * CropSize * (1 - RepetitionRate)) + CropSize,
(width - CropSize): width]
# 写图像
writeTiff(cropped, geotrans, proj, SavePath + "/%d.tif" % new_name)
new_name = new_name + 1
# 向前裁剪最后一行
for j in range(int((width - CropSize * RepetitionRate) / (CropSize * (1 - RepetitionRate)))):
if (len(img.shape) == 2):
cropped = img[(height - CropSize): height,
int(j * CropSize * (1 - RepetitionRate)): int(j * CropSize * (1 - RepetitionRate)) + CropSize]
else:
cropped = img[:,
(height - CropSize): height,
int(j * CropSize * (1 - RepetitionRate)): int(j * CropSize * (1 - RepetitionRate)) + CropSize]
writeTiff(cropped, geotrans, proj, SavePath + "/%d.tif" % new_name)
# 文件名 + 1
new_name = new_name + 1
# 裁剪右下角
if (len(img.shape) == 2):
cropped = img[(height - CropSize): height,
(width - CropSize): width]
else:
cropped = img[:,
(height - CropSize): height,
(width - CropSize): width]
writeTiff(cropped, geotrans, proj, SavePath + "/%d.tif" % new_name)
new_name = new_name + 1
# # 将影像1裁剪为重复率为0的256×256的数据集
# TifCrop(img_path, img_save, range , rate)
# TifCrop(label_path, label_save, range, rate)
TifCrop(r"G:\BFINet\cla_image\HN_image.tif",
r"G:\BFINet\\cla_image\image", 256, 0)
TifCrop(r"G:\BFINet\cla_mask\HN_mask.tif",
r"G:\BFINet\\cla_mask\mask", 256, 0)