Python crops satellite images and modifies their xml files
project scene
The ZY3 or GF7 stereo pair image is too large, and the test is very slow, so a smaller test data needs to be produced. Since the coordinates of the four corners in the xml need to be used for coordinate conversion and visualization, it is necessary to modify the relevant attribute values in the xml synchronously. In the past, it was done separately. First, the image was cut into a fixed size, and then the corner coordinates in the xml were copied, calculated, and then modified. The efficiency was very low. Since this operation may be required in the future, simply write a Python script to automatically handle it.
Precautions
- Since the program requires satellite rpc support, only the upper left corner of the image is cropped
- Calculate the latitude and longitude coordinates of the cropped corner points in vector mode, there may be some errors
- Mainly modify the corner coordinates, and modify the width, height, range, etc.
- The cropped small-scale image and the modified xml are not renamed and stored in the newly created directory
- The name of the new directory can be drawn up by yourself, or it can be automatically generated by the code
- The xml structure of different images may be different. This code uses the xml of ZY3 as an example to implement
- If there are only images or xml in the given path, the cropping operation will also be performed
- Part of the code of the cropped image is not original. I forgot which kind person I referred to a long time ago. Welcome to claim it
- Requires gdal and lxml library support
- For debugging related codes of matplotlib and icecream libraries, can be deleted
solution
the code
# File :crop_tif_and_xml.py
# Auther :WooChi
# Time :2023/03/02
# Version :1.0
# Function :
import numpy as np
from icecream import ic
from matplotlib import pyplot as plt
from lxml import etree
import gdal
import os
def cut_img(srcpath, width_size, height_size, ColN=int(1), RowN=int(1), outpath="cut.tif"):
"""
用来裁剪影像
:param srcpath:
:param width_size:
:param height_size:
:param ColN: 列向需要多少块
:param RowN: 行向需要多少块 在这个场景下只需要一张
:return:
"""
in_ds = gdal.Open(srcpath) # 读取要切的原图
print("open tif file succeed")
width = in_ds.RasterXSize # 获取数据宽度
height = in_ds.RasterYSize # 获取数据高度
outbandsize = in_ds.RasterCount # 获取数据波段数
datatype = in_ds.GetRasterBand(1).DataType
nBands = in_ds.RasterCount # 获取波段数
# 读取原图中的每个波段
in_band = []
for n in range(nBands):
in_band.append(in_ds.GetRasterBand(n + 1))
# 定义切图的起始点坐标
# 定义切图的大小（矩形框）
col_num = int(width / width_size) # 宽度可以分成几块
row_num = int(height / height_size) # 高度可以分成几块
if (width % width_size != 0):
col_num += 1
if (height % height_size != 0):
row_num += 1
# 这边就知道我们一共是分成了多少个 如果说有多余的 那我们就让那些也自己一小块好吧
if (ColN * RowN != 0 and ColN <= col_num and RowN <= row_num):
col_num = ColN
row_num = RowN
num = 1 # 这个就用来记录一共有多少块的
print("row_num:%d col_num:%d" % (row_num, col_num))
for i in range(col_num):
for j in range(row_num):
offset_x = i * width_size
offset_y = j * height_size
## 从每个波段中切需要的矩形框内的数据(注意读取的矩形框不能超过原图大小)
b_ysize = min(width - offset_y, height_size)
b_xsize = min(height - offset_x, width_size)
print("width:%d height:%d offset_x:%d offset_y:%d b_xsize:%d b_ysize:%d" % (
width, height, offset_x, offset_y, b_xsize, b_ysize))
outband = []
for n in range(nBands):
outband.append(in_band[n].ReadAsArray(offset_y, offset_x, b_ysize, b_xsize))
# 获取Tif的驱动，为创建切出来的图文件做准备
gtif_driver = gdal.GetDriverByName("GTiff")
num += 1
# 创建切出来的要存的文件
out_ds = gtif_driver.Create(outpath, b_ysize, b_xsize, outbandsize, datatype)
print("create new tif file succeed")
# 获取原图的原点坐标信息
ori_transform = in_ds.GetGeoTransform()
if ori_transform:
print(ori_transform)
print("Origin = ({}, {})".format(ori_transform[0], ori_transform[3]))
print("Pixel Size = ({}, {})".format(ori_transform[1], ori_transform[5]))
# 读取原图仿射变换参数值
top_left_x = ori_transform[0] # 左上角x坐标
w_e_pixel_resolution = ori_transform[1] # 东西方向像素分辨率
top_left_y = ori_transform[3] # 左上角y坐标
n_s_pixel_resolution = ori_transform[5] # 南北方向像素分辨率
# 根据反射变换参数计算新图的原点坐标
top_left_x = top_left_x + offset_y * w_e_pixel_resolution
top_left_y = top_left_y + offset_x * n_s_pixel_resolution
# 将计算后的值组装为一个元组，以方便设置
dst_transform = (
top_left_x, ori_transform[1], ori_transform[2], top_left_y, ori_transform[4], ori_transform[5])
print("dst_transform = ", dst_transform)
# 设置裁剪出来图的原点坐标
out_ds.SetGeoTransform(dst_transform)
# 设置SRS属性（投影信息）
out_ds.SetProjection(in_ds.GetProjection())
# 写入目标文件
for n in range(nBands):
print(outband[n])
out_ds.GetRasterBand(n + 1).WriteArray(outband[n])
# 将缓存写入磁盘
out_ds.FlushCache()
print("FlushCache succeed")
del out_ds, outband
def zoom_bounds(bounds, width, height, width_new, height_new):
"""
用来计算裁剪后的新的经纬度坐标
"""
bounds_new = np.zeros_like(bounds)
bounds_new[0, :] = bounds[0, :]
newRightTop = (bounds[1, :] - bounds[0, :]) * width_new / width + bounds[0, :]
newLeftBottom = (bounds[3, :] - bounds[0, :]) * height_new / height + bounds[0, :]
newRightBottom = (bounds[3, :] - bounds[0, :]) * height_new / height + newRightTop
bounds_new[1, :] = newRightTop
bounds_new[2, :] = newRightBottom
bounds_new[3, :] = newLeftBottom
plt.figure()
plt.plot(bounds[:, 0], bounds[:, 1], c="r")
plt.plot([bounds[3, 0], bounds[0, 0]], [bounds[3, 1], bounds[0, 1]], c="r")
plt.plot(bounds_new[:, 0], bounds_new[:, 1], c="b")
plt.plot([bounds_new[3, 0], bounds_new[0, 0]], [bounds_new[3, 1], bounds_new[0, 1]], c="b")
plt.pause(2)
return bounds_new
def ZY_xml_process(xmlfile, width_new, height_new, outxmlpath):
"""
处理xml，由于不同影像xml结构不同。在此以ZY3为例实现资源系列影像xml处理功能
"""
et = etree.parse(xmlfile)
WidthInPixels = et.find('//WidthInPixels')
HeightInPixels = et.find('//HeightInPixels')
width = float(WidthInPixels.text)
height = float(HeightInPixels.text)
if (width <= width_new or height <= height_new):
print("Out of Bounds (width:%d height:%d)" % (width, height))
return
LeftTopLon = et.find('//LeftTopPoint//Longtitude')
LeftTopLat = et.find('//LeftTopPoint//Latitude')
RightTopLon = et.find('//RightTopPoint//Longtitude')
RightTopLat = et.find('//RightTopPoint//Latitude')
RightBottomLon = et.find('//RightBottomPoint//Longtitude')
RightBottomLat = et.find('//RightBottomPoint//Latitude')
LeftBottomLon = et.find('//LeftBottomPoint//Longtitude')
LeftBottomLat = et.find('//LeftBottomPoint//Latitude')
boundary = np.array([[float(LeftTopLon.text), float(LeftTopLat.text)],
[float(RightTopLon.text), float(RightTopLat.text)],
[float(RightBottomLon.text), float(RightBottomLat.text)],
[float(LeftBottomLon.text), float(LeftBottomLat.text)]])
ic(boundary)
boundary_new = zoom_bounds(boundary, width, height, width_new, height_new)
ic(boundary_new)
# 接下来修改xml相关属性值
# 修改四个角点的坐标值（这部分是必须的，因为后续读取的就是这部分值）
LeftTopLon.text = str(boundary_new[0, 0])
LeftTopLat.text = str(boundary_new[0, 1])
RightTopLon.text = str(boundary_new[1, 0])
RightTopLat.text = str(boundary_new[1, 1])
RightBottomLon.text = str(boundary_new[2, 0])
RightBottomLat.text = str(boundary_new[2, 1])
LeftBottomLon.text = str(boundary_new[3, 0])
LeftBottomLat.text = str(boundary_new[3, 1])
et.find('//RightTopPoint//Sample').text = str(width_new)
et.find('//RightBottomPoint//Sample').text = str(width_new)
et.find('//RightBottomPoint//Line').text = str(height_new)
et.find('//LeftBottomPoint//Line').text = str(height_new)
# 修改中心点以及四个角点对应的像素坐标
center_col = int(width_new / 2)
center_row = int(height_new / 2)
center_lon = (boundary_new[1, 0] + boundary_new[2, 0] - boundary_new[0, 0] - boundary_new[
3, 0]) / 2 * center_col / width_new + np.min(boundary_new[:, 0])
center_lat = (boundary_new[0, 1] + boundary_new[1, 1] - boundary_new[2, 1] - boundary_new[
3, 1]) / 2 * center_row / height_new + np.min(boundary_new[:, 1])
CenterPointSample = et.find('//CenterPoint//Sample')
CenterPointLine = et.find('//CenterPoint//Line')
CenterPointLongtitude = et.find('//CenterPoint//Longtitude')
CenterPointLatitude = et.find('//CenterPoint//Latitude')
CenterPointSample.text = str(center_col)
CenterPointLine.text = str(center_row)
CenterPointLongtitude.text = str(center_lon)
CenterPointLatitude.text = str(center_lat)
# 修改宽高
WidthInPixels.text = str(width_new)
HeightInPixels.text = str(height_new)
WidthInMeters = et.find('//WidthInMeters')
HeightInMeters = et.find('//HeightInMeters')
WidthInMeters.text = str(float(WidthInMeters.text) * width_new / width)
HeightInMeters.text = str(float(HeightInMeters.text) * height_new / height)
et.write(outxmlpath, encoding="utf-8")
def crop_tif_and_xml(imgPath, width_new, height_new, new_folder=""):
"""
主函数，在原始影像所在目录下建立一个新目录，判断是否存在影像以及xml，把裁剪后的影像和xml存到新建的目录下，不改变其命名方式
"""
(folder, imgFile) = os.path.split(imgPath)
(name, ext) = os.path.splitext(imgFile)
xmlFile = name + ".xml"
xmlPath = os.path.join(folder, xmlFile)
if (len(new_folder) == 0):
new_folder = name[:3] + "_w" + str(width_new) + "_h" + str(height_new)
outFolder = os.path.join(folder, new_folder)
imgOutPath = os.path.join(outFolder, imgFile)
xmlOutPath = os.path.join(outFolder, xmlFile)
ic(outFolder)
ic(imgOutPath)
ic(xmlOutPath)
if not os.path.exists(outFolder):
os.makedirs(outFolder) # makedirs 创建文件时如果路径不存在会创建这个路径
if (os.path.exists(imgPath)):
print(ext[1:], " processing...")
cut_img(imgPath, int(width_new), int(height_new), outpath=imgOutPath)
else:
print("no img file")
if (os.path.exists(xmlPath) and (name[:2] == "zy" or name[:2] == "ZY")):
print("xml processing...")
ZY_xml_process(xmlPath, width_new, height_new, xmlOutPath)
else:
print("no xml file")
print("done")
if __name__ == '__main__':
imgPath = r"T:\ProjectData\ZY302_2YW\ZY302_2YW\zy302a_bwd_016497_003126_20190520112310_01_sec_0001_1905228482.tif"
width_new = 3000
height_new = 3000
crop_tif_and_xml(imgPath, width_new, height_new)
output
D:\ProgramData\Anaconda3\envs\Env36\python.exe D:/Project/PythonProject/Crop_tif_and_xml/Crop_tif_and_xml.py
outFolder: 'T:\\ProjectData\\ZY302_2YW\\ZY302_2YW\\zy3_w3000_h3000'
imgOutPath: 'T:\\ProjectData\\ZY302_2YW\\ZY302_2YW\\zy3_w3000_h3000\\zy302a_bwd_016497_003126_20190520112310_01_sec_0001_1905228482.tif'
xmlOutPath: 'T:\\ProjectData\\ZY302_2YW\\ZY302_2YW\\zy3_w3000_h3000\\zy302a_bwd_016497_003126_20190520112310_01_sec_0001_1905228482.xml'
tif processing...
open tif file succeed
row_num:1 col_num:1
width:24513 height:19995 offset_x:0 offset_y:0 b_xsize:3000 b_ysize:3000
create new tif file succeed
(0.0, 1.0, 0.0, 0.0, 0.0, 1.0)
Origin = (0.0, 0.0)
Pixel Size = (1.0, 1.0)
dst_transform = (0.0, 1.0, 0.0, 0.0, 0.0, 1.0)
[[277 284 298 ... 258 257 268]
[284 294 296 ... 263 265 271]
[292 306 295 ... 271 267 267]
...
[263 257 256 ... 167 151 137]
[269 268 265 ... 165 170 164]
[266 274 263 ... 188 193 191]]
FlushCache succeed
xml processing...
boundary: array([[115.414386, 39.663782],
[116.04798 , 39.553868],
[115.921683, 39.116167],
[115.29204 , 39.225697]])
done
boundary_new: array([[115.414386 , 39.663782 ],
[115.49192779, 39.65033028],
[115.47357131, 39.5846011 ],
[115.39602951, 39.59805282]])
进程已结束,退出代码0
examine
Display (cropping range indication)
Create a new folder zy3_w3000_h3000 according to the width and height of the output, and put the processed files under it
Check the image, there is nothing wrong
Check the xml, the modification is successful
Upload the code and call it a day.