410 lines
13 KiB
Python
410 lines
13 KiB
Python
|
# -*- coding: UTF-8 -*-
|
|||
|
|
"""
|
|||
|
|
@Project ：microproduct
|
|||
|
|
@File ：Imagehandle.py
|
|||
|
|
@Function ：实现对待处理SAR数据的读取、格式标准化和处理完后保存文件功能
|
|||
|
|
@Author ：SHJ
|
|||
|
|
@Date ：2021/10/15
|
|||
|
|
@Version ：1.0.0
|
|||
|
|
"""
|
|||
|
|
import logging
|
|||
|
|
import os
|
|||
|
|
from xml.etree.ElementTree import ElementTree
|
|||
|
|
|
|||
|
|
from osgeo import gdal
|
|||
|
|
import numpy as np
|
|||
|
|
from PIL import Image
|
|||
|
|
import cv2
|
|||
|
|
|
|||
|
|
|
|||
|
|
logger = logging.getLogger("mylog")
|
|||
|
|
|
|||
|
|
|
|||
|
|
class DemImageHandler:
|
|||
|
|
"""
|
|||
|
|
影像读取、编辑、保存
|
|||
|
|
"""
|
|||
|
|
def __init__(self):
|
|||
|
|
pass
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_dataset(filename):
|
|||
|
|
"""
|
|||
|
|
:param filename: tif路径
|
|||
|
|
:return: 图像句柄
|
|||
|
|
"""
|
|||
|
|
gdal.AllRegister()
|
|||
|
|
dataset = gdal.Open(filename)
|
|||
|
|
if dataset is None:
|
|||
|
|
return None
|
|||
|
|
return dataset
|
|||
|
|
|
|||
|
|
def get_scope(self, filename):
|
|||
|
|
"""
|
|||
|
|
:param filename: tif路径
|
|||
|
|
:return: 图像范围
|
|||
|
|
"""
|
|||
|
|
gdal.AllRegister()
|
|||
|
|
dataset = gdal.Open(filename)
|
|||
|
|
if dataset is None:
|
|||
|
|
return None
|
|||
|
|
im_scope = self.cal_img_scope(dataset)
|
|||
|
|
del dataset
|
|||
|
|
return im_scope
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_projection(filename):
|
|||
|
|
"""
|
|||
|
|
:param filename: tif路径
|
|||
|
|
:return: 地图投影信息
|
|||
|
|
"""
|
|||
|
|
gdal.AllRegister()
|
|||
|
|
dataset = gdal.Open(filename)
|
|||
|
|
if dataset is None:
|
|||
|
|
return None
|
|||
|
|
im_proj = dataset.GetProjection()
|
|||
|
|
del dataset
|
|||
|
|
return im_proj
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_geotransform(filename):
|
|||
|
|
"""
|
|||
|
|
:param filename: tif路径
|
|||
|
|
:return: 从图像坐标空间（行、列），也称为（像素、线）到地理参考坐标空间（投影或地理坐标）的仿射变换
|
|||
|
|
"""
|
|||
|
|
gdal.AllRegister()
|
|||
|
|
dataset = gdal.Open(filename)
|
|||
|
|
if dataset is None:
|
|||
|
|
return None
|
|||
|
|
geotransform = dataset.GetGeoTransform()
|
|||
|
|
del dataset
|
|||
|
|
return geotransform
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_bands(filename):
|
|||
|
|
"""
|
|||
|
|
:param filename: tif路径
|
|||
|
|
:return: 影像的波段数
|
|||
|
|
"""
|
|||
|
|
gdal.AllRegister()
|
|||
|
|
dataset = gdal.Open(filename)
|
|||
|
|
if dataset is None:
|
|||
|
|
return None
|
|||
|
|
bands = dataset.RasterCount
|
|||
|
|
del dataset
|
|||
|
|
return bands
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_band_array(filename, num=1):
|
|||
|
|
"""
|
|||
|
|
:param filename: tif路径
|
|||
|
|
:param num: 波段序号
|
|||
|
|
:return: 对应波段的矩阵数据
|
|||
|
|
"""
|
|||
|
|
gdal.AllRegister()
|
|||
|
|
dataset = gdal.Open(filename)
|
|||
|
|
if dataset is None:
|
|||
|
|
return None
|
|||
|
|
bands = dataset.GetRasterBand(num)
|
|||
|
|
array = bands.ReadAsArray(0, 0, bands.XSize, bands.YSize)
|
|||
|
|
del dataset
|
|||
|
|
return array
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_data(filename):
|
|||
|
|
"""
|
|||
|
|
:param filename: tif路径
|
|||
|
|
:return: 获取所有波段的数据
|
|||
|
|
"""
|
|||
|
|
gdal.AllRegister()
|
|||
|
|
dataset = gdal.Open(filename)
|
|||
|
|
if dataset is None:
|
|||
|
|
return None
|
|||
|
|
im_width = dataset.RasterXSize
|
|||
|
|
im_height = dataset.RasterYSize
|
|||
|
|
im_data = dataset.ReadAsArray(0, 0, im_width, im_height)
|
|||
|
|
del dataset
|
|||
|
|
return im_data
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_img_width(filename):
|
|||
|
|
"""
|
|||
|
|
:param filename: tif路径
|
|||
|
|
:return: 影像宽度
|
|||
|
|
"""
|
|||
|
|
gdal.AllRegister()
|
|||
|
|
dataset = gdal.Open(filename)
|
|||
|
|
if dataset is None:
|
|||
|
|
return None
|
|||
|
|
width = dataset.RasterXSize
|
|||
|
|
|
|||
|
|
del dataset
|
|||
|
|
return width
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_img_height(filename):
|
|||
|
|
"""
|
|||
|
|
:param filename: tif路径
|
|||
|
|
:return: 影像高度
|
|||
|
|
"""
|
|||
|
|
gdal.AllRegister()
|
|||
|
|
dataset = gdal.Open(filename)
|
|||
|
|
if dataset is None:
|
|||
|
|
return None
|
|||
|
|
height = dataset.RasterYSize
|
|||
|
|
del dataset
|
|||
|
|
return height
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def read_img(filename):
|
|||
|
|
"""
|
|||
|
|
影像读取
|
|||
|
|
:param filename:
|
|||
|
|
:return:
|
|||
|
|
"""
|
|||
|
|
gdal.AllRegister()
|
|||
|
|
img_dataset = gdal.Open(filename) # 打开文件
|
|||
|
|
|
|||
|
|
if img_dataset is None:
|
|||
|
|
msg = 'Could not open ' + filename
|
|||
|
|
logger.error(msg)
|
|||
|
|
return None, None, None
|
|||
|
|
|
|||
|
|
im_proj = img_dataset.GetProjection() # 地图投影信息
|
|||
|
|
if im_proj is None:
|
|||
|
|
return None, None, None
|
|||
|
|
im_geotrans = img_dataset.GetGeoTransform() # 仿射矩阵
|
|||
|
|
|
|||
|
|
im_width = img_dataset.RasterXSize # 栅格矩阵的行数
|
|||
|
|
im_height = img_dataset.RasterYSize # 栅格矩阵的行数
|
|||
|
|
im_arr = img_dataset.ReadAsArray(0, 0, im_width, im_height)
|
|||
|
|
del img_dataset
|
|||
|
|
return im_proj, im_geotrans, im_arr
|
|||
|
|
|
|||
|
|
def cal_img_scope(self, dataset):
|
|||
|
|
"""
|
|||
|
|
计算影像的地理坐标范围
|
|||
|
|
根据GDAL的六参数模型将影像图上坐标（行列号）转为投影坐标或地理坐标（根据具体数据的坐标系统转换）
|
|||
|
|
:param dataset :GDAL地理数据
|
|||
|
|
:return: list[point_upleft, point_upright, point_downleft, point_downright]
|
|||
|
|
"""
|
|||
|
|
if dataset is None:
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
img_geotrans = dataset.GetGeoTransform()
|
|||
|
|
if img_geotrans is None:
|
|||
|
|
return None
|
|||
|
|
|
|||
|
|
width = dataset.RasterXSize # 栅格矩阵的列数
|
|||
|
|
height = dataset.RasterYSize # 栅格矩阵的行数
|
|||
|
|
|
|||
|
|
point_upleft = self.trans_rowcol2geo(img_geotrans, 0, 0)
|
|||
|
|
point_upright = self.trans_rowcol2geo(img_geotrans, width, 0)
|
|||
|
|
point_downleft = self.trans_rowcol2geo(img_geotrans, 0, height)
|
|||
|
|
point_downright = self.trans_rowcol2geo(img_geotrans, width, height)
|
|||
|
|
|
|||
|
|
return [point_upleft, point_upright, point_downleft, point_downright]
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def trans_rowcol2geo(img_geotrans,img_col, img_row):
|
|||
|
|
"""
|
|||
|
|
据GDAL的六参数模型仿射矩阵将影像图上坐标（行列号）转为投影坐标或地理坐标（根据具体数据的坐标系统转换）
|
|||
|
|
:param img_geotrans: 仿射矩阵
|
|||
|
|
:param img_col:图像纵坐标
|
|||
|
|
:param img_row:图像横坐标
|
|||
|
|
:return: [geo_x,geo_y]
|
|||
|
|
"""
|
|||
|
|
geo_x = img_geotrans[0] + img_geotrans[1] * img_col + img_geotrans[2]* img_row
|
|||
|
|
geo_y = img_geotrans[3] + img_geotrans[4] * img_col + img_geotrans[5]* img_row
|
|||
|
|
return [geo_x, geo_y]
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def write_img(filename, im_proj, im_geotrans, im_data):
|
|||
|
|
"""
|
|||
|
|
影像保存
|
|||
|
|
:param filename:
|
|||
|
|
:param im_proj:
|
|||
|
|
:param im_geotrans:
|
|||
|
|
:param im_data:
|
|||
|
|
:return:
|
|||
|
|
"""
|
|||
|
|
|
|||
|
|
gdal_dtypes = {
|
|||
|
|
'int8': gdal.GDT_Byte,
|
|||
|
|
'unit16': gdal.GDT_UInt16,
|
|||
|
|
'int16': gdal.GDT_Int16,
|
|||
|
|
'unit32': gdal.GDT_UInt32,
|
|||
|
|
'int32': gdal.GDT_Int32,
|
|||
|
|
'float32': gdal.GDT_Float32,
|
|||
|
|
'float64': gdal.GDT_Float64,
|
|||
|
|
}
|
|||
|
|
if not gdal_dtypes.get(im_data.dtype.name, None) is None:
|
|||
|
|
datatype = gdal_dtypes[im_data.dtype.name]
|
|||
|
|
else:
|
|||
|
|
datatype = gdal.GDT_Float32
|
|||
|
|
|
|||
|
|
# 判读数组维数
|
|||
|
|
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
|
|||
|
|
|
|||
|
|
# 创建文件
|
|||
|
|
if os.path.exists(os.path.split(filename)[0]) is False:
|
|||
|
|
os.makedirs(os.path.split(filename)[0])
|
|||
|
|
|
|||
|
|
driver = gdal.GetDriverByName("GTiff") # 数据类型必须有，因为要计算需要多大内存空间
|
|||
|
|
dataset = driver.Create(filename, im_width, im_height, im_bands, datatype)
|
|||
|
|
|
|||
|
|
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) # 写入数组数据
|
|||
|
|
else:
|
|||
|
|
for i in range(im_bands):
|
|||
|
|
# dataset.GetRasterBand(i + 1).WriteArray(im_data[:, :, im_bands - 1 - i])
|
|||
|
|
dataset.GetRasterBand(i + 1).WriteArray(im_data[i])
|
|||
|
|
del dataset
|
|||
|
|
|
|||
|
|
# 写GeoTiff文件
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def write_img_rpc(filename, im_proj, im_geotrans, im_data, rpc_dict):
|
|||
|
|
"""
|
|||
|
|
图像中写入rpc信息
|
|||
|
|
"""
|
|||
|
|
# 判断栅格数据的数据类型
|
|||
|
|
if 'int8' in im_data.dtype.name:
|
|||
|
|
datatype = gdal.GDT_Byte
|
|||
|
|
elif 'int16' in im_data.dtype.name:
|
|||
|
|
datatype = gdal.GDT_Int16
|
|||
|
|
else:
|
|||
|
|
datatype = gdal.GDT_Float32
|
|||
|
|
|
|||
|
|
# 判读数组维数
|
|||
|
|
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
|
|||
|
|
|
|||
|
|
# 创建文件
|
|||
|
|
driver = gdal.GetDriverByName("GTiff")
|
|||
|
|
dataset = driver.Create(filename, im_width, im_height, im_bands, datatype)
|
|||
|
|
|
|||
|
|
dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数
|
|||
|
|
dataset.SetProjection(im_proj) # 写入投影
|
|||
|
|
|
|||
|
|
# 写入RPC参数
|
|||
|
|
for k in rpc_dict.keys():
|
|||
|
|
dataset.SetMetadataItem(k, rpc_dict[k], 'RPC')
|
|||
|
|
|
|||
|
|
if im_bands == 1:
|
|||
|
|
dataset.GetRasterBand(1).WriteArray(im_data) # 写入数组数据
|
|||
|
|
else:
|
|||
|
|
for i in range(im_bands):
|
|||
|
|
dataset.GetRasterBand(i + 1).WriteArray(im_data[i])
|
|||
|
|
|
|||
|
|
del dataset
|
|||
|
|
|
|||
|
|
|
|||
|
|
def transtif2mask(self,out_tif_path, in_tif_path, threshold):
|
|||
|
|
"""
|
|||
|
|
:param out_tif_path:输出路径
|
|||
|
|
:param in_tif_path:输入的路径
|
|||
|
|
:param threshold:阈值
|
|||
|
|
"""
|
|||
|
|
im_proj, im_geotrans, im_arr, im_scope = self.read_img(in_tif_path)
|
|||
|
|
im_arr_mask = (im_arr < threshold).astype(int)
|
|||
|
|
self.write_img(out_tif_path, im_proj, im_geotrans, im_arr_mask)
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def get_polarization(IW1):
|
|||
|
|
"""
|
|||
|
|
读取极化方式
|
|||
|
|
:param IW1:
|
|||
|
|
:return:
|
|||
|
|
"""
|
|||
|
|
tree = ElementTree()
|
|||
|
|
tree.parse(IW1)
|
|||
|
|
root = tree.getroot()
|
|||
|
|
RadarCenterFrequency = root.find('component').find('component').find('component')
|
|||
|
|
element_trees = list(RadarCenterFrequency)
|
|||
|
|
value = 0
|
|||
|
|
for element in element_trees:
|
|||
|
|
if len(element.attrib) == 1:
|
|||
|
|
if element.attrib["name"] == "polarization":
|
|||
|
|
value = element.find('value').text
|
|||
|
|
return value
|
|||
|
|
|
|||
|
|
def write_quick_view(self, tif_path, color_img=False, quick_view_path=None):
|
|||
|
|
"""
|
|||
|
|
生成快视图,默认快视图和影像同路径且同名
|
|||
|
|
:param tif_path:影像路径
|
|||
|
|
:param color_img:是否生成随机伪彩色图
|
|||
|
|
:param quick_view_path:快视图路径
|
|||
|
|
"""
|
|||
|
|
if quick_view_path is None:
|
|||
|
|
quick_view_path = os.path.splitext(tif_path)[0]+'.jpg'
|
|||
|
|
|
|||
|
|
n = self.get_bands(tif_path)
|
|||
|
|
if n == 1: # 单波段
|
|||
|
|
t_data = self.get_data(tif_path)
|
|||
|
|
else: # 多波段，转为强度数据
|
|||
|
|
t_data = self.get_data(tif_path)
|
|||
|
|
t_data = t_data.astype(float)
|
|||
|
|
t_data = np.sqrt(t_data[0] ** 2 + t_data[1] ** 2)
|
|||
|
|
|
|||
|
|
t_r = self.get_img_height(tif_path)
|
|||
|
|
t_c = self.get_img_width(tif_path)
|
|||
|
|
if t_r > 10000 or t_c > 10000:
|
|||
|
|
q_r = int(t_r / 10)
|
|||
|
|
q_c = int(t_c / 10)
|
|||
|
|
elif 1024 < t_r < 10000 or 1024 < t_c < 10000:
|
|||
|
|
if t_r > t_c:
|
|||
|
|
q_r = 1024
|
|||
|
|
q_c = int(t_c/t_r * 1024)
|
|||
|
|
else:
|
|||
|
|
q_c = 1024
|
|||
|
|
q_r = int(t_r/t_c * 1024)
|
|||
|
|
else:
|
|||
|
|
q_r = t_r
|
|||
|
|
q_c = t_c
|
|||
|
|
|
|||
|
|
if color_img is True:
|
|||
|
|
# 生成伪彩色图
|
|||
|
|
img = np.zeros((t_r, t_c, 3), dtype=np.uint8) # (高，宽，维度)
|
|||
|
|
u = np.unique(t_data)
|
|||
|
|
for i in u:
|
|||
|
|
if i != 0:
|
|||
|
|
w = np.where(t_data == i)
|
|||
|
|
img[w[0], w[1], 0] = np.random.randint(0, 255) # 随机生成一个0到255之间的整数 可以通过挑参数设定不同的颜色范围
|
|||
|
|
img[w[0], w[1], 1] = np.random.randint(0, 255)
|
|||
|
|
img[w[0], w[1], 2] = np.random.randint(0, 255)
|
|||
|
|
|
|||
|
|
img = cv2.resize(img, (q_c, q_r)) # (宽，高)
|
|||
|
|
cv2.imwrite(quick_view_path, img)
|
|||
|
|
# cv2.imshow("result4", img)
|
|||
|
|
# cv2.waitKey(0)
|
|||
|
|
else:
|
|||
|
|
# 灰度图
|
|||
|
|
min = np.nanmin(t_data)
|
|||
|
|
max = np.nanmax(t_data)
|
|||
|
|
t_data[np.isnan(t_data)] = max
|
|||
|
|
if (max - min) < 256:
|
|||
|
|
t_data = (t_data - min) / (max - min) * 255
|
|||
|
|
out_img = Image.fromarray(t_data)
|
|||
|
|
out_img = out_img.resize((q_c, q_r)) # 重采样
|
|||
|
|
out_img = out_img.convert("L") # 转换成灰度图
|
|||
|
|
out_img.save(quick_view_path)
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
# if __name__ == '__main__':
|
|||
|
|
# ih = ImageHandler()
|
|||
|
|
# path = 'D:\Dual1_1_feature1.tif'
|
|||
|
|
# # ih.write_quick_view(path, color_img=False)
|
|||
|
|
# print('done')
|