378 lines
12 KiB
Python
378 lines
12 KiB
Python
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# -*- coding: UTF-8 -*-
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"""
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@Project ：microproduct
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@File ：Imagehandle.py
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@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
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@Author ：SHJ
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@Date ：2021/10/15
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@Version ：1.0.0
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"""
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import logging
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import os
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from osgeo import gdal
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import numpy as np
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from PIL import Image
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import cv2
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logger = logging.getLogger("mylog")
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class ImageHandler:
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"""
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影像读取、编辑、保存
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"""
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def __init__(self):
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pass
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@staticmethod
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def get_dataset(filename):
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"""
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:param filename: tif路径
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:return: 图像句柄
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"""
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gdal.AllRegister()
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dataset = gdal.Open(filename)
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if dataset is None:
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return None
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return dataset
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def get_scope(self, filename):
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"""
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:param filename: tif路径
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:return: 图像范围
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"""
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gdal.AllRegister()
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dataset = gdal.Open(filename)
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if dataset is None:
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return None
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im_scope = self.cal_img_scope(dataset)
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del dataset
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return im_scope
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@staticmethod
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def get_projection(filename):
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"""
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:param filename: tif路径
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:return: 地图投影信息
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"""
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gdal.AllRegister()
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dataset = gdal.Open(filename)
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if dataset is None:
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return None
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im_proj = dataset.GetProjection()
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del dataset
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return im_proj
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@staticmethod
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def get_geotransform(filename):
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"""
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:param filename: tif路径
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: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
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"""
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gdal.AllRegister()
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dataset = gdal.Open(filename)
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if dataset is None:
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return None
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geotransform = dataset.GetGeoTransform()
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del dataset
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return geotransform
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@staticmethod
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def get_bands(filename):
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"""
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:param filename: tif路径
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:return: 影像的波段数
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"""
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gdal.AllRegister()
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dataset = gdal.Open(filename)
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if dataset is None:
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return None
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bands = dataset.RasterCount
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del dataset
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return bands
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@staticmethod
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def get_band_array(filename, num=1):
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"""
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:param filename: tif路径
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:param num: 波段序号
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:return: 对应波段的矩阵数据
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"""
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gdal.AllRegister()
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dataset = gdal.Open(filename)
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if dataset is None:
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return None
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bands = dataset.GetRasterBand(num)
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array = bands.ReadAsArray(0, 0, bands.XSize, bands.YSize)
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del dataset
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return array
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@staticmethod
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def get_data(filename):
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"""
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:param filename: tif路径
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:return: 获取所有波段的数据
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"""
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gdal.AllRegister()
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dataset = gdal.Open(filename)
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if dataset is None:
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return None
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im_width = dataset.RasterXSize
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im_height = dataset.RasterYSize
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im_data = dataset.ReadAsArray(0, 0, im_width, im_height)
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del dataset
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return im_data
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@staticmethod
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def get_img_width(filename):
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"""
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:param filename: tif路径
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:return: 影像宽度
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"""
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gdal.AllRegister()
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dataset = gdal.Open(filename)
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if dataset is None:
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return None
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width = dataset.RasterXSize
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del dataset
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return width
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@staticmethod
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def get_img_height(filename):
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"""
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:param filename: tif路径
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:return: 影像高度
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"""
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gdal.AllRegister()
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dataset = gdal.Open(filename)
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if dataset is None:
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return None
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height = dataset.RasterYSize
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del dataset
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return height
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@staticmethod
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def read_img(filename):
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"""
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影像读取
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:param filename:
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:return:
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"""
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gdal.AllRegister()
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img_dataset = gdal.Open(filename) # 打开文件
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if img_dataset is None:
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msg = 'Could not open ' + filename
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logger.error(msg)
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return None, None, None
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im_proj = img_dataset.GetProjection() # 地图投影信息
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if im_proj is None:
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return None, None, None
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im_geotrans = img_dataset.GetGeoTransform() # 仿射矩阵
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im_width = img_dataset.RasterXSize # 栅格矩阵的行数
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im_height = img_dataset.RasterYSize # 栅格矩阵的行数
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im_arr = img_dataset.ReadAsArray(0, 0, im_width, im_height)
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del img_dataset
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return im_proj, im_geotrans, im_arr
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def cal_img_scope(self, dataset):
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"""
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计算影像的地理坐标范围
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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）
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:param dataset :GDAL地理数据
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:return: list[point_upleft, point_upright, point_downleft, point_downright]
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"""
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if dataset is None:
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return None
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img_geotrans = dataset.GetGeoTransform()
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if img_geotrans is None:
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return None
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width = dataset.RasterXSize # 栅格矩阵的列数
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height = dataset.RasterYSize # 栅格矩阵的行数
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point_upleft = self.trans_rowcol2geo(img_geotrans, 0, 0)
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point_upright = self.trans_rowcol2geo(img_geotrans, width, 0)
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point_downleft = self.trans_rowcol2geo(img_geotrans, 0, height)
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point_downright = self.trans_rowcol2geo(img_geotrans, width, height)
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return [point_upleft, point_upright, point_downleft, point_downright]
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@staticmethod
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def trans_rowcol2geo(img_geotrans,img_col, img_row):
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"""
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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）
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:param img_geotrans: 仿射矩阵
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:param img_col:图像纵坐标
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:param img_row:图像横坐标
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:return: [geo_x,geo_y]
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"""
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geo_x = img_geotrans[0] + img_geotrans[1] * img_col + img_geotrans[2]* img_row
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geo_y = img_geotrans[3] + img_geotrans[4] * img_col + img_geotrans[5]* img_row
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return [geo_x, geo_y]
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@staticmethod
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def write_img(filename, im_proj, im_geotrans, im_data):
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"""
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影像保存
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:param filename:
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:param im_proj:
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:param im_geotrans:
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:param im_data:
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:return:
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"""
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gdal_dtypes = {
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'int8': gdal.GDT_Byte,
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'unit16': gdal.GDT_UInt16,
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'int16': gdal.GDT_Int16,
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'unit32': gdal.GDT_UInt32,
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'int32': gdal.GDT_Int32,
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'float32': gdal.GDT_Float32,
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'float64': gdal.GDT_Float64,
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}
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if not gdal_dtypes.get(im_data.dtype.name, None) is None:
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datatype = gdal_dtypes[im_data.dtype.name]
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else:
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datatype = gdal.GDT_Float32
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# 判读数组维数
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if len(im_data.shape) == 3:
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im_bands,im_height, im_width, = im_data.shape
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else:
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im_bands, (im_height, im_width) = 1, im_data.shape
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# 创建文件
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if os.path.exists(os.path.split(filename)[0]) is False:
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os.makedirs(os.path.split(filename)[0])
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driver = gdal.GetDriverByName("GTiff") # 数据类型必须有，因为要计算需要多大内存空间
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dataset = driver.Create(filename, im_width, im_height, im_bands, datatype)
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dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数
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dataset.SetProjection(im_proj) # 写入投影
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if im_bands == 1:
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dataset.GetRasterBand(1).WriteArray(im_data) # 写入数组数据
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else:
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for i in range(im_bands):
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# dataset.GetRasterBand(i + 1).WriteArray(im_data[:, :, im_bands - 1 - i])
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dataset.GetRasterBand(i + 1).WriteArray(im_data[i])
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del dataset
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# 写GeoTiff文件
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@staticmethod
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def write_img_rpc(filename, im_proj, im_geotrans, im_data, rpc_dict):
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"""
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图像中写入rpc信息
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"""
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# 判断栅格数据的数据类型
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if 'int8' in im_data.dtype.name:
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datatype = gdal.GDT_Byte
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elif 'int16' in im_data.dtype.name:
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datatype = gdal.GDT_Int16
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else:
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datatype = gdal.GDT_Float32
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# 判读数组维数
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if len(im_data.shape) == 3:
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im_bands, im_height, im_width = im_data.shape
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else:
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im_bands, (im_height, im_width) = 1, im_data.shape
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# 创建文件
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driver = gdal.GetDriverByName("GTiff")
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dataset = driver.Create(filename, im_width, im_height, im_bands, datatype)
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dataset.SetGeoTransform(im_geotrans) # 写入仿射变换参数
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dataset.SetProjection(im_proj) # 写入投影
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# 写入RPC参数
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for k in rpc_dict.keys():
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dataset.SetMetadataItem(k, rpc_dict[k], 'RPC')
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if im_bands == 1:
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dataset.GetRasterBand(1).WriteArray(im_data) # 写入数组数据
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else:
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for i in range(im_bands):
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dataset.GetRasterBand(i + 1).WriteArray(im_data[i])
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del dataset
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def transtif2mask(self,out_tif_path, in_tif_path, threshold):
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"""
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:param out_tif_path:输出路径
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:param in_tif_path:输入的路径
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:param threshold:阈值
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"""
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im_proj, im_geotrans, im_arr, im_scope = self.read_img(in_tif_path)
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im_arr_mask = (im_arr < threshold).astype(int)
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self.write_img(out_tif_path, im_proj, im_geotrans, im_arr_mask)
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def write_quick_view(self, tif_path, color_img=False, quick_view_path=None):
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"""
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生成快视图,默认快视图和影像同路径且同名
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:param tif_path:影像路径
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:param color_img:是否生成随机伪彩色图
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:param quick_view_path:快视图路径
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"""
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if quick_view_path is None:
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quick_view_path = os.path.splitext(tif_path)[0]+'.jpg'
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n = self.get_bands(tif_path)
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if n == 1: # 单波段
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t_data = self.get_data(tif_path)
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else: # 多波段，转为强度数据
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t_data = self.get_data(tif_path)
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t_data = t_data.astype(float)
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t_data = np.sqrt(t_data[0] ** 2 + t_data[1] ** 2)
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t_r = self.get_img_height(tif_path)
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t_c = self.get_img_width(tif_path)
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if t_r > 1024 or t_c > 1024:
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if t_r > t_c:
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q_r = 1024
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q_c = int(t_c/t_r * 1024)
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else:
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q_c = 1024
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q_r = int(t_r/t_c * 1024)
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else:
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q_r = t_r
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q_c = t_c
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|
|
|
|||
|
|
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
|
|||
|
|
t_data = (t_data - np.min(t_data)) / (np.max(t_data) - np.min(t_data)) * 255
|
|||
|
|
out_img = Image.fromarray(t_data)
|
|||
|
|
out_img = out_img.resize((q_r, q_c)) # 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')
|