652 lines
22 KiB
Python
652 lines
22 KiB
Python
"""
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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 ：LMM
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@Date ：2021/10/19 14:39
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@Version ：1.0.0
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"""
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import os
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from PIL import Image
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from osgeo import gdal
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from osgeo import osr
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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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import logging
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import math
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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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def get_invgeotransform(filename):
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"""
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:param filename: tif路径
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:return: 从地理参考坐标空间（投影或地理坐标）的到图像坐标空间（行、列
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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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geotransform=gdal.InvGeoTransform(geotransform)
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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 geo2lonlat(dataset, x, y):
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"""
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将投影坐标转为经纬度坐标（具体的投影坐标系由给定数据确定）
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:param dataset: GDAL地理数据
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:param x: 投影坐标x
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:param y: 投影坐标y
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:return: 投影坐标(x, y)对应的经纬度坐标(lon, lat)
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"""
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prosrs = osr.SpatialReference()
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prosrs.ImportFromWkt(dataset.GetProjection())
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geosrs = prosrs.CloneGeogCS()
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ct = osr.CoordinateTransformation(prosrs, geosrs)
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coords = ct.TransformPoint(x, y)
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return coords[:2]
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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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# if 'int' in str(array.dtype):
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# array[np.where(array == -9999)] = np.inf
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# else:
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# array[np.where(array < -9000.0)] = np.nan
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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_all_band_array(filename):
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"""
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（大气延迟算法）
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将ERA-5影像所有波段存为一个数组, 波段数在第三维度 get_data（）->（37，8，8）
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:param filename： 影像路径 get_all_band_array ->（8，8，37）
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:return: 影像数组
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"""
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dataset = gdal.Open(filename)
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x_size = dataset.RasterXSize
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y_size = dataset.RasterYSize
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nums = dataset.RasterCount
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array = np.zeros((y_size, x_size, nums), dtype=float)
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if nums == 1:
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bands_0 = dataset.GetRasterBand(1)
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array = bands_0.ReadAsArray(0, 0, x_size, y_size)
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else:
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for i in range(0, nums):
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bands = dataset.GetRasterBand(i+1)
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arr = bands.ReadAsArray(0, 0, x_size, y_size)
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array[:, :, i] = arr
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return array
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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 get_scope_ori_sim(filename):
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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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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 # 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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band1 = dataset.GetRasterBand(1)
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array1 = band1.ReadAsArray(0, 0, band1.XSize, band1.YSize)
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band2 = dataset.GetRasterBand(2)
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array2 = band2.ReadAsArray(0, 0, band2.XSize, band2.YSize)
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if array1[0, 0] < array1[0, width-1]:
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point_upleft = [array1[0, 0], array2[0, 0]]
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point_upright = [array1[0, width-1], array2[0, width-1]]
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else:
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point_upright = [array1[0, 0], array2[0, 0]]
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point_upleft = [array1[0, width-1], array2[0, width-1]]
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if array1[height-1, 0] < array1[height-1, width-1]:
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point_downleft = [array1[height - 1, 0], array2[height - 1, 0]]
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point_downright = [array1[height - 1, width - 1], array2[height - 1, width - 1]]
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else:
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point_downright = [array1[height - 1, 0], array2[height - 1, 0]]
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point_downleft = [array1[height - 1, width - 1], array2[height - 1, width - 1]]
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if(array2[0, 0] < array2[height - 1, 0]):
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#上下调换顺序
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tmp1 = point_upleft
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point_upleft = point_downleft
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point_downleft = tmp1
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tmp2 = point_upright
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point_upright = point_downright
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point_downright = tmp2
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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_era_into_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_height, im_width, im_bands = im_data.shape # shape[0] 行数
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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[:, :, 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(filename, im_proj, im_geotrans, im_data, no_data='null'):
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"""
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影像保存
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:param filename: 保存的路径
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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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:param no_data: 把无效值设置为 nodata
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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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# outRaster.GetRasterBand(1).WriteArray(array) # 写入数组数据
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outband = dataset.GetRasterBand(1)
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outband.WriteArray(im_data)
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if no_data != 'null':
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outband.SetNoDataValue(np.double(no_data))
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outband.FlushCache()
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else:
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for i in range(im_bands):
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outband = dataset.GetRasterBand(1 + i)
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outband.WriteArray(im_data[i])
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if no_data != 'null':
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outband.SetNoDataValue(np.double(no_data))
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outband.FlushCache()
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# outRaster.GetRasterBand(i + 1).WriteArray(array[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_envi(filename, im_proj, im_geotrans, im_data, no_data='null'):
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"""
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影像保存
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:param filename: 保存的路径
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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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:param no_data: 把无效值设置为 nodata
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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("ENVI") # 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
|
||
|
||
dataset.SetProjection(im_proj) # 写入投影
|
||
|
||
if im_bands == 1:
|
||
# outRaster.GetRasterBand(1).WriteArray(array) # 写入数组数据
|
||
outband = dataset.GetRasterBand(1)
|
||
outband.WriteArray(im_data)
|
||
if no_data != 'null':
|
||
outband.SetNoDataValue(no_data)
|
||
outband.FlushCache()
|
||
else:
|
||
for i in range(im_bands):
|
||
outband = dataset.GetRasterBand(1 + i)
|
||
outband.WriteArray(im_data[i])
|
||
outband.FlushCache()
|
||
# outRaster.GetRasterBand(i + 1).WriteArray(array[i])
|
||
del dataset
|
||
|
||
@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)
|
||
|
||
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.percentile(t_data, 2) # np.nanmin(t_data)
|
||
max = np.percentile(t_data, 98) # np.nanmax(t_data)
|
||
t_data[np.isnan(t_data)] = max
|
||
if (max - min) < 256 & (max-min) != 0: # 会报异常RuntimeWarning: divide by zero encountered in true_divide t_data = (t_data - min) / (max - min) * 255 jia's
|
||
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)
|
||
|
||
def limit_field(self, out_path, in_path, min_value, max_value):
|
||
"""
|
||
:param out_path:输出路径
|
||
:param in_path:主mask路径，输出影像采用主mask的地理信息
|
||
:param min_value
|
||
:param max_value
|
||
"""
|
||
proj = self.get_projection(in_path)
|
||
geotrans = self.get_geotransform(in_path)
|
||
array = self.get_band_array(in_path, 1)
|
||
array[array < min_value] = min_value
|
||
array[array > max_value] = max_value
|
||
self.write_img(out_path, proj, geotrans, array)
|
||
return True
|
||
|
||
def band_merge(self, lon, lat, ori_sim):
|
||
lon_arr = self.get_data(lon)
|
||
lat_arr = self.get_data(lat)
|
||
temp = np.zeros((2, lon_arr.shape[0], lon_arr.shape[1]), dtype=float)
|
||
temp[0, :, :] = lon_arr[:, :]
|
||
temp[1, :, :] = lat_arr[:, :]
|
||
self.write_img(ori_sim, '', [0.0, 1.0, 0.0, 0.0, 0.0, 1.0], temp, '0')
|
||
|
||
|
||
def get_scopes(self, ori_sim):
|
||
ori_sim_data = self.get_data(ori_sim)
|
||
lon = ori_sim_data[0, :, :]
|
||
lat = ori_sim_data[1, :, :]
|
||
|
||
min_lon = np.nanmin(np.where((lon != 0) & ~np.isnan(lon), lon, np.inf))
|
||
max_lon = np.nanmax(np.where((lon != 0) & ~np.isnan(lon), lon, -np.inf))
|
||
min_lat = np.nanmin(np.where((lat != 0) & ~np.isnan(lat), lat, np.inf))
|
||
max_lat = np.nanmax(np.where((lat != 0) & ~np.isnan(lat), lat, -np.inf))
|
||
|
||
scopes = [[min_lon, max_lat], [max_lon, max_lat], [min_lon, min_lat], [max_lon, min_lat]]
|
||
return scopes
|
||
|
||
|
||
# if __name__ == '__main__':
|
||
# path = r'D:\BaiduNetdiskDownload\GZ\lon.rdr'
|
||
# path2 = r'D:\BaiduNetdiskDownload\GZ\lat.rdr'
|
||
# path3 = r'D:\BaiduNetdiskDownload\GZ\lon_lat.tif'
|
||
# s = ImageHandler().band_merge(path, path2, path3)
|
||
# print(s)
|
||
# pass |