490 lines
19 KiB
Python
490 lines
19 KiB
Python
# -*- coding: UTF-8 -*-
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"""
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@Project : microproduct
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@File : blockprocess.py
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@Function : tif、tiff图像分块处理拼接功能
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@Contact : https://blog.csdn.net/qq_38308388/article/details/102978755
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@Author:SHJ
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@Date:2021/9/6
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@Version:1.0.0
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"""
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import glob
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import zipfile
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from osgeo import osr, gdal
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import numpy as np
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import os
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from PIL import Image
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# import time
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# from skimage import io
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from tool.algorithm.image.ImageHandle import ImageHandler
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class BlockProcess:
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def __init__(self):
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pass
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@staticmethod
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def get_block_size(rows, cols):
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block_size = 1024
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if rows <= 2048 or cols <= 2048:
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block_size = 512
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if (rows // 1024) <= 5 or (cols // 1024) <= 5:
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block_size = 1024
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elif 5 < (rows // 1024) and 5 < (cols // 1024):
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block_size = 2048
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return block_size
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# def get_block_size(rows, cols, block_size_config):
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# block_size = 512 if block_size_config < 512 else block_size_config
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# if rows > 2048 and cols > 2048:
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# block_size = block_size_config
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# return block_size
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@staticmethod
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def get_suffix(path_name):
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name = path_name
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suffix = '_' + name.split('_')[-4] + '_' + name.split('_')[-3] + '_' + name.split('_')[-2] + '_' + \
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name.split('_')[-1]
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return suffix
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@staticmethod
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def get_file_names(data_dir, file_type=['tif', 'tiff']):
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"""
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获取data_dir文件夹下file_type类型的文件路径
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"""
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result_dir = []
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result_name = []
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for maindir, subdir, file_name_list in os.walk(data_dir):
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for filename in file_name_list:
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apath = os.path.join(maindir, filename)
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ext = apath.split('.')[-1]
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if ext in file_type:
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result_dir.append(apath)
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result_name.append(filename)
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else:
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pass
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return result_dir, result_name
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@staticmethod
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def unzip_file(zip_file_path, out_path): # ):
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# 获取压缩文件所在的目录
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# extract_folder = os.path.dirname(zip_file_path)
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if zip_file_path.endswith("dem.zip"):
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out_path_merg = os.path.join(out_path)
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# os.mkdir(out_path_merg)
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para_value_list = zip_file_path.split(";")
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for n in para_value_list:
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with zipfile.ZipFile(n, 'r') as zip_ref:
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# 解压到和压缩文件同名的文件夹中
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zip_ref.extractall(out_path_merg)
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return out_path
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else:
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basename = os.path.splitext(os.path.basename(zip_file_path))[0]
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extract_folder = os.path.join(out_path, basename)
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with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
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# 解压到和压缩文件同名的文件夹中
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zip_ref.extractall(extract_folder)
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files = list(glob.glob(os.path.join(extract_folder, '*')))
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for file in files:
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if basename in os.path.basename(file):
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if not file.endswith(".xml"):
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unzipped_folder_path = file
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return unzipped_folder_path
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@staticmethod
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def get_same_img(img_dir, img_name):
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"""
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在img_dir路径下，用img_name的子图像路径集合，将集合以字典输出
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"""
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result = {}
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for idx, name in enumerate(img_name):
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temp_name = ''
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for idx2, item in enumerate(name.split('_')[:-4]):
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if idx2 == 0:
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temp_name = temp_name + item
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else:
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temp_name = temp_name + '_' + item
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if temp_name in result:
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result[temp_name].append(img_dir[idx])
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else:
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result[temp_name] = []
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result[temp_name].append(img_dir[idx])
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return result
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@staticmethod
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def assign_spatial_reference_byfile(src_path, dst_path):
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"""
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将src_path的地理信息，输入到dst_path图像中
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"""
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src_ds = gdal.Open(src_path, gdal.GA_ReadOnly)
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if src_ds is None:
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return False
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sr = osr.SpatialReference()
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sr.ImportFromWkt(src_ds.GetProjectionRef())
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geo_transform = src_ds.GetGeoTransform()
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dst_ds = gdal.Open(dst_path, gdal.GA_Update)
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if dst_ds is None:
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return False
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dst_ds.SetProjection(sr.ExportToWkt())
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dst_ds.SetGeoTransform(geo_transform)
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del dst_ds
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del src_ds
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return True
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@staticmethod
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def assign_spatial_reference_bypoint(row_begin, col_begin, src_proj, src_geo, img_path):
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"""
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将src_path的地理信息，输入到dst_path图像中
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"""
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sr = osr.SpatialReference()
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sr.ImportFromWkt(src_proj)
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geo_transform = src_geo
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geo_transform[0] = src_geo[0] + col_begin * src_geo[1] + row_begin * src_geo[2]
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geo_transform[3] = src_geo[3] + col_begin * src_geo[4] + row_begin * src_geo[5]
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dst_ds = gdal.Open(img_path, gdal.GA_Update)
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if dst_ds is None:
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return False
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dst_ds.SetProjection(sr.ExportToWkt())
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dst_ds.SetGeoTransform(geo_transform)
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del dst_ds
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return True
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@staticmethod
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def __get_band_array(filename, num):
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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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def get_tif_dtype(self, filename):
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"""
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:param filename: tif路径
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:return: tif数据类型
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"""
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image = self.__get_band_array(filename, 1)
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return image.dtype.name
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def cut(self, in_dir, out_dir, file_type=['tif', 'tiff'], out_type='tif', out_size=2048):
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"""
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:param in_dir:存放待裁剪的影像文件夹，不用指定到tif文件
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:param out_dir:存放裁剪结果的影像文件夹
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:param file_type:待裁剪的影像文件类型tif、tiff、bmp、jpg、png等等
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:param out_type:裁剪结果影像文件类型
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:param out_size:裁剪尺寸，裁剪为n*n的方形
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:return: True or Flase
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"""
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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data_dir_list, _ = self.get_file_names(in_dir, file_type)
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count = 0
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for each_dir in data_dir_list:
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name_suffix = os.path.basename(each_dir)
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img_name = os.path.splitext(name_suffix)[0]
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# gdal读取方法
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image = self.__get_band_array(each_dir, 1)
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cut_factor_row = int(np.ceil(image.shape[0] / out_size))
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cut_factor_clo = int(np.ceil(image.shape[1] / out_size))
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for i in range(cut_factor_row):
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for j in range(cut_factor_clo):
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if i == cut_factor_row - 1:
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i = image.shape[0] / out_size - 1
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else:
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pass
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if j == cut_factor_clo - 1:
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j = image.shape[1] / out_size - 1
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else:
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pass
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start_x = int(np.rint(i * out_size))
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start_y = int(np.rint(j * out_size))
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end_x = int(np.rint((i + 1) * out_size))
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end_y = int(np.rint((j + 1) * out_size))
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out_dir_images = os.path.join(out_dir, img_name + '_' + str(start_x) + '_' + str(end_x) + '_' + str(start_y) + '_' + str(
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end_y) + '.' + out_type)
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# + '/' + img_name \
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# + '_' + str(start_x) + '_' + str(end_x) + '_' + str(start_y) + '_' + str(
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# end_y) + '.' + out_type
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# temp_image = image[start_x:end_x, start_y:end_y]
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# out_image = Image.fromarray(temp_data)
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# out_image = Image.fromarray(temp_image)
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# out_image.save(out_dir_images)
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data = ImageHandler.get_data(each_dir)
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if ImageHandler.get_bands(each_dir) > 1:
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temp_data = data[:,start_x:end_x, start_y:end_y]
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else:
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temp_data = data[start_x:end_x, start_y:end_y]
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ImageHandler.write_img(out_dir_images, '', [0, 0, 0, 0, 0, 0], temp_data)
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count += 1
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return True
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def cut_new(self, in_dir, out_dir, file_type=['tif', 'tiff'], out_type='tif', out_size=2048):
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"""
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:param in_dir:存放待裁剪的影像文件夹，不用指定到tif文件
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:param out_dir:存放裁剪结果的影像文件夹
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:param file_type:待裁剪的影像文件类型tif、tiff、bmp、jpg、png等等
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:param out_type:裁剪结果影像文件类型
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:param out_size:裁剪尺寸，裁剪为n*n的方形
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:return: True or Flase
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20230831修改 ----tjx
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"""
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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data_dir_list, _ = self.get_file_names(in_dir, file_type)
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count = 0
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for each_dir in data_dir_list:
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name_suffix = os.path.basename(each_dir)
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img_name = os.path.splitext(name_suffix)[0]
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# gdal读取方法
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image = self.__get_band_array(each_dir, 1)
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block_x = int(np.ceil(image.shape[1] / out_size))
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block_y = int(np.ceil(image.shape[0] / out_size)) # todo 修改分块
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for i in range(block_y):
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for j in range(block_x):
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start_x = j * out_size
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start_y = i * out_size
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end_x = image.shape[1] if (j + 1) * out_size > image.shape[1] else (j + 1) * out_size
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end_y = image.shape[0] if (i + 1) * out_size > image.shape[0] else (i + 1) * out_size
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out_dir_images = os.path.join(out_dir, img_name + '_' + str(start_x) + '_' + str(end_x) + '_' + str(start_y) + '_' + str(
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end_y) + '.' + out_type)
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# print(out_dir_images)
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data = ImageHandler.get_data(each_dir)
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if ImageHandler.get_bands(each_dir) > 1:
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# temp_data = data[:,start_x:end_x, start_y:end_y]
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temp_data = data[:,start_y:end_y, start_x:end_x]
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else:
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# temp_data = data[start_x:end_x, start_y:end_y]
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temp_data = data[start_y:end_y, start_x:end_x]
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ImageHandler.write_img(out_dir_images, '', [0, 0, 0, 0, 0, 0], temp_data)
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count += 1
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return True
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def combine(self, data_dir, w, h, out_dir, out_type='tif', file_type=['tif', 'tiff'], datetype='float16'):
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"""
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:param data_dir: 存放待裁剪的影像文件夹，不用指定到tif文件
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:param w 拼接影像的宽度，
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:param h 拼接影像的高度
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:param out_dir: 存放裁剪结果的影像文件夹
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:param out_type: 裁剪结果影像文件类型
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:param file_type: 待裁剪的影像文件类型
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:param datetype:数据类型 int8，int16，float16，float32 等
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:return: True or Flase
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"""
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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img_dir, img_name = self.get_file_names(data_dir, file_type)
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dir_dict = self.get_same_img(img_dir, img_name)
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count = 0
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for key in dir_dict.keys():
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temp_label = np.zeros(shape=(h, w), dtype=datetype)
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dir_list = dir_dict[key]
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for item in dir_list:
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name_split = item.split('_')
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x_start = int(name_split[-4])
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x_end = int(name_split[-3])
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y_start = int(name_split[-2])
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y_end = int(name_split[-1].split('.')[0])
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# img = Image.open(item)
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img = ImageHandler.get_band_array(item, 1)
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img = np.array(img)
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temp_label[x_start:x_end, y_start:y_end] = img
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img_name = key + '.' + out_type
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new_out_dir = os.path.join(out_dir, img_name)
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ImageHandler.write_img(new_out_dir, '', [0, 0, 0, 0, 0, 0], temp_label)
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# label = Image.fromarray(temp_label)
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# label.save(new_out_dir)
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count += 1
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return True
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# todo 20230901 修改分块同步修改合并代码
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def combine_new(self, data_dir, w, h, out_dir, out_type='tif', file_type=['tif', 'tiff'], datetype='float16'):
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"""
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:param data_dir: 存放待裁剪的影像文件夹，不用指定到tif文件
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:param w 拼接影像的宽度，
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:param h 拼接影像的高度
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:param out_dir: 存放裁剪结果的影像文件夹
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:param out_type: 裁剪结果影像文件类型
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:param file_type: 待裁剪的影像文件类型
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:param datetype:数据类型 int8，int16，float16，float32 等
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:return: True or Flase
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"""
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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img_dir, img_name = self.get_file_names(data_dir, file_type)
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dir_dict = self.get_same_img(img_dir, img_name)
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count = 0
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for key in dir_dict.keys():
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dir_list = dir_dict[key]
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bands = ImageHandler.get_bands(dir_list[0])
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if bands > 1:
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temp_label = np.zeros(shape=(bands, h, w), dtype=datetype)
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for item in dir_list:
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name_split = item.split('_')
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x_start = int(name_split[-4])
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x_end = int(name_split[-3])
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y_start = int(name_split[-2])
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y_end = int(name_split[-1].split('.')[0])
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# img = Image.open(item)
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img = ImageHandler.get_band_array(item, 1)
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img = np.array(img)
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temp_label[:, y_start:y_end, x_start:x_end] = img
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img_name = key + '.' + out_type
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new_out_dir = os.path.join(out_dir, img_name)
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ImageHandler.write_img(new_out_dir, '', [0, 0, 0, 0, 0, 0], temp_label)
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# label = Image.fromarray(temp_label)
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# label.save(new_out_dir)
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count += 1
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else:
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temp_label = np.zeros(shape=(h, w), dtype=datetype)
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for item in dir_list:
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name_split = item.split('_')
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x_start = int(name_split[-4])
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x_end = int(name_split[-3])
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y_start = int(name_split[-2])
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y_end = int(name_split[-1].split('.')[0])
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# img = Image.open(item)
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img = ImageHandler.get_band_array(item, 1)
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img = np.array(img)
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temp_label[y_start:y_end, x_start:x_end] = img
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img_name = key + '.' + out_type
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new_out_dir = os.path.join(out_dir, img_name)
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ImageHandler.write_img(new_out_dir, '', [0, 0, 0, 0, 0, 0], temp_label)
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# label = Image.fromarray(temp_label)
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# label.save(new_out_dir)
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count += 1
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return True
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def combine_Tif(self, data_dir, w, h, out_dir, proj, geo, out_type='tif', file_type=['tif', 'tiff'],
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datetype='float16'):
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"""
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将文件夹下的tif拼接成一个大的tif
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:param data_dir: 存放待裁剪的影像文件夹，不用指定到tif文件
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:param w 拼接影像的宽度，
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:param h 拼接影像的高度
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:param out_dir: 存放裁剪结果的影像文件夹
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:param proj: 指定投影系
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:param geo: 指定变换参数
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:param out_type: 裁剪结果影像文件类型
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:param file_type: 待裁剪的影像文件类型
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:param datetype:数据类型 int8，int16，float16，float32 等
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:return: True or Flase
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"""
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image_handler = ImageHandler()
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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img_dir, img_name = self.get_file_names(data_dir, file_type)
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dir_dict = self.get_same_img(img_dir, img_name)
|
||
count = 0
|
||
for key in dir_dict.keys():
|
||
temp_label = np.zeros(shape=(h, w), dtype=datetype)
|
||
dir_list = dir_dict[key]
|
||
for item in dir_list:
|
||
name_split = item.split('_')
|
||
x_start = int(name_split[-4])
|
||
x_end = int(name_split[-3])
|
||
y_start = int(name_split[-2])
|
||
y_end = int(name_split[-1].split('.')[0])
|
||
img = image_handler.get_data(item)
|
||
temp_label[x_start:x_end, y_start:y_end] = img
|
||
|
||
img_name = key + '.' + out_type
|
||
new_out_dir = os.path.join(out_dir,img_name)
|
||
image_handler.write_img(new_out_dir, proj, geo, temp_label)
|
||
count += 1
|
||
return True
|
||
|
||
if __name__ == '__main__':
|
||
bp = BlockProcess()
|
||
# # # cut
|
||
# data_dir = r"D:\micro\WorkSpace\LandCover\Temporary\processing\feature_tif\cut"
|
||
# out_dir = r"D:\micro\WorkSpace\LandCover\Temporary\processing\feature_tif\combine"
|
||
# file_type = ['tif']
|
||
# out_type = 'tif'
|
||
# cut_size = 1024
|
||
# #
|
||
# bp.cut_new(data_dir, out_dir, file_type, out_type, cut_size)
|
||
# # # combine
|
||
data_dir=r"D:\micro\WorkSpace\VegetationPhenology\Temporary\processing\GF3C_MYC_QPSI_006270_E100.4_N27.0_20230615_L1A_AHV_L10000158764-ortho\features_geo\feature"
|
||
w= 9548
|
||
h= 9613
|
||
out_dirs=r"D:\micro\WorkSpace\VegetationPhenology\Temporary\processing\GF3C_MYC_QPSI_006270_E100.4_N27.0_20230615_L1A_AHV_L10000158764-ortho\features_geo"
|
||
out_type='tif'
|
||
file_type=['tif']
|
||
datetype = 'float'
|
||
# src_path = r"D:\Workspace\SoilMoisture\Temporary\preprocessed\HH_preprocessed.tif"
|
||
# datetype = bp.get_tif_dtype(src_path)
|
||
bp.combine(data_dir, w, h, out_dirs, out_type, file_type, datetype)
|
||
|
||
#
|
||
# # 添加地理信息
|
||
# new_out_dir =r"D:\DATA\testdata1\combine\TEST_20200429_NDVI.tif"
|
||
# bp.assign_spatial_reference_byfile(src_path, new_out_dir)
|
||
|
||
# fn = r'D:\Workspace\SoilMoisture\Temporary\combine\soil_moisture.tif'
|
||
# product_path = r'D:\Workspace\SoilMoisture\Temporary\combine\soil_moisture_1.tif'
|
||
#
|
||
# proj, geos, img = ImageHandler.read_img(fn)
|
||
# img[img>1] = 1
|
||
# img[img<0] = 0
|
||
# ImageHandler.write_img(product_path, proj, geos, img)
|
||
|