semantic-segmentation/data_preprocessing/cut_smalltif.bak

import numpy as np
from osgeo import gdal


def read_tif(fileName):
    dataset = gdal.Open(fileName)

    im_width = dataset.RasterXSize  # 栅格矩阵的列数
    im_height = dataset.RasterYSize  # 栅格矩阵的行数
    im_bands = dataset.RasterCount  # 波段数
    im_data = dataset.ReadAsArray().astype(np.float32)  # 获取数据
    if len(im_data.shape) == 2:
        im_data = im_data[np.newaxis, :]
    im_geotrans = dataset.GetGeoTransform()  # 获取仿射矩阵信息
    im_proj = dataset.GetProjection()  # 获取投影信息

    return im_data, im_width, im_height, im_bands, im_geotrans, im_proj


def write_tif(im_data, im_width, im_height, path, im_geotrans, im_proj):
    if 'int8' in im_data.dtype.name:
        datatype = gdal.GDT_Byte
    elif 'int16' in im_data.dtype.name:
        datatype = gdal.GDT_UInt16
    else:
        datatype = gdal.GDT_Float32

    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(path, im_width, im_height, im_bands, datatype)
    if dataset != None and im_geotrans != None and im_proj != None:
        dataset.SetGeoTransform(im_geotrans)  # 写入仿射变换参数
        dataset.SetProjection(im_proj)  # 写入投影
    for i in range(im_bands):
        dataset.GetRasterBand(i + 1).WriteArray(im_data[i])
    del dataset


fileName = 'E:/RSdata/wlk_tif/wlk_right/wlk_right_cj.tif'
im_data, im_width, im_height, im_bands, im_geotrans, im_proj = read_tif(
    fileName)

fileName2 = 'E:/RSdata/wlk_tif/wlk_right/wlk_right_cjdan.tif'
im_data2, im_width2, im_height2, im_bands2, im_geotrans2, im_proj2 = read_tif(
    fileName2)

mask_fileName = 'E:/RSdata/wlk_tif/wlk_right/label_right_cj.tif'
mask_im_data, mask_im_width, mask_im_height, mask_im_bands, mask_im_geotrans, mask_im_proj = read_tif(
    mask_fileName)
mask_im_data = np.int8(mask_im_data)


# geotiff归一化
for i in range(im_bands):
    arr = im_data[i, :, :]
    Min = arr.min()
    Max = arr.max()
    normalized_arr = (arr-Min)/(Max-Min)*255
    im_data[i] = normalized_arr

for i in range(im_bands2):
    arr = im_data2[i, :, :]
    Min = arr.min()
    Max = arr.max()
    normalized_arr = (arr-Min)/(Max-Min)*255
    im_data2[i] = normalized_arr


# 计算大图每个波段的均值和方差，train.py里transform会用到
im_data = im_data/255
for i in range(im_bands):
    pixels = im_data[i, :, :].ravel()
    print("波段{} mean: {:.4f}, std: {:.4f}".format(
        i, np.mean(pixels), np.std(pixels)))
im_data = im_data*255

im_data2 = im_data2/255
for i in range(im_bands2):
    pixels = im_data2[i, :, :].ravel()
    print("波段{} mean: {:.4f}, std: {:.4f}".format(
        i, np.mean(pixels), np.std(pixels)))
im_data2 = im_data2*255


# 切成小图
a = 0
size = 224
for i in range(0, int(mask_im_height / size)):
    for j in range(0, int(mask_im_width / size)):
        im_cut = im_data[:, i * size:i * size + size, j * size:j * size + size]
        im_cut2 = im_data2[:, i * size:i *size + size, j * size:j * size + size]
        mask_cut = mask_im_data[:, i * size:i *size + size, j * size:j * size + size]

        # 以mask为判断基准，同时处理geotiff和mask
        labelfla = np.array(mask_cut).flatten()
        if np.all(labelfla == 15):  # 15为NoData
            print("Skip!!!")
        else:
            # 5m
            left_h = i * size * im_geotrans[5] + im_geotrans[3]
            left_w = j * size * im_geotrans[1] + im_geotrans[0]
            new_geotrans = np.array(im_geotrans)
            new_geotrans[0] = left_w
            new_geotrans[3] = left_h
            out_geotrans = tuple(new_geotrans)

            im_out = 'E:/RSdata/wlk_right_224_2/dataset_5m/geotiff' + str(a) + '.tif'
            write_tif(im_cut, size, size, im_out, out_geotrans, im_proj)

            # dan
            left_h = i * size * im_geotrans2[5] + im_geotrans2[3]
            left_w = j * size * im_geotrans2[1] + im_geotrans2[0]
            new_geotrans = np.array(im_geotrans2)
            new_geotrans[0] = left_w
            new_geotrans[3] = left_h
            out_geotrans = tuple(new_geotrans)

            im_out = 'E:/RSdata/wlk_right_224_2/dataset_dan/geotiff' + str(a) + '.tif'
            write_tif(im_cut2, size, size, im_out, out_geotrans, im_proj2)

            # 存mask
            mask_left_h = i * size * mask_im_geotrans[5] + mask_im_geotrans[3]
            mask_left_w = j * size * mask_im_geotrans[1] + mask_im_geotrans[0]
            mask_new_geotrans = np.array(mask_im_geotrans)
            mask_new_geotrans[0] = mask_left_w
            mask_new_geotrans[3] = mask_left_h
            mask_out_geotrans = tuple(mask_new_geotrans)
            mask_out = 'E:/RSdata/wlk_right_224_2/mask/geotiff' + str(a) + '.tif'
            write_tif(mask_cut, size, size, mask_out,
                      mask_out_geotrans, mask_im_proj)

            print(mask_out + 'Cut to complete')

            a = a+1