semantic-segmentation/data_preprocessing/cut_smalltif.py

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)


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
lower_percent = 2
upper_percent = 98
for i in range(im_bands):
    arr = im_data[i, :, :]
    lower = np.percentile(arr, lower_percent)
    upper = np.percentile(arr, upper_percent)
    stretched = np.clip((arr - lower) / (upper - lower), 0, 1)
    im_data[i] = (stretched * 255).astype(np.uint8)


# 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


# 切成小图
a = 0
size = 448
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]
        mask_cut = mask_im_data[:, i * size:i *
                                size + size, j * size:j * size + size]

        labelfla = np.array(mask_cut).flatten()
        if np.all(labelfla == 15):  # 15为NoData
            print("Skip!!!")
        else:
            # 取5、4、3波段，注意顺序
            rgb_cut = np.stack([
                im_cut[4],  # 第5波段
                im_cut[3],  # 第4波段
                im_cut[2],  # 第3波段
            ], axis=0)  # shape: (3, H, W)
            # 转为 (H, W, 3)
            rgb_cut = np.transpose(rgb_cut, (1, 2, 0))
            # 归一化到0-255并转uint8
            rgb_cut = np.clip(rgb_cut, 0, 255)
            rgb_cut = rgb_cut.astype(np.uint8)
            # 保存为jpg
            from PIL import Image
            rgb_img = Image.fromarray(rgb_cut)
            rgb_img.save(
                f'E:/RSdata/wlk_right_448/dataset_5m_jpg/img_{a}.jpg')

            # mask只取第一个波段（如果是单通道），转uint8保存为png
            mask_arr = mask_cut[0] if mask_cut.shape[0] == 1 else mask_cut
            mask_arr = np.clip(mask_arr, 0, 255).astype(np.uint8)
            mask_img = Image.fromarray(mask_arr)
            mask_img.save(f'E:/RSdata/wlk_right_448/mask_png/mask_{a}.png')

            print(f'img_{a}.jpg and mask_{a}.png saved')
            a = a+1