python影像裁剪并保存成tiff格式_bmp转换成tiff python

from read_landsat8 import read_landsat8_bands
import numpy as np
from osgeo import gdal_array
 
def get_img(base_path):
    """
    叠加波段的简单demo
    与mask矩阵一起构建成3维数组结构"""
    bands = read_landsat8_bands(base_path)
 
    # 读取数据
    B1_gdal = gdal_array.LoadFile(bands[0][0])
    B2_gdal = gdal_array.LoadFile(bands[0][1])
    B3_gdal = gdal_array.LoadFile(bands[0][2])
 
    # 转化成ndarray形式
    B1_np = np.array(B1_gdal)
    B2_np = np.array(B2_gdal)
    B3_np = np.array(B3_gdal)
    print(B1_np.shape)
 
    B123 = np.stack([B1_np, B2_np, B3_np], axis=0)
    print(B123.shape)  # 3,7301,7341
 
    # 构建0-1 mask矩阵
    height = B123.shape[1]
    width = B123.shape[2]
    mask = np.random.randint(0, 2, (1, height, width))
 
    # 按照通道堆叠
    img = np.concatenate([B123, mask], axis=0)
 
    return img

这里得到的img其实是一个3维数组，在本例中，它的维度是
4×7301×7341

envi查看一下堆叠后的图像

图像裁剪

有了上面构建的img数据，再设定一个裁剪尺寸，我们就可以进行图像裁剪了。

def crop_img(img, cropsize):
    """
    裁剪图像为指定格式并保存成tiff
    输入为array形式的数组
    """
    num = 0
    height = img.shape[1]
    width = img.shape[2]
 
    # 从左上开始裁剪
    for i in range(int((height) / (cropsize))):  # 行裁剪次数
        for j in range(int((width) / (cropsize))):  # 列裁剪次数
            cropped = img[:,  # 通道不裁剪
                      i * cropsize: i * cropsize + cropsize,
                      j * cropsize: j * cropsize + cropsize,
                      ]
 
            num = num + 1
            target = 'tiff_crop' + '/cropped{n}.tif'.format(n=num)
            out = gdal_array.SaveArray(cropped, target, format="GTiff")
 
    # #  向前裁剪最后一列
    for i in range(int((height) / (cropsize))):
        for j in range(int((width) / (cropsize))):
            cropped = img[:,  # 通道不裁剪
                      i * cropsize: i * cropsize + cropsize,  # 所有行
                      width - cropsize: width,  # 最后256列
                      ]
 
            num = num + 1
            target = 'tiff_crop' + '/cropped{n}.tif'.format(n=num)
            out = gdal_array.SaveArray(cropped, target, format="GTiff")
 
    # #  向前裁剪最后一行
    for i in range(int((height) / (cropsize))):
        for j in range(int((width) / (cropsize))):
            cropped = img[:,  # 通道不裁剪
                      height - cropsize: height,  # 最后256行
                      j * cropsize: j * cropsize + cropsize,  # 所有列
                      ]
 
            num = num + 1
            target = 'tiff_crop' + '/cropped{n}.tif'.format(n=num)
            out = gdal_array.SaveArray(cropped, target, format="GTiff")
   
    # 裁剪右下角
    cropped = img[:,  # 通道不裁剪
              height - cropsize: height,
              width - cropsize: width,
              ]
 
    num = num + 1
    target = 'tiff_crop' + '/cropped{n}.tif'.format(n=num)
    gdal_array.SaveArray(cropped, target, format="GTiff")

python影像裁剪并保存成tiff格式（规则网格法）_一只眠羊e的博客-CSDN博客_python 存tiff格式