1、遇到的问题：numpy版本
im_data = dataset.ReadAsArray(0,0,im_width,im_height)#获取数据 这句报错
升级numpy：pip install -U numpy 但是提示已经是最新版本
解决：卸载numpy 重新安装
2.直接从压缩包中读取tiff图像
参考：http://gdal.org/gdal_virtual_file_systems.html#gdal_virtual_file_systems_vsizip
当前情况是2层压缩： /’/vsitar/C:/Users/summer/Desktop/a_PAN1.tiff’
3.读tiff
def readTif(fileName): merge_img = 0 driver = gdal.GetDriverByName( ‘GTiff’) driver.Register() dataset = gdal.Open(fileName) if dataset == None: print(fileName+ “掩膜失败，文件无法打开”) return im_width = dataset.RasterXSize #栅格矩阵的列数 print( ‘im_width:’, im_width) im_height = dataset.RasterYSize #栅格矩阵的行数 print( ‘im_height:’, im_height) im_bands = dataset.RasterCount #波段数 im_geotrans = dataset.GetGeoTransform() #获取仿射矩阵信息 im_proj = dataset.GetProjection() #获取投影信息 if im_bands == 1: band = dataset.GetRasterBand( 1) im_data = dataset.ReadAsArray( 0, 0,im_width,im_height) #获取数据 cdata = im_data.astype(np.uint8) merge_img = cv2.merge([cdata,cdata,cdata]) cv2.imwrite( ‘C:/Users/summer/Desktop/a.jpg’, merge_img) # elif im_bands == 4: # # im_data = dataset.ReadAsArray(0,0,im_width,im_height)#获取数据 # # im_blueBand = im_data[0,0:im_width,0:im_height] #获取蓝波段 # # im_greenBand = im_data[1,0:im_width,0:im_height] #获取绿波段 # # im_redBand = im_data[2,0:im_width,0:im_height] #获取红波段 # # # im_nirBand = im_data[3,0:im_width,0:im_height] #获取近红外波段 # # merge_img=cv2.merge([im_redBand,im_greenBand,im_blueBand]) # # zeros = np.zeros([im_height,im_width],dtype = “uint8”) # # data1 = im_redBand.ReadAsArray # band1=dataset.GetRasterBand(1) # band2=dataset.GetRasterBand(2) # band3=dataset.GetRasterBand(3) # band4=dataset.GetRasterBand(4) data1=band1.ReadAsArray( 0, 0,im_width,im_height).astype(np.uint16) #r #获取数据 data2=band2.ReadAsArray( 0, 0,im_width,im_height).astype(np.uint16) #g #获取数据 data3=band3.ReadAsArray( 0, 0,im_width,im_height).astype(np.uint16) #b #获取数据 data4=band4.ReadAsArray( 0, 0,im_width,im_height).astype(np.uint16) #R #获取数据 # print(data1[1][45]) # output1= cv2.convertScaleAbs(data1, alpha=(255.0/65535.0)) # print(output1[1][45]) # output2= cv2.convertScaleAbs(data2, alpha=(255.0/65535.0)) # output3= cv2.convertScaleAbs(data3, alpha=(255.0/65535.0)) merge_img1 = cv2.merge([output3,output2,output1]) #B G R cv2.imwrite( ‘C:/Users/summer/Desktop/merge_img1.jpg’, merge_img1)4.图像裁剪：
import cv2 import numpy as np import os tiff_file = './try_img/2.tiff' save_folder = './try_img_re/' if not os.path.exists(save_folder): os.makedirs(save_folder) tif_img = cv2.imread(tiff_file) width, height, channel = tif_img.shape # print height, width, channel : 6908 7300 3 threshold = 1000 overlap = 100 step = threshold - overlap x_num = width/step + 1 y_num = height/step + 1 print x_num, y_num N = 0 yj = 0 for xi in range(x_num): for yj in range(y_num): # print xi if yj <= y_num: print yj x = step*xi y = step*yj wi = min(width,x+threshold) hi = min(height,y+threshold) # print wi , hi if wi-x < 1000 and hi-y < 1000: im_block = tif_img[wi -1000:wi, hi -1000:hi] elif wi-x > 1000 and hi-y < 1000: im_block = tif_img[x:wi, hi -1000:hi] elif wi-x < 1000 and hi-y > 1000: im_block = tif_img[wi -1000:wi, y:hi] else: im_block = tif_img[x:wi,y:hi] cv2.imwrite(save_folder + 'try' + str(N) + '.jpg', im_block) N += 1 https://blog.csdn.net/summermaoz/article/details/78346929