Python3+gdal 讀取tiff格式數據

1、遇到的問題：numpy版本

im_data = dataset.ReadAsArray(0,0,im_width,im_height)#獲取數據 這句報錯

升級numpy：pip install -U numpy 但是提示已經是最新版本

解決：卸載numpy 重新安裝

2.直接從壓縮包中讀取tiff圖像

參考：http://gdal.org/gdal_virtual_file_systems.html#gdal_virtual_file_systems_vsizip

當前情況是2層壓縮： /’/vsitar/C:/Users/summer/Desktop/a_PAN1.tiff’

3.讀tiff

def readTif(fileName):
 
 merge_img = 0
   driver = gdal.GetDriverByName(‘GTiff’)
  driver.Register()
 
    dataset = gdal.Open(fileName)
    if dataset == None:
      print(fileName+ “掩膜失敗，文件無法打開”)
     return
    im_width = dataset.RasterXSize #柵格矩陣的列數
   print(‘im_width:’, im_width) 
 
 im_height = dataset.RasterYSize #柵格矩陣的行數
  print(‘im_height:’, im_height) 
    im_bands = dataset.RasterCount #波段數
   im_geotrans = dataset.GetGeoTransform()#獲取仿射矩陣信息
  im_proj = dataset.GetProjection()#獲取投影信息
  
 
   if im_bands == 1:
        band = dataset.GetRasterBand(1)
        im_data = dataset.ReadAsArray(0,0,im_width,im_height) #獲取數據
     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)#獲取數據
 #    # im_blueBand =  im_data[0,0:im_width,0:im_height] #獲取藍波段
    #    # im_greenBand = im_data[1,0:im_width,0:im_height] #獲取綠波段
    #    # im_redBand =   im_data[2,0:im_width,0:im_height] #獲取紅波段
    #    # # im_nirBand = im_data[3,0:im_width,0:im_height]   #獲取近紅外波段
    #    # 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 #獲取數據
      data2=band2.ReadAsArray(0,0,im_width,im_height).astype(np.uint16) #g #獲取數據
      data3=band3.ReadAsArray(0,0,im_width,im_height).astype(np.uint16) #b #獲取數據
      data4=band4.ReadAsArray(0,0,im_width,im_height).astype(np.uint16) #R #獲取數據
  #    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.圖像裁剪：

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