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 cv2import numpy as npimport 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 3threshold = 1000overlap = 100 step = threshold - overlapx_num = width/step + 1y_num = height/step + 1print x_num, y_num N = 0yj = 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