浅谈python下tiff图像的读取和保存方法

import numpy as npfrom scipy import miscfrom PIL import Imagefrom libtiff import TIFF ## 读入已有图像,数据类型和原图像一致tif32 = misc.imread('./test/lena32.tif') #<class 'numpy.float32'>tif16 = misc.imread('./test/lena16.tif') #<class 'numpy.uint16'>tif8 = misc.imread('./test/lena8.tif') #<class 'numpy.uint8'># 产生随机矩阵,数据类型float64np.random.seed(12345)flt = np.random.randn(512, 512)   #<class 'numpy.float64'># 转换float64矩阵type,为后面作测试z8 = (flt.astype(np.uint8))    #<class 'numpy.uint8'>z16 = (flt.astype(np.uint16))   #<class 'numpy.uint16'>z32 = (flt.astype(np.float32))   #<class 'numpy.float32'> 

# scipy.misc『不论输入数据是何类型，输出图像均为uint8』misc.imsave('./test/lena32_scipy.tif', tif32) #--> 8bit(tif16和tif8同)misc.imsave('./test//randmat64_scipy.tif', flt) #--> 8bitmisc.imsave('./test//randmat8_scipy.tif', z8) #--> 8bit(z16和z32同)# PIL.Image『8位16位输出图像与输入数据类型保持一致，64位会存成32位』Image.fromarray(tif32).save('./test/lena32_Image.tif') #--> 32bitImage.fromarray(tif16).save('./test/lena16_Image.tif') #--> 16bitImage.fromarray(tif8).save('./test/lena8_Image.tif') #--> 8bitImage.fromarray(flt).save('./test//randmat_Image.tif') #--> 32bit(flt.min~flt.max)im = Image.fromarray(flt.astype(np.float32))      im.save('./test//randmat32_Image.tif')     #--> 32bit(灰度值范围同上)#『uint8和uint16类型转换,会使输出图像灰度变换到255和65535』im = Image.frombytes('I;16', (512, 512), flt.tostring())im.save('./test//randmat16_Image1.tif')    #--> 16bit(0~65535)im = Image.fromarray(flt.astype(np.uint16))      im.save('./test//randmat16_Image2.tif')    #--> 16bit(0~65535)im = Image.fromarray(flt.astype(np.uint8))      im.save('./test//randmat8_Image.tif')     #--> 8bit(0~255)# libtiff.TIFF『输出图像与输入数据类型保持一致』tif = TIFF.open('./test//randmat_TIFF.tif', mode='w') tif.write_image(flt, compression=None)tif.close() #float64可以存储,但因BitsPerSample=64,一些图像软件不识别tif = TIFF.open('./test//randmat32_TIFF.tif', mode='w') tif.write_image(flt.astype(np.float32), compression=None)tif.close() #--> 32bit(flt.min~flt.max)#『uint8和uint16类型转换,会使输出图像灰度变换到255和65535』tif = TIFF.open('./test//randmat16_TIFF.tif', mode='w') tif.write_image(flt.astype(np.uint16), compression=None)tif.close() #--> 16bit(0~65535,8位则0~255)

# 『使用scipy,只能存成uint8』z16Norm = (z16-np.min(z16))/(np.max(z16)-np.min(z16)) #<class 'numpy.float64'>z32Norm = (z32-np.min(z32))/(np.max(z32)-np.min(z32))scipy.misc.imsave('./test//randmat16_norm_scipy.tif', z16Norm) #--> 8bit(0~255)# 『使用Image,归一化后变成np.float64 直接转8bit或16bit都会超出阈值,要*255或*65535』# 『如果没有astype的位数设置,float64会直接存成32bit』im = Image.fromarray(z16Norm)im.save('./test//randmat16_norm_Image.tif')  #--> 32bit(0~1)im = Image.fromarray(z16Norm.astype(np.float32))im.save('./test//randmat16_norm_to32_Image.tif') #--> 32bit(灰度范围值同上)im = Image.fromarray(z16Norm.astype(np.uint16))im.save('./test//randmat16_norm_to16_Image.tif') #--> 16bit(0~1)超出阈值im = Image.fromarray(z16Norm.astype(np.uint8))im.save('./test//randmat16_norm_to8_Image.tif') #--> 8bit(0~1)超出阈值im = Image.fromarray((z16Norm*65535).astype(np.uint16))im.save('./test//randmat16_norm_to16_Image1.tif') #--> 16bit(0~65535)im = Image.fromarray((z16Norm*255).astype(np.uint16))im.save('./test//randmat16_norm_to16_Image2.tif') #--> 16bit(0~255)im = Image.fromarray((z16Norm*255).astype(np.uint8))im.save('./test//randmat16_norm_to8_Image2.tif') #--> 8bit(0~255)# 『使用TIFF结果同Image』

#tiff文件解析成图像序列：读取tiff图像def tiff_to_read(tiff_image_name):  tif = TIFF.open(tiff_image_name, mode = "r")  im_stack = list() for im in list(tif.iter_images()):   im_stack.append(im) return  #根据文档,应该是这样实现,但测试中不管是tif.read_image还是tif.iter_images读入的矩阵数值都有问题#图像序列保存成tiff文件：保存tiff图像 def write_to_tiff(tiff_image_name, im_array, image_num): tif = TIFF.open(tiff_image_name, mode = 'w')  for i in range(0, image_num):   im = Image.fromarray(im_array[i])  #缩放成统一尺寸   im = im.resize((480, 480), Image.ANTIALIAS)   tif.write_image(im, compression = None)   out_tiff.close()  return 

from libtiff import TIFFfiletif = TIFFfile('./test/lena32-3.tif')samples, _ = tif.get_samples()