對(duì)比測(cè)試 scipy.misc 和 PIL.Image 和 libtiff.TIFF 三個(gè)庫

輸入：

1. (讀取矩陣) 讀入uint8、uint16、float32的lena.tif

2. (生成矩陣) 使用numpy產(chǎn)生隨機(jī)矩陣，float64的mat

import numpy as npfrom scipy import miscfrom PIL import Imagefrom libtiff import TIFF ## 讀入已有圖像,數(shù)據(jù)類型和原圖像一致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'># 產(chǎn)生隨機(jī)矩陣,數(shù)據(jù)類型float64np.random.seed(12345)flt = np.random.randn(512, 512)   #<class 'numpy.float64'># 轉(zhuǎn)換float64矩陣type,為后面作測(cè)試z8 = (flt.astype(np.uint8))    #<class 'numpy.uint8'>z16 = (flt.astype(np.uint16))   #<class 'numpy.uint16'>z32 = (flt.astype(np.float32))   #<class 'numpy.float32'> 

①對(duì)讀取圖像和隨機(jī)矩陣的存儲(chǔ)

# scipy.misc『不論輸入數(shù)據(jù)是何類型，輸出圖像均為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位輸出圖像與輸入數(shù)據(jù)類型保持一致，64位會(huì)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(灰度值范圍同上)#『uint8和uint16類型轉(zhuǎn)換,會(huì)使輸出圖像灰度變換到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『輸出圖像與輸入數(shù)據(jù)類型保持一致』tif = TIFF.open('./test//randmat_TIFF.tif', mode='w') tif.write_image(flt, compression=None)tif.close() #float64可以存儲(chǔ),但因BitsPerSample=64,一些圖像軟件不識(shí)別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類型轉(zhuǎn)換,會(huì)使輸出圖像灰度變換到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位則0~255)

②圖像或矩陣歸一化對(duì)存儲(chǔ)的影響

# 『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,歸一化后變成np.float64 直接轉(zhuǎn)8bit或16bit都會(huì)超出閾值,要*255或*65535』# 『如果沒有astype的位數(shù)設(shè)置,float64會(huì)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(灰度范圍值同上)im = Image.fromarray(z16Norm.astype(np.uint16))im.save('./test//randmat16_norm_to16_Image.tif') #--> 16bit(0~1)超出閾值im = Image.fromarray(z16Norm.astype(np.uint8))im.save('./test//randmat16_norm_to8_Image.tif') #--> 8bit(0~1)超出閾值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結(jié)果同Image』