import numpy as np
from scipy import misc
from PIL import Image
from 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float64
np.random.seed(12345)
flt = np.random.randn(512, 512)   #<class 'numpy.float64'>
# 轉(zhuǎn)換float64矩陣type,為后面作測試
z8 = (flt.astype(np.uint8))    #<class 'numpy.uint8'>
z16 = (flt.astype(np.uint16))   #<class 'numpy.uint16'>
z32 = (flt.astype(np.float32))   #<class 'numpy.float32'> 

# 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) #--> 8bit
misc.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') #--> 32bit
Image.fromarray(tif16).save('.\test\lena16_Image.tif') #--> 16bit
Image.fromarray(tif8).save('.\test\lena8_Image.tif') #--> 8bit

Image.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)

# 『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』

#tiff文件解析成圖像序列：讀取tiff圖像
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 
 #根據(jù)文檔,應(yīng)該是這樣實(shí)現(xiàn),但測試中不管是tif.read_image還是tif.iter_images讀入的矩陣數(shù)值都有問題

#圖像序列保存成tiff文件：保存tiff圖像 
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])
  #縮放成統(tǒng)一尺寸 
  im = im.resize((480, 480), Image.ANTIALIAS) 
  tif.write_image(im, compression = None)  
 out_tiff.close() 
 return 

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