In [1]:

    

%run common.ipynb
from __future__ import division


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

import glob
path = "/Users/arve/Dokumenter/TFY4500/well z-scan/slide--S00/chamber--U00--V00/field--X01--Y01/"
files = glob.glob(path + "*.ome.tif")


    

In [3]:

    

# read images to a list
images = []
for f in files:
    images.append(pylab.imread(f)[:,:,1]) # green channel


    

In [4]:

    

imshow(images[0])


    

    
Out[4]:





<matplotlib.image.AxesImage at 0x110882b10>






    

In [5]:

    

images[0].shape


    

    
Out[5]:





(512, 512)

In [6]:

    

def maximize_image(images, stitch_size=32):
    """
    Stitch images to one image based on max value to image area.
    
    :list images: Source images
    :int stitch_size: How large areas should be stitched
    
    :return ndarray: Stitched image
    """
    max_image = np.copy(images[0])
    y_max, x_max = images[0].shape
    Y,X = meshgrid(range(0,y_max,stitch_size), range(0,x_max,stitch_size))
    points = zip(Y.flatten(), X.flatten()) # create list of tuples for less loops
    for y,x in points:
        # selective part of image
        y_stop = y+stitch_size
        x_stop = x+stitch_size
        if y_stop > y_max:
            y_stop = y_max
        if x_stop > x_max:
            x_stop = x_max
        current_max = max_image[y:y_stop, x:x_stop].max()
        for image in images[1:-1]: # exclude first, already copied
            image_part = image[y:y_stop, x:x_stop]
            image_max = image_part.max()
            if image_max > current_max:
                max_image[y:y_stop, x:x_stop] = np.copy(image_part)
                current_max = image_max
    return max_image


    

In [7]:

    

for step in range(4,32,4):
    img = maximize_image(images, step)
    imsave(path+str(step)+"max.tif", img)
for step in range(32,128,16):
    img = maximize_image(images, step)
    imsave(path+str(step)+"max.tif", img)
imsave(path+"2max.tif", maximize_image(images, 2))


    

In [10]:

    

# do the same with smoothed images
images = []
for f in files:
    images.append(smooth(pylab.imread(f)[:,:,1])) # green channel


    

In [11]:

    

for step in range(4,32,4):
    img = maximize_image(images, step)
    imsave(path+str(step)+"smooth max.tif", img)
for step in range(32,128,16):
    img = maximize_image(images, step)
    imsave(path+str(step)+"smooth max.tif", img)
imsave(path+"2smooth max.tif", maximize_image(images, 2))