

In [20]:

    
%matplotlib inline 

import numpy as np
from scipy.misc import imread

from plt_utils import *



In [21]:

    
# img = np.array(data.lena()).astype('uint8')
img = imread('ducks.jpg')
img_float = img.astype('float32')
img_float_gray = img.mean(-1) # will be float type
img_stacked = np.concatenate((img, img), axis=2)

grayshow

Display 2D arrays as a grayscale image (or as a heatmap).



In [22]:

    
# basic use case
grayshow(img_float_gray)



In [23]:

    
# inputs are always scaled to be between 0 and 1 before displaying
# (also: british spelling)
greyshow(img_float_gray*400)



In [24]:

    
# can also display as a heatmap
grayshow(img_float_gray, heat=True)



In [25]:

    
# clip values in array before displaying
grayshow(img_float_gray, clip=[0, 125])

colorshow

Display a 3D NxMx3 array as a color (RGB) image.



In [26]:

    
# basic use case (uint8 array)
colorshow(img)



In [27]:

    
# float inputs are scale to be between 0 and 1 before displaying
colorshow(img_float*400)

imshow

Convenience wrapper around grayshow and colorshow that "does the right thing" (most of the time).



In [28]:

    
# display 2D array as grayscale image
imshow(img_float_gray)



In [29]:

    
# display 3D array with 3 channels as RGB image
imshow(img)



In [30]:

    
# Float inputs are scaled to be between 0 and 1 before display
imshow(img_float*1000)



In [31]:

    
# display 3D array with 3 channels as mosaic of separate channels
imshow(img, split=True)



In [32]:

    
# display 3D array with number of channels != 3 as mosaic of separate channels
imshow(img_stacked)

Mosaic

Stitch multiple images (of the same shape) into one.



In [33]:

    
# display a list of color images in a mosaic
imshow(mosaic([img]*5))



In [34]:

    
# control how images get stitched together
imshow(mosaic([img]*15, padding=50, num_row=2))



In [35]:

    
# display a list of grayscale images
imshow(mosaic([img_float_gray]*5))



In [36]:

    
# if images have different ranges, it might be difficult to 
# see some of them after stitching
imshow(mosaic([img_float_gray, img_float_gray*2, img_float_gray*3], num_row=1))



In [37]:

    
# can normalize to (0,1) and/or clip images before stitching together
imshow(mosaic([img_float_gray, img_float_gray*2, img_float_gray*3], num_row=1, normalize=True, clip=(.3,.5)))



In [38]:

    
# can do the same for color images
imshow(mosaic([img_float, img_float*2, img_float*3], num_row=1, normalize=True))