In [1]:

import os
import dask
import dask_image.imread
import matplotlib.pyplot as plt
import imageio

In [2]:

# Set up figure defaults
# plt.rc('image', cmap='gray', interpolation='nearest')  # Display all images in b&w and with 'nearest' interpolation
plt.rcParams['figure.figsize'] = (16, 9)  # Size up figures a bit
plt.rcParams['figure.dpi'] = 300  # Increase dpi

In [3]:

Images = dask_image.imread.imread(os.path.join('img', '*.jpg'))

In [4]:

Images

Out[4]:

	Array	Chunk
Bytes	1.50 GiB	34.88 MiB
Shape	(44, 3024, 4032, 3)	(1, 3024, 4032, 3)
Dask graph	44 chunks in 3 graph layers
Data type	uint8 numpy.ndarray

In [5]:

Images.shape

Out[5]:

(44, 3024, 4032, 3)

In [6]:

# 'Roll' the array so that autumn is in the middle of the array and not at the start
# Roll along the image axis, otherwise we roll the RGB channels :)
Images = dask.array.roll(Images, shift=len(Images)//2, axis=0)

In [7]:

# Show every xth image
x = 5
for c, img in enumerate(Images[::x]):
    plt.subplot(1,len(Images[::x]), c+1)
    plt.imshow(img)
    plt.title(range(len(Images))[::x][c])
    plt.axis('off')
plt.show()