In [1]:

    

"""
 Basic imgae load, plot, resize, etc..
 Sungjoon Choi (sungjoon.choi@cpslab.snu.ac.kr)
"""
# Import packs
import numpy as np
import os
from scipy.misc import imread, imresize
import matplotlib.pyplot as plt
import skimage.io
import skimage.transform
# import tensorflow as tf

%matplotlib inline  

print ("Packs loaded")


    

    




Packs loaded

In [2]:

    

# Print Current Folder 
cwd = os.getcwd()
print ("Current folder is %s" % (cwd) ) 

# Useful function
def print_typeshape(img):
    print("Type is %s" % (type(img)))
    print("Shape is %s" % (img.shape,))


    

    




Current folder is /home/enginius/github/tensorflow-101/notebooks

In [3]:

    

# Load 
cat = imread(cwd + "/images/cat.jpg")
print_typeshape(cat)
# Plot
plt.figure(0)
plt.imshow(cat)
plt.title("Original Image with imread")
plt.draw()


    

    




Type is <type 'numpy.ndarray'>
Shape is (1026, 1368, 3)






    

In [4]:

    

# Load
cat2 = imread(cwd + "/images/cat.jpg").astype(np.float)
print_typeshape(cat2)
# Plot
plt.figure(0)
plt.imshow(cat2)
plt.title("Original Image with imread.astype(np.float)")
plt.draw()


    

    




Type is <type 'numpy.ndarray'>
Shape is (1026, 1368, 3)






    

In [5]:

    

# Load
cat3 = imread(cwd + "/images/cat.jpg").astype(np.float)
print_typeshape(cat3)
# Plot
plt.figure(0)
plt.imshow(cat3/255.)
plt.title("Original Image with imread.astype(np.float)/255.")
plt.draw()


    

    




Type is <type 'numpy.ndarray'>
Shape is (1026, 1368, 3)






    

In [6]:

    

# Resize
catsmall = imresize(cat, [100, 100, 3])
print_typeshape(catsmall)
# Plot
plt.figure(1)
plt.imshow(catsmall)
plt.title("Resized Image")
plt.draw()


    

    




Type is <type 'numpy.ndarray'>
Shape is (100, 100, 3)






    

In [7]:

    

# Grayscale
def rgb2gray(rgb):
    if len(rgb.shape) is 3:
        return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])
    else:
        print ("Current Image if GRAY!")
        return rgb
catsmallgray = rgb2gray(catsmall)

print ("size of catsmallgray is %s" % (catsmallgray.shape,))
print ("type of catsmallgray is", type(catsmallgray))

plt.figure(2)
plt.imshow(catsmallgray, cmap=plt.get_cmap("gray"))
plt.title("[imshow] Gray Image")
plt.colorbar()
plt.draw()


    

    




size of catsmallgray is (100, 100)
('type of catsmallgray is', <type 'numpy.ndarray'>)






    




/usr/lib/pymodules/python2.7/matplotlib/collections.py:548: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  if self._edgecolors == 'face':






    

In [8]:

    

# Convert to Vector
catrowvec = np.reshape(catsmallgray, (1, -1));
print ("size of catrowvec is %s" % (catrowvec.shape,))
print ("type of catrowvec is", type(catrowvec))

# Convert to Matrix
catmatrix = np.reshape(catrowvec, (100, 100));
print ("size of catmatrix is %s" % (catmatrix.shape,))
print ("type of catmatrix is", type(catmatrix))


    

    




size of catrowvec is (1, 10000)
('type of catrowvec is', <type 'numpy.ndarray'>)
size of catmatrix is (100, 100)
('type of catmatrix is', <type 'numpy.ndarray'>)

In [9]:

    

# Load from Folder
cwd  = os.getcwd()
path = cwd + "/images/cats"
valid_exts = [".jpg",".gif",".png",".tga", ".jpeg"]

# print ("Images in %s are: \n %s" % (path, os.listdir(path)))
print ("%d files in %s" % (len(os.listdir(path)), path))

# Append Images and their Names to Lists
imgs = []
names = []
for f in os.listdir(path):
    # For all files 
    ext = os.path.splitext(f)[1]
    # Check types 
    if ext.lower() not in valid_exts:
        continue
    fullpath = os.path.join(path,f)
    imgs.append(imread(fullpath))
    names.append(os.path.splitext(f)[0]+os.path.splitext(f)[1])
print ("%d images loaded" % (len(imgs)))


    

    




38 files in /home/enginius/github/tensorflow-101/notebooks/images/cats
38 images loaded

In [10]:

    

# Check
nimgs = len(imgs)
randidx = np.sort(np.random.randint(nimgs, size=3))
print ("Type of 'imgs': ", type(imgs))
print ("Length of 'imgs': ", len(imgs))
for curr_img, curr_name, i \
    in zip([imgs[j] for j in randidx]
           , [names[j] for j in randidx]
           , range(len(randidx))):
    print ("[%d] Type of 'curr_img': %s" % (i, type(curr_img)))
    print ("    Name is: %s" % (curr_name))
    print ("    Size of 'curr_img': %s" % (curr_img.shape,))


    

    




("Type of 'imgs': ", <type 'list'>)
("Length of 'imgs': ", 38)
[0] Type of 'curr_img': <type 'numpy.ndarray'>
    Name is: images (19).jpeg
    Size of 'curr_img': (246, 205, 3)
[1] Type of 'curr_img': <type 'numpy.ndarray'>
    Name is: images (32).jpeg
    Size of 'curr_img': (194, 259, 3)
[2] Type of 'curr_img': <type 'numpy.ndarray'>
    Name is: images (11).jpeg
    Size of 'curr_img': (183, 275, 3)

In [11]:

    

# Plot Images in 'imgs' list
nimgs = len(imgs)
randidx = np.sort(np.random.randint(nimgs, size=3))
for curr_img, curr_name, i \
    in zip([imgs[j] for j in randidx]
           , [names[j] for j in randidx], range(len(randidx))):
    plt.figure(i)
    plt.imshow(curr_img)
    plt.title("[" + str(i) + "] ")
    plt.draw()


    

In [12]:

    

print "That was all!"


    

    




That was all!