WARNING:

When running locally you must cd in the following cell into the root of the cloned repo.



In [22]:

    
cd /Users/dharness/dev/seam









    



/Users/dharness/dev/seam



In [34]:

    
%reload_ext autoreload
%autoreload
import matplotlib.animation as animation
import matplotlib.pyplot as plt
from scipy import misc
from scipy.ndimage.filters import gaussian_filter
import numpy as np
from IPython.display import Image
from seam_carver.seam_carver import  (
    normalize,
    compute_eng_grad,
    compute_eng_color,
    compute_eng,
    remove_seam,
    add_seam,
    find_seams,
    get_best_seam,
    reduce_width,
    reduce_height,
    increase_width,
    increase_height,
    intelligent_resize
)

Original Image



In [9]:

    
Image(filename='./demo/cat.png')









    Out[9]:

Simple Energy Gradient with Sobel Filter



In [10]:

    
cat_img = misc.imread('./demo/cat.png')
eng = compute_eng_grad(cat_img)
misc.imsave('./demo/cat_eng_grad.png', eng)
Image(filename='./demo/cat_eng_grad.png')









    Out[10]:

Simple Color Energy



In [11]:

    
rgb_weights = [-3, 1, -3]
cat_img = misc.imread('./demo/cat.png')
eng = compute_eng_color(cat_img, rgb_weights)
misc.imsave('./demo/cat_eng_color.png', eng)
Image(filename='./demo/cat_eng_color.png')









    Out[11]:

Total Energy



In [12]:

    
rgb_weights = [-3, 1, -3]
mask_weight = 10
cat_img = misc.imread('./demo/cat.png')
mask = np.zeros(cat_img.shape)
img4 = np.dstack((cat_img, mask))
eng = compute_eng(img4, rgb_weights, mask_weight)
misc.imsave('./demo/cat_eng_total.png', eng)
Image(filename='./demo/cat_eng_total.png')









    Out[12]:

Add Seam



In [13]:

    
%autoreload
rgb_weights = [-3, 1, -3]
mask_weight = 10
cat_img = misc.imread('./demo/cat.png')
dims = (cat_img.shape[0], cat_img.shape[1])

img4 = np.dstack((
    cat_img,
    np.zeros(dims)
))
eng = compute_eng(img4, rgb_weights, mask_weight)
seam, adjusted_img4, cost, adjusted_eng = increase_width(img4, eng)

%matplotlib inline
plt.imshow(adjusted_eng, cmap='gray')









    Out[13]:





<matplotlib.image.AxesImage at 0x113613a90>



In [14]:

    
%autoreload
rgb_weights = [-3, 1, -3]
mask_weight = 10
cat_img = misc.imread('./demo/cat.png')
dims = (cat_img.shape[0], cat_img.shape[1])

img4 = np.dstack((
    cat_img,
    np.zeros(dims)
))
eng = compute_eng(img4, rgb_weights, mask_weight)
seam, adjusted_img4, cost, adjusted_eng = increase_height(img4, eng)

%matplotlib inline
plt.imshow(adjusted_eng, cmap='gray')









    Out[14]:





<matplotlib.image.AxesImage at 0x113e79278>

Intelligent Resize



In [19]:

    
%autoreload
rgb_weights = [-3, 1, -3]
mask_weight = 10
cat_img = misc.imread('./demo/cat.png')
mask = np.zeros(cat_img.shape)

resized_img = intelligent_resize(cat_img, 0, -20, rgb_weights, mask, mask_weight)
misc.imsave('./demo/cat_shrunk.png', resized_img)
Image(filename='./demo/cat_shrunk.png')









    Out[19]:



In [20]:

    
%autoreload
resized_img = intelligent_resize(cat_img, 0, 50, rgb_weights, mask, mask_weight)
misc.imsave('./demo/cat_grown.png', resized_img)
Image(filename='./demo/cat_grown.png')









    Out[20]: