スタンフォード/CS131/宿題4-4 画像の拡大・縮小

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import rc
from skimage import color,io, util
from time import time
from IPython.display import HTML
from __future__ import print_function
from seam_carving import compute_cost,energy_function,enlarge
from seam_carving import backtrack_seam,reduce,remove_seam

%matplotlib inline
plt.rcParams['figure.figsize'] = (15.0, 12.0) # set default size of plots
plt.rcParams["font.size"] = "17"
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'

img = io.imread('imgs/broadway_tower.jpg')
img = util.img_as_float(img)
# Reduce image width
H, W, _ = img.shape
W_new = 200

start = time()
out = reduce(img, W_new)
end = time()

print("Reducing width from %d to %d: %f seconds." % (W, W_new, end - start))

plt.subplot(2, 1, 1)
plt.title('Original')
plt.imshow(img)

plt.subplot(2, 1, 2)
plt.title('Resized')
plt.imshow(out)
plt.show()

def reduce_fast(image, size, axis=1, efunc=energy_function, cfunc=compute_cost):
    """Reduces the size of the image using the seam carving process. Faster than `reduce`.
    Use your own implementation (you can use auxiliary functions if it helps like `energy_fast`)
    to implement a faster version of `reduce`.
    Hint: do we really need to compute the whole cost map again at each iteration?
    Args:
        image: numpy array of shape (H, W, C)
        size: size to reduce height or width to (depending on axis)
        axis: reduce in width (axis=1) or height (axis=0)
        efunc: energy function to use
        cfunc: cost function to use
    Returns:
        out: numpy array of shape (size, W, C) if axis=0, or (H, size, C) if axis=1
    """
    out = np.copy(image)
    if axis == 0:
        out = np.transpose(out, (1, 0, 2))
    H = out.shape[0]
    W = out.shape[1]
    assert W > size, "Size must be smaller than %d" % W
    assert size > 0, "Size must be greater than zero"
    ### YOUR CODE HERE
    energy = efunc(out)
    for i in range(W-size):
        cost,path = cfunc(out,energy)
        seam = backtrack_seam(path,np.argmin(cost[-1]))
        out = remove_seam(out,seam)
        i = np.min(seam)
        j = np.max(seam) 
        if i <= 3:
            energy = np.c_[efunc(out[:,0:j+2])[:,:-1],\
                           energy[:,j+2:]]
        elif j >= i-3:
            energy = np.c_[energy[:,0:i-1],\
                           efunc(out[:,i-3:])[:,2:]]
        else:
            energy = np.c_[energy[:,0:i-1], \
             efunc(out[:,i-3:j+2])[:,2:-1],energy[:,j+2:]]
    ### END YOUR CODE
    assert out.shape[1] == size, "Output doesn't have the right shape"
    if axis == 0:
        out = np.transpose(out, (1, 0, 2))
    return out

# Reduce image width
H, W, _ = img.shape
W_new = 400

start = time()
out = reduce(img, W_new)
end = time()

print("Normal reduce width from %d to %d: %f seconds." % (W, W_new, end - start))

start = time()
out_fast = reduce_fast(img, W_new)
end = time()

print("Faster reduce width from %d to %d: %f seconds." % (W, W_new, end - start))

assert np.allclose(out, out_fast), "Outputs don't match."

plt.subplot(3, 3, 1)
plt.title('Original')
plt.imshow(img)

plt.subplot(3, 3, 2)
plt.title('Resized')
plt.imshow(out)

plt.subplot(3, 3, 3)
plt.title('Faster resized')
plt.imshow(out)
plt.show()

# Load image
img2 = io.imread('imgs/wave.jpg')
img2 = util.img_as_float(img2)

plt.title('Original Image')
plt.imshow(img2)
plt.show()

out = reduce(img2, 300)
plt.imshow(out)
plt.show()

out = enlarge(img2, 800)
plt.imshow(out)
plt.show()