from PIL import Image
import pylab as pl
import numpy as np
from common import imtools
im = np.array(Image.open('data/empire.jpg'))
pl.figure('Orignal')
pl.imshow(im)
im_resz = imtools.imresize(im, [142, 200])
pl.figure('Resized')
pl.imshow(im_resz)
pl.show()
from PIL import Image
import numpy as np
import pylab as plt
from common import imtools
from common import harris
# This is the Harris point matching example in Figure 2-2.
im1 = np.array(Image.open("data/crans_1_small.jpg").convert("L"))
im2 = np.array(Image.open("data/crans_2_small.jpg").convert("L"))
# resize to make matching faster
im1 = imtools.imresize(im1, (int(im1.shape[1] / 2), int(im1.shape[0] / 2)))
im2 = imtools.imresize(im2, (int(im2.shape[1] / 2), int(im2.shape[0] / 2)))
wid = 5
harrisim = harris.compute_harris_response(im1, 5)
filtered_coords1 = harris.get_harris_points(harrisim, wid + 1)
d1 = harris.get_descriptors(im1, filtered_coords1, wid)
harrisim = harris.compute_harris_response(im2, 5)
filtered_coords2 = harris.get_harris_points(harrisim, wid + 1)
d2 = harris.get_descriptors(im2, filtered_coords2, wid)
print('starting matching')
matches = harris.match_twosided(d1, d2)
plt.figure()
plt.gray()
harris.plot_matches(im1, im2, filtered_coords1, filtered_coords2, matches)
plt.show()
from common import sift
import pydot
download_path = "data/EiffelTower"
thumbnail_path = os.getcwd() + "/graphs/thumbnails/"
# list of downloaded filenames
im_paths = imtools.get_imlist(download_path)
nbr_images = len(im_paths)
im_list = []
for path in im_paths:
im = np.array(Image.open(path).convert("L"))
im_list.append(
imtools.imresize(im, (int(im.shape[1] / 2), int(im.shape[0] / 2))))
matchscores = np.zeros((nbr_images, nbr_images))
for i in range(nbr_images):
for j in range(i, nbr_images): # only compute upper triangle
print('comparing ', im_paths[i], im_paths[j])
l1, d1 = sift.detect_and_compute(im_list[i])
l2, d2 = sift.detect_and_compute(im_list[j])
matches = sift.match_twosided(d1, d2)
nbr_matches = np.sum(matches > 0)
print('number of matches = ', nbr_matches)
matchscores[i, j] = nbr_matches
# copy values
for i in range(nbr_images):
from common import imtools
from common import sift
import pydot
download_path = "data/WhiteHouse"
thumbnail_path = os.getcwd() + "/graphs/thumbnails/"
# list of downloaded filenames
im_paths = imtools.get_imlist(download_path)
nbr_images = len(im_paths)
im_list = []
for path in im_paths:
im = np.array(Image.open(path).convert("L"))
im_list.append(imtools.imresize(im,(int(im.shape[1]/2),int(im.shape[0]/2))))
matchscores = np.zeros((nbr_images,nbr_images))
for i in range(nbr_images):
for j in range(i,nbr_images): # only compute upper triangle
print('comparing ', im_paths[i], im_paths[j])
l1,d1 = sift.detect_and_compute(im_list[i])
l2,d2 = sift.detect_and_compute(im_list[j])
matches = sift.match_twosided(d1,d2)
nbr_matches = np.sum(matches > 0)
print('number of matches = ', nbr_matches)
matchscores[i,j] = nbr_matches
# copy values
"""
Exercise 4
Create copies of an image with different resolutions (for example by halving the
size a few times). Extract SIFT features for each image. Plot and match features
to get a feel for how and when the scale independence breaks down.
"""
from PIL import Image
import numpy as np
import pylab as plt
from common import imtools
from common import sift
# Seems to break down around 8, clearly breaks down at 16.
div_factor = 8
im = np.array(Image.open('data/empire.jpg').convert("L"))
im_resized = imtools.imresize(im, (int(im.shape[1]/div_factor),int(im.shape[0]/div_factor)))
kp1, desc1 = sift.detect_and_compute(im)
kp2, desc2 = sift.detect_and_compute(im_resized)
print('starting matching')
matches = sift.match_twosided(desc1,desc2)
plt.figure()
plt.gray()
sift.plot_matches(im,im_resized,kp1,kp2,matches)
plt.show()