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):
for j in range(i+1,nbr_images): # no need to copy diagonal
matchscores[j,i] = matchscores[i,j]
threshold = 2 # min number of matches needed to create link
g = pydot.Dot(graph_type='graph') # don't want the default directed graph
for i in range(nbr_images):
for j in range(i+1,nbr_images):
from PIL import Image
import numpy as np
import pylab as plt
from common import imtools
from common import sift
im1 = np.array(Image.open("data/climbing_1_small.jpg").convert("L"))
im2 = np.array(Image.open("data/climbing_2_small.jpg").convert("L"))
kp1, desc1 = sift.detect_and_compute(im1)
kp2, desc2 = sift.detect_and_compute(im2)
print('starting matching')
matches = sift.match_twosided(desc1, desc2)
plt.figure("SIFT Rock Wall 2-Side Matching")
plt.gray()
sift.plot_matches(im1, im2, kp1, kp2, matches)
plt.show()
import pickle
from common import objects3d
from common import homography
from common import camera
from common import sift
im_book_frontal = np.array(Image.open('data/book_frontal.JPG'))
im_book_perspective = np.array(Image.open('data/book_perspective.JPG'))
# Compute features
l0, d0 = sift.detect_and_compute(im_book_frontal)
l1, d1 = sift.detect_and_compute(im_book_perspective)
# Match features and estimate homography
matches = sift.match_twosided(d0, d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
tp = homography.make_homog(l1[ndx2, :2].T)
# This differs from the book: Swap coordinates in the from/to points because they are row/col
# whereas the Camera plots are x/y. If they aren't swapped before goint into the
# H_from_ransac method, H will not be computed correctly for the camera translation.
fp_xy = np.array([fp[1, :], fp[0, :], fp[2, :]])
tp_xy = np.array([tp[1, :], tp[0, :], tp[2, :]])
model = homography.RansacModel()
H = homography.H_from_ransac(fp_xy, tp_xy, model)[0]
# camera calibration
K = camera.book_calibration((747, 1000))