def get_visual_words(im, mask, vocabulary):
interest_points = get_interest_points(mask)
descriptors, _ = compute_boundary_desc(im, mask, interest_points)
desc_count = len(descriptors)
words = np.zeros(desc_count)
if desc_count > 0:
dists = euclidean_distances(descriptors, vocabulary)
words = map(lambda i: dists[i].argmin(), range(desc_count))
return words
def get_visual_words(im, mask, vocabulary):
interest_points = get_interest_points(mask)
descriptors, _ = compute_boundary_desc(im, mask, interest_points)
desc_count = len(descriptors)
words = np.zeros(desc_count)
if desc_count > 0:
dists = euclidean_distances(descriptors, vocabulary)
words = map(lambda i: dists[i].argmin(), range(desc_count))
return words
def score_results(coords, desc, search_results, names, voc, verbose=False):
"""
Scores the 200 best results
params
------
coords:
desc:
search_results:
names: ndarray,
image database
voc: ndarray
vocabulary
verbose: boolean, optional, default: False
Make output more verbose
returns:
search_results: ndarray
indxs, scores
"""
# FIXME doxstring
for j, (result, score) in enumerate(search_results):
if verbose:
print "Scoring %d / %d" % (j, len(search_results))
im2, mask2 = get_image(names[result, 0])
interest_points = get_interest_points(mask2)
desc2, coords2 = compute_boundary_desc(im2,
mask2,
interest_points)
if desc2:
search_results[j, 1] += score_(desc, desc2, coords, coords2)
idxs = search_results[:, 1].argsort()[::-1]
return search_results[idxs, :]
import load import matplotlib.pyplot as plt from descriptors import get_interest_points gen = load.load_data() _, _ = gen.next() im, mask = gen.next() points = get_interest_points(mask, min_dist=40) plt.figure() plt.imshow(im) plt.scatter(points[:, 1], points[:, 0])
