def grid_search(model_images, query_images, dist_type, hist_type, num_bins):
[best_match,
D] = match_module.find_best_match(model_images, query_images, dist_type,
hist_type, num_bins)
num_correct = sum(best_match == range(len(query_images)))
recog_rate = num_correct / len(query_images)
return [dist_type, hist_type, num_bins, num_correct, recog_rate]
def compare_dist_rpc(model_images, query_images, dist_types, hist_type, num_bins, plot_colors):
assert len(plot_colors) == len(dist_types)
for idx in range(len(dist_types)):
[best_match, D] = match_module.find_best_match(model_images, query_images, dist_types[idx], hist_type, int(num_bins))
plot_rpc(D, plot_colors[idx])
plt.axis([0, 1, 0, 1])
plt.xlabel('1 - precision')
plt.ylabel('recall')
#legend(dist_types, 'Location', 'Best')
plt.legend( dist_types, loc='best')
def compare_dist_rpc(model_images, query_images, dist_types, hist_type, num_bins, plot_colors):
assert len(plot_colors) == len(dist_types), 'number of distance types should match the requested plot colors'
for idx in range( len(dist_types) ):
[best_match, D] = match_module.find_best_match(model_images, query_images, dist_types[idx], hist_type, num_bins)
plot_rpc(D, plot_colors[idx])
plt.axis([0, 1, 0, 1]);
plt.xlabel('1 - precision');
plt.ylabel('recall');
# legend(dist_types, 'Location', 'Best')
plt.legend( dist_types, loc='best')
## Find best match (Question 3.a)
with open('model.txt') as fp:
model_images = fp.readlines()
model_images = [x.strip() for x in model_images]
with open('query.txt') as fp:
query_images = fp.readlines()
query_images = [x.strip() for x in query_images]
dist_type = 'intersect'
hist_type = 'rg'
num_bins = 30
[best_match, D] = match_module.find_best_match(model_images, query_images,
dist_type, hist_type, num_bins)
## visualize nearest neighbors (Question 3.b)
query_images_vis = [query_images[i] for i in np.array([0, 4, 9])]
match_module.show_neighbors(model_images, query_images_vis, dist_type,
hist_type, num_bins)
## compute recognition percentage (Question 3.c)
# import ipdb; ipdb.set_trace()
num_correct = sum(best_match == range(len(query_images)))
print('number of correct matches: %d (%f)\n' %
(num_correct, 1.0 * num_correct / len(query_images)))
# %%
# All combinations
with open('query.txt') as fp:
query_images = fp.readlines()
query_images = [x.strip() for x in query_images]
#chi2
#intersect
#l2
eval_dist_type = 'intersect'
#grayvalue
#rgb
#rg
#dxdy
eval_hist_type = 'rgb'
eval_num_bins = 60
[best_match,
D] = match_module.find_best_match(model_images, query_images, eval_dist_type,
eval_hist_type, eval_num_bins)
## visualize nearest neighbors (Question 3.b)
query_images_vis = [query_images[i] for i in np.array([0, 4, 9])]
match_module.show_neighbors(model_images, query_images_vis, eval_dist_type,
eval_hist_type, eval_num_bins)
## compute recognition percentage (Question 3.c)
# import ipdb; ipdb.set_trace()
num_correct = sum(best_match == range(len(query_images)))
print('number of correct matches: %d (%f)\n' %
(num_correct, 1.0 * num_correct / len(query_images)))
# plot recall_precision curves (Question 4)
with open('model.txt') as fp:
