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
def grid_search(model_images, query_images, dist_type, hist_type, num_bins):
[best_match,
#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:
model_images = fp.readlines()
model_images = [x.strip() for x in model_images]
with open('query.txt') as fp:
query_images = fp.readlines()