import numpy as np
from sklearn.externals.joblib import Memory
from load import load_data
from descriptors import compute_boundary_desc, get_interest_points
from histograms import compute_visual_words
from retrieval import search, show_results
mem = Memory(cachedir='.')
postings = np.load('./data/postings.npy')
vocabulary = np.load('./data/vocabulary.npy')
image_names = np.load('./data/images.npy')
gen = load_data()
im, mask = gen.next()
interest_points = mem.cache(get_interest_points)(mask)
descriptors, _ = mem.cache(compute_boundary_desc)(im, mask, interest_points)
vw = compute_visual_words(descriptors, vocabulary)
results = search(vw, postings)
show_results(results, image_names)
# Awesome ! We're finding the the first image as best result !
visual_words = compute_visual_words(desc, voc)
if visual_words is None:
continue
#search_results = retrieval.search2(visual_words, postings, max_im=20)
query_document, _ = np.histogram(visual_words,
bins=np.arange(len(voc) + 1))
search_results = retrieval2.search(query_document, max_im=20)
#search_results2 = mem.cache(search2)(visual_words,postings,max_im=20)
indices = search_results[:, 0].astype(int)
search_file_names = all_names[indices]
queries_total += 1
ave_prec = average_precision(search_file_names, positive_file_names,
ignore_file_names)
mean_average_precision += ave_prec
print "Prec: ", ave_prec
if ave_prec > best_prec:
best_query_file_name = query_file_name
best_search_file_names = search_file_names
best_prec = ave_prec
best_results = search_results
mean_average_precision /= queries_total
retrieval.show_results(best_results,
best_search_file_names,
"%s (%f) mAP: %.2f" % (best_query_file_name,
best_prec,
mean_average_precision))
pass
interest_points = mem.cache(get_interest_points)(mask)
desc, coords = mem.cache(compute_boundary_desc)(im, mask, interest_points)
visual_words = compute_visual_words(desc, voc)
if visual_words is None:
continue
#search_results = retrieval.search2(visual_words, postings, max_im=20)
query_document, _ = np.histogram(visual_words,
bins=np.arange(len(voc) + 1))
search_results = retrieval2.search(query_document, max_im=20)
#search_results2 = mem.cache(search2)(visual_words,postings,max_im=20)
indices = search_results[:, 0].astype(int)
search_file_names = all_names[indices]
queries_total += 1
ave_prec = average_precision(search_file_names, positive_file_names,
ignore_file_names)
mean_average_precision += ave_prec
print "Prec: ", ave_prec
if ave_prec > best_prec:
best_query_file_name = query_file_name
best_search_file_names = search_file_names
best_prec = ave_prec
best_results = search_results
mean_average_precision /= queries_total
retrieval.show_results(
best_results, best_search_file_names, "%s (%f) mAP: %.2f" %
(best_query_file_name, best_prec, mean_average_precision))
pass