def eval(retrieval_top_n=2):
dataset = Fashion_inshop()
length = dataset.test_len
deep_feats, color_feats, labels = load_feat_db()
deep_feats, color_feats, labels = deep_feats[-length:], color_feats[
-length:], labels[-length:]
feat_dict = {
labels[i]: (deep_feats[i], color_feats[i])
for i in range(len(labels))
}
include_once = 0
include_zero = 0
include_times = 0
should_include_times = 0
for iter_id, item_id in enumerate(dataset.test_list):
item_imgs = dataset.test_dict[item_id]
item_img = random.choice(item_imgs)
result = get_deep_color_top_n(feat_dict[item_img], deep_feats,
color_feats, labels, retrieval_top_n)
keys = list(map(lambda x: x[0], result))
included = list(map(lambda x: x in item_imgs, keys))
should_include_times += (len(item_imgs) - 1)
include_once += (1 if included.count(True) >= 2 else 0)
include_zero += (1 if included.count(True) <= 1 else 0)
include_times += (included.count(True) - 1)
if iter_id % 10 == 0:
print("{}/{}, is included: {}/{}, included times: {}/{}".format(
iter_id, len(dataset.test_list), include_once,
include_once + include_zero, include_times,
should_include_times))
return include_times, should_include_times, include_once, include_zero
from file import Anno, Eval
import random
import re
#----- CONFIG -----#
# set static url path
app = flask.Flask(__name__, static_url_path='/static')
app.config['DEBUG'] = True
# first part of path of images hosted by aws
static_path = "https://capstone-deepfashion.s3.us-east-2.amazonaws.com/"
#-------- FEATURES DATA -----------#
deep_feats, color_feats, labels = load_feat_db()
#----- ROUTES -----#
@app.route("/")
def random_query_sample(batch=1):
'''Get an image from the test dataset, upper wear only'''
ann = Anno(is_train=False)
paths = []
for i in range(batch):
ran = random.randrange(0, len(ann))
paths.append(ann.loc[ran]['image_name'])
selected_path = static_path + paths[0]
return flask.render_template('query_styling.html',selected_path=selected_path)
# -*- coding:utf-8 -*-
from sklearn.cluster import KMeans
from retrieval import load_feat_db
from sklearn.externals import joblib
from config import DATASET_BASE, N_CLUSTERS
import os
if __name__ == '__main__':
feats, _, labels = load_feat_db() #d_feats. c_feats
#model = KMeans(n_clusters=N_CLUSTERS, random_state=0, n_jobs=-1).fit(feats)
model = KMeans(n_clusters=N_CLUSTERS, random_state=1, n_jobs=1).fit(feats)
model_path = os.path.join(DATASET_BASE, r'models', r'kmeans.m')
joblib.dump(model, model_path)
from sklearn.cluster import KMeans
from retrieval import load_feat_db
from sklearn.externals import joblib
from config import DATASET_BASE, N_CLUSTERS
import os
if __name__ == '__main__':
feats, color, labels = load_feat_db()
model = KMeans(n_clusters=N_CLUSTERS,
init='k-means++',
random_state=0,
n_jobs=-1).fit(feats)
model_path = os.path.join(DATASET_BASE, r'models', r'kmeans.m')
joblib.dump(model, model_path)
# if __name__ == '__main__':
# feats, labels = load_feat_db()
# model = KMeans(n_clusters=N_CLUSTERS, random_state=0, n_jobs=-1).fit(feats)
# model_path = os.path.join(DATASET_BASE, r'models', r'kmeans.m')
# joblib.dump(model, model_path)
# Modified version for scrapped data
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--scrapped", help="run kmeans on scrapped dataset rather than on deepfashion",
action="store_true")
args = parser.parse_args()
if args.scrapped:
print("Performing kmeans clustering on scrapped dataset.")
# feats, labels = load_feat_db(custom=True)
feats, color_feats, labels = load_feat_db(custom=True)
model = KMeans(n_clusters=N_CLUSTERS, random_state=0, n_jobs=-1).fit(feats)
model_path = os.path.join(DATASET_BASE, r'models', r'kmeans_scrapped.m')
joblib.dump(model, model_path)
else:
print("Performing kmeans clustering on deepfashion dataset.")
# feats, labels = load_feat_db()
feats, color_feats, labels = load_feat_db()
model = KMeans(n_clusters=N_CLUSTERS, random_state=0, n_jobs=-1).fit(feats)
model_path = os.path.join(DATASET_BASE, r'models', r'kmeans.m')
joblib.dump(model, model_path)
# feats, labels = load_feat_db()
# model = KMeans(n_clusters=N_CLUSTERS, random_state=0, n_jobs=-1).fit(feats)
# -*- coding:utf-8 -*-
from sklearn.cluster import KMeans
from retrieval import load_feat_db
from sklearn.externals import joblib
from config import DATASET_BASE, N_CLUSTERS
import os
if __name__ == '__main__':
feats, labels = load_feat_db()
model = KMeans(n_clusters=N_CLUSTERS, random_state=0, n_jobs=-1).fit(feats)
model_path = os.path.join(DATASET_BASE, r'models', r'kmeans.m')
joblib.dump(model, model_path)