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import tempfile as tfile
from datetime import datetime
from urllib.request import urlopen
import requests
from keras.utils import img_to_array
from lxml import etree
import keras
from keras.applications.imagenet_utils import decode_predictions, preprocess_input
from keras.models import Model
from PIL import Image
from io import BytesIO
import numpy as np
from sklearn.decomposition import PCA
from scipy.spatial import distance
from collections import OrderedDict
from remove import remove_files
from generate_csv_file import generate_csv_files
from load_data import load_data, get_shops
def get_ids_from_feed(feed_url):
# create temp xml file
temp_file = tfile.NamedTemporaryFile(mode="w", suffix=".xml", prefix="feed")
f = temp_file.name
temp_file.write(urlopen(feed_url).read().decode('utf-8'))
# open xml file
tree = etree.parse(f)
temp_file.close()
root = tree.getroot()
# get image ids and shop base url
list_ids = []
shop_url = root[0][1].text
for item in root.findall(".//g:mpn", root.nsmap):
list_ids.append(item.text)
return list_ids, shop_url
def get_image(url):
res = requests.get(url)
im = Image.open(BytesIO(res.content)).convert("RGB").resize((224, 224))
img = img_to_array(im)
x = img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
return img, x
def load_image(url, img_id):
print('get image url', id)
request_url = '{}/flat_thumb/{}/1/224'.format(url, img_id)
print('get image', request_url)
img, x = get_image(request_url)
return img, x
def create_feature_files():
model = keras.applications.VGG16(weights='imagenet', include_top=True)
feat_extractor = Model(inputs=model.input, outputs=model.get_layer("fc2").output)
final_json = []
data = get_shops()
if data:
for p in data:
final_json.append(calculate_shop(p, feat_extractor))
load_data(generate_csv_files(final_json))
remove_files()
return
def calculate_shop(shop, feat_extractor):
start = datetime.today()
if shop['id'] not in ['']: # temp
print(shop['id'], shop['base_url'])
google_xml_feed_url = '{}/google_xml_feed'.format(shop['base_url'])
try:
list_ids, shop_url = get_ids_from_feed(google_xml_feed_url)
except Exception as e:
list_ids = []
print('could not get images from ', shop['id'], e)
features = []
list_of_fitted_designs = []
design_json = {}
if len(list_ids) > 0:
for l in list_ids:
try:
img, x = load_image(shop_url, l)
feat = feat_extractor.predict(x)[0]
features.append(feat)
list_of_fitted_designs.append(l)
except Exception as e:
print(l, ' failed loading feature extraction', e)
try:
features = np.array(features)
# print(features.shape)
components = len(features) if len(features) < 300 else 300
pca = PCA(n_components=components) # 300
pca.fit(features)
pca_features = pca.transform(features)
except Exception as e:
print('pca too small?', e)
if len(list_of_fitted_designs) >= 80:
max_list_per_design = 80
else:
max_list_per_design = len(list_of_fitted_designs)
try:
for im in list_of_fitted_designs:
query_image_idx = list_of_fitted_designs.index(im)
similar_idx = [distance.cosine(pca_features[query_image_idx], feat) for feat in pca_features]
filterd_idx = dict()
for i in range(len(similar_idx)):
filterd_idx[i] = {"dist": similar_idx[i], "id": list_of_fitted_designs[i]}
sorted_dict = dict(
OrderedDict(sorted(filterd_idx.items(), key=lambda i: i[1]['dist'])[1:max_list_per_design]))
design_list = []
for k, v in sorted_dict.items():
design_list.append(v)
design_dict = {im: design_list}
# idx_closest = sorted(range(len(similar_idx)), key=lambda k: similar_idx[k])
design_json.update(design_dict)
# print(idx_closest)
except Exception as e:
print("could not create json with look-a-like for shop:", shop['id'], e)
end = datetime.today()
return {'shop_id': shop['id'], 'start_time': start, 'end_time': end, 'designs': design_json}
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