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import asyncio
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
from schemas import Shop
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
async def create_feature_files(shop: Shop):
model = keras.applications.VGG16(weights='imagenet', include_top=True)
feat_extractor = Model(inputs=model.input, outputs=model.get_layer("fc2").output)
await calculate_shop(shop, feat_extractor)
async def calculate_shop(shop: Shop, feat_extractor) -> None:
start = datetime.today()
if shop.id: # 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[:100]:
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 = {"shop_id": shop.id, "design": im,
"results": design_list
}
print(design_dict)
try:
response = requests.post(shop.webhook_url, json=design_dict)
if response.status_code == 200:
print(f"result for {im} is sent to {shop.webhook_url}")
else:
print(f"Error sending data {shop.webhook_url} to for result {im}: {response.status_code}")
except Exception as e:
print(f"Error sending data {shop.webhook_url} to for result {im}: {response.status_code}", e)
# 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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