def __init__(self, GPU=True):
self.conn = psycopg2.connect(dbname="dogs2", user="******")
self.psql = self.conn.cursor()
self.features = []
self.GPU = GPU
self.binar = BinarizeArray()
self.binar.run()
class DoggyMatchEngine(object):
def __init__(self, GPU=True):
self.conn = psycopg2.connect(dbname="dogs2", user="******")
self.psql = self.conn.cursor()
self.features = []
self.GPU = GPU
self.binar = BinarizeArray()
self.binar.run()
def fit(self, user_input, session_id):
self.session_id = session_id
if "http" in user_input or "https" in user_input:
r = requests.get(user_input, stream=True)
if r.status_code == 200:
rand_name = "app/static/uploads/img_" + session_id + ".jpg"
with open(rand_name, "wb") as f:
r.raw.decode_content = True
shutil.copyfileobj(r.raw, f)
img_file = rand_name
else:
img_file = user_input
img = imread(img_file)
i_size = img.shape
if i_size[0] < 221 or i_size[1] < 221 or (i_size[2] != 3 and i_size[2] != 4):
raise Exception("Img too small to process.")
imgr = resize(img, [221, 221])
self.orig_file = img_file
img_file = img_file.replace("app/static/uploads/", "app/static/uploads/r_")
imsave(img_file, imgr)
if self.GPU:
p = subprocess.check_output(
[
"/OverFeat/bin/linux_64/cuda/\
overfeat_cuda",
"-f",
"-l",
img_file,
]
)
else:
p = subprocess.check_output(["/OverFeat/bin/linux_64/overfeat", "-f", "-l", img_file])
features = p.replace("\n", "").replace("1-", "").strip(" ").split(" ")[2:]
features = [float(x) for x in features]
if len(features) != 4096:
raise Exception("Feature size is off.")
self.user_features = features
def sql_import(self, user_zip, user_radius):
self.psql.execute(
"""select lng,lat from zipcodes where zip=%s limit 1;
"""
% user_zip
)
lnglat = self.psql.fetchall()
user_long = lnglat[0][0]
user_lat = lnglat[0][1]
self.psql.execute(
"""select id,features_bin from records where ST_Distance_Sphere
(geom, ST_MakePoint(%s, %s))*0.000621371 < %s;
"""
% (user_long, user_lat, user_radius)
)
filtered = self.psql.fetchall()
self.geo_ids_lst = [x[0] for x in filtered]
self.geo_feature_lst = [x[1] for x in filtered]
def match(self, num_matches=10):
sim_scores = pairwise_distances(
np.array(self.user_features), np.array(self.geo_feature_lst), "cosine", n_jobs=-1
)
sim_scores = np.array(sim_scores).flatten()
ids = np.array(self.geo_ids_lst)
match_inds = sim_scores.argsort()
min_arr = match_inds[:num_matches]
self.top10_scores = sim_scores[min_arr]
self.top10_ids = ids[min_arr]
ids_subset = [re.sub("_.*", "", x) for x in self.top10_ids]
c_ids = Counter(ids_subset)
if c_ids.most_common()[0][1] > 1:
dupes = True
else:
dupes = False
data_in = num_matches
while dupes:
arr_ids = np.array(ids_subset)
repeats = np.where(arr_ids == c_ids.most_common()[0][0])
dup_ind = repeats[0][1]
new_top_ids = np.delete(self.top10_ids, dup_ind)
new_top_scores = np.delete(self.top10_scores, dup_ind)
repl_match_ind = match_inds[data_in]
self.top10_scores = np.append(new_top_scores, sim_scores[repl_match_ind])
self.top10_ids = np.append(new_top_ids, ids[repl_match_ind])
ids_subset = [re.sub("_.*", "", x) for x in self.top10_ids]
c_ids = Counter(ids_subset)
if c_ids.most_common()[0][1] > 1:
data_in += 1
else:
dupes = False
def results(self):
conn = boto.connect_s3(os.environ["access_key"], os.environ["access_secret_key"])
bucket_name = os.environ["bucket_name"]
bucket = conn.get_bucket(bucket_name)
img_data = []
for x, val in enumerate(self.top10_ids):
self.psql.execute(
"""select name,gender,age,city,state,
zip from records where id='%s';"""
% val
)
img_details = self.psql.fetchall()[0]
key = val.encode("ascii", "ignore") + ".jpg"
img = bucket.get_key(key)
outfile = img.generate_url(6000)
img_data.append(
[
val,
outfile,
self.top10_scores[x],
img_details[0],
img_details[1],
img_details[2],
img_details[3],
img_details[4],
img_details[5],
]
)
self.df = pd.DataFrame(
np.array(img_data), columns=["id", "img_loc", "score", "name", "gender", "age", "city", "state", "zip"]
)
self.df.to_csv("tmp/" + self.session_id + ".csv")
