# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
from pathing_utils import image_name_from_path
from random import sample
from .models import Image
from utils import ci_lower_bound
from pathing_utils import path_to_static
indices_euclidean_path = path_to_static() + "indices_euclidean.csv"
INDICIES = np.genfromtxt(indices_euclidean_path, delimiter=',', dtype=str)
# INDICIES = INDICIES[:1000]
distances_euclidean_path = path_to_static() + "distances_euclidean.csv"
DISTANCES = np.genfromtxt(distances_euclidean_path, delimiter=',')
# DISTANCES = DISTANCES[:1000]
def index_from_image_name(image_path):
return int(image_name_from_path(image_path).split(".jpg")[0])
def get_images_similar_to_image(img_name, indices, distances, k, form="df"):
""" Returns list or df of k similar images, with or without path
Parameters:
1. img_name - "00001.jpg"
2. indices - nested np array with top 50 closest images for each image
3. distances - nested np array with top 50 distances for each image
build_Index, \
get_tl_vector, \
get_ol_vector
from inception import Inception, maybe_download
import gc
from pathing_utils import path_to_image_frontend, \
path_to_static, \
path_to_images_folder_absolute, \
image_name_from_path,\
path_to_uploads
from utils import image_from_base64str
maybe_download()
inception_layer_path = path_to_static() + "inception_output_layer2"
inverted_index_path = path_to_static() + "inverted_index"
vocab_path = path_to_static() + "vocab.csv"
word2vec_vocab_path = path_to_static() + "word2vecvocab.csv"
word_vec_path = path_to_static() + "GoogleNews-vectors-negative300.bin.gz"
print("READ inception_layer_path")
inverted_index = pd.read_pickle(inverted_index_path)
df_in = pd.read_pickle(inception_layer_path)
df_in.columns = ['img', 'output_layer', 'scores']
# print("DROP column - output_layer")
df_in.drop('output_layer', axis=1, inplace=True)
# print(df_in.head())
known_words = defaultdict(list)
annoy_index = None
inception_object = None
vocabs = list(pd.read_csv(vocab_path).vocab)
def __init__(self):
self.HISTORY_FILE_PATH = path.join(path_to_static(), "history.csv")
self.HISTORY_COLS = ["query", "times"]
import numpy as np
import tensorflow as tf
import download
from cache import cache
import os
import sys
from pathing_utils import path_to_static
########################################################################
# Various directories and file-names.
# Internet URL for the tar-file with the Inception model.
# Note that this might change in the future and will need to be updated.
data_url = "http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz"
# Directory to store the downloaded data.
data_dir = path_to_static() + "inception" + os.path.sep
# File containing the mappings between class-number and uid. (Downloaded)
path_uid_to_cls = "imagenet_2012_challenge_label_map_proto.pbtxt"
# File containing the mappings between uid and string. (Downloaded)
path_uid_to_name = "imagenet_synset_to_human_label_map.txt"
# File containing the TensorFlow graph definition. (Downloaded)
path_graph_def = "classify_image_graph_def.pb"
########################################################################
def maybe_download():
"""
def __init__(self):
self.FEEDBACK_FILE_PATH = path_to_static() + "feedback.csv"
self.FEEDBACK_COLS = ["query", "step", "image", "status"]
self.df_feedback = self.read_feedback()