def __init__(self, chunk=1500, img_path_many=None, folder_path=None):
from keras.models import Model
self._Model = Model
from keras.preprocessing import image
self._image = image
from keras.applications.xception import Xception as key_model
self._key_model = key_model
from keras.applications.xception import preprocess_input, decode_predictions
self._preprocess_input = preprocess_input
self._decode_predictions = decode_predictions
base_model_4 = key_model(weights='imagenet', include_top=False)
self._base_model_4 = base_model_4
model = Model(inputs=base_model_4.input, outputs=base_model_4.get_layer(index=-3).output)
self._model = model
self.chunk = chunk
if img_path_many == folder_path == None:
raise Exception(" choice img_path_many or folder_path")
elif img_path_many != None:
self.img_path_many = img_path_many
elif folder_path != None:
# self.folder_path = folder_path
self.folder_path = folder_path
self.img_path_many = self._Img_List()
else:
raise Exception("at least specify img_path_many or folder_path")
self.Get_Target()
from database import DATABASE
import pickle
import faiss
import numpy as np
from keras.models import Model
from keras.preprocessing import image
from keras.applications.xception import Xception as key_model
from keras.applications.xception import preprocess_input, decode_predictions
base_model_4 = key_model(weights='imagenet', include_top=False)
model = Model(inputs=base_model_4.input,
outputs=base_model_4.get_layer(index=-3).output)
reduced_database = DATABASE()
reduced_database.database_chose("bar")
reduced_database.collection_chose("raw_vector01_redu")
data_from_database = reduced_database.get_data(movie_name=0).astype("float32")
compare_data = data_from_database[:, :-2]
compare_target = data_from_database[:, -2:]
img_path = "/data/bar03/screenshot01/0_160.jpg"
def Jpg_To_Vector(img_path):
if isinstance(img_path, list):
img_path = img_path[0]
img = image.load_img(img_path, target_size=(299, 299))
x = image.img_to_array(img)