def query_model(dogo, model, images):
neighbours = get_images_from_ids(model.query(dogo, k=20), images)
image_list = SFrame(data=None)
shown_dogs = {dogo['images'][0][0]}
for i in range(0, len(neighbours)):
if len(shown_dogs) < 6:
if neighbours[i]['images'][0] not in shown_dogs:
# neighbours[i]['image'].show()
dogo_clone = neighbours[i:i + 1].copy()
image_list = image_list.append(SFrame(dogo_clone))
shown_dogs.add(neighbours[i]['images'][0])
else:
break
return image_list
def append_images(json_file):
# we fill an SFrame with all the given metadata of the dogs
meta = SFrame.read_json(json_file, orient='records')
# this is the SFrame that we will fill with the data plus the image, which will be saved in the final file
image_list = SFrame(data=None)
# for each image in the images column in the meta SFrame, we add one line in the final SF with one image per line
for i in range(0, len(meta) - 1):
dogo = meta[i:i + 1]
for image in dogo['images'][0]:
# print image
dogo_clone = dogo.copy()
dogo_clone.add_column(SArray([(graphlab.Image(images_path + image))
]),
name='image')
dogo_clone.add_column(SArray([image]), name='image_filename')
image_list = image_list.append(SFrame(dogo_clone))
image_list.save(filename='prepared_data/')