def main(_):
dataset_api = Images()
writer = tf.python_io.TFRecordWriter(FLAGS.output_path)
offset = 0
limit = 50
while True:
try:
res = dataset_api.get_images_by_source("deepfashion",
offset=offset,
limit=limit)
for image in res:
print(image)
exit(1)
tf_data = dict_to_tf_example(image)
writer.write(tf_data.SerializeToString())
if limit > len(res):
print("done")
break
else:
offset = offset + len(res)
except Exception as e:
print(str(e))
writer.close()
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)
if __name__ == '__main__':
print('start')
image_api = Images()
offset = 0
limit = 100
while True:
images = None
try:
images = image_api.get_images_by_source("deepfashion", offset, limit)
except Exception as e:
print(str(e))
if len(images) == 0:
break
else:
offset = offset + limit
for img in images:
if 'url_with_box' in img:
continue
img_file = download_image(img.get('url'))
image = Image.open(img_file)
from __future__ import print_function
from stylelens_dataset.images import Images
from pprint import pprint
# create an instance of the API class
api_instance = Images()
try:
api_response = api_instance.get_images_by_source("deepfashion")
pprint(api_response)
except Exception as e:
print("Exception when calling get_images_by_source: %s\n" % e)
return uploaded_path
if __name__ == '__main__':
print('start')
image_api = Images()
object_api = Objects()
offset = 0
limit = 100
while True:
try:
res = image_api.get_images_by_source(source='deepfashion',
offset=offset,
limit=limit)
for image in res:
cropped_file = crop(image)
image['url'] = cropped_file
image['image_id'] = str(image['_id'])
image['bbox'] = None
image['width'] = None
image['height'] = None
object_api.add_object(image)
if limit > len(res):
break
else:
offset = offset + limit