def get_images_dataset_categories_counts_by_category(source, category=None):
image_api = Images()
res = GetImagesCategoriesCountsByCategoryResponse()
try:
res_data = GetImagesCategoriesCountsByCategoryResponseData()
total_count = image_api.get_images_count_by_category_name(category_name=category)
valid_count = image_api.get_images_count_by_category_name(category_name=category, valid=True)
invalid_count = image_api.get_images_count_by_category_name(category_name=category, valid=False)
res_data.total_count = total_count
res_data.valid_count = valid_count
res_data.invalid_count = invalid_count
res.message = "Successful"
res.data = res_data
response_status = 200
except Exception as e:
log.error(str(e))
res.message = str(e)
response_status = 400
return res, response_status
def get_playground_images_by_category(source,
category=None,
offset=0,
limit=100):
image_api = Images()
res = GetImagesByCategoryResponse()
try:
res_data = GetImagesByCategoryResponseData()
count = image_api.get_images_count_by_category_name(
category_name=category)
images = image_api.get_images_by_category_name(
category_name=category, offset=offset, limit=limit)
res_data.total_count = count
imgs = []
for i in images:
i['id'] = i.get('_id')
imgs.append(ImageDataset().from_dict(i))
res_data.images = imgs
res.message = "Successful"
res.data = res_data
response_status = 200
except Exception as e:
log.error(str(e))
res.message = str(e)
response_status = 400
return res, response_status
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 update_images_dataset_by_ids(source, ids, valid=True):
image_api = Images()
res = UpdateImageDatasetResponse()
try:
image_api.validate_images(ids, valid)
res.message = "Successful"
response_status = 200
except Exception as e:
log.error(str(e))
res.message = str(e)
response_status = 400
return res, response_status
def main(_):
dataset_api = Images()
train_writer = tf.python_io.TFRecordWriter(FLAGS.train_output_path)
eval_writer = tf.python_io.TFRecordWriter(FLAGS.eval_output_path)
train_images = []
eval_images = []
for i in range(1, 4):
category_class = str(i)
(category_train_images, category_eval_images) = read_from_db(dataset_api,
category_class)
train_images.extend(category_train_images)
eval_images.extend(category_eval_images)
print("category_class: {} read from db done!".format(category_class))
random.shuffle(train_images)
random.shuffle(eval_images)
for image in train_images:
tf_data = dict_to_tf_example(image)
train_writer.write(tf_data.SerializeToString())
for image in eval_images:
tf_data = dict_to_tf_example(image)
eval_writer.write(tf_data.SerializeToString())
print("make train/eval TFRecord done!")
train_writer.close()
eval_writer.close()
def main(_):
dataset_api = Images()
train_writer = tf.python_io.TFRecordWriter(FLAGS.train_output_path)
eval_writer = tf.python_io.TFRecordWriter(FLAGS.eval_output_path)
train_images = []
eval_images = []
category_class = str(FLAGS.category_class)
(train_images, eval_images) = read_from_db(dataset_api, category_class)
print("category_class: {} read from db done!".format(category_class))
random.shuffle(train_images)
random.shuffle(eval_images)
print("----images_num-------")
print(len(train_images))
print(len(eval_images))
print("---------------------")
for image in train_images:
if 'is_valid' in image:
if image['is_valid']:
tf_data = dict_to_tf_example(image)
train_writer.write(tf_data.SerializeToString())
for image in eval_images:
if 'is_valid' in image:
if image['is_valid']:
tf_data = dict_to_tf_example(image)
eval_writer.write(tf_data.SerializeToString())
print("make train/eval TFRecord done!")
train_writer.close()
eval_writer.close()
print("id = " + str(id))
print("img_file = " + file)
key = os.path.join('deepfashion', 'img', 'with_box', id + '.jpg')
is_public = True
file_url = storage.upload_file_to_bucket(AWS_BUCKET, file, key, is_public=is_public)
return file_url
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
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:
ids = []
ids.append('5a718ce0bd44107fbae98994')
res = api_instance.validate_images(ids, valid=True)
pprint(res)
except Exception as e:
print("Exception when calling validate_images: %s\n" % e)
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:
offset = 0
limit = 100
while True:
res = api_instance.get_images_by_category_class("1",
offset=offset,
limit=limit)
pprint(res)
if limit > len(res):
break
else:
offset = offset + limit
except Exception as e:
print("Exception when calling get_images_by_source: %s\n" % e)
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()
image = {}
image['file'] = "xxxkkkx"
image['source'] = 'deepfashion'
size = {}
size['width'] = 300
size['height'] = 500
size['depth'] = 3
objects = []
object = {}
box = {}
box['x1'] = 10.0
box['y1'] = 10.0
box['x2'] = 40.0
box['y2'] = 60.0
object['name'] = 'xxx'
object['box'] = box
objects.append(object)
image['size'] = size
image['objects'] = objects
try:
api_response = api_instance.add_image(image)
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:
offset = 0
limit = 10
res = api_instance.get_images_by_category_name("Blouse",
offset=offset,
limit=limit)
pprint(res)
except Exception as e:
print("Exception when calling get_images_by_category_name: %s\n" % e)
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)
im = Image.open(f)
area = (left, top, left + abs(left - right), top + abs(bottom - top))
cropped = im.crop(area)
cropped.save(new_file_name)
uploaded_path = upload_image_to_storage(new_file_name)
os.remove(new_file_name)
except Exception as e:
print(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'])
file_url = storage.upload_file_to_bucket(AWS_BUCKET,
file,
key,
is_public=is_public)
return file_url
def get_image_size(img_file):
with Image.open(img_file) as img:
return img.size
if __name__ == '__main__':
print('start')
dataset_api = Images()
bboxes = get_bbox()
category_clothes = get_category_clothes()
category_images = get_category_images(category_clothes)
attribute_clothes = get_attribute_clothes()
attribute_images = get_attribute_images(attribute_clothes)
i = 0
for img in attribute_images:
image = {}
image['file'] = img['file']
image['source'] = 'deepfashion'
width, height = get_image_size(img['file'])
image['width'] = width
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:
res = api_instance.get_images_count_by_category_name("Blouse")
pprint(res)
except Exception as e:
print("Exception when calling get_images_by_category_name: %s\n" % e)
