1. Upload & Manage Data & Metadata¶

1.1. Upload specific files¶

When you have specific files you want to upload, you can upload them all into a dataset using this script:

import dtlpy as dl
if dl.token_expired():
    dl.login()
project = dl.projects.get(project_name='project_name')
dataset = project.datasets.get(dataset_name='dataset_name')
dataset.items.upload(local_path=[r'C:/home/project/images/John Morris.jpg',
                                 r'C:/home/project/images/John Benton.jpg',
                                 r'C:/home/project/images/Liu Jinli.jpg'],
                     remote_path='/folder_name')  # Remote path is optional, images will go to the main directory by default

1.2. Upload all files in a folder¶

If you want to upload all files from a folder, you can do that by just specifying the folder name:

import dtlpy as dl
if dl.token_expired():
    dl.login()
project = dl.projects.get(project_name='project_name')
dataset = project.datasets.get(dataset_name='dataset_name')
dataset.items.upload(local_path=r'C:/home/project/images',
                     remote_path='/folder_name')  # Remote path is optional, images will go to the main directory by default

1.3. Upload items from URL link¶

You can provide Dataloop with the link to the item, and not necessarily the item itself.

dataset = project.datasets.get(dataset_name='dataset_name')
url_path = 'http://ww.some_website/beautiful_flower.jpg'
# Create link
link = dl.UrlLink(ref=url_path, mimetype='image', name='file_name.jpg')
# Upload link
item = dataset.items.upload(local_path=link)

You can open an item uploaded to Dataloop by opening it in a viewer.

show
item.open_in_web()

1.3.1. Additional upload options¶

Additional upload options include using buffer, pillow, openCV, and NdArray - see our complete documentation for code examples.

1.4. Upload Items and Annotations Metadata¶

You can upload items as a table using a pandas data frame that will let you upload items with info (annotations, metadata such as confidence, filename, etc.) attached to it.

import pandas
import dtlpy as dl
dataset = dl.datasets.get(dataset_id='id')  # Get dataset
to_upload = list()
# First item and info attached:
to_upload.append({'local_path': r"E:\TypesExamples\000000000064.jpg",  # Item file path
                  'local_annotations_path': r"E:\TypesExamples\000000000776.json",  # Annotations file path
                  'remote_path': "/first",  # Dataset folder to upload the item to
                  'remote_name': 'f.jpg',  # Dataset folder name
                  'item_metadata': {'user': {'dummy': 'fir'}}})  # Added user metadata
# Second item and info attached:
to_upload.append({'local_path': r"E:\TypesExamples\000000000776.jpg",  # Item file path
                  'local_annotations_path': r"E:\TypesExamples\000000000776.json",  # Annotations file path
                  'remote_path': "/second",  # Dataset folder to upload the item to
                  'remote_name': 's.jpg',  # Dataset folder name
                  'item_metadata': {'user': {'dummy': 'sec'}}})  # Added user metadata
df = pandas.DataFrame(to_upload)  # Make data into table
items = dataset.items.upload(local_path=df,
                             overwrite=True)  # Upload table to platform