# Install gsutil, google cloud's storage CLI utility
# https://cloud.google.com/storage/docs/gsutil_install#sdk-install
​
gsutil --version
​
# Activate the service account credentials
# https://cloud.google.com/sdk/gcloud/reference/auth/activate-service-account
​
gcloud auth activate-service-account \
    aquarium-customer-[email protected]-266018.iam.gserviceaccount.com \
    --key-file /path/to/credentials.json
​
# Copy directories up! This command will recursively copy a directory
# Feel free to check out the docs for more options:
#    https://cloud.google.com/storage/docs/gsutil/commands/cp
​
gsutil -m rsync -r \
    ./projectA/imgs/ gs://aquarium-customer-abcd1234/projectA/imgs/
​

image_url = "https://storage.cloud.google.com/aquarium-customer-abcd1234/path/to/image.png"

image_url = "s3://yourbucket/path/to/img.jpg"
image_url = "gs://yourbucket/path/to/img.jpg"

# Python 3
python3 -m http.server 5000
​
# Python 2
python -m SimpleHTTPServer 5000
​
# NPM
npx http-server --cors='*' --port=5000

image_url = "http://localhost:5000/path/to/img/.jpg"

CLASSNAMES = ['dog', 'cat', 'horse', 'hamster']
# Explicitly specify handling of each class
label_class_map = al.LabelClassMap(entries=[
    al.ClassMapEntry(
        name='dog',
        class_id=0,
        color=(255,0,0)
    ),
    al.ClassMapEntry(
        name='cat',
        class_id=1,
        color=(0,255,0)
    ),
    al.ClassMapEntry(
        name='horse',
        class_id=2,
        color=(0,0,255)
    ),
    al.ClassMapEntry(
        name='hamster',
        class_id=3,
        color=(0,128,128)
    )
])

​
# Explicitly specify handling of each class
#
# Here, we have three pet GT classes, which get
# predicted as either large or small pet. We also have
# an "ignore" class which may exist in the labels,
# but should be ignored in evaluation without a
# corresponding predicted label. 
​
label_class_map = al.LabelClassMap(entries=[
    al.ClassMapEntry(
        name='dog',
        class_id=0,
        color=(255,0,0),
        train_name='small_pet',
        train_id=0,
        train_color=(200,200,0)
    ),
    al.ClassMapEntry(
        name='cat',
        class_id=1,
        color=(0,255,0),
        train_name='small_pet',
        train_id=0,
        train_color=(200,200,0)
    ),
    al.ClassMapEntry(
        name='horse',
        class_id=2,
        color=(0,0,255),
        train_name='large_pet',
        train_id=1,
        train_color=(0,0,200)
    ),
    al.ClassMapEntry(
        name='ignore',
        class_id=255,
        color=(0,0,0),
        train_name="",
        train_id=255,
        ignore_in_eval=True
    )
])

# You can refer to a specific label by id or by name
label_class_changes = [
    al.ClassMapUpdateEntry(class_id=5, color=(0, 255, 255)),
    al.ClassMapUpdateEntry(name='jellyfish', train_color=(255,0,255))
]
​
al_client.update_label_class_map_colors(AL_PROJECT, label_class_changes)

al_client.create_project(AL_PROJECT, class_map, primary_task="CLASSIFICATION")

al_client.create_project(AL_PROJECT, class_map, primary_task="2D_SEMSEG")

al_client.create_project(AL_PROJECT, class_map, primary_task="MULTI_LABEL_CLASSIFICATION")

frame.add_user_metadata('deployment_id', value)
​
# In the case of nullable values, you can also provide an explicit type
frame.add_user_matadata('nullable_field', maybe_null, val_type='int')

# EPSG:4326 WGS84 Latitute Longitude coordinates
frame.add_geo_latlong_data(lat, lon)

frame.add_audio(
    # A unique name to refer to this image by
    sensor_id='mic_1',
    # A URL to load the mp3 file from
    audio_url='',
    # Optional: ISO formatted date-time string
    date_captured=''
)

frame.add_point_cloud_pcd(
    sensor_id='my_lidar_scanner',
    pcd_url='',
    # Optional: If your point cloud is relative to a specific
    # coordinate frame, you can reference it by name here.
    coord_frame_id="",
    # Optional: ISO formatted date-time string
    date_captured=''
)

frame.add_point_cloud_bins(
    sensor_id='my_lidar_scanner',
    # URL for the point positions: 
    #    float32 [x1, y1, z1, x2, y2, z2, ...]
    point_cloud_url='',
    # URL for the point intensities:
    #    unsigned int32 [i1, i2, i3, ...]
    intensity_url='',
    # URL for the point ranges
    #    float32 [r1, r2, r3, ...]
    range_url'',
    # Optional: If your point cloud is relative to a specific
    # coordinate frame, you can reference it by name here.
    coord_frame_id="",
    # Optional: ISO formatted date-time string
    date_captured=''
)

frame.add_obj(
    sensor_id='some_id',
    # A url to a *.obj formatted text
    obj_url='',
    # Optional: If your object geometry is relative to a specific
    # coordinate frame, you can reference it by name here.
    coord_frame_id="",
    # Optional: ISO formatted date-time string
    date_captured=''
)

frame.add_coordinate_frame_3d(
    coord_frame_id='robot_ego_frame',
    # Position offset of this coordinate frame
    position={'x': 0, 'y': 0, 'z': 0},
    # Rotation/Orientation of this coordinate frame,
    # represented as a quaternion
    orientation={'w': 1, 'x': 0, 'y': 0, 'z': 0},
    # Optional: string ID of the parent coordinate frame
    # that this one is relative to.
    parent_frame_id=""
)

frame.add_label_2d_bbox(
    # The sensor id of the image this label corresponds to
    sensor_id='some_camera',
    # A unique id across all other labels in this dataset
    label_id='unique_id_for_this_label',
    classification='dog',
    # Coordinates are in absolute pixel space
    top=200,
    left=300,
    width=250,
    height=150
)

frame.add_label_3d_cuboid(
    label_id="unique_id_for_this_label",
    classification="car",
    # XYZ dimensions of this cuboid
    dimensions=[1.0, 0.5, 0.5],
    # XYZ position of the center of this object
    position=[2.0, 2.0, 1.0],
    # An XYZW ordered object rotation quaternion
    rotation=[0.0, 0.0, 0.0, 1.0],
    # Optional: If your cuboid is relative to a specific
    # coordinate frame, you can reference it by name here.
    coord_frame_id="robot_ego_frame"
)

! pip3 install pillow
​
from PIL import Image
...
​
# 2D array, where each value is [0,255] corresponding to a class_id
# in the project's label_class_map.
int_arr = your_2d_ndarray.astype('uint8')
​
Image.fromarray(int_arr).save(f"{imagename}.png")

mask_url = al_client.upload_asset_from_filepath(project_id, dataset_id, filepath)

frame.add_label_2d_semseg(
    # The sensor id of the image this label corresponds to
    sensor_id='some_camera',
    # A unique id across all other labels in this dataset
    label_id='unique_id_for_this_label',
    # Expected to be a PNG, with values in [0,255] that correspond
    # to the class_id of classes in the label_class_map
    mask_url='url_to_greyscale_png'
)

frame.add_label_2d_polygon_list(
    # The sensor id of the image this label corresponds to
    sensor_id='some_camera',
    # A unique id across all other labels in this dataset
    label_id='unique_id_for_this_label',
    classification='dog',
    # All coordinates are in absolute pixel space
    #
    # These are polygon vertices, not a line string. This means
    # that no vertices are duplicated in the lists.
    polygons=[
        {'vertices': [(x1, y1), (x2, y2), ...]},
        {'vertices': [(x1, y1), (x2, y2), ...]}
    ],
    # Optional: indicate the center position of the object
    center: [center_x, center_y]
)