Datalake - Metadata and Custom Metadata

We created a system of Metadata to enhance your Data management. Here is how to use it.

For each Data you push on Picsellia, you can add additional information that you want to keep throughout your pipelines. Two systems coexist:

• Metadata: a fixed, predefined schema of keys, available to every Data (detailed below).
• Custom Metadata: your own arbitrary key/value fields, detailed here.

For a Computer Vision project, this means you can capture the acquisition context of each image or video, GPS position, timestamp, weather, sensor or camera settings, alongside a business reference from your own systems, and later use it to build geographically or temporally balanced Dataset, debug a model's failures against specific shooting conditions, or simply trace a Data back to the batch, site, or inspection it came from.

Please note that even if Metadata and Custom Metadata are linked to a Data at the Datalake level, they will always be displayed, searchable, and editable at every step of the Picsellia platform, for instance in Datalake, DatasetVersion, Annotation Studio, Evaluation, etc.

1. Metadata

Here is what can be stored in Picsellia and its associated format:

latitude: float
longitude: float
altitude: float
acquired_at: datetime
acquired_by: str = Field(max_length=128)
weather: str = Field(max_length=1024)
resolution_x: float
resolution_y: float
resolution_unit: str = Field(max_length=128)
compression: str = Field(max_length=128)
manufacturer: str = Field(max_length=128)
software: str = Field(max_length=128)
color_space: str = Field(max_length=128)
custom_id: str = Field(max_length=128)
reference: str = Field(max_length=128)
yaw: float
pitch: float
roll: float
focal_length: float
sensor_width: float
brand: str = Field(max_length=128)
region_of_interest: str = Field(max_length=128)

For a given Data the associated Metadata are displayed in the Details view:

2. Custom Metadata

Unlike the fixed Metadata schema, Custom Metadata lets you define your own key/value fields, each with a chosen type: String, Integer, Float, Datetime, or Boolean.

Custom Metadata can be managed directly from the UI, from the Metadata section of any Details view across the platform (Datalake, Dataset, Annotation Studio, Evaluation, etc.):

• Add: click + Add, provide a key, select its type, and provide a value.

  

  

• Edit: hover over an existing entry and click the pencil icon. Please note that only the value can be edited, not the key.

• Delete: hover an existing entry and click the trash icon.

As is the case for Metadata, Custom Metadata is always displayed in the Details view across the platform; it is stacked on the Metadata list.

3. Searching Data by Metadata

Both Metadata systems can be used to retrieve Data, but not through the same mechanism:

• The fixed Metadata schema is queryable directly from the Query Language, detailed here, using the metadata.<key> notation, for instance metadata.latitude > 43.5.

  

• Custom Metadata is not part of the Query Language. Instead, use the Add filter button next to the Search Bar to filter Data by Custom Metadata.

  

  

4. Uploading Metadata via SDK

If you want to upload one Data with additional business reference, a different date than the uploaded time for example, you can use the SDK this way:

datalake = client.get_datalake()
data = datalake.upload_data(
  filepaths="./files/1.jpg",
  metadata={
    "reference": "7NGOMI",
    "acquired_by": "USER-178",
    "acquired_at": datetime.utcnow(),
  },
)

When sending multiple Data, you need 2 lists ("filepaths" & "metadata") of the exact same size:

datalake = client.get_datalake()
data = datalake.upload_data(
    filepaths=[
        "./files/1.jpg",
        "./files/2.jpg",
        "./files/3.jpg",
    ],
    metadata=[
        {
            "latitude": 43.6027394,
            "longitude": 1.4540158,
        },
        {
            "latitude": 43.601717,
            "longitude": 1.456135,
        },
        {
            "latitude": 43.6013007,
            "longitude": 1.4560448,
        },
    ],
)

Custom Metadata can also be uploaded the same way, using the custom_metadata parameter instead of metadata:

datalake = client.get_datalake()
data = datalake.upload_data(
    filepaths="./files/1.jpg",
    custom_metadata={
        "batch_id": "B-2024-042",
        "inspection_passed": True,
    },
)

5. Fill Metadata from Exif Tags

If you want to upload Data and automatically fill EXIF tags into the Picsellia Metadata system, you can give the parameter fill_metadata=True when calling upload_data:

datalake = client.get_datalake()
data = datalake.upload_data(
  filepaths="./files/1.jpg",
  fill_metadata=True
)

These are Metadata filled automatically with the PIL library.

You can display the Metadata automatically attached to your data as shown below:

exif_data = image.getexif()
acquired_at = exif_data.get(0x0132)
acquired_by = exif_data.get(0x013B)
resolution_x = exif_data.get(0x011A)
resolution_y = exif_data.get(0x011B)
resolution_unit = exif_data.get(0x0128)
compression = exif_data.get(0x0103)
manufacturer = exif_data.get(0x010F)
software = exif_data.get(0x0131)
color_space = exif_data.get(0xA001)