ee.data.getPixels (Python only)

  • ee.data.getPixels fetches raw image data or visualized 8-bit RGB data from an Earth Engine image asset.

  • The function requires specifying the asset ID and allows customization of file format, pixel grid, region, bands, and visualization options.

  • Users can define the output region, select specific bands for extraction, and apply visualization parameters for an RGB representation.

  • Python examples demonstrate the usage of ee.data.getPixels with the necessary parameters and retrieving image data.

Fetches pixels from an image asset.

Returns: The pixels as raw image data.

UsageReturns
ee.data.getPixels(params)Object|Value
ArgumentTypeDetails
paramsObjectAn object containing parameters with the following possible values:
assetId - The asset ID for which to get pixels. Must be an image asset.
fileFormat - The resulting file format. Defaults to png. See ImageFileFormat for the available formats. There are additional formats that convert the downloaded object to a Python data object. These include: NUMPY_NDARRAY, which converts to a structured NumPy array.
grid - Parameters describing the pixel grid in which to fetch data. Defaults to the native pixel grid of the data.
region - If present, the region of data to return, specified as a GeoJSON geometry object (see RFC 7946).
bandIds - If present, specifies a specific set of bands from which to get pixels.
visualizationOptions - If present, a set of visualization options to apply to produce an 8-bit RGB visualization of the data, rather than returning the raw data.

Examples

Python setup

See the Python Environment page for information on the Python API and using geemap for interactive development.

import ee
import geemap.core as geemap

Colab (Python)

# Region of interest.
coords = [
    -121.58626826832939,
    38.059141484827485,
]
region = ee.Geometry.Point(coords)

# Get a Sentinel-2 image.
image = (ee.ImageCollection('COPERNICUS/S2')
  .filterBounds(region)
  .filterDate('2020-04-01', '2020-09-01')
  .sort('CLOUD_COVERAGE_ASSESSMENT')
  .first())
image_id = image.getInfo()['id']

# Make a projection to discover the scale in degrees.
proj = ee.Projection('EPSG:4326').atScale(10).getInfo()

# Get scales out of the transform.
scale_x = proj['transform'][0]
scale_y = -proj['transform'][4]

# Make a request object.
request = {
    'assetId': image_id,
    'fileFormat': 'PNG',
    'bandIds': ['B4', 'B3', 'B2'],
    'grid': {
        'dimensions': {
            'width': 640,
            'height': 640
        },
        'affineTransform': {
            'scaleX': scale_x,
            'shearX': 0,
            'translateX': coords[0],
            'shearY': 0,
            'scaleY': scale_y,
            'translateY': coords[1]
        },
        'crsCode': proj['crs'],
    },
    'visualizationOptions': {'ranges': [{'min': 0, 'max': 3000}]},
}

image_png = ee.data.getPixels(request)
# Do something with the image...