Detects instances of real-world entities within an image (JPEG or PNG) provided
as
input.
This includes objects like flower, tree, and table; events like wedding,
graduation,
and birthday party; and concepts like landscape, evening, and nature.
For an example, see Analyzing images stored in an Amazon S3 bucket in the
Amazon Rekognition Developer Guide.
You pass the input image as base64-encoded image bytes or as a reference to an
image in
an Amazon S3 bucket. If you use the
AWS
CLI to call Amazon Rekognition operations, passing image bytes is not
supported. The image must be either a PNG or JPEG formatted file.
You can specify one or both of the GENERAL_LABELS and
IMAGE_PROPERTIES feature types when calling the DetectLabels API. Including
GENERAL_LABELS will ensure the response includes the labels detected in the
input image, while including IMAGE_PROPERTIESwill ensure the response
includes
information about the image quality and color.
When using GENERAL_LABELS and/or IMAGE_PROPERTIES you can
provide filtering criteria to the Settings parameter. You can filter with sets
of individual
labels or with label categories. You can specify inclusive filters, exclusive
filters, or a
combination of inclusive and exclusive filters. For more information on
filtering see Detecting Labels in an
Image.
When getting labels, you can specify MinConfidence to control the
confidence threshold for the labels returned. The default is 55%. You can also
add the
MaxLabels parameter to limit the number of labels returned. The default and
upper limit is 1000 labels. These arguments are only valid when supplying
GENERAL_LABELS as a
feature type.
For each object, scene, and concept the API returns one or more labels. The API
returns the following types of information about labels:
*
Name - The name of the detected label.
*
Confidence - The level of confidence in the label assigned to a detected object.
*
Parents - The ancestor labels for a detected label. DetectLabels returns a
hierarchical taxonomy of detected labels. For example, a detected car might be
assigned
the label car. The label car has two parent labels: Vehicle (its parent) and
Transportation (its grandparent). The response includes the all ancestors for a
label,
where every ancestor is a unique label. In the previous example, Car, Vehicle,
and
Transportation are returned as unique labels in the response.
*
Aliases - Possible Aliases for the label.
*
Categories - The label categories that the detected label belongs to.
*
BoundingBox — Bounding boxes are described for all instances of detected common
object labels, returned in an array of Instance objects. An Instance object
contains a
BoundingBox object, describing the location of the label on the input image. It
also
includes the confidence for the accuracy of the detected bounding box.
The API returns the following information regarding the image, as part of the
ImageProperties structure:
*
Quality - Information about the Sharpness, Brightness, and Contrast of the input
image, scored between 0 to 100. Image quality is returned for the entire image,
as well as
the background and the foreground.
*
Dominant Color - An array of the dominant colors in the image.
*
Foreground - Information about the sharpness, brightness, and dominant colors of
the
input image’s foreground.
*
Background - Information about the sharpness, brightness, and dominant colors of
the
input image’s background.
The list of returned labels will include at least one label for every detected
object,
along with information about that label. In the following example, suppose the
input image has
a lighthouse, the sea, and a rock. The response includes all three labels, one
for each
object, as well as the confidence in the label:
{Name: lighthouse, Confidence: 98.4629}
{Name: rock,Confidence: 79.2097}
{Name: sea,Confidence: 75.061}
The list of labels can include multiple labels for the same object. For example,
if the
input image shows a flower (for example, a tulip), the operation might return
the following
three labels.
{Name: flower,Confidence: 99.0562}
{Name: plant,Confidence: 99.0562}
{Name: tulip,Confidence: 99.0562}
In this example, the detection algorithm more precisely identifies the flower as
a
tulip.
If the object detected is a person, the operation doesn't provide the same
facial
details that the DetectFaces operation provides.
This is a stateless API operation that doesn't return any data.
This operation requires permissions to perform the
rekognition:DetectLabels action.