Detects instances of real-world entities within an image (JPEG or PNG) provided as input
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.
detect_labels does not support the
detection of activities. However, activity detection is supported for
label detection in videos. For more information, see StartLabelDetection
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.
For each object, scene, and concept the API returns one or more labels. Each label provides the object name, and the level of confidence that the image contains the object. For example, suppose the input image has a lighthouse, the sea, and a rock. The response includes all three labels, one for each object.
{Name: lighthouse, Confidence: 98.4629}
{Name: rock,Confidence: 79.2097}
{Name: sea,Confidence: 75.061}
In the preceding example, the operation returns one label for each of the three objects. The operation can also return multiple labels for the same object in the image. 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.
In response, the API returns an array of labels. In addition, the
response also includes the orientation correction. Optionally, 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.
If the object detected is a person, the operation doesn't provide the
same facial details that the detect_faces
operation provides.
detect_labels returns bounding boxes for
instances of common object labels in an array of Instance objects. An
Instance object contains a BoundingBox object, for the location of the
label on the image. It also includes the confidence by which the
bounding box was detected.
detect_labels also 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 returns the entire list of ancestors for a label. Each ancestor
is a unique label in the response. In the previous example, Car,
Vehicle, and Transportation are returned as unique labels in the
response.
This is a stateless API operation. That is, the operation does not persist any data.
This operation requires permissions to perform the
rekognition:DetectLabels action.
rekognition_detect_labels(Image, MaxLabels, MinConfidence)
Image |
[required] The input image as base64-encoded bytes or an S3 object. If you use the AWS CLI to call Amazon Rekognition operations, passing image bytes is not supported. Images stored in an S3 Bucket do not need to be base64-encoded. If you are using an AWS SDK to call Amazon Rekognition, you might not
need to base64-encode image bytes passed using the |
MaxLabels |
Maximum number of labels you want the service to return in the response. The service returns the specified number of highest confidence labels. |
MinConfidence |
Specifies the minimum confidence level for the labels to return. Amazon Rekognition doesn't return any labels with confidence lower than this specified value. If |
A list with the following syntax:
list(
Labels = list(
list(
Name = "string",
Confidence = 123.0,
Instances = list(
list(
BoundingBox = list(
Width = 123.0,
Height = 123.0,
Left = 123.0,
Top = 123.0
),
Confidence = 123.0
)
),
Parents = list(
list(
Name = "string"
)
)
)
),
OrientationCorrection = "ROTATE_0"|"ROTATE_90"|"ROTATE_180"|"ROTATE_270",
LabelModelVersion = "string"
)svc$detect_labels(
Image = list(
Bytes = raw,
S3Object = list(
Bucket = "string",
Name = "string",
Version = "string"
)
),
MaxLabels = 123,
MinConfidence = 123.0
)## Not run:
# This operation detects labels in the supplied image
svc$detect_labels(
Image = list(
S3Object = list(
Bucket = "mybucket",
Name = "myphoto"
)
),
MaxLabels = 123L,
MinConfidence = 70L
)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.