/
api_op_DetectLabels.go
262 lines (240 loc) · 11.5 KB
/
api_op_DetectLabels.go
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
// Code generated by smithy-go-codegen DO NOT EDIT.
package rekognition
import (
"context"
"fmt"
awsmiddleware "github.com/aws/aws-sdk-go-v2/aws/middleware"
"github.com/aws/aws-sdk-go-v2/aws/signer/v4"
"github.com/aws/aws-sdk-go-v2/service/rekognition/types"
"github.com/aws/smithy-go/middleware"
smithyhttp "github.com/aws/smithy-go/transport/http"
)
// 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.
// Optional Parameters 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_PROPERTIES will 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 (https://docs.aws.amazon.com/rekognition/latest/dg/labels-detect-labels-image.html)
// . 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. Response Elements 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.
func (c *Client) DetectLabels(ctx context.Context, params *DetectLabelsInput, optFns ...func(*Options)) (*DetectLabelsOutput, error) {
if params == nil {
params = &DetectLabelsInput{}
}
result, metadata, err := c.invokeOperation(ctx, "DetectLabels", params, optFns, c.addOperationDetectLabelsMiddlewares)
if err != nil {
return nil, err
}
out := result.(*DetectLabelsOutput)
out.ResultMetadata = metadata
return out, nil
}
type DetectLabelsInput struct {
// 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 Bytes field. For more information, see Images in the
// Amazon Rekognition developer guide.
//
// This member is required.
Image *types.Image
// A list of the types of analysis to perform. Specifying GENERAL_LABELS uses the
// label detection feature, while specifying IMAGE_PROPERTIES returns information
// regarding image color and quality. If no option is specified GENERAL_LABELS is
// used by default.
Features []types.DetectLabelsFeatureName
// Maximum number of labels you want the service to return in the response. The
// service returns the specified number of highest confidence labels. Only valid
// when GENERAL_LABELS is specified as a feature type in the Feature input
// parameter.
MaxLabels *int32
// 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 MinConfidence is not specified, the operation returns labels with a
// confidence values greater than or equal to 55 percent. Only valid when
// GENERAL_LABELS is specified as a feature type in the Feature input parameter.
MinConfidence *float32
// A list of the filters to be applied to returned detected labels and image
// properties. Specified filters can be inclusive, exclusive, or a combination of
// both. Filters can be used for individual labels or label categories. The exact
// label names or label categories must be supplied. For a full list of labels and
// label categories, see Detecting labels (https://docs.aws.amazon.com/rekognition/latest/dg/labels.html)
// .
Settings *types.DetectLabelsSettings
noSmithyDocumentSerde
}
type DetectLabelsOutput struct {
// Information about the properties of the input image, such as brightness,
// sharpness, contrast, and dominant colors.
ImageProperties *types.DetectLabelsImageProperties
// Version number of the label detection model that was used to detect labels.
LabelModelVersion *string
// An array of labels for the real-world objects detected.
Labels []types.Label
// The value of OrientationCorrection is always null. If the input image is in
// .jpeg format, it might contain exchangeable image file format (Exif) metadata
// that includes the image's orientation. Amazon Rekognition uses this orientation
// information to perform image correction. The bounding box coordinates are
// translated to represent object locations after the orientation information in
// the Exif metadata is used to correct the image orientation. Images in .png
// format don't contain Exif metadata. Amazon Rekognition doesn’t perform image
// correction for images in .png format and .jpeg images without orientation
// information in the image Exif metadata. The bounding box coordinates aren't
// translated and represent the object locations before the image is rotated.
OrientationCorrection types.OrientationCorrection
// Metadata pertaining to the operation's result.
ResultMetadata middleware.Metadata
noSmithyDocumentSerde
}
func (c *Client) addOperationDetectLabelsMiddlewares(stack *middleware.Stack, options Options) (err error) {
if err := stack.Serialize.Add(&setOperationInputMiddleware{}, middleware.After); err != nil {
return err
}
err = stack.Serialize.Add(&awsAwsjson11_serializeOpDetectLabels{}, middleware.After)
if err != nil {
return err
}
err = stack.Deserialize.Add(&awsAwsjson11_deserializeOpDetectLabels{}, middleware.After)
if err != nil {
return err
}
if err := addProtocolFinalizerMiddlewares(stack, options, "DetectLabels"); err != nil {
return fmt.Errorf("add protocol finalizers: %v", err)
}
if err = addlegacyEndpointContextSetter(stack, options); err != nil {
return err
}
if err = addSetLoggerMiddleware(stack, options); err != nil {
return err
}
if err = awsmiddleware.AddClientRequestIDMiddleware(stack); err != nil {
return err
}
if err = smithyhttp.AddComputeContentLengthMiddleware(stack); err != nil {
return err
}
if err = addResolveEndpointMiddleware(stack, options); err != nil {
return err
}
if err = v4.AddComputePayloadSHA256Middleware(stack); err != nil {
return err
}
if err = addRetryMiddlewares(stack, options); err != nil {
return err
}
if err = awsmiddleware.AddRawResponseToMetadata(stack); err != nil {
return err
}
if err = awsmiddleware.AddRecordResponseTiming(stack); err != nil {
return err
}
if err = addClientUserAgent(stack, options); err != nil {
return err
}
if err = smithyhttp.AddErrorCloseResponseBodyMiddleware(stack); err != nil {
return err
}
if err = smithyhttp.AddCloseResponseBodyMiddleware(stack); err != nil {
return err
}
if err = addSetLegacyContextSigningOptionsMiddleware(stack); err != nil {
return err
}
if err = addOpDetectLabelsValidationMiddleware(stack); err != nil {
return err
}
if err = stack.Initialize.Add(newServiceMetadataMiddleware_opDetectLabels(options.Region), middleware.Before); err != nil {
return err
}
if err = awsmiddleware.AddRecursionDetection(stack); err != nil {
return err
}
if err = addRequestIDRetrieverMiddleware(stack); err != nil {
return err
}
if err = addResponseErrorMiddleware(stack); err != nil {
return err
}
if err = addRequestResponseLogging(stack, options); err != nil {
return err
}
if err = addDisableHTTPSMiddleware(stack, options); err != nil {
return err
}
return nil
}
func newServiceMetadataMiddleware_opDetectLabels(region string) *awsmiddleware.RegisterServiceMetadata {
return &awsmiddleware.RegisterServiceMetadata{
Region: region,
ServiceID: ServiceID,
OperationName: "DetectLabels",
}
}