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# 检测视频中的标签
Amazon Rekognition Video 可以检测视频中的标签（对象和概念）以及检测到标签的时间。有关开发工具包代码示例，请参阅[使用 Java 或 Python 分析存储在 Amazon S3 存储桶中的视频 (SDK)](video-analyzing-with-sqs.md)。有关 AWS CLI 示例，请参阅[使用分析视频 AWS Command Line Interface](video-cli-commands.md)。
Amazon Rekognition Video 标签检测是一项异步操作。要开始检测视频中的标签，请致电[StartLabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_StartlabelDetection.html)。
Amazon Rekognition Video 会将视频分析的完成状态发布到 Amazon Simple Notification Service 主题。如果视频分析成功，请调用 [GetLabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_GetLabelDetection.html) 来获取检测到的标签。有关调用视频分析 API 操作的信息，请参阅[调用 Amazon Rekognition Video 操作](api-video.md)。
## StartLabelDetection 请求
以下示例是 `StartLabelDetection` 操作的请求。您为 `StartLabelDetection` 操作提供了存储在 Amazon S3 存储桶中的视频。在示例请求 JSON 中，指定了 Amazon S3 存储桶和视频名称，以及 `MinConfidence`、`Features`、`Settings` 和 `NotificationChannel`。
`MinConfidence` 是 Amazon Rekognition Video 对检测到的标签或实例边界框（如果检测到）要在响应中返回而对其准确度所具有的最小置信度。
使用 `Features`，您可以指定要将 GENERAL\_LABELS 作为响应的一部分返回。
使用 `Settings`，您可以筛选 GENERAL\_LABELS 的返回项目。对于标签，您可以使用纳入和排除筛选器。您也可以按特定标签、单个标签或标签类别进行筛选：
+ `LabelInclusionFilters` – 用于指定要在响应中纳入哪些标签
+ `LabelExclusionFilters` – 用于指定要从响应中排除哪些标签。
+ `LabelCategoryInclusionFilters` – 用于指定要在响应中纳入哪些标签类别。
+ `LabelCategoryExclusionFilters` - 用于指定要从响应中排除哪些标签类别。
您还可以根据需要组合纳入和排除筛选器，排除某些标签或类别，而纳入其他标签或类别。
`NotificationChannel` 是您希望 Amazon Rekognition Video 向其发布标签检测操作完成状态的 Amazon SNS 主题的 ARN。如果您使用的是 `AmazonRekognitionServiceRole` 权限策略，那么 Amazon SNS 主题的主题名称必须以 Rekognition 开头。
以下是 JSON 格式的示例 `StartLabelDetection` 请求，包括筛选器：
```
{
"ClientRequestToken": "5a6e690e-c750-460a-9d59-c992e0ec8638",
"JobTag": "5a6e690e-c750-460a-9d59-c992e0ec8638",
"Video": {
"S3Object": {
"Bucket": "bucket",
"Name": "video.mp4"
}
},
"Features": ["GENERAL_LABELS"],
"MinConfidence": 75,
"Settings": {
"GeneralLabels": {
"LabelInclusionFilters": ["Cat", "Dog"],
"LabelExclusionFilters": ["Tiger"],
"LabelCategoryInclusionFilters": ["Animals and Pets"],
"LabelCategoryExclusionFilters": ["Popular Landmark"]
}
},
"NotificationChannel": {
"RoleArn": "arn:aws:iam::012345678910:role/SNSAccessRole",
"SNSTopicArn": "arn:aws:sns:us-east-1:012345678910:notification-topic",
}
}
```
## GetLabelDetection 操作响应
`GetLabelDetection` 将返回一个数组 (`Labels`)，其中包含有关在视频中检测到的标签的信息。数组可以按时间排序，也可以按指定 `SortBy` 参数时检测到的标签进行排序。也可以使用 `AggregateBy` 参数选择如何汇总响应项。
以下示例是 `GetLabelDetection` 的 JSON 响应。在响应中，请注意以下内容：
+ **排序顺序** – 返回的标签数组按时间进行排序。要按标签进行排序，请为 `GetLabelDetection` 在 `SortBy` 输入参数中指定 `NAME`。如果此标签在视频中多次出现，则会有 ([LabelDetection](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_LabelDetection.html)) 元素的多个实例。默认排序顺序为 `TIMESTAMP`，而辅助排序顺序为 `NAME`。
+ **标签信息** – `LabelDetection` 数组元素包含一个（[标签](https://docs.aws.amazon.com/rekognition/latest/APIReference/API_Label.html)）对象，该对象包含标签名称和 Amazon Rekognition 在检测到的标签的准确性中具有的置信度。`Label` 对象还包括标签的分层分类和常见标签的边界框信息。`Timestamp` 是从视频开始到检测到标签的时间，以毫秒为单位。
还会返回与标签关联的任何类别或别名的相关信息。对于按视频`SEGMENTS`汇总的结果，将返回 `StartTimestampMillis`、`EndTimestampMillis` 和 `DurationMillis` 结构，它们分别定义了片段的开始时间、结束时间和持续时间。
+ **汇总** – 指定返回结果时的汇总方式。默认为按 `TIMESTAMPS` 汇总。您也可以选择按 `SEGMENTS` 汇总，即在某个时间段内汇总结果。如果按 `SEGMENTS` 汇总，则不会返回有关检测到的带有边界框的实例的信息。只返回在分段期间检测到的标签。
+ **分页信息** - 此示例显示一页标签检测信息。您可以为 `GetLabelDetection` 在 `MaxResults` 输入参数中指定要返回的 `LabelDetection` 对象的数量。如果存在的结果的数量超过了 `MaxResults`，则 `GetLabelDetection` 会返回一个令牌 (`NextToken`)，用于获取下一页的结果。有关更多信息，请参阅 [获取 Amazon Rekognition Video 分析结果](api-video.md#api-video-get)。
+ **视频信息** – 此响应包含有关由 `GetLabelDetection` 返回的每页信息中的视频格式（`VideoMetadata`）的信息。
以下是 JSON 格式的示例 GetLabelDetection 响应，其中包含由 TIMESTAMPS 进行聚合：
```
{
"JobStatus": "SUCCEEDED",
"LabelModelVersion": "3.0",
"Labels": [
{
"Timestamp": 1000,
"Label": {
"Name": "Car",
"Categories": [
{
"Name": "Vehicles and Automotive"
}
],
"Aliases": [
{
"Name": "Automobile"
}
],
"Parents": [
{
"Name": "Vehicle"
}
],
"Confidence": 99.9364013671875, // Classification confidence
"Instances": [
{
"BoundingBox": {
"Width": 0.26779675483703613,
"Height": 0.8562285900115967,
"Left": 0.3604024350643158,
"Top": 0.09245597571134567
},
"Confidence": 99.9364013671875 // Detection confidence
}
]
}
},
{
"Timestamp": 1000,
"Label": {
"Name": "Cup",
"Categories": [
{
"Name": "Kitchen and Dining"
}
],
"Aliases": [
{
"Name": "Mug"
}
],
"Parents": [],
"Confidence": 99.9364013671875, // Classification confidence
"Instances": [
{
"BoundingBox": {
"Width": 0.26779675483703613,
"Height": 0.8562285900115967,
"Left": 0.3604024350643158,
"Top": 0.09245597571134567
},
"Confidence": 99.9364013671875 // Detection confidence
}
]
}
},
{
"Timestamp": 2000,
"Label": {
"Name": "Kangaroo",
"Categories": [
{
"Name": "Animals and Pets"
}
],
"Aliases": [
{
"Name": "Wallaby"
}
],
"Parents": [
{
"Name": "Mammal"
}
],
"Confidence": 99.9364013671875,
"Instances": [
{
"BoundingBox": {
"Width": 0.26779675483703613,
"Height": 0.8562285900115967,
"Left": 0.3604024350643158,
"Top": 0.09245597571134567,
},
"Confidence": 99.9364013671875
}
]
}
},
{
"Timestamp": 4000,
"Label": {
"Name": "Bicycle",
"Categories": [
{
"Name": "Hobbies and Interests"
}
],
"Aliases": [
{
"Name": "Bike"
}
],
"Parents": [
{
"Name": "Vehicle"
}
],
"Confidence": 99.9364013671875,
"Instances": [
{
"BoundingBox": {
"Width": 0.26779675483703613,
"Height": 0.8562285900115967,
"Left": 0.3604024350643158,
"Top": 0.09245597571134567
},
"Confidence": 99.9364013671875
}
]
}
}
],
"VideoMetadata": {
"ColorRange": "FULL",
"DurationMillis": 5000,
"Format": "MP4",
"FrameWidth": 1280,
"FrameHeight": 720,
"FrameRate": 24
}
}
```
以下是 JSON 格式的示例 GetLabelDetection 响应，其中包含按分段进行聚合：
```
{
"JobStatus": "SUCCEEDED",
"LabelModelVersion": "3.0",
"Labels": [
{
"StartTimestampMillis": 225,
"EndTimestampMillis": 3578,
"DurationMillis": 3353,
"Label": {
"Name": "Car",
"Categories": [
{
"Name": "Vehicles and Automotive"
}
],
"Aliases": [
{
"Name": "Automobile"
}
],
"Parents": [
{
"Name": "Vehicle"
}
],
"Confidence": 99.9364013671875 // Maximum confidence score for Segment mode
}
},
{
"StartTimestampMillis": 7578,
"EndTimestampMillis": 12371,
"DurationMillis": 4793,
"Label": {
"Name": "Kangaroo",
"Categories": [
{
"Name": "Animals and Pets"
}
],
"Aliases": [
{
"Name": "Wallaby"
}
],
"Parents": [
{
"Name": "Mammal"
}
],
"Confidence": 99.9364013671875
}
},
{
"StartTimestampMillis": 22225,
"EndTimestampMillis": 22578,
"DurationMillis": 2353,
"Label": {
"Name": "Bicycle",
"Categories": [
{
"Name": "Hobbies and Interests"
}
],
"Aliases": [
{
"Name": "Bike"
}
],
"Parents": [
{
"Name": "Vehicle"
}
],
"Confidence": 99.9364013671875
}
}
],
"VideoMetadata": {
"ColorRange": "FULL",
"DurationMillis": 5000,
"Format": "MP4",
"FrameWidth": 1280,
"FrameHeight": 720,
"FrameRate": 24
}
}
```
## 转变回 GetLabelDetection 应
使用 GetLabelDetection API 操作检索结果时，您可能需要响应结构来模仿旧的 API 响应结构，其中主标签和别名都包含在同一个列表中。
上一节中的 JSON 响应示例，显示了来自的 API 响应的当前形式 GetLabelDetection。
以下示例显示了 GetLabelDetection API 之前的响应：
```
{
"Labels": [
{
"Timestamp": 0,
"Label": {
"Instances": [],
"Confidence": 60.51791763305664,
"Parents": [],
"Name": "Leaf"
}
},
{
"Timestamp": 0,
"Label": {
"Instances": [],
"Confidence": 99.53411102294922,
"Parents": [],
"Name": "Human"
}
},
{
"Timestamp": 0,
"Label": {
"Instances": [
{
"BoundingBox": {
"Width": 0.11109819263219833,
"Top": 0.08098889887332916,
"Left": 0.8881205320358276,
"Height": 0.9073750972747803
},
"Confidence": 99.5831298828125
},
{
"BoundingBox": {
"Width": 0.1268676072359085,
"Top": 0.14018426835536957,
"Left": 0.0003282368124928324,
"Height": 0.7993982434272766
},
"Confidence": 99.46029663085938
}
],
"Confidence": 99.63411102294922,
"Parents": [],
"Name": "Person"
}
},
.
.
.
{
"Timestamp": 166,
"Label": {
"Instances": [],
"Confidence": 73.6471176147461,
"Parents": [
{
"Name": "Clothing"
}
],
"Name": "Sleeve"
}
}
],
"LabelModelVersion": "2.0",
"JobStatus": "SUCCEEDED",
"VideoMetadata": {
"Format": "QuickTime / MOV",
"FrameRate": 23.976024627685547,
"Codec": "h264",
"DurationMillis": 5005,
"FrameHeight": 674,
"FrameWidth": 1280
}
}
```
如果需要，您可以转换当前响应以遵循旧响应的格式。您可以使用以下示例代码将最新的 API 响应转换为之前的 API 响应结构：
```
from copy import deepcopy
VIDEO_LABEL_KEY = "Labels"
LABEL_KEY = "Label"
ALIASES_KEY = "Aliases"
INSTANCE_KEY = "Instances"
NAME_KEY = "Name"
#Latest API response sample for AggregatedBy SEGMENTS
EXAMPLE_SEGMENT_OUTPUT = {
"Labels": [
{
"Timestamp": 0,
"Label":{
"Name": "Person",
"Confidence": 97.530106,
"Parents": [],
"Aliases": [
{
"Name": "Human"
},
],
"Categories": [
{
"Name": "Person Description"
}
],
},
"StartTimestampMillis": 0,
"EndTimestampMillis": 500666,
"DurationMillis": 500666
},
{
"Timestamp": 6400,
"Label": {
"Name": "Leaf",
"Confidence": 89.77790069580078,
"Parents": [
{
"Name": "Plant"
}
],
"Aliases": [],
"Categories": [
{
"Name": "Plants and Flowers"
}
],
},
"StartTimestampMillis": 6400,
"EndTimestampMillis": 8200,
"DurationMillis": 1800
},
]
}
#Output example after the transformation for AggregatedBy SEGMENTS
EXPECTED_EXPANDED_SEGMENT_OUTPUT = {
"Labels": [
{
"Timestamp": 0,
"Label":{
"Name": "Person",
"Confidence": 97.530106,
"Parents": [],
"Aliases": [
{
"Name": "Human"
},
],
"Categories": [
{
"Name": "Person Description"
}
],
},
"StartTimestampMillis": 0,
"EndTimestampMillis": 500666,
"DurationMillis": 500666
},
{
"Timestamp": 6400,
"Label": {
"Name": "Leaf",
"Confidence": 89.77790069580078,
"Parents": [
{
"Name": "Plant"
}
],
"Aliases": [],
"Categories": [
{
"Name": "Plants and Flowers"
}
],
},
"StartTimestampMillis": 6400,
"EndTimestampMillis": 8200,
"DurationMillis": 1800
},
{
"Timestamp": 0,
"Label":{
"Name": "Human",
"Confidence": 97.530106,
"Parents": [],
"Categories": [
{
"Name": "Person Description"
}
],
},
"StartTimestampMillis": 0,
"EndTimestampMillis": 500666,
"DurationMillis": 500666
},
]
}
#Latest API response sample for AggregatedBy TIMESTAMPS
EXAMPLE_TIMESTAMP_OUTPUT = {
"Labels": [
{
"Timestamp": 0,
"Label": {
"Name": "Person",
"Confidence": 97.530106,
"Instances": [
{
"BoundingBox": {
"Height": 0.1549897,
"Width": 0.07747964,
"Top": 0.50858885,
"Left": 0.00018205095
},
"Confidence": 97.530106
},
],
"Parents": [],
"Aliases": [
{
"Name": "Human"
},
],
"Categories": [
{
"Name": "Person Description"
}
],
},
},
{
"Timestamp": 6400,
"Label": {
"Name": "Leaf",
"Confidence": 89.77790069580078,
"Instances": [],
"Parents": [
{
"Name": "Plant"
}
],
"Aliases": [],
"Categories": [
{
"Name": "Plants and Flowers"
}
],
},
},
]
}
#Output example after the transformation for AggregatedBy TIMESTAMPS
EXPECTED_EXPANDED_TIMESTAMP_OUTPUT = {
"Labels": [
{
"Timestamp": 0,
"Label": {
"Name": "Person",
"Confidence": 97.530106,
"Instances": [
{
"BoundingBox": {
"Height": 0.1549897,
"Width": 0.07747964,
"Top": 0.50858885,
"Left": 0.00018205095
},
"Confidence": 97.530106
},
],
"Parents": [],
"Aliases": [
{
"Name": "Human"
},
],
"Categories": [
{
"Name": "Person Description"
}
],
},
},
{
"Timestamp": 6400,
"Label": {
"Name": "Leaf",
"Confidence": 89.77790069580078,
"Instances": [],
"Parents": [
{
"Name": "Plant"
}
],
"Aliases": [],
"Categories": [
{
"Name": "Plants and Flowers"
}
],
},
},
{
"Timestamp": 0,
"Label": {
"Name": "Human",
"Confidence": 97.530106,
"Parents": [],
"Categories": [
{
"Name": "Person Description"
}
],
},
},
]
}
def expand_aliases(inferenceOutputsWithAliases):
if VIDEO_LABEL_KEY in inferenceOutputsWithAliases:
expandInferenceOutputs = []
for segmentLabelDict in inferenceOutputsWithAliases[VIDEO_LABEL_KEY]:
primaryLabelDict = segmentLabelDict[LABEL_KEY]
if ALIASES_KEY in primaryLabelDict:
for alias in primaryLabelDict[ALIASES_KEY]:
aliasLabelDict = deepcopy(segmentLabelDict)
aliasLabelDict[LABEL_KEY][NAME_KEY] = alias[NAME_KEY]
del aliasLabelDict[LABEL_KEY][ALIASES_KEY]
if INSTANCE_KEY in aliasLabelDict[LABEL_KEY]:
del aliasLabelDict[LABEL_KEY][INSTANCE_KEY]
expandInferenceOutputs.append(aliasLabelDict)
inferenceOutputsWithAliases[VIDEO_LABEL_KEY].extend(expandInferenceOutputs)
return inferenceOutputsWithAliases
if __name__ == "__main__":
segmentOutputWithExpandAliases = expand_aliases(EXAMPLE_SEGMENT_OUTPUT)
assert segmentOutputWithExpandAliases == EXPECTED_EXPANDED_SEGMENT_OUTPUT
timestampOutputWithExpandAliases = expand_aliases(EXAMPLE_TIMESTAMP_OUTPUT)
assert timestampOutputWithExpandAliases == EXPECTED_EXPANDED_TIMESTAMP_OUTPUT
```