Human Attribute Recognition

Recognize the attributes of the human body in the image, and return the human body position coordinates and attribute analysis in each area, including the semantic information of 16 attributes such as gender, age, and clothing.

Applicable scenarios

Applicable to scenarios such as smart security, smart retail, and pedestrian search.

List of attributes

Name	Semantic
Upper wear	Short sleeve, Long sleeve
Lower wear	Shorts/Skirts, Pants/Skirts
Upper wear texture	Pattern, Solid, Stripe/Check
Backpack	Without bag, With bag
Whether to wear glasses	No, Yes
Whether to wear a hat	No, Yes
Body orientation	Front, Back, Left, Right
Truncate above	No, Yes
Truncate below	No, Yes
Occlusion	None, Light, Heavy
Whether to wear a mask	No, Yes
Gender	Male, Female
Age	Children, teenager, middle-aged, elderly
Smoking	No, Yes
Telephone	No, Yes
Whether to carry things	No, Yes

API reference

HTTP request method: POST
Request body parameters

Name	Type	Required	Description
url	String	Choose url or img.	Image URL address, which supports HTTP/HTTPS and S3 protocols. Supported image formats are jpg/jpeg/png/bmp, with the longest side not exceeding 4096px.
img	String	Choose url or img.	Base64 encoded image data.

Example JSON request

{
  "url": "Image URL address"
}

{
  "img": "Base64-encoded image data"
}

Response parameters

Name	Type	Description
Labels	List	List of human bodies recognized in the image.
upper_wear	Dict	Short Sleeve, Long Sleeve
upper_wear_texture	Dict	Pattern, Solid, Stripe/Check
lower_wear	Dict	Shorts/Skirts, Pants/Skirts
glasses	Dict	With glasses, without glasses
bag	Dict	With backpack, without backpack
headwear	Dict	With hat, without hat
orientation	Dict	left side, back side, front side, right side
upper_cut	Dict	with truncation, without truncation
lower_cut	Dict	with truncation, without truncation
occlusion	Dict	No Occlusion, Light Occlusion, Heavy Occlusion
face_mask	Dict	With mask, without mask
gender	Dict	Male, Female
age	Dict	Children, Teens, Middle-aged, Seniors
smoke	Dict	Smoking, non-smoking
cellphone	Dict	with telephone, without telephone
carrying_item	Dict	With or without carry
BoundingBox	Dict	Coordinate values of the human body in the image, including the percentage of top, left, width, height relative to the full screen
LabelModelVersion	String	Current model version number

Example JSON response

{
    "Labels": [
        {
            "upper_wear": {
                "短袖": 0.01, 
                "长袖": 99.99
            }, 
            "upper_wear_texture": {
                "图案": 0, 
                "纯色": 99.55, 
                "条纹/格子": 0.45
            }, 
            "lower_wear": {
                "短裤/裙": 0.15, 
                "长裤/裙": 99.85
            }, 
            "glasses": {
                "有眼镜": 57.74, 
                "无眼镜": 42.26
            }, 
            "bag": {
                "有背包": 0.69, 
                "无背包": 99.31
            }, 
            "headwear": {
                "有帽": 97.02, 
                "无帽": 2.98
            }, 
            "orientation": {
                "左侧面": 99.99, 
                "背面": 0, 
                "正面": 0, 
                "右侧面": 0.01
            }, 
            "upper_cut": {
                "有截断": 0, 
                "无截断": 100
            }, 
            "lower_cut": {
                "无截断": 0.18, 
                "有截断": 99.82
            }, 
            "occlusion": {
                "无遮挡": 100, 
                "重度遮挡": 0, 
                "轻度遮挡": 0
            }, 
            "face_mask": {
                "无口罩": 100, 
                "戴口罩": 0
            }, 
            "gender": {
                "男性": 100, 
                "女性": 0
            }, 
            "age": {
                "幼儿": 0, 
                "青少年": 100, 
                "中年": 0, 
                "老年": 0
            }, 
            "smoke": {
                "吸烟": 0, 
                "未吸烟": 100
            }, 
            "cellphone": {
                "使用手机": 0, 
                "未使用手机": 100
            }, 
            "carrying_item": {
                "有手提物": 0.03, 
                "无手提物": 99.97
            }, 
            "BoundingBox": {
                "Width": 0.11781848725818456, 
                "Height": 0.43450208474661556, 
                "Left": 0.5310931977771577, 
                "Top": 0.45263674786982644
            }
        }, 
        ...
    ], 
    "LabelModelVersion": "1.2.0"
}

API test

You can use the following tools (API explorer, Postman, cURL, Python, Java) to test calling APIs.

API Explorer

Prerequisites

When deploying the solution, you need to：

set the parameter API Explorer to yes.
set the parameter API Gateway Authorization to NONE.

Otherwise, you can only view the API definitions in the API explorer, but cannot test calling API online.

Steps

Sign in to the AWS CloudFormation console.
On the Stacks page, select the solution’s root stack. Do not select the NESTED stack.
Choose the Outputs tab, and find the URL for APIExplorer.
Click the URL to access the API explorer. The APIs that you have selected during deployment will be displayed.
For the API you want to test, click the down arrow to display the request method.
Choose the Try it out button, and enter the correct Body data to test API and check the test result.
Make sure the format is correct, and choose Execute.
Check the returned result in JSON format in the Responses body. If needed, copy or download the result.
Check the Response headers.
(Optional) Choose Clear next to the Execute button to clear the request body and responses.

Postman (AWS_IAM Authentication）

Sign in to the AWS CloudFormation console.
On the Stacks page, select the solution’s root stack.
Choose the Outputs tab, and find the URL with the prefix GeneralOCR.
Create a new tab in Postman. Paste the URL into the address bar, and select POST as the HTTP call method.
Open the Authorization configuration, select Amazon Web Service Signature from the drop-down list, and enter the AccessKey, SecretKey and Amazon Web Service Region of the corresponding account (such as cn-north-1 or cn-northwest-1 ).
Open the Body configuration item and select the raw and JSON data types.
Enter the test data in the Body, and click the Send button to see the corresponding return results.

{
  "url": "Image URL address"
}

cURL

Windows

curl --location --request POST "https://[API_ID].execute-api.[AWS_REGION].amazonaws.com/[STAGE]/custom_ocr" ^
--header "Content-Type: application/json" ^
--data-raw "{\"url\": \"Image URL address\"}"

Linux/MacOS

curl --location --request POST 'https://[API_ID].execute-api.[AWS_REGION].amazonaws.com/[STAGE]/custom_ocr' \
--header 'Content-Type: application/json' \
--data-raw '{
  "url":"Image URL address"
}'

Python (AWS_IAM Authentication)

import requests
import json
from aws_requests_auth.boto_utils import BotoAWSRequestsAuth

auth = BotoAWSRequestsAuth(aws_host='[API_ID].execute-api.[AWS_REGION].amazonaws.com',
                           aws_region='[AWS_REGION]',
                           aws_service='execute-api')

url = 'https://[API_ID].execute-api.[AWS_REGION].amazonaws.com/[STAGE]/custom_ocr'
payload = {
    'url': 'Image URL address'
}
response = requests.request("POST", url, data=json.dumps(payload), auth=auth)
print(json.loads(response.text))

Python (NONE Authentication)

import requests
import json

url = "https://[API_ID].execute-api.[AWS_REGION].amazonaws.com/[STAGE]/custom_ocr"

payload = json.dumps({
  "url": "Image URL address"
})
headers = {
  'Content-Type': 'application/json'
}

response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

Java

OkHttpClient client = new OkHttpClient().newBuilder()
  .build();
MediaType mediaType = MediaType.parse("application/json");
RequestBody body = RequestBody.create(mediaType, "{\n  \"url\":\"Image URL address\"\n}");
Request request = new Request.Builder()
  .url("https://xxxxxxxxxxx.execute-api.xxxxxxxxx.amazonaws.com/[STAGE]/custom_ocr")
  .method("POST", body)
  .addHeader("Content-Type", "application/json")
  .build();
Response response = client.newCall(request).execute();

Cost estimation

You are responsible for the cost of using each Amazon Web Services service when running the solution. As of this revision, the main cost factors affecting the solution include.

Amazon API Gateway calls
Amazon API Gateway data output
Amazon CloudWatch Logs storage
Amazon Elastic Container Registry storage

If you choose an Amazon Lambda based deployment, the factors also include:

Amazon Lambda invocations
Amazon Lambda running time

If you choose an Amazon SageMaker based deployment, the factors also include:

Amazon SageMaker endpoint node instance type
Amazon SageMaker endpoint node data input
Amazon SageMaker endpoint node data output

Cost estimation example 1

In AWS China (Ningxia) Region operated by NWCD (cn-northwest-1), process an image of 1MB in 1 second

The cost of using this solution to process the image is shown below:

Service	Dimensions	Cost
AWS Lambda	1 million invocations	¥1.36
AWS Lambda	8192MB memory, 1 seconds run each time	¥907.8
Amazon API Gateway	1 million invocations	¥28.94
Amazon API Gateway	100KB data output each time, ¥0.933/GB	¥93.3
Amazon CloudWatch Logs	10KB each time, ¥6.228/GB	¥62.28
Amazon Elastic Container Registry	0.5GB storage, ¥0.69/GB each month	¥0.35
Total		¥1010.06

Cost estimation example 2

In US East (Ohio) Region (us-east-2), process an image of 1MB in 1 seconds

The cost of using this solution to process this image is shown below:

Service	Dimensions	Cost
AWS Lambda	1 million invocations	$0.20
AWS Lambda	8192MB memory, 1 seconds run each time	$133.3
Amazon API Gateway	1 million invocations	$3.5
Amazon API Gateway	100KB data output each time, $0.09/GB	$9
Amazon CloudWatch Logs	10KB each time, $0.50/GB	$5
Amazon Elastic Container Registry	0.5GB存储，$0.1/GB each month	$0.05
Total		$142.95

Uninstall the deployment

You can uninstall the Human Attribute Recognition feature via Amazon CloudFormation as described in Add or remove AI features and make sure the HumanAttributeRecognition parameter is set to no in the parameters section.

Note

Time to uninstall the deployment is approximately: 10 Minutes