Age and Gender Recognition via REST API¶

Introduction¶

This article describes how to use OpenVINO Model Server to execute inference requests sent over the REST API interface. The demo uses a pretrained model from the Open Model Zoo repository.

Steps¶

Download the pretrained model for age and gender recognition¶

Download both components of the model (xml and bin file) using curl in the model directory

curl --create-dirs https://download.01.org/opencv/2021/openvinotoolkit/2021.1/open_model_zoo/models_bin/1/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013.bin https://download.01.org/opencv/2021/openvinotoolkit/2021.1/open_model_zoo/models_bin/1/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013.xml -o model/1/age-gender-recognition-retail-0013.bin -o model/1/age-gender-recognition-retail-0013.xml

Pull and tag the docker image with the OVMS component¶

Pull the latest version of OpenVINO Model Server from Dockerhub :

docker pull openvino/model_server:latest

Start OVMS docker container with downloaded model¶

Start OVMS container with image pulled in previous step and mount model directory :

docker run --rm -d -u $(id -u):$(id -g) -v $(pwd)/model:/models/age_gender -p 9000:9000 -p 9001:9001 openvino/model_server:latest --model_path /models/age_gender --model_name age_gender --port 9000 --rest_port 9001

Download Sample Image¶

Download sample image using the command :

wget https://raw.githubusercontent.com/openvinotoolkit/open_model_zoo/2021.4/models/intel/age-gender-recognition-retail-0013/assets/age-gender-recognition-retail-0001.jpg

#### Format the json request and send the inference request to the OVMS REST API endpoint
1. Create a sample python script using the command :
```Bash
touch sample.py

Format the downloaded image using the following Python code snippet. Output of the code snippet is a json including the downloaded image in BGR format and 0-255 normalization. Paste the following code snippet in sample.py.

import cv2
import numpy as np
import json
import requests


def getJpeg(path, size):

    img = cv2.imread(path, cv2.IMREAD_COLOR)
    # retrieved array has BGR format and 0-255 normalization
    img = cv2.resize(img, (size, size))
    img = img.transpose(2,0,1).reshape(1,3,size,size)
    print(path, img.shape, "; data range:",np.amin(img),":",np.amax(img))
    return img

my_image = getJpeg('age-gender-recognition-retail-0001.jpg',62)


data_obj = {'inputs':  my_image.tolist()}
data_json = json.dumps(data_obj)

result = requests.post("http://localhost:9001/v1/models/age_gender:predict", data=data_json)
result_dict = json.loads(result.text)
print(result_dict)

Run the above code snippet to send POST API request to predict results by providing formatted json as request body using the command :
```
python3 sample.py
```
Sample Output :
```
{'outputs': {'age_conv3': [[[[0.2519038915634155]]]], 'prob': [[[[0.9874807000160217]], [[0.012519358657300472]]]]}}
```
Output format :

Output Name

Shape

Description

age_conv3

[1, 1, 1, 1]

Estimated age divided by 100

prob

[1, 2, 1, 1]

Softmax output across 2 type classes [female, male]

Output Name	Shape	Description
age_conv3	[1, 1, 1, 1]	Estimated age divided by 100
prob	[1, 2, 1, 1]	Softmax output across 2 type classes [female, male]

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