Face Analysis

In this tutorial we will learn to write a python application to perform face analysis using deepsight

Python code

Create a file face_analysis.py with the following code in it.

import requests
import argparse

face_api = "http://localhost:5000/inferImage"

parser = argparse.ArgumentParser(description='Analyze faces')
parser.add_argument('--double', metavar='d', action='store', 
        type=int, nargs='?', default=0, help='specified no. of times the image should be doubled')
parser.add_argument('--expandBy', metavar='x', action='store', 
        type=float, nargs='?', default=0.0, help='specify %% by which to expand face bounding boxes')
parser.add_argument('--id',  action='store_true',help='return face id')
parser.add_argument('--attr', action='store_true',help='return gender age')
parser.add_argument('--lmk', action='store_true', help='return landmarks')
parser.add_argument('image', action='store', help='path to image')
args = parser.parse_args()

params = {
    "returnFaceId": args.id,
    "returnFaceAttributes": args.attr,
    "returnFaceLandmarks": args.lmk,
    "expandBy": args.expandBy,
    "dblScale": args.double
url_params = "?"
for k,v in params.items():
    url_params += "%s=%s&"%(k,str(v).lower())
face_api += url_params

files = {'pic':open(args.image,'rb')}
r = requests.post(face_api,files=files)
result = r.json()

# The result is a list as follows 
# [
# {face1},
# {face2},
#  .
# {faceN},
# {diagnostics}
# ] 

for i, face in enumerate(result[:-1]):
    print("Face %d  "%i, face)


# For help with usage
$ python face_analysis.py -h
usage: face_analysis.py [-h] [--double [d]] [--expandBy [x]] [--id] [--attr]

Analyze faces

positional arguments:
  image           path to image

optional arguments:
  -h, --help      show this help message and exit
  --double [d]    specified no. of times the image should be doubled
  --expandBy [x]  specify % by which to expand face bounding boxes
  --id            return face id
  --attr          return gender age
  --lmk           return landmarks

Running the program

Download an image from the internet and put it in the current folder. Then run the program as follows

# To get gender/age
$ python face_analysis.py face.jpg --attr
Face 0   {'gender': 'Female', 'faceRectangle': {'height': 96, 'top': 170, 'left': 168, 'width': 96}, 'age': '25-32'}
Face 1   {'gender': 'Female', 'faceRectangle': {'height': 95, 'top': 113, 'left': 379, 'width': 96}, 'age': '25-32'}
Face 2   {'gender': 'Male', 'faceRectangle': {'height': 96, 'top': 141, 'left': 62, 'width': 96}, 'age': '25-32'}
Face 3   {'gender': 'Male', 'faceRectangle': {'height': 80, 'top': 148, 'left': 252, 'width': 80}, 'age': '25-32'}
Face 4   {'gender': 'Male', 'faceRectangle': {'height': 80, 'top': 28, 'left': 220, 'width': 80}, 'age': '25-32'}
Face 5   {'gender': 'Male', 'faceRectangle': {'height': 80, 'top': 20, 'left': 316, 'width': 80}, 'age': '25-32'}

# To get everything after expanding bounding boxes by 10% and doubling up the image 
$ python face_analysis.py face.jpg --attr --id --lmk --expandBy 0.1 --double 1