Data Science: Predict the Gender and Age Using OpenCV in Python

Introduction

Age and gender prediction are used extensively in the field of computer vision for surveillance. Advancement in computer vision makes this prediction even more practical and open to all. Significant improvements have been made in this research area due to its usefulness in intelligent real-world applications.

Application

A human face contains features that determine the identity, age, gender, emotions, and ethnicity of people. Among these features, age and gender classification can be especially helpful in several real-world applications including security and video surveillance, electronic customer relationship management, biometrics, electronic vending machines, human-computer interaction, entertainment, cosmetology, and forensic art.

Implementation

Typically, you’ll see age detection implemented as a two-stage process:

1. Stage #1: Detect faces from the input image

2. Stage #2: Extract the face Region of Interest (ROI), and apply the age detector algorithm to predict the age of the person

For Stage #1, any face detector capable of producing bounding boxes for faces in an image can be used

The face detector produces the bounding box coordinates of the face in the image.

For Stage #2 — identifying the age of the person.

Given the bounding box (x, y)-coordinates of the face, we first extract the face ROI, ignoring the rest of the image/frame. Doing so allows the age detector to focus solely on the person’s face and not any other irrelevant “noise” in the image.

The face ROI is then passed through the model, yielding the actual age prediction.

Task: Identify and predict Gender and Age range from Photo.

Step 1: Importing libraries

# Import required modules
import cv2 as cv
import math
import time
from google.colab.patches import cv2_imshow

Step 2: Finding bounding box coordinates

def getFaceBox(net, frame, conf_threshold=0.7):
frameOpencvDnn = frame.copy()
frameHeight = frameOpencvDnn.shape[0]
frameWidth = frameOpencvDnn.shape[1]
blob = cv.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False) net.setInput(blob)
detections = net.forward()
bboxes = []
for i in range(detections.shape[2]):
confidence = detections[0, 0, i, 2]
if confidence > conf_threshold:
x1 = int(detections[0, 0, i, 3] * frameWidth)
y1 = int(detections[0, 0, i, 4] * frameHeight)
x2 = int(detections[0, 0, i, 5] * frameWidth)
y2 = int(detections[0, 0, i, 6] * frameHeight)
bboxes.append([x1, y1, x2, y2])
cv.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight/150)), 8)
return frameOpencvDnn, bboxes

Step 3: Loading model and weight files

faceProto = "/content/opencv_face_detector.pbtxt"
faceModel = "/content/opencv_face_detector_uint8.pb"
ageProto = "/content/age_deploy.prototxt"
ageModel = "/content/age_net.caffemodel"
genderProto = "/content/gender_deploy.prototxt"
genderModel = "/content/gender_net.caffemodel"

Step 4: Mentioning age and gender category list

ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(34-39)', '(48-53)', '(60-100)']
genderList = ['Male', 'Female']

Step 5: Function to predict gender and age

def age_gender_detector(frame):
# Read frame
t = time.time()
frameFace, bboxes = getFaceBox(faceNet, frame)
for bbox in bboxes:
# print(bbox)
face = frame[max(0,bbox[1]-padding):min(bbox[3]+padding,frame.shape[0]-1),max(0,bbox[0]-padding):min(bbox[2]+padding, frame.shape[1]-1)]blob = cv.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
genderNet.setInput(blob)
genderPreds = genderNet.forward()
gender = genderList[genderPreds[0].argmax()]
# print("Gender Output : {}".format(genderPreds))
print("Gender : {}, conf = {:.3f}".format(gender, genderPreds[0].max()))ageNet.setInput(blob)
agePreds = ageNet.forward()
age = ageList[agePreds[0].argmax()]
print("Age Output : {}".format(agePreds))
print("Age : {}, conf = {:.3f}".format(age, agePreds[0].max()))label = "{},{}".format(gender, age)
cv.putText(frameFace, label, (bbox[0], bbox[1]-10), cv.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv.LINE_AA)
return frameFace

Step 6: Uploading photo

from google.colab import files
uploaded = files.upload()
input = cv.imread("2.jpg")
output = age_gender_detector(input)
cv2_imshow(output)

In this blog, We have learned how to create an Age predictor that can also detect your face and highlight it with the border.

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