Age and Gender Detection In Python - Creation Code

Gender Identity:

Gender identity is the personal sense of one's own gender. Gender identity can correlate with a person's assigned sex or can differ from it. In most individuals, the various biological determinants of sex are congruent and consistent with the individual's gender identity. Gender expression typically reflects a person's gender identity, but this is not always the case. While a person may express behaviors, attitudes, and appearances consistent with a particular gender role, such expression may not necessarily reflect their gender identity. The term gender identity was coined by Robert J Stoller in 1964.

Modules and assets:

In this project, we will use the assetsOpenCV library which will help us to play with the camera 

We also use some datasets to identify the faces and ages you can find those datasets here

Explanation:

We will first train our models

test.py

import cv2

def faceBox(net, frame, conf_threshold=0.7):

    frameDnn = frame.copy()

    frameHeight = frameDnn.shape[0]

    frameWidth = frameDnn.shape[1]

    blob = cv2.dnn.blobFromImage(frameDnn, 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])

            cv2.rectangle(frameDnn, (x1, y1), (x2, y2), (0, 255, 0), 1)

    return frameDnn, bboxes

faceProto = "opencv_face_detector.pbtxt"

faceModel = "opencv_face_detector_uint8.pb"

ageProto = "age_deploy.prototxt"

ageModel = "age_net.caffemodel"

genderProto = "gender_deploy.prototxt"

genderModel = "gender_net.caffemodel"

MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)

ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']

genderList = ['Male', 'Female']

# Load network

ageNet = cv2.dnn.readNet(ageModel, ageProto)

genderNet = cv2.dnn.readNet(genderModel, genderProto)

faceNet = cv2.dnn.readNet(faceModel, faceProto)

# Open a video file or an image file or a camera stream

video = cv2.VideoCapture('4.mp4')

while True:

    ret, frame = video.read()

    frameFace, bboxes = faceBox(faceNet, frame)

    for bbox in bboxes:

        face = frame[bbox[1]:bbox[3],bbox[0]:bbox[2]]

        blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)

        genderNet.setInput(blob)

        genderPreds = genderNet.forward()

        gender = genderList[genderPreds[0].argmax()]

        ageNet.setInput(blob)

        agePreds = ageNet.forward()

        age = ageList[agePreds[0].argmax()]

        label = "{},{}".format(gender, age)

        cv2.rectangle(frameFace, (bbox[0], bbox[1]-30), (bbox[2], bbox[1]), (0, 255, 0), -1)

        cv2.putText(frameFace, label, (bbox[0], bbox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2, cv2.LINE_AA)

    cv2.imshow("Age-Gender", frameFace)

    k=cv2.waitKey(1)

    if k==ord('q'):

        break

video.release()

cv2.destroyAllWindows()

Now this we will create the main program to detect age and gender

main.py

import cv2

def faceBox(faceNet,frame):

    frameHeight=frame.shape[0]

    frameWidth=frame.shape[1]

    blob=cv2.dnn.blobFromImage(frame, 1.0, (300,300), [104,117,123], swapRB=False)

    faceNet.setInput(blob)

    detection=faceNet.forward()

    bboxs=[]

    for i in range(detection.shape[2]):

        confidence=detection[0,0,i,2]

        if confidence>0.7:

            x1=int(detection[0,0,i,3]*frameWidth)

            y1=int(detection[0,0,i,4]*frameHeight)

            x2=int(detection[0,0,i,5]*frameWidth)

            y2=int(detection[0,0,i,6]*frameHeight)

            bboxs.append([x1,y1,x2,y2])

            cv2.rectangle(frame, (x1,y1),(x2,y2),(0,255,0), 1)

    return frame, bboxs

faceProto = "opencv_face_detector.pbtxt"

faceModel = "opencv_face_detector_uint8.pb"

ageProto = "age_deploy.prototxt"

ageModel = "age_net.caffemodel"

genderProto = "gender_deploy.prototxt"

genderModel = "gender_net.caffemodel"

faceNet=cv2.dnn.readNet(faceModel, faceProto)

ageNet=cv2.dnn.readNet(ageModel,ageProto)

genderNet=cv2.dnn.readNet(genderModel,genderProto)

MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)

ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']

genderList = ['Male', 'Female']

video=cv2.VideoCapture(0)

padding=20

while True:

    ret,frame=video.read()

    frame,bboxs=faceBox(faceNet,frame)

    for bbox in bboxs:

        # face=frame[bbox[1]:bbox[3], bbox[0]:bbox[2]]

        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=cv2.dnn.blobFromImage(face, 1.0, (227,227), MODEL_MEAN_VALUES, swapRB=False)

        genderNet.setInput(blob)

        genderPred=genderNet.forward()

        gender=genderList[genderPred[0].argmax()]

        ageNet.setInput(blob)

        agePred=ageNet.forward()

        age=ageList[agePred[0].argmax()]

        label="{},{}".format(gender,age)

        cv2.rectangle(frame,(bbox[0], bbox[1]-30), (bbox[2], bbox[1]), (0,255,0),-1)

        cv2.putText(frame, label, (bbox[0], bbox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255,255,255), 2,cv2.LINE_AA)

    cv2.imshow("Age-Gender",frame)

    k=cv2.waitKey(1)

    if k==ord('q'):

        break

video.release()

cv2.destroyAllWindows()

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