Practical - 10

 

Practical 10

 Aim: Using image data, predict the gender and age range of an individual in Python. Test the data science model using your own image.

Theory:

OpenCV is the huge open-source library for the computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems. By using it, one can process images and videos to identify objects, faces, or even handwriting of a human. When it integrated with various libraries, such as Numpuy, python is capable of processing the OpenCV array structure for analysis. To Identify image pattern and its various features we use vector space and perform mathematical operations on these features.

Output:

Code:

import cv2

import math

import argparse

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

    frameOpencvDnn=frame.copy()

    frameHeight=frameOpencvDnn.shape[0]

    frameWidth=frameOpencvDnn.shape[1]

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

    net.setInput(blob)

    detections=net.forward()

    faceBoxes=[]

    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)

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

            cv2.rectangle(frameOpencvDnn, (x1,y1), (x2,y2), (0,255,0), int(round(frameHeight/150)), 8)

    return frameOpencvDnn,faceBoxes

parser=argparse.ArgumentParser()

parser.add_argument('--image')

args=parser.parse_args()

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']

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

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

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

video=cv2.VideoCapture(args.image if args.image else 0)

padding=20

while cv2.waitKey(1)<0 :

    hasFrame,frame=video.read()

    if not hasFrame:

        cv2.waitKey()

        break

    

    resultImg,faceBoxes=highlightFace(faceNet,frame)

    if not faceBoxes:

        print("No face detected")

    for faceBox in faceBoxes:

        face=frame[max(0,faceBox[1]-padding):

                   min(faceBox[3]+padding,frame.shape[0]-1),max(0,faceBox[0]-padding)

                   :min(faceBox[2]+padding, frame.shape[1]-1)]

        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()]

        print(f'Gender: {gender}')

        ageNet.setInput(blob)

        agePreds=ageNet.forward()

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

        print(f'Age: {age[1:-1]} years')

        cv2.putText(resultImg, f'{gender}, {age}', (faceBox[0], faceBox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0,255,255), 2, cv2.LINE_AA)

        cv2.imshow("Detecting age and gender", resultImg)