神经网络 | 基于DNN神经网络实现人的年龄及性别预测（代码类）

博主github：https://github.com/MichaelBeechan   

博主CSDN：https://blog.csdn.net/u011344545 

最近一段时间，大论文完稿了，可以搞搞自己之前想做但没有时间做的——机器视觉（神经网络）。So 开始看代码学习人脸识别方面的事。
这是一篇通过人脸图像预测图像中人的性别及年龄的文章。
好了！！！！ 鲜花不聊，上干货。。。。。

代码中所需包下载地址：https://download.csdn.net/download/u011344545/11015035

# Name: Michael Beechan
# Time: 2019.3.10
# Function: predict age and gender from a face image

import cv2 as cv
import time
import math
import argparse

# Detect face
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

# 命令注释解析
parser = argparse.ArgumentParser(description='Use this script to run age and gender recognition using OpenCV.')
parser.add_argument('--input', help='Path to input image or video file. Skip this argument to capture frames from a camera')

args = parser.parse_args()

faceProto = "opencv_face_detector.pbtxt"
faceModel = "opencv_face_detector_uint8.pb"

ageProto = "deploy_age.prototxt"
ageModel = "age_net.caffemodel"

genderProto = "deploy_gender.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 = cv.dnn.readNet(ageModel, ageProto)
genderNet = cv.dnn.readNet(genderModel, genderProto)
faceNet = cv.dnn.readNet(faceModel, faceProto)

#Open a video file or an image file or a camera stream
cap = cv.VideoCapture(args.input if args.input else 0)
padding = 20
while cv.waitKey(1) < 0:
    # Read frame
    t = time.time()
    hasFrame, frame = cap.read()
    if not hasFrame:
        cv.waitKey()
        break
    frameFace, bboxes = getFaceBox(faceNet, frame)
    if not bboxes:
        print("No face detected, checking next frame")
        continue
    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(gender, genderPreds[0].max()))
        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)
        cv.imshow("Age Gender Demo", frameFace)
        # cv.imwrite("age-gender-out-{}".format(args.input),frameFace)
    print("time : {:.3f}".format(time.time() - t))

 真实年龄：

 侧脸预测年龄：

 

 

从上面的预测分析发现，本文算法预测精度还是很粗略，需要进一步完善。不过也是一个人脸识别预测的学习典例。

加油吧！！！！骚年们，欢迎评论交流。。。。。。。。