• Opencv dnn实现人类性别检测和年龄预测


    概述

    前面我写了很多篇关于OpenCV DNN应用相关的文章,这里再来一篇文章,用OpenCV DNN实现一个很有趣好玩的例子,基于Caffe的预训练模型实现年龄与性别预测,这个在很多展会上都有展示,OpenCV DNN实现这里非常简洁明了,总共不到100行的代码。下面就来说一下怎么实现的,首先下载两个Caffe的预训练模型:

    Gender Net and Age Net

    https://www.dropbox.com/s/iyv483wz7ztr9gh/gender_net.caffemodel?dl=0"

    https://www.dropbox.com/s/xfb20y596869vbb/age_net.caffemodel?dl=0"

    上述两个模型一个是预测性别的,一个是预测年龄的,性别预测返回的是一个二分类结果

    Male
    Female

    年龄预测返回的是8个年龄的阶段!

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

    人脸检测是基于OPenCV DNN模块自带的残差网络的人脸检测算法模型!非常的强大与好用!

    实现步骤

    完整的实现步骤需要如下几步:

    1. 预先加载三个网络模型
    2. 打开摄像头视频流/加载图像
    3. 对每一帧进行人脸检测 - 对检测到的人脸进行性别与年龄预测 - 解析预测结果 - 显示结果

    代码实现详解

    加载模型

    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)

    人脸检测

    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)

    性别与年龄预测

        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)
            cv.imshow("Age Gender Demo", frameFace)
        print("time : {:.3f} ms".format(time.time() - t))

    运行效果(看到这个预测,我又相信技术了!@_@):

    完整源代码:

    import cv2 as cv
    import time
    
    
    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
    
    
    faceProto = "D:/projects/opencv_tutorial/data/models/face_detector/opencv_face_detector.pbtxt"
    faceModel = "D:/projects/opencv_tutorial/data/models/face_detector/opencv_face_detector_uint8.pb"
    
    ageProto = "D:/projects/opencv_tutorial/data/models/cnn_age_gender_models/age_deploy.prototxt"
    ageModel = "D:/projects/opencv_tutorial/data/models/cnn_age_gender_models/age_net.caffemodel"
    
    genderProto = "D:/projects/opencv_tutorial/data/models/cnn_age_gender_models/gender_deploy.prototxt"
    genderModel = "D:/projects/opencv_tutorial/data/models/cnn_age_gender_models/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(0)
    padding = 20
    while cv.waitKey(1) < 0:
        # Read frame
        t = time.time()
        hasFrame, frame = cap.read()
        frame = cv.flip(frame, 1)
        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(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)
            cv.imshow("Age Gender Demo", frameFace)
        print("time : {:.3f} ms".format(time.time() - t))

    任何程序错误,以及技术疑问或需要解答的,请扫码添加作者VX

    源码与模型下载地址

    https://github.com/xiaobingchan/opencv_tutorial

     
  • 相关阅读:
    Failed to load resource: the server responded with a status of 404 (Not Found) favicon.ico文件找不到
    合并排序
    python爬虫入门笔记--爬取垃圾分类查询【还有待改善】
    快速排序、合并排序和分治策略的基本思想
    动态规划的基本要素
    【转载】算法时间复杂度分析方法
    python爬虫入门笔记--知乎发现(爬取失败了)
    管理主界面的两个刷新操作
    把Excel选手名单信息导入到评委计分软件Access数据库的步骤
    评委打分回避功能的详细操作步骤
  • 原文地址:https://www.cnblogs.com/luyanjie/p/14646004.html
Copyright © 2020-2023  润新知