树莓派实现人脸年龄、性别预测

首先下载对应的模型
AgeGenderDeepLearning 可以到github里下载
这里不全

下载得到六个文件，分别是以下等号右边六个

> 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"

将下好的模型放在程序的同一目录下

2.代码如下

#-*- coding: utf-8 -*-
import cv2 as cv
import time


# 检测人脸并绘制人脸bounding box
def getFaceBox(net, frame, conf_threshold=0.7):
    frameOpencvDnn = frame.copy()
    frameHeight = frameOpencvDnn.shape[0]  # 高就是矩阵有多少行
    frameWidth = frameOpencvDnn.shape[1]  # 宽就是矩阵有多少列
    blob = cv.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)
    #  blobFromImage(image[, scalefactor[, size[, mean[, swapRB[, crop[, ddepth]]]]]]) -> retval  返回值   # swapRB是交换第一个和最后一个通道   返回按NCHW尺寸顺序排列的4 Mat值
    net.setInput(blob)
    detections = net.forward()  # 网络进行前向传播，检测人脸
    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])  # bounding box 的坐标
            cv.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight / 150)),
                         8)  # rectangle(img, pt1, pt2, color[, thickness[, lineType[, shift]]]) -> img
    return frameOpencvDnn, bboxes


# 网络模型  和  预训练模型
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"

# 模型均值
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']

# 加载网络
ageNet = cv.dnn.readNet(ageModel, ageProto)
genderNet = cv.dnn.readNet(genderModel, genderProto)
# 人脸检测的网络和模型
faceNet = cv.dnn.readNet(faceModel, faceProto)

# 打开一个视频文件或一张图片或一个摄像头
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)   # 取出box框住的脸部进行检测,返回的是脸部图片
        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)]
        print("=======", type(face), face.shape)  #  <class 'numpy.ndarray'> (166, 154, 3)
        #
        blob = cv.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
        print("======", type(blob), blob.shape)  # <class 'numpy.ndarray'> (1, 3, 227, 227)
        genderNet.setInput(blob)   # blob输入网络进行性别的检测
        genderPreds = genderNet.forward()   # 性别检测进行前向传播
        print("++++++", type(genderPreds), genderPreds.shape, genderPreds)   # <class 'numpy.ndarray'> (1, 2)  [[9.9999917e-01 8.6268375e-07]]  变化的值
        gender = genderList[genderPreds[0].argmax()]   # 分类  返回性别类型
        # 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(agePreds[0].argmax())  # 3
        print("*********", agePreds[0])   #  [4.5557402e-07 1.9009208e-06 2.8783199e-04 9.9841607e-01 1.5261240e-04 1.0924522e-03 1.3928890e-05 3.4708322e-05]
        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)  # putText(img, text, org, fontFace, fontScale, color[, thickness[, lineType[, bottomLeftOrigin]]]) -> img
        cv.imshow("Age Gender Demo", frameFace)
    print("time : {:.3f} ms".format(time.time() - t))

如果出现AttributeError: ‘module’ object has no attribute ‘dnn’
说明opencv版本不够高，可以参考一下
python虚拟环境virtualenv高级篇
可以先使用

python
import cv2
cv2.__version__

查看opencv版本可参考这里

sudo pip3 install opencv-python

安装新版本的opencv库
如果在虚拟环境里运行也需要安装新版本的opencv库
具体以后解决

效果图

树莓派实现人脸年龄、性别预测

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