opencv实现人脸检测、性别和年龄预测

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主要是通过opencv加载已经训练好的模型，实现性别和年龄预测

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20191009更新
不好意思大家，最近搞乱七八糟的东西，模型也没有来得急上传，其实上面的链接是有模型的，只是需要借助梯子，目前我已经把模型上传到百度网盘啦，供大家享用！

百度网盘模型文件：

链接：地址
提取码：fl6c

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一、下载预训练的模型

1、下载模型

基于Caffe的预训练模型实现年龄与性别预测

• 性别预训练模型
  https://www.dropbox.com/s/iyv483wz7ztr9gh/gender_net.caffemodel?dl=0"
• 年龄预训练模型
  https://www.dropbox.com/s/xfb20y596869vbb/age_net.caffemodel?dl=0"

2、模型说明

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！,如下：

'(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！

二、实现步骤

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

1、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)

2、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)

3、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))

从显示的精确度来看，精确度达到了用美颜隔离的效果，你懂得！！！

4、完整代码

opencv 使用预训练模型的流程
1、读取预训练模型
genderNet = cv.dnn.readNet(genderModel, genderProto)
模型的输入blob
blob = cv.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
print("======", type(blob), blob.shape) # (1, 3, 227, 227)
2、blob输入网络
genderNet.setInput(blob) # blob输入网络进行性别的检测
3、网络前向传播（因为用的是已经训练好的模型参数，所以只有前向传播，没有训练过程，因此没有反向传播）
genderPreds = genderNet.forward() # 性别检测进行前向传播
print("++++++", type(genderPreds), genderPreds.shape, genderPreds) # (1, 2) [[9.9999917e-01 8.6268375e-07]] 变化的值
4、返回分类结果，根据argmax()判断类别
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()))

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 = "E:/workOpencv/opencv_tutorial/data/models/face_detector/opencv_face_detector.pbtxt"
faceModel = "E:/workOpencv/opencv_tutorial/data/models/face_detector/opencv_face_detector_uint8.pb"

ageProto = "E:/workOpencv/opencv_tutorial/data/models/cnn_age_gender_models/age_deploy.prototxt"
ageModel = "E:/workOpencv/opencv_tutorial/data/models/cnn_age_gender_models/age_net.caffemodel"

genderProto = "E:/workOpencv/opencv_tutorial/data/models/cnn_age_gender_models/gender_deploy.prototxt"
genderModel = "E:/workOpencv/opencv_tutorial/data/models/cnn_age_gender_models/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)  #   (166, 154, 3)
        #
        blob = cv.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
        print("======", type(blob), blob.shape)  #  (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)   #  (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))

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Reference：
https://mp.weixin.qq.com/s/-G6XW0vUlFsGFSyEBNnjQQ

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