深度学习AI识别人脸年龄

以下链接来自 @落痕的寒假

GitHub - luohenyueji/OpenCV-Practical-Exercise: OpenCV practical exercise

GitHub - luohenyueji/OpenCV-Practical-Exercise: OpenCV practical exercise

import cv2 as cv
import time
import argparse
 
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
 
 
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 = "age_gender/model/opencv_face_detector.pbtxt"
faceModel = "age_gender/model/opencv_face_detector_uint8.pb"
 
ageProto = "age_gender/model/age_deploy.prototxt"
ageModel = "age_gender/model/age_net.caffemodel"
 
genderProto = "age_gender/model/gender_deploy.prototxt"
genderModel = "age_gender/model/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(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)
        # cv.imwrite("age-gender-out-{}".format(args.input),frameFace)
    print("time : {:.3f}".format(time.time() - t))

1. 导入必要的模块：

   • cv2：用于图像处理和显示
   • time：用于计时
   • argparse：用于解析命令行参数

2. 定义函数 getFaceBox 用于检测人脸框：

   • 通过 DNN 模型进行人脸检测，筛选出置信度高于阈值的人脸框，并在原图上绘制矩形框。

3. 使用 argparse 解析命令行参数：

   • 支持从图像或视频文件中读取，如果没有指定输入则使用摄像头捕获。

4. 定义人脸检测和年龄、性别识别模型的路径：

   • faceProto 和 faceModel 是人脸检测模型的配置文件和权重文件的路径。
   • ageProto 和 ageModel 是年龄识别模型的配置文件和权重文件的路径。
   • genderProto 和 genderModel 是性别识别模型的配置文件和权重文件的路径。

5. 加载模型：

   • 使用 cv.dnn.readNet 加载人脸检测、年龄识别和性别识别模型。

6. 打开视频文件或图像文件或者摄像头流，并设置填充值：

   • 使用 cv.VideoCapture 打开视频文件或图像文件或者摄像头流，并设置填充值为 20。

7. 在循环中处理每帧图像：

   • 读取一帧图像，然后调用 getFaceBox 函数检测人脸框。
   • 对检测到的人脸框进行处理，提取人脸区域，并使用年龄和性别模型进行识别。
   • 将识别结果标记在图像上并显示。