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 argparsedef 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, bboxesparser = 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 framet = time.time()hasFrame, frame = cap.read()if not hasFrame:cv.waitKey()breakframeFace, bboxes = getFaceBox(faceNet, frame)if not bboxes:print("No face Detected, Checking next frame")continuefor 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。