宁晋做网站,网页游戏排行榜传奇,杭州住房和城乡建设局网站,东莞网红打卡旅游景点文章目录 1、任务描述2、网络结构2.1 人脸检测2.2 性别分类2.3 年龄分类 3、代码实现4、结果展示5、参考 1、任务描述
性别分类和年龄分类预测
2、网络结构
2.1 人脸检测 输出最高的 200 个 RoI#xff0c;每个 RoI 7 个值#xff0c;#xff08;xx#xff0c;xx#x… 文章目录 1、任务描述2、网络结构2.1 人脸检测2.2 性别分类2.3 年龄分类 3、代码实现4、结果展示5、参考 1、任务描述
性别分类和年龄分类预测
2、网络结构
2.1 人脸检测 输出最高的 200 个 RoI每个 RoI 7 个值xxxxscorex0y0x1y1
2.2 性别分类
二分类 2.3 年龄分类
按年龄区间分类 ageList [(0-2), (4-6), (8-12), (15-20), (25-32), (38-43), (48-53), (60-100)] 3、代码实现
先检测人脸人脸外扩再性别检测再年龄检测最后结果绘制输出
# Import required modules
import cv2 as cv
import math
import time
import argparsedef getFaceBox(net, frame, conf_threshold0.7):frameOpencvDnn frame.copy()frameHeight frameOpencvDnn.shape[0] # 333frameWidth frameOpencvDnn.shape[1] # 500blob cv.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)net.setInput(blob)detections net.forward() # (1, 1, 200, 7) (xxx, xxx, confidence, x0, y0, x1, y1)bboxes []for i in range(detections.shape[2]): # 遍历 top 200 RoIconfidence 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(descriptionUse this script to run age and gender recognition using OpenCV.)
parser.add_argument(--input, helpPath to input image or video file. Skip this argument to capture frames from a camera.,defaultjolie.jpg)
parser.add_argument(--device, defaultcpu, helpDevice to inference on)args parser.parse_args()args parser.parse_args()faceProto opencv_face_detector.pbtxt
faceModel opencv_face_detector_uint8.pbageProto age_deploy.prototxt
ageModel age_net.caffemodelgenderProto gender_deploy.prototxt
genderModel gender_net.caffemodelMODEL_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)if args.device cpu:ageNet.setPreferableBackend(cv.dnn.DNN_TARGET_CPU)genderNet.setPreferableBackend(cv.dnn.DNN_TARGET_CPU)faceNet.setPreferableBackend(cv.dnn.DNN_TARGET_CPU)print(Using CPU device)elif args.device gpu:ageNet.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA)ageNet.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA)genderNet.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA)genderNet.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA)genderNet.setPreferableBackend(cv.dnn.DNN_BACKEND_CUDA)genderNet.setPreferableTarget(cv.dnn.DNN_TARGET_CUDA)print(Using GPU device)# 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) # (333, 500, 3), 4 bboxif not bboxes:print(No face Detected, Checking next frame)continuefor bbox in bboxes: # 遍历检测出来的人脸# 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)] # 人脸外扩blob cv.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRBFalse)genderNet.setInput(blob)genderPreds genderNet.forward()gender genderList[genderPreds[0].argmax()]# array([[9.9999559e-01, 4.4012304e-06]], dtypefloat32), Male# print(Gender Output : {}.format(genderPreds))print(Gender : {}, conf {:.3f}.format(gender, genderPreds[0].max()))ageNet.setInput(blob)agePreds ageNet.forward()array([[5.3957672e-05, 5.3967893e-02, 9.4579268e-01, 1.0875276e-04, 5.0436443e-05, 1.2142612e-05, 1.0151542e-05, 3.9845672e-06]],dtypefloat32)age ageList[agePreds[0].argmax()] # (8-12)# print(Age Output : {}.format(agePreds))# print(Age : {}, conf {:.3f}.format(age, agePreds[0].max()))label {},{}.format(gender, age) # Out[15]: Male,(8-12)cv.putText(frameFace, label, (bbox[0], bbox[1]-5), cv.FONT_HERSHEY_SIMPLEX,0.6, (0, 0, 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))4、结果展示
输入图片 人脸检测结果 人脸外扩 输出结果 性别还是比较准的
输入图片 输出结果 输入图片 输出结果 输入图片 输出结果 输入图片 输出结果 5、参考
OpenCV进阶8性别和年龄识别
