百度网站首页收录,软件开发具体工作内容,怎样做化妆品公司网站,开电商网站需要多少钱概述 前面我写了很多篇关于OpenCV DNN应用相关的文章#xff0c;这里再来一篇文章#xff0c;用OpenCV DNN实现一个很有趣好玩的例子#xff0c;基于Caffe的预训练模型实现年龄与性别预测#xff0c;这个在很多展会上都有展示#xff0c;OpenCV DNN实现这里非常简洁明了这里再来一篇文章用OpenCV DNN实现一个很有趣好玩的例子基于Caffe的预训练模型实现年龄与性别预测这个在很多展会上都有展示OpenCV DNN实现这里非常简洁明了总共不到100行的代码。下面就来说一下怎么实现的首先下载两个Caffe的预训练模型 Gender Net and Age Net https://www.dropbox.com/s/iyv483wz7ztr9gh/gender_net.caffemodel?dl0 https://www.dropbox.com/s/xfb20y596869vbb/age_net.caffemodel?dl0 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
(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
实现步骤
完整的实现步骤需要如下几步 预先加载三个网络模型 打开摄像头视频流/加载图像 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 代码实现详解
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) 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) 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, swapRBFalse)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)) 运行效果(看到这个预测我又相信技术了_) 完整源代码
import cv2 as cv
import timedef getFaceBox(net, frame, conf_threshold0.7):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, bboxesfaceProto D:/projects/opencv_tutorial/data/models/face_detector/opencv_face_detector.pbtxt
faceModel D:/projects/opencv_tutorial/data/models/face_detector/opencv_face_detector_uint8.pbageProto D:/projects/opencv_tutorial/data/models/cnn_age_gender_models/age_deploy.prototxt
ageModel D:/projects/opencv_tutorial/data/models/cnn_age_gender_models/age_net.caffemodelgenderProto D:/projects/opencv_tutorial/data/models/cnn_age_gender_models/gender_deploy.prototxt
genderModel D:/projects/opencv_tutorial/data/models/cnn_age_gender_models/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)# Open a video file or an image file or a camera stream
cap cv.VideoCapture(0)
padding 20
while cv.waitKey(1) 0:# Read framet time.time()hasFrame, frame cap.read()frame cv.flip(frame, 1)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, swapRBFalse)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))
任何程序错误以及技术疑问或需要解答的请添加 源码与模型下载地址
GitHub - xiaobingchan/opencv_tutorial: 基于OpenCV4.0 C/Python SDK的案例代码演示程序与效果图像
