2022年 11月 16日

基于Python+Opencv识别性别年龄（摄像头实现，可换成图片）

环境要求：Python环境+Opencv有DNN的版本

模型文件:链接：https://pan.baidu.com/s/1W4ar8L8FiJ41IYcZQkf4mA 
提取码：jp06 

代码：

  1. import cv2 as cv
  2. import time
  3.  
  4.  
  5. # 检测人脸并绘制人脸bounding box
  6. def getFaceBox(net, frame, conf_threshold=0.7):
  7.     frameOpencvDnn = frame.copy()
  8.     frameHeight = frameOpencvDnn.shape[0]  # 高就是矩阵有多少行
  9.     frameWidth = frameOpencvDnn.shape[1]  # 宽就是矩阵有多少列
  10.     blob = cv.dnn.blobFromImage(frameOpencvDnn, 1.0, (300, 300), [104, 117, 123], True, False)
  11.     #  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
  12.     net.setInput(blob)
  13.     detections = net.forward()  # 网络进行前向传播，检测人脸
  14.     bboxes = []
  15.     for i in range(detections.shape[2]):
  16.         confidence = detections[0, 0, i, 2]
  17.         if confidence > conf_threshold:
  18.             x1 = int(detections[0, 0, i, 3] * frameWidth)
  19.             y1 = int(detections[0, 0, i, 4] * frameHeight)
  20.             x2 = int(detections[0, 0, i, 5] * frameWidth)
  21.             y2 = int(detections[0, 0, i, 6] * frameHeight)
  22.             bboxes.append([x1, y1, x2, y2])  # bounding box 的坐标
  23.             cv.rectangle(frameOpencvDnn, (x1, y1), (x2, y2), (0, 255, 0), int(round(frameHeight / 150)),
  24.                          8)  # rectangle(img, pt1, pt2, color[, thickness[, lineType[, shift]]]) -> img
  25.     return frameOpencvDnn, bboxes
  26.  
  27.  
  28. # 网络模型  和  预训练模型
  29. faceProto = "age_gender/opencv_face_detector.pbtxt"
  30. faceModel = "age_gender/opencv_face_detector_uint8.pb"
  31.  
  32. ageProto = "age_gender/age_deploy.prototxt"
  33. ageModel = "age_gender/age_net.caffemodel"
  34.  
  35. genderProto = "age_gender/gender_deploy.prototxt"
  36. genderModel = "age_gender/gender_net.caffemodel"
  37.  
  38. # 模型均值
  39. MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
  40. ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
  41. genderList = ['Male', 'Female']
  42.  
  43. # 加载网络
  44. ageNet = cv.dnn.readNet(ageModel, ageProto)
  45. genderNet = cv.dnn.readNet(genderModel, genderProto)
  46. # 人脸检测的网络和模型
  47. faceNet = cv.dnn.readNet(faceModel, faceProto)
  48.  
  49. # 打开一个视频文件或一张图片或一个摄像头
  50. cap = cv.VideoCapture(0)
  51. padding = 20
  52. while cv.waitKey(1) < 0:
  53.     # Read frame
  54.     t = time.time()
  55.     hasFrame, frame = cap.read()
  56.     frame = cv.flip(frame, 1)
  57.     if not hasFrame:
  58.         cv.waitKey()
  59.         break
  60.  
  61.     frameFace, bboxes = getFaceBox(faceNet, frame)
  62.     if not bboxes:
  63.         print("No face Detected, Checking next frame")
  64.         continue
  65.  
  66.     for bbox in bboxes:
  67.         # print(bbox)   # 取出box框住的脸部进行检测,返回的是脸部图片
  68.         face = frame[max(0, bbox[1] - padding):min(bbox[3] + padding, frame.shape[0] - 1),
  69.                max(0, bbox[0] - padding):min(bbox[2] + padding, frame.shape[1] - 1)]
  70.         print("=======", type(face), face.shape)  #  <class 'numpy.ndarray'> (166, 154, 3)
  71.         #
  72.         blob = cv.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
  73.         print("======", type(blob), blob.shape)  # <class 'numpy.ndarray'> (1, 3, 227, 227)
  74.         genderNet.setInput(blob)   # blob输入网络进行性别的检测
  75.         genderPreds = genderNet.forward()   # 性别检测进行前向传播
  76.         print("++++++", type(genderPreds), genderPreds.shape, genderPreds)   # <class 'numpy.ndarray'> (1, 2)  [[9.9999917e-01 8.6268375e-07]]  变化的值
  77.         gender = genderList[genderPreds[0].argmax()]   # 分类  返回性别类型
  78.         # print("Gender Output : {}".format(genderPreds))
  79.         print("Gender : {}, conf = {:.3f}".format(gender, genderPreds[0].max()))
  80.  
  81.         ageNet.setInput(blob)
  82.         agePreds = ageNet.forward()
  83.         age = ageList[agePreds[0].argmax()]
  84.         print(agePreds[0].argmax())  # 3
  85.         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]
  86.         print("Age Output : {}".format(agePreds))
  87.         print("Age : {}, conf = {:.3f}".format(age, agePreds[0].max()))
  88.  
  89.         label = "{},{}".format(gender, age)
  90.         cv.putText(frameFace, label, (bbox[0], bbox[1] - 10), cv.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2,
  91.                    cv.LINE_AA)  # putText(img, text, org, fontFace, fontScale, color[, thickness[, lineType[, bottomLeftOrigin]]]) -> img
  92.         cv.imshow("Age Gender Demo", frameFace)
  93.     print("time : {:.3f} ms".format(time.time() - t))
  94.  
  95.