在Python中使用OpenCV进行年龄和性别检测

了解如何使用带有相机或图像输入的 Python 中的 OpenCV 库执行年龄和性别检测。
在本OpenCV年龄和性别检测教程中，我们将结合性别检测和年龄检测教程来编写一个代码来检测两者。
OpenCV如何检测年龄和性别？如果你还没有安装 OpenCV，请确保这样做：

$ pip install opencv-python numpy

打开一个新文件。导入库：

# Import Libraries import cv2 import numpy as np

接下来，定义人脸、年龄和性别检测模型的权重和架构变量：

# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt FACE_PROTO = "weights/deploy.prototxt.txt" # https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodel FACE_MODEL = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel" # The gender model architecture # https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQ GENDER_MODEL = 'weights/deploy_gender.prototxt' # The gender model pre-trained weights # https://drive.google.com/open?id=1AW3WduLk1haTVAxHOkVS_BEzel1WXQHP GENDER_PROTO = 'weights/gender_net.caffemodel' # Each Caffe Model impose the shape of the input image also image preprocessing is required like mean # substraction to eliminate the effect of illunination changes MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746) # Represent the gender classes GENDER_LIST = [ 'Male', 'Female'] # The model architecture # download from: https://drive.google.com/open?id=1kiusFljZc9QfcIYdU2s7xrtWHTraHwmW AGE_MODEL = 'weights/deploy_age.prototxt' # The model pre-trained weights # download from: https://drive.google.com/open?id=1kWv0AjxGSN0g31OeJa02eBGM0R_jcjIl AGE_PROTO = 'weights/age_net.caffemodel' # Represent the 8 age classes of this CNN probability layer AGE_INTERVALS = [ '(0, 2)', '(4, 6)', '(8, 12)', '(15, 20)', '(25, 32)', '(38, 43)', '(48, 53)', '(60, 100)']

OpenCV年龄和性别检测 - 以下是要包含在项目目录中的必要文??件：

gender_net.caffemodel：它是用于性别检测的预训练模型权重。你可以在这里下载。
deploy_gender.prototxt: 是性别检测模型的模型架构（一个带有类似 JSON 结构的纯文本文件，包含所有神经网络层的定义）。从这里获取。
age_net.caffemodel：这是用于年龄检测的预训练模型权重。你可以在这里下载。
deploy_age.prototxt: 是年龄检测模型的模型架构（一个带有类似 JSON 结构的纯文本文件，包含所有神经网络层的定义）。从这里获取。
res10_300x300_ssd_iter_140000_fp16.caffemodel：用于人脸检测的预训练模型权重，请在此处下载。
deploy.prototxt.txt：这是人脸检测模型的模型架构，在这里下载。

接下来，加载模型：

# Initialize frame size frame_width = 1280 frame_height = 720 # load face Caffe model face_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL) # Load age prediction model age_net = cv2.dnn.readNetFromCaffe(AGE_MODEL, AGE_PROTO) # Load gender prediction model gender_net = cv2.dnn.readNetFromCaffe(GENDER_MODEL, GENDER_PROTO)

OpenCV检测年龄和性别示例：在尝试检测年龄和性别之前，我们首先需要一个检测人脸的函数：

def get_faces(frame, confidence_threshold=0.5): # convert the frame into a blob to be ready for NN input blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104, 177.0, 123.0)) # set the image as input to the NN face_net.setInput(blob) # perform inference and get predictions output = np.squeeze(face_net.forward()) # initialize the result list faces = [ ] # Loop over the faces detected for i in range(output.shape[ 0]): confidence = output[ i, 2] if confidence > confidence_threshold: box = output[ i, 3:7] * \ np.array([ frame.shape[ 1], frame.shape[ 0], frame.shape[ 1], frame.shape[ 0]]) # convert to integers start_x, start_y, end_x, end_y = box.astype(np.int) # widen the box a little start_x, start_y, end_x, end_y = start_x - \ 10, start_y - 10, end_x + 10, end_y + 10 start_x = 0 if start_x < 0 else start_x start_y = 0 if start_y < 0 else start_y end_x = 0 if end_x < 0 else end_x end_y = 0 if end_y < 0 else end_y # append to our list faces.append((start_x, start_y, end_x, end_y)) return faces

该get_faces()功能是从人脸检测教程中抓取的，如果你需要更多信息，请查看它。
下面是一个简单显示图像的函数：

def display_img(title, img): """Displays an image on screen and maintains the output until the user presses a key""" # Display Image on screen cv2.imshow(title, img) # Mantain output until user presses a key cv2.waitKey(0) # Destroy windows when user presses a key cv2.destroyAllWindows()

OpenCV年龄和性别检测：下面是一个动态调整图像大小的函数，当超过一定宽度时，我们将需要它来调整输入图像的大小：

# from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencv def image_resize(image, width = None, height = None, inter = cv2.INTER_AREA): # initialize the dimensions of the image to be resized and # grab the image size dim = None (h, w) = image.shape[ :2] # if both the width and height are None, then return the # original image if width is None and height is None: return image # check to see if the width is None if width is None: # calculate the ratio of the height and construct the # dimensions r = height / float(h) dim = (int(w * r), height) # otherwise, the height is None else: # calculate the ratio of the width and construct the # dimensions r = width / float(w) dim = (width, int(h * r)) # resize the image return cv2.resize(image, dim, interpolation = inter)

现在一切准备就绪，让我们定义年龄和性别检测的两个函数：

def get_gender_predictions(face_img): blob = cv2.dnn.blobFromImage( image=face_img, scalefactor=1.0, size=(227, 227), mean=MODEL_MEAN_VALUES, swapRB=False, crop=False ) gender_net.setInput(blob) return gender_net.forward()def get_age_predictions(face_img): blob = cv2.dnn.blobFromImage( image=face_img, scalefactor=1.0, size=(227, 227), mean=MODEL_MEAN_VALUES, swapRB=False ) age_net.setInput(blob) return age_net.forward()

get_gender_predictions()和get_age_predictions()在执行预测gender_net和age_net模型分别推断出输入图像的性别和年龄。
OpenCV检测年龄和性别示例：最后，我们编写我们的主函数：

def predict_age_and_gender(input_path: str): """Predict the gender of the faces showing in the image""" # Initialize frame size # frame_width = 1280 # frame_height = 720 # Read Input Image img = cv2.imread(input_path) # resize the image, uncomment if you want to resize the image # img = cv2.resize(img, (frame_width, frame_height)) # Take a copy of the initial image and resize it frame = img.copy() if frame.shape[ 1] > frame_width: frame = image_resize(frame, width=frame_width) # predict the faces faces = get_faces(frame) # Loop over the faces detected # for idx, face in enumerate(faces): for i, (start_x, start_y, end_x, end_y) in enumerate(faces): face_img = frame[ start_y: end_y, start_x: end_x] age_preds = get_age_predictions(face_img) gender_preds = get_gender_predictions(face_img) i = gender_preds[ 0].argmax() gender = GENDER_LIST[ i] gender_confidence_score = gender_preds[ 0][ i] i = age_preds[ 0].argmax() age = AGE_INTERVALS[ i] age_confidence_score = age_preds[ 0][ i] # Draw the box label = f"{gender}-{gender_confidence_score*100:.1f}%, {age}-{age_confidence_score*100:.1f}%" # label = "{}-{:.2f}%".format(gender, gender_confidence_score*100) print(label) yPos = start_y - 15 while yPos < 15: yPos += 15 box_color = (255, 0, 0) if gender == "Male" else (147, 20, 255) cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), box_color, 2) # Label processed image font_scale = 0.54 cv2.putText(frame, label, (start_x, yPos), cv2.FONT_HERSHEY_SIMPLEX, font_scale, box_color, 2)# Display processed image display_img("Gender Estimator", frame) # uncomment if you want to save the image cv2.imwrite("output.jpg", frame) # Cleanup cv2.destroyAllWindows()

OpenCV如何检测年龄和性别？主要功能执行以下操作：