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In this tutorial, we will combinegender detection andage detection tutorials to develop a single code that detects both.

Let's get started. If you haven't OpenCV already installed, make sure to do so:

$ pip install opencv-python numpy

Open up a new file. Importing the libraries:

# Import Librariesimport cv2import numpy as np

Next, defining the variables of weights and architectures for face, age, and gender detection models:

# https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxtFACE_PROTO = "weights/deploy.prototxt.txt"# https://raw.githubusercontent.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20180205_fp16/res10_300x300_ssd_iter_140000_fp16.caffemodelFACE_MODEL = "weights/res10_300x300_ssd_iter_140000_fp16.caffemodel"# The gender model architecture# https://drive.google.com/open?id=1W_moLzMlGiELyPxWiYQJ9KFaXroQ_NFQGENDER_MODEL = 'weights/deploy_gender.prototxt'# The gender model pre-trained weights# https://drive.google.com/open?id=1AW3WduLk1haTVAxHOkVS_BEzel1WXQHPGENDER_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 changesMODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)# Represent the gender classesGENDER_LIST = ['Male', 'Female']# The model architecture# download from: https://drive.google.com/open?id=1kiusFljZc9QfcIYdU2s7xrtWHTraHwmWAGE_MODEL = 'weights/deploy_age.prototxt'# The model pre-trained weights# download from: https://drive.google.com/open?id=1kWv0AjxGSN0g31OeJa02eBGM0R_jcjIlAGE_PROTO = 'weights/age_net.caffemodel'# Represent the 8 age classes of this CNN probability layerAGE_INTERVALS = ['(0, 2)', '(4, 6)', '(8, 12)', '(15, 20)',                 '(25, 32)', '(38, 43)', '(48, 53)', '(60, 100)']

Below are the necessary files to be included in the project directory:

• gender_net.caffemodel: It is the pre-trained model weights for gender detection. You can download ithere.
• deploy_gender.prototxt: is the model architecture for the gender detection model (a plain text file with a JSON-like structure containing all the neural network layer’s definitions). Get ithere.
• age_net.caffemodel: It is the pre-trained model weights for age detection. You can download ithere.
• deploy_age.prototxt: is the model architecture for the age detection model (a plain text file with a JSON-like structure containing all the neural network layer’s definitions). Get ithere.
• res10_300x300_ssd_iter_140000_fp16.caffemodel: The pre-trained model weights for face detection, downloadhere.
• deploy.prototxt.txt: This is the model architecture for the face detection model, downloadhere.

Next, loading the models:

# Initialize frame sizeframe_width = 1280frame_height = 720# load face Caffe modelface_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL)# Load age prediction modelage_net = cv2.dnn.readNetFromCaffe(AGE_MODEL, AGE_PROTO)# Load gender prediction modelgender_net = cv2.dnn.readNetFromCaffe(GENDER_MODEL, GENDER_PROTO)

Before trying to detect age and gender, we need a function to detect faces first:

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

Theget_faces() function was grabbed from theface detection tutorial, so check it out if you want more information.

Below is a function for simply displaying an image:

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()

Below are is a function for dynamically resizing an image, we're going to need it to resize the input images when exceeding a certain width:

# from: https://stackoverflow.com/questions/44650888/resize-an-image-without-distortion-opencvdef 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)

Now that everything is ready, let's define our two functions for age and gender detection:

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()

Theget_gender_predictions() andget_age_predictions() perform prediction on thegender_net andage_net models to infer the gender and age of the input image respectively.

Finally, we write our main function:

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()

The main function does the following:

• First, it reads the image using thecv2.imread() method.
• After the image is resized to the appropriate size, we use ourget_faces() function to get all the detected faces from the image.
• We iterate on each detected face image and call ourget_age_predictions() andget_gender_predictions() to get the predictions.
• We print the age and gender.
• We draw a rectangle surrounding the face and also put the label that contains the age and gender text along with confidence on the image.
• Finally, we show the image.

Let's call it:

if __name__ == "__main__":    import sys    input_path = sys.argv[1]    predict_age_and_gender(input_path)

Done, let's run the script now (testing on this image):

$ python age_and_gender_detection.py images/girl.jpg

Output in the console:

Male-99.1%, (4, 6)-71.9%Female-96.0%, (4, 6)-70.9%

The resulting image:

Here is another example:

Or this:

Awesome! If you see the text in the image is large or small, make sure to tweak thefont_scale floating-point variable on your image in thepredict_age_and_gender() function.

For more detail on how the gender and age prediction works, I suggest you check the individual tutorials:

• Age Detection using OpenCV in Python
• Gender Detection using OpenCV in Python

If you want to use your camera, I madea Python script to read images from your webcam and perform inference in real time.

Check the full codehere.

Happy coding ♥

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