Age and Gender Detection using OpenCV in Python

Learn how to perform age and gender detection using OpenCV library in Python with camera or image input.
  · 7 min read · Updated may 2024 · Machine Learning · Computer Vision

Welcome! Meet our Python Code Assistant, your new coding buddy. Why wait? Start exploring now!

In this tutorial, we will combine gender detection and age 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 Libraries
import cv2
import 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.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)']

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 it here.
  • 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 it here.
  • age_net.caffemodel: It is the pre-trained model weights for age detection. You can download it here.
  • 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 it here.
  • res10_300x300_ssd_iter_140000_fp16.caffemodel: The pre-trained model weights for face detection, download here.
  • deploy.prototxt.txt: This is the model architecture for the face detection model, download here.

Next, loading the models:

# 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)

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

Mastering YOLO: Build an Automatic Number Plate Recognition System

Building a real-time automatic number plate recognition system using YOLO and OpenCV library in Python

Download EBook

The get_faces() function was grabbed from the face 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-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)

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()

The get_gender_predictions() and get_age_predictions() perform prediction on the gender_net and age_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 the cv2.imread() method.
  • After the image is resized to the appropriate size, we use our get_faces() function to get all the detected faces from the image.
  • We iterate on each detected face image and call our get_age_predictions() and get_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:

Resulting ImageHere is another example:

Resulting image 2 on age & gender detection using OpenCVOr this:

Age & Gender detection tutorial resulting imageAwesome! If you see the text in the image is large or small, make sure to tweak the font_scale floating-point variable on your image in the predict_age_and_gender() function.

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

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

Check the full code here.

Mastering YOLO: Build an Automatic Number Plate Recognition System

Building a real-time automatic number plate recognition system using YOLO and OpenCV library in Python

Download EBook

Happy coding ♥

Found the article interesting? You'll love our Python Code Generator! Give AI a chance to do the heavy lifting for you. Check it out!

View Full Code Create Code for Me
Sharing is caring!



Read Also



Comment panel

    Got a coding query or need some guidance before you comment? Check out this Python Code Assistant for expert advice and handy tips. It's like having a coding tutor right in your fingertips!