Real time Age Prediction Blog

age-prediction.py

# capture the video from default camera
# video_stream = cv2.VideoCapture(0)

# read video from video file
video_file_path = 'test/face-demographics-walking.mp4'
video_stream = cv2.VideoCapture(video_file_path)

# initialize the number of frame needed to be skipped
skip = 0

while True:

  #Read the frame
  for i in range(skip):
      video_stream.grab()

  # get current frame
  stat, frame = video_stream.read()

  # check that frame is left to read
  if stat == False:
    print('Video has ended')
    break  

  # reduce size of image to 50%
  scaled_frame = cv2.resize(frame, (0,0), fx=0.5, fy=0.5)

  # find all face locations using face_locations() function
  # model can be "cnn" or "hog"
  # number_of_times_to_upsample = 1 higher and detect more faces

  all_face_locations = face_recognition.face_locations(scaled_frame, model="cnn")

  for index,face_location in enumerate(all_face_locations):

    # split the tuple 
    top_pos, right_pos, bottom_pos, left_pos = face_location

    # scale up the coordinates by 2 times
    top_pos = top_pos * 2
    right_pos = right_pos * 2
    bottom_pos = bottom_pos * 2
    left_pos = left_pos * 2

    #---------------------------------------------------------------------------
    # Age Detection
    #---------------------------------------------------------------------------

    # Slice frame image array by positions face_locations
    face_image = frame[top_pos:bottom_pos, left_pos:right_pos]

    # The 'AGE_GENDER_MODEL_MEAN_VALUES' calculated by using the numpy.mean()
    AGE_GENDER_MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)

    # create blob of sliced face image
    face_image_blob = cv2.dnn.blobFromImage(face_image, 1, (227, 227), AGE_GENDER_MODEL_MEAN_VALUES, swapRB=False)

    # give input to age detection model
    age_cov_net.setInput(face_image_blob)

    # get the predicted label from the model
    age_prediction = age_cov_net.forward()

    # find the max value of predicted index
    # pass the index to age_label_list to get the associated label text
    age_label = age_label_list[age_prediction[0].argmax()]

    # draw rectangle around each face
    cv2.rectangle(frame, (left_pos,top_pos), (right_pos,bottom_pos), (0,0,255), 2)

    # disply the name of age as text along the face
    font = cv2.FONT_HERSHEY_SIMPLEX
    cv2.rectangle(frame, (left_pos-1, bottom_pos + 18), (right_pos+1, bottom_pos), (0, 0, 255), -1)
    cv2.putText(frame, age_label, (left_pos+2,bottom_pos+11), font, 0.5, (255,255,255), 1)

    #---------------------------------------------------------------------------

  # display frame
  cv2.imshow("Video",frame)

  # for only google colab
  # cv2_imshow(frame)

  # Press 'q' on the keyboard to break the while loop
  if cv2.waitKey(1) & 0xFF == ord('q'):
    break
    
# Release the video stream resource
video_stream.release()
cv2.destroyAllWindows()

dir-tree.md

•
├── test
│   ├── modi-obama-1.jpg
│   ├── modi-obama-2.jpg
│   └── face-demographics-walking.mp4
├── models
│   ├── age_deploy.prototxt
│   └── age_net.caffemodel
├── age-prediction-from-image.py
└── realtime-age-prediction.py

import-libraries.py

# importing required libraries
import cv2
import numpy as np
from keras.preprocessing import image
from keras.models import model_from_json
import  face_recognition

# only for google colab
# from google.colab.patches import cv2_imshow

initialize-model-and-weights.py

# declare age range list
age_label_list = ['0-2','4-6','8-12','15-20','25-32','38-43','48-53','60-100']

# initialize the model
age_prototxt = "models/age_deploy.prototxt"
age_caffemodel = "models/age_net.caffemodel"

age_cov_net =  cv2.dnn.readNet(age_caffemodel, age_prototxt)

realtime-age-prediction.py

# importing required libraries
import cv2
import numpy as np
from keras.preprocessing import image
from keras.models import model_from_json
import  face_recognition

# only for google colab
# from google.colab.patches import cv2_imshow

# declare age range list
age_label_list = ['0-2','4-6','8-12','15-20','25-32','38-43','48-53','60-100']

# initialize the model
age_prototxt = "models/age_deploy.prototxt"
age_caffemodel = "models/age_net.caffemodel"

age_cov_net =  cv2.dnn.readNet(age_caffemodel, age_prototxt)

# capture the video from default camera
# video_stream = cv2.VideoCapture(0)

# read video from video file
video_file_path = 'test/face-demographics-walking.mp4'
video_stream = cv2.VideoCapture(video_file_path)

# initialize the number of frame needed to be skipped
skip = 0

while True:

  #Read the frame
  for i in range(skip):
      video_stream.grab()

  # get current frame
  stat, frame = video_stream.read()

  # check that frame is left to read
  if stat == False:
    print('Video has ended')
    break  

  # reduce size of image to 50%
  scaled_frame = cv2.resize(frame, (0,0), fx=0.5, fy=0.5)

  # find all face locations using face_locations() function
  # model can be "cnn" or "hog"
  # number_of_times_to_upsample = 1 higher and detect more faces

  all_face_locations = face_recognition.face_locations(scaled_frame, model="cnn")

  for index,face_location in enumerate(all_face_locations):

    # split the tuple 
    top_pos, right_pos, bottom_pos, left_pos = face_location

    # scale up the coordinates by 2 times
    top_pos = top_pos * 2
    right_pos = right_pos * 2
    bottom_pos = bottom_pos * 2
    left_pos = left_pos * 2

    #---------------------------------------------------------------------------
    # Age Detection
    #---------------------------------------------------------------------------

    # Slice frame image array by positions face_locations
    face_image = frame[top_pos:bottom_pos, left_pos:right_pos]

    # The 'AGE_GENDER_MODEL_MEAN_VALUES' calculated by using the numpy.mean()
    AGE_GENDER_MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)

    # create blob of sliced face image
    face_image_blob = cv2.dnn.blobFromImage(face_image, 1, (227, 227), AGE_GENDER_MODEL_MEAN_VALUES, swapRB=False)

    # give input to age detection model
    age_cov_net.setInput(face_image_blob)

    # get the predicted label from the model
    age_prediction = age_cov_net.forward()

    # find the max value of predicted index
    # pass the index to age_label_list to get the associated label text
    age_label = age_label_list[age_prediction[0].argmax()]

    # draw rectangle around each face
    cv2.rectangle(frame, (left_pos,top_pos), (right_pos,bottom_pos), (0,0,255), 2)

    # disply the name of age as text along the face
    font = cv2.FONT_HERSHEY_SIMPLEX
    cv2.rectangle(frame, (left_pos-1, bottom_pos + 18), (right_pos+1, bottom_pos), (0, 0, 255), -1)
    cv2.putText(frame, age_label, (left_pos+2,bottom_pos+11), font, 0.5, (255,255,255), 1)

    #---------------------------------------------------------------------------

  # display frame
  cv2.imshow("Video",frame)

  # for only google colab
  # cv2_imshow(frame)

  # Press 'q' on the keyboard to break the while loop
  if cv2.waitKey(1) & 0xFF == ord('q'):
    break
    
# Release the video stream resource
video_stream.release()
cv2.destroyAllWindows()