Code for Age Detection using OpenCV in Python Tutorial
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predict_age.py
# Import Libraries
import cv2
import os
import filetype
import numpy as np
# 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'
# 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 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)']
# download from: https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
FACE_PROTO = "weights/deploy.prototxt.txt"
# download from: 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"
# 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)
def get_faces(frame, confidence_threshold=0.5):
"""Returns the box coordinates of all detected faces"""
# 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
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()
def get_optimal_font_scale(text, width):
"""Determine the optimal font scale based on the hosting frame width"""
for scale in reversed(range(0, 60, 1)):
textSize = cv2.getTextSize(text, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=scale/10, thickness=1)
new_width = textSize[0][0]
if (new_width <= width):
return scale/10
return 1
# 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)
def predict_age(input_path: str):
"""Predict the age of the faces showing in the image"""
# Read Input Image
img = cv2.imread(input_path)
# 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)
faces = get_faces(frame)
for i, (start_x, start_y, end_x, end_y) in enumerate(faces):
face_img = frame[start_y: end_y, start_x: end_x]
# image --> Input image to preprocess before passing it through our dnn for classification.
blob = cv2.dnn.blobFromImage(
image=face_img, scalefactor=1.0, size=(227, 227),
mean=MODEL_MEAN_VALUES, swapRB=False
)
# Predict Age
age_net.setInput(blob)
age_preds = age_net.forward()
print("="*30, f"Face {i+1} Prediction Probabilities", "="*30)
for i in range(age_preds[0].shape[0]):
print(f"{AGE_INTERVALS[i]}: {age_preds[0, i]*100:.2f}%")
i = age_preds[0].argmax()
age = AGE_INTERVALS[i]
age_confidence_score = age_preds[0][i]
# Draw the box
label = f"Age:{age} - {age_confidence_score*100:.2f}%"
print(label)
# get the position where to put the text
yPos = start_y - 15
while yPos < 15:
yPos += 15
# write the text into the frame
cv2.putText(frame, label, (start_x, yPos),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), thickness=2)
# draw the rectangle around the face
cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2)
# Display processed image
display_img('Age Estimator', frame)
# save the image if you want
# cv2.imwrite("predicted_age.jpg", frame)
if __name__ == '__main__':
# Parsing command line arguments entered by user
import sys
image_path = sys.argv[1]
predict_age(image_path)
predict_age_live.py
# Import Libraries
import cv2
import os
import filetype
import numpy as np
# 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'
# 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 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)']
# download from: https://raw.githubusercontent.com/opencv/opencv/master/samples/dnn/face_detector/deploy.prototxt
FACE_PROTO = "weights/deploy.prototxt.txt"
# download from: 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"
# 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)
def get_faces(frame, confidence_threshold=0.5):
"""Returns the box coordinates of all detected faces"""
# 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
def get_optimal_font_scale(text, width):
"""Determine the optimal font scale based on the hosting frame width"""
for scale in reversed(range(0, 60, 1)):
textSize = cv2.getTextSize(text, fontFace=cv2.FONT_HERSHEY_DUPLEX, fontScale=scale/10, thickness=1)
new_width = textSize[0][0]
if (new_width <= width):
return scale/10
return 1
# 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)
def predict_age():
"""Predict the age of the faces showing in the image"""
# create a new cam object
cap = cv2.VideoCapture(0)
while True:
_, img = cap.read()
# 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)
faces = get_faces(frame)
for i, (start_x, start_y, end_x, end_y) in enumerate(faces):
face_img = frame[start_y: end_y, start_x: end_x]
# image --> Input image to preprocess before passing it through our dnn for classification.
blob = cv2.dnn.blobFromImage(
image=face_img, scalefactor=1.0, size=(227, 227),
mean=MODEL_MEAN_VALUES, swapRB=False
)
# Predict Age
age_net.setInput(blob)
age_preds = age_net.forward()
print("="*30, f"Face {i+1} Prediction Probabilities", "="*30)
for i in range(age_preds[0].shape[0]):
print(f"{AGE_INTERVALS[i]}: {age_preds[0, i]*100:.2f}%")
i = age_preds[0].argmax()
age = AGE_INTERVALS[i]
age_confidence_score = age_preds[0][i]
# Draw the box
label = f"Age:{age} - {age_confidence_score*100:.2f}%"
print(label)
# get the position where to put the text
yPos = start_y - 15
while yPos < 15:
yPos += 15
# write the text into the frame
cv2.putText(frame, label, (start_x, yPos),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), thickness=2)
# draw the rectangle around the face
cv2.rectangle(frame, (start_x, start_y), (end_x, end_y), color=(255, 0, 0), thickness=2)
# Display processed image
cv2.imshow('Age Estimator', frame)
if cv2.waitKey(1) == ord("q"):
break
# save the image if you want
# cv2.imwrite("predicted_age.jpg", frame)
cv2.destroyAllWindows()
if __name__ == '__main__':
predict_age()
