Code for Gender Detection using OpenCV in Python Tutorial
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predict_gender.py
# Import Libraries
import cv2
import numpy as np
# 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']
# 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"
# load face Caffe model
face_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL)
# Load gender prediction model
gender_net = cv2.dnn.readNetFromCaffe(GENDER_MODEL, GENDER_PROTO)
# Initialize frame size
frame_width = 1280
frame_height = 720
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
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_gender(input_path: str):
"""Predict the gender of the faces showing in the image"""
# 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]
# image --> Input image to preprocess before passing it through our dnn for classification.
# scale factor = After performing mean substraction we can optionally scale the image by some factor. (if 1 -> no scaling)
# size = The spatial size that the CNN expects. Options are = (224*224, 227*227 or 299*299)
# mean = mean substraction values to be substracted from every channel of the image.
# swapRB=OpenCV assumes images in BGR whereas the mean is supplied in RGB. To resolve this we set swapRB to True.
blob = cv2.dnn.blobFromImage(image=face_img, scalefactor=1.0, size=(
227, 227), mean=MODEL_MEAN_VALUES, swapRB=False, crop=False)
# Predict Gender
gender_net.setInput(blob)
gender_preds = gender_net.forward()
i = gender_preds[0].argmax()
gender = GENDER_LIST[i]
gender_confidence_score = gender_preds[0][i]
# Draw the box
label = "{}-{:.2f}%".format(gender, gender_confidence_score*100)
print(label)
yPos = start_y - 15
while yPos < 15:
yPos += 15
# get the font scale for this image size
optimal_font_scale = get_optimal_font_scale(label,((end_x-start_x)+25))
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
cv2.putText(frame, label, (start_x, yPos),
cv2.FONT_HERSHEY_SIMPLEX, optimal_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()
if __name__ == '__main__':
# Parsing command line arguments entered by user
import sys
predict_gender(sys.argv[1])
predict_gender_live.py
# Import Libraries
import cv2
import numpy as np
# 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']
# 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"
# load face Caffe model
face_net = cv2.dnn.readNetFromCaffe(FACE_PROTO, FACE_MODEL)
# Load gender prediction model
gender_net = cv2.dnn.readNetFromCaffe(GENDER_MODEL, GENDER_PROTO)
# Initialize frame size
frame_width = 1280
frame_height = 720
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
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_gender():
"""Predict the gender of the faces showing in the image"""
# create a new cam object
cap = cv2.VideoCapture(0)
while True:
_, img = cap.read()
# 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]
# image --> Input image to preprocess before passing it through our dnn for classification.
# scale factor = After performing mean substraction we can optionally scale the image by some factor. (if 1 -> no scaling)
# size = The spatial size that the CNN expects. Options are = (224*224, 227*227 or 299*299)
# mean = mean substraction values to be substracted from every channel of the image.
# swapRB=OpenCV assumes images in BGR whereas the mean is supplied in RGB. To resolve this we set swapRB to True.
blob = cv2.dnn.blobFromImage(image=face_img, scalefactor=1.0, size=(
227, 227), mean=MODEL_MEAN_VALUES, swapRB=False, crop=False)
# Predict Gender
gender_net.setInput(blob)
gender_preds = gender_net.forward()
i = gender_preds[0].argmax()
gender = GENDER_LIST[i]
gender_confidence_score = gender_preds[0][i]
# Draw the box
label = "{}-{:.2f}%".format(gender, gender_confidence_score*100)
print(label)
yPos = start_y - 15
while yPos < 15:
yPos += 15
# get the font scale for this image size
optimal_font_scale = get_optimal_font_scale(label,((end_x-start_x)+25))
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
cv2.putText(frame, label, (start_x, yPos),
cv2.FONT_HERSHEY_SIMPLEX, optimal_font_scale, box_color, 2)
# Display processed image
# frame = cv2.resize(frame, (frame_height, frame_width))
cv2.imshow("Gender Estimator", frame)
if cv2.waitKey(1) == ord("q"):
break
# uncomment if you want to save the image
# cv2.imwrite("output.jpg", frame)
# Cleanup
cv2.destroyAllWindows()
if __name__ == '__main__':
predict_gender()
