-
Notifications
You must be signed in to change notification settings - Fork 1.9k
/
age_and_gender_detection_live.py
166 lines (151 loc) · 6.46 KB
/
age_and_gender_detection_live.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
# 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"
# 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)']
# 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)
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
# 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 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()
def predict_age_and_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()
# Take a copy of the initial image and resize it
frame = img.copy()
# resize if higher than frame_width
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]
# predict age
age_preds = get_age_predictions(face_img)
# predict gender
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
cv2.putText(frame, label, (start_x, yPos),
cv2.FONT_HERSHEY_SIMPLEX, 0.54, box_color, 2)
# Display processed image
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_age_and_gender()