-
Notifications
You must be signed in to change notification settings - Fork 0
/
FaceRecognition_Webcam_FastVersion.py
214 lines (153 loc) · 6.83 KB
/
FaceRecognition_Webcam_FastVersion.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
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
# -*- coding: utf-8 -*-
"""
Created on Mon Apr 16 17:14:14 2018
@author: aakash.chotrani
"""
import face_recognition
import cv2
import os
import time
known_face_names = [
# 'Aakash',
# 'Shivam'
]
# Create arrays of known face encodings and their names
known_face_encodings = [
# Aakash_face_encoding,
# shivam_face_encoding
]
def Get_Existing_Directories_Training_Images(path):
for root, dirs, files in os.walk(path, topdown=False):
#for each directory that already exists get the name and push it to known faces array.
for name in dirs:
print(name)
known_face_names.append(name)
#Go in each directory and get the first image and call Train on the image.
#NOTE BUG: Check if there are files in the directory.
for rootx, dirsx, filesx in os.walk(os.path.join(root, name), topdown=False):
print(filesx)
print(os.path.join(root, name)+'/'+filesx[0])
imagePath = os.path.join(root, name)+'/'+filesx[0]
Train_Known_Person(imagePath)
def Train_Known_Person(path):
global known_face_encodings
known_person_face_image = face_recognition.load_image_file(path)
known_person_face_encoding = face_recognition.face_encodings(known_person_face_image)[0]
#record the encoding
known_face_encodings.append(known_person_face_encoding)
Image_Capture_Delay_Seconds = 10
img_counter = 0
def capture_images(name,top,right,bottom,left):
print('capture images called')
global img_counter
#Resetting the path and names
img_name = ""
path = ""
#saving the faces
img_name = name+ str(img_counter) + '.jpg'
crop_img = frame[top:bottom,left:right]
#giving folder path to store the captured images.
path = 'C:/Users/aakash.chotrani/Desktop/OpenCV/FaceRecognitionImages' +'/'+name
if not os.path.exists(path):
os.makedirs(path)
cv2.imwrite(os.path.join(path,img_name),crop_img)
img_counter = img_counter + 1
print(img_counter," ",path+img_name)
return path
def Train_New_Person(name,face_locations):
global known_face_encodings
global known_face_names
top = face_locations[0][0]
right = face_locations[0][1]
bottom = face_locations[0][2]
left = face_locations[0][3]
top *= 4
right *= 4
bottom *= 4
left *= 4
path = capture_images(name,top,right,bottom,left)
#image counter is increased in the capture image function hence decresasing it and storing in temp
temp = img_counter - 1
name += str(temp)
new_person_face_image = face_recognition.load_image_file(os.path.join(path,name) + '.jpg')
new_person_face_encoding = face_recognition.face_encodings(new_person_face_image)[0]
known_face_encodings.append(new_person_face_encoding)
known_face_names.append(name)
# Load a sample picture and learn how to recognize it.
#Aakash_image = face_recognition.load_image_file("Aakash_LinkedIn.jpg")
#Aakash_face_encoding = face_recognition.face_encodings(Aakash_image)[0]
shivam_image = face_recognition.load_image_file("shivam.jpg")
shivam_face_encoding = face_recognition.face_encodings(shivam_image)[0]
# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
end_time = time.time() + Image_Capture_Delay_Seconds
unknown_person_counter = 0
def Start_Webcam():
global process_this_frame
global unknown_person_counter
video_capture = cv2.VideoCapture(0)
video_capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1920);
video_capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080);
global frame
global end_time
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
rgb_small_frame = small_frame[:, :, ::-1]
# Only process every other frame of video to save time
if process_this_frame:
# Find all the faces and face encodings in the current frame of video
face_locations = face_recognition.face_locations(rgb_small_frame)
face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
face_names = []
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
# If a match was found in known_face_encodings, just use the first one.
if True in matches:
first_match_index = matches.index(True)
name = known_face_names[first_match_index]
else:
name = "Person"+str(unknown_person_counter)
Train_New_Person(name,face_locations)
# Person_image = face_recognition.load_image_file("Aakash_LinkedIn.jpg")
# Aakash_face_encoding = face_recognition.face_encodings(Aakash_image)[0]
unknown_person_counter = unknown_person_counter + 1
face_names.append(name)
process_this_frame = not process_this_frame
# Display the results
for (top, right, bottom, left), name in zip(face_locations, face_names):
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
top *= 4
right *= 4
bottom *= 4
left *= 4
#capture image every few seconds
if(time.time() > end_time):
capture_images(name,top,right,bottom,left)
end_time = time.time() + Image_Capture_Delay_Seconds
# Draw a box around the face
cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
font = cv2.FONT_HERSHEY_DUPLEX
cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
# Display the resulting image
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
def main():
path = 'C:/Users/aakash.chotrani/Desktop/OpenCV/FaceRecognitionImages'
Get_Existing_Directories_Training_Images(path)
Start_Webcam()
if __name__ == "__main__":
main()