forked from suhayryz/maskface_attendance
-
Notifications
You must be signed in to change notification settings - Fork 0
/
03recognizer.py
243 lines (208 loc) · 9.23 KB
/
03recognizer.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
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
# -*- coding: utf-8 -*-
"""
@author: suhairisuhaimin
"""
import cv2
import numpy as np
import os
import pandas as pd
from matplotlib import pyplot as plt
import time
import datetime
# images properties
def plt_show(image, title=""):
if len(image.shape) == 3:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.axis("off")
plt.title(title)
plt.imshow(image, cmap="Greys_r")
plt.show()
# face detection
class FaceDetector(object):
def __init__(self, xml_path):
self.classifier = cv2.CascadeClassifier(xml_path)
def detect(self, image, biggest_only=True):
scale_factor = 1.2
min_neighbors = 5
min_size = (75, 75)
biggest_only = True
flags = cv2.CASCADE_FIND_BIGGEST_OBJECT | cv2.CASCADE_DO_ROUGH_SEARCH if biggest_only else cv2.CASCADE_SCALE_IMAGE
faces_coord = self.classifier.detectMultiScale(image,
scaleFactor=scale_factor,
minNeighbors=min_neighbors,
minSize=min_size,
flags=flags)
return faces_coord
# video camera
class VideoCamera(object):
def __init__(self, index=1):
self.video = cv2.VideoCapture(index)
self.index = index
print (self.video.isOpened())
def __del__(self):
self.video.release()
def get_frame(self, in_grayscale=False):
_, frame = self.video.read()
if in_grayscale:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
return frame
# crop images
def cut_faces(image, faces_coord):
faces = []
for (x, y, w, h) in faces_coord:
w_rm = int(0.3 * w / 2)
faces.append(image[y: y + h, x + w_rm: x + w - w_rm])
return faces
# normalize images
def normalize_intensity(images):
images_norm = []
for image in images:
is_color = len(image.shape) == 3
if is_color:
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
images_norm.append(cv2.equalizeHist(image))
return images_norm
# resize images
def resize(images, size=(100, 100)):
images_norm = []
for image in images:
if image.shape < size:
image_norm = cv2.resize(image, size,
interpolation=cv2.INTER_AREA)
else:
image_norm = cv2.resize(image, size,
interpolation=cv2.INTER_CUBIC)
images_norm.append(image_norm)
return images_norm
# normalize faces
def normalize_faces(frame, faces_coord):
faces = cut_faces(frame, faces_coord)
faces = normalize_intensity(faces)
faces = resize(faces)
return faces
# rectangle line
def draw_rectangle(image, coords):
for (x, y, w, h) in coords:
w_rm = int(0.2 * w / 2)
cv2.rectangle(image, (x + w_rm, y), (x + w - w_rm, y + h),
(102, 255, 0), 1)
# acquire images from dataset
def collect_dataset():
images = []
labels = []
labels_dic = {}
members = [person for person in os.listdir("members/")]
for i, person in enumerate(members): # loop over
labels_dic[i] = person
for image in os.listdir("members/" + person):
images.append(cv2.imread("members/" + person + '/' + image,
0))
labels.append(i)
return (images, np.array(labels), labels_dic)
images, labels, labels_dic = collect_dataset()
# train image (algorithm sets)
rec_eig = cv2.face.EigenFaceRecognizer_create()
rec_eig.train(images, labels)
rec_fisher = cv2.face.FisherFaceRecognizer_create()
rec_fisher.train(images, labels)
rec_lbph = cv2.face.LBPHFaceRecognizer_create()
rec_lbph.train(images, labels)
print ("Models Trained Succesfully")
# cascade face and mask
detector = FaceDetector("xml/frontal_face.xml")
detector_mask = cv2.CascadeClassifier("xml/mask_cascade.xml")
# 0 usb webcam additional
# 1 back cam acer
# 2 front cam acer
webcam0 = VideoCamera(0)
webcam1 = VideoCamera(2)
ts = time.time()
date = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d')
timeStamp = datetime.datetime.fromtimestamp(ts).strftime('%H:%M:%S')
# mask detection and face recognition (in)
while True:
frame0 = webcam0.get_frame()
mask = detector_mask.detectMultiScale(frame0,
scaleFactor=1.2,
minNeighbors=5,
minSize=(100, 100),
maxSize=(150, 150),
flags=cv2.CASCADE_SCALE_IMAGE)
for(x1,y1,x2,y2) in mask:
cv2.rectangle(frame0,(x1,y1),(x1+x2,y1+y2),(0,255,0),2)
cv2.putText(frame0, 'Using Mask',(x1, y1+y2 + 30), cv2.FONT_HERSHEY_PLAIN, 1.5, (255,255,255), 2)
faces_coord = detector.detect(frame0, False) # detect more than one face
col_names = ['Name','Date','Time']
attendance = pd.DataFrame(columns = col_names)
if len(faces_coord):
faces = normalize_faces(frame0, faces_coord) # norm pipeline
for i, face in enumerate(faces): # for each detected face
collector = cv2.face.StandardCollector_create()
rec_lbph.predict_collect(face, collector) # chosen algorithm
conf = collector.getMinDist()
pred = collector.getMinLabel()
threshold = 76 # eigen, fisher, lbph [mean 3375,1175,65] [high lbph 76]
print ("Prediction Entry: " + labels_dic[pred].capitalize() + "\nConfidence Entry: " + str(round(conf)))
if conf < threshold: # apply threshold
cv2.putText(frame0, labels_dic[pred].capitalize(),
(faces_coord[i][0], faces_coord[i][1] - 20),
cv2.FONT_HERSHEY_DUPLEX, 1.0, (102, 255, 0), 1)
attendance.loc[len(attendance)] = [labels_dic[pred],date,timeStamp]
Hour,Minute,Second=timeStamp.split(":")
fileName="attendancein\Attendance_"+labels_dic[pred]+"-"+date+"_"+Hour+"-"+Minute+"-"+Second+".csv" # write to output file (in)
attendance.to_csv(fileName,index=False)
else:
cv2.putText(frame0, "Unknown",
(faces_coord[i][0], faces_coord[i][1] - 10),
cv2.FONT_HERSHEY_DUPLEX, 1.0, (66, 55, 245), 1)
draw_rectangle(frame0, faces_coord) # rectangle around face
cv2.putText(frame0, "ESC to exit", (5, frame0.shape[0] - 5),
cv2.FONT_HERSHEY_DUPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA)
cv2.imshow("Entry Cam", frame0) # live feed in external
if cv2.waitKey(33) & 0xFF == 27:
cv2.destroyAllWindows()
break
# mask detection and face recognition (out)
frame1 = webcam1.get_frame()
mask = detector_mask.detectMultiScale(frame1,
scaleFactor=1.2,
minNeighbors=5,
minSize=(100, 100),
maxSize=(150, 150),
flags=cv2.CASCADE_SCALE_IMAGE)
for(x1,y1,x2,y2) in mask:
cv2.rectangle(frame1,(x1,y1),(x1+x2,y1+y2),(0,255,0),2)
cv2.putText(frame1, 'Using Mask',(x1, y1+y2 + 30), cv2.FONT_HERSHEY_PLAIN, 1.5, (255,255,255), 2)
faces_coord = detector.detect(frame1, False) # detect more than one face
col_names = ['Name','Date','Time']
attendance = pd.DataFrame(columns = col_names)
if len(faces_coord):
faces = normalize_faces(frame1, faces_coord) # norm pipeline
for i, face in enumerate(faces): # for each detected face
collector = cv2.face.StandardCollector_create()
rec_lbph.predict_collect(face, collector) # chosen algorithm
conf = collector.getMinDist()
pred = collector.getMinLabel()
threshold = 76 # eigen, fisher, lbph [mean 3375,1175,65] [high lbph 76]
print ("Prediction Exit: " + labels_dic[pred].capitalize() + "\nConfidence Exit: " + str(round(conf)))
if conf < threshold: # apply threshold
cv2.putText(frame1, labels_dic[pred].capitalize(),
(faces_coord[i][0], faces_coord[i][1] - 20),
cv2.FONT_HERSHEY_DUPLEX, 1.0, (102, 255, 0), 1)
attendance.loc[len(attendance)] = [labels_dic[pred],date,timeStamp]
Hour,Minute,Second=timeStamp.split(":")
fileName="attendanceout\Attendance_"+labels_dic[pred]+"-"+date+"_"+Hour+"-"+Minute+"-"+Second+".csv" # write to output file (out)
attendance.to_csv(fileName,index=False)
else:
cv2.putText(frame1, "Unknown",
(faces_coord[i][0], faces_coord[i][1] - 10),
cv2.FONT_HERSHEY_DUPLEX, 1.0, (66, 55, 245), 1)
draw_rectangle(frame1, faces_coord) # rectangle around face
cv2.putText(frame1, "ESC to exit", (5, frame1.shape[0] - 5),
cv2.FONT_HERSHEY_DUPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA)
cv2.imshow("Exit Cam", frame1) # live feed in external
if cv2.waitKey(33) & 0xFF == 27:
cv2.destroyAllWindows()
break
del webcam0
del webcam1