def live():
fd = face()
cap = cv2.VideoCapture(0)
ret = True
while ret:
ret, frame = cap.read()
detected, x, y, w, h = fd.detectFace(frame)
if (detected is not None):
f = detected
detected = cv2.cvtColor(detected, cv2.COLOR_BGR2GRAY)
detected = cv2.resize(detected, (48, 48))
detected = detected[:, :, np.newaxis]
detected = detected.astype('float') / 255.0
detected = np.expand_dims(detected, axis=0)
prediction = model.predict(detected)[0]
index = int(np.argmax(prediction))
print("Result: ", result[index])
#cv2.putText(frame, (x,y),cv2.FONT_HERSHEY_SIMPLEX,1,(255,255,255),2)
cv2.rectangle(frame, (x, y), (x + w, y + h), (252, 160, 39), 3)
cv2.imshow('onlyFace', f)
cv2.imshow('frame', frame)
if (cv2.waitKey(1) & 0XFF == ord('q')):
break
cap.release()
cv2.destroyAllWindows()
def photo_analyse(img_list):
img_path_list = []
filtered_img_list = []
for img in img_list:
detect = face(img)
if detect is not '[]':
img_path_list.append(str(img))
filtered_img_list.append(img)
analyzed_imgs = []
text = []
for i in range(len(filtered_img_list)):
filtered_img_list[i] = handwriting(filtered_img_list[i])
lines = json.loads(filtered_img_list[i])['recognitionResult']['lines']
text.append('')
for line in lines:
text[i] += line['text'] + '\n'
for i in range(len(text)):
if text[i] not in text[i + 1:]:
analyzed_imgs.append(img_path_list[i])
print(analyzed_imgs)
return analyzed_imgs
import cv2
from face_detection import face
from keras.models import load_model
import numpy as np
from embedding import emb
from retreive_pymongo_data import database
label = None
a = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0}
people = {0: "satinder", 1: "sagat", 2: "mandeep", 3: "sobhu", 4: "ravi"}
abhi = None
data = database()
e = emb()
fd = face()
print('attendance till now is ')
data.view()
model = load_model('face_reco1.MODEL')
def test():
test_run = cv2.imread('1.jpg', 1)
test_run = cv2.resize(test_run, (160, 160))
test_run = test_run.astype('float') / 255.0
test_run = np.expand_dims(test_run, axis=0)
test_run = e.calculate(test_run)
test_run = np.expand_dims(test_run, axis=0)
test_run = model.predict(test_run)[0]
def recognize(self, get_attendance = False, FRAMES = 30):
with keras.backend.get_session().graph.as_default():
e = emb()
fd = face()
model = load_model('face_reco2.MODEL')
n_frames = 0
attendance = dict()
ret = True
cap = cv2.VideoCapture(CAM_CONSTANT, cv2.CAP_DSHOW)
WindowName = 'StudentDetector'
cv2.namedWindow(WindowName, cv2.WINDOW_NORMAL)
cv2.setWindowProperty(WindowName, cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
cv2.setWindowProperty(WindowName, cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_NORMAL)
for person in self.people.values():
attendance[person] = 0
print(attendance.items())
for i in range(FRAMES):
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
try:
det, coor = fd.detectFace(frame)
except:
det, coor = None, None
if (det is not None):
for i in range(len(det)):
detected = det[i]
k = coor[i]
f = detected
detected = cv2.resize(detected, (160, 160))
# detected=np.rollaxis(detected,2,0)
detected = detected.astype('float') / 255.0
detected = np.expand_dims(detected, axis=0)
feed = e.calculate(detected)
feed = np.expand_dims(feed, axis=0)
prediction = model.predict(feed)[0]
result = int(np.argmax(prediction))
if get_attendance:
if (np.max(prediction) > .70):
for i in self.people:
if (result == i):
self.label = self.people[i]
attendance[self.label] += 1
if (self.a[i] == 0):
print("a")
self.a[i] = 1
abhi = i
else:
self.label = 'unknown'
cv2.putText(frame, self.label, (k[0], k[1]), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.rectangle(frame, (k[0], k[1]), (k[0] + k[2], k[1] + k[3]), (252, 160, 39), 3)
n_frames += 1
cv2.imshow(WindowName, frame)
if (cv2.waitKey(1) & 0XFF == ord('q')):
break
for person in attendance.keys():
attendance[person] /= n_frames
cap.release()
cv2.destroyAllWindows()
return attendance
def detect(num):
subno = num
data = database()
a = {}
people = {}
def getdata():
name = []
records = data.db.pa.find()
j = 0
for i in records:
j += 1
name.append(i["name"])
k = len(name)
for i in range(0, k):
people[i] = name[i]
a[i] = 0
getdata()
print(a)
print(people)
label = None
abhi = None
x = a
e = emb()
fd = face()
model = load_model('face_reco2.MODEL')
def test():
test_run = cv2.imread('1.jpg', 1)
test_run = cv2.resize(test_run, (160, 160))
#test_run=np.rollaxis(test_run,2,0)
test_run = test_run.astype('float') / 255.0
test_run = np.expand_dims(test_run, axis=0)
test_run = e.calculate(test_run)
test_run = np.expand_dims(test_run, axis=0)
test_run = model.predict(test_run)[0]
cap = cv2.VideoCapture(-1)
ret = True
test()
while ret:
ret, frame = cap.read()
frame = cv2.flip(frame, 1)
det, coor = fd.detectFace(frame)
if (det is not None):
for i in range(len(det)):
detected = det[i]
k = coor[i]
f = detected
detected = cv2.resize(detected, (160, 160))
#detected=np.rollaxis(detected,2,0)
detected = detected.astype('float') / 255.0
detected = np.expand_dims(detected, axis=0)
feed = e.calculate(detected)
feed = np.expand_dims(feed, axis=0)
prediction = model.predict(feed)[0]
result = int(np.argmax(prediction))
if (np.max(prediction) > .70):
for i in people:
if (result == i):
label = people[i]
if (a[i] == 0):
print("a")
data.update(label, subno)
a[i] = 1
abhi = i
else:
label = 'unknown'
cv2.putText(frame, label, (k[0], k[1]),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
x = k[0]
y = k[1]
if (abhi is not None):
if (a[abhi] == 1):
cv2.putText(frame, "your attendance is complete",
(x, y - 30), cv2.FONT_HERSHEY_SIMPLEX, 1,
(255, 255, 255), 2)
cv2.rectangle(frame, (k[0], k[1]),
(k[0] + k[2], k[1] + k[3]),
(252, 160, 39), 3)
cv2.imshow('onlyFace', f)
cv2.imshow('frame', frame)
if (cv2.waitKey(1) & 0XFF == ord('q')):
break
cap.release()
cv2.destroyAllWindows()
data.export_csv()