def face():
speak('Processing Facial Recognition')
test_img = cv2.imread('saved/{}.jpg'.format(always))
faces_detected, gray_img = fr.faceDetection(test_img)
speak("I found your data from our system")
faces, faceID = fr.labels_for_training_data('test')
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.write('trainingData.yml')
users_list()
for face in faces_detected:
(x, y, w, h) = face
roi_gray = gray_img[y:y + h, x:x + h]
label, confidence = face_recognizer.predict(
roi_gray) #predicting the label of given image
print("Confidence:", confidence)
print("label:", label)
fr.draw_rect(test_img, face)
predicted_name = name[label]
if (
confidence > 37
): #If confidence more than 37 then don't print predicted face text on screen
welcome()
break
fr.put_text(test_img, predicted_name, x, y)
resized_img = cv2.resize(test_img, (1000, 1000))
cv2.imshow("Face", resized_img)
cv2.waitKey(0) #Waits indefinitely until a key is pressed
cv2.destroyAllWindows
def saveDetails(name):
path = '/home/pi/Desktop/Project3/trainingImages/'
for i, j, k in os.walk(path):
number_of_dirs = len(j)
break
number_of_dirs += 1
p1 = '/home/pi/Desktop/Project3/trainingImages/' + str(number_of_dirs)
os.mkdir(p1)
#namesAndPath[len(namesAndPath)+1] = name
#print(namesAndPath)
print(name + " get ready for 10 pictures to be taken")
print("Also as you are ready for next shot, please press 's' ")
for i in range(10):
j = raw_input("Ready, press 's': ")
if j == 's':
take_picture(name, i, number_of_dirs)
else:
print("Sorry wrong key is pressed")
i -= 2
print("Thanks " + name)
print("Now classifier is running on the taken images")
#use for training data if new images are introduced:-
try:
faces, faceId = fr.labels_for_training_data(
'/home/pi/Desktop/Face Recognition/trainingImages')
face_recognizer = fr.train_classifier(faces, faceId)
face_recognizer.save('trainingData.yml')
except:
print("Something is wrong with the captured Image")
return
print("Face classified Successfully")
return
def testerAuthorization():
# This module takes images stored in disk and performs face recognition
# test_img = cv2.imread('TestImages/Kangana.jpg') # test_img path
# faces_detected, gray_img = fr.faceDetection(test_img)
# print("faces_detected:", faces_detected)
# Comment belows lines when running this program second time.Since it saves training.yml file in directory
# faces, faceID = fr.labels_for_training_data('trainingImages')
faces, faceID = fr.labels_for_training_data2()
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.write('trainingData.yml')
return True
def train():
# read time table and class names
read_tt.read_tt_and_names()
# making excel template
excel_template.make_template()
# This module takes images stored in disk and performs face recognition
test_img_name = str(input("image name : "))
test_img = cv2.imread('TestImages/' + test_img_name +
'.jpg') # test_img path
# detect all the faces in image
faces_detected, gray_img = fr.faceDetection(test_img)
print("faces_detected:", faces_detected)
# counter for keeping face count
face_count = len(faces_detected)
print("face_count:", face_count)
faces, faceID = fr.labels_for_training_data('trainingImages')
face_recognizer = fr.train_classifier(faces, faceID)
# have to store our trained data so we can use it later without going through the training process again
face_recognizer.write('trainingData.yml')
# use this .yml file in future to avoid training time
# creating dictionary containing names for each label
name = np.load("names.npy", allow_pickle=True, fix_imports=True)
name = name.item()
# id of students present in class
present = []
for face in faces_detected:
(x, y, w, h) = face
roi_gray = gray_img[y:y + h, x:x + h]
label, confidence = face_recognizer.predict(
roi_gray) # predicting the label of given image
present.append(label)
fr.draw_rect(test_img, face) # drawing rectangle on face
predicted_name = name[label]
fr.put_text(test_img, predicted_name, x,
y) # printing name of the person
# # print ids of present students
# present.sort()
print(present)
# cv2.imshow("test image", resized_img)
cv2.waitKey(0) # Waits indefinitely until a key is pressed
cv2.destroyAllWindows()
# train()
def dete():
def audio():
my_text = "user found"
language = 'en'
myobj = gTTS(text=my_text, lang=language, slow=False)
myobj.save("welcome.mp3")
os.system("welcome.mp3")
test_img = cv2.imread('TestImages/divyanshu.jpg')
faces_detected, gray_img = fr.faceDetection(test_img)
print("faces_detected:", faces_detected)
faces, faceID = fr.labels_for_training_data('trainingImages')
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.write('trainingData.yml')
name = {0: "chirag", 1: "divyanshu"}
for face in faces_detected:
(x, y, w, h) = face
roi_gray = gray_img[y:y + h, x:x + h]
label, confidence = face_recognizer.predict(roi_gray)
print("confidence:", confidence)
print("label:", label)
fr.draw_rect(test_img, face)
predicted_name = name[label]
if (confidence > 37):
continue
fr.put_text(test_img, predicted_name, x, y)
audio()
resized_img = cv2.resize(test_img, (1000, 1000))
cv2.imshow("face dtecetion ", resized_img)
cv2.waitKey(0)
cv2.destroyAllWindows
faces_detected, gray_img = fr.faceDetection(test_img)
print("faces_detected:", faces_detected)
# Comment lines 17-34 after 1st run
faces, faceID = None, None
if sys.argv[2] == "2":
faces, faceID = fr.getFaces('./trainingImages2/')
elif sys.argv[2] == "3":
faces, faceID = fr.getFaces('./trainingImages3/')
elif sys.argv[2] == "4":
faces, faceID = fr.getFaces('./trainingImages4/')
else:
print("Number of types of images should be 2/3/4")
exit(0)
face_recognizer = fr.train_classifier(faces, faceID)
if sys.argv[2] == "2":
face_recognizer.write('./trainingData2.yml')
elif sys.argv[2] == "3":
face_recognizer.write('./trainingData3.yml')
else:
face_recognizer.write('./trainingData4.yml')
# Uncomment lines 37-43 for subsequent runs
# face_recognizer=cv2.face.LBPHFaceRecognizer_create()
# if sys.argv[2] == "2":
# face_recognizer.read('trainingData2.yml')
# elif sys.argv[2] == "3":
# face_recognizer.read('trainingData3.yml')
# else :
# face_recognizer.read('trainingData4.yml')
def train(absolutePathModel,list):
test = os.path.exists(path_of_model)
if(not test):
faces,faceID=fr.labels_for_training_data('trainingImages')
face_recognizer=fr.train_classifier(faces,faceID)
face_recognizer.write('trainingData.yml')
def training_student():
faces, faceID = fr.labels_for_training_data(
'/home/siva_ganesh/zips/projects/AttendanceSystem/static/')
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.write('trainingData.yml')
