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 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
import os
import faceRecognition as fr
print(fr)
test_img = cv2.imread(
r'C:\Users\Dell\Desktop\Data Science\projects\Face_Recognition\Face-Recognition-master\myphoto.jpg'
) #Give path to the image which you want to test
faces_detected, gray_img = fr.faceDetection(test_img)
print("face Detected: ", faces_detected)
#Training will begin from here
faces, faceID = fr.labels_for_training_data(
r'C:\Users\Dell\Desktop\Data Science\projects\Face_Recognition\Face-Recognition-master\images'
) #Give path to the train-images folder which has both labeled folder as 0 and 1
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.save(
r'C:\Users\Dell\Desktop\Data Science\projects\Face_Recognition\Face-Recognition-master\trainingData.yml'
) #It will save the trained model. Just give path to where you want to save
name = {
0: "Uday"
} #Change names accordingly. If you want to recognize only one person then write:- name={0:"name"} thats all. Dont write for id number 1.
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)
parser = ap.ArgumentParser()
parser.add_argument('-w',
'--width',
help="width to resize",
default=100,
type=int)
parser.add_argument('-e',
'--height',
help="height to resize",
default=100,
type=int)
parser.add_argument('-a',
'--algorithm',
help="1-LBPH, 2-Eigenfaces",
default=1,
type=int)
args = vars(parser.parse_args())
faces, faceID = fr.labels_for_training_data('Dataset/train', args["width"],
args["height"])
if args["algorithm"] == 1:
face_recognizer = fr.train_classifierLBPH(faces, faceID)
elif args["algorithm"] == 2:
face_recognizer = fr.train_classifierEigen(faces, faceID)
else:
print("Option not valid")
exit(0)
face_recognizer.save("trainingData.yml")
import cv2
import os
import faceRecognition as fr
print(fr)
test_img = cv2.imread(r'C:\Python37\Projects\Face Recognition\1.jpg'
) #Give path to the image which you want to test
faces_detected, gray_img = fr.faceDetection(test_img)
print("face Detected: ", faces_detected)
#Training will begin from here
faces, faceID = fr.labels_for_training_data(
r'Give Path Here\train-images'
) #Give path to the train-images folder which has both labeled folder as 0 and 1
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.save(
r'Give Path Here\trainingData.yml'
) #It will save the trained model. Just give path to where you want to save
name = {
0: "Ashish",
1: "Vijay Deverakonda"
} #Change names accordingly. If you want to recognize only one person then write:- name={0:"name"} thats all. Dont write for id number 1.
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)
import numpy as np
import cv2
import os
import faceRecognition as fr
print (fr)
test_img=cv2.imread(r'C:\Users\hp\Downloads\1.jpg') #Give path to the image which you want to test
faces_detected,gray_img=fr.faceDetection(test_img)
print("face Detected: ",faces_detected)
#Training will begin from here
faces,faceID=fr.labels_for_training_data(r'G:\prsnal\Face-Recognition-master\img') #Give path to the train-images folder which has both labeled folder as 0 and 1
face_recognizer=fr.train_classifier(faces,faceID)
face_recognizer.save(r'trainingData.yml') #It will save the trained model. Just give path to where you want to save
name={0:"Mohan",1:"Sanju"} #Change names accordingly. If you want to recognize only one person then write:- name={0:"name"} thats all. Dont write for id number 1.
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]
fr.put_text(test_img,predicted_name,x,y)
import cv2
import os
import numpy as np
import faceRecognition as fr
#This module takes images stored in disk and performs face recognition
test_img = cv2.imread(
'E:\ppts\deep learning\FaceRecognition-master\FaceRecognition-master\TestImages\p3.jpg'
) #test_img path
faces_detected, gray_img = fr.faceDetection(test_img)
print("faces_detected:", faces_detected)
#Comment below lines when running this program second time.Since it saves training.yml file in directory
faces, faceID = fr.labels_for_training_data(
'E:\ppts\deep learning\FaceRecognition-master\FaceRecognition-master\trainingImages'
)
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.write(
'E:\ppts\deep learning\FaceRecognition-master\FaceRecognition-master\trainingData.yml'
)
#Uncomment below line for subsequent runs
#face_recognizer=cv2.face.LBPHFaceRecognizer_create()
#face_recognizer.read('E:\ppts\deep learning\FaceRecognition-master\FaceRecognition-master\trainingData.yml')
#use this to #load training data for subsequent runs
name = {
0: "Priyanka",
1: "Kangana",
2: "Mahesh"
} #creating dictionary containing names for each label
import cv2
import os
import numpy as np
import faceRecognition as fr
#This module takes images stored in diskand performs face recognition
test_img=cv2.imread('Tutorial_faceRecognition/TestImages/neha1.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('Tutorial_faceRecognition/trainingImages')
face_recognizer=fr.train_classifier(faces,faceID)
face_recognizer.save('trainingData.yml')
face_recognizer=cv2.face.LBPHFaceRecognizer_create()
#Uncomment below line for subsequent runs
# face_recognizer.read('trainingData.yml')#use this to load training data for subsequent runs
name={0:"Priyanka",1:"Neha"}#creating dictionary containing names for each label
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]
import numpy as np
import faceRecognition as fr
test_img = cv2.imread('Locate the captured image to be detected')
faces_detected, gray_img = fr.faceDetection(test_img)
print("The detected face:", faces_detected)
for (x, y, w, h) in faces_detected:
cv2.rectangle(test_img, (x, y), (x + w, y + h), (255, 0, 0), thickness=1)
#resized_img=cv2.resize(test_img,(1000,700))
#cv2.imshow("Internship Project in DDU",resized_img)
#cv2.waitKey(0)
#cv2.destroyAllWindows
faces, faceID = fr.labels_for_training_data(
'Locate Path of the folder where we train our images')
face_recognizer = fr.train_classifier(faces, faceID)
name = {0: "Unlicenced", 1: "Licenced"}
for face in faces_detected:
(x, y, w, h) = face
roi_gray = gray_img[y:y + h, x:x + h]
lable, confidence = face_recognizer.predict(roi_gray)
print("confidence:", confidence)
print("lable:", lable)
fr.draw_rect(test_img, face)
predicted_name = name[lable]
fr.put_text(test_img, predicted_name, x, y)
resized_img = cv2.resize(test_img, (600, 400))
cv2.imshow("Final Project in ASTU", resized_img)
import cv2
import os
import numpy as np
import faceRecognition as fr
test_img = cv2.imread('image.jpg')
faces_detected, gray_img = fr.faceDetection(test_img)
print("face-detected:", faces_detected)
#give the position of the rectangle to detect the face
for(x, y, w, h) in faces_detected:
cv2.rectangle(test_img, (x, y), (x+w, y+h), (255, 0, 0), thickness = 5)
faces, faceID = fr.labels_for_training_data('/media/chaand/New Volume4/Coding/Python/face-recon/training')
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.save('trainingData.yml')
#face_recognizer = cv2.face.LBPHFaceRecognizer_create()
face_recognizer.read('/media/chaand/New Volume4/Coding/Python/face-recon/trainingData.yml')
name = { 0: 'Deepika',
1:'Riley Reid'
}
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
import cv2
import os
import numpy as np
import faceRecognition as fr
faces, faceID = fr.labels_for_training_data('Dataset/train')
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.save("trainingData.yml")
import cv2
import os
import faceRecognition as fr
from user_data import name
test_img = cv2.imread(
r'C:\\Users\\Bas\\Documents\\College\\Project\\Face-Recognition\\train-images\\0\\image0000.jpg'
) #Give path to the image which you want to test
faces_detected, gray_img = fr.faceDetection(test_img)
print("face Detected: ", faces_detected)
#Give path to the train-images folder
faces, faceID = fr.labels_for_training_data(
r'C:\\Users\\Bas\\Documents\\College\\Project\\Face-Recognition\\train-images\\'
)
face_recognizer = fr.train_classifier(faces, faceID)
#It will save the trained model.
face_recognizer.save(
r'C:\\Users\\Bas\\Documents\\College\\Project\\Face-Recognition\\train-images\\training_data.yml'
)
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)
import cv2
import os
import numpy as np
import faceRecognition as fr
#This module takes images stored in diskand performs face recognition
test_img = cv2.imread('F://face//test_img//rohit.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('F://face//training_images')
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.write('F://face//trainingData.yml')
#Uncomment below line for subsequent runs
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
face_recognizer.read(
'trainingData.yml') #use this to load training data for subsequent runs
name = {
0: "Mask on",
1: "Without mask"
} #creating dictionary containing names for each label
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)
test_img = cv2.imread(' ') #test_img path
faces_detected, gray_img = fr.faceDetection(test_img)
print("faces_detected:", faces_detected)
for (x, y, w, h) in faces_detected:
cv2.rectangle(test_img, (x, y), (x + w, y + h), (250, 0, 0), thickness=3)
# =============================================================================
# resized_img=cv2.resize(test_img,(600,800))
# cv2.imshow("face detection ",resized_img)
# cv2.waitKey(0)
# cv2.destroyAllWindows
# =============================================================================
#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')
face_recognizer = fr.train_classifier(faces, faceid)
face_recognizer.save('trainingdata.yml')
#Uncomment below line for subsequent runs
# =============================================================================
# face_recognizer=cv2.face.LBPHFaceRecognizer_create()
# face_recognizer.read('trainingdata.yml')
# =============================================================================
name = {
0: "name1",
1: "name2"
} #creating dictionary containing names for each label
for face in faces_detected:
import numpy as np
import cv2
import os
import faceRecognition as fr
print (fr)
test_img=cv2.imread(r'C:\Users\adithya\Face-Recognition\test_image.jpg') #Give path to the image which you want to test
faces_detected,gray_img=fr.faceDetection(test_img)
print("face Detected: ",faces_detected)
#Training will begin from here
faces,faceID=fr.labels_for_training_data(r'C:\Users\adithya\Face-Recognition\train_images') #Give path to the train-images folder which has both labeled folder as 0 and 1
face_recognizer=fr.train_classifier(faces,faceID)
face_recognizer.save(r'C:\Users\adithya\Face-Recognition\trainingData.yml') #It will save the trained model. Just give path to where you want to save
name={0:"Adithya Suresh"} #Change names accordingly. If you want to recognize only one person then write:- name={0:"name"} thats all. Dont write for id number 1.
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]
fr.put_text(test_img,predicted_name,x,y)
print(f"faces_detected : {faces_detected}")
# faces,face_id = fr.labels_for_training_data("/home/avisek/Desktop/FaceRecognition/dataset/training/Batch-2018-2019")
# face_recognizer = fr.train_classifier(faces,face_id)
# face_recognizer.save("trainedModel/Batch-2018-2019.yml")
face_recognizer = cv2.face.LBPHFaceRecognizer_create()
try:
face_recognizer.read(
"/home/avisek/Desktop/FaceRecognition/trainedModel/Batch-2018-2019.yml"
)
except:
face_recognizer = fr.train_classifier(faces, face_id)
face_recognizer.save("trainedModel/Batch-2018-2019.yml")
faces, face_id = fr.labels_for_training_data(
"/home/avisek/Desktop/FaceRecognition/dataset/training/Batch-2018-2019"
)
name = {
35: "Atiab kalam",
51: "Avisek shaw",
60: "Agnibesh Mukherjee",
64: "Abhishek Charan",
37: "Madhurima Maji",
32: "Arnab kumar Pati"
}
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)
import faceRecognition as fr
print(fr)
test_img = cv2.imread(
r'C:/Users/balajiam/Documents/ML Data Analysis/Face-Recognition-master/1.jpg'
)
faces_detected, gray_img = fr.faceDetection(test_img)
print("face Detected: ", faces_detected)
#Training will begin from here
faces, faceID = fr.labels_for_training_data(
r'C:/Users/balajiam/Documents/ML Data Analysis/Face-Recognition-master/train-images'
) #Give path to the train-images folder which has both labeled folder as 0 and 1
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.save(
r'C:/Users/balajiam/Documents/ML Data Analysis/Face-Recognition-master/trainingData.yml'
) #It will save the trained model. Just give path to where you want to save
name = {0: "Balaji", 1: "Srini"}
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)
import numpy as np
import faceRecognition as fr
test_img=cv2.imread("test_img.jpg")
faces_Detected,gray_img=fr.faceDetection(test_img)
print("faces_detected:",faces_Detected)
#for (x,y,w,h) in faces_detected:
#cv2.rectangle(test_img,(x,y),(x+w,y+h),(255,0,0),2)
#resized_img=cv2.resize(test_img,(1000,700))
#cv2.imshow("face dtecetion",resized_img)
#cv2.waitKey(0)
#cv2.destroyAllWindows
faces,faceID=fr.labels_for_training_data("C://Users//Mounika Reddy P//Desktop//FACE//trainingimages")
face_recognizer= fr.train_classifier(faces,faceID)
face_recognizer.save('trainingData.yml')
name={0:"Priyanka",1:"Nick"}
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]
fr.put_text(test_img,predicted_name,x,y)
test_img=cv2.resize(test_img,(1000,1000))
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')
import os
import numpy as np
import faceRecognition as fr
from os import path
#This module takes images stored in diskand performs face recognition
#print(os.path.dirname(__file__))
filePath = os.path.dirname(__file__)
# print(filePath)
test_img=cv2.imread(filePath +'/TestImages/WIN_20201002_17_13_01_Pro.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(filePath +'/trainingImages')
face_recognizer=fr.train_classifier(faces,faceID)
face_recognizer.write(filePath +'/trainingData.yml')
#Uncomment below line for subsequent runs
# face_recognizer=cv2.face.LBPHFaceRecognizer_create()
# face_recognizer.read('trainingData.yml')#use this to load training data for subsequent runs
name={1:"Manoj"}#creating dictionary containing names for each label
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)
import os
import faceRecognition as fr
print(fr)
test_img = cv2.imread(
r'/home/bhanu/PycharmProjects/Face-Recogn/Face-Recognition/1.jpg'
) #Give path to the image which you want to test
faces_detected, gray_img = fr.faceDetection(test_img)
print("face Detected: ", faces_detected)
#Training will begin from here
faces, faceID = fr.labels_for_training_data(
r'/home/bhanu/PycharmProjects/Face-Recogn/Face-Recognition/train-images'
) #Give path to the train-images folder which has both labeled folder as 0 and 1
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.save(
r'/home/bhanu/PycharmProjects/Face-Recogn/Face-Recognition/trainingData.yml'
) #It will save the trained model. Just give path to where you want to save
name = {
0: "Bhanu"
} #Change names accordingly. If you want to recognize only one person then write:- name={0:"name"} thats all. Dont write for id number 1.
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)
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')
import numpy as np
import faceRecognition as fr
test_img = cv2.imread("shushkov(0).jpg")
faces_detected, gray_img = fr.faceDetection(test_img)
print("face_detected:", faces_detected)
# for (x, y, w, h) in faces_detected:
# cv2.rectangle(test_img, (x, y), (x + w, y + h), (255, 0, 0), 5)
# resized_img = cv2.resize(test_img, (1000, 700))
# cv2.imshow("face detection tutorial", resized_img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
faces, faceID = fr.labels_for_training_data("shushkov(0).jpg")
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.save("trainingData.yaml")
name = {0: "Priyanka", 1: "Neha"}
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]
fr.put_text(test_img, predicted_name, x, y)
resized_img = cv2.resize(test_img, (1000, 700))
import os
import faceRecognition as fr
print(fr)
test_img = cv2.imread(
r'C:/Users/SAFI UDDIN/Desktop/face/train-images/0/image0000.jpg'
) #Give path to the image which you want to test
faces_detected, gray_img = fr.faceDetection(test_img)
print("face Detected: ", faces_detected)
#Training will begin from here
faces, faceID = fr.labels_for_training_data(
r'C:/Users/SAFI UDDIN/Desktop/face/train-images'
) #Give path to the train-images folder which has both labeled folder as 0 and 1
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.save(
r'C:/Users/SAFI UDDIN/Desktop/face/train-images/trainingData.yml'
) #It will save the trained model. Just give path to where you want to save
name = {
0: "unknown",
1: "Kalam"
} #Change names accordingly. If you want to recognize only one person then write:- name={0:"name"} thats all. Dont write for id number 1.
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)
import numpy as np
import faceRecognition as fr
test_img=cv2.imread('/Users/duongkhieu/documents/face-recognition/testimages/Tt1.jpg')
faces_detected, gray_img=fr.faceDetection(test_img)
print("faces_detected:", faces_detected)
# for (x, y, w, h) in faces_detected:
# cv2.rectangle(test_img,(x, y), (x+w, y+h), (255, 0, 0), thickness=5)
# resized_img=cv2.resize(test_img, (1000, 700))
# cv2.imshow("face dtection tutorial", resized_img)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
faces, faceID=fr.labels_for_training_data('/Users/duongkhieu/documents/face-recognition/trainingImages')
face_recognizer=fr.train_classifier(faces, faceID)
name={0:"T", 1:"G"}
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]
fr.put_text(test_img,predicted_name,x,y)
resized_img=cv2.resize(test_img, (1000, 700))
cv2.imshow("face dtection tutorial", resized_img)
import cv2
import os
import numpy as np
import faceRecognition as fr
test_img = cv2.imread(
'C:\\Users\\swara\\Desktop\\Envision\\FaceRecognition-master\\TestImages\\2.jpg'
) #test_img path
faces_detected, gray_img = fr.faceDetection(test_img)
print("faces_detected:", faces_detected)
faces, faceID = fr.labels_for_training_data(
'C:\\Users\\swara\\Desktop\\Envision\\FaceRecognition-master\\trainingImages'
)
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.write('trainingData.yml')
c = 0
name = {
0: "Akshya RA1711003010111",
1: "Swaraaj RA1711003010118",
2: "Devesh RA1711003010116",
3: "Himanshu RA1711003010131",
4: "Anish RA1711003010115",
6: "AbhishiktH RA1711003010128",
7: "Mouli RA1711003010076"
}
name1 = {
0: "Actor",
1: "CSE",
2: "Studnt",
import cv2
import os
import numpy as np
import faceRecognition as fr
#This module takes images stored in diskand 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')
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.write('trainingData.yml')
#Uncomment below line for subsequent runs
# face_recognizer=cv2.face.LBPHFaceRecognizer_create()
# face_recognizer.read('trainingData.yml')#use this to load training data for subsequent runs
name = {
0: "Priyanka",
1: "Kangana"
} #creating dictionary containing names for each label
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)
import cv2
import os
import faceRecognition as fr
print(fr)
test_img = cv2.imread(
r'C:\Users\A707272\PycharmProjects\Face_Recognition\Test_image\test1.jpg'
) #Give path to the image which you want to test
faces_detected, gray_img = fr.faceDetection(test_img)
print("face Detected: ", faces_detected)
# Training begins here
faces, faceID = fr.labels_for_training_data(
r'C:\Users\A707272\PycharmProjects\Face_Recognition\Training_images')
face_recognizer = fr.train_classifier(faces, faceID)
face_recognizer.save(
r'C:\Users\A707272\PycharmProjects\Face_Recognition\trainingData.yml')
name = {0: "Aniket", 1: " Ramavati"}
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]
fr.put_text(test_img, predicted_name, x, y)
