def capture(self):
from Name import name
user = name()
with open('write_data.txt') as f:
for line1 in f:
info = line1.split()
user.id.append(info[0])
user.name.append(info[1])
print user.id
print user.name
faceDetect = cv2.CascadeClassifier('haarcascade_frontalface_default.xml');
rec = cv2.createLBPHFaceRecognizer()
rec.load('recognizer/trainningData.yml')
ids = 0
font = cv2.FONT_HERSHEY_SIMPLEX
while (True):
ret, img = cap.read();
if not ret: continue
if len(img.shape) == 3 or len(img.shape) == 4:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img
faces = faceDetect.detectMultiScale(gray, 2, 5);
x=0
y=0
w=0
h=0
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
ids, conf = rec.predict(gray[y:y + h, x:x + w])
idss=ids
#print ids,":",conf
if (conf < 70):
ids = user.name[ids-1]
else:
ids = "stranger"
print ids,":",conf
names=ids
path_img=os.path.join('dataSet/'+str(idss),names)
print path_img
Exist2=os.path.exists(path_img)
if Exist2:
img2=cv2.imread(path_img+ids+'.jpg')
#cv2.cvtColor(img2, cv2.COLOR_BGR2RGB, img)
cv2.imshow('face',img2)
#self.image2=QImage()
#self.label_img.setPixmap(QPixmap.fromImage(self.image2).scaled(self.label_img.size(),Qt.KeepAspectRatio,Qt.SmoothTransformation))
#self.ncImage=QImage('../图/2.jpg')
#self.label_img.setPixmap(QPixmap.fromImage(self.ncImage).scaled(self.label_img.size(),Qt.KeepAspectRatio,Qt.SmoothTransformation))
cv2.putText(img, str(ids), (x, y + h), font, 1,(0,255,255))
height, width, bytesPerComponent = img.shape
bytesPerLine = bytesPerComponent * width
cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
self.image=QImage(img.data,width,height,bytesPerLine,QImage.Format_RGB888)
self.label_camera.setPixmap(QPixmap.fromImage(self.image).scaled(self.label_camera.width(),self.label_camera.height()))
#cv2.imshow("Face", img);
if (cv2.waitKey(1) == ord('q')):
break
cv2.destroyAllWindows()
def input():
user = name()
filename = 'write_data.txt'
with open(filename, 'a') as f:
id = input("Please input an id: ")
name = input("please input a name: ")
add(id, name)
f.write((str)(user.id[user.index - 1]) + " " +
user.name[user.index - 1])
f.write("\n")
def capture(self):
import cv2
import numpy as np
from Name import name
user = name()
with open('write_data.txt') as f:
for line1 in f:
info = line1.split()
user.id.append(info[0])
user.name.append(info[1])
faceDetect = cv2.CascadeClassifier(
'haarcascade_frontalface_default.xml')
#cam = cv2.VideoCapture(0);
rec = cv2.createLBPHFaceRecognizer()
rec.load('recognizer/trainningData.yml')
id = 0
font = cv2.FONT_HERSHEY_SIMPLEX
while (True):
#if (self.cap.isOpened()):
ret, img = self.cap.read()
if not ret: continue
if len(img.shape) == 3 or len(img.shape) == 4:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = faceDetect.detectMultiScale(gray, 1.3, 5)
x = 0
y = 0
h = 0
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
id, conf = rec.predict(gray[y:y + h, x:x + w])
if (conf < 70):
id = user.name[id - 1]
else:
id = "stranger"
cv2.putText(img, str(id), (x, y + h), font, 1, (0, 255, 255))
height, width, bytesPerComponent = img.shape
bytesPerLine = bytesPerComponent * width
cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)
self.image = QImage(img.data, width, height, bytesPerLine,
QImage.Format_RGB888)
self.label_camera.setPixmap(
QPixmap.fromImage(self.image).scaled(
self.label_camera.width(), self.label_camera.height()))
cv2.imshow("Face", img)
if (cv2.waitKey(1) == ord('q')):
break
#
cv2.destroyAllWindows()
def create(a, b):
from Name import name
user = name()
filename = 'write_data.txt'
with open(filename, 'a') as f:
#id = input("Please input an id: ")
#Sname = input("please input a name: ")
id = a
user.id.append(a)
user.name.append(b)
user.index = user.index + 1
f.write((str)(user.id[user.index - 1]) + " " +
user.name[user.index - 1])
f.write("\n")
faceDetect = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
#cam = cv2.VideoCapture(0)
sampleNum = 0
while (True):
ret, img = self.cap.read()
if len(img.shape) == 3 or len(img.shape) == 4:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img
faces = faceDetect.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
sampleNum = sampleNum + 1
cv2.imwrite(
"dataSet/User." + str(id) + "." + str(sampleNum) + ".jpg",
gray[y:y + h, x:x + w])
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
cv2.waitKey(100)
cv2.imshow("Face", img)
cv2.waitKey(1)
cv2.destroyAllwindows()
if (sampleNum > 100):
break
#cv2.destroyAllWindows()
cam.release()
#-*-coding:utf8-*-
import cv2
import numpy as np
from Name import name
user = name()
with open('write_data.txt') as f:
for line1 in f:
info = line1.split()
user.id.append(info[0])
user.name.append(info[1])
faceDetect = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
cam = cv2.VideoCapture(0)
rec = cv2.createLBPHFaceRecognizer()
rec.load(
'/home/zsf/桌面/FaceRecognizer-master (3)/faceRecogination/recognizer/trainningData.yml'
)
id = 0
font = cv2.FONT_HERSHEY_SIMPLEX
while (True):
ret, img = cam.read()
if not ret: continue
if len(img.shape) == 3 or len(img.shape) == 4:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
else:
gray = img
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = faceDetect.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)