def test_onBatch(path):
model = Model()
model.load()
index = 0
img_list, label_lsit, counter = read_file(path)
for img in img_list:
picType, prob = model.predict(img)
if picType != -1:
index += 1
name_list = read_name_list('/root/dataset')
print name_list[picType]
else:
print " Don't know this person"
return index
def onBatch(path):
model = Model()
model.load()
index = 0
img_list, label_lsit, counter = read_file(path)
for img in img_list:
picType, prob = model.predict(img)
if picType != -1:
index += 1
name_list = read_name_list('F:\myProject\pictures\dataset')
print(name_list)
print(name_list[picType])
else:
print(" Don't know this person")
return index
def onePicture(path):
model = Model()
model.load()
img = cv2.imread(path)
img = cv2.resize(img, (92, 112))
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
picType, prob = model.predict(img)
if picType != -1:
name_list = read_name_list('res')
print(name_list)
print(picType)
print(name_list[picType], prob)
print("done")
else:
print(" Don't know this person")
def test_Pictures(path):
model = Model()
model.load()
for child_dir in os.listdir(path):
if endwith(child_dir, 'jpg'): # 找到以jpg结尾的文件路径
str = os.path.join(path, child_dir) # 具体的图片文件的路径
img = cv2.imread(str)
resized_img = cv2.resize(img, (128, 128),
interpolation=cv2.INTER_CUBIC)
img = cv2.cvtColor(resized_img, cv2.COLOR_BGR2GRAY)
picType, prob = model.predict(img)
if picType != -1:
name_list = read_name_list('pictures/dataset')
print(name_list[picType], prob)
else:
print(" Don't know this person")
class Camera_reader(object):
#在初始化camera的时候建立模型，并加载已经训练好的模型
def __init__(self):
self.model = Model()
self.model.load()
self.img_size = 128
def build_camera(self):
#opencv文件中人脸级联文件的位置，用于帮助识别图像或者视频流中的人脸
face_cascade = cv2.CascadeClassifier(
'C:\\Face recognition\\classifier\\haarcascade_frontalface_alt2.xml'
)
#读取dataset数据集下的子文件夹名称
name_list = read_name_list('C:\\Face recognition\\data')
#打开摄像头并开始读取画面
cap = cv2.VideoCapture(0)
ret, frame = cap.read() #读取一帧视频
while ret and cv2.waitKey(10) == -1:
ret, frame = cap.read()
try:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #图像灰化
faces = face_cascade.detectMultiScale(
gray, scaleFactor=1.2, minNeighbors=3,
minSize=(32, 32)) #识别人脸,******
for (x, y, w, h) in faces:
ROI = gray[y - 10:y + h + 10, x - 10:x + w + 10]
ROI = cv2.resize(ROI, (self.img_size, self.img_size),
interpolation=cv2.INTER_LINEAR)
label, prob = self.model.predict(ROI) #利用模型对cv2识别出的人脸进行比对
if prob > 0.90: #如果模型认为概率高于95%则显示为模型中已有的label
show_name = name_list[label]
else:
show_name = 'Stranger'
cv2.putText(frame, show_name, (x, y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) #显示名字
frame = cv2.rectangle(frame, (x, y), (x + w, y + h),
(255, 0, 0), 2) #在人脸区域画一个正方形
cv2.imshow("Camera", frame)
except:
continue
cap.release()
cv2.destroyAllWindows()
return show_name
class Camera_reader(object):
# 在初始化camera的时候建立模型，并加载已经训练好的模型
def __init__(self):
self.model = Model()
self.model.load()
self.img_size = 128
def build_camera(self):
# opencv文件中人脸级联文件的位置，用于帮助识别图像或者视频流中的人脸
face_cascade = cv2.CascadeClassifier(
r'C:\Users\01436179\PycharmProjects\pyhton_face\haarcascade_frontalface_alt.xml'
)
# 读取dataset数据集下的子文件夹名称
name_list = read_name_list(
r'C:\Users\01436179\PycharmProjects\pyhton_face\dataset')
# 打开摄像头并开始读取画面
cameraCapture = cv2.VideoCapture(0)
success, frame = cameraCapture.read()
while success and cv2.waitKey(1) == -1:
success, frame = cameraCapture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 图像灰化
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
# 识别人脸
for (x, y, w, h) in faces:
ROI = gray[x:x + w, y:y + h]
ROI = cv2.resize(ROI, (self.img_size, self.img_size),
interpolation=cv2.INTER_LINEAR)
label, prob = self.model.predict(ROI)
# 利用模型对cv2识别出的人脸进行比对
if prob > 0.7:
# 如果模型认为概率高于70%则显示为模型中已有的label
show_name = name_list[label]
else:
show_name = 'Stranger'
cv2.putText(frame, show_name, (x, y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2)
# 显示名字
frame = cv2.rectangle(frame, (x, y), (x + w, y + h),
(255, 0, 0), 2)
# 在人脸区域画一个正方形出来
cv2.imshow("Camera", frame)
cameraCapture.release()
cv2.destroyAllWindows()
class Camera_reader(object):
#在初始化camera的时候建立模型，并加载已经训练好的模型
def __init__(self):
self.model = Model()
self.model.load()
self.img_size = 128
classname = 0
titlename = "untitled (hzy_order) - Sublime Text (UNREGISTERED)"
#获取句柄
self.hwnd = win32gui.FindWindow(classname, titlename)
print("得到句柄")
def build_camera(self):
#opencv文件中人脸级联文件的位置，用于帮助识别图像或者视频流中的人脸C:\\Users\\jan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site-packages\\cv2\\data\\
face_cascade = cv2.CascadeClassifier(
'C:\\Users\\jan\\AppData\\Local\\Programs\\Python\\Python36\\Lib\\site-packages\\cv2\\data\\haarcascade_frontalface_alt.xml'
)
#读取dataset数据集下的子文件夹名称
name_list = read_name_list('E:\\WWW\\faceRecognition\\dataset')
#打开摄像头并开始读取画面
cameraCapture = cv2.VideoCapture(0)
success, frame = cameraCapture.read()
while success and cv2.waitKey(1) == -1:
success, frame = cameraCapture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #图像灰化
faces = face_cascade.detectMultiScale(gray, 1.3, 5) #识别人脸
for (x, y, w, h) in faces:
ROI = gray[x:x + w, y:y + h]
ROI = cv2.resize(ROI, (self.img_size, self.img_size),
interpolation=cv2.INTER_LINEAR)
label, prob = self.model.predict(ROI) #利用模型对cv2识别出的人脸进行比对
if prob > 0.7: #如果模型认为概率高于70%则显示为模型中已有的label
show_name = name_list[label]
win32gui.SetForegroundWindow(self.hwnd)
print("got you %s " % show_name)
win32gui.ShowWindow(self.hwnd, win32con.SW_MAXIMIZE)
else:
show_name = 'Stranger'
#cv2.putText(frame, show_name, (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) #显示名字
#frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) #在人脸区域画一个正方形出来
time.sleep(3)
#cv2.imshow("Camera", frame)
cameraCapture.release()
cv2.destroyAllWindows()
def test_onePicture(path):
model= Model(read_save=True)
model.load()
img = cv2.imread(path)
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray,1.1,5)
for (x, y, w, h) in faces:
face = cv2.resize(gray[y:(y + h), x:(x + w)], (128, 128))
picType,prob = model.predict(face)
print (picType)
name_list = read_name_list('G:\Python/FaceRecognition\datasets')
if picType != -1:
# if picType>=0 and picType<len(name_list):
print (name_list[picType],prob)
else:
print (" Don't know this person")
def test_onBatch(path):
model = Model()
model.load()
index = 0
img_list, label_lsit, counter = read_file(path)
for img in img_list:
picType, prob = model.predict(img)
if picType != -1:
index += 1
# name_list = read_name_list('D:\myProject\pictures\dataset')
name_list = read_name_list(
r'D:/my_laboratory/face_detection20180516/dataset')
print(name_list[picType])
else:
print(" Don't know this person")
return index
class Camera_reader(object):
#在初始化camera的时候建立模型，并加载已经训练好的模型
def __init__(self):
self.model = Model()
self.model.load()
self.img_size = 128
def build_camera(self):
#opencv文件中人脸级联文件的位置，用于帮助识别图像或者视频流中的人脸
#face_cascade = cv2.CascadeClassifier('D:/anaconda3/envs/tensorflow-1.13.1/Lib/site-packages/cv2/data/haarcascade_frontalface_alt.xml')
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
#读取dataset数据集下的子文件夹名称
name_list = read_name_list('pictures\dataset')
#打开摄像头并开始读取画面
cameraCapture = cv2.VideoCapture(0)
success, frame = cameraCapture.read()
num = 1
while success and cv2.waitKey(1) == -1:
success, frame = cameraCapture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #图像灰化
faces = face_cascade.detectMultiScale(gray, 1.1, 3) # 识别人脸
for (x, y, w, h) in faces:
ROI = gray[y:(y + h), x:(x + w)]
ROI = cv2.resize(ROI, (self.img_size, self.img_size),
interpolation=cv2.INTER_LINEAR)
#cv2.imwrite("pictures/take_photos/p_" + str(num) + ".jpg", ROI)
#num += 1...............................................................
label, prob = self.model.predict(ROI) # 利用模型对cv2识别出的人脸进行比对
show_name = ''
if prob > 0.9: # 如果模型认为概率高于88%则显示为模型中已有的label
show_name = name_list[label]
if show_name == 'zhouyicheng':
#else:
#show_name = ''
cv2.putText(frame, show_name, (x + 11, y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) #显示名字
frame = cv2.rectangle(frame, (x, y), (x + w, y + h),
(0, 255, 255), 1) #在人脸区域画一个正方形出来
cv2.imshow("Camera", frame)
cameraCapture.release()
cv2.destroyAllWindows()
def test_onePicture(path):
# 加载模型
model = Model()
model.load()
# 读取图片
img = cv2.imread(path)
# 重置图片尺寸为：128*128
img = cv2.resize(img, (128, 128))
# 图片灰度化
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# labelIndex为概率最高的label的索引号, prob为对应概率
labelIndex, prob = model.predict(img)
if labelIndex != -1:
name_list = read_name_list('./dataset')
print(name_list[labelIndex], prob)
else:
print("Don't know this person.")
def test_onBatch(path):
model = Model()
model.load()
index = 0
img_list, label_lsit, counter = read_file(path)
for img in img_list:
picType, prob = model.predict(img)
if picType != -1:
index += 1
name_list = read_name_list(
'C:\\Users\\jimmychen\\Desktop\\chernger\\chernger_faceRecognition\\dataset'
)
print(name_list[picType])
else:
print("Don't know this person")
return index
class Camera_reader(object):
# 在初始化camera的时候建立模型，并加载已经训练好的模型
def __init__(self):
self.model = Model()
self.model.load()
self.img_size = 128
def build_camera(self):
# opencv文件中人脸级联文件的位置，用于帮助识别图像或者视频流中的人脸
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_alt.xml')
# 读取dataset数据集下的子文件夹名称
name_list = read_name_list('./dataset')
# 打开摄像头并开始读取画面
cameraCapture = cv2.VideoCapture(700)
success, frame = cameraCapture.read()
while success and cv2.waitKey(1) == -1:
success, frame = cameraCapture.read()
# 图像灰度化
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 检测人脸：scaleFactor是放大比例（此参数必须大于1）；minNeighbors是重复识别次数（此参数用来调整精确度，越大则越精确）
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=8)
for (x, y, w, h) in faces:
origin = gray[x:x + w, y:y + h]
# origin是原图，self.img_size是输出图像的尺寸，interpolation是插值方法
origin = cv2.resize(origin, (self.img_size, self.img_size), interpolation=cv2.INTER_LINEAR)
# 利用模型对cv2识别出的人脸进行比对
label, prob = self.model.predict(origin)
# 如果模型认为概率高于70%则显示为模型中已有的label
if prob > 0.7:
show_name = name_list[label]
else:
show_name = 'unknown'
# 框出人脸
frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 1)
# 显示人名
cv2.putText(frame, show_name, (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2)
cv2.imshow("Recognizing...", frame)
# 释放摄像头
cameraCapture.release()
# 关闭所有窗口
cv2.destroyAllWindows()
def test_onBatch(path):
# 加载模型
model = Model()
model.load()
# 计数器
index = 0
# 读取所有图片；img_list是所有图片的集合，label_lsit是所有标签的集合，label_num是标签数量
img_list, label_lsit, label_num = read_file(path)
for img in img_list:
# labelIndex为概率最高的label的索引号, prob为对应概率
labelIndex, prob = model.predict(img)
if labelIndex != -1:
index += 1
name_list = read_name_list('./dataset')
print(name_list[labelIndex])
else:
print("Don't know this person.")
return index
def Batch(path):
model = Model()
model.load()
index = 0
img_list, label_lsit, counter = read_file(path)
for img in img_list:
picType, prob = model.predict(img)
if picType != -1:
index += 1
name_list = read_name_list('./res')
print("done")
print(name_list)
print(picType)
print(name_list[picType])
else:
print(" Don't know this person")
return index
class Camera_reader(object):
#在初始化camera的时候建立模型，并加载已经训练好的模型
def __init__(self):
self.model = Model(read_save=True)
self.model.load()
self.img_size = 128
#opencv文件中人脸级联文件的位置，用于帮助识别图像或者视频流中的人脸
self.face_cascade = cv2.CascadeClassifier(
"haarcascade_frontalface_default.xml")
#读取dataset数据集下的子文件夹名称
self.name_list = read_name_list(dataset_path)
def build_camera(self):
#打开摄像头并开始读取画面
# cv2.namedWindow("Face_Recognization")
cameraCapture = cv2.VideoCapture(0)
while cameraCapture.isOpened():
success, frame = cameraCapture.read()
if success == True:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #图像灰化
faces = self.face_cascade.detectMultiScale(gray, 1.1, 5) #识别人脸
for (x, y, w, h) in faces:
face_img = gray[x:x + w, y:y + h]
face_img = cv2.resize(face_img,
(self.img_size, self.img_size),
interpolation=cv2.INTER_LINEAR)
label, prob = self.model.predict(
face_img) #利用模型对cv2识别出的人脸进行比对
if prob > 0.7: #如果模型认为概率高于70%则显示为模型中已有的label
show_name = self.name_list[label]
else:
show_name = 'Stranger'
cv2.putText(frame, show_name, (x, y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0),
2) #显示名字
frame = cv2.rectangle(frame, (x, y), (x + w, y + h),
(0, 0, 255), 2) #在人脸区域画一个正方形出来
cv2.imshow("Face_Recognization", frame)
key = cv2.waitKey(10)
c = chr(key & 255)
if c in ['q', 'Q', chr(27)]: #chr(27)是Esc键
break
cameraCapture.release()
cv2.destroyAllWindows()
def test_onBatch(path):
model = Model()
model.load()
index = 0
img_list, label_list, counter = read_file(path)
# img_list = img_list.reshape(img_list.shape[0], 174, 212, 1)
# print(img_list.shape[0:])
# img_list = img_list.astype('float32')/255
# Label_list = np_utils.to_categorical(label_list, num_classes=counter)
for img in img_list:
picType, prob = model.predict(img)
if picType != -1:
index += 1
name_list = read_name_list('G:/desktop/myProject/pictures/test')
print(name_list)
print(name_list[picType])
else:
print(" Don't know this person")
return index
class Camera_reader(object):
#在初始化camera的时候建立模型，并加载已经训练好的模型
def __init__(self):
self.model = Model()
self.model.load()
self.img_size = 128
def build_camera(self):
#opencv文件中人脸级联文件的位置，用于帮助识别图像或者视频流中的人脸
# face_cascade = cv2.CascadeClassifier('E:\openCV\opencv\sources\data\haarcascades\haarcascade_frontalface_alt.xml')
# face_cascade = cv2.CascadeClassifier('D:\Program Files(x86)\opencv\sources\data\haarcascades\haarcascade_frontalface_alt.xml')
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
#读取dataset数据集下的子文件夹名称
# name_list = read_name_list('D:\myProject\pictures\dataset')
name_list = read_name_list(r'D:/my_laboratory/face_detection20180516/dataset')
#打开摄像头并开始读取画面
cameraCapture = cv2.VideoCapture(0)
success, frame = cameraCapture.read()
while success and cv2.waitKey(1) == -1:
success, frame = cameraCapture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #图像灰化
faces = face_cascade.detectMultiScale(gray, 1.3, 5) #识别人脸
for (x, y, w, h) in faces:
ROI = gray[x:x + w, y:y + h]
ROI = cv2.resize(ROI, (self.img_size, self.img_size), interpolation=cv2.INTER_LINEAR)
frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 3) # 在人脸区域画一个正方形出来
label,prob = self.model.predict(ROI) #利用模型对cv2识别出的人脸进行比对
if prob >0.1: #如果模型认为概率高于70%则显示为模型中已有的label
show_name = name_list[label]
else:
show_name = 'Stranger'
cv2.putText(frame, show_name, (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) #显示名字
# frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2) #在人脸区域画一个正方形出来
# cv2.rectangle(img, (x2, x1), (y2, y1), (255, 0, 0), 3)
cv2.imshow("Camera", frame)
cameraCapture.release()
cv2.destroyAllWindows()
def test_onBatch(path):
model= Model(read_save=True)
model.load()
index = 0
img_list,img_name_list = readAllImg(path,'.jpg')
face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
for img in img_list:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray,1.1,5)
for (x, y, w, h) in faces:
face = cv2.resize(gray[y:(y + h), x:(x + w)], (128, 128))
picType,prob = model.predict(face)
name_list = read_name_list('G:\Python/FaceRecognition\datasets')
if picType != -1:
# if picType>=0 and picType<len(name_list):
print (img_name_list[index],":",name_list[picType],",",prob)
index += 1
else:
print (" Don't know this person")
return index
class img_reader(object):
#在初始化camera的时候建立模型，并加载已经训练好的模型
def __init__(self):
self.model = Model()
self.model.load()
self.img_size = 128
def build_img(self):
#opencv文件中人脸级联文件的位置，用于帮助识别图像或者视频流中的人脸
face_cascade = cv2.CascadeClassifier(
'C:\pylearning\ml&dl\\face_detect_v0\config\haarcascade_frontalface_alt.xml'
)
#读取dataset数据集下的子文件夹名称
name_list = read_name_list('face')
img = cv2.imread('test.jpg')
#打开摄像头并开始读取画面
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #图像灰化
faces = face_cascade.detectMultiScale(gray, 1.3, 5) #识别人脸
for (x, y, w, h) in faces:
ROI = gray[x:x + w, y:y + h]
ROI = cv2.resize(ROI, (self.img_size, self.img_size),
interpolation=cv2.INTER_LINEAR)
label, prob = self.model.predict(ROI) #利用模型对cv2识别出的人脸进行比对
if prob > 0.9: #如果模型认为概率高于70%则显示为模型中已有的label
show_name = name_list[label]
else:
show_name = 'Stranger'
cv2.putText(img, show_name, (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX,
1, 255, 2) #显示名字
img = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0),
2) #在人脸区域画一个正方形出来
cv2.imshow("face", img)
cv2.waitKey(0)
def test_onePicture2():
global image_file, im, image_label
path = ent.get()
if path == "":
path = "TwoF.jpg"
t.delete(0.0, END)
t.insert('insert',
" Driver info(left - > right)\n\n")
model = Model()
model.load()
color = (0, 255, 0)
name_list = read_name_list('dataset')
img1 = cv2.imread("images\\" + path)
gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) # 图像灰化
face_cascade = cv2.CascadeClassifier(
'C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\cv2\\data\\haarcascade_frontalface_alt2.xml'
)
faceRects = face_cascade.detectMultiScale(gray, 1.2, 3,
minSize=(32, 32)) # 识别人脸
hight = 0
if len(faceRects) > 0: # 大于0则检测到人脸
for (x, y, w, h) in faceRects: # 单独框出每一张人脸
hight = hight + 1
ROI = gray[x:x + w, y:y + h]
ROI = cv2.resize(ROI, (128, 128), interpolation=cv2.INTER_LINEAR)
label, prob = model.predict(ROI) # 利用模型对cv2识别出的人脸进行比对
if prob > 0.4: # 如果模型认为概率高于70%则显示为模型中已有的label
show_name = name_list[label]
else:
show_name = 'Stranger'
img1 = cv2.rectangle(img1, (x, y), (x + w, y + h), color,
2) # 在人脸区域画一个正方形出来
if show_name != "Stranger":
info = show_name.split('_')
cv2.putText(img1, info[0], (x, y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) # 显示陌生人信息
infoName = 'Name: ' + info[0]
infoAge = 'Age: ' + info[1]
infoUniversity = 'University: ' + info[2]
infoSex = 'Sex: ' + info[3]
infoDrivingLN = 'DriverNumber: ' + info[4]
t.insert(
'insert', " driver" + str(hight) + " " +
infoDrivingLN + "\n")
t.insert('insert',
" " + infoName + "\n")
t.insert('insert',
" " + infoAge + "\n")
t.insert(
'insert',
" " + infoUniversity + "\n")
t.insert('insert',
" " + infoSex + "\n\n")
else:
cv2.putText(img1, show_name, (x, y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) # 显示陌生人信息
t.insert('insert', "It is a Stranger!!!")
cv2.imwrite("images/result.jpg", img1)
image_file = Image.open("images/result.jpg")
im = ImageTk.PhotoImage(image_file)
image_label = Label(image_frame, image=im)
image_label.grid(row=4, column=0, pady=20, padx=30)
else:
image_file = Image.open("images/" + path)
im = ImageTk.PhotoImage(image_file)
image_label = Label(image_frame, image=im)
image_label.grid(row=4, column=0, pady=20, padx=30)
t.insert('insert', "No driver in this picture !!!")
class Camera_reader(object):
#在初始化camera的时候建立模型，并加载已经训练好的模型
def __init__(self):
self.model = Model()
self.model.load()
self.img_size = 128
def build_camera(self):
hight = 0
color = (0, 255, 0)
#opencv文件中人脸级联文件的位置，用于帮助识别图像或者视频流中的人脸
face_cascade = cv2.CascadeClassifier('C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\cv2\\data\\haarcascade_frontalface_alt2.xml')
#读取dataset数据集下的子文件夹名称
name_list = read_name_list('dataset')
#打开摄像头并开始读取画面
cameraCapture = cv2.VideoCapture(0)
success, frame = cameraCapture.read()
while success and cv2.waitKey(1) == -1:
success, frame = cameraCapture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #图像灰化
faces = face_cascade.detectMultiScale(gray, 1.3, 5) #识别人脸
cv2.putText(frame, "Driver Info", (80, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.8, 255, 2, 2) # 显示信息标题
if len(faces) == 0:
cv2.rectangle(frame, (0, 0), (300, 100), color, 3) # 5控制绿色框的粗细
cv2.putText(frame, "No people !!!", (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, 255,
2) # 显示陌生人信息
else:
for (x, y, w, h) in faces:
ROI = gray[x:x + w, y:y + h]
ROI = cv2.resize(ROI, (self.img_size, self.img_size), interpolation=cv2.INTER_LINEAR)
label, prob = self.model.predict(ROI) # 利用模型对cv2识别出的人脸进行比对
if prob > 0.7: # 如果模型认为概率高于70%则显示为模型中已有的label
show_name = name_list[label]
else:
show_name = 'Stranger'
# 当识别出来的不是陌生人时，需要显示出司机信息
if show_name != "Stranger":
info = show_name.split('_')
infoName = 'Name:' + info[0]
infoAge = 'Age:' + info[1]
infoUniversity = 'University:' + info[2]
infoSex = 'Sex:' + info[3]
infoDrivingLN = 'DriverNumber:' + info[4]
cv2.rectangle(frame, (0, 0), (280, len(faces) * 220), color, 3) # 5控制绿色框的粗细
cv2.putText(frame, info[0], (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) # 显示姓名信息
cv2.putText(frame, infoDrivingLN, (0, hight + 60), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2,
3) # 显示驾驶证号码
cv2.putText(frame, infoName, (0, hight + 95), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2,
3) # 显示名字
cv2.putText(frame, infoAge, (0, hight + 130), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2,
3) # 显示年龄
cv2.putText(frame, infoUniversity, (0, hight + 165), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2,
3) # 显示学历
cv2.putText(frame, infoSex, (0, hight + 200), cv2.FONT_HERSHEY_SIMPLEX, 0.8, color, 2,
3) # 显示性别
else:
cv2.rectangle(frame, (0, 0), (300, 100), color, 3) # 5控制绿色框的粗细
cv2.putText(frame, "Stranger", (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) # 显示陌生人信息
cv2.putText(frame, "It is a Stranger", (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 1, 255,
2) # 显示陌生人信息
frame = cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) # 在人脸区域画一个正方形出来
cv2.imshow("Camera", frame)
cameraCapture.release()
cv2.destroyAllWindows()
gray_face = cv2.resize(gray_face, (emotion_target_size))
face_recog = cv2.resize(face_recog, (face_target_size))
except:
continue
gray_face = preprocess_input(gray_face, False)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
# emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction,axis=1)
emotion_text = class_names[int(emotion_label_arg)]
picType,prob = face_model.predict(face_recog)
if picType != -1:
name_list = read_name_list('/Users/gaoxingyun/Documents/uw/courses/Sp19/EE576_CV/project/realtime_emotion_recognition/dataset')
print (name_list[picType],prob)
face_text = name_list[picType]
else:
print (" Don't know this person")
face_text = 'unknown'
color = (0,255,0)
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, emotion_text,
color, 0, 45, 1, 1)
draw_text(face_coordinates, rgb_image, face_text, color, 0, -45, 1, 1)
class Camera_reader(object):
#在初始化camera的时候建立模型，并加载已经训练好的模型
def __init__(self):
self.model = Model()
#self.model.load()
self.img_size = 128
if os.path.exists("images\\yjd_sc"):
shutil.rmtree("images\\yjd_sc")
if os.path.exists("images\\yjd_deal"):
shutil.rmtree("images\\yjd_deal")
if os.path.exists("dataset\\yjd_21_NEU_boy_001231"):
shutil.rmtree("dataset\\yjd_21_NEU_boy_001231")
os.mkdir("images\\yjd_sc")
os.mkdir("images\\yjd_deal")
os.mkdir("dataset\\yjd_21_NEU_boy_001231")
def build_camera(self):
hight = 0
color = (0, 255, 0)
#opencv文件中人脸级联文件的位置，用于帮助识别图像或者视频流中的人脸
face_cascade = cv2.CascadeClassifier(
'C:\\ProgramData\\Anaconda3\\Lib\\site-packages\\cv2\\data\\haarcascade_frontalface_alt2.xml'
)
#打开摄像头并开始读取画面
cameraCapture = cv2.VideoCapture(0)
success, frame = cameraCapture.read()
name_list = read_name_list('dataset')
count = 0
while success and cv2.waitKey(1) == -1:
success, frame = cameraCapture.read()
count += 1
if count < 100:
cv2.imwrite("images/yjd_sc/" + str(count) + ".jpg", frame)
elif count == 200: #将摄像头采集的钱200张图像当做原始图片进行实时建模和模型的训练
cv2.imwrite("images/yjd_sc/" + str(count) + ".jpg", frame)
dealImg('images\\yjd_sc', 'images\\yjd_deal')
readPicSaveFace('images\\yjd_deal',
'dataset\\yjd_21_NEU_boy_001231', '.jpg',
'.JPG', 'png', 'PNG')
dataset = DataSet("dataset")
self.model.read_trainData(dataset)
self.model.build_model()
self.model.train_model()
self.model.evaluate_model()
self.model.save()
else: #将其实时训练好的模型应用于之后检测的图像
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) #图像灰化
faces = face_cascade.detectMultiScale(gray, 1.3, 5) #识别人脸
cv2.putText(frame, "Driver Info", (80, 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, 255, 2, 2) # 显示信息标题
if len(faces) == 0:
cv2.rectangle(frame, (0, 0), (300, 100), color,
3) # 5控制绿色框的粗细
cv2.putText(frame, "No people !!!", (20, 60),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) # 显示陌生人信息
else:
for (x, y, w, h) in faces:
ROI = gray[x:x + w, y:y + h]
ROI = cv2.resize(ROI, (self.img_size, self.img_size),
interpolation=cv2.INTER_LINEAR)
label, prob = self.model.predict(
ROI) # 利用模型对cv2识别出的人脸进行比对
if prob > 0.7: # 如果模型认为概率高于70%则显示为模型中已有的label
show_name = name_list[label]
else:
show_name = 'Stranger'
# 当识别出来的不是陌生人时，需要显示出司机信息
if show_name != "Stranger":
info = show_name.split('_')
infoName = 'Name:' + info[0]
infoAge = 'Age:' + info[1]
infoUniversity = 'University:' + info[2]
infoSex = 'Sex:' + info[3]
infoDrivingLN = 'DriverNumber:' + info[4]
cv2.rectangle(frame, (0, 0),
(280, len(faces) * 220), color,
3) # 5控制绿色框的粗细
cv2.putText(frame, info[0], (x, y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255,
2) # 显示姓名信息
cv2.putText(frame, infoDrivingLN, (0, hight + 60),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, color,
2, 3) # 显示驾驶证号码
cv2.putText(frame, infoName, (0, hight + 95),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, color,
2, 3) # 显示名字
cv2.putText(frame, infoAge, (0, hight + 130),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, color,
2, 3) # 显示年龄
cv2.putText(frame, infoUniversity,
(0, hight + 165),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, color,
2, 3) # 显示学历
cv2.putText(frame, infoSex, (0, hight + 200),
cv2.FONT_HERSHEY_SIMPLEX, 0.8, color,
2, 3) # 显示性别
else:
cv2.rectangle(frame, (0, 0), (300, 100), color,
3) # 5控制绿色框的粗细
cv2.putText(frame, "Stranger", (x, y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255,
2) # 显示陌生人信息
cv2.putText(frame, "It is a Stranger", (20, 60),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255,
2) # 显示陌生人信息
frame = cv2.rectangle(frame, (x, y), (x + w, y + h),
color, 2) # 在人脸区域画一个正方形出来
cv2.imshow("Camera", frame)
cameraCapture.release()
cv2.destroyAllWindows()
class Camera_reader(object):
# Build the model when initializing the camera and load the trained model
def __init__(self):
self.model = Model()
self.model.load()
self.img_size = 128
def build_camera(self):
# The location of the face cascade file in the opencv file, used to help identify faces in images or video streams
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades +
'haarcascade_frontalface_alt.xml')
# Read the subfolder name under the dataset
name_list = read_name_list('./dataset')
# Turn on the camera and start reading
cameraCapture = cv2.VideoCapture(700)
success, frame = cameraCapture.read()
while success and cv2.waitKey(1) == -1:
success, frame = cameraCapture.read()
# Grayscale image
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# Face detection: scaleFactor is
#the magnification ratio (this parameter must be greater than 1);
#minNeighbors is the number of repeated identifications (this parameter is used to adjust the accuracy, the larger the more accurate)
faces = face_cascade.detectMultiScale(gray,
scaleFactor=1.3,
minNeighbors=8)
for (x, y, w, h) in faces:
origin = gray[x:x + w, y:y + h]
# origin is the original image, self.img_size is the size of the output image, and interpolation is the interpolation method
origin = cv2.resize(origin, (self.img_size, self.img_size),
interpolation=cv2.INTER_LINEAR)
# Use the model to compare the faces recognized by cv2
label, prob = self.model.predict(origin)
# If the model believes that the probability is higher than 70%, it will be displayed as the existing label in the model
if prob > 0.7:
show_name = name_list[label]
else:
show_name = 'unknown'
# Frame a face
frame = cv2.rectangle(frame, (x, y), (x + w, y + h),
(255, 0, 0), 1)
if prob > 0.7:
#storing recognized folder in folder named with date of that day
today = datetime.now()
recpath = './Recognized/' + today.strftime("%d-%m-%Y")
if not os.path.exists(recpath):
os.makedirs(recpath)
#print(os.getcwd())
cv2.imwrite(
recpath + '/' + today.strftime("%H%M%S") + '.jpg',
frame[y:y + h, x:x + w])
#if not os.path.exists()
# Show name
cv2.putText(frame, show_name, (x, y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2)
cv2.imshow("Recognizing...", frame)
# Release camera
cameraCapture.release()
# Close all windows
cv2.destroyAllWindows()
class Camera_reader(object):
# 在初始化camera的时候建立模型，并加载已经训练好的模型
def __init__(self):
self.model = Model()
self.model.load()
self.img_size = 128
print("init")
# 摄像头初始化 加快使用速度
self.face_cascade = cv2.CascadeClassifier(
'./haarcascade_frontalface_alt.xml')
# 读取dataset数据集下的子文件夹名称
self.name_list = read_name_list('res')
# 打开摄像头并开始读取画面
self.cameraCapture = cv2.VideoCapture(0)
def build_camera(self):
# opencv文件中人脸级联文件的位置，用于帮助识别图像或者视频流中的人脸
success, frame = self.cameraCapture.read()
while success and cv2.waitKey(1) == -1:
success, frame = self.cameraCapture.read()
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 图像灰化
faces = self.face_cascade.detectMultiScale(gray, 1.3, 5) # 识别人脸
for (x, y, w, h) in faces:
ROI = gray[x:x + w, y:y + h]
ROI = cv2.resize(ROI, (92, 112),
interpolation=cv2.INTER_LINEAR)
label, prob = self.model.predict(ROI) # 利用模型对cv2识别出的人脸进行比对
if prob > 0.9: # 如果模型认为概率高于70%则显示为模型中已有的label
show_name = self.name_list[label]
print("get a face %s " % show_name)
data = {
"key": "7b97e1bed4d6a452ab5bc68b9fc1e681",
"id": 1,
"status": "open"
}
response = requests.get(
"http://kuailezhai.cn/update/?key=7b97e1bed4d6a452ab5bc68b9fc1e681&id=1&status=open"
)
print(response.text)
print("是管理员，允许开门")
else:
show_name = 'Stranger'
cv2.putText(frame, show_name, (x, y - 20),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255, 2) # 显示名字
frame = cv2.rectangle(frame, (x, y), (x + w, y + h),
(255, 0, 0), 2) # 在人脸区域画一个正方形出来
cv2.imshow("Camera", frame)
#self.cameraCapture.release()
cv2.destroyAllWindows()
def get_one_picture(self):
success, frame = self.cameraCapture.read()
if success:
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 图像灰化
faces = self.face_cascade.detectMultiScale(gray, 1.3, 5) # 识别人脸
# 找不到人脸的情况
cv2.imwrite('./picture' + '.jpg', frame)
if len(faces) == 0:
print("can not find a face ")
return False
# 找到人脸
else:
for (x, y, w, h) in faces:
ROI = gray[x:x + w, y:y + h]
ROI = cv2.resize(ROI, (92, 112),
interpolation=cv2.INTER_LINEAR)
label, prob = self.model.predict(ROI) # 利用模型对cv2识别出的人脸进行比对
if prob > 0.9: # 如果模型认为概率高于70%则显示为模型中已有的label
show_name = self.name_list[label]
print("get a face %s " % show_name)
return True
else:
show_name = 'Stranger'
print("stranger")
return False
return False
def camera_done(self):
self.cameraCapture.release()
cv2.destroyAllWindows()
small_gray = cv2.cvtColor(small_image, cv2.COLOR_BGR2GRAY)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 检测人脸，探测图片中的人脸
faces = face_engine.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=15)
# 读取dataset数据集下的子文件夹名称
name_list = read_name_list('./dataset')
# 框出人脸
for (x, y, w, h) in faces:
origin = gray[x:x + w, y:y + h]
# origin是原图，self.img_size是输出图像的尺寸，interpolation是插值方法
origin = cv2.resize(origin, (128, 128), interpolation=cv2.INTER_LINEAR)
# 利用模型对cv2识别出的人脸进行比对
# 第一个返回值为概率最高的label的index,第二个返回值为对应概率
labelIndex, prob = model.predict(origin)
# 如果模型认为概率高于70%则显示为模型中已有的label
if prob > 0.7:
show_name = name_list[labelIndex]
else:
show_name = 'unknown'
# 1.原始图片 2.人脸坐标原点 3.标记的高度 4，线的颜色 5，线宽
frame = cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 1)
# 显示人名
cv2.putText(frame, show_name, (x, y - 20), cv2.FONT_HERSHEY_SIMPLEX, 1,
255, 2)
# 显示图片
cv2.imshow("Recognizing...", image)
# 暂停窗口
