def __init__(self,detector,net):
super(DetectionThread, self).__init__()
#导入识别和检测模型
self.net = net
self.detector = detector
self.db = PyMySQL('localhost', 'root', 'CockTail', 'TESTDATABASE')
self.thres = 0.5 #判断人脸相似度的阈值
self.show_time = 100 #动态显示人脸时间
def __init__(self, detector, net):
super(DetectionThread, self).__init__()
#导入识别和检测模型
self.net = net
self.detector = detector
self.db = PyMySQL('localhost', 'root', 'Asd980517', 'WEININGFACE')
self.thres = 0.5 #判断人脸相似度的阈值
self.show_time = 100 #动态显示人脸时间
def __init__(self, parent=None):
super(Ui_MainWindow, self).__init__(parent)
#数据库调用
self.dbWidge = DBWidge()
self.dbWidge.setHidden(True)
self.db = PyMySQL('localhost','root','Asd980517','WEININGFACE')
#相机区域
#人脸识别与记录线程
self.detector = MTCNN()
self.FaceThread = DetectionThread(self.detector,net)
#添加新人脸的线程
self.AddFaceThread = AddFaceThread(self.detector,net)
#定时器
self.timer_camera = QTimer()
self.timer_camera_counter = 0
self.timer_clear_label = QTimer()
self.timer_dynamic_recog = QTimer()
self.timer_long_name = QTimer()
self.cap = cv2.VideoCapture()
self.CAM_NUM = 0 #Camera used
self.dynamic_draw_flag = False
#初始化
# self.setBackGround()
self.facelabel_list = []
self.textlabel_list = []
self.name_list = []
self.long_name_list = []
self.set_ui()
self.slot_init()
self.__flag_work = 0
self.initMenu()
self.initAnimation()
self.setStyleSheet(qdarkstyle.load_stylesheet_pyqt5())
self.timer_clear_label.start(5000)
self.timer_long_name.start(60000)
class DetectionThread(QThread):
#传出的信号为图片中人脸的位置矩形以及识别出的人名
Bound_Name = pyqtSignal(int, int, int, int, str)
Dynamic_Bound_Name = pyqtSignal(int, int, int, int, str)
Dynamic_Show_Time = pyqtSignal(int)
Face_Count = pyqtSignal(int)
def __init__(self, detector, net):
super(DetectionThread, self).__init__()
#导入识别和检测模型
self.net = net
self.detector = detector
self.db = PyMySQL('localhost', 'root', 'Asd980517', 'WEININGFACE')
self.thres = 0.5 #判断人脸相似度的阈值
self.show_time = 100 #动态显示人脸时间
def SetImg(self, img, method=0):
self.img = img
self.method = method
#传入图片后执行run方法
self.start()
def SetThresHold(self, thres):
self.thres = thres
def run(self):
result = self.detector.detect_faces(self.img)
print('results', result)
self.Face_Count.emit(len(result))
#如果没有检测出人脸，发出一个信号并且提前停止线程
if len(result) == 0:
return
aligment_imgs = []
originfaces = []
# 检测，标定landmark
for face in result:
temp_landmarks = []
bouding_boxes = face['box']
for axis in bouding_boxes:
if axis <= 0 or axis >= self.img.shape[
0] - 1 or axis >= self.img.shape[1] - 1:
return
if bouding_boxes[0] + bouding_boxes[2] <= 0 or bouding_boxes[
1] + bouding_boxes[3] <= 0:
self.No_face.emit()
return
if bouding_boxes[0] + bouding_boxes[2] >= self.img.shape[1] - 1 or bouding_boxes[1] + bouding_boxes[3] >= \
self.img.shape[0] - 1:
return
keypoints = face['keypoints']
faces = self.img[bouding_boxes[1]:bouding_boxes[1] +
bouding_boxes[3],
bouding_boxes[0]:bouding_boxes[0] +
bouding_boxes[2]]
originfaces.append(faces)
lefteye = keypoints['left_eye']
righteye = keypoints['right_eye']
nose = keypoints['nose']
mouthleft = keypoints['mouth_left']
mouthright = keypoints['mouth_right']
temp_landmarks.append(lefteye[0])
temp_landmarks.append(lefteye[1])
temp_landmarks.append(righteye[0])
temp_landmarks.append(righteye[1])
temp_landmarks.append(nose[0])
temp_landmarks.append(nose[1])
temp_landmarks.append(mouthleft[0])
temp_landmarks.append(mouthleft[1])
temp_landmarks.append(mouthright[0])
temp_landmarks.append(mouthright[1])
for i, num in enumerate(temp_landmarks):
if i % 2:
temp_landmarks[i] = num - bouding_boxes[1]
else:
temp_landmarks[i] = num - bouding_boxes[0]
faces = self.alignment(faces, temp_landmarks)
faces = np.transpose(faces, (2, 0, 1)).reshape(1, 3, 112, 96)
faces = (faces - 127.5) / 128.0
aligment_imgs.append(faces)
# print('face ok')
length = len(aligment_imgs)
aligment_imgs = np.array(aligment_imgs)
aligment_imgs = np.reshape(aligment_imgs, (length, 3, 112, 96))
output_imgs_features = self.get_imgs_features(aligment_imgs)
cos_distances_list = []
#和数据库内的每一个向量进行计算对比
imgs_features = self.db.get_all_vector()
NameIndb = self.db.get_all_name()
NameList = []
for img_feature in output_imgs_features:
cos_distance_list = [
self.cal_cosdistance(img_feature, test_img_feature)
for test_img_feature in imgs_features
]
cos_distances_list.append(cos_distance_list)
# print('\n',cos_distances_list)
for sub_cos_distances_list in cos_distances_list:
if max(sub_cos_distances_list) < self.thres:
NameList.append('Unknown')
else:
NameList.append(NameIndb[sub_cos_distances_list.index(
max(sub_cos_distances_list))])
#method = 0: 签到
#method = 1: 动态识别（画人脸）
if self.method == 0:
for i, name in enumerate(NameList):
bound = result[i]['box']
#发送信号
# print('Signal emit:',bound,name)
self.Bound_Name.emit(bound[0], bound[1], bound[2], bound[3],
name)
elif self.method == 1:
for i, name in enumerate(NameList):
bound = result[i]['box']
#发送信号
self.Dynamic_Bound_Name.emit(bound[0], bound[1], bound[2],
bound[3], name)
self.Dynamic_Show_Time.emit(self.show_time)
else:
pass
def cal_cosdistance(self, vec1, vec2):
vec1 = np.reshape(vec1, (1, -1))
vec2 = np.reshape(vec2, (-1, 1))
length1 = np.sqrt(np.square(vec1).sum())
length2 = np.sqrt(np.square(vec2).sum())
cosdistance = vec1.dot(vec2) / (length1 * length2)
cosdistance = cosdistance[0][0]
return cosdistance
def get_imgs_features(self, imgs_alignment):
input_images = Variable(torch.from_numpy(imgs_alignment).float(),
volatile=True)
output_features = self.net(input_images)
output_features = output_features.data.numpy()
return output_features
def alignment(self, src_img, src_pts):
ref_pts = [[30.2946, 51.6963], [65.5318, 51.5014], [48.0252, 71.7366],
[33.5493, 92.3655], [62.7299, 92.2041]]
crop_size = (96, 112)
src_pts = np.array(src_pts).reshape(5, 2)
s = np.array(src_pts).astype(np.float32)
r = np.array(ref_pts).astype(np.float32)
tfm = get_similarity_transself_for_cv2(s, r)
face_img = cv2.warpAffine(src_img, tfm, crop_size)
return face_img
def __init__(self, detector, net):
super(AddFaceThread, self).__init__()
self.detector = detector
self.net = net
self.db = PyMySQL('localhost', 'root', 'CockTail', 'TESTDATABASE')
self.inputWidget = QWidget()
class AddFaceThread(QThread):
#传出的信号为图片中人脸的位置矩形
Bound_box = pyqtSignal(int, int, int, int)
No_face = pyqtSignal()
def __init__(self, detector, net):
super(AddFaceThread, self).__init__()
self.detector = detector
self.net = net
self.db = PyMySQL('localhost', 'root', 'CockTail', 'TESTDATABASE')
self.inputWidget = QWidget()
def SetImg(self, img):
self.img = img
#传入图片后执行run方法
self.start()
def Cal_Area_Index(self, result):
areas = []
for face in result:
bounding_boxes = face['box']
areas.append(bounding_boxes[3] * bounding_boxes[2])
return areas.index(max(areas))
def run(self):
try:
result = self.detector.detect_faces(self.img)
except:
return
#如果没有检测出人脸，发出一个信号并且提前停止线程
if len(result) == 0:
self.No_face.emit()
return
aligment_imgs = []
temp_landmarks = []
maxIndex = self.Cal_Area_Index(result)
face = result[maxIndex]
bouding_boxes = face['box']
keypoints = face['keypoints']
if bouding_boxes[0] + bouding_boxes[2] <= 0 or bouding_boxes[
1] + bouding_boxes[3] <= 0:
self.No_face.emit()
return
if bouding_boxes[0] + bouding_boxes[2] >= self.img.shape[
1] - 1 or bouding_boxes[1] + bouding_boxes[
3] >= self.img.shape[0] - 1:
return
faces = self.img[bouding_boxes[1]:bouding_boxes[1] + bouding_boxes[3],
bouding_boxes[0]:bouding_boxes[0] + bouding_boxes[2]]
lefteye = keypoints['left_eye']
righteye = keypoints['right_eye']
nose = keypoints['nose']
mouthleft = keypoints['mouth_left']
mouthright = keypoints['mouth_right']
temp_landmarks.append(lefteye[0])
temp_landmarks.append(lefteye[1])
temp_landmarks.append(righteye[0])
temp_landmarks.append(righteye[1])
temp_landmarks.append(nose[0])
temp_landmarks.append(nose[1])
temp_landmarks.append(mouthleft[0])
temp_landmarks.append(mouthleft[1])
temp_landmarks.append(mouthright[0])
temp_landmarks.append(mouthright[1])
for i, num in enumerate(temp_landmarks):
if i % 2:
temp_landmarks[i] = num - bouding_boxes[1]
else:
temp_landmarks[i] = num - bouding_boxes[0]
faces = self.alignment(faces, temp_landmarks)
#手动 normalization
faces = np.transpose(faces, (2, 0, 1)).reshape(1, 3, 112, 96)
faces = (faces - 127.5) / 128.0
aligment_imgs.append(faces)
length = len(aligment_imgs)
aligment_imgs = np.array(aligment_imgs)
aligment_imgs = np.reshape(aligment_imgs, (length, 3, 112, 96))
#获取feature 向量
output_imgs_features = self.get_imgs_features(aligment_imgs)
# print('get image featrure ok')
try:
name, ok = QInputDialog.getText(self.inputWidget, "Get name",
"Your name:", QLineEdit.Normal, "")
current_time = time.strftime('%Y-%m-%d\n%H:%M:%S')
try:
if ok and name != '':
self.db.insert([name], [20], [output_imgs_features],
[current_time])
except:
return
except:
return
# print('insert ok')
def cal_cosdistance(self, vec1, vec2):
vec1 = np.reshape(vec1, (1, -1))
vec2 = np.reshape(vec2, (-1, 1))
length1 = np.sqrt(np.square(vec1).sum())
length2 = np.sqrt(np.square(vec2).sum())
cosdistance = vec1.dot(vec2) / (length1 * length2)
cosdistance = cosdistance[0][0]
return cosdistance
def get_imgs_features(self, imgs_alignment):
input_images = Variable(torch.from_numpy(imgs_alignment).float(),
volatile=True)
output_features = self.net(input_images)
output_features = output_features.data.numpy()
return output_features
def alignment(self, src_img, src_pts):
ref_pts = [[30.2946, 51.6963], [65.5318, 51.5014], [48.0252, 71.7366],
[33.5493, 92.3655], [62.7299, 92.2041]]
crop_size = (96, 112)
src_pts = np.array(src_pts).reshape(5, 2)
s = np.array(src_pts).astype(np.float32)
r = np.array(ref_pts).astype(np.float32)
tfm = get_similarity_transself_for_cv2(s, r)
face_img = cv2.warpAffine(src_img, tfm, crop_size)
return face_img
def __init__(self, detector, net):
super(AddFaceThread, self).__init__()
self.detector = detector
self.net = net
self.db = PyMySQL('localhost', 'root', 'Asd980517', 'WEININGFACE')
self.inputWidget = QWidget()