def __init__(self):
super(MyDesignerShow, self).__init__()
self.timer_camera = QtCore.QTimer() # 本地摄像头定时器
self.timer_udp_video = QtCore.QTimer() # UDP获取视频定时器
self.cap = cv2.VideoCapture() # 获得摄像头对象
self.CAM_NUM = 0 # 获取摄像头编号
self.time = time # 获取时间对象
self.PREDICTOR_PATH = './face_lib/shape_predictor_68_face_landmarks.dat' # 关键点提取模型路径
self.my_align = align_dlib.AlignDlib(self.PREDICTOR_PATH) # 获取人脸对齐对象
self.pix = QtGui.QPixmap() # 获取QPixmap对象
self.pic_show = None
self.face_photo = None # 人脸图片
self.face_recog = face_recg.Recognize() # 获取人脸识别对象
self.setupUi(self) # 加载窗体
self.btn_close.clicked.connect(self.close) # 关闭程序
self.btn_local_camera.clicked.connect(self.get_local_camera) # 打开本地相机
self.btn_web_camera.clicked.connect(self.get_udp_video) # 打开UDP视频数据
self.btn_get_face.clicked.connect(self.get_face) # 得到人脸图像
self.btn_debug.clicked.connect(self.debug) # 报错
self.btn_new_face.clicked.connect(self.new_face) # 新建人脸数据
self.btn_face_recognize.clicked.connect(self.face_recognize) # 人脸识别
self.timer_camera.timeout.connect(
self.show_local_camera) # 计时结束调用show_camera()方法
self.timer_udp_video.timeout.connect(
self.show_udp_video) # 计时结束调用show_udp_video()方法
def photo_read(self, path, num):
# 使用dlib自带的frontal_face_detector作为我们的特征提取器
detector = align_dlib.AlignDlib(self.PREDICTOR_PATH)
path = self.input_dir + '/' + path
print(path + " 正在处理...")
name_file = str(num) + '_' + path.split('/')[-1]
name_file = self.output_dir + '/' + name_file
# 如果不存在目录 就创造目录
if not os.path.exists(name_file):
os.makedirs(name_file)
index = 1
for filename in os.listdir(path):
if filename.endswith('.jpg'):
img_path = path + '/' + filename
print(img_path)
# 从文件读取图片
img_bgr = cv2.imread(img_path) # 从文件读取bgr图片
img_rgb = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB) # 转为RGB图片
face_align = detector.align(size, img_rgb)
if face_align is None:
pass
else:
face_align = cv2.cvtColor(face_align,
cv2.COLOR_RGB2BGR) # 转为BGR图片
# 保存图片
cv2.imwrite(name_file + '/' + str(index) + '.jpg',
face_align)
index += 1
def get_pair_image(self, pair):
detector = align_dlib.AlignDlib(self.PREDICTOR_PATH)
x, y = pair
path_name_x = self.path + self.path_array[x - 1]
path_name_y = self.path + self.path_array[y - 1]
x_img = self.get_one_image(x, detector)
y_img = self.get_one_image(y, detector)
return x_img, y_img
