def __init__(self):
# 创建mtcnn对象
# 检测图片中的人脸
self.mtcnn_model = mtcnn()
# 门限函数
self.threshold = [0.5,0.6,0.8]
self.Crop_HEIGHT = 160
self.Crop_WIDTH = 160
self.classes_path = "model_data/classes.txt"
self.NUM_CLASSES = 2
self.mask_model = MobileNet(input_shape=[self.Crop_HEIGHT,self.Crop_WIDTH,3],classes=self.NUM_CLASSES)
self.mask_model.load_weights("./logs/last_one.h5")
self.class_names = self._get_class()
def __init__(self):
# create a mtcnn object
# detect the face in img
self.mtcnn_model = mtcnn()
# threshold
self.threshold = [0.5, 0.6, 0.8]
self.Crop_HEIGHT = 160
self.Crop_WIDTH = 160
self.classes_path = "model_data/classes.txt"
self.NUM_CLASSES = 2
self.mask_model = MobileNet(
input_shape=[self.Crop_HEIGHT, self.Crop_WIDTH, 3],
classes=self.NUM_CLASSES)
self.mask_model.load_weights("./logs/last_one.h5")
self.class_names = self._get_class()
# 打开数据集的txt
with open(r".\data\train.txt", "r") as f:
lines = f.readlines()
# 打乱行，这个txt主要用于帮助读取数据来训练
# 打乱的数据更有利于训练
np.random.seed(10101)
np.random.shuffle(lines)
np.random.seed(None)
# 90%用于训练，10%用于估计。
num_val = int(len(lines) * 0.1)
num_train = len(lines) - num_val
# 建立MobileNet模型
model = MobileNet(input_shape=[HEIGHT, WIDTH, 3], classes=NUM_CLASSES)
model_name = 'mobilenet_1_0_224_tf_no_top.h5'
weight_path = BASE_WEIGHT_PATH + model_name
weights_path = get_file(model_name, weight_path, cache_subdir='models')
model.load_weights(weights_path, by_name=True)
# 保存的方式，3世代保存一次
checkpoint_period1 = ModelCheckpoint(
log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',
monitor='acc',
save_weights_only=False,
save_best_only=True,
period=3)
# 学习率下降的方式，acc三次不下降就下降学习率继续训练
reduce_lr = ReduceLROnPlateau(monitor='acc',
class face_rec():
def __init__(self):
# 创建mtcnn对象
# 检测图片中的人脸
self.mtcnn_model = mtcnn()
# 门限函数
self.threshold = [0.5, 0.6, 0.8]
self.Crop_HEIGHT = 160
self.Crop_WIDTH = 160
self.classes_path = "model_data/classes.txt"
self.NUM_CLASSES = 2
self.mask_model = MobileNet(
input_shape=[self.Crop_HEIGHT, self.Crop_WIDTH, 3],
classes=self.NUM_CLASSES)
self.mask_model.load_weights("./logs/last_one.h5")
self.class_names = self._get_class()
# ["mask","nomask"]
def _get_class(self):
classes_path = os.path.expanduser(self.classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def recognize(self, draw):
#-----------------------------------------------#
# 人脸识别
# 先定位，再进行数据库匹配
#-----------------------------------------------#
height, width, _ = np.shape(draw)
draw_rgb = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
# 检测人脸
rectangles = self.mtcnn_model.detectFace(draw_rgb, self.threshold)
if len(rectangles) == 0:
return
rectangles = np.array(rectangles, dtype=np.int32)
rectangles[:, 0] = np.clip(rectangles[:, 0], 0, width)
rectangles[:, 1] = np.clip(rectangles[:, 1], 0, height)
rectangles[:, 2] = np.clip(rectangles[:, 2], 0, width)
rectangles[:, 3] = np.clip(rectangles[:, 3], 0, height)
rectangles_temp = utils.rect2square(
np.array(rectangles, dtype=np.int32))
rectangles_temp[:, 0] = np.clip(rectangles_temp[:, 0], 0, width)
rectangles_temp[:, 1] = np.clip(rectangles_temp[:, 1], 0, height)
rectangles_temp[:, 2] = np.clip(rectangles_temp[:, 2], 0, width)
rectangles_temp[:, 3] = np.clip(rectangles_temp[:, 3], 0, height)
# 转化成正方形
#-----------------------------------------------#
# 对检测到的人脸进行编码
#-----------------------------------------------#
classes_all = []
for rectangle in rectangles_temp:
# 获取landmark在小图中的坐标
landmark = (np.reshape(rectangle[5:15], (5, 2)) - np.array(
[int(rectangle[0]), int(rectangle[1])])) / (rectangle[3] -
rectangle[1]) * 160
# 截取图像
crop_img = draw_rgb[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
crop_img = cv2.resize(crop_img,
(self.Crop_HEIGHT, self.Crop_WIDTH))
# 对齐
new_img, _ = utils.Alignment_1(crop_img, landmark)
# 归一化
new_img = preprocess_input(
np.reshape(np.array(new_img, np.float64),
[1, self.Crop_HEIGHT, self.Crop_WIDTH, 3]))
classes = self.class_names[np.argmax(
self.mask_model.predict(new_img)[0])]
classes_all.append(classes)
rectangles = rectangles[:, 0:4]
#-----------------------------------------------#
# 画框~!~
#-----------------------------------------------#
for (left, top, right, bottom), c in zip(rectangles, classes_all):
cv2.rectangle(draw, (left, top), (right, bottom), (0, 0, 255), 2)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(draw, c, (left, bottom - 15), font, 0.75,
(255, 255, 255), 2)
return draw
class face_rec():
def __init__(self):
# create a mtcnn object
# detect the face in img
self.mtcnn_model = mtcnn()
# threshold
self.threshold = [0.5, 0.6, 0.8]
self.Crop_HEIGHT = 160
self.Crop_WIDTH = 160
self.classes_path = "model_data/classes.txt"
self.NUM_CLASSES = 2
self.mask_model = MobileNet(
input_shape=[self.Crop_HEIGHT, self.Crop_WIDTH, 3],
classes=self.NUM_CLASSES)
self.mask_model.load_weights("./logs/last_one.h5")
self.class_names = self._get_class()
# ["mask","nomask"]
def _get_class(self):
classes_path = os.path.expanduser(self.classes_path)
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return class_names
def recognize(self, draw):
#-----------------------------------------------#
# recognise the face
# Position the face the match in database
#-----------------------------------------------#
height, width, _ = np.shape(draw)
draw_rgb = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
# recognise the face
rectangles = self.mtcnn_model.detectFace(draw_rgb, self.threshold)
if len(rectangles) == 0:
return
rectangles = np.array(rectangles, dtype=np.int32)
rectangles[:, 0] = np.clip(rectangles[:, 0], 0, width)
rectangles[:, 1] = np.clip(rectangles[:, 1], 0, height)
rectangles[:, 2] = np.clip(rectangles[:, 2], 0, width)
rectangles[:, 3] = np.clip(rectangles[:, 3], 0, height)
rectangles_temp = utils.rect2square(
np.array(rectangles, dtype=np.int32))
rectangles_temp[:, 0] = np.clip(rectangles_temp[:, 0], 0, width)
rectangles_temp[:, 1] = np.clip(rectangles_temp[:, 1], 0, height)
rectangles_temp[:, 2] = np.clip(rectangles_temp[:, 2], 0, width)
rectangles_temp[:, 3] = np.clip(rectangles_temp[:, 3], 0, height)
# reshape to square
#-----------------------------------------------#
# encode the face
#-----------------------------------------------#
classes_all = []
for rectangle in rectangles_temp:
# get the axis in landmark
landmark = (np.reshape(rectangle[5:15], (5, 2)) - np.array(
[int(rectangle[0]), int(rectangle[1])])) / (rectangle[3] -
rectangle[1]) * 160
# Intercept the image
crop_img = draw_rgb[int(rectangle[1]):int(rectangle[3]),
int(rectangle[0]):int(rectangle[2])]
crop_img = cv2.resize(crop_img,
(self.Crop_HEIGHT, self.Crop_WIDTH))
# Oppose
new_img, _ = utils.Alignment_1(crop_img, landmark)
# Normalization
new_img = preprocess_input(
np.reshape(np.array(new_img, np.float64),
[1, self.Crop_HEIGHT, self.Crop_WIDTH, 3]))
classes = self.class_names[np.argmax(
self.mask_model.predict(new_img)[0])]
classes_all.append(classes)
rectangles = rectangles[:, 0:4]
#-----------------------------------------------#
# draw the square
#-----------------------------------------------#
for (left, top, right, bottom), c in zip(rectangles, classes_all):
cv2.rectangle(draw, (left, top), (right, bottom), (0, 0, 255), 2)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(draw, c, (left, bottom - 15), font, 0.75,
(255, 255, 255), 2)
return draw