def __init__(self,model_path=None):
# 预训练模型路径
pnet_path = "./datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/pnet.h5"
rnet_path = "./datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/rnet.h5"
onet_path = "./datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/onet.h5"
classes_path = "./datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/classes.txt"
# 创建 mtcnn 对象 检测图片中的人脸
self.mtcnn_model = mtcnn(pnet_path,rnet_path,onet_path)
# 门限函数
self.threshold = [0.5,0.6,0.8]
self.Crop_HEIGHT = 160
self.Crop_WIDTH = 160
# self.classes_path = "./data/model_data/classes.txt"
self.classes_path = classes_path
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(model_path)
# self.mask_model.load_weights("./results/temp.h5")
self.class_names = self._get_class()
class mask_rec():
def __init__(self, model_path=None):
# 预训练模型路径
pnet_path = "../datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/pnet.h5"
rnet_path = "../datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/rnet.h5"
onet_path = "../datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/onet.h5"
classes_path = "../datasets/5f680a696ec9b83bb0037081-momodel/data/keras_model_data/classes.txt"
# 创建 mtcnn 对象 检测图片中的人脸
self.mtcnn_model = mtcnn(pnet_path, rnet_path, onet_path)
# 门限函数
self.threshold = [0.5, 0.6, 0.8]
self.Crop_HEIGHT = 160
self.Crop_WIDTH = 160
# self.classes_path = "../data/model_data/classes.txt"
self.classes_path = classes_path
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(model_path)
self.mask_model.load_weights("../results/temp.h5")
self.class_names = self._get_class()
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，再进行匹配
#-----------------------------------------------#
height, width, _ = np.shape(draw)
draw = cv.cvtColor(np.asarray(draw), cv.COLOR_RGB2BGR)
draw_rgb = cv.cvtColor(draw, cv.COLOR_BGR2RGB)
# 检测人脸
rectangles = self.mtcnn_model.detectFace(draw_rgb, self.threshold)
if len(rectangles) == 0:
return draw, 0, 0
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 = cv.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]
all_num = rectangles.shape[0]
mask_num = 0
#-----------------------------------------------#
# 画框
#-----------------------------------------------#
for (left, top, right, bottom), c in zip(rectangles, classes_all):
if c == "YES":
mask_num = mask_num + 1
cv.rectangle(draw, (left, top), (right, bottom), (0, 0, 255),
2)
font = cv.FONT_HERSHEY_SIMPLEX
cv.putText(draw, c, (left, bottom - 15), font, 0.75,
(255, 255, 255), 2)
else:
cv.rectangle(draw, (left, top), (right, bottom), (255, 0, 0),
2)
font = cv.FONT_HERSHEY_SIMPLEX
cv.putText(draw, c, (left, bottom - 15), font, 0.75,
(255, 255, 255), 2)
return draw, all_num, mask_num
detect.recognize(img)
# 展示结果
fig = plt.figure(figsize=(8, 8))
ax1 = fig.add_subplot(111)
ax1.set_xticks([])
ax1.set_yticks([])
ax1.set_title('mask_1')
ax1.imshow(img)
#口罩识别
#预训练模型MobileNet
# 加载 MobileNet 的预训练模型权重
weights_path = basic_path + 'keras_model_data/mobilenet_1_0_224_tf_no_top.h5'
# 图像数据的行数和列数
height, width = 160, 160
model = MobileNet(input_shape=[height, width, 3], classes=2)
model.load_weights(weights_path, by_name=True)
print('加载完成...')
# 4.2 准备训练模型 Tip
# 4.2.1 加载和保存模型(可调参）
def save_model(model, checkpoint_save_path, model_dir):
"""
保存模型，每迭代3次保存一次
:param model: 训练的模型
:param checkpoint_save_path: 加载历史模型
:param model_dir:
:return:
"""
if os.path.exists(checkpoint_save_path):