def train(self):
dataset = DataSet('./data/')
model = Model()
model.read_trainData(dataset)
model.build_model()
model.train_model()
model.evaluate_model()
model.save()
def load_model(fn):
m = Model(filter_sizes=[3, 3, 2, 2],
kernel_nums=[10, 10, 10, 10],
strides=[1, 1, 1, 1],
batch_size=50,
training_mode=False)
m.build_model()
m.model.load_weights(fn)
return m
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()
