hog_face_detector = dlib.get_frontal_face_detector()
# 关键点 检测模型
shape_detector = dlib.shape_predictor('./weights/shape_predictor_68_face_landmarks.dat')
# resnet模型
face_descriptor_extractor = dlib.face_recognition_model_v1('./weights/dlib_face_recognition_resnet_model_v1.dat')
while True:
ret, frame = cap.read()
# 镜像
frame = cv2.flip(frame,1)
# 转为灰度图
frame_gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
# 检测人脸
detections = hog_face_detector(frame,1)
for face in detections:
# 人脸框坐标 左上和右下
l, t, r, b = face.left(), face.top(), face.right(), face.bottom()
# 获取68个关键点
points = shape_detector(frame,face)
# 绘制关键点
for point in points.parts():
cv2.circle(frame,(point.x,point.y),2,(0,255,0),1)
# 绘制矩形框
cv2.rectangle(frame,(l,t),(r,b),(0,255,0),2)
cv2.imshow("face",frame)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows
faceRegister()
此时一张帅脸如下：
#### 2、描述符的采集
之后，我们根据参数，即faceCount 和 Interval 进行**描述符的生成和采集**。
(这里我默认是faceCount=3，Interval=3，即每3秒采集一次，共3次）
def faceRegister(faceId=1, userName=‘default’, interval=3, faceCount=3, resize_w=700, resize_h=400):
‘’’
faceId:人脸ID
userName: 人脸姓名
faceCount: 采集该人脸图片的数量
interval: 采集间隔
‘’’
cap = cv2.VideoCapture(0)
# 人脸检测模型
hog_face_detector = dlib.get_frontal_face_detector()
# 关键点 检测模型
shape_detector = dlib.shape_predictor('./weights/shape_predictor_68_face_landmarks.dat')
# resnet模型
face_descriptor_extractor = dlib.face_recognition_model_v1('./weights/dlib_face_recognition_resnet_model_v1.dat')
# 开始时间
start_time = time.time()
# 执行次数
collect_times = 0
while True:
ret, frame = cap.read()
# 镜像
frame = cv2.flip(frame,1)
# 转为灰度图
frame_gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
# 检测人脸
detections = hog_face_detector(frame,1)
for face in detections:
# 人脸框坐标 左上和右下
l, t, r, b = face.left(), face.top(), face.right(), face.bottom()
# 获取68个关键点
points = shape_detector(frame,face)
# 绘制人脸关键点
for point in points.parts():
cv2.circle(frame, (point.x, point.y), 2, (0, 255, 0), 1)
# 绘制矩形框
cv2.rectangle(frame, (l, t), (r, b), (0, 255, 0), 2)
# 采集：
if collect_times < faceCount:
# 获取当前时间
now = time.time()
# 时间限制
if now - start_time > interval:
# 获取特征描述符
face_descriptor = face_descriptor_extractor.compute_face_descriptor(frame,points)
# dlib格式转为数组
face_descriptor = [f for f in face_descriptor]
collect_times += 1
start_time = now
print("成功采集{}次".format(collect_times))
else:
# 时间间隔不到interval
print("等待进行下一次采集")
pass
else:
# 已经成功采集完3次了
print("采集完毕")
cap.release()
cv2.destroyAllWindows()
return
cv2.imshow("face",frame)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
faceRegister()
等待进行下一次采集
…
成功采集1次
等待进行下一次采集
…
成功采集2次
等待进行下一次采集
…
成功采集3次
采集完毕
#### 3、完整的注册
最后就是**写入csv文件**
这里加入了注册成功等的提示，且把一些变量放到了全局，因为后面人脸识别打卡时也会用到。
加载人脸检测器
hog_face_detector = dlib.get_frontal_face_detector()
cnn_detector = dlib.cnn_face_detection_model_v1(‘./weights/mmod_human_face_detector.dat’)
haar_face_detector = cv2.CascadeClassifier(‘./weights/haarcascade_frontalface_default.xml’)
加载关键点检测器
points_detector = dlib.shape_predictor(‘./weights/shape_predictor_68_face_landmarks.dat’)
加载resnet模型
face_descriptor_extractor = dlib.face_recognition_model_v1(‘./weights/dlib_face_recognition_resnet_model_v1.dat’)
绘制中文
def cv2AddChineseText(img, text, position, textColor=(0, 255, 0), textSize=30):
if (isinstance(img, np.ndarray)): # 判断是否OpenCV图片类型
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
# 创建一个可以在给定图像上绘图的对象
draw = ImageDraw.Draw(img)
# 字体的格式
fontStyle = ImageFont.truetype(
“./fonts/songti.ttc”, textSize, encoding=“utf-8”)
# 绘制文本
draw.text(position, text, textColor, font=fontStyle)
# 转换回OpenCV格式
return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
绘制左侧信息
def drawLeftInfo(frame, fpsText, mode=“Reg”, detector=‘haar’, person=1, count=1):
# 帧率
cv2.putText(frame, "FPS: " + str(round(fpsText, 2)), (30, 50), cv2.FONT_ITALIC, 0.8, (0, 255, 0), 2)
# 模式：注册、识别
cv2.putText(frame, "Mode: " + str(mode), (30, 80), cv2.FONT_ITALIC, 0.8, (0, 255, 0), 2)
if mode == 'Recog':
# 检测器
cv2.putText(frame, "Detector: " + detector, (30, 110), cv2.FONT_ITALIC, 0.8, (0, 255, 0), 2)
# 人数
cv2.putText(frame, "Person: " + str(person), (30, 140), cv2.FONT_ITALIC, 0.8, (0, 255, 0), 2)
# 总人数
cv2.putText(frame, "Count: " + str(count), (30, 170), cv2.FONT_ITALIC, 0.8, (0, 255, 0), 2)
注册人脸
def faceRegiser(faceId=1, userName=‘default’, interval=3, faceCount=3, resize_w=700, resize_h=400):
# 计数
count = 0
# 开始注册时间
startTime = time.time()
# 视频时间
frameTime = startTime
# 控制显示打卡成功的时长
show_time = (startTime - 10)
# 打开文件
f = open('./data/feature.csv', 'a', newline='')
csv_writer = csv.writer(f)
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
frame = cv2.resize(frame, (resize_w, resize_h))
frame = cv2.flip(frame, 1)
# 检测
face_detetion = hog_face_detector(frame, 1)
for face in face_detetion:
# 识别68个关键点
points = points_detector(frame, face)
# 绘制人脸关键点
for point in points.parts():
cv2.circle(frame, (point.x, point.y), 2, (0, 255, 0), 1)
# 绘制框框
l, t, r, b = face.left(), face.top(), face.right(), face.bottom()
cv2.rectangle(frame, (l, t), (r, b), (0, 255, 0), 2)
now = time.time()
if (now - show_time) < 0.5:
frame = cv2AddChineseText(frame,
"注册成功 {count}/{faceCount}".format(count=(count + 1), faceCount=faceCount),
(l, b + 30), textColor=(255, 0, 255), textSize=30)
# 检查次数
if count < faceCount:
# 检查时间
if now - startTime > interval:
# 特征描述符
face_descriptor = face_descriptor_extractor.compute_face_descriptor(frame, points)
face_descriptor = [f for f in face_descriptor]
# 描述符增加进data文件
line = [faceId, userName, face_descriptor]
# 写入
csv_writer.writerow(line)
# 保存照片样本
print('人脸注册成功 {count}/{faceCount}，faceId:{faceId}，userName:{userName}'.format(count=(count + 1),
faceCount=faceCount,
faceId=faceId,
userName=userName))
frame = cv2AddChineseText(frame,
"注册成功 {count}/{faceCount}".format(count=(count + 1), faceCount=faceCount),
(l, b + 30), textColor=(255, 0, 255), textSize=30)
show_time = time.time()
# 时间重置
startTime = now
# 次数加一
count += 1
else:
print('人脸注册完毕')
f.close()
cap.release()
cv2.destroyAllWindows()
return
now = time.time()
fpsText = 1 / (now - frameTime)
frameTime = now
# 绘制
drawLeftInfo(frame, fpsText, 'Register')
cv2.imshow('Face Attendance Demo: Register', frame)
if cv2.waitKey(10) & 0xFF == ord('q'):
break
f.close()
cap.release()
cv2.destroyAllWindows()
此时执行：
faceRegiser(3,“用户B”)
人脸注册成功 1/3，faceId:3，userName:用户B
人脸注册成功 2/3，faceId:3，userName:用户B
人脸注册成功 3/3，faceId:3，userName:用户B
人脸注册完毕
其features文件：
### B. 识别、打卡
识别步骤如下：
* 打开摄像头获取画面
* 根据画面中的图片获取里面的人脸特征描述符
* 根据特征描述符将其与feature.csv文件里特征做距离判断
* 获取ID、NAME
* 考勤记录写入attendance.csv里
这里与上面流程相似，不过是加了一个对比功能，距离小于阈值，则表示匹配成功。就加快速度不一步步来了，代码如下：
刷新右侧考勤信息
def updateRightInfo(frame, face_info_list, face_img_list):
# 重新绘制逻辑：从列表中每隔3个取一批显示，新增人脸放在最前面
# 如果有更新，重新绘制
# 如果没有，定时往后移动
left_x = 30
left_y = 20
resize_w = 80
offset_y = 120
index = 0
frame_h = frame.shape[0]
frame_w = frame.shape[1]
for face in face_info_list[:3]:
name = face[0]
time = face[1]
face_img = face_img_list[index]
# print(face_img.shape)
face_img = cv2.resize(face_img, (resize_w, resize_w))
offset_y_value = offset_y * index
frame[(left_y + offset_y_value):(left_y + resize_w + offset_y_value), -(left_x + resize_w):-left_x] = face_img
cv2.putText(frame, name, ((frame_w - (left_x + resize_w)), (left_y + resize_w) + 15 + offset_y_value),
cv2.FONT_ITALIC, 0.5, (0, 255, 0), 1)
cv2.putText(frame, time, ((frame_w - (left_x + resize_w)), (left_y + resize_w) + 30 + offset_y_value),
cv2.FONT_ITALIC, 0.5, (0, 255, 0), 1)
index += 1
return frame
返回DLIB格式的face
def getDlibRect(detector=‘hog’, face=None):
l, t, r, b = None, None, None, None
if detector == 'hog':
l, t, r, b = face.left(), face.top(), face.right(), face.bottom()
if detector == 'cnn':
l = face.rect.left()
t = face.rect.top()
r = face.rect.right()
b = face.rect.bottom()
if detector == 'haar':
l = face[0]
t = face[1]
r = face[0] + face[2]
b = face[1] + face[3]
nonnegative = lambda x: x if x >= 0 else 0
return map(nonnegative, (l, t, r, b))
获取CSV中信息
def getFeatList():
print(‘加载注册的人脸特征’)
feature_list = None
label_list = []
name_list = []
# 加载保存的特征样本
with open('./data/feature.csv', 'r') as f:
csv_reader = csv.reader(f)
for line in csv_reader:
# 重新加载数据
faceId = line[0]
userName = line[1]
face_descriptor = eval(line[2])
label_list.append(faceId)
name_list.append(userName)
# 转为numpy格式
face_descriptor = np.asarray(face_descriptor, dtype=np.float64)
# 转为二维矩阵，拼接
face_descriptor = np.reshape(face_descriptor, (1, -1))
# 初始化
if feature_list is None:
feature_list = face_descriptor
else:
# 拼接
feature_list = np.concatenate((feature_list, face_descriptor), axis=0)
print("特征加载完毕")
return feature_list, label_list, name_list
人脸识别
def faceRecognize(detector=‘haar’, threshold=0.5, write_video=False, resize_w=700, resize_h=400):
# 视频时间
frameTime = time.time()
# 加载特征
feature_list, label_list, name_list = getFeatList()
face_time_dict = {}
# 保存name,time人脸信息
face_info_list = []
# numpy格式人脸图像数据
face_img_list = []
# 侦测人数
person_detect = 0
# 统计人脸数
face_count = 0
# 控制显示打卡成功的时长
show_time = (frameTime - 10)
# 考勤记录
f = open('./data/attendance.csv', 'a')
csv_writer = csv.writer(f)
cap = cv2.VideoCapture(0)
# resize_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))//2
# resize_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) //2
videoWriter = cv2.VideoWriter('./record_video/out' + str(time.time()) + '.mp4', cv2.VideoWriter_fourcc(*'MP4V'), 15,
(resize_w, resize_h))
while True:
ret, frame = cap.read()
frame = cv2.resize(frame, (resize_w, resize_h))
frame = cv2.flip(frame, 1)
# 切换人脸检测器
if detector == 'hog':
face_detetion = hog_face_detector(frame, 1)
if detector == 'cnn':
face_detetion = cnn_detector(frame, 1)
if detector == 'haar':
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
face_detetion = haar_face_detector.detectMultiScale(frame_gray, minNeighbors=7, minSize=(100, 100))
person_detect = len(face_detetion)
for face in face_detetion:
l, t, r, b = getDlibRect(detector, face)
face = dlib.rectangle(l, t, r, b)
# 识别68个关键点
points = points_detector(frame, face)
cv2.rectangle(frame, (l, t), (r, b), (0, 255, 0), 2)
# 人脸区域
face_crop = frame[t:b, l:r]
# 特征
face_descriptor = face_descriptor_extractor.compute_face_descriptor(frame, points)
face_descriptor = [f for f in face_descriptor]
face_descriptor = np.asarray(face_descriptor, dtype=np.float64)
# 计算距离
distance = np.linalg.norm((face_descriptor - feature_list), axis=1)
# 最小距离索引
min_index = np.argmin(distance)
# 最小距离
min_distance = distance[min_index]
predict_name = "Not recog"
if min_distance < threshold:
# 距离小于阈值，表示匹配
predict_id = label_list[min_index]
predict_name = name_list[min_index]
# 判断是否新增记录：如果一个人距上次检测时间>3秒，或者换了一个人，将这条记录插入
need_insert = False
now = time.time()
if predict_name in face_time_dict:
if (now - face_time_dict[predict_name]) > 3:
# 刷新时间
face_time_dict[predict_name] = now
need_insert = True
else:
# 还是上次人脸
need_insert = False
else:
# 新增数据记录
face_time_dict[predict_name] = now
need_insert = True
if (now - show_time) < 1:
frame = cv2AddChineseText(frame, "打卡成功", (l, b + 30), textColor=(0, 255, 0), textSize=40)
if need_insert:
# 连续显示打卡成功1s
frame = cv2AddChineseText(frame, "打卡成功", (l, b + 30), textColor=(0, 255, 0), textSize=40)
show_time = time.time()
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