人脸跟踪、识别、脸部标识

知识点：

1. 人脸跟踪
2. 人脸特征
3. 要识别的人脸特征距离

 

看效果图，鼻子部分已经被标识出来了：

 

 

 

主要用到了dlib

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import dlib   predictor_path = 'models\\shape_predictor_68_face_landmarks.dat' face_rec_model_path = 'models\\dlib_face_recognition_resnet_model_v1.dat' detector = dlib.get_frontal_face_detector()                       #人脸跟踪 predictor = dlib.shape_predictor(predictor_path)                  #人脸68个特征点检测器 facerec = dlib.face_recognition_model_v1(face_rec_model_path)     #映射人脸为128维特征值

 

由于目标里还希望把鼻子区域标识出来（方便后续自己贴图上去），因此：

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from imutils import face_utils   (noseStart, noseEnd) = face_utils.FACIAL_LANDMARKS_IDXS["nose"]   #省略一部分代码，主要是省略了opencv调用摄像头逻辑代码           success, img = cap.read()                                  #读取摄像头视频信息         frame = imutils.resize(img, width=300)                     #resize，尺寸越小处理越快         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)             #变灰度图         rects = detector(gray, 0)                                  #人脸区域跟踪         rect = rects[0]                                            #假设只有1个区域存在人脸         shape = predictor(gray, rect)                              #识别68个特征点         shape = face_utils.shape_to_np(shape)                      #转换为numpy格式         nose = shape[noseStart:noseEnd]                            #只拿鼻子区域的点         noseHull = cv2.convexHull(nose)                            #把这些点转换为凸包         cv2.drawContours(frame, [noseHull], -1, (0, 255, 0), 1)    #画出这些凸包外形         cv2.putText(frame, "nose", (nose[0][0], nose[0][1]), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)         #文本标注

　　

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1	face_utils.FACIAL_LANDMARKS_IDXS，这个是对68个点的描述，有很多（有兴趣大家自己试试）：

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#For dlib’s 68-point facial landmark detector: FACIAL_LANDMARKS_68_IDXS = OrderedDict([     ("mouth", (48, 68)),     ("inner_mouth", (60, 68)),     ("right_eyebrow", (17, 22)),     ("left_eyebrow", (22, 27)),     ("right_eye", (36, 42)),     ("left_eye", (42, 48)),     ("nose", (27, 36)),     ("jaw", (0, 17)) ])

　　

接下来就是识别人脸到底是谁了

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def get_feature(path):     img = imread(path)     frame = img     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)     dets = detector(gray, 0)     shape = predictor(gray, dets[0])     face_vector = facerec.compute_face_descriptor(img, shape)     return face_vector   faces = [            (get_feature('faces\\dbh.jpg'), 'McKay')         ]

　　目前就1张，所以只load了1个到faces array里

       实际匹配代码如下：

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def distance(a, b):     a, b = np.array(a), np.array(b)     sub = np.sum((a - b) ** 2)     add = (np.sum(a ** 2) + np.sum(b ** 2)) / 2.     r = sub / add     return r def process_face_id(faces, frame, rect, shape):     found_face_id = 'Unknown'     if len(faces) > 0:         face_descriptor = facerec.compute_face_descriptor(frame, shape)         min_face_id = found_face_id         min_face_distance = 1         for face_feature, face_id in faces:             cur_distance = distance(face_feature, face_descriptor)             if cur_distance < min_face_distance:                 min_face_distance = cur_distance                 min_face_id = face_id           if min_face_distance < threshold:             found_face_id = min_face_id     cv2.rectangle(frame, (rect.left(), rect.top() + 10), (rect.right(), rect.bottom()), (0, 255, 0), 2)     cv2.putText(frame, found_face_id, (rect.left(), rect.top()), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2, cv2.LINE_AA)     if found_face_id != 'Unknown':         events.append(('user_found', found_face_id, time.time()))

　　

需要pip install的有：

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import dlib import cv2 import numpy as np import imutils from imutils import face_utils from imageio import imread     pip install cmake pip install dlib pip install opencv-python pip install numpy pip install imutils pip install imageio

　　

完整代码

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import dlib import cv2 import numpy as np import imutils from imutils import face_utils from imageio import imread import time   predictor_path = 'models\\shape_predictor_68_face_landmarks.dat' face_rec_model_path = 'models\\dlib_face_recognition_resnet_model_v1.dat'   predictor = dlib.shape_predictor(predictor_path) detector = dlib.get_frontal_face_detector() facerec = dlib.face_recognition_model_v1(face_rec_model_path)   (noseStart, noseEnd) = face_utils.FACIAL_LANDMARKS_IDXS["nose"]   threshold = 0.12     def get_feature(path):     img = imread(path)     frame = img     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)     dets = detector(gray, 0)     # print('检测到了 %d 个人脸' % len(dets))     # 这里假设每张图只有一个人脸     shape = predictor(gray, dets[0])     face_vector = facerec.compute_face_descriptor(img, shape)     return face_vector     def distance(a, b):     a, b = np.array(a), np.array(b)     sub = np.sum((a - b) ** 2)     add = (np.sum(a ** 2) + np.sum(b ** 2)) / 2.     r = sub / add     return r     faces = None cap = None success = None events = []     def init():     global faces     global cap     global success     faces = [                 (get_feature('faces\\dbh.jpg'), 'McKay')             ]       cap = cv2.VideoCapture(0)     success, img = cap.read()   def start():     global faces     global cap     global success     while success:         success, img = cap.read()         frame = imutils.resize(img, width=300)         gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)         rects = detector(gray, 0)         for rect in rects:             shape = predictor(gray, rect)               process_face_id(faces, frame, rect, shape)               shape = face_utils.shape_to_np(shape)             nose = shape[noseStart:noseEnd]             noseHull = cv2.convexHull(nose)             cv2.drawContours(frame, [noseHull], -1, (0, 255, 0), 1)             cv2.putText(frame, "nose", (nose[0][0], nose[0][1]), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)           cv2.imshow("Frame", frame)         key = cv2.waitKey(1) & 0xFF       cv2.destroyAllWindows()     def process_face_id(faces, frame, rect, shape):     found_face_id = 'Unknown'     if len(faces) > 0:         face_descriptor = facerec.compute_face_descriptor(frame, shape)         min_face_id = found_face_id         min_face_distance = 1         for face_feature, face_id in faces:             cur_distance = distance(face_feature, face_descriptor)             if cur_distance < min_face_distance:                 min_face_distance = cur_distance                 min_face_id = face_id           if min_face_distance < threshold:             found_face_id = min_face_id     cv2.rectangle(frame, (rect.left(), rect.top() + 10), (rect.right(), rect.bottom()), (0, 255, 0), 2)     cv2.putText(frame, found_face_id, (rect.left(), rect.top()), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2, cv2.LINE_AA)     if found_face_id != 'Unknown':         events.append(('user_found', found_face_id, time.time()))