dlib实现人脸landmark点检测以及一些其他的应用

首先从中这里下载下代码：

https://github.com/ageitgey/face_recognition#face-recognition

然后安装所以必须的组件，我用的Python3.5

进入example里面跑他的demo，主要就是掉了dlib的接口比如：

    face_locations = face_recognition.face_locations(rgb_frame)
    face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
    face_landmarks_list = face_recognition.face_landmarks(rgb_frame)

 引入了OpenCV稍微改了下显示，camera实时跟踪人脸的特征点，速度奇慢，而且还不准。

代码很简单：

import face_recognition
import cv2

# This is a super simple (but slow) example of running face recognition on live video from your webcam.
# There's a second example that's a little more complicated but runs faster.

# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("obama.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]

# Load a second sample picture and learn how to recognize it.
biden_image = face_recognition.load_image_file("biden.jpg")
biden_face_encoding = face_recognition.face_encodings(biden_image)[0]

# Create arrays of known face encodings and their names
known_face_encodings = [
    obama_face_encoding,
    biden_face_encoding
]
known_face_names = [
    "Barack Obama",
    "Joe Biden"
]

while True:
    # Grab a single frame of video
    ret, frame = video_capture.read()

    # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
    rgb_frame = frame[:, :, ::-1]

    # Find all the faces and face enqcodings in the frame of video
    # face_locations = face_recognition.face_locations(rgb_frame)
    # face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)
    face_landmarks_list = face_recognition.face_landmarks(rgb_frame)

    # Loop through each face in this frame of video
    # for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):
    #     # See if the face is a match for the known face(s)
    #     matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
    #
    #     name = "Unknown"

        # If a match was found in known_face_encodings, just use the first one.
        # if True in matches:
        #     first_match_index = matches.index(True)
        #     name = known_face_names[first_match_index]

        # # Draw a box around the face
        # cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
        #
        # # Draw a label with a name below the face
        # cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
        # font = cv2.FONT_HERSHEY_DUPLEX
        # cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

    for face_landmarks in face_landmarks_list:

        # Print the location of each facial feature in this image
        facial_features = [
            'chin',
            'left_eyebrow',
            'right_eyebrow',
            'nose_bridge',
            'nose_tip',
            'left_eye',
            'right_eye',
            'top_lip',
            'bottom_lip'
        ]


        for facial_feature in facial_features:
            for point in face_landmarks[facial_feature]:
                cv2.circle(frame, point, 1, (255, 0, 0))

    # Display the resulting image
    cv2.imshow('Video', frame)

    # Hit 'q' on the keyboard to quit!
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()