人工智能之基於face_recognition的人臉檢測與識別

不久乘高鐵出行，看見高鐵火車站已經實現了“刷臉進站”，而且效率很高，很感興趣，今天抽時間研究一下，其實沒那么復雜。

我基本上是基於https://github.com/ageitgey/face_recognition上的資料和源碼做一些嘗試和試驗。

首先，需要配置我們的python環境，我懸着的python27(比較穩定)，具體過程不多說了。

然后，需要安裝這次的主角face_recognition庫，這個的安裝花了我不少時間，需要注意一下幾點(按照本人的環境)：

　　1，首先，安裝visual studio 2015，因為vs2015默認只安裝c#相關組件，所以需要安裝c++相關組件。

　　　　ps:vs2015安裝c++相關組件的方法：在vs2015中新建c++項目，出現下面場景

　　　　

　　　　選擇第二項，確定后就會自動安裝。

　　　　為什么需要安裝c++,因為安裝face_recognition時會先安裝dlib,dlib是基於c++的一個庫。

　　2，安裝cmake(一個跨平台編譯工具),然后需要將cmake的安裝路徑加入到系統環境變量path中去。

最后，就可以直接在dos中執行安裝命令了(需要切換到python目錄下的Script目錄下)：pip install  face_recognition,命令會自動幫你安裝好需要的dlib庫。　

到此為止，我們完成了face_recognition安裝工作。

 

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下面給出幾個實例來逐步了解“人臉識別”：

1.一行代碼實現“人臉識別”

 

在Python目錄中新建兩個文件夾：分別表示“已知姓名的人”和“未知姓名的人”，圖片以額、人名命名，如下：

 

 接下來，我們通過“認識的人”來識別“不認識的人”：

結果表明：1.jpg不認識，3.jpg是obama,unkown.jpg中有兩個人，一個是obama,另一個不認識

結果還挺准確的！很給力！！

 

2.識別圖片中所有的人臉，並顯示出來

import Image
import face_recognition
image = face_recognition.load_image_file('F:/Python27/Scripts/all.jpg')
face_locations = face_recognition.face_locations(image)

#face_locations =face_recognition.

#face_locations(image,number_of_times_to_upsample=0,model='cnn')
print('i found {} face(s) in this photograph.'.format(len(face_locations)))
for face_location in face_locations:
    top,right,bottom,left = face_location
    print('A face is located at pixel location Top:{},Left:{},Bottom:{},Right:{}'.format(top,right,bottom,left))
    face_image = image[top:bottom,left:right]
    pil_image=Image.fromarray(face_image)
    pil_image.show()

避坑指南：import Image需要先安裝PIL庫，在pycharm中安裝的時候會報錯（因為pil沒有64位的版本），這時我們安裝Pillow-PIL就好了。

我們的all.jpg如下：

 

 執行以下，看看結果：

沒有錯，總共12個人臉都被識別出來了！！！

 

3.給照片“美顏”

face_recognition可以識別人像的下巴，眼睛，鼻子，嘴唇，眼球等區域，包含以下這些個特征:

　　facial_features = [ 'chin', 'left_eyebrow', 'right_eyebrow', 'nose_bridge', 'nose_tip', 'left_eye', 'right_eye', 'top_lip', 'bottom_lip' ]

       利用這些特征屬性，可以輕松的給人像“美顏”

from PIL import Image, ImageDraw
face_recognition
import face_recognition


image = face_recognition.load_image_file("F:/Python27/Scripts/known_people/obama.jpg")

#查找圖像中所有面部的所有面部特征
face_landmarks_list = face_recognition.face_landmarks(image)

for face_landmarks in face_landmarks_list:
    pil_image = Image.fromarray(image)
    d = ImageDraw.Draw(pil_image, 'RGBA')

    #讓眉毛變成了一場噩夢
    d.polygon(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 128))
    d.polygon(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 128))
    d.line(face_landmarks['left_eyebrow'], fill=(68, 54, 39, 150), width=5)
    d.line(face_landmarks['right_eyebrow'], fill=(68, 54, 39, 150), width=5)

    #光澤的嘴唇
    d.polygon(face_landmarks['top_lip'], fill=(150, 0, 0, 128))
    d.polygon(face_landmarks['bottom_lip'], fill=(150, 0, 0, 128))
    d.line(face_landmarks['top_lip'], fill=(150, 0, 0, 64), width=8)
    d.line(face_landmarks['bottom_lip'], fill=(150, 0, 0, 64), width=8)

    #閃耀眼睛
    d.polygon(face_landmarks['left_eye'], fill=(255, 255, 255, 30))
    d.polygon(face_landmarks['right_eye'], fill=(255, 255, 255, 30))

    #塗一些眼線
    d.line(face_landmarks['left_eye'] + [face_landmarks['left_eye'][0]], fill=(0, 0, 0, 110), width=6)
    d.line(face_landmarks['right_eye'] + [face_landmarks['right_eye'][0]], fill=(0, 0, 0, 110), width=6)

    pil_image.show()

執行下看看結果：

有點辣眼睛！！！！

 

4.利用筆記本攝像頭識別人像

回到前面說的高鐵站的“刷臉”，其實就是基於攝像頭的“人像識別”。

這里要調用電腦的攝像頭，而且涉及一些計算機視覺系統的計算，所以我們要先安裝opencv庫，

安裝方法：

pip install --upgrade setuptools
pip install numpy Matplotlib
pip install opencv-python

　ps:如果報錯：EnvironmentError: [Errno 13] Permission denied: 在install后加上--user即可

         小技巧：可以在python命令行中用 import site; site.getsitepackages()來確定當前的python環境的site-packages目錄的位置

目的：這里我們需要用攝像頭識別自己，那么首先需要有一張自己的照片，我將我的照片命名為mike.jpg,然后使用攝像頭來識別我自己。

 看看代碼：

import face_recognition
import cv2

# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
# other example, but it includes some basic performance tweaks to make things run a lot faster:
#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
#   2. Only detect faces in every other frame of video.

# 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("F:/Python27/Scripts/known_people/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("F:/Python27/Scripts/known_people/mike.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",
    "mike"
]

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

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

    # Resize frame of video to 1/4 size for faster face recognition processing
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

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

    # Only process every other frame of video to save time
    if process_this_frame:
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)

        face_names = []
        for face_encoding in 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]

            face_names.append(name)

    process_this_frame = not process_this_frame


    # Display the results
    for (top, right, bottom, left), name in zip(face_locations, face_names):
        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
        top *= 4
        right *= 4
        bottom *= 4
        left *= 4

        # 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)

    # 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()

只想看看結果：

 

看來，我被識別成功了。看起來有點小激動呢。

 

 

 

通過上面四個小例子基本了解face_recognition的用法，這只是小試牛刀，具體在現實中的應用要復雜很多，

我們需要大量的人臉數據，會涉及到機器學習和數學算法等等，而且根據應用場景的不同也會出現很多不同的要求。

這里只是一起學習分享，期待后續關於"人工智能"的內容。