首先是face_recognition安裝
一、到github上面查找中文資料，然後查看樹莓派安裝教程
https://github.com/ageitgey/face_recognition
樹莓派安裝的教程路徑是下面這個（中間有些地方和我不一樣，我照這個鏈接，沒成功過，安裝那個face_rec網速太慢，根本沒下載下來過）
https://gist.github.com/ageitgey/1ac8dbe8572f3f533df6269dab35df65
二、需要更正的步驟
1、libatlas-dev 無法候選，這裏安裝替換的 libatlas-base-dev
2、dlib安裝按照例子如下，但是這裏需要更改
我是按照這樣安裝，網上查資料聽說這個版本才般配
sudo pip3 install dlib==19.7.0
然後直接開始安裝就好了
sudo pip3 install face_recognition
三、進行測試face_recognition
然後按照原鏈接還原更換交換區大小，不用下載示例代碼，我下載了之後好像不能用啥的，你可以參考這個鏈接直接使用
https://github.com/ageitgey/face_recognition/blob/master/README_Simplified_Chinese.md
主要使用就是這句話，中間那個文件夾是你知道的人照片文件夾，名字是圖片的文件名，後面那個文件夾放的是你不認識的人的圖片，然後會識別出名字並且打印出來，但是經過測試，速度真慢，而且只是單純的圖片識別，你難道不想玩玩在線攝像頭實時識別嗎？
face_recognition ./pictures_of_people_i_know/ ./unknown_pictures/
如果有攝像頭的小朋友，可以開始安裝openCV開始攝像頭識別了。
最後是安裝opencv、opencv_contrib
一、配置樹莓派並打開攝像頭
打開攝像頭設置，啓用攝像頭
sudo raspi-config
選擇是就好了
更新樹莓派軟件
sudo apt-get update
sudo apt-get upgrade
二、安裝OpenCV的相關工具
sudo apt-get install build-essential cmake git pkg-config
三、安裝OpenCV的圖像工具包
sudo apt-get install libjpeg8-dev
sudo apt-get install libtiff5-dev
sudo apt-get install libjasper-dev
sudo apt-get install libpng12-dev
四、安裝視頻I/O包
sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
五、安裝gtk2.0和優化函數包
sudo apt-get install libgtk2.0-dev
sudo apt-get install libatlas-base-dev gfortran
六、下載OpenCV源碼
git clone https://github.com/opencv/opencv.git
七、下載OpenCV_contrib
git clone https://github.com/opencv/opencv_contrib.git
八、安裝OpenCV
// 根據下載的版本而定
cd opencv
// 創建release文件夾
mkdir release
// 進入release目錄下
cd release
// cmake讀入所有源文件之後，自動生成makefile，複製下面所有(更改第三行的路徑)，粘貼回車
cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D OPENCV_EXTRA_MODULES_PATH=/home/pi/software/opencv_contrib/modules \
-D ENABLE_NEON=ON \
-D ENABLE_VFPV3=ON \
-D BUILD_TESTS=OFF \
-D OPENCV_ENABLE_NONFREE=ON \
-D INSTALL_PYTHON_EXAMPLES=OFF \
-DCMAKE_SHARED_LINKER_FLAGS='-latomic' \
-D BUILD_EXAMPLES=OFF ..
// 編譯(建議 sudo make -j4 速度快呀)
sudo make -j4
// 安裝
sudo make install
//更新動態鏈接庫
sudo ldconfig
九、測試代碼來了
https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam_faster.py
這個鏈接，就是測試代碼，速度比較快
import face_recognition
import cv2
import numpy as np
# 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("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"
]
# 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]
# Or instead, use the known face with the smallest distance to the new face
face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
best_match_index = np.argmin(face_distances)
if matches[best_match_index]:
name = known_face_names[best_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()
//下載一下奧巴馬和拜登兩個人的照片各一張放到路徑下
python3 python3 facerec_from_webcam_faster.py
這裏沒有屏幕的兄弟姐妹們，下載個Xmanager，然後就可以了，會顯示一個圖相框來，我這裏識別王力宏和蒲巴甲
十、測試demo下載
速度還比較快速，比直接對比兩張圖片快得多，demo下載的路徑是這個
https://github.com/ageitgey/face_recognition
我用的那個網絡攝像頭識別實時視頻中的人臉，更快的版本，大家裝好了以上三個，就可以開啓自己人臉識別旅程了。
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