1、安装dlib
sudo pip install dlib
如果不能安装参见前文
sudo pip install face_recognition
python
>>>import face_recognition
不报错识别成功
2、识别照片中的人脸
#coding:utf-8
# 检测人脸
import face_recognition
import cv2
# 读取图片并识别人脸
img = face_recognition.load_image_file("silicon_valley.jpg")
face_locations = face_recognition.face_locations(img)
print face_locations
# 调用opencv函数显示图片
img = cv2.imread("silicon_valley.jpg")
cv2.namedWindow("原图")
cv2.imshow("原图", img)
# 遍历每个人脸,并标注
faceNum = len(face_locations)
for i in range(0, faceNum):
top = face_locations[i][0]
right = face_locations[i][1]
bottom = face_locations[i][2]
left = face_locations[i][3]
start = (left, top)
end = (right, bottom)
color = (55,255,155)
thickness = 3
cv2.rectangle(img, start, end, color, thickness)
# 显示识别结果
cv2.namedWindow("识别")
cv2.imshow("识别", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
3、视频实时识别
import face_recognition
import cv2
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
# Load a sample picture and learn how to recognize it.
m1_image = face_recognition.load_image_file("M1_B1.jpg")
m1_face_encoding = face_recognition.face_encodings(m1_image)[0]
m2_image = face_recognition.load_image_file("M2.jpg")
m2_face_encoding = face_recognition.face_encodings(m2_image)[0]
k1_image = face_recognition.load_image_file("k1.jpg")
k1_face_encoding = face_recognition.face_encodings(k1_image)[0]
B1_image = face_recognition.load_image_file("B1.jpg")
B1_face_encoding = face_recognition.face_encodings(B1_image)[0]
B2_image = face_recognition.load_image_file("B2.jpg")
B2_face_encoding = face_recognition.face_encodings(B2_image)[0]
# 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)
# 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(small_frame)
face_encodings = face_recognition.face_encodings(small_frame, face_locations)
face_names = []
faces_to_compare = [
m1_face_encoding,
m2_face_encoding,
k1_face_encoding,
B1_face_encoding,
B2_face_encoding]
for face_encoding in face_encodings:
# See if the face is a match for the known face(s)
match = face_recognition.compare_faces(faces_to_compare, face_encoding, tolerance=0.5)
name = "Unknown"
print(match)
if match[0]:
name = "M1"
if match[1]:
name = "M2"
if match[2]:
name = "K1"
if match[3]:
name = "B1"
if match[4]:
name = "B2"
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()
参考备注:
https://face-recognition.readthedocs.io/en/latest/readme.html
http://blog.youkuaiyun.com/hongbin_xu/article/details/76284134
https://zhuanlan.zhihu.com/p/27275307
http://blog.youkuaiyun.com/yang_xian521/article/category/910716/5