一、安装相关包
安装dlib、cmake、face_recognition
二、获取人脸的128位数组编码
使用基本演绎法这两个演员的脸测试，只用了这两张正脸进行识别，如果要识别准确，得准备多种角度的照片，才能比较精准。
# 使用Face recognition获取人脸128位数组
from imutils.video import VideoStream
from imutils import paths
import face_recognition
import argparse
import pickle
import cv2
import os
import imutils
def train():
# grab the paths to the input images in our dataset
print("[INFO] quantifying faces...")
imagePaths = ['C:Users/xia/Desktop/Watson.png','C:/Users/xia/Desktop/Sherlock.png']
# initialize the list of known encodings and known names
knownEncodings = []
knownNames = []
# loop over the image paths
for (i, imagePath) in enumerate(imagePaths):
# extract the person name from the image path
print("[INFO] processing image {}/{}".format(i + 1, len(imagePaths)))
name = 'Watson'
if i==1:
name = 'Sherlock'
# load the input image and convert it from BGR (OpenCV ordering)
# to dlib ordering (RGB)
image = cv2.imread(imagePath)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# detect the (x, y)-coordinates of the bounding boxes
# corresponding to each face in the input image
boxes = face_recognition.face_locations(rgb, model='cnn')
# compute the facial embedding for the face
encodings = face_recognition.face_encodings(rgb, boxes)
# loop over the encodings
for encoding in encodings:
# add each encoding + name to our set of known names and
# encodings
knownEncodings.append(encoding)
knownNames.append(name)
# dump the facial encodings + names to disk
print("[INFO] serializing encodings...")
data = {"encodings": knownEncodings, "names": knownNames}
f = open('C:/Users/xia/Desktop/encodings.pickle', "wb")
f.write(pickle.dumps(data))
f.close()三、在图片上测试
# 使用Face recognition获取人脸128位
from imutils.video import VideoStream
from imutils import paths
import face_recognition
import argparse
import pickle
import cv2
import os
import imutils
# 在图片上测试
def reimage():
# load the known faces and embeddings
print("[INFO] loading encodings...")
data = pickle.loads(open('C:/Users/xia/Desktop/encodings.pickle', "rb").read())
# load the input image and convert it from BGR to RGB
image = cv2.imread('C:/Users/xia/Desktop/v2.jpg')
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# detect the (x, y)-coordinates of the bounding boxes corresponding
# to each face in the input image, then compute the facial embeddings
# for each face
print("[INFO] recognizing faces...")
boxes = face_recognition.face_locations(rgb, model='cnn')
encodings = face_recognition.face_encodings(rgb, boxes)
# initialize the list of names for each face detected
names = []
# loop over the facial embeddings
for encoding in encodings:
# attempt to match each face in the input image to our known
# encodings
matches = face_recognition.compare_faces(data["encodings"], encoding)
name = "Unknown"
# check to see if we have found a match
if True in matches:
# find the indexes of all matched faces then initialize a
# dictionary to count the total number of times each face
# was matched
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}
# loop over the matched indexes and maintain a count for
# each recognized face face
for i in matchedIdxs:
name = data["names"][i]
counts[name] = counts.get(name, 0) + 1
# determine the recognized face with the largest number of
# votes (note: in the event of an unlikely tie Python will
# select first entry in the dictionary)
name = max(counts, key=counts.get)
# update the list of names
names.append(name)
# loop over the recognized faces
for ((top, right, bottom, left), name) in zip(boxes, names):
# draw the predicted face name on the image
cv2.rectangle(image, (left, top), (right, bottom), (0, 255, 0), 2)
y = top - 15 if top - 15 > 15 else top + 15
cv2.putText(image, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 255, 0), 2)
# show the output image
cv2.imshow("Image", image)
cv2.waitKey(0)
四、摄像头实时测试
使用cpu+hpg也是很卡，得上gpu测试
# 使用Face recognition获取人脸128位
from imutils.video import VideoStream
from imutils import paths
import face_recognition
import argparse
import pickle
import cv2
import os
import imutils
# 实时视频，但是需要gpu，即使用cpu+hog，也会卡顿
def revideo():
# load the known faces and embeddings
print("[INFO] loading encodings...")
data = pickle.loads(open('C:/Users/zyh/Desktop/encodings.pickle', "rb").read())
# initialize the video stream and pointer to output video file, then
# allow the camera sensor to warm up
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
writer = None
#time.sleep(2.0)
# loop over frames from the video file stream
while True:
# grab the frame from the threaded video stream
frame = vs.read()
# convert the input frame from BGR to RGB then resize it to have
# a width of 750px (to speedup processing)
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
rgb = imutils.resize(frame, width=750)
r = frame.shape[1] / float(rgb.shape[1])
# detect the (x, y)-coordinates of the bounding boxes
# corresponding to each face in the input frame, then compute
# the facial embeddings for each face
boxes = face_recognition.face_locations(rgb, model='hog')
encodings = face_recognition.face_encodings(rgb, boxes)
names = []
# loop over the facial embeddings
for encoding in encodings:
# attempt to match each face in the input image to our known
# encodings
matches = face_recognition.compare_faces(data["encodings"],
encoding)
name = "Unknown"
# check to see if we have found a match
if True in matches:
# find the indexes of all matched faces then initialize a
# dictionary to count the total number of times each face
# was matched
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}
# loop over the matched indexes and maintain a count for
# each recognized face face
for i in matchedIdxs:
name = data["names"][i]
counts[name] = counts.get(name, 0) + 1
# determine the recognized face with the largest number
# of votes (note: in the event of an unlikely tie Python
# will select first entry in the dictionary)
name = max(counts, key=counts.get)
# update the list of names
names.append(name)
# loop over the recognized faces
for ((top, right, bottom, left), name) in zip(boxes, names):
# rescale the face coordinates
top = int(top * r)
right = int(right * r)
bottom = int(bottom * r)
left = int(left * r)
# draw the predicted face name on the image
cv2.rectangle(frame, (left, top), (right, bottom),
(0, 255, 0), 2)
y = top - 15 if top - 15 > 15 else top + 15
cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 255, 0), 2)
# 写入视频
# # if the video writer is None *AND* we are supposed to write
# # the output video to disk initialize the writer
# if writer is None and args["output"] is not None:
# fourcc = cv2.VideoWriter_fourcc(*"MJPG")
# writer = cv2.VideoWriter(args["output"], fourcc, 20,
# (frame.shape[1], frame.shape[0]), True)
# # if the writer is not None, write the frame with recognized
# # faces to disk
# if writer is not None:
# writer.write(frame)
# check to see if we are supposed to display the output frame to
# the screen
if 1 > 0:
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()
# check to see if the video writer point needs to be released
if writer is not None:
writer.release()五、打开视频测试
# 使用Face recognition获取人脸128位
from imutils.video import VideoStream
from imutils import paths
import face_recognition
import argparse
import pickle
import cv2
import os
import imutils
def revideo1():
# load the known faces and embeddings
print("[INFO] loading encodings...")
data = pickle.loads(open('C:/Users/xia/Desktop/encodings.pickle', "rb").read())
# initialize the video stream and pointer to output video file, then
cap = cv2.VideoCapture('C:/Users/xia/Desktop/123.mp4')
# loop over frames from the video file stream
while(cap.isOpened()):
# grab the frame from the threaded video stream
ret, frame = cap.read()
# convert the input frame from BGR to RGB then resize it to have
# a width of 750px (to speedup processing)
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
rgb = imutils.resize(frame, width=750)
r = frame.shape[1] / float(rgb.shape[1])
# detect the (x, y)-coordinates of the bounding boxes
# corresponding to each face in the input frame, then compute
# the facial embeddings for each face
boxes = face_recognition.face_locations(rgb, model='hog')
encodings = face_recognition.face_encodings(rgb, boxes)
names = []
# loop over the facial embeddings
for encoding in encodings:
# attempt to match each face in the input image to our known
# encodings
matches = face_recognition.compare_faces(data["encodings"], encoding)
name = "Unknown"
# check to see if we have found a match
if True in matches:
# find the indexes of all matched faces then initialize a
# dictionary to count the total number of times each face
# was matched
matchedIdxs = [i for (i, b) in enumerate(matches) if b]
counts = {}
# loop over the matched indexes and maintain a count for
# each recognized face face
for i in matchedIdxs:
name = data["names"][i]
counts[name] = counts.get(name, 0) + 1
# determine the recognized face with the largest number
# of votes (note: in the event of an unlikely tie Python
# will select first entry in the dictionary)
name = max(counts, key=counts.get)
# update the list of names
names.append(name)
# loop over the recognized faces
for ((top, right, bottom, left), name) in zip(boxes, names):
# rescale the face coordinates
top = int(top * r)
right = int(right * r)
bottom = int(bottom * r)
left = int(left * r)
# draw the predicted face name on the image
cv2.rectangle(frame, (left, top), (right, bottom),
(0, 255, 0), 2)
y = top - 15 if top - 15 > 15 else top + 15
cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
0.75, (0, 255, 0), 2)
# check to see if we are supposed to display the output frame to
# the screen
if 1 > 0:
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
# do a bit of cleanup
cap.release()
cv2.destroyAllWindows()六、理论参考
http://blog.dlib.net/2017/02/high-quality-face-recognition-with-deep.html