python版本 3.7.0 

1、 安装 cmake

pip install cmake 

2、安装 boost

pip install boost 

3、安装 dlib

pip install dlib 

4、安装 face_recognition

pip install face_recognition 

5、验证

face_recognition 本地模型路径 要识别图片路径 
输出:文件名 识别的人名 

注意:文件名以人名命名 

6、寻找人脸位置

face_detection “路径” 
输出:人脸像素坐标 

7、调整灵敏度

face_recognition –tolerance 灵敏度 本地模型路径 要识别图片路径 
注:默认0.6,识别度越低识别难度越高 

8、计算每次面部距离

face_recognition –show-distance true 本地模型路径 要识别图片路径 

9、只是想知道每张照片中人物的姓名,却不关心文件名,可以这样做:

face_recognition 本地模型路径 要识别图片路径 | cut -d ‘,’ -f2

10、加速识别

face_recognition –cpus 使用内核数 本地模型路径 要识别图片路径 
使用四核识别: 
face_recognition –cpus 4 本地模型路径 要识别图片路径 
 
使用全部内核识别: 
face_recognition –cpus -1 本地模型路径 要识别图片路径

11、自动查找图像中的所有面孔

import face_recognition

image = face_recognition.load_image_file(“吴京.jpg”) 
face_locations = face_recognition.face_locations(image)

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()  

彩蛋

import cv2
import threading
import face_recognition
import numpy as np
import os class camThread(threading.Thread):
def __init__(self, previewName, camID):
threading.Thread.__init__(self)
self.previewName = previewName
self.camID = camID
def run(self):
print("Starting " + self.previewName)
camPreview(self.previewName, self.camID) def camPreview(previewName, camID):
cv2.namedWindow(previewName)
video_capture = cv2.VideoCapture(camID)
if video_capture.isOpened():
rval, frame = video_capture.read()
else:
rval = False known_face_encodings = []
known_face_names = [] imagelist = os.listdir('./face/')
for imagename in imagelist:
image = face_recognition.load_image_file("./face/"+imagename)
face_encoding = face_recognition.face_encodings(image)[0]
known_face_encodings.append(face_encoding)
subname=imagename.split('.')[0]
known_face_names.append(subname)
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True while rval:
#cv2.imshow(previewName, frame)
rval, frame = video_capture.read()
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
rgb_small_frame = small_frame[:, :, ::-1]
if process_this_frame:
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:
matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
name = "Unknown" 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 for (top, right, bottom, left), name in zip(face_locations, face_names):
top *= 4
right *= 4
bottom *= 4
left *= 4 cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2) 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) cv2.imshow(previewName, frame) if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyWindow(previewName) thread1 = camThread("Camera 1", 0)
thread2 = camThread("Camera 2", 1) thread1.start()
thread2.start()

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