我正在尝试构建一个使用面部识别库实时检测面部的软件。 我使用网络摄像头进行了尝试，结果令人鼓舞，帧频也相当稳定，但是当我切换到.mp4视频时，在fps方面效果非常差。 我在OpenCV中使用Python 3.6，这是我正在使用的代码：

import face_recognition

import cv2

# Load a sample picture and learn how to recognize it.

totti_image = face_recognition.load_image_file("totti.jpg")

totti_face_encoding = face_recognition.face_encodings(totti_image)[0]

# Create arrays of known face encodings and their names

known_face_encodings = [

totti_face_encoding

]

known_face_names = [

"Francesco Totti"

]

def get_faces(frame):

# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)

rgb_frame = frame[:, :, ::-1]

# Find all the faces and face enqcodings in the frame of video

face_locations = face_recognition.face_locations(rgb_frame)

face_encodings = face_recognition.face_encodings(rgb_frame, face_locations)

# Loop through each face in this frame of video

for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):

# See if the face is a match for the known face(s)

matches = face_recognition.compare_faces(known_face_encodings, face_encoding, tolerance=0.50)

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]

# 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)

return frame

在每帧的while循环内都会调用函数" get_faces"，而我的性能约为0.5 fps。

如果有人有建议以便获得更好的fps输出，请告诉我，谢谢。

编辑：

我使用下面的示例(使其适应我的需求)，并且一切工作得更好：

链接

最终代码：

import face_recognition

import cv2

# Load a sample picture and learn how to recognize it.

image = face_recognition.load_image_file("totti.jpg")

encoding = face_recognition.face_encodings(image)[0]

# Create arrays of known face encodings and their names

known_face_encodings = [

encoding

]

known_face_names = [

"Totti",

]

# Initialize some variables

face_locations = []

face_encodings = []

face_names = []

def get_faces(frame):

# Resize frame of video to 1/10 size for faster face recognition processing

small_frame = cv2.resize(frame, (0, 0), fx=0.1, fy=0.1)

# Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)

rgb_small_frame = small_frame[:, :, ::-1]

# 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 ="Person"

# 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)

# 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 1/10 size

top *= 10

right *= 10

bottom *= 10

left *= 10

# 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)

return frame

要确定脚本的哪些部分运行时间最长，请使用探查器。 这将输出执行每个调用的时间，因此您可以更好地了解函数的哪些部分不是最佳的。 有关如何配置代码的示例，请参见Python Profilers。

从文档中：

SPEEDING UP FACE RECOGNITION

Face recognition can be done in parallel if you have a computer with

multiple CPU cores. For example if your system has 4 CPU cores, you

can process about 4 times as many images in the same amount of time by

using all your CPU cores in parallel. If you are using Python 3.4 or

newer, pass in a --cpus parameter:

face_recognition --cpus 4 ./pictures_of_people_i_know/ ./unknown_pictures/

You can also pass in --cpus -1 to use all CPU cores in your system.

使用一个，然后使用最大核数，在计算机上测试操作。 如果这可以显着缩短执行时间，那么最好的做法是在自己的脚本中实施多处理。

谢谢您的回答，但我已经知道问题出在以下几行：face_locations = face_recognition.face_locations(rgb_frame)face_encodings = face_recognition.face_encodings(rgb_frame，face_locations)但是我无法更改它们，因为这些是我需要的库函数。 这是我在网上找到的最好的处理方法，但是确实很慢，所以我的问题不是要找到运行时间最长的部分，而是是否有使用此代码的方法或我没有找到的其他方法 为了得到更快的分析。

@ J.Blackadar，我可以在我的python代码中添加face_recognition --cpus 4吗？ 我正在使用pi 2进行人脸识别，但是速度很慢。 :(

@ M.D.P仅通过示例使用face_recognition本身就可以增加CPU。 在您的实际代码中，您需要实现多处理，以将计算负荷分配到多个内核。