此程序使用网络摄像头，并在OpenCV的帮助下使用face_recignition库定位和识别面部。我没有合适的网络摄像头（我的内置网络摄像头很糟糕），所以我决定为其使用DroidCam Client（https://www.dev47apps.com/）。通过从网络摄像头中取出单个框架然后识别它，可以起作用。
CODE（使用默认摄像头）
import face_recognition
import cv2
import numpy as np
import time
# 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.
# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)
# Load a sample picture and learn how to recognize it.
aarna_image = face_recognition.load_image_file("aarna.jpg")
aarna_face_encoding = face_recognition.face_encodings(aarna_image)[0]
# Load a second sample picture and learn how to recognize it.
aditya_image = face_recognition.load_image_file("aditya.jpg")
aditya_face_encoding = face_recognition.face_encodings(aditya_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
aarna_face_encoding,
aditya_face_encoding
]
known_face_names = [
"Aarna Gupta",
"Aditya"
]
# Initialize some variables
while True:
face_locations = []
face_encodings = []
face_names = []
process_this_frame = 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)
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
运行以上代码后，它可以正常工作。这是DroidCam的代码
import face_recognition
import cv2
import numpy as np
import time
# 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.
# Load a sample picture and learn how to recognize it.
aarna_image = face_recognition.load_image_file("aarna.jpg")
aarna_face_encoding = face_recognition.face_encodings(aarna_image)[0]
# Load a second sample picture and learn how to recognize it.
aditya_image = face_recognition.load_image_file("aditya.jpg")
aditya_face_encoding = face_recognition.face_encodings(aditya_image)[0]
# Create arrays of known face encodings and their names
known_face_encodings = [
aarna_face_encoding,
aditya_face_encoding
]
known_face_names = [
"Aarna Gupta",
"Aditya"
]
# Initialize some variables
while True:
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
# Grab a single frame of video
cap = cv2.VideoCapture(http://192.168.0.0:4747/video)
#THE link redirects to the WEBCAM =====^^^^^^^===================================================
ret, frame = cap.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)
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()
运行时，它仅显示视频的一个处理过的帧，而不显示多个处理过的帧，并返回以下输出：
Traceback (most recent call last):
File "F:/Webcam Face/main.py", line 49, in <module>
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
cv2.error: OpenCV(4.2.0) C:\projects\opencv-python\opencv\modules\imgproc\src\resize.cpp:4045: error: (-215:Assertion failed) !ssize.empty() in function 'cv::resize'
请帮助提供代码。
您确定网络流正常吗？使用VLC等媒体播放器签出视频流。另外，我可以在代码中看到多个问题：1> VideoCapture对象cap = cv2.VideoCapture（“”）应该在while循环之外定义，否则会大大降低处理速度。2>您在代码的两个不同版本之间误认了cap和video_capture变量。