Ubuntu22.04.1下运行OpenCV人脸识别报错：缺失Qt Wayland插件

问题描述 #

在Ubuntu 22.04.1系统运行基于OpenCV和face_recognition的人脸识别代码时，持续出现如下报错：
qt.qpa.plugin: Could not find the Qt platform plugin "wayland" in "/usr/local/lib/python3.10/dist-packages/cv2/qt/plugins"

已尝试谷歌搜索解决方案，用Pacman安装插件未成功，怀疑插件已安装但路径错误，或需要替代实现方法。

完整代码如下：

import cv2
import os
import face_recognition
import numpy as np

# Get a reference to webcam #0 (the default one)
video_capture = cv2.VideoCapture(0)

#loop over all the images in the folder and put them in a list
known_face_encodings = []
known_face_names = []
for file in os.listdir("known/"):
    if file.endswith(".jpg"):
        #load the image
        known_image = face_recognition.load_image_file("known/" + file)
        #get the face encoding
        known_face_encoding = face_recognition.face_encodings(known_image)[0]
        #add the encoding to the list
        known_face_encodings.append(known_face_encoding)
        #add the name to the list
        known_face_names.append(file[:-4])

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

#loop over the frames
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]

            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)

   #save the image
    cv2.imwrite("Tested/test.jpg", frame)

解决方案 #

方法1：强制使用X11平台绕过Wayland问题 #

Ubuntu 22.04默认使用Wayland，但OpenCV的Qt插件可能存在兼容性问题，可强制指定使用X11（xcb）平台：

• 代码内设置：在代码最开头添加以下两行：

import os
os.environ['QT_QPA_PLATFORM'] = 'xcb'

• 命令行运行时设置：执行脚本时直接指定环境变量：
  QT_QPA_PLATFORM=xcb python your_script_name.py

方法2：修复Wayland插件路径 #

如果系统已安装Wayland插件但路径不匹配，可通过软链接修复：

1. 查找系统中Wayland插件的位置：
   find /usr -name "libqwayland-egl.so"
2. 将找到的插件路径链接到OpenCV的Qt插件目录：
   sudo ln -s /path/to/libqwayland-egl.so /usr/local/lib/python3.10/dist-packages/cv2/qt/plugins/platforms/
   （替换/path/to/libqwayland-egl.so为实际查到的路径）

方法3：移除Qt窗口显示（非必要时） #

如果代码仅需要保存识别后的图片，不需要实时显示窗口，可直接注释掉cv2.imshow('Video', frame)这一行，彻底规避Qt相关问题。

内容的提问来源于stack exchange，提问作者tjmc608