qt5可能不支持我们的cuda版本，改用qt6
YOLO11+QT6+Opencv+C++训练加载模型全过程讲解_yolov11 模型转换成opencv c++模型-CSDN博客
下面是qt5版本的案例，和yolo及cuda有冲突
安装qt
切换到虚拟环境，例如pyqt，conda activate pyqt
pip install PyQt5验证安装结果
写一个简单程序
import sys
from PyQt5.QtWidgets import QApplication, QLabel
app = QApplication([])
label = QLabel('Hello PyQt5!')
label.show()
sys.exit(app.exec_())可以直接在虚拟环境中命令行输入：python，输入上面代码，按回车。
弹出一个小窗口是安装成功。
复杂的
import sys
from PyQt5.QtCore import Qt, QTimer
from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QVBoxLayout, QWidget, QPushButton, QHBoxLayout, \
QMessageBox, QFileDialog
from PyQt5.QtGui import QImage, QPixmap, QIcon
import cv2
from ultralytics import YOLO
class Worker:
def __init__(self):
self.model = None
def load_model(self):
model_path, _ = QFileDialog.getOpenFileName(None, "选择模型文件", "", "模型文件 (*.pt)")
if model_path:
self.model = YOLO(model_path)
return self.model is not None
return False
def detect_image(self, image):
results = self.model.predict(image)
return results
class MainWindow(QMainWindow):
def __init__(self):
super().__init__()
self.setWindowTitle("@author：笑脸惹桃花")
# self.setWindowIcon(QIcon("icon.png"))
self.setGeometry(300, 150, 800, 400)
# 创建两个 QLabel 分别显示左右图像
self.label1 = QLabel()
self.label1.setAlignment(Qt.AlignCenter)
self.label1.setMinimumSize(580, 450) # 设置大小
self.label1.setStyleSheet('border:3px solid #6950a1; background-color: black;') # 添加边框并设置背景颜色为黑色
self.label2 = QLabel()
self.label2.setAlignment(Qt.AlignCenter)
self.label2.setMinimumSize(580, 450) # 设置大小
self.label2.setStyleSheet('border:3px solid #6950a1; background-color: black;') # 添加边框并设置背景颜色为黑色
# 水平布局，用于放置左右两个 QLabel
layout = QVBoxLayout()
# layout.addWidget(self.label1)
hbox_video = QHBoxLayout()
hbox_video.addWidget(self.label1) # 左侧显示原始图像
hbox_video.addWidget(self.label2) # 右侧显示检测后的图像
layout.addLayout(hbox_video)
self.worker = Worker()
# 创建按钮布局
hbox_buttons = QHBoxLayout()
# 添加模型选择按钮
self.load_model_button = QPushButton("📁模型选择")
self.load_model_button.clicked.connect(self.load_model)
self.load_model_button.setFixedSize(120, 30)
hbox_buttons.addWidget(self.load_model_button)
# 添加图片检测按钮
self.image_detect_button = QPushButton("💾图片检测")
self.image_detect_button.clicked.connect(self.detect_image)
self.image_detect_button.setEnabled(False)
self.image_detect_button.setFixedSize(120, 30)
hbox_buttons.addWidget(self.image_detect_button)
# 添加显示检测物体按钮
self.display_objects_button = QPushButton("🔍显示检测物体")
self.display_objects_button.clicked.connect(self.show_detected_objects)
self.display_objects_button.setEnabled(False)
self.display_objects_button.setFixedSize(120, 30)
hbox_buttons.addWidget(self.display_objects_button)
# 添加退出按钮
self.exit_button = QPushButton("❌退出")
self.exit_button.clicked.connect(self.exit_application)
self.exit_button.setFixedSize(120, 30)
hbox_buttons.addWidget(self.exit_button)
layout.addLayout(hbox_buttons)
central_widget = QWidget()
central_widget.setLayout(layout)
self.setCentralWidget(central_widget)
self.current_results = None
def detect_image(self):
image_path, _ = QFileDialog.getOpenFileName(None, "选择图片文件", "", "图片文件 (*.jpg *.jpeg *.png)")
if image_path:
image = cv2.imread(image_path)
if image is not None:
self.current_results = self.worker.detect_image(image)
if self.current_results:
annotated_image = self.current_results[0].plot()
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # 转换为 RGB
height1, width1, channel1 = image_rgb.shape
bytesPerLine1 = 3 * width1
qimage1 = QImage(image_rgb.data, width1, height1, bytesPerLine1, QImage.Format_RGB888)
pixmap1 = QPixmap.fromImage(qimage1)
self.label1.setPixmap(pixmap1.scaled(self.label1.size(), Qt.KeepAspectRatio))
annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB) # 转换为 RGB
height2, width2, channel2 = annotated_image.shape
bytesPerLine2 = 3 * width2
qimage2 = QImage(annotated_image.data, width2, height2, bytesPerLine2, QImage.Format_RGB888)
pixmap2 = QPixmap.fromImage(qimage2)
self.label2.setPixmap(pixmap2.scaled(self.label2.size(), Qt.KeepAspectRatio))
def show_detected_objects(self):
if self.current_results:
det_info = self.current_results[0].boxes.cls
object_count = len(det_info)
object_info = f"识别到的物体总个数：{object_count}\n"
object_dict = {}
class_names_dict = self.current_results[0].names
for class_id in det_info:
class_name = class_names_dict[int(class_id)]
if class_name in object_dict:
object_dict[class_name] += 1
else:
object_dict[class_name] = 1
sorted_objects = sorted(object_dict.items(), key=lambda x: x[1], reverse=True)
for obj_name, obj_count in sorted_objects:
object_info += f"{obj_name}: {obj_count}\n"
self.show_message_box("识别结果", object_info)
else:
self.show_message_box("识别结果", "未检测到物体")
def show_message_box(self, title, message):
msg_box = QMessageBox(self)
msg_box.setWindowTitle(title)
msg_box.setText(message)
msg_box.exec_()
def load_model(self):
if self.worker.load_model():
self.image_detect_button.setEnabled(True)
self.display_objects_button.setEnabled(True)
def exit_application(self):
# 终止程序运行
sys.exit()
if __name__ == '__main__':
app = QApplication(sys.argv)
window = MainWindow()
window.show()
sys.exit(app.exec_())报错：
安装opencv
conda install -c conda-forge opencv安装训练库
在虚拟环境安装
pip install -i https://mirrors.aliyun.com/pypi/simple/ ultralytics torch torchvision检查yolo
yolo checks
export QT_PLUGIN_PATH=~/miniconda3/envs/yoloenv/lib/python3.13/site-packages/cv2/qt/plugins
export QT_QPA_PLATFORM_PLUGIN_PATH=$QT_PLUGIN_PATH/platforms检查插件文件状态
-
确认
libqxcb.so文件存在于$QT_PLUGIN_PATH/platforms/目录 -
赋予执行权限：
chmod +x ~/miniconda3/envs/yoloenv/lib/python3.13/site-packages/cv2/qt/plugins/platforms/libqxcb.so
PyQt5+Anaconda+PyCharm安装、配置和使用_anaconda pyqt5 pycharm-CSDN博客