如果你还不懂YOLOv8的安装、推理，自定义数据集训练与搭建的，可以看一下老师这篇文章和视频
https://blog.csdn.net/chajinglong/article/details/149105590?spm=1001.2014.3001.5501https://blog.csdn.net/chajinglong/article/details/149105590?spm=1001.2014.3001.5501
https://www.bilibili.com/video/BV1qtHeeMEnC/?spm_id_from=333.337.search-card.all.click&vd_source=27c8ea1c143ecfe9f586177e5e7027cfhttps%3A%2F%2Fwww.bilibili.com%2Fvideo%2FBV1qtHeeMEnC%2F%3Fspm_id_from%3D333.337.search-card.all.click&vd_source=27c8ea1c143ecfe9f586177e5e7027cfhttps://www.bilibili.com/video/BV1qtHeeMEnC/?spm_id_from=333.337.search-card.all.click&vd_source=27c8ea1c143ecfe9f586177e5e7027cfhttps%3A%2F%2Fwww.bilibili.com%2Fvideo%2FBV1qtHeeMEnC%2F%3Fspm_id_from%3D333.337.search-card.all.click&vd_source=27c8ea1c143ecfe9f586177e5e7027cf
很多同学问老师，这些课程能不能把文档写出来，分享到更多的人，那么今天它来了，整理成大纲给到同学们，希望能够帮助到你们，如果觉得写的可以，可以对照上面的视频和详细资料进行学习。
【YOLOv8零基础从入门到实战进阶系列教程】
https://www.bilibili.com/cheese/play/ep1342527?query_from=0&search_id=6883789517812043464&search_query=yolov8&csource=common_hpsearch_null_null&spm_id_from=333.337.search-card.all.clickhttps://www.bilibili.com/cheese/play/ep1342527?query_from=0&search_id=6883789517812043464&search_query=yolov8&csource=common_hpsearch_null_null&spm_id_from=333.337.search-card.all.click
基于yolov8图形化目标检测视频（PyQt5 + ultralytics）：
- 使用YOLO模型进行目标检测
- 处理图片和视频流
- 实现检测结果GUI可视化
import sys #用于退出程序 from PyQt5.QtCore import Qt,QTimer #这些是 PyQt5 库的模块，用于构建图形用户界面（GUI）应用程序 from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QVBoxLayout, QWidget, QPushButton, QHBoxLayout, \ QMessageBox, QFileDialog from PyQt5.QtGui import QImage, QPixmap import cv2 #图像处理 from ultralytics import YOLO #深度学习模型接口，用于加载 YOLO（You Only Look Once）模型进行对象检测 #负责处理模型加载和图像检测的逻辑。 class Worker: def __init__(self): self.model = None #加载模型 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) if self.model: return True else: return False #检测图片 def detect_image(self, image_path): image = cv2.imread(image_path) if image is not None: results = self.model.predict(image) return results else: return None #打开视频 def detect_video(self, video_path): # 打开视频文件 self.cap = cv2.VideoCapture(video_path) if not self.cap.isOpened(): return False return True #获取视频标注图像 def get_frame(self): if self.cap is not None and self.cap.isOpened(): ret, frame = self.cap.read() if ret: # 使用 YOLO 进行对象检测 results = self.model.predict(frame) annotated_frame = results[0].plot() # 用检测结果标注图像 return annotated_frame return None #释放 def release(self): if self.cap: self.cap.release() #应用程序的主窗口，负责创建界面，处理用户交互和显示检测结果 class MainWindow(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle("YOLOv8-GUI(图形化界面)") """ 第一个：100，左上角屏幕左边缘水平距离（像素） 第二个：100，左上角屏幕上边缘垂直距离（像素） 第三个：100，设置窗口的宽度 第四个：100，设置窗口的高度 """ self.setGeometry(100, 100, 1200, 800) self.label = QLabel() self.label.setAlignment(Qt.AlignCenter) self.label.setFixedSize(640,480) layout = QVBoxLayout() layout.addWidget(self.label) central_widget = QWidget() central_widget.setLayout(layout) self.setCentralWidget(central_widget) self.worker = Worker() # 创建按钮布局 hbox_buttons = QHBoxLayout() layout.addLayout(hbox_buttons) # 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) 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) hbox_buttons.addWidget(self.image_detect_button) #添加视频检测按钮 self.video_detect_button = QPushButton("视频检测") self.video_detect_button.clicked.connect(self.detect_video) self.video_detect_button.setEnabled(False) hbox_buttons.addWidget(self.video_detect_button) # 添加退出按钮 self.exit_button = QPushButton("退出") self.exit_button.clicked.connect(self.exit_application) hbox_buttons.addWidget(self.exit_button) #定时器更新视频 self.timer = QTimer(self) self.timer.timeout.connect(self.update_frame) 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: results = self.worker.detect_image(image_path) if results: annotated_image = results[0].plot() height, width, channel = annotated_image.shape bytesPerLine = 3 * width qimage = QImage(annotated_image.data, width, height, bytesPerLine, QImage.Format_BGR888) pixmap = QPixmap.fromImage(qimage) self.label.setPixmap(pixmap.scaled(self.label.size(), Qt.KeepAspectRatio)) else: QMessageBox.critical(self, "错误", "请选择图片文件") #选择视频文件 def detect_video(self): video_path, _ = QFileDialog.getOpenFileName(None, "选择视频文件", "", "视频文件 (*.mp4 *.avi *.mov)") if video_path: if self.worker.detect_video(video_path): # 开始定时器逐帧处理视频 self.timer.start(30) # 每30ms更新一次 (大约每秒更新33帧) else: QMessageBox.critical(self, "错误", "无法打开视频文件") else: QMessageBox.critical(self, "错误", "请选择视频文件") #更新视频 def update_frame(self): frame = self.worker.get_frame() if frame is not None: # 获取视频帧的宽高 height, width, channel = frame.shape bytesPerLine = 3 * width qimage = QImage(frame.data, width, height, bytesPerLine, QImage.Format_BGR888) # 创建 QPixmap 并按固定尺寸显示 pixmap = QPixmap.fromImage(qimage) # 按照 Qlabel 固定的尺寸显示，保持宽高比 self.label.setPixmap(pixmap.scaled(self.label.size(), Qt.KeepAspectRatio)) else: self.timer.stop() # 停止定时器，视频播放完毕 QMessageBox.information(self, "提示", "视频播放完毕") def load_model(self): if self.worker.load_model(): self.image_detect_button.setEnabled(True) self.video_detect_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_())如果这篇文章对你有所帮助，那就感谢同学的分享，转载，你们的分享是老师最大的动力
