手把手教你yolov8 pyqt5

### YOLOv8与PyQt5结合的开发指南 #### 了解YOLOv8特性 YOLOv8作为YOLO系列的新成员，在性能上有了显著提升，支持更高效的训练和推理过程。对于目标检测任务而言，其改进之处在于优化了网络架构以及提升了小物体检测能力[^2]。 #### 创建基于PyQt5的应用程序框架 为了构建图形界面应用程序，需先搭建好PyQt5的基础结构。这通常涉及创建主窗口类并设置布局管理器来安排各个控件的位置。下面是一个简单的例子用于初始化GUI： ```python import sys from PyQt5.QtWidgets import QApplication, QMainWindow, QLabel, QVBoxLayout, QWidget class MainWindow(QMainWindow): def __init__(self): super().__init__() self.setWindowTitle("YOLOv8 Object Detection GUI") layout = QVBoxLayout() label = QLabel("Welcome to the YOLOv8 Detector!") layout.addWidget(label) container = QWidget() container.setLayout(layout) self.setCentralWidget(container) if __name__ == "__main__": app = QApplication(sys.argv) window = MainWindow() window.show() sys.exit(app.exec_()) ``` #### 集成YOLOv8模型到PyQt5应用中 为了让YOLOv8能够在PyQt5环境中运行，可以利用`ultralytics/yolov8`库加载预训练好的权重文件，并通过自定义函数实现图像输入处理、预测执行及结果显示等功能。这里给出一段伪代码片段说明这一流程： ```python from ultralytics.yolo.engine.model import YOLO as yolo_v8_model import cv2 import numpy as np def load_yolov8(): model_path = "path/to/best.pt" device = 'cuda' if torch.cuda.is_available() else 'cpu' return yolo_v8_model(model_path).to(device) model = load_yolov8() def detect_objects(image_np_array): results = model.predict(source=image_np_array, conf=0.45)[0].boxes.data.tolist() detections = [] for result in results: box = list(map(int, result[:4])) confidence = float(result[4]) class_id = int(result[-1]) detection_info = { "box": box, "confidence": confidence, "label": str(class_id), } detections.append(detection_info) return detections # 假设有一个按钮点击事件触发此方法来进行实时视频流中的对象识别 def on_video_frame_received(frame_as_numpy_array): predictions = detect_objects(frame_as_numpy_array) annotated_image = frame_as_numpy_array.copy() for pred in predictions: start_point = (pred["box"][0], pred["box"][1]) end_point = (pred["box"][2], pred["box"][3]) color = (0, 255, 0) # Green bounding boxes thickness = 2 font_scale = 0.75 text_thickness = 1 font_face = cv2.FONT_HERSHEY_SIMPLEX annotated_image = cv2.rectangle(annotated_image, start_point, end_point, color, thickness) annotated_image = cv2.putText( img=annotated_image, text=f"{pred['label']} {pred['confidence']:.2f}", org=start_point, fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, color=color, thickness=text_thickness ) display_in_gui(annotated_image) # 将带有标注框的画面显示出来 ``` 上述代码展示了如何将YOLOv8集成至PyQt5项目里，实现了基本的对象检测功能。需要注意的是实际部署时还需考虑更多细节如错误处理机制等。