yolov8实战第九天-pyqt5-yolov8实现道路病害识别系统（环境配置+完整代码+代码使用说明+训练好的模型+数据集）

from PyQt5 import QtWidgets from csdn import Ui_Form # 确保这个 import 路径和类名正确 from PyQt5.QtWidgets import QFileDialog, QMessageBox, QListWidget, QListWidgetItem from PyQt5.QtGui import QImage, QPixmap, QIcon from PyQt5.QtGui import QFont from PyQt5.QtCore import Qt, QTimer, QSize from PyQt5.QtWidgets import QApplication import cv2 from PyQt5.QtGui import QImage, QPixmap from PyQt5.QtWidgets import QGraphicsPixmapItem, QGraphicsScene import numpy as np from PIL import Image, ImageDraw, ImageFont from PyQt5.QtCore import QTimer import datetime from ultralytics import YOLO from PIL import Image, ImageDraw, ImageFont import numpy as np import math class mywindow(QtWidgets.QWidget, Ui_Form): def __init__(self): super(mywindow, self).__init__() self.setupUi(self) self.pushButton.clicked.connect(self.read_file) self.pushButton_4.clicked.connect(self.open_video) self.pushButton_2.clicked.connect(self.close_video) self.pushButton_5.clicked.connect(self.open_rtsp_stream) self.listWidget.itemClicked.connect(self.display_image_popup) self.pushButton_6.clicked.connect(self.batch_detect) # Load a pretrained YOLOv8n-pose Pose model self.model = YOLO('20000/best.pt') self.class_names = ["裂缝", "井盖", "龟裂", "坑洼", "裂缝修补","龟裂修补", "坑洼修补"] self.video_capture = None self.current_frame = 0 self.timer = QTimer(self) # 用于处理视频帧的计时器 self.timer.timeout.connect(self.process_frame) # 初始化事件记录时间字典和记录间隔 self.last_event_time = {} self.event_record_interval = 5 # 事件记录的时间间隔，单位为秒 self.event_detected = False def open_video(self): filename, _ = QFileDialog.getOpenFileName(self, "打开视频", "./", "Video Files (*.mp4 *.avi *.ts)") if not filename: return self.is_read = 0 self.video_capture = cv2.VideoCapture(filename) if not self.video_capture.isOpened(): QMessageBox.critical(self, "错误", "无法打开视频文件。") return self.timer.start(30) def close_video(self): if self.timer.isActive(): self.timer.stop() # 停止计时器 if self.video_capture and self.video_capture.isOpened(): self.video_capture.release() # 释放视频资源 self.video_capture = None # 将 video_capture 设置为 None 确保完全关闭 self.graphicsView.setScene(None) # 清除显示的图像 self.is_read = 0 # 重置读取状态标记 self.current_frame = 0 # 重置帧计数器 def open_rtsp_stream(self): rtsp_url = self.lineEdit.text() if not rtsp_url: QMessageBox.warning(self, "错误", "请输入RTSP流的URL。") return self.is_read = 2 self.video_capture = cv2.VideoCapture(rtsp_url) if not self.video_capture.isOpened(): QMessageBox.critical(self, "错误", "无法打开RTSP流。") return self.timer.start(30) def batch_detect(self): folder_path = QFileDialog.getExistingDirectory(self, "选择图片文件夹", "./") if not folder_path: return import os images = [os.path.join(folder_path, f) for f in os.listdir(folder_path) if f.endswith(('.png', '.jpg', '.jpeg'))] for image_path in images: image = cv2.imread(image_path) if image is None: continue results = self.model(image) self.display_image(image, results) QApplication.processEvents() # 处理界面更新事件 ##############################注意：在此设置检测间隔################## def process_frame(self): skip_frames = 25 # 每隔 25 帧进行一次检测 ret, frame = self.video_capture.read() if not ret: self.close_video() return # 检查当前帧是否为检测帧 if self.current_frame % (skip_frames + 1) == 0: results = self.model(frame) # 使用模型进行检测 self.display_image(frame, results) # 显示检测结果 else: self.display_frame(frame) # 只显示帧，不进行检测 self.current_frame += 1 # 更新帧计数器 def display_frame(self, frame): # 将帧从 BGR 转换为 RGB，因为 QImage 需要 RGB img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # 转换到 QImage 对象 height, width, channel = img.shape bytesPerLine = 3 * width qImage = QImage(img.data, width, height, bytesPerLine, QImage.Format_RGB888) pixmap = QPixmap.fromImage(qImage) # 显示到 QGraphicsView item = QGraphicsPixmapItem(pixmap) scene = QGraphicsScene() scene.addItem(item) self.graphicsView.setScene(scene) def read_file(self): if self.video_capture and self.video_capture.isOpened(): QMessageBox.warning(self, "警告", "请先关闭当前的视频或视频流检测。") return filename, _ = QFileDialog.getOpenFileName(self, "选取文件", "./", "Images (*.png *.jpeg *.jpg)") if not filename: return self.is_read = 1 # 标记为图片处理 image = cv2.imread(filename) if image is None: QMessageBox.critical(self, "错误", "无法读取图像文件。") return results = self.model(image) # results list self.display_image(image, results) def display_image(self, image, results): img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) xyxy = results[0].boxes.xyxy.cpu().numpy() # 边界框坐标 confs = results[0].boxes.conf.cpu().numpy() # 置信度 classes = results[0].boxes.cls.cpu().numpy()# 类别索引 # 使用 Pillow 绘制检测结果 frame_pil = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) draw = ImageDraw.Draw(frame_pil) font = ImageFont.truetype("simsun.ttc", 30) # 确保你有这个字体文件 R= 0 #过滤条件 p=[] for (box, conf, cls_id) in zip(xyxy, confs, classes): x1, y1, x2, y2 = map(int, box) cls_name = self.class_names[int(cls_id)] # 使用中文标签 label = f"{cls_name} {conf:.2f}" if cls_id in [0,2,3] and conf>=0.3: p.append(cls_name) # 绘制边界框 draw.rectangle([x1, y1, x2, y2], outline="red", width=4) # 绘制文本 draw.text((x1, y1 - 30), label, font=font, fill="yellow") R = 1 # 转换回 OpenCV 格式并显示 image = cv2.cvtColor(np.array(frame_pil), cv2.COLOR_RGB2BGR) if R==1: image1 = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # 将图像保存 current_time = datetime.datetime.now().strftime("%Y%m%d%H%M%S%f") thumbnail_path = f'picture/{current_time}.jpg' cv2.imwrite(thumbnail_path, image1) # 保存缩略图 p = list(set(p)) # 去重 classes_string = ", ".join(p) # 用逗号连接类名 self.update_list_widget(classes_string, thumbnail_path) # 更新列表 # Convert to Qt image format height, width, channel = image.shape bytesPerLine = 3 * width qImage = QImage(image.data, width, height, bytesPerLine, QImage.Format_RGB888) pixmap = QPixmap.fromImage(qImage) self.item = QGraphicsPixmapItem(pixmap) self.scene = QGraphicsScene() self.scene.addItem(sel