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库车网站建设,响应式企业网站 下载,百度竞价规则,免费网站建站申请一、项目介绍 项目背景#xff1a; 虾类养殖是全球水产养殖业的重要组成部分#xff0c;但虾类病害#xff08;如黑斑病、黑鳃病、肝胰腺苍白病、白斑综合症病毒等#xff09;严重影响了虾类的健康生长和养殖效益。传统的病害检测方法依赖于人工观察和经验判断#xff0c…一、项目介绍项目背景虾类养殖是全球水产养殖业的重要组成部分但虾类病害如黑斑病、黑鳃病、肝胰腺苍白病、白斑综合症病毒等严重影响了虾类的健康生长和养殖效益。传统的病害检测方法依赖于人工观察和经验判断效率低且容易出错。基于深度学习的目标检测技术可以自动识别虾类病害帮助养殖户及时采取防治措施减少经济损失。项目目标本项目利用YOLOv10You Only Look Once version 10目标检测算法开发一个高效的虾病害检测系统。该系统能够自动从虾类图像中检测出病害区域并区分病害类型如黑斑病、黑鳃病、肝胰腺苍白病、白斑综合症病毒为养殖户提供病害预警和防治建议。技术栈深度学习框架PyTorch目标检测算法YOLOv10数据集包含4类病害标签的虾类图像数据集开发环境Python、CUDA、OpenCV等系统功能病害检测系统能够从输入的虾类图像中自动检测出病害区域并标注出病害类型。分类与定位系统不仅能识别病害还能区分病害类型black-spots、blackgill、pale-hepatopancreas、wssv并给出病害区域的位置信息边界框。可视化结果系统将检测结果可视化标注出病害区域并提供置信度评分。批量处理支持批量处理虾类图像适用于大规模养殖场的病害监测需求。应用场景虾类养殖场辅助养殖户快速筛查虾类病害及时采取防治措施。水产研究机构用于虾类病害的研究和分析推动病害防治技术的进步。远程诊断通过自动化的病害检测系统为偏远地区的养殖户提供及时的病害诊断服务。目录一、项目介绍二、项目功能展示系统功能图片检测视频检测摄像头实时检测三、数据集介绍数据集配置文件data.yaml数据集制作流程四、项目环境配置创建虚拟环境pycharm中配置anaconda安装所需要库五、模型训练训练代码训练结果六、核心代码七、项目基于深度学习的虾病害检测系统YOLOv10YOLO数据集UI界面Python项目源码模型_哔哩哔哩_bilibili基于深度学习的虾病害检测系统YOLOv10YOLO数据集UI界面Python项目源码模型二、项目功能展示系统功能✅图片检测可对单张图片进行检测返回检测框及类别信息。✅批量图片检测支持文件夹输入一次性检测多张图片生成批量检测结果。✅视频检测支持视频文件输入检测视频中每一帧的情况。✅摄像头实时检测连接USB 摄像头实现实时监测图片检测该功能允许用户通过单张图片进行目标检测。输入一张图片后YOLO模型会实时分析图像识别出其中的目标并在图像中框出检测到的目标输出带有目标框的图像。批量图片检测用户可以一次性上传多个图片进行批量处理。该功能支持对多个图像文件进行并行处理并返回每张图像的目标检测结果适用于需要大规模处理图像数据的应用场景。视频检测视频检测功能允许用户将视频文件作为输入。YOLO模型将逐帧分析视频并在每一帧中标记出检测到的目标。最终结果可以是带有目标框的视频文件或实时展示适用于视频监控和分析等场景。摄像头实时检测该功能支持通过连接摄像头进行实时目标检测。YOLO模型能够在摄像头拍摄的实时视频流中进行目标检测实时识别并显示检测结果。此功能非常适用于安防监控、无人驾驶、智能交通等应用提供即时反馈。核心特点高精度基于YOLO模型提供精确的目标检测能力适用于不同类型的图像和视频。实时性特别优化的算法使得实时目标检测成为可能无论是在视频还是摄像头实时检测中响应速度都非常快。批量处理支持高效的批量图像和视频处理适合大规模数据分析。三、数据集介绍数据集概述本项目的数据集包含四类病害标签black-spots黑斑病、blackgill黑鳃病、pale-hepatopancreas肝胰腺苍白病、wssv白斑综合症病毒。数据集分为训练集、验证集和测试集数据集配置文件data.yamltrain: .\datasets\images\train val: .\datasets\images\val test: .\datasets\images\test nc: 4 names: [black-spots, blackgill, pale-hepatopancreas, wssv]数据集制作流程标注数据使用标注工具如LabelImg、CVAT等对图像中的目标进行标注。每个目标需要标出边界框并且标注类别。转换格式将标注的数据转换为YOLO格式。YOLO标注格式为每行object-class x_center y_center width height这些坐标是相对于图像尺寸的比例。分割数据集将数据集分为训练集、验证集和测试集通常的比例是80%训练集、10%验证集和10%测试集。准备标签文件为每张图片生成一个对应的标签文件确保标签文件与图片的命名一致。调整图像尺寸根据YOLO网络要求统一调整所有图像的尺寸如416x416或608x608。四、项目环境配置创建虚拟环境首先新建一个Anaconda环境每个项目用不同的环境这样项目中所用的依赖包互不干扰。终端输入conda create -n yolov10 python3.9激活虚拟环境conda activate yolov10安装cpu版本pytorchpip install torch torchvision torchaudiopycharm中配置anaconda安装所需要库pip install -r requirements.txt五、模型训练训练代码from ultralytics import YOLOv10 model_path yolov10s.pt data_path datasets/data.yaml if __name__ __main__: model YOLOv10(model_path) results model.train(datadata_path, epochs500, batch64, device0, workers0, projectruns/detect, nameexp, )根据实际情况更换模型 yolov10n.yaml (nano)轻量化模型适合嵌入式设备速度快但精度略低。 yolov10s.yaml (small)小模型适合实时任务。 yolov10m.yaml (medium)中等大小模型兼顾速度和精度。 yolov10b.yaml (base)基本版模型适合大部分应用场景。 yolov10l.yaml (large)大型模型适合对精度要求高的任务。--batch 64每批次64张图像。--epochs 500训练500轮。--datasets/data.yaml数据集配置文件。--weights yolov10s.pt初始化模型权重yolov10s.pt是预训练的轻量级YOLO模型。训练结果六、核心代码# -*- coding: utf-8 -*- import time from PyQt5.QtWidgets import QApplication , QMainWindow, QFileDialog,QMessageBox,QWidget,QHeaderView,QTableWidgetItem, QAbstractItemView import sys import os from PIL import ImageFont from ultralytics import YOLOv10 sys.path.append(UIProgram) from UIProgram.UiMain import Ui_MainWindow import sys from PyQt5.QtCore import QTimer, Qt, QThread, pyqtSignal,QCoreApplication import detect_tools as tools import cv2 import Config from UIProgram.QssLoader import QSSLoader from UIProgram.precess_bar import ProgressBar import numpy as np # import torch class MainWindow(QMainWindow): def __init__(self, parentNone): super(QMainWindow, self).__init__(parent) self.ui Ui_MainWindow() self.ui.setupUi(self) self.initMain() self.signalconnect() # 加载css渲染效果 style_file UIProgram/style.css qssStyleSheet QSSLoader.read_qss_file(style_file) self.setStyleSheet(qssStyleSheet) def signalconnect(self): self.ui.PicBtn.clicked.connect(self.open_img) self.ui.comboBox.activated.connect(self.combox_change) self.ui.VideoBtn.clicked.connect(self.vedio_show) self.ui.CapBtn.clicked.connect(self.camera_show) self.ui.SaveBtn.clicked.connect(self.save_detect_video) self.ui.ExitBtn.clicked.connect(QCoreApplication.quit) self.ui.FilesBtn.clicked.connect(self.detact_batch_imgs) def initMain(self): self.show_width 700 self.show_height 500 self.org_path None self.is_camera_open False self.cap None # self.device 0 if torch.cuda.is_available() else cpu # 加载检测模型 self.model YOLOv10(runs/detect/exp/weights/best.pt, taskdetect) self.model(np.zeros((48, 48, 3))) #预先加载推理模型 self.fontC ImageFont.truetype(Font/platech.ttf, 25, 0) self.colors tools.Colors() self.timer_camera QTimer() # 更新检测信息表格 # self.timer_info QTimer() # 保存视频 self.timer_save_video QTimer() # 表格 self.ui.tableWidget.verticalHeader().setSectionResizeMode(QHeaderView.Fixed) self.ui.tableWidget.verticalHeader().setDefaultSectionSize(40) self.ui.tableWidget.setColumnWidth(0, 80) # 设置列宽 self.ui.tableWidget.setColumnWidth(1, 200) self.ui.tableWidget.setColumnWidth(2, 150) self.ui.tableWidget.setColumnWidth(3, 90) self.ui.tableWidget.setColumnWidth(4, 230) self.ui.tableWidget.setSelectionBehavior(QAbstractItemView.SelectRows) # 设置表格整行选中 self.ui.tableWidget.verticalHeader().setVisible(False) # 隐藏列标题 self.ui.tableWidget.setAlternatingRowColors(True) # 表格背景交替 def open_img(self): if self.cap: # 打开图片前关闭摄像头 self.video_stop() self.is_camera_open False self.ui.CaplineEdit.setText(摄像头未开启) self.cap None file_path, _ QFileDialog.getOpenFileName(None, 打开图片, ./, Image files (*.jpg *.jepg *.png)) if not file_path: return self.ui.comboBox.setDisabled(False) self.org_path file_path self.org_img tools.img_cvread(self.org_path) # 目标检测 t1 time.time() self.results self.model(self.org_path)[0] t2 time.time() take_time_str {:.3f} s.format(t2 - t1) self.ui.time_lb.setText(take_time_str) location_list self.results.boxes.xyxy.tolist() self.location_list [list(map(int, e)) for e in location_list] cls_list self.results.boxes.cls.tolist() self.cls_list [int(i) for i in cls_list] self.conf_list self.results.boxes.conf.tolist() self.conf_list [%.2f %% % (each*100) for each in self.conf_list] total_nums len(location_list) cls_percents [] for i in range(1): if total_nums 0: res 0 else: res self.cls_list.count(i) / total_nums cls_percents.append(res) self.set_percent(cls_percents) now_img self.results.plot() self.draw_img now_img # 获取缩放后的图片尺寸 self.img_width, self.img_height self.get_resize_size(now_img) resize_cvimg cv2.resize(now_img,(self.img_width, self.img_height)) pix_img tools.cvimg_to_qpiximg(resize_cvimg) self.ui.label_show.setPixmap(pix_img) self.ui.label_show.setAlignment(Qt.AlignCenter) # 设置路径显示 self.ui.PiclineEdit.setText(self.org_path) # 目标数目 target_nums len(self.cls_list) self.ui.label_nums.setText(str(target_nums)) # 设置目标选择下拉框 choose_list [全部] target_names [Config.names[id] _ str(index) for index,id in enumerate(self.cls_list)] choose_list choose_list target_names self.ui.comboBox.clear() self.ui.comboBox.addItems(choose_list) if target_nums 1: self.ui.type_lb.setText(Config.CH_names[self.cls_list[0]]) self.ui.label_conf.setText(str(self.conf_list[0])) self.ui.label_xmin.setText(str(self.location_list[0][0])) self.ui.label_ymin.setText(str(self.location_list[0][1])) self.ui.label_xmax.setText(str(self.location_list[0][2])) self.ui.label_ymax.setText(str(self.location_list[0][3])) else: self.ui.type_lb.setText() self.ui.label_conf.setText() self.ui.label_xmin.setText() self.ui.label_ymin.setText() self.ui.label_xmax.setText() self.ui.label_ymax.setText() # # 删除表格所有行 self.ui.tableWidget.setRowCount(0) self.ui.tableWidget.clearContents() self.tabel_info_show(self.location_list, self.cls_list, self.conf_list,pathself.org_path) def detact_batch_imgs(self): if self.cap: # 打开图片前关闭摄像头 self.video_stop() self.is_camera_open False self.ui.CaplineEdit.setText(摄像头未开启) self.cap None directory QFileDialog.getExistingDirectory(self, 选取文件夹, ./) # 起始路径 if not directory: return self.org_path directory img_suffix [jpg,png,jpeg,bmp] for file_name in os.listdir(directory): full_path os.path.join(directory,file_name) if os.path.isfile(full_path) and file_name.split(.)[-1].lower() in img_suffix: # self.ui.comboBox.setDisabled(False) img_path full_path self.org_img tools.img_cvread(img_path) # 目标检测 t1 time.time() self.results self.model(img_path)[0] t2 time.time() take_time_str {:.3f} s.format(t2 - t1) self.ui.time_lb.setText(take_time_str) location_list self.results.boxes.xyxy.tolist() self.location_list [list(map(int, e)) for e in location_list] cls_list self.results.boxes.cls.tolist() self.cls_list [int(i) for i in cls_list] self.conf_list self.results.boxes.conf.tolist() self.conf_list [%.2f %% % (each * 100) for each in self.conf_list] total_nums len(location_list) cls_percents [] for i in range(1): if total_nums 0: res 0 else: res self.cls_list.count(i) / total_nums cls_percents.append(res) self.set_percent(cls_percents) now_img self.results.plot() self.draw_img now_img # 获取缩放后的图片尺寸 self.img_width, self.img_height self.get_resize_size(now_img) resize_cvimg cv2.resize(now_img, (self.img_width, self.img_height)) pix_img tools.cvimg_to_qpiximg(resize_cvimg) self.ui.label_show.setPixmap(pix_img) self.ui.label_show.setAlignment(Qt.AlignCenter) # 设置路径显示 self.ui.PiclineEdit.setText(img_path) # 目标数目 target_nums len(self.cls_list) self.ui.label_nums.setText(str(target_nums)) # 设置目标选择下拉框 choose_list [全部] target_names [Config.names[id] _ str(index) for index, id in enumerate(self.cls_list)] choose_list choose_list target_names self.ui.comboBox.clear() self.ui.comboBox.addItems(choose_list) if target_nums 1: self.ui.type_lb.setText(Config.CH_names[self.cls_list[0]]) self.ui.label_conf.setText(str(self.conf_list[0])) self.ui.label_xmin.setText(str(self.location_list[0][0])) self.ui.label_ymin.setText(str(self.location_list[0][1])) self.ui.label_xmax.setText(str(self.location_list[0][2])) self.ui.label_ymax.setText(str(self.location_list[0][3])) else: self.ui.type_lb.setText() self.ui.label_conf.setText() self.ui.label_xmin.setText() self.ui.label_ymin.setText() self.ui.label_xmax.setText() self.ui.label_ymax.setText() # # 删除表格所有行 self.tabel_info_show(self.location_list, self.cls_list, self.conf_list, pathimg_path) self.ui.tableWidget.scrollToBottom() QApplication.processEvents() #刷新页面 def draw_rect_and_tabel(self, results, img): now_img img.copy() location_list results.boxes.xyxy.tolist() self.location_list [list(map(int, e)) for e in location_list] cls_list results.boxes.cls.tolist() self.cls_list [int(i) for i in cls_list] self.conf_list results.boxes.conf.tolist() self.conf_list [%.2f %% % (each * 100) for each in self.conf_list] for loacation, type_id, conf in zip(self.location_list, self.cls_list, self.conf_list): type_id int(type_id) color self.colors(int(type_id), True) # cv2.rectangle(now_img, (int(x1), int(y1)), (int(x2), int(y2)), colors(int(type_id), True), 3) now_img tools.drawRectBox(now_img, loacation, Config.CH_names[type_id], self.fontC, color) # 获取缩放后的图片尺寸 self.img_width, self.img_height self.get_resize_size(now_img) resize_cvimg cv2.resize(now_img, (self.img_width, self.img_height)) pix_img tools.cvimg_to_qpiximg(resize_cvimg) self.ui.label_show.setPixmap(pix_img) self.ui.label_show.setAlignment(Qt.AlignCenter) # 设置路径显示 self.ui.PiclineEdit.setText(self.org_path) # 目标数目 target_nums len(self.cls_list) self.ui.label_nums.setText(str(target_nums)) if target_nums 1: self.ui.type_lb.setText(Config.CH_names[self.cls_list[0]]) self.ui.label_conf.setText(str(self.conf_list[0])) self.ui.label_xmin.setText(str(self.location_list[0][0])) self.ui.label_ymin.setText(str(self.location_list[0][1])) self.ui.label_xmax.setText(str(self.location_list[0][2])) self.ui.label_ymax.setText(str(self.location_list[0][3])) else: self.ui.type_lb.setText() self.ui.label_conf.setText() self.ui.label_xmin.setText() self.ui.label_ymin.setText() self.ui.label_xmax.setText() self.ui.label_ymax.setText() # 删除表格所有行 self.ui.tableWidget.setRowCount(0) self.ui.tableWidget.clearContents() self.tabel_info_show(self.location_list, self.cls_list, self.conf_list, pathself.org_path) return now_img def combox_change(self): com_text self.ui.comboBox.currentText() if com_text 全部: cur_box self.location_list cur_img self.results.plot() self.ui.type_lb.setText(Config.CH_names[self.cls_list[0]]) self.ui.label_conf.setText(str(self.conf_list[0])) else: index int(com_text.split(_)[-1]) cur_box [self.location_list[index]] cur_img self.results[index].plot() self.ui.type_lb.setText(Config.CH_names[self.cls_list[index]]) self.ui.label_conf.setText(str(self.conf_list[index])) # 设置坐标位置值 self.ui.label_xmin.setText(str(cur_box[0][0])) self.ui.label_ymin.setText(str(cur_box[0][1])) self.ui.label_xmax.setText(str(cur_box[0][2])) self.ui.label_ymax.setText(str(cur_box[0][3])) resize_cvimg cv2.resize(cur_img, (self.img_width, self.img_height)) pix_img tools.cvimg_to_qpiximg(resize_cvimg) self.ui.label_show.clear() self.ui.label_show.setPixmap(pix_img) self.ui.label_show.setAlignment(Qt.AlignCenter) def get_video_path(self): file_path, _ QFileDialog.getOpenFileName(None, 打开视频, ./, Image files (*.avi *.mp4 *.jepg *.png)) if not file_path: return None self.org_path file_path self.ui.VideolineEdit.setText(file_path) return file_path def video_start(self): # 删除表格所有行 self.ui.tableWidget.setRowCount(0) self.ui.tableWidget.clearContents() # 清空下拉框 self.ui.comboBox.clear() # 定时器开启每隔一段时间读取一帧 self.timer_camera.start(1) self.timer_camera.timeout.connect(self.open_frame) def tabel_info_show(self, locations, clses, confs, pathNone): path path for location, cls, conf in zip(locations, clses, confs): row_count self.ui.tableWidget.rowCount() # 返回当前行数(尾部) self.ui.tableWidget.insertRow(row_count) # 尾部插入一行 item_id QTableWidgetItem(str(row_count1)) # 序号 item_id.setTextAlignment(Qt.AlignHCenter | Qt.AlignVCenter) # 设置文本居中 item_path QTableWidgetItem(str(path)) # 路径 # item_path.setTextAlignment(Qt.AlignHCenter | Qt.AlignVCenter) item_cls QTableWidgetItem(str(Config.CH_names[cls])) item_cls.setTextAlignment(Qt.AlignHCenter | Qt.AlignVCenter) # 设置文本居中 item_conf QTableWidgetItem(str(conf)) item_conf.setTextAlignment(Qt.AlignHCenter | Qt.AlignVCenter) # 设置文本居中 item_location QTableWidgetItem(str(location)) # 目标框位置 # item_location.setTextAlignment(Qt.AlignHCenter | Qt.AlignVCenter) # 设置文本居中 self.ui.tableWidget.setItem(row_count, 0, item_id) self.ui.tableWidget.setItem(row_count, 1, item_path) self.ui.tableWidget.setItem(row_count, 2, item_cls) self.ui.tableWidget.setItem(row_count, 3, item_conf) self.ui.tableWidget.setItem(row_count, 4, item_location) self.ui.tableWidget.scrollToBottom() def video_stop(self): self.cap.release() self.timer_camera.stop() # self.timer_info.stop() def open_frame(self): ret, now_img self.cap.read() if ret: # 目标检测 t1 time.time() results self.model(now_img)[0] t2 time.time() take_time_str {:.3f} s.format(t2 - t1) self.ui.time_lb.setText(take_time_str) location_list results.boxes.xyxy.tolist() self.location_list [list(map(int, e)) for e in location_list] cls_list results.boxes.cls.tolist() self.cls_list [int(i) for i in cls_list] self.conf_list results.boxes.conf.tolist() self.conf_list [%.2f %% % (each * 100) for each in self.conf_list] total_nums len(location_list) cls_percents [] for i in range(1): if total_nums! 0 : res self.cls_list.count(i) / total_nums else : res0 cls_percents.append(res) self.set_percent(cls_percents) now_img results.plot() # 获取缩放后的图片尺寸 self.img_width, self.img_height self.get_resize_size(now_img) resize_cvimg cv2.resize(now_img, (self.img_width, self.img_height)) pix_img tools.cvimg_to_qpiximg(resize_cvimg) self.ui.label_show.setPixmap(pix_img) self.ui.label_show.setAlignment(Qt.AlignCenter) # 目标数目 target_nums len(self.cls_list) self.ui.label_nums.setText(str(target_nums)) # 设置目标选择下拉框 choose_list [全部] target_names [Config.names[id] _ str(index) for index, id in enumerate(self.cls_list)] choose_list choose_list target_names self.ui.comboBox.clear() self.ui.comboBox.addItems(choose_list) if target_nums 1: self.ui.type_lb.setText(Config.CH_names[self.cls_list[0]]) self.ui.label_conf.setText(str(self.conf_list[0])) self.ui.label_xmin.setText(str(self.location_list[0][0])) self.ui.label_ymin.setText(str(self.location_list[0][1])) self.ui.label_xmax.setText(str(self.location_list[0][2])) self.ui.label_ymax.setText(str(self.location_list[0][3])) else: self.ui.type_lb.setText() self.ui.label_conf.setText() self.ui.label_xmin.setText() self.ui.label_ymin.setText() self.ui.label_xmax.setText() self.ui.label_ymax.setText() self.tabel_info_show(self.location_list, self.cls_list, self.conf_list, pathself.org_path) else: self.cap.release() self.timer_camera.stop() def vedio_show(self): if self.is_camera_open: self.is_camera_open False self.ui.CaplineEdit.setText(摄像头未开启) video_path self.get_video_path() if not video_path: return None self.cap cv2.VideoCapture(video_path) self.video_start() self.ui.comboBox.setDisabled(True) def camera_show(self): self.is_camera_open not self.is_camera_open if self.is_camera_open: self.ui.CaplineEdit.setText(摄像头开启) self.cap cv2.VideoCapture(0) self.video_start() self.ui.comboBox.setDisabled(True) else: self.ui.CaplineEdit.setText(摄像头未开启) self.ui.label_show.setText() if self.cap: self.cap.release() cv2.destroyAllWindows() self.ui.label_show.clear() def get_resize_size(self, img): _img img.copy() img_height, img_width , depth _img.shape ratio img_width / img_height if ratio self.show_width / self.show_height: self.img_width self.show_width self.img_height int(self.img_width / ratio) else: self.img_height self.show_height self.img_width int(self.img_height * ratio) return self.img_width, self.img_height def save_detect_video(self): if self.cap is None and not self.org_path: QMessageBox.about(self, 提示, 当前没有可保存信息请先打开图片或视频) return if self.is_camera_open: QMessageBox.about(self, 提示, 摄像头视频无法保存!) return if self.cap: res QMessageBox.information(self, 提示, 保存视频检测结果可能需要较长时间请确认是否继续保存,QMessageBox.Yes | QMessageBox.No , QMessageBox.Yes) if res QMessageBox.Yes: self.video_stop() com_text self.ui.comboBox.currentText() self.btn2Thread_object btn2Thread(self.org_path, self.model, com_text) self.btn2Thread_object.start() self.btn2Thread_object.update_ui_signal.connect(self.update_process_bar) else: return else: if os.path.isfile(self.org_path): fileName os.path.basename(self.org_path) name , end_name fileName.rsplit(.,1) save_name name _detect_result. end_name save_img_path os.path.join(Config.save_path, save_name) # 保存图片 cv2.imwrite(save_img_path, self.draw_img) QMessageBox.about(self, 提示, 图片保存成功!\n文件路径:{}.format(save_img_path)) else: img_suffix [jpg, png, jpeg, bmp] for file_name in os.listdir(self.org_path): full_path os.path.join(self.org_path, file_name) if os.path.isfile(full_path) and file_name.split(.)[-1].lower() in img_suffix: name, end_name file_name.rsplit(.,1) save_name name _detect_result. end_name save_img_path os.path.join(Config.save_path, save_name) results self.model(full_path)[0] now_img results.plot() # 保存图片 cv2.imwrite(save_img_path, now_img) QMessageBox.about(self, 提示, 图片保存成功!\n文件路径:{}.format(Config.save_path)) def update_process_bar(self,cur_num, total): if cur_num 1: self.progress_bar ProgressBar(self) self.progress_bar.show() if cur_num total: self.progress_bar.close() QMessageBox.about(self, 提示, 视频保存成功!\n文件在{}目录下.format(Config.save_path)) return if self.progress_bar.isVisible() is False: # 点击取消保存时终止进程 self.btn2Thread_object.stop() return value int(cur_num / total *100) self.progress_bar.setValue(cur_num, total, value) QApplication.processEvents() def set_percent(self, probs): pass class btn2Thread(QThread): update_ui_signal pyqtSignal(int,int) def __init__(self, path, model, com_text): super(btn2Thread, self).__init__() self.org_path path self.model model self.com_text com_text # 用于绘制不同颜色矩形框 self.colors tools.Colors() self.is_running True # 标志位表示线程是否正在运行 def run(self): # VideoCapture方法是cv2库提供的读取视频方法 cap cv2.VideoCapture(self.org_path) # 设置需要保存视频的格式“xvid” # 该参数是MPEG-4编码类型文件名后缀为.avi fourcc cv2.VideoWriter_fourcc(*XVID) # 设置视频帧频 fps cap.get(cv2.CAP_PROP_FPS) # 设置视频大小 size (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))) # VideoWriter方法是cv2库提供的保存视频方法 # 按照设置的格式来out输出 fileName os.path.basename(self.org_path) name, end_name fileName.split(.) save_name name _detect_result.avi save_video_path os.path.join(Config.save_path, save_name) out cv2.VideoWriter(save_video_path, fourcc, fps, size) prop cv2.CAP_PROP_FRAME_COUNT total int(cap.get(prop)) print([INFO] 视频总帧数{}.format(total)) cur_num 0 # 确定视频打开并循环读取 while (cap.isOpened() and self.is_running): cur_num 1 print(当前第{}帧总帧数{}.format(cur_num, total)) ret, frame cap.read() if ret True: # 检测 results self.model(frame)[0] frame results.plot() out.write(frame) self.update_ui_signal.emit(cur_num, total) else: break # 释放资源 cap.release() out.release() def stop(self): self.is_running False if __name__ __main__: app QApplication(sys.argv) win MainWindow() win.show() sys.exit(app.exec_())七、项目演示与介绍视频基于深度学习的虾病害检测系统YOLOv10YOLO数据集UI界面Python项目源码模型_哔哩哔哩_bilibili基于深度学习的虾病害检测系统YOLOv10YOLO数据集UI界面Python项目源码模型