（篇三）基于PyDracula搭建一个深度学习的软件之解析yolo算法融合

文章目录

1YoloPredictor类——检测器
- 1.1继承BasePredictor解析
- 1.2继承QObject解析
2MainWindow类——主窗口

在前面两篇中，篇一介绍了启动界面的制作，篇二介绍了如何修改PyDracula的界面，那么这一篇我们学习一下yolo要融合进入软件中，需要了解的两个类。

1YoloPredictor类——检测器

class YoloPredictor(BasePredictor, QObject):# 定义信号，传递不同类型的数据给GUI或其他部分yolo2main_pre_img = Signal(np.ndarray)  # 原始图像信号（图像数据类型为NumPy数组）yolo2main_res_img = Signal(np.ndarray)  # 测试结果图像信号（图像数据类型为NumPy数组）yolo2main_status_msg = Signal(str)  # 状态消息信号（如：检测中、暂停、停止、错误等）yolo2main_fps = Signal(str)  # FPS（帧率）信号（类型为字符串，显示每秒处理的帧数）yolo2main_labels = Signal(dict)  # 检测到的目标类别及其数量（字典类型）yolo2main_progress = Signal(int)  # 检测进度信号（表示任务完成的进度百分比）yolo2main_class_num = Signal(int)  # 检测到的类别数量信号（整数类型）yolo2main_target_num = Signal(int)  # 检测到的目标数量信号（整数类型）def __init__(self, cfg=DEFAULT_CFG, overrides=None):super(YoloPredictor, self).__init__()  # 调用父类BasePredictor的构造函数进行初始化QObject.__init__(self)  # 初始化QObject，支持信号和槽机制self.args = get_cfg(cfg, overrides)  # 获取配置信息，最终会从这里获取DEFAULT_CFG_PATH = ROOT / 'yolo/cfg/default.yaml'''''下面这个配置信息是为了填补yaml文件的空白'''# 设置项目路径：如果args.project为空，则使用默认路径并加上任务名称project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task# 设置模型名称：根据模式（mode）命名name = f'{self.args.mode}'# 设置保存目录：调用increment_path以避免覆盖现有文件夹self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)self.done_warmup = False  # 标记是否完成预热阶段（如：模型加载、预处理等）if self.args.show:self.args.show = check_imshow(warn=True)  # 如果需要显示图像，则检查imshow是否可用# GUI相关参数初始化self.used_model_name = None  # 当前使用的模型名称self.new_model_name = None  # 实时更新的模型名称self.source = ''  # 输入源路径（如视频、图像路径）self.stop_dtc = False  # 停止检测标志self.continue_dtc = True  # 是否继续检测（用于暂停检测）self.save_res = False  # 是否保存测试结果self.save_txt = False  # 是否保存标签文件（txt格式）self.iou_thres = 0.45  # IoU阈值（用于判断是否为目标）self.conf_thres = 0.25  # 置信度阈值（检测到目标的置信度要求）self.speed_thres = 10  # 延迟阈值，单位毫秒（检测的最大延迟时间）self.labels_dict = {}  # 存储检测结果的字典（类别名和数量）self.progress_value = 0  # 进度条的当前进度（0-100）# 以下为后续任务需要的变量初始化self.model = None  # 存储YOLO检测模型self.data = self.args.data  # 存储数据字典（包含数据集路径等）self.imgsz = None  # 输入图像的大小（例如：640x640）self.device = None  # 设备（GPU或CPU）self.dataset = None  # 数据集对象self.vid_path, self.vid_writer = None, None  # 存储视频路径和视频写入器（如果需要处理视频）self.annotator = None  # 注释器对象（用于图像标注）self.data_path = None  # 数据路径（用于加载数据）self.source_type = None  # 输入源类型（视频、图像等）self.batch = None  # 批次大小（用于批处理预测）# 添加回调函数（用于处理检测过程中的各种操作）self.callbacks = defaultdict(list, callbacks.default_callbacks)  # 默认为空列表的回调字典callbacks.add_integration_callbacks(self)  # 将回调集成到当前对象中# 主函数用于目标检测@smart_inference_mode()  # 装饰器，可能用于切换推理模式（例如加速或禁用某些操作）def run(self):try:# 如果开启详细模式，则输出空行日志if self.args.verbose:LOGGER.info('')# 设置模型self.yolo2main_status_msg.emit('Loding Model...')  # 向主界面发射模型加载的状态信息if not self.model:  # 如果没有已加载模型self.setup_model(self.new_model_name)  # 初始化并加载新模型self.used_model_name = self.new_model_name  # 设置当前使用的模型名称# 设置输入源self.setup_source(self.source if self.source is not None else self.args.source)# 检查保存路径/标签文件夹if self.save_res or self.save_txt:  # 如果需要保存结果或标签(self.save_dir / 'labels' if self.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)# 热身模型if not self.done_warmup:  # 如果模型未热身self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.dataset.bs, 3, *self.imgsz))self.done_warmup = True  # 设置热身完成标志# 初始化状态变量self.seen, self.windows, self.dt, self.batch = 0, [], (ops.Profile(), ops.Profile(), ops.Profile()), None# 开始目标检测count = 0  # 当前帧数start_time = time.time()  # 用于计算帧率的开始时间batch = iter(self.dataset)  # 将数据集转化为迭代器while True:# 检查是否终止检测if self.stop_dtc:if isinstance(self.vid_writer[-1], cv2.VideoWriter):self.vid_writer[-1].release()  # 释放视频写入器self.yolo2main_status_msg.emit('Detection terminated!')  # 向主界面发射终止信息break# 检查是否需要切换模型if self.used_model_name != self.new_model_name:self.setup_model(self.new_model_name)  # 加载新的模型self.used_model_name = self.new_model_name  # 更新当前使用的模型名称# 检查是否暂停检测if self.continue_dtc:self.yolo2main_status_msg.emit('Detecting...')  # 向主界面发射“正在检测”信息batch = next(self.dataset)  # 获取下一批数据self.batch = batch  # 保存当前批次path, im, im0s, vid_cap, s = batch  # 从批次中提取路径、图像、原始图像、视频捕获和其他信息visualize = increment_path(self.save_dir / Path(path).stem,mkdir=True) if self.args.visualize else False  # 是否可视化# 计算进度和帧率（待优化）count += 1  # 帧计数加1if vid_cap:all_count = vid_cap.get(cv2.CAP_PROP_FRAME_COUNT)  # 获取视频总帧数else:all_count = 1  # 如果不是视频，则设置总帧数为1self.progress_value = int(count / all_count * 1000)  # 更新进度条（0~1000）if count % 5 == 0 and count >= 5:  # 每5帧计算一次帧率self.yolo2main_fps.emit(str(int(5 / (time.time() - start_time))))  # 向主界面发射每秒帧率start_time = time.time()  # 重置开始时间# 预处理图像with self.dt[0]:im = self.preprocess(im)  # 预处理图像if len(im.shape) == 3:  # 如果图像维度为3，则扩展批次维度im = im[None]  # 扩展为批次维度# 推理过程with self.dt[1]:preds = self.model(im, augment=self.args.augment, visualize=visualize)  # 进行推理# 后处理with self.dt[2]:self.results = self.postprocess(preds, im, im0s)  # 后处理推理结果# 可视化、保存和写入结果n = len(im)  # 图像数量（通常是1，除非是批次推理）for i in range(n):# 保存速度信息self.results[i].speed = {'preprocess': self.dt[0].dt * 1E3 / n,'inference': self.dt[1].dt * 1E3 / n,'postprocess': self.dt[2].dt * 1E3 / n}p, im0 = (path[i], im0s[i].copy()) if self.source_type.webcam or self.source_type.from_img \else (path, im0s.copy())  # 设置路径p = Path(p)  # 转换路径为Path对象# 获取检测框和标签label_str = self.write_results(i, self.results, (p, im, im0))  # 获取标签字符串# 处理标签和目标数量class_nums = 0  # 类别数target_nums = 0  # 目标数self.labels_dict = {}  # 标签字典if 'no detections' in label_str:  # 如果没有检测到目标passelse:# 解析标签字符串，更新目标和类别数量for ii in label_str.split(',')[:-1]:nums, label_name = ii.split('~')self.labels_dict[label_name] = int(nums)target_nums += int(nums)class_nums += 1# 保存检测结果图像或视频if self.save_res:self.save_preds(vid_cap, i, str(self.save_dir / p.name))# 向主界面发送检测结果self.yolo2main_res_img.emit(im0)  # 检测后图像self.yolo2main_pre_img.emit(im0s if isinstance(im0s, np.ndarray) else im0s[0])  # 检测前图像self.yolo2main_class_num.emit(class_nums)  # 发送类别数量self.yolo2main_target_num.emit(target_nums)  # 发送目标数量if self.speed_thres != 0:time.sleep(self.speed_thres / 1000)  # 延时控制（单位：毫秒）# 更新进度条self.yolo2main_progress.emit(self.progress_value)# 检测完成if count + 1 >= all_count:if isinstance(self.vid_writer[-1], cv2.VideoWriter):self.vid_writer[-1].release()  # 释放视频写入器self.yolo2main_status_msg.emit('Detection completed')  # 向主界面发射“检测完成”信息break  # 退出循环except Exception as e:  # 捕获异常pass  # 忽略异常print(e)  # 打印异常self.yolo2main_status_msg.emit('%s' % e)  # 向主界面发射异常信息def get_annotator(self, img):return Annotator(img, line_width=self.args.line_thickness, example=str(self.model.names))def preprocess(self, img):img = torch.from_numpy(img).to(self.model.device)img = img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32img /= 255  # 0 - 255 to 0.0 - 1.0return imgdef postprocess(self, preds, img, orig_img):### importantpreds = ops.non_max_suppression(preds,self.conf_thres,self.iou_thres,agnostic=self.args.agnostic_nms,max_det=self.args.max_det,classes=self.args.classes)results = []for i, pred in enumerate(preds):orig_img = orig_img[i] if isinstance(orig_img, list) else orig_imgshape = orig_img.shapepred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()path, _, _, _, _ = self.batchimg_path = path[i] if isinstance(path, list) else pathresults.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred))# print(results)return resultsdef write_results(self, idx, results, batch):p, im, im0 = batchlog_string = ''if len(im.shape) == 3:im = im[None]  # expand for batch dimself.seen += 1imc = im0.copy() if self.args.save_crop else im0if self.source_type.webcam or self.source_type.from_img:  # batch_size >= 1         # attentionlog_string += f'{idx}: 'frame = self.dataset.countelse:frame = getattr(self.dataset, 'frame', 0)self.data_path = pself.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')# log_string += '%gx%g ' % im.shape[2:]         # !!! don't add img size~self.annotator = self.get_annotator(im0)det = results[idx].boxes  # TODO: make boxes inherit from tensorsif len(det) == 0:return f'{log_string}(no detections), ' # if no, send this~~for c in det.cls.unique():n = (det.cls == c).sum()  # detections per classlog_string += f"{n}~{self.model.names[int(c)]},"   #   {'s' * (n > 1)}, "   # don't add 's'# now log_string is the classes 👆# writefor d in reversed(det):cls, conf = d.cls.squeeze(), d.conf.squeeze()if self.save_txt:  # Write to fileline = (cls, *(d.xywhn.view(-1).tolist()), conf) \if self.args.save_conf else (cls, *(d.xywhn.view(-1).tolist()))  # label formatwith open(f'{self.txt_path}.txt', 'a') as f:f.write(('%g ' * len(line)).rstrip() % line + '\n')if self.save_res or self.args.save_crop or self.args.show or True:  # Add bbox to image(must)c = int(cls)  # integer classname = f'id:{int(d.id.item())} {self.model.names[c]}' if d.id is not None else self.model.names[c]label = None if self.args.hide_labels else (name if self.args.hide_conf else f'{name} {conf:.2f}')self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))if self.args.save_crop:save_one_box(d.xyxy,imc,file=self.save_dir / 'crops' / self.model.model.names[c] / f'{self.data_path.stem}.jpg',BGR=True)return log_string

class YoloPredictor(BasePredictor, QObject):这里的继承了BasePredictor和QObject。
自定义的一些参数

        '''GUI相关参数初始化'''self.used_model_name = None  # 当前使用的模型名称self.new_model_name = None  # 实时更新的模型名称self.source = ''  # 输入源路径（如视频、图像路径）self.stop_dtc = False  # 停止检测标志self.continue_dtc = True  # 是否继续检测（用于暂停检测）self.save_res = False  # 是否保存测试结果self.save_txt = False  # 是否保存标签文件（txt格式）self.iou_thres = 0.45  # IoU阈值（用于判断是否为目标）self.conf_thres = 0.25  # 置信度阈值（检测到目标的置信度要求）self.speed_thres = 10  # 延迟阈值，单位毫秒（检测的最大延迟时间）self.labels_dict = {}  # 存储检测结果的字典（类别名和数量）self.progress_value = 0  # 进度条的当前进度（0-100）# 以下为后续任务需要的变量初始化self.model = None  # 存储YOLO检测模型self.data = self.args.data  # 存储数据字典（包含数据集路径等）self.imgsz = None  # 输入图像的大小（例如：640x640）self.device = None  # 设备（GPU或CPU）self.dataset = None  # 数据集对象self.vid_path, self.vid_writer = None, None  # 存储视频路径和视频写入器（如果需要处理视频）self.annotator = None  # 注释器对象（用于图像标注）self.data_path = None  # 数据路径（用于加载数据）self.source_type = None  # 输入源类型（视频、图像等）self.batch = None  # 批次大小（用于批处理预测）入代码片

1.1继承BasePredictor解析

BasePredictor类是 YOLOv8 目标检测/分类/分割的推理代码，封装了从数据输入 → 预处理 → 模型推理 → 后处理 → 结果可视化/保存的完整流程。
继承该函数之后需要对其中与处理函数、绘图函数、结果输出函数、后处理函数进行重写。整体代码中也包含了这三个函数的重写。

在这里插入图片描述

完善YoloPredictor检测器的参数

 get_cfg函数会从ROOT / 'yolo/cfg/default.yaml'将默认参数去除，但是这个文件中有很多参数是空白，需要之后填写的。

在这里插入图片描述

        super(YoloPredictor, self).__init__()  # 调用父类BasePredictor的构造函数进行初始化QObject.__init__(self)  # 初始化QObject，支持信号和槽机制self.args = get_cfg(cfg, overrides)  # 获取配置信息，最终会从这里获取DEFAULT_CFG_PATH = ROOT / 'yolo/cfg/default.yaml'''''下面这个配置信息是为了填补yaml文件的空白'''# 设置项目路径：如果args.project为空，则使用默认路径并加上任务名称project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task# 设置模型名称：根据模式（mode）命名name = f'{self.args.mode}'# 设置保存目录：调用increment_path以避免覆盖现有文件夹self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok)self.done_warmup = False  # 标记是否完成预热阶段（如：模型加载、预处理等）if self.args.show:self.args.show = check_imshow(warn=True)  # 如果需要显示图像，则检查imshow是否可用

1.2继承QObject解析

YoloPredictor需要使用信号与槽函数。

    yolo2main_pre_img = Signal(np.ndarray)  # 原始图像信号（图像数据类型为NumPy数组）yolo2main_res_img = Signal(np.ndarray)  # 测试结果图像信号（图像数据类型为NumPy数组）yolo2main_status_msg = Signal(str)  # 状态消息信号（如：检测中、暂停、停止、错误等）yolo2main_fps = Signal(str)  # FPS（帧率）信号（类型为字符串，显示每秒处理的帧数）yolo2main_labels = Signal(dict)  # 检测到的目标类别及其数量（字典类型）yolo2main_progress = Signal(int)  # 检测进度信号（表示任务完成的进度百分比）yolo2main_class_num = Signal(int)  # 检测到的类别数量信号（整数类型）yolo2main_target_num = Signal(int)  # 检测到的目标数量信号（整数类型）

咋们单独看一下每个信号都在做些什么？
yolo2main_pre_img

#向主界面发送检测结果
self.yolo2main_pre_img.emit(im0s if isinstance(im0s, np.ndarray) else im0s[0])#检测前图像
#信号触发后x会被显示出来，x 是 yolo2main_pre_img 这个信号 emit() 时传递的参数。
self.yolo_predict.yolo2main_pre_img.connect(lambda x: self.show_image(x, self.pre_video))

yolo2main_res_img

#向主界面发送检测结果
self.yolo2main_res_img.emit(im0)  # 检测后图像
#信号触发后x会被显示出来，x 是 yolo2main_res_img 这个信号 emit() 时传递的参数。
self.yolo_predict.yolo2main_res_img.connect(lambda x: self.show_image(x, self.res_video))

yolo2main_status_msg

self.yolo2main_status_msg.emit('Loding Model...')  # 向主界面发射模型加载的状态信息
self.yolo2main_status_msg.emit('Detection terminated!')  # 向主界面发射终止信息
self.yolo2main_status_msg.emit('Detecting...')  # 向主界面发射“正在检测”信息
self.yolo2main_status_msg.emit('Detection completed')  # 向主界面发射“检测完成”信息
self.yolo2main_status_msg.emit('%s' % e)  # 向主界面发射异常信息
#信号触发后x会被显示出来，x 是 yolo2main_status_msg这个信号 emit() 时传递的参数。
self.yolo_predict.yolo2main_status_msg.connect(lambda x: self.show_status(x))

yolo2main_fps

#向主界面发送self.yolo2main_fps.emit(str(int(5 / (time.time() - start_time))))  # 向主界面发射每秒帧率
#被主界面接收
self.yolo_predict.yolo2main_fps.connect(lambda x: self.fps_label.setText(x))

yolo2main_progress

# 更新进度条
self.yolo2main_progress.emit(self.progress_value)
#主界面设置进度值
self.yolo_predict.yolo2main_progress.connect(lambda x: self.progress_bar.setValue(x))

yolo2main_class_num

self.yolo2main_class_num.emit(class_nums)  # 发送类别数量
self.yolo_predict.yolo2main_class_num.connect(lambda x:self.Class_num.setText(str(x)))

yolo2main_target_num

self.yolo2main_target_num.emit(target_nums)  # 发送目标数量
self.yolo_predict.yolo2main_target_num.connect(lambda x:self.Target_num.setText(str(x)))

至此，检测器的剖析已经到位了。

2MainWindow类——主窗口

class MainWindow(QMainWindow, Ui_MainWindow):main2yolo_begin_sgl = Signal()  # The main window sends an execution signal to the yolo instancedef __init__(self, parent=None):super(MainWindow, self).__init__(parent)# basic interfaceself.setupUi(self)self.setAttribute(Qt.WA_TranslucentBackground)  # rounded transparentself.setWindowFlags(Qt.FramelessWindowHint)  # Set window flag: hide window bordersUIFuncitons.uiDefinitions(self)# Show module shadowsUIFuncitons.shadow_style(self, self.Class_QF, QColor(162,129,247))UIFuncitons.shadow_style(self, self.Target_QF, QColor(251, 157, 139))UIFuncitons.shadow_style(self, self.Fps_QF, QColor(170, 128, 213))UIFuncitons.shadow_style(self, self.Model_QF, QColor(64, 186, 193))# read model folderself.pt_list = os.listdir('./models')self.pt_list = [file for file in self.pt_list if file.endswith('.pt')]self.pt_list.sort(key=lambda x: os.path.getsize('./models/' + x))   # sort by file sizeself.model_box.clear()self.model_box.addItems(self.pt_list)self.Qtimer_ModelBox = QTimer(self)     # Timer: Monitor model file changes every 2 secondsself.Qtimer_ModelBox.timeout.connect(self.ModelBoxRefre)self.Qtimer_ModelBox.start(2000)# Yolo-v8 threadself.yolo_predict = YoloPredictor()                           # Create a Yolo instanceself.select_model = self.model_box.currentText()                   # default modelself.yolo_predict.new_model_name = "./models/%s" % self.select_model  self.yolo_thread = QThread()                                  # Create yolo threadself.yolo_predict.yolo2main_pre_img.connect(lambda x: self.show_image(x, self.pre_video)) self.yolo_predict.yolo2main_res_img.connect(lambda x: self.show_image(x, self.res_video))self.yolo_predict.yolo2main_status_msg.connect(lambda x: self.show_status(x))             self.yolo_predict.yolo2main_fps.connect(lambda x: self.fps_label.setText(x))              # self.yolo_predict.yolo2main_labels.connect(self.show_labels)                            self.yolo_predict.yolo2main_class_num.connect(lambda x:self.Class_num.setText(str(x)))         self.yolo_predict.yolo2main_target_num.connect(lambda x:self.Target_num.setText(str(x)))       self.yolo_predict.yolo2main_progress.connect(lambda x: self.progress_bar.setValue(x))     self.main2yolo_begin_sgl.connect(self.yolo_predict.run)     self.yolo_predict.moveToThread(self.yolo_thread)              # Model parametersself.model_box.currentTextChanged.connect(self.change_model)     self.iou_spinbox.valueChanged.connect(lambda x:self.change_val(x, 'iou_spinbox'))    # iou boxself.iou_slider.valueChanged.connect(lambda x:self.change_val(x, 'iou_slider'))      # iou scroll barself.conf_spinbox.valueChanged.connect(lambda x:self.change_val(x, 'conf_spinbox'))  # conf boxself.conf_slider.valueChanged.connect(lambda x:self.change_val(x, 'conf_slider'))    # conf scroll barself.speed_spinbox.valueChanged.connect(lambda x:self.change_val(x, 'speed_spinbox'))# speed boxself.speed_slider.valueChanged.connect(lambda x:self.change_val(x, 'speed_slider'))  # speed scroll bar# Prompt window initializationself.Class_num.setText('--')self.Target_num.setText('--')self.fps_label.setText('--')self.Model_name.setText(self.select_model)# Select detection sourceself.src_file_button.clicked.connect(self.open_src_file)  # select local file# self.src_cam_button.clicked.connect(self.show_status("The function has not yet been implemented."))#chose_cam# self.src_rtsp_button.clicked.connect(self.show_status("The function has not yet been implemented."))#chose_rtsp# start testing buttonself.run_button.clicked.connect(self.run_or_continue)   # pause/startself.stop_button.clicked.connect(self.stop)             # termination# Other function buttonsself.save_res_button.toggled.connect(self.is_save_res)  # save image optionself.save_txt_button.toggled.connect(self.is_save_txt)  # Save label optionself.ToggleBotton.clicked.connect(lambda: UIFuncitons.toggleMenu(self, True))   # left navigation buttonself.settings_button.clicked.connect(lambda: UIFuncitons.settingBox(self, True))   # top right settings button# initializationself.load_config()# The main window displays the original image and detection results@staticmethoddef show_image(img_src, label):try:ih, iw, _ = img_src.shapew = label.geometry().width()h = label.geometry().height()# keep the original data ratioif iw/w > ih/h:scal = w / iwnw = wnh = int(scal * ih)img_src_ = cv2.resize(img_src, (nw, nh))else:scal = h / ihnw = int(scal * iw)nh = himg_src_ = cv2.resize(img_src, (nw, nh))frame = cv2.cvtColor(img_src_, cv2.COLOR_BGR2RGB)img = QImage(frame.data, frame.shape[1], frame.shape[0], frame.shape[2] * frame.shape[1],QImage.Format_RGB888)label.setPixmap(QPixmap.fromImage(img))except Exception as e:print(repr(e))# Control start/pausedef run_or_continue(self):if self.yolo_predict.source == '':self.show_status('Please select a video source before starting detection...')self.run_button.setChecked(False)else:self.yolo_predict.stop_dtc = Falseif self.run_button.isChecked():self.run_button.setChecked(True)    # start buttonself.save_txt_button.setEnabled(False)  # It is forbidden to check and save after starting the detectionself.save_res_button.setEnabled(False)self.show_status('Detecting...')           self.yolo_predict.continue_dtc = True   # Control whether Yolo is pausedif not self.yolo_thread.isRunning():self.yolo_thread.start()self.main2yolo_begin_sgl.emit()else:self.yolo_predict.continue_dtc = Falseself.show_status("Pause...")self.run_button.setChecked(False)    # start button# bottom status bar informationdef show_status(self, msg):self.status_bar.setText(msg)if msg == 'Detection completed' or msg == '检测完成':self.save_res_button.setEnabled(True)self.save_txt_button.setEnabled(True)self.run_button.setChecked(False)    self.progress_bar.setValue(0)if self.yolo_thread.isRunning():self.yolo_thread.quit()         # end processelif msg == 'Detection terminated!' or msg == '检测终止':self.save_res_button.setEnabled(True)self.save_txt_button.setEnabled(True)self.run_button.setChecked(False)    self.progress_bar.setValue(0)if self.yolo_thread.isRunning():self.yolo_thread.quit()         # end processself.pre_video.clear()           # clear image display  self.res_video.clear()          self.Class_num.setText('--')self.Target_num.setText('--')self.fps_label.setText('--')# select local filedef open_src_file(self):config_file = 'config/fold.json'    config = json.load(open(config_file, 'r', encoding='utf-8'))open_fold = config['open_fold']     if not os.path.exists(open_fold):open_fold = os.getcwd()name, _ = QFileDialog.getOpenFileName(self, 'Video/image', open_fold, "Pic File(*.mp4 *.mkv *.avi *.flv *.jpg *.png)")if name:self.yolo_predict.source = nameself.show_status('Load File：{}'.format(os.path.basename(name))) config['open_fold'] = os.path.dirname(name)config_json = json.dumps(config, ensure_ascii=False, indent=2)  with open(config_file, 'w', encoding='utf-8') as f:f.write(config_json)self.stop()             # Select camera source----  have one bugdef chose_cam(self):try:self.stop()MessageBox(self.close_button, title='Note', text='loading camera...', time=2000, auto=True).exec()# get the number of local cameras_, cams = Camera().get_cam_num()popMenu = QMenu()popMenu.setFixedWidth(self.src_cam_button.width())popMenu.setStyleSheet('''QMenu {font-size: 16px;font-family: "Microsoft YaHei UI";font-weight: light;color:white;padding-left: 5px;padding-right: 5px;padding-top: 4px;padding-bottom: 4px;border-style: solid;border-width: 0px;border-color: rgba(255, 255, 255, 255);border-radius: 3px;background-color: rgba(200, 200, 200,50);}''')for cam in cams:exec("action_%s = QAction('%s')" % (cam, cam))exec("popMenu.addAction(action_%s)" % cam)x = self.src_cam_button.mapToGlobal(self.src_cam_button.pos()).x()     y = self.src_cam_button.mapToGlobal(self.src_cam_button.pos()).y()     y = y + self.src_cam_button.frameGeometry().height()pos = QPoint(x, y)action = popMenu.exec(pos)if action:self.yolo_predict.source = action.text()self.show_status('Loading camera：{}'.format(action.text()))except Exception as e:self.show_status('%s' % e)# select network sourcedef chose_rtsp(self):self.rtsp_window = Window()config_file = 'config/ip.json'if not os.path.exists(config_file):ip = "rtsp://admin:admin888@192.168.1.2:555"new_config = {"ip": ip}new_json = json.dumps(new_config, ensure_ascii=False, indent=2)with open(config_file, 'w', encoding='utf-8') as f:f.write(new_json)else:config = json.load(open(config_file, 'r', encoding='utf-8'))ip = config['ip']self.rtsp_window.rtspEdit.setText(ip)self.rtsp_window.show()self.rtsp_window.rtspButton.clicked.connect(lambda: self.load_rtsp(self.rtsp_window.rtspEdit.text()))# load network sourcesdef load_rtsp(self, ip):try:self.stop()MessageBox(self.close_button, title='提示', text='加载 rtsp...', time=1000, auto=True).exec()self.yolo_predict.source = ipnew_config = {"ip": ip}new_json = json.dumps(new_config, ensure_ascii=False, indent=2)with open('config/ip.json', 'w', encoding='utf-8') as f:f.write(new_json)self.show_status('Loading rtsp：{}'.format(ip))self.rtsp_window.close()except Exception as e:self.show_status('%s' % e)# Save test result button--picture/videodef is_save_res(self):if self.save_res_button.checkState() == Qt.CheckState.Unchecked:self.show_status('NOTE: Run image results are not saved.')self.yolo_predict.save_res = Falseelif self.save_res_button.checkState() == Qt.CheckState.Checked:self.show_status('NOTE: Run image results will be saved.')self.yolo_predict.save_res = True# Save test result button -- label (txt)def is_save_txt(self):if self.save_txt_button.checkState() == Qt.CheckState.Unchecked:self.show_status('NOTE: Labels results are not saved.')self.yolo_predict.save_txt = Falseelif self.save_txt_button.checkState() == Qt.CheckState.Checked:self.show_status('NOTE: Labels results will be saved.')self.yolo_predict.save_txt = True# Configuration initialization  ~~~wait to change~~~def load_config(self):config_file = 'config/setting.json'if not os.path.exists(config_file):iou = 0.26conf = 0.33   rate = 10save_res = 0   save_txt = 0    new_config = {"iou": iou,"conf": conf,"rate": rate,"save_res": save_res,"save_txt": save_txt}new_json = json.dumps(new_config, ensure_ascii=False, indent=2)with open(config_file, 'w', encoding='utf-8') as f:f.write(new_json)else:config = json.load(open(config_file, 'r', encoding='utf-8'))if len(config) != 5:iou = 0.26conf = 0.33rate = 10save_res = 0save_txt = 0else:iou = config['iou']conf = config['conf']rate = config['rate']save_res = config['save_res']save_txt = config['save_txt']self.save_res_button.setCheckState(Qt.CheckState(save_res))self.yolo_predict.save_res = (False if save_res==0 else True )self.save_txt_button.setCheckState(Qt.CheckState(save_txt)) self.yolo_predict.save_txt = (False if save_txt==0 else True )self.run_button.setChecked(False)  self.show_status("Welcome~")# Terminate button and associated statedef stop(self):if self.yolo_thread.isRunning():self.yolo_thread.quit()         # end threadself.yolo_predict.stop_dtc = Trueself.run_button.setChecked(False)    # start key recoveryself.save_res_button.setEnabled(True)   # Ability to use the save buttonself.save_txt_button.setEnabled(True)   # Ability to use the save buttonself.pre_video.clear()           # clear image displayself.res_video.clear()           # clear image displayself.progress_bar.setValue(0)self.Class_num.setText('--')self.Target_num.setText('--')self.fps_label.setText('--')# Change detection parametersdef change_val(self, x, flag):if flag == 'iou_spinbox':self.iou_slider.setValue(int(x*100))    # The box value changes, changing the sliderelif flag == 'iou_slider':self.iou_spinbox.setValue(x/100)        # The slider value changes, changing the boxself.show_status('IOU Threshold: %s' % str(x/100))self.yolo_predict.iou_thres = x/100elif flag == 'conf_spinbox':self.conf_slider.setValue(int(x*100))elif flag == 'conf_slider':self.conf_spinbox.setValue(x/100)self.show_status('Conf Threshold: %s' % str(x/100))self.yolo_predict.conf_thres = x/100elif flag == 'speed_spinbox':self.speed_slider.setValue(x)elif flag == 'speed_slider':self.speed_spinbox.setValue(x)self.show_status('Delay: %s ms' % str(x))self.yolo_predict.speed_thres = x  # ms# change modeldef change_model(self,x):self.select_model = self.model_box.currentText()self.yolo_predict.new_model_name = "./models/%s" % self.select_modelself.show_status('Change Model：%s' % self.select_model)self.Model_name.setText(self.select_model)# label result# def show_labels(self, labels_dic):#     try:#         self.result_label.clear()#         labels_dic = sorted(labels_dic.items(), key=lambda x: x[1], reverse=True)#         labels_dic = [i for i in labels_dic if i[1]>0]#         result = [' '+str(i[0]) + '：' + str(i[1]) for i in labels_dic]#         self.result_label.addItems(result)#     except Exception as e:#         self.show_status(e)# Cycle monitoring model file changesdef ModelBoxRefre(self):pt_list = os.listdir('./models')pt_list = [file for file in pt_list if file.endswith('.pt')]pt_list.sort(key=lambda x: os.path.getsize('./models/' + x))# It must be sorted before comparing, otherwise the list will be refreshed all the timeif pt_list != self.pt_list:self.pt_list = pt_listself.model_box.clear()self.model_box.addItems(self.pt_list)# Get the mouse position (used to hold down the title bar and drag the window)def mousePressEvent(self, event):p = event.globalPosition()globalPos = p.toPoint()self.dragPos = globalPos# Optimize the adjustment when dragging the bottom and right edges of the window sizedef resizeEvent(self, event):# Update Size GripsUIFuncitons.resize_grips(self)# Exit Exit thread, save settingsdef closeEvent(self, event):config_file = 'config/setting.json'config = dict()config['iou'] = self.iou_spinbox.value()config['conf'] = self.conf_spinbox.value()config['rate'] = self.speed_spinbox.value()config['save_res'] = (0 if self.save_res_button.checkState()==Qt.Unchecked else 2)config['save_txt'] = (0 if self.save_txt_button.checkState()==Qt.Unchecked else 2)config_json = json.dumps(config, ensure_ascii=False, indent=2)with open(config_file, 'w', encoding='utf-8') as f:f.write(config_json)# Exit the process before closingif self.yolo_thread.isRunning():self.yolo_predict.stop_dtc = Trueself.yolo_thread.quit()MessageBox(self.close_button, title='Note', text='Exiting, please wait...', time=3000, auto=True).exec()sys.exit(0)else:sys.exit(0)