一、YOLOv8环境搭建

这篇文章将跳过基础的深度学习环境的搭建，如果没有完成的可以看我的这篇博客：超详细||深度学习环境搭建记录cuda+anaconda+pytorch+pycharm-CSDN博客

1. 在github上下载源码：

ONNX > OpenVINO > CoreML > TFLite">GitHub - ultralytics/ultralytics: NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite

2. 安装ultralytics（YOLOv8改名为ultralytics）

这里有两种方式安装ultralytics

• 直接使用CLI

  pip install ultralytics
  

  • 使用requirements.txt安装，这种方法是在上面下载的源码处安装，方便对yolov8进行改进

    cd ultralytics
    pip install -r requirements.txt
    

    3. 安装wandb

    pip install wandb

    登录自己的wandb账号

    wandb login

    二、开始训练

    1. 构建数据集

    数据集要严格按照下面的目录格式，image的格式为jpg，label的格式为txt，对应的image和label的名字要一致

    Dataset
    	└─images
    	   └─train
           └─val
    	└─labels
    	   └─train
           └─val

    2. 创建一个dataset.yaml文件

    更换自己的image train和image val的地址，labels地址不用，它会自动索引

    将classes改为自己的类别，从0开始

    path: ../datasets/coco128  # dataset root dir
    train: images/train2017  # train images (relative to 'path') 128 images
    val: images/train2017  # val images (relative to 'path') 128 images
    test:  # test images (optional)
    # Classes
    names:
      0: person
      1: bicycle
      2: car
      3: motorcycle
      4: airplane
      5: bus
      6: train
      7: truck
      8: boat

    3. 新建一个train.py，修改相关参数，运行即可开始训练

    from ultralytics import YOLO
    if __name__ == '__main__':
        # Load a model
        model = YOLO(r'\ultralytics\detection\yolov8n\yolov8n.yaml')  # 不使用预训练权重训练
        # model = YOLO(r'yolov8p.yaml').load("yolov8n.pt")  # 使用预训练权重训练
        # Trainparameters ----------------------------------------------------------------------------------------------
        model.train(
            data=r'\ultralytics\detection\dataset\appledata.yaml',
            epochs= 30 , # (int) number of epochs to train for
            patience= 50 , # (int) epochs to wait for no observable improvement for early stopping of training
            batch= 8 , # (int) number of images per batch (-1 for AutoBatch)
            imgsz= 320 , # (int) size of input images as integer or w,h
            save= True , # (bool) save train checkpoints and predict results
            save_period= -1, # (int) Save checkpoint every x epochs (disabled if < 1)
            cache= False , # (bool) True/ram, disk or False. Use cache for data loading
            device= 0 , # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
            workers= 16 , # (int) number of worker threads for data loading (per RANK if DDP)
            project= 'result', # (str, optional) project name
            name= 'yolov8n' ,# (str, optional) experiment name, results saved to 'project/name' directory
            exist_ok= False , # (bool) whether to overwrite existing experiment
            pretrained= False , # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
            optimizer= 'SGD',  # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
            verbose= True ,# (bool) whether to print verbose output
            seed= 0 , # (int) random seed for reproducibility
            deterministic= True , # (bool) whether to enable deterministic mode
            single_cls= True , # (bool) train multi-class data as single-class
            rect= False  ,# (bool) rectangular training if mode='train' or rectangular validation if mode='val'
            cos_lr= False , # (bool) use cosine learning rate scheduler
            close_mosaic= 0,  # (int) disable mosaic augmentation for final epochs
            resume= False , # (bool) resume training from last checkpoint
            amp= False,  # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
            fraction= 1.0 , # (float) dataset fraction to train on (default is 1.0, all images in train set)
            profile= False,  # (bool) profile ONNX and TensorRT speeds during training for loggers
            # Segmentation
            overlap_mask= True , # (bool) masks should overlap during training (segment train only)
            mask_ratio= 4,  # (int) mask downsample ratio (segment train only)
            # Classification
            dropout= 0.0,  # (float) use dropout regularization (classify train only)
            # Hyperparameters ----------------------------------------------------------------------------------------------
            lr0=0.01,  # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
            lrf=0.01,  # (float) final learning rate (lr0 * lrf)
            momentum=0.937,  # (float) SGD momentum/Adam beta1
            weight_decay=0.0005,  # (float) optimizer weight decay 5e-4
            warmup_epochs=3.0,  # (float) warmup epochs (fractions ok)
            warmup_momentum=0.8,  # (float) warmup initial momentum
            warmup_bias_lr=0.1,  # (float) warmup initial bias lr
            box=7.5,  # (float) box loss gain
            cls=0.5,  # (float) cls loss gain (scale with pixels)
            dfl=1.5,  # (float) dfl loss gain
            pose=12.0,  # (float) pose loss gain
            kobj=1.0,  # (float) keypoint obj loss gain
            label_smoothing=0.0,  # (float) label smoothing (fraction)
            nbs=64,  # (int) nominal batch size
            hsv_h=0.015,  # (float) image HSV-Hue augmentation (fraction)
            hsv_s=0.7,  # (float) image HSV-Saturation augmentation (fraction)
            hsv_v=0.4,  # (float) image HSV-Value augmentation (fraction)
            degrees=0.0,  # (float) image rotation (+/- deg)
            translate=0.1,  # (float) image translation (+/- fraction)
            scale=0.5,  # (float) image scale (+/- gain)
            shear=0.0,  # (float) image shear (+/- deg)
            perspective=0.0,  # (float) image perspective (+/- fraction), range 0-0.001
            flipud=0.0,  # (float) image flip up-down (probability)
            fliplr=0.5,  # (float) image flip left-right (probability)
            mosaic=1.0,  # (float) image mosaic (probability)
            mixup=0.0,  # (float) image mixup (probability)
            copy_paste=0.0,  # (float) segment copy-paste (probability)
                    )
    

    三、测试与验证

    1. 新建一个test.py, 这个可以打印网路信息，参数量以及FLOPs，还有每一层网络的信息

    from ultralytics import YOLO
    if __name__ == '__main__':
        # Load a model
        model = YOLO(r'\ultralytics\detection\yolov8n\yolov8n.yaml')  # build a new model from YAML
        model.info()

    2. 新建一个val.py，这个可以打印模型在验证集上的结果，如mAP，推理速度等

    from ultralytics import YOLO
    if __name__ == '__main__':
        # Load a model
        model = YOLO(r'\ultralytics\detection\yolov8n\result\yolov8n4\weights\best.pt')  # build a new model from YAML
        # Validate the model
        model.val(
            val=True,  # (bool) validate/test during training
            data=r'\ultralytics\detection\dataset\appledata.yaml',
            split='val',  # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
            batch=1,  # 测试速度时一般设置为 1 ，设置越大速度越快。   (int) number of images per batch (-1 for AutoBatch)
            imgsz=320,  # (int) size of input images as integer or w,h
            device=0,  # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
            workers=8,  # (int) number of worker threads for data loading (per RANK if DDP)
            save_json=False,  # (bool) save results to JSON file
            save_hybrid=False,  # (bool) save hybrid version of labels (labels + additional predictions)
            conf=0.001,  # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
            iou=0.7,  # (float) intersection over union (IoU) threshold for NMS
            project='val',  # (str, optional) project name
            name='',  # (str, optional) experiment name, results saved to 'project/name' directory
            max_det=300,  # (int) maximum number of detections per image
            half=True,  # (bool) use half precision (FP16)
            dnn=False,  # (bool) use OpenCV DNN for ONNX inference
            plots=True,  # (bool) save plots during train/val
        )

    3. 新建一个predict.py，这个可以根据训练好的权重文件进行推理，权重文件格式支持pt，onnx等，支持图片，视频，摄像头等进行推理

    from ultralytics import YOLO
    if __name__ == '__main__':
        # Load a model
        model = YOLO(r'\deploy\yolov8n.pt')  # pretrained YOLOv8n model
        model.predict(
            source=r'\deploy\output_video.mp4',
            save=False,  # save predict results
            imgsz=320,  # (int) size of input images as integer or w,h
            conf=0.25,  # object confidence threshold for detection (default 0.25 predict, 0.001 val)
            iou=0.7,  # # intersection over union (IoU) threshold for NMS
            show=True,  # show results if possible
            project='',  # (str, optional) project name
            name='',  # (str, optional) experiment name, results saved to 'project/name' directory
            save_txt=False,  # save results as .txt file
            save_conf=True,  # save results with confidence scores
            save_crop=False,  # save cropped images with results
            show_labels=True,  # show object labels in plots
            show_conf=True,  # show object confidence scores in plots
            vid_stride=1,  # video frame-rate stride
            line_width=1,  # bounding box thickness (pixels)
            visualize=False,  # visualize model features
            augment=False,  # apply image augmentation to prediction sources
            agnostic_nms=False,  # class-agnostic NMS
            retina_masks=False,  # use high-resolution segmentation masks
            boxes=True,  # Show boxes in segmentation predictions
        )

    四、onnx模型部署

    1. 新建一个export.py，将pt文件转化为onnx文件

    from ultralytics import YOLO
    # Load a model
    model = YOLO('/ultralytics/weight file/yolov8n.pt')  # load a custom trained model
    # Export the model
    model.export(format='onnx')

    2. 将onnx文件添加到之前提到的predict.py中，进行推理。

    3. 如果想在推理的视频中添加FPS信息，请把ultralytics/engine/predictor.py替换为下面的代码。

    # Ultralytics YOLO 🚀, AGPL-3.0 license
    """
    Run prediction on images, videos, directories, globs, YouTube, webcam, streams, etc.
    Usage - sources:
        $ yolo mode=predict model=yolov8n.pt source=0                               # webcam
     img.jpg                         # image
     vid.mp4                         # video
     screen                          # screenshot
     path/                           # directory
     list.txt                        # list of images
     list.streams                    # list of streams
     'path/*.jpg'                    # glob
     'https://youtu.be/Zgi9g1ksQHc'  # YouTube
     'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
    Usage - formats:
        $ yolo mode=predict model=yolov8n.pt                 # PyTorch
                                  yolov8n.torchscript        # TorchScript
                                  yolov8n.onnx               # ONNX Runtime or OpenCV DNN with dnn=True
                                  yolov8n_openvino_model     # OpenVINO
                                  yolov8n.engine             # TensorRT
                                  yolov8n.mlmodel            # CoreML (macOS-only)
                                  yolov8n_saved_model        # TensorFlow SavedModel
                                  yolov8n.pb                 # TensorFlow GraphDef
                                  yolov8n.tflite             # TensorFlow Lite
                                  yolov8n_edgetpu.tflite     # TensorFlow Edge TPU
                                  yolov8n_paddle_model       # PaddlePaddle
    """
    import platform
    from pathlib import Path
    import cv2
    import numpy as np
    import torch
    from ultralytics.cfg import get_cfg
    from ultralytics.data import load_inference_source
    from ultralytics.data.augment import LetterBox, classify_transforms
    from ultralytics.nn.autobackend import AutoBackend
    from ultralytics.utils import DEFAULT_CFG, LOGGER, MACOS, SETTINGS, WINDOWS, callbacks, colorstr, ops
    from ultralytics.utils.checks import check_imgsz, check_imshow
    from ultralytics.utils.files import increment_path
    from ultralytics.utils.torch_utils import select_device, smart_inference_mode
    STREAM_WARNING = """
        WARNING ⚠️ stream/video/webcam/dir predict source will accumulate results in RAM unless `stream=True` is passed,
        causing potential out-of-memory errors for large sources or long-running streams/videos.
        Usage:
            results = model(source=..., stream=True)  # generator of Results objects
            for r in results:
                boxes = r.boxes  # Boxes object for bbox outputs
                masks = r.masks  # Masks object for segment masks outputs
                probs = r.probs  # Class probabilities for classification outputs
    """
    class BasePredictor:
        """
        BasePredictor
        A base class for creating predictors.
        Attributes:
            args (SimpleNamespace): Configuration for the predictor.
            save_dir (Path): Directory to save results.
            done_warmup (bool): Whether the predictor has finished setup.
            model (nn.Module): Model used for prediction.
            data (dict): Data configuration.
            device (torch.device): Device used for prediction.
            dataset (Dataset): Dataset used for prediction.
            vid_path (str): Path to video file.
            vid_writer (cv2.VideoWriter): Video writer for saving video output.
            data_path (str): Path to data.
        """
        def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
            """
            Initializes the BasePredictor class.
            Args:
                cfg (str, optional): Path to a configuration file. Defaults to DEFAULT_CFG.
                overrides (dict, optional): Configuration overrides. Defaults to None.
            """
            self.args = get_cfg(cfg, overrides)
            self.save_dir = self.get_save_dir()
            if self.args.conf is None:
                self.args.conf = 0.25  # default conf=0.25
            self.done_warmup = False
            if self.args.show:
                self.args.show = check_imshow(warn=True)
            # Usable if setup is done
            self.model = None
            self.data = self.args.data  # data_dict
            self.imgsz = None
            self.device = None
            self.dataset = None
            self.vid_path, self.vid_writer = None, None
            self.plotted_img = None
            self.data_path = None
            self.source_type = None
            self.batch = None
            self.results = None
            self.transforms = None
            self.callbacks = _callbacks or callbacks.get_default_callbacks()
            self.txt_path = None
            callbacks.add_integration_callbacks(self)
        def get_save_dir(self):
            project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task
            name = self.args.name or f'{self.args.mode}'
            return increment_path(Path(project) / name, exist_ok=self.args.exist_ok)
        def preprocess(self, im):
            """Prepares input image before inference.
            Args:
                im (torch.Tensor | List(np.ndarray)): BCHW for tensor, [(HWC) x B] for list.
            """
            not_tensor = not isinstance(im, torch.Tensor)
            if not_tensor:
                im = np.stack(self.pre_transform(im))
                im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW, (n, 3, h, w)
                im = np.ascontiguousarray(im)  # contiguous
                im = torch.from_numpy(im)
            img = im.to(self.device)
            img = img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32
            if not_tensor:
                img /= 255  # 0 - 255 to 0.0 - 1.0
            return img
        def inference(self, im, *args, **kwargs):
            visualize = increment_path(self.save_dir / Path(self.batch[0][0]).stem,
                                       mkdir=True) if self.args.visualize and (not self.source_type.tensor) else False
            return self.model(im, augment=self.args.augment, visualize=visualize)
        def pre_transform(self, im):
            """Pre-transform input image before inference.
            Args:
                im (List(np.ndarray)): (N, 3, h, w) for tensor, [(h, w, 3) x N] for list.
            Return: A list of transformed imgs.
            """
            same_shapes = all(x.shape == im[0].shape for x in im)
            auto = same_shapes and self.model.pt
            return [LetterBox(self.imgsz, auto=auto, stride=self.model.stride)(image=x) for x in im]
        def write_results(self, idx, results, batch):
            """Write inference results to a file or directory."""
            p, im, _ = batch
            log_string = ''
            if len(im.shape) == 3:
                im = im[None]  # expand for batch dim
            if self.source_type.webcam or self.source_type.from_img or self.source_type.tensor:  # batch_size >= 1
                log_string += f'{idx}: '
                frame = self.dataset.count
            else:
                frame = getattr(self.dataset, 'frame', 0)
            self.data_path = p
            self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
            log_string += '%gx%g ' % im.shape[2:]  # print string
            result = results[idx]
            log_string += result.verbose()
            if self.args.save or self.args.show:  # Add bbox to image
                plot_args = {
                    'line_width': self.args.line_width,
                    'boxes': self.args.boxes,
                    'conf': self.args.show_conf,
                    'labels': self.args.show_labels}
                if not self.args.retina_masks:
                    plot_args['im_gpu'] = im[idx]
                self.plotted_img = result.plot(**plot_args)
            # Write
            if self.args.save_txt:
                result.save_txt(f'{self.txt_path}.txt', save_conf=self.args.save_conf)
            if self.args.save_crop:
                result.save_crop(save_dir=self.save_dir / 'crops',
                                 file_name=self.data_path.stem + ('' if self.dataset.mode == 'image' else f'_{frame}'))
            return log_string
        def postprocess(self, preds, img, orig_imgs):
            """Post-processes predictions for an image and returns them."""
            return preds
        def __call__(self, source=None, model=None, stream=False, *args, **kwargs):
            """Performs inference on an image or stream."""
            self.stream = stream
            if stream:
                return self.stream_inference(source, model, *args, **kwargs)
            else:
                return list(self.stream_inference(source, model, *args, **kwargs))  # merge list of Result into one
        def predict_cli(self, source=None, model=None):
            """Method used for CLI prediction. It uses always generator as outputs as not required by CLI mode."""
            gen = self.stream_inference(source, model)
            for _ in gen:  # running CLI inference without accumulating any outputs (do not modify)
                pass
        def setup_source(self, source):
            """Sets up source and inference mode."""
            self.imgsz = check_imgsz(self.args.imgsz, stride=self.model.stride, min_dim=2)  # check image size
            self.transforms = getattr(self.model.model, 'transforms', classify_transforms(
                self.imgsz[0])) if self.args.task == 'classify' else None
            self.dataset = load_inference_source(source=source, imgsz=self.imgsz, vid_stride=self.args.vid_stride)
            self.source_type = self.dataset.source_type
            if not getattr(self, 'stream', True) and (self.dataset.mode == 'stream' or  # streams
       len(self.dataset) > 1000 or  # images
       any(getattr(self.dataset, 'video_flag', [False]))):  # videos
                LOGGER.warning(STREAM_WARNING)
            self.vid_path, self.vid_writer = [None] * self.dataset.bs, [None] * self.dataset.bs
        @smart_inference_mode()
        def stream_inference(self, source=None, model=None, *args, **kwargs):
            """Streams real-time inference on camera feed and saves results to file."""
            if self.args.verbose:
                LOGGER.info('')
            # Setup model
            if not self.model:
                self.setup_model(model)
            # Setup source every time predict is called
            self.setup_source(source if source is not None else self.args.source)
            # Check if save_dir/ label file exists
            if self.args.save or self.args.save_txt:
                (self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True)
            # Warmup model
            if not self.done_warmup:
                self.model.warmup(imgsz=(1 if self.model.pt or self.model.triton else self.dataset.bs, 3, *self.imgsz))
                self.done_warmup = True
            self.seen, self.windows, self.batch, self.profilers = 0, [], None, (ops.Profile(), ops.Profile(), ops.Profile())
            self.run_callbacks('on_predict_start')
            for batch in self.dataset:
                self.run_callbacks('on_predict_batch_start')
                self.batch = batch
                path, im0s, vid_cap, s = batch
                # Preprocess
                with self.profilers[0]:
                    im = self.preprocess(im0s)
                # Inference
                with self.profilers[1]:
                    preds = self.inference(im, *args, **kwargs)
                # Postprocess
                with self.profilers[2]:
                    self.results = self.postprocess(preds, im, im0s)
                self.run_callbacks('on_predict_postprocess_end')
                # Visualize, save, write results
                n = len(im0s)
                for i in range(n):
                    self.seen += 1
                    self.results[i].speed = {
                        'preprocess': self.profilers[0].dt * 1E3 / n,
                        'inference': self.profilers[1].dt * 1E3 / n,
                        'postprocess': self.profilers[2].dt * 1E3 / n}
                    p, im0 = path[i], None if self.source_type.tensor else im0s[i].copy()
                    p = Path(p)
                    if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:
                        s += self.write_results(i, self.results, (p, im, im0))
                    if self.args.save or self.args.save_txt:
                        self.results[i].save_dir = self.save_dir.__str__()
                    if self.args.show and self.plotted_img is not None:
                        self.show(p)
                    if self.args.save and self.plotted_img is not None:
                        self.save_preds(vid_cap, i, str(self.save_dir / p.name))
                self.run_callbacks('on_predict_batch_end')
                yield from self.results
                # Print time (inference-not only)
                if self.args.verbose:
                    LOGGER.info(f'{s}{(self.profilers[0].dt+self.profilers[1].dt+self.profilers[2].dt) * 1E3:.2f}ms')
            # Release assets
            if isinstance(self.vid_writer[-1], cv2.VideoWriter):
                self.vid_writer[-1].release()  # release final video writer
            # Print results
            if self.args.verbose and self.seen:
                t = tuple(x.t / self.seen * 1E3 for x in self.profilers)  # speeds per image
                LOGGER.info(f'Speed: %.2fms preprocess, %.2fms inference, %.2fms postprocess per image at shape '
                            f'{(1, 3, *im.shape[2:])}' % t)
            if self.args.save or self.args.save_txt or self.args.save_crop:
                nl = len(list(self.save_dir.glob('labels/*.txt')))  # number of labels
                s = f"\n{nl} label{'s' * (nl > 1)} saved to {self.save_dir / 'labels'}" if self.args.save_txt else ''
                LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
            self.run_callbacks('on_predict_end')
        def setup_model(self, model, verbose=True):
            """Initialize YOLO model with given parameters and set it to evaluation mode."""
            self.model = AutoBackend(model or self.args.model,
                                     device=select_device(self.args.device, verbose=verbose),
                                     dnn=self.args.dnn,
                                     data=self.args.data,
                                     fp16=self.args.half,
                                     fuse=True,
                                     verbose=verbose)
            self.device = self.model.device  # update device
            self.args.half = self.model.fp16  # update half
            self.model.eval()
        def show(self, p):
            """Display an image in a window using OpenCV imshow()."""
            
            im0 = self.plotted_img
            
            #--------------------后添加-----------------------------
            str_FPS = "FPS: %.2f" % (1. / (self.profilers[0].dt + self.profilers[1].dt + self.profilers[2].dt))
            cv2.putText(im0, str_FPS, (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 3)
            # --------------------后添加-----------------------------
            
            if platform.system() == 'Linux' and p not in self.windows:
                self.windows.append(p)
                cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
                cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
            cv2.imshow(str(p), im0)
            cv2.waitKey(500 if self.batch[3].startswith('image') else 1)  # 1 millisecond
        def save_preds(self, vid_cap, idx, save_path):
            """Save video predictions as mp4 at specified path."""
            im0 = self.plotted_img
            # Save imgs
            if self.dataset.mode == 'image':
                cv2.imwrite(save_path, im0)
            else:  # 'video' or 'stream'
                if self.vid_path[idx] != save_path:  # new video
                    self.vid_path[idx] = save_path
                    if isinstance(self.vid_writer[idx], cv2.VideoWriter):
                        self.vid_writer[idx].release()  # release previous video writer
                    if vid_cap:  # video
                        fps = int(vid_cap.get(cv2.CAP_PROP_FPS))  # integer required, floats produce error in MP4 codec
                        w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                        h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                    else:  # stream
                        fps, w, h = 30, im0.shape[1], im0.shape[0]
                    suffix = '.mp4' if MACOS else '.avi' if WINDOWS else '.avi'
                    fourcc = 'avc1' if MACOS else 'WMV2' if WINDOWS else 'MJPG'
                    save_path = str(Path(save_path).with_suffix(suffix))
                    self.vid_writer[idx] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
                self.vid_writer[idx].write(im0)
        def run_callbacks(self, event: str):
            """Runs all registered callbacks for a specific event."""
            for callback in self.callbacks.get(event, []):
                callback(self)
        def add_callback(self, event: str, func):
            """
            Add callback
            """
            self.callbacks[event].append(func)
    

     
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