Reference for ultralytics/engine/results.py

class BaseTensor(SimpleClass):
    """Base tensor class with additional methods for easy manipulation and device handling.

    This class provides a foundation for tensor-like objects with device management capabilities, supporting both
    PyTorch tensors and NumPy arrays. It includes methods for moving data between devices and converting between tensor
    types.

    Attributes:
        data (torch.Tensor | np.ndarray): Prediction data such as bounding boxes, masks, or keypoints.
        orig_shape (tuple[int, int]): Original shape of the image, typically in the format (height, width).

    Methods:
        cpu: Return a copy of the tensor stored in CPU memory.
        numpy: Return a copy of the tensor as a numpy array.
        cuda: Move the tensor to GPU memory, returning a new instance if necessary.
        to: Return a copy of the tensor with the specified device and dtype.

    Examples:
        >>> import torch
        >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]])
        >>> orig_shape = (720, 1280)
        >>> base_tensor = BaseTensor(data, orig_shape)
        >>> cpu_tensor = base_tensor.cpu()
        >>> numpy_array = base_tensor.numpy()
        >>> gpu_tensor = base_tensor.cuda()
    """

    def __init__(self, data: torch.Tensor | np.ndarray, orig_shape: tuple[int, int]) -> None:
        """Initialize BaseTensor with prediction data and the original shape of the image.

        Args:
            data (torch.Tensor | np.ndarray): Prediction data such as bounding boxes, masks, or keypoints.
            orig_shape (tuple[int, int]): Original shape of the image in (height, width) format.
        """
        assert isinstance(data, (torch.Tensor, np.ndarray)), "data must be torch.Tensor or np.ndarray"
        self.data = data
        self.orig_shape = orig_shape

@property
def shape(self) -> tuple[int, ...]:
    """Return the shape of the underlying data tensor.

    Returns:
        (tuple[int, ...]): The shape of the data tensor.

    Examples:
        >>> data = torch.rand(100, 4)
        >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280))
        >>> print(base_tensor.shape)
        (100, 4)
    """
    return self.data.shape

def __getitem__(self, idx):
    """Return a new BaseTensor instance containing the specified indexed elements of the data tensor.

    Args:
        idx (int | list[int] | torch.Tensor): Index or indices to select from the data tensor.

    Returns:
        (BaseTensor): A new BaseTensor instance containing the indexed data.

    Examples:
        >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]])
        >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280))
        >>> result = base_tensor[0]  # Select the first row
        >>> print(result.data)
        tensor([1, 2, 3])
    """
    return self.__class__(self.data[idx], self.orig_shape)

def __len__(self) -> int:
    """Return the length of the underlying data tensor.

    Returns:
        (int): The number of elements in the first dimension of the data tensor.

    Examples:
        >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]])
        >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280))
        >>> len(base_tensor)
        2
    """
    return len(self.data)

def cpu(self):
    """Return a copy of the tensor stored in CPU memory.

    Returns:
        (BaseTensor): A new BaseTensor object with the data tensor moved to CPU memory.

    Examples:
        >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]]).cuda()
        >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280))
        >>> cpu_tensor = base_tensor.cpu()
        >>> isinstance(cpu_tensor, BaseTensor)
        True
        >>> cpu_tensor.data.device
        device(type='cpu')
    """
    return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.cpu(), self.orig_shape)

def cuda(self):
    """Move the tensor to GPU memory.

    Returns:
        (BaseTensor): A new BaseTensor instance with the data moved to GPU memory if it's not already a numpy array,
            otherwise returns self.

    Examples:
        >>> import torch
        >>> from ultralytics.engine.results import BaseTensor
        >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]])
        >>> base_tensor = BaseTensor(data, orig_shape=(720, 1280))
        >>> gpu_tensor = base_tensor.cuda()
        >>> print(gpu_tensor.data.device)
        cuda:0
    """
    return self.__class__(torch.as_tensor(self.data).cuda(), self.orig_shape)

def numpy(self):
    """Return a copy of the tensor as a numpy array.

    Returns:
        (np.ndarray): A numpy array containing the same data as the original tensor.

    Examples:
        >>> data = torch.tensor([[1, 2, 3], [4, 5, 6]])
        >>> orig_shape = (720, 1280)
        >>> base_tensor = BaseTensor(data, orig_shape)
        >>> numpy_array = base_tensor.numpy()
        >>> print(type(numpy_array))
        <class 'numpy.ndarray'>
    """
    return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.numpy(), self.orig_shape)

def to(self, *args, **kwargs):
    """Return a copy of the tensor with the specified device and dtype.

    Args:
        *args (Any): Variable length argument list to be passed to torch.Tensor.to().
        **kwargs (Any): Arbitrary keyword arguments to be passed to torch.Tensor.to().

    Returns:
        (BaseTensor): A new BaseTensor instance with the data moved to the specified device and/or dtype.

    Examples:
        >>> base_tensor = BaseTensor(torch.randn(3, 4), orig_shape=(480, 640))
        >>> cuda_tensor = base_tensor.to("cuda")
        >>> float16_tensor = base_tensor.to(dtype=torch.float16)
    """
    return self.__class__(torch.as_tensor(self.data).to(*args, **kwargs), self.orig_shape)

class Results(SimpleClass, DataExportMixin):
    """A class for storing and manipulating inference results.

    This class provides comprehensive functionality for handling inference results from various Ultralytics models,
    including detection, segmentation, classification, and pose estimation. It supports visualization, data export, and
    various coordinate transformations.

    Attributes:
        orig_img (np.ndarray): The original image as a numpy array.
        orig_shape (tuple[int, int]): Original image shape in (height, width) format.
        boxes (Boxes | None): Detected bounding boxes.
        masks (Masks | None): Segmentation masks.
        probs (Probs | None): Classification probabilities.
        keypoints (Keypoints | None): Detected keypoints.
        obb (OBB | None): Oriented bounding boxes.
        speed (dict): Dictionary containing inference speed information.
        names (dict): Dictionary mapping class indices to class names.
        path (str): Path to the input image file.
        save_dir (str | None): Directory to save results.

    Methods:
        update: Update the Results object with new detection data.
        cpu: Return a copy of the Results object with all tensors moved to CPU memory.
        numpy: Convert all tensors in the Results object to numpy arrays.
        cuda: Move all tensors in the Results object to GPU memory.
        to: Move all tensors to the specified device and dtype.
        new: Create a new Results object with the same image, path, names, and speed attributes.
        plot: Plot detection results on an input RGB image.
        show: Display the image with annotated inference results.
        save: Save annotated inference results image to file.
        verbose: Return a log string for each task in the results.
        save_txt: Save detection results to a text file.
        save_crop: Save cropped detection images to specified directory.
        summary: Convert inference results to a summarized dictionary.
        to_df: Convert detection results to a Polars Dataframe.
        to_json: Convert detection results to JSON format.
        to_csv: Convert detection results to a CSV format.

    Examples:
        >>> results = model("path/to/image.jpg")
        >>> result = results[0]  # Get the first result
        >>> boxes = result.boxes  # Get the boxes for the first result
        >>> masks = result.masks  # Get the masks for the first result
        >>> for result in results:
        >>>     result.plot()  # Plot detection results
    """

    def __init__(
        self,
        orig_img: np.ndarray,
        path: str,
        names: dict[int, str],
        boxes: torch.Tensor | None = None,
        masks: torch.Tensor | None = None,
        probs: torch.Tensor | None = None,
        keypoints: torch.Tensor | None = None,
        obb: torch.Tensor | None = None,
        speed: dict[str, float] | None = None,
    ) -> None:
        """Initialize the Results class for storing and manipulating inference results.

        Args:
            orig_img (np.ndarray): The original image as a numpy array.
            path (str): The path to the image file.
            names (dict): A dictionary of class names.
            boxes (torch.Tensor | None): A 2D tensor of bounding box coordinates for each detection.
            masks (torch.Tensor | None): A 3D tensor of detection masks, where each mask is a binary image.
            probs (torch.Tensor | None): A 1D tensor of probabilities of each class for classification task.
            keypoints (torch.Tensor | None): A 2D tensor of keypoint coordinates for each detection.
            obb (torch.Tensor | None): A 2D tensor of oriented bounding box coordinates for each detection.
            speed (dict | None): A dictionary containing preprocess, inference, and postprocess speeds (ms/image).

        Notes:
            For the default pose model, keypoint indices for human body pose estimation are:
            0: Nose, 1: Left Eye, 2: Right Eye, 3: Left Ear, 4: Right Ear
            5: Left Shoulder, 6: Right Shoulder, 7: Left Elbow, 8: Right Elbow
            9: Left Wrist, 10: Right Wrist, 11: Left Hip, 12: Right Hip
            13: Left Knee, 14: Right Knee, 15: Left Ankle, 16: Right Ankle
        """
        self.orig_img = orig_img
        self.orig_shape = orig_img.shape[:2]
        self.boxes = Boxes(boxes, self.orig_shape) if boxes is not None else None  # native size boxes
        self.masks = Masks(masks, self.orig_shape) if masks is not None else None  # native size or imgsz masks
        self.probs = Probs(probs) if probs is not None else None
        self.keypoints = Keypoints(keypoints, self.orig_shape) if keypoints is not None else None
        self.obb = OBB(obb, self.orig_shape) if obb is not None else None
        self.speed = speed if speed is not None else {"preprocess": None, "inference": None, "postprocess": None}
        self.names = names
        self.path = path
        self.save_dir = None
        self._keys = "boxes", "masks", "probs", "keypoints", "obb"

def __getitem__(self, idx):
    """Return a Results object for a specific index of inference results.

    Args:
        idx (int | slice): Index or slice to retrieve from the Results object.

    Returns:
        (Results): A new Results object containing the specified subset of inference results.

    Examples:
        >>> results = model("path/to/image.jpg")  # Perform inference
        >>> single_result = results[0]  # Get the first result
        >>> subset_results = results[1:4]  # Get a slice of results
    """
    return self._apply("__getitem__", idx)

def __len__(self) -> int:
    """Return the number of detections in the Results object.

    Returns:
        (int): The number of detections, determined by the length of the first non-empty attribute in (masks, probs,
            keypoints, or obb).

    Examples:
        >>> results = Results(orig_img, path, names, boxes=torch.rand(5, 4))
        >>> len(results)
        5
    """
    for k in self._keys:
        v = getattr(self, k)
        if v is not None:
            return len(v)

def _apply(self, fn: str, *args, **kwargs):
    """Apply a function to all non-empty attributes and return a new Results object with modified attributes.

    This method is internally called by methods like .to(), .cuda(), .cpu(), etc.

    Args:
        fn (str): The name of the function to apply.
        *args (Any): Variable length argument list to pass to the function.
        **kwargs (Any): Arbitrary keyword arguments to pass to the function.

    Returns:
        (Results): A new Results object with attributes modified by the applied function.

    Examples:
        >>> results = model("path/to/image.jpg")
        >>> for result in results:
        ...     result_cuda = result.cuda()
        ...     result_cpu = result.cpu()
    """
    r = self.new()
    for k in self._keys:
        v = getattr(self, k)
        if v is not None:
            setattr(r, k, getattr(v, fn)(*args, **kwargs))
    return r

def cpu(self):
    """Return a copy of the Results object with all its tensors moved to CPU memory.

    This method creates a new Results object with all tensor attributes (boxes, masks, probs, keypoints, obb)
    transferred to CPU memory. It's useful for moving data from GPU to CPU for further processing or saving.

    Returns:
        (Results): A new Results object with all tensor attributes on CPU memory.

    Examples:
        >>> results = model("path/to/image.jpg")  # Perform inference
        >>> cpu_result = results[0].cpu()  # Move the first result to CPU
        >>> print(cpu_result.boxes.device)  # Output: cpu
    """
    return self._apply("cpu")

def cuda(self):
    """Move all tensors in the Results object to GPU memory.

    Returns:
        (Results): A new Results object with all tensors moved to CUDA device.

    Examples:
        >>> results = model("path/to/image.jpg")
        >>> cuda_results = results[0].cuda()  # Move first result to GPU
        >>> for result in results:
        ...     result_cuda = result.cuda()  # Move each result to GPU
    """
    return self._apply("cuda")

def new(self):
    """Create a new Results object with the same image, path, names, and speed attributes.

    Returns:
        (Results): A new Results object with copied attributes from the original instance.

    Examples:
        >>> results = model("path/to/image.jpg")
        >>> new_result = results[0].new()
    """
    return Results(orig_img=self.orig_img, path=self.path, names=self.names, speed=self.speed)

def numpy(self):
    """Convert all tensors in the Results object to numpy arrays.

    Returns:
        (Results): A new Results object with all tensors converted to numpy arrays.

    Examples:
        >>> results = model("path/to/image.jpg")
        >>> numpy_result = results[0].numpy()
        >>> type(numpy_result.boxes.data)
        <class 'numpy.ndarray'>

    Notes:
        This method creates a new Results object, leaving the original unchanged. It's useful for
        interoperability with numpy-based libraries or when CPU-based operations are required.
    """
    return self._apply("numpy")

def plot(
    self,
    conf: bool = True,
    line_width: float | None = None,
    font_size: float | None = None,
    font: str = "Arial.ttf",
    pil: bool = False,
    img: np.ndarray | None = None,
    im_gpu: torch.Tensor | None = None,
    kpt_radius: int = 5,
    kpt_line: bool = True,
    labels: bool = True,
    boxes: bool = True,
    masks: bool = True,
    probs: bool = True,
    show: bool = False,
    save: bool = False,
    filename: str | None = None,
    color_mode: str = "class",
    txt_color: tuple[int, int, int] = (255, 255, 255),
) -> np.ndarray:
    """Plot detection results on an input RGB image.

    Args:
        conf (bool): Whether to plot detection confidence scores.
        line_width (float | None): Line width of bounding boxes. If None, scaled to image size.
        font_size (float | None): Font size for text. If None, scaled to image size.
        font (str): Font to use for text.
        pil (bool): Whether to return the image as a PIL Image.
        img (np.ndarray | None): Image to plot on. If None, uses original image.
        im_gpu (torch.Tensor | None): Normalized image on GPU for faster mask plotting.
        kpt_radius (int): Radius of drawn keypoints.
        kpt_line (bool): Whether to draw lines connecting keypoints.
        labels (bool): Whether to plot labels of bounding boxes.
        boxes (bool): Whether to plot bounding boxes.
        masks (bool): Whether to plot masks.
        probs (bool): Whether to plot classification probabilities.
        show (bool): Whether to display the annotated image.
        save (bool): Whether to save the annotated image.
        filename (str | None): Filename to save image if save is True.
        color_mode (str): Specify the color mode, e.g., 'instance' or 'class'.
        txt_color (tuple[int, int, int]): Specify the RGB text color for classification task.

    Returns:
        (np.ndarray): Annotated image as a numpy array.

    Examples:
        >>> results = model("image.jpg")
        >>> for result in results:
        >>>     im = result.plot()
        >>>     im.show()
    """
    assert color_mode in {"instance", "class"}, f"Expected color_mode='instance' or 'class', not {color_mode}."
    if img is None and isinstance(self.orig_img, torch.Tensor):
        img = (self.orig_img[0].detach().permute(1, 2, 0).contiguous() * 255).byte().cpu().numpy()

    names = self.names
    is_obb = self.obb is not None
    pred_boxes, show_boxes = self.obb if is_obb else self.boxes, boxes
    pred_masks, show_masks = self.masks, masks
    pred_probs, show_probs = self.probs, probs
    annotator = Annotator(
        deepcopy(self.orig_img if img is None else img),
        line_width,
        font_size,
        font,
        pil or (pred_probs is not None and show_probs),  # Classify tasks default to pil=True
        example=names,
    )

    # Plot Segment results
    if pred_masks and show_masks:
        if im_gpu is None:
            img = LetterBox(pred_masks.shape[1:])(image=annotator.result())
            im_gpu = (
                torch.as_tensor(img, dtype=torch.float16, device=pred_masks.data.device)
                .permute(2, 0, 1)
                .flip(0)
                .contiguous()
                / 255
            )
        idx = (
            pred_boxes.id
            if pred_boxes.is_track and color_mode == "instance"
            else pred_boxes.cls
            if pred_boxes and color_mode == "class"
            else reversed(range(len(pred_masks)))
        )
        annotator.masks(pred_masks.data, colors=[colors(x, True) for x in idx], im_gpu=im_gpu)

    # Plot Detect results
    if pred_boxes is not None and show_boxes:
        for i, d in enumerate(reversed(pred_boxes)):
            c, d_conf, id = int(d.cls), float(d.conf) if conf else None, int(d.id.item()) if d.is_track else None
            name = ("" if id is None else f"id:{id} ") + names[c]
            label = (f"{name} {d_conf:.2f}" if conf else name) if labels else None
            box = d.xyxyxyxy.squeeze() if is_obb else d.xyxy.squeeze()
            annotator.box_label(
                box,
                label,
                color=colors(
                    c
                    if color_mode == "class"
                    else id
                    if id is not None
                    else i
                    if color_mode == "instance"
                    else None,
                    True,
                ),
            )

    # Plot Classify results
    if pred_probs is not None and show_probs:
        text = "\n".join(f"{names[j] if names else j} {pred_probs.data[j]:.2f}" for j in pred_probs.top5)
        x = round(self.orig_shape[0] * 0.03)
        annotator.text([x, x], text, txt_color=txt_color, box_color=(64, 64, 64, 128))  # RGBA box

    # Plot Pose results
    if self.keypoints is not None:
        for i, k in enumerate(reversed(self.keypoints.data)):
            annotator.kpts(
                k,
                self.orig_shape,
                radius=kpt_radius,
                kpt_line=kpt_line,
                kpt_color=colors(i, True) if color_mode == "instance" else None,
            )

    # Show results
    if show:
        annotator.show(self.path)

    # Save results
    if save:
        annotator.save(filename or f"results_{Path(self.path).name}")

    return annotator.result(pil)

def save(self, filename: str | None = None, *args, **kwargs) -> str:
    """Save annotated inference results image to file.

    This method plots the detection results on the original image and saves the annotated image to a file. It
    utilizes the `plot` method to generate the annotated image and then saves it to the specified filename.

    Args:
        filename (str | Path | None): The filename to save the annotated image. If None, a default filename is
            generated based on the original image path.
        *args (Any): Variable length argument list to be passed to the `plot` method.
        **kwargs (Any): Arbitrary keyword arguments to be passed to the `plot` method.

    Returns:
        (str): The filename where the image was saved.

    Examples:
        >>> results = model("path/to/image.jpg")
        >>> for result in results:
        >>>     result.save("annotated_image.jpg")
        >>> # Or with custom plot arguments
        >>> for result in results:
        >>>     result.save("annotated_image.jpg", conf=False, line_width=2)
    """
    if not filename:
        filename = f"results_{Path(self.path).name}"
    self.plot(save=True, filename=filename, *args, **kwargs)
    return filename

def save_crop(self, save_dir: str | Path, file_name: str | Path = Path("im.jpg")):
    """Save cropped detection images to specified directory.

    This method saves cropped images of detected objects to a specified directory. Each crop is saved in a
    subdirectory named after the object's class, with the filename based on the input file_name.

    Args:
        save_dir (str | Path): Directory path where cropped images will be saved.
        file_name (str | Path): Base filename for the saved cropped images.

    Examples:
        >>> results = model("path/to/image.jpg")
        >>> for result in results:
        >>>     result.save_crop(save_dir="path/to/crops", file_name="detection")

    Notes:
        - This method does not support Classify or Oriented Bounding Box (OBB) tasks.
        - Crops are saved as 'save_dir/class_name/file_name.jpg'.
        - The method will create necessary subdirectories if they don't exist.
        - Original image is copied before cropping to avoid modifying the original.
    """
    if self.probs is not None:
        LOGGER.warning("Classify task do not support `save_crop`.")
        return
    if self.obb is not None:
        LOGGER.warning("OBB task do not support `save_crop`.")
        return
    for d in self.boxes:
        save_one_box(
            d.xyxy,
            self.orig_img.copy(),
            file=Path(save_dir) / self.names[int(d.cls)] / Path(file_name).with_suffix(".jpg"),
            BGR=True,
        )

def save_txt(self, txt_file: str | Path, save_conf: bool = False) -> str:
    """Save detection results to a text file.

    Args:
        txt_file (str | Path): Path to the output text file.
        save_conf (bool): Whether to include confidence scores in the output.

    Returns:
        (str): Path to the saved text file.

    Examples:
        >>> from ultralytics import YOLO
        >>> model = YOLO("yolo11n.pt")
        >>> results = model("path/to/image.jpg")
        >>> for result in results:
        >>>     result.save_txt("output.txt")

    Notes:
        - The file will contain one line per detection or classification with the following structure:
          - For detections: `class confidence x_center y_center width height`
          - For classifications: `confidence class_name`
          - For masks and keypoints, the specific formats will vary accordingly.
        - The function will create the output directory if it does not exist.
        - If save_conf is False, the confidence scores will be excluded from the output.
        - Existing contents of the file will not be overwritten; new results will be appended.
    """
    is_obb = self.obb is not None
    boxes = self.obb if is_obb else self.boxes
    masks = self.masks
    probs = self.probs
    kpts = self.keypoints
    texts = []
    if probs is not None:
        # Classify
        [texts.append(f"{probs.data[j]:.2f} {self.names[j]}") for j in probs.top5]
    elif boxes:
        # Detect/segment/pose
        for j, d in enumerate(boxes):
            c, conf, id = int(d.cls), float(d.conf), int(d.id.item()) if d.is_track else None
            line = (c, *(d.xyxyxyxyn.view(-1) if is_obb else d.xywhn.view(-1)))
            if masks:
                seg = masks[j].xyn[0].copy().reshape(-1)  # reversed mask.xyn, (n,2) to (n*2)
                line = (c, *seg)
            if kpts is not None:
                kpt = torch.cat((kpts[j].xyn, kpts[j].conf[..., None]), 2) if kpts[j].has_visible else kpts[j].xyn
                line += (*kpt.reshape(-1).tolist(),)
            line += (conf,) * save_conf + (() if id is None else (id,))
            texts.append(("%g " * len(line)).rstrip() % line)

    if texts:
        Path(txt_file).parent.mkdir(parents=True, exist_ok=True)  # make directory
        with open(txt_file, "a", encoding="utf-8") as f:
            f.writelines(text + "\n" for text in texts)

    return str(txt_file)

def show(self, *args, **kwargs):
    """Display the image with annotated inference results.

    This method plots the detection results on the original image and displays it. It's a convenient way to
    visualize the model's predictions directly.

    Args:
        *args (Any): Variable length argument list to be passed to the `plot()` method.
        **kwargs (Any): Arbitrary keyword arguments to be passed to the `plot()` method.

    Examples:
        >>> results = model("path/to/image.jpg")
        >>> results[0].show()  # Display the first result
        >>> for result in results:
        >>>     result.show()  # Display all results
    """
    self.plot(show=True, *args, **kwargs)

def summary(self, normalize: bool = False, decimals: int = 5) -> list[dict[str, Any]]:
    """Convert inference results to a summarized dictionary with optional normalization for box coordinates.

    This method creates a list of detection dictionaries, each containing information about a single detection or
    classification result. For classification tasks, it returns the top class and its
    confidence. For detection tasks, it includes class information, bounding box coordinates, and
    optionally mask segments and keypoints.

    Args:
        normalize (bool): Whether to normalize bounding box coordinates by image dimensions.
        decimals (int): Number of decimal places to round the output values to.

    Returns:
        (list[dict[str, Any]]): A list of dictionaries, each containing summarized information for a single
            detection or classification result. The structure of each dictionary varies based on the task type
            (classification or detection) and available information (boxes, masks, keypoints).

    Examples:
        >>> results = model("image.jpg")
        >>> for result in results:
        >>>     summary = result.summary()
        >>>     print(summary)
    """
    # Create list of detection dictionaries
    results = []
    if self.probs is not None:
        class_id = self.probs.top1
        results.append(
            {
                "name": self.names[class_id],
                "class": class_id,
                "confidence": round(self.probs.top1conf.item(), decimals),
            }
        )
        return results

    is_obb = self.obb is not None
    data = self.obb if is_obb else self.boxes
    h, w = self.orig_shape if normalize else (1, 1)
    for i, row in enumerate(data):  # xyxy, track_id if tracking, conf, class_id
        class_id, conf = int(row.cls), round(row.conf.item(), decimals)
        box = (row.xyxyxyxy if is_obb else row.xyxy).squeeze().reshape(-1, 2).tolist()
        xy = {}
        for j, b in enumerate(box):
            xy[f"x{j + 1}"] = round(b[0] / w, decimals)
            xy[f"y{j + 1}"] = round(b[1] / h, decimals)
        result = {"name": self.names[class_id], "class": class_id, "confidence": conf, "box": xy}
        if data.is_track:
            result["track_id"] = int(row.id.item())  # track ID
        if self.masks:
            result["segments"] = {
                "x": (self.masks.xy[i][:, 0] / w).round(decimals).tolist(),
                "y": (self.masks.xy[i][:, 1] / h).round(decimals).tolist(),
            }
        if self.keypoints is not None:
            x, y, visible = self.keypoints[i].data[0].cpu().unbind(dim=1)  # torch Tensor
            result["keypoints"] = {
                "x": (x / w).numpy().round(decimals).tolist(),  # decimals named argument required
                "y": (y / h).numpy().round(decimals).tolist(),
                "visible": visible.numpy().round(decimals).tolist(),
            }
        results.append(result)

    return results

def to(self, *args, **kwargs):
    """Move all tensors in the Results object to the specified device and dtype.

    Args:
        *args (Any): Variable length argument list to be passed to torch.Tensor.to().
        **kwargs (Any): Arbitrary keyword arguments to be passed to torch.Tensor.to().

    Returns:
        (Results): A new Results object with all tensors moved to the specified device and dtype.

    Examples:
        >>> results = model("path/to/image.jpg")
        >>> result_cuda = results[0].to("cuda")  # Move first result to GPU
        >>> result_cpu = results[0].to("cpu")  # Move first result to CPU
        >>> result_half = results[0].to(dtype=torch.float16)  # Convert first result to half precision
    """
    return self._apply("to", *args, **kwargs)

def update(
    self,
    boxes: torch.Tensor | None = None,
    masks: torch.Tensor | None = None,
    probs: torch.Tensor | None = None,
    obb: torch.Tensor | None = None,
    keypoints: torch.Tensor | None = None,
):
    """Update the Results object with new detection data.

    This method allows updating the boxes, masks, probabilities, and oriented bounding boxes (OBB) of the Results
    object. It ensures that boxes are clipped to the original image shape.

    Args:
        boxes (torch.Tensor | None): A tensor of shape (N, 6) containing bounding box coordinates and confidence
            scores. The format is (x1, y1, x2, y2, conf, class).
        masks (torch.Tensor | None): A tensor of shape (N, H, W) containing segmentation masks.
        probs (torch.Tensor | None): A tensor of shape (num_classes,) containing class probabilities.
        obb (torch.Tensor | None): A tensor of shape (N, 5) containing oriented bounding box coordinates.
        keypoints (torch.Tensor | None): A tensor of shape (N, 17, 3) containing keypoints.

    Examples:
        >>> results = model("image.jpg")
        >>> new_boxes = torch.tensor([[100, 100, 200, 200, 0.9, 0]])
        >>> results[0].update(boxes=new_boxes)
    """
    if boxes is not None:
        self.boxes = Boxes(ops.clip_boxes(boxes, self.orig_shape), self.orig_shape)
    if masks is not None:
        self.masks = Masks(masks, self.orig_shape)
    if probs is not None:
        self.probs = probs
    if obb is not None:
        self.obb = OBB(obb, self.orig_shape)
    if keypoints is not None:
        self.keypoints = Keypoints(keypoints, self.orig_shape)

def verbose(self) -> str:
    """Return a log string for each task in the results, detailing detection and classification outcomes.

    This method generates a human-readable string summarizing the detection and classification results. It includes
    the number of detections for each class and the top probabilities for classification tasks.

    Returns:
        (str): A formatted string containing a summary of the results. For detection tasks, it includes the number
            of detections per class. For classification tasks, it includes the top 5 class probabilities.

    Examples:
        >>> results = model("path/to/image.jpg")
        >>> for result in results:
        >>>     print(result.verbose())
        2 persons, 1 car, 3 traffic lights,
        dog 0.92, cat 0.78, horse 0.64,

    Notes:
        - If there are no detections, the method returns "(no detections), " for detection tasks.
        - For classification tasks, it returns the top 5 class probabilities and their corresponding class names.
        - The returned string is comma-separated and ends with a comma and a space.
    """
    boxes = self.obb if self.obb is not None else self.boxes
    if len(self) == 0:
        return "" if self.probs is not None else "(no detections), "
    if self.probs is not None:
        return f"{', '.join(f'{self.names[j]} {self.probs.data[j]:.2f}' for j in self.probs.top5)}, "
    if boxes:
        counts = boxes.cls.int().bincount()
        return "".join(f"{n} {self.names[i]}{'s' * (n > 1)}, " for i, n in enumerate(counts) if n > 0)

class Boxes(BaseTensor):
    """A class for managing and manipulating detection boxes.

    This class provides comprehensive functionality for handling detection boxes, including their coordinates,
    confidence scores, class labels, and optional tracking IDs. It supports various box formats and offers methods for
    easy manipulation and conversion between different coordinate systems.

    Attributes:
        data (torch.Tensor | np.ndarray): The raw tensor containing detection boxes and associated data.
        orig_shape (tuple[int, int]): The original image dimensions (height, width).
        is_track (bool): Indicates whether tracking IDs are included in the box data.
        xyxy (torch.Tensor | np.ndarray): Boxes in [x1, y1, x2, y2] format.
        conf (torch.Tensor | np.ndarray): Confidence scores for each box.
        cls (torch.Tensor | np.ndarray): Class labels for each box.
        id (torch.Tensor | None): Tracking IDs for each box (if available).
        xywh (torch.Tensor | np.ndarray): Boxes in [x, y, width, height] format.
        xyxyn (torch.Tensor | np.ndarray): Normalized [x1, y1, x2, y2] boxes relative to orig_shape.
        xywhn (torch.Tensor | np.ndarray): Normalized [x, y, width, height] boxes relative to orig_shape.

    Methods:
        cpu: Return a copy of the object with all tensors on CPU memory.
        numpy: Return a copy of the object with all tensors as numpy arrays.
        cuda: Return a copy of the object with all tensors on GPU memory.
        to: Return a copy of the object with tensors on specified device and dtype.

    Examples:
        >>> import torch
        >>> boxes_data = torch.tensor([[100, 50, 150, 100, 0.9, 0], [200, 150, 300, 250, 0.8, 1]])
        >>> orig_shape = (480, 640)  # height, width
        >>> boxes = Boxes(boxes_data, orig_shape)
        >>> print(boxes.xyxy)
        >>> print(boxes.conf)
        >>> print(boxes.cls)
        >>> print(boxes.xywhn)
    """

    def __init__(self, boxes: torch.Tensor | np.ndarray, orig_shape: tuple[int, int]) -> None:
        """Initialize the Boxes class with detection box data and the original image shape.

        This class manages detection boxes, providing easy access and manipulation of box coordinates, confidence
        scores, class identifiers, and optional tracking IDs. It supports multiple formats for box coordinates,
        including both absolute and normalized forms.

        Args:
            boxes (torch.Tensor | np.ndarray): A tensor or numpy array with detection boxes of shape (num_boxes, 6) or
                (num_boxes, 7). Columns should contain [x1, y1, x2, y2, (optional) track_id, confidence, class].
            orig_shape (tuple[int, int]): The original image shape as (height, width). Used for normalization.
        """
        if boxes.ndim == 1:
            boxes = boxes[None, :]
        n = boxes.shape[-1]
        assert n in {6, 7}, f"expected 6 or 7 values but got {n}"  # xyxy, track_id, conf, cls
        super().__init__(boxes, orig_shape)
        self.is_track = n == 7
        self.orig_shape = orig_shape