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Referencia para ultralytics/engine/results.py

Nota

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ultralytics.engine.results.BaseTensor

Bases: SimpleClass

Clase base tensor con m茅todos adicionales para facilitar la manipulaci贸n y el manejo de dispositivos.

C贸digo fuente en ultralytics/engine/results.py
class BaseTensor(SimpleClass):
    """Base tensor class with additional methods for easy manipulation and device handling."""

    def __init__(self, data, orig_shape) -> None:
        """
        Initialize BaseTensor with data and original shape.

        Args:
            data (torch.Tensor | np.ndarray): Predictions, such as bboxes, masks and keypoints.
            orig_shape (tuple): Original shape of image.
        """
        assert isinstance(data, (torch.Tensor, np.ndarray))
        self.data = data
        self.orig_shape = orig_shape

    @property
    def shape(self):
        """Return the shape of the data tensor."""
        return self.data.shape

    def cpu(self):
        """Return a copy of the tensor on CPU memory."""
        return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.cpu(), self.orig_shape)

    def numpy(self):
        """Return a copy of the tensor as a numpy array."""
        return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.numpy(), self.orig_shape)

    def cuda(self):
        """Return a copy of the tensor on GPU memory."""
        return self.__class__(torch.as_tensor(self.data).cuda(), self.orig_shape)

    def to(self, *args, **kwargs):
        """Return a copy of the tensor with the specified device and dtype."""
        return self.__class__(torch.as_tensor(self.data).to(*args, **kwargs), self.orig_shape)

    def __len__(self):  # override len(results)
        """Return the length of the data tensor."""
        return len(self.data)

    def __getitem__(self, idx):
        """Return a BaseTensor with the specified index of the data tensor."""
        return self.__class__(self.data[idx], self.orig_shape)

shape property

Devuelve la forma de los datos tensor.

__getitem__(idx)

Devuelve un BaseTensor con el 铆ndice especificado de los datos tensor.

C贸digo fuente en ultralytics/engine/results.py
def __getitem__(self, idx):
    """Return a BaseTensor with the specified index of the data tensor."""
    return self.__class__(self.data[idx], self.orig_shape)

__init__(data, orig_shape)

Inicializa BaseTensor con los datos y la forma original.

Par谩metros:

Nombre Tipo Descripci贸n Por defecto
data Tensor | ndarray

Predicciones, como bboxes, m谩scaras y puntos clave.

necesario
orig_shape tuple

Forma original de la imagen.

necesario
C贸digo fuente en ultralytics/engine/results.py
def __init__(self, data, orig_shape) -> None:
    """
    Initialize BaseTensor with data and original shape.

    Args:
        data (torch.Tensor | np.ndarray): Predictions, such as bboxes, masks and keypoints.
        orig_shape (tuple): Original shape of image.
    """
    assert isinstance(data, (torch.Tensor, np.ndarray))
    self.data = data
    self.orig_shape = orig_shape

__len__()

Devuelve la longitud de los datos tensor.

C贸digo fuente en ultralytics/engine/results.py
def __len__(self):  # override len(results)
    """Return the length of the data tensor."""
    return len(self.data)

cpu()

Devuelve una copia del tensor en la memoria de la CPU.

C贸digo fuente en ultralytics/engine/results.py
def cpu(self):
    """Return a copy of the tensor on CPU memory."""
    return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.cpu(), self.orig_shape)

cuda()

Devuelve una copia del tensor en la memoria de la GPU.

C贸digo fuente en ultralytics/engine/results.py
def cuda(self):
    """Return a copy of the tensor on GPU memory."""
    return self.__class__(torch.as_tensor(self.data).cuda(), self.orig_shape)

numpy()

Devuelve una copia de tensor como matriz numpy.

C贸digo fuente en ultralytics/engine/results.py
def numpy(self):
    """Return a copy of the tensor as a numpy array."""
    return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.numpy(), self.orig_shape)

to(*args, **kwargs)

Devuelve una copia de la p谩gina tensor con el dispositivo y dtype especificados.

C贸digo fuente en ultralytics/engine/results.py
def to(self, *args, **kwargs):
    """Return a copy of the tensor with the specified device and dtype."""
    return self.__class__(torch.as_tensor(self.data).to(*args, **kwargs), self.orig_shape)



ultralytics.engine.results.Results

Bases: SimpleClass

Una clase para almacenar y manipular los resultados de las inferencias.

Atributos:

Nombre Tipo Descripci贸n
orig_img ndarray

Imagen original como matriz numpy.

orig_shape tuple

Forma de la imagen original en formato (alto, ancho).

boxes Boxes

Objeto que contiene cuadros delimitadores de detecci贸n.

masks Masks

Objeto que contiene m谩scaras de detecci贸n.

probs Probs

Objeto que contiene probabilidades de clase para tareas de clasificaci贸n.

keypoints Keypoints

Objeto que contiene los puntos clave detectados para cada objeto.

speed dict

Diccionario de velocidades de preproceso, inferencia y postproceso (ms/imagen).

names dict

Diccionario de nombres de clase.

path str

Ruta al archivo de imagen.

M茅todos:

Nombre Descripci贸n
update

Actualiza los atributos del objeto con los nuevos resultados de la detecci贸n.

cpu

Devuelve una copia del objeto Resultados con todos los tensores en la memoria de la CPU.

numpy

Devuelve una copia del objeto Resultados con todos los tensores como matrices numpy.

cuda

Devuelve una copia del objeto Resultados con todos los tensores en la memoria de la GPU.

to

Devuelve una copia del objeto Resultados con tensores en un dispositivo y dtype especificados.

new

Devuelve un nuevo objeto Resultados con la misma imagen, ruta y nombres.

plot

Traza los resultados de la detecci贸n en una imagen de entrada, devolviendo una imagen anotada.

show

Mostrar resultados anotados en pantalla.

save

Guarda los resultados anotados en un archivo.

verbose

Devuelve una cadena de registro para cada tarea, detallando las detecciones y clasificaciones.

save_txt

Guarda los resultados de la detecci贸n en un archivo de texto.

save_crop

Guarda las im谩genes de detecci贸n recortadas.

tojson

Convierte los resultados de la detecci贸n al formato JSON.

C贸digo fuente en ultralytics/engine/results.py
class Results(SimpleClass):
    """
    A class for storing and manipulating inference results.

    Attributes:
        orig_img (numpy.ndarray): Original image as a numpy array.
        orig_shape (tuple): Original image shape in (height, width) format.
        boxes (Boxes, optional): Object containing detection bounding boxes.
        masks (Masks, optional): Object containing detection masks.
        probs (Probs, optional): Object containing class probabilities for classification tasks.
        keypoints (Keypoints, optional): Object containing detected keypoints for each object.
        speed (dict): Dictionary of preprocess, inference, and postprocess speeds (ms/image).
        names (dict): Dictionary of class names.
        path (str): Path to the image file.

    Methods:
        update(boxes=None, masks=None, probs=None, obb=None): Updates object attributes with new detection results.
        cpu(): Returns a copy of the Results object with all tensors on CPU memory.
        numpy(): Returns a copy of the Results object with all tensors as numpy arrays.
        cuda(): Returns a copy of the Results object with all tensors on GPU memory.
        to(*args, **kwargs): Returns a copy of the Results object with tensors on a specified device and dtype.
        new(): Returns a new Results object with the same image, path, and names.
        plot(...): Plots detection results on an input image, returning an annotated image.
        show(): Show annotated results to screen.
        save(filename): Save annotated results to file.
        verbose(): Returns a log string for each task, detailing detections and classifications.
        save_txt(txt_file, save_conf=False): Saves detection results to a text file.
        save_crop(save_dir, file_name=Path("im.jpg")): Saves cropped detection images.
        tojson(normalize=False): Converts detection results to JSON format.
    """

    def __init__(
        self, orig_img, path, names, boxes=None, masks=None, probs=None, keypoints=None, obb=None, speed=None
    ) -> None:
        """
        Initialize the Results class.

        Args:
            orig_img (numpy.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, optional): A 2D tensor of bounding box coordinates for each detection.
            masks (torch.tensor, optional): A 3D tensor of detection masks, where each mask is a binary image.
            probs (torch.tensor, optional): A 1D tensor of probabilities of each class for classification task.
            keypoints (torch.tensor, optional): A 2D tensor of keypoint coordinates for each detection.
            obb (torch.tensor, optional): A 2D tensor of oriented bounding box coordinates for each detection.
        """
        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 the specified index."""
        return self._apply("__getitem__", idx)

    def __len__(self):
        """Return the number of detections in the Results object."""
        for k in self._keys:
            v = getattr(self, k)
            if v is not None:
                return len(v)

    def update(self, boxes=None, masks=None, probs=None, obb=None):
        """Update the boxes, masks, and probs attributes of the Results object."""
        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)

    def _apply(self, fn, *args, **kwargs):
        """
        Applies a function to all non-empty attributes and returns a new Results object with modified attributes. This
        function is internally called by methods like .to(), .cuda(), .cpu(), etc.

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

        Returns:
            Results: A new Results object with attributes modified by the applied function.
        """
        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 tensors on CPU memory."""
        return self._apply("cpu")

    def numpy(self):
        """Return a copy of the Results object with all tensors as numpy arrays."""
        return self._apply("numpy")

    def cuda(self):
        """Return a copy of the Results object with all tensors on GPU memory."""
        return self._apply("cuda")

    def to(self, *args, **kwargs):
        """Return a copy of the Results object with tensors on the specified device and dtype."""
        return self._apply("to", *args, **kwargs)

    def new(self):
        """Return a new Results object with the same image, path, names and speed."""
        return Results(orig_img=self.orig_img, path=self.path, names=self.names, speed=self.speed)

    def plot(
        self,
        conf=True,
        line_width=None,
        font_size=None,
        font="Arial.ttf",
        pil=False,
        img=None,
        im_gpu=None,
        kpt_radius=5,
        kpt_line=True,
        labels=True,
        boxes=True,
        masks=True,
        probs=True,
        show=False,
        save=False,
        filename=None,
    ):
        """
        Plots the detection results on an input RGB image. Accepts a numpy array (cv2) or a PIL Image.

        Args:
            conf (bool): Whether to plot the detection confidence score.
            line_width (float, optional): The line width of the bounding boxes. If None, it is scaled to the image size.
            font_size (float, optional): The font size of the text. If None, it is scaled to the image size.
            font (str): The font to use for the text.
            pil (bool): Whether to return the image as a PIL Image.
            img (numpy.ndarray): Plot to another image. if not, plot to original image.
            im_gpu (torch.Tensor): Normalized image in gpu with shape (1, 3, 640, 640), for faster mask plotting.
            kpt_radius (int, optional): Radius of the drawn keypoints. Default is 5.
            kpt_line (bool): Whether to draw lines connecting keypoints.
            labels (bool): Whether to plot the label of bounding boxes.
            boxes (bool): Whether to plot the bounding boxes.
            masks (bool): Whether to plot the masks.
            probs (bool): Whether to plot classification probability
            show (bool): Whether to display the annotated image directly.
            save (bool): Whether to save the annotated image to `filename`.
            filename (str): Filename to save image to if save is True.

        Returns:
            (numpy.ndarray): A numpy array of the annotated image.

        Example:
            ```python
            from PIL import Image
            from ultralytics import YOLO

            model = YOLO('yolov8n.pt')
            results = model('bus.jpg')  # results list
            for r in results:
                im_array = r.plot()  # plot a BGR numpy array of predictions
                im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
                im.show()  # show image
                im.save('results.jpg')  # save image
            ```
        """
        if img is None and isinstance(self.orig_img, torch.Tensor):
            img = (self.orig_img[0].detach().permute(1, 2, 0).contiguous() * 255).to(torch.uint8).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.cls if pred_boxes else 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 d in reversed(pred_boxes):
                c, conf, id = int(d.cls), float(d.conf) if conf else None, None if d.id is None else int(d.id.item())
                name = ("" if id is None else f"id:{id} ") + names[c]
                label = (f"{name} {conf:.2f}" if conf else name) if labels else None
                box = d.xyxyxyxy.reshape(-1, 4, 2).squeeze() if is_obb else d.xyxy.squeeze()
                annotator.box_label(box, label, color=colors(c, True), rotated=is_obb)

        # 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=(255, 255, 255))  # TODO: allow setting colors

        # Plot Pose results
        if self.keypoints is not None:
            for k in reversed(self.keypoints.data):
                annotator.kpts(k, self.orig_shape, radius=kpt_radius, kpt_line=kpt_line)

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

        # Save results
        if save:
            annotator.save(filename)

        return annotator.result()

    def show(self, *args, **kwargs):
        """Show annotated results image."""
        self.plot(show=True, *args, **kwargs)

    def save(self, filename=None, *args, **kwargs):
        """Save annotated results image."""
        if not filename:
            filename = f"results_{Path(self.path).name}"
        self.plot(save=True, filename=filename, *args, **kwargs)
        return filename

    def verbose(self):
        """Return log string for each task."""
        log_string = ""
        probs = self.probs
        boxes = self.boxes
        if len(self) == 0:
            return log_string if probs is not None else f"{log_string}(no detections), "
        if probs is not None:
            log_string += f"{', '.join(f'{self.names[j]} {probs.data[j]:.2f}' for j in probs.top5)}, "
        if boxes:
            for c in boxes.cls.unique():
                n = (boxes.cls == c).sum()  # detections per class
                log_string += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "
        return log_string

    def save_txt(self, txt_file, save_conf=False):
        """
        Save predictions into txt file.

        Args:
            txt_file (str): txt file path.
            save_conf (bool): save confidence score or not.
        """
        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), None if d.id is None else int(d.id.item())
                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") as f:
                f.writelines(text + "\n" for text in texts)

    def save_crop(self, save_dir, file_name=Path("im.jpg")):
        """
        Save cropped predictions to `save_dir/cls/file_name.jpg`.

        Args:
            save_dir (str | pathlib.Path): Save path.
            file_name (str | pathlib.Path): File name.
        """
        if self.probs is not None:
            LOGGER.warning("WARNING 鈿狅笍 Classify task do not support `save_crop`.")
            return
        if self.obb is not None:
            LOGGER.warning("WARNING 鈿狅笍 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)] / f"{Path(file_name)}.jpg",
                BGR=True,
            )

    def summary(self, normalize=False, decimals=5):
        """Convert the results to a summarized format."""
        # 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

        data = self.boxes or self.obb
        is_obb = self.obb is not None
        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 tojson(self, normalize=False, decimals=5):
        """Convert the results to JSON format."""
        import json

        return json.dumps(self.summary(normalize=normalize, decimals=decimals), indent=2)

__getitem__(idx)

Devuelve un objeto Resultados para el 铆ndice especificado.

C贸digo fuente en ultralytics/engine/results.py
def __getitem__(self, idx):
    """Return a Results object for the specified index."""
    return self._apply("__getitem__", idx)

__init__(orig_img, path, names, boxes=None, masks=None, probs=None, keypoints=None, obb=None, speed=None)

Inicializa la clase Resultados.

Par谩metros:

Nombre Tipo Descripci贸n Por defecto
orig_img ndarray

La imagen original como matriz numpy.

necesario
path str

La ruta al archivo de imagen.

necesario
names dict

Un diccionario de nombres de clases.

necesario
boxes tensor

Un tensor 2D de coordenadas de cuadro delimitador para cada detecci贸n.

None
masks tensor

Un tensor 3D de m谩scaras de detecci贸n, donde cada m谩scara es una imagen binaria.

None
probs tensor

Un 1D tensor de probabilidades de cada clase para la tarea de clasificaci贸n.

None
keypoints tensor

Un tensor 2D de coordenadas de puntos clave para cada detecci贸n.

None
obb tensor

Un tensor 2D de coordenadas de cuadro delimitador orientado para cada detecci贸n.

None
C贸digo fuente en ultralytics/engine/results.py
def __init__(
    self, orig_img, path, names, boxes=None, masks=None, probs=None, keypoints=None, obb=None, speed=None
) -> None:
    """
    Initialize the Results class.

    Args:
        orig_img (numpy.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, optional): A 2D tensor of bounding box coordinates for each detection.
        masks (torch.tensor, optional): A 3D tensor of detection masks, where each mask is a binary image.
        probs (torch.tensor, optional): A 1D tensor of probabilities of each class for classification task.
        keypoints (torch.tensor, optional): A 2D tensor of keypoint coordinates for each detection.
        obb (torch.tensor, optional): A 2D tensor of oriented bounding box coordinates for each detection.
    """
    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"

__len__()

Devuelve el n煤mero de detecciones en el objeto Resultados.

C贸digo fuente en ultralytics/engine/results.py
def __len__(self):
    """Return the number of detections in the Results object."""
    for k in self._keys:
        v = getattr(self, k)
        if v is not None:
            return len(v)

cpu()

Devuelve una copia del objeto Resultados con todos los tensores en la memoria de la CPU.

C贸digo fuente en ultralytics/engine/results.py
def cpu(self):
    """Return a copy of the Results object with all tensors on CPU memory."""
    return self._apply("cpu")

cuda()

Devuelve una copia del objeto Resultados con todos los tensores en la memoria de la GPU.

C贸digo fuente en ultralytics/engine/results.py
def cuda(self):
    """Return a copy of the Results object with all tensors on GPU memory."""
    return self._apply("cuda")

new()

Devuelve un nuevo objeto Resultados con la misma imagen, ruta, nombres y velocidad.

C贸digo fuente en ultralytics/engine/results.py
def new(self):
    """Return a new Results object with the same image, path, names and speed."""
    return Results(orig_img=self.orig_img, path=self.path, names=self.names, speed=self.speed)

numpy()

Devuelve una copia del objeto Resultados con todos los tensores como matrices numpy.

C贸digo fuente en ultralytics/engine/results.py
def numpy(self):
    """Return a copy of the Results object with all tensors as numpy arrays."""
    return self._apply("numpy")

plot(conf=True, line_width=None, font_size=None, font='Arial.ttf', pil=False, img=None, im_gpu=None, kpt_radius=5, kpt_line=True, labels=True, boxes=True, masks=True, probs=True, show=False, save=False, filename=None)

Traza los resultados de la detecci贸n en una imagen RGB de entrada. Acepta una matriz numpy (cv2) o una imagen PIL.

Par谩metros:

Nombre Tipo Descripci贸n Por defecto
conf bool

Si se traza la puntuaci贸n de confianza de la detecci贸n.

True
line_width float

La anchura de l铆nea de las cajas delimitadoras. Si es Ninguno, se escala al tama帽o de la imagen.

None
font_size float

El tama帽o de la fuente del texto. Si es Ninguno, se escala al tama帽o de la imagen.

None
font str

El tipo de letra a utilizar para el texto.

'Arial.ttf'
pil bool

Si se devuelve la imagen como Imagen PIL.

False
img ndarray

Traza a otra imagen. si no, traza a la imagen original.

None
im_gpu Tensor

Imagen normalizada en gpu con forma (1, 3, 640, 640), para un trazado m谩s r谩pido de la m谩scara.

None
kpt_radius int

Radio de los puntos clave dibujados. Por defecto es 5.

5
kpt_line bool

Si dibujar l铆neas que conecten los puntos clave.

True
labels bool

Si se traza la etiqueta de las cajas delimitadoras.

True
boxes bool

Si se trazan las cajas delimitadoras.

True
masks bool

Si se trazan las m谩scaras.

True
probs bool

Si se traza la probabilidad de clasificaci贸n

True
show bool

Si quieres mostrar directamente la imagen anotada.

False
save bool

Si guardar la imagen anotada en filename.

False
filename str

Nombre de archivo en el que guardar la imagen si guardar es True.

None

Devuelve:

Tipo Descripci贸n
ndarray

Una matriz numpy de la imagen anotada.

Ejemplo
from PIL import Image
from ultralytics import YOLO

model = YOLO('yolov8n.pt')
results = model('bus.jpg')  # results list
for r in results:
    im_array = r.plot()  # plot a BGR numpy array of predictions
    im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
    im.show()  # show image
    im.save('results.jpg')  # save image
C贸digo fuente en ultralytics/engine/results.py
def plot(
    self,
    conf=True,
    line_width=None,
    font_size=None,
    font="Arial.ttf",
    pil=False,
    img=None,
    im_gpu=None,
    kpt_radius=5,
    kpt_line=True,
    labels=True,
    boxes=True,
    masks=True,
    probs=True,
    show=False,
    save=False,
    filename=None,
):
    """
    Plots the detection results on an input RGB image. Accepts a numpy array (cv2) or a PIL Image.

    Args:
        conf (bool): Whether to plot the detection confidence score.
        line_width (float, optional): The line width of the bounding boxes. If None, it is scaled to the image size.
        font_size (float, optional): The font size of the text. If None, it is scaled to the image size.
        font (str): The font to use for the text.
        pil (bool): Whether to return the image as a PIL Image.
        img (numpy.ndarray): Plot to another image. if not, plot to original image.
        im_gpu (torch.Tensor): Normalized image in gpu with shape (1, 3, 640, 640), for faster mask plotting.
        kpt_radius (int, optional): Radius of the drawn keypoints. Default is 5.
        kpt_line (bool): Whether to draw lines connecting keypoints.
        labels (bool): Whether to plot the label of bounding boxes.
        boxes (bool): Whether to plot the bounding boxes.
        masks (bool): Whether to plot the masks.
        probs (bool): Whether to plot classification probability
        show (bool): Whether to display the annotated image directly.
        save (bool): Whether to save the annotated image to `filename`.
        filename (str): Filename to save image to if save is True.

    Returns:
        (numpy.ndarray): A numpy array of the annotated image.

    Example:
        ```python
        from PIL import Image
        from ultralytics import YOLO

        model = YOLO('yolov8n.pt')
        results = model('bus.jpg')  # results list
        for r in results:
            im_array = r.plot()  # plot a BGR numpy array of predictions
            im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
            im.show()  # show image
            im.save('results.jpg')  # save image
        ```
    """
    if img is None and isinstance(self.orig_img, torch.Tensor):
        img = (self.orig_img[0].detach().permute(1, 2, 0).contiguous() * 255).to(torch.uint8).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.cls if pred_boxes else 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 d in reversed(pred_boxes):
            c, conf, id = int(d.cls), float(d.conf) if conf else None, None if d.id is None else int(d.id.item())
            name = ("" if id is None else f"id:{id} ") + names[c]
            label = (f"{name} {conf:.2f}" if conf else name) if labels else None
            box = d.xyxyxyxy.reshape(-1, 4, 2).squeeze() if is_obb else d.xyxy.squeeze()
            annotator.box_label(box, label, color=colors(c, True), rotated=is_obb)

    # 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=(255, 255, 255))  # TODO: allow setting colors

    # Plot Pose results
    if self.keypoints is not None:
        for k in reversed(self.keypoints.data):
            annotator.kpts(k, self.orig_shape, radius=kpt_radius, kpt_line=kpt_line)

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

    # Save results
    if save:
        annotator.save(filename)

    return annotator.result()

save(filename=None, *args, **kwargs)

Guardar imagen de resultados anotados.

C贸digo fuente en ultralytics/engine/results.py
def save(self, filename=None, *args, **kwargs):
    """Save annotated results image."""
    if not filename:
        filename = f"results_{Path(self.path).name}"
    self.plot(save=True, filename=filename, *args, **kwargs)
    return filename

save_crop(save_dir, file_name=Path('im.jpg'))

Guardar predicciones recortadas en save_dir/cls/file_name.jpg.

Par谩metros:

Nombre Tipo Descripci贸n Por defecto
save_dir str | Path

Guardar ruta.

necesario
file_name str | Path

Nombre del archivo.

Path('im.jpg')
C贸digo fuente en ultralytics/engine/results.py
def save_crop(self, save_dir, file_name=Path("im.jpg")):
    """
    Save cropped predictions to `save_dir/cls/file_name.jpg`.

    Args:
        save_dir (str | pathlib.Path): Save path.
        file_name (str | pathlib.Path): File name.
    """
    if self.probs is not None:
        LOGGER.warning("WARNING 鈿狅笍 Classify task do not support `save_crop`.")
        return
    if self.obb is not None:
        LOGGER.warning("WARNING 鈿狅笍 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)] / f"{Path(file_name)}.jpg",
            BGR=True,
        )

save_txt(txt_file, save_conf=False)

Guarda las predicciones en un archivo txt.

Par谩metros:

Nombre Tipo Descripci贸n Por defecto
txt_file str

ruta del archivo txt.

necesario
save_conf bool

guardar puntuaci贸n de confianza o no.

False
C贸digo fuente en ultralytics/engine/results.py
def save_txt(self, txt_file, save_conf=False):
    """
    Save predictions into txt file.

    Args:
        txt_file (str): txt file path.
        save_conf (bool): save confidence score or not.
    """
    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), None if d.id is None else int(d.id.item())
            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") as f:
            f.writelines(text + "\n" for text in texts)

show(*args, **kwargs)

Mostrar imagen de resultados anotados.

C贸digo fuente en ultralytics/engine/results.py
def show(self, *args, **kwargs):
    """Show annotated results image."""
    self.plot(show=True, *args, **kwargs)

summary(normalize=False, decimals=5)

Convierte los resultados a un formato resumido.

C贸digo fuente en ultralytics/engine/results.py
def summary(self, normalize=False, decimals=5):
    """Convert the results to a summarized format."""
    # 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

    data = self.boxes or self.obb
    is_obb = self.obb is not None
    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

to(*args, **kwargs)

Devuelve una copia del objeto Resultados con tensores en el dispositivo y dtype especificados.

C贸digo fuente en ultralytics/engine/results.py
def to(self, *args, **kwargs):
    """Return a copy of the Results object with tensors on the specified device and dtype."""
    return self._apply("to", *args, **kwargs)

tojson(normalize=False, decimals=5)

Convierte los resultados al formato JSON.

C贸digo fuente en ultralytics/engine/results.py
def tojson(self, normalize=False, decimals=5):
    """Convert the results to JSON format."""
    import json

    return json.dumps(self.summary(normalize=normalize, decimals=decimals), indent=2)

update(boxes=None, masks=None, probs=None, obb=None)

Actualiza los atributos casillas, m谩scaras y probs del objeto Resultados.

C贸digo fuente en ultralytics/engine/results.py
def update(self, boxes=None, masks=None, probs=None, obb=None):
    """Update the boxes, masks, and probs attributes of the Results object."""
    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)

verbose()

Devuelve la cadena de registro de cada tarea.

C贸digo fuente en ultralytics/engine/results.py
def verbose(self):
    """Return log string for each task."""
    log_string = ""
    probs = self.probs
    boxes = self.boxes
    if len(self) == 0:
        return log_string if probs is not None else f"{log_string}(no detections), "
    if probs is not None:
        log_string += f"{', '.join(f'{self.names[j]} {probs.data[j]:.2f}' for j in probs.top5)}, "
    if boxes:
        for c in boxes.cls.unique():
            n = (boxes.cls == c).sum()  # detections per class
            log_string += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "
    return log_string



ultralytics.engine.results.Boxes

Bases: BaseTensor

Gestiona las cajas de detecci贸n, facilitando el acceso y la manipulaci贸n de las coordenadas de las cajas, las puntuaciones de confianza, los identificadores de clase y los identificadores de seguimiento opcionales. identificadores de clase e identificadores de seguimiento opcionales. Admite varios formatos para las coordenadas de las casillas, tanto absolutos como absolutas y normalizadas.

Atributos:

Nombre Tipo Descripci贸n
data Tensor

El tensor en bruto que contiene las casillas de detecci贸n y sus datos asociados.

orig_shape tuple

El tama帽o original de la imagen como tupla (alto, ancho), utilizada para la normalizaci贸n.

is_track bool

Indica si los ID de seguimiento se incluyen en los datos de la caja.

Propiedades

xyxy (torch.Tensor | numpy.ndarray): Cajas en formato [x1, y1, x2, y2]. conf (torch.Tensor | numpy.ndarray): Puntuaciones de confianza de cada casilla. cls (torch.Tensor | numpy.ndarray): Etiquetas de clase para cada casilla. id (torch.Tensor | numpy.ndarray, opcional): IDs de seguimiento para cada casilla, si est谩n disponibles. xywh (torch.Tensor | numpy.ndarray): Cajas en formato [x, y, anchura, altura], calculadas bajo demanda. xyxyn (torch.Tensor | numpy.ndarray): Cajas normalizadas [x1, y1, x2, y2], relativas a orig_shape. xywhn (torch.Tensor | numpy.ndarray): Cajas [x, y, anchura, altura] normalizadas, relativas a orig_shape.

M茅todos:

Nombre Descripci贸n
cpu

Mueve las cajas a la memoria de la CPU.

numpy

Convierte las cajas a un formato de matriz numpy.

cuda

Mueve las cajas a la memoria CUDA (GPU).

to

Mueve las cajas al dispositivo especificado.

C贸digo fuente en ultralytics/engine/results.py
class Boxes(BaseTensor):
    """
    Manages detection boxes, providing easy access and manipulation of box coordinates, confidence scores, class
    identifiers, and optional tracking IDs. Supports multiple formats for box coordinates, including both absolute and
    normalized forms.

    Attributes:
        data (torch.Tensor): The raw tensor containing detection boxes and their associated data.
        orig_shape (tuple): The original image size as a tuple (height, width), used for normalization.
        is_track (bool): Indicates whether tracking IDs are included in the box data.

    Properties:
        xyxy (torch.Tensor | numpy.ndarray): Boxes in [x1, y1, x2, y2] format.
        conf (torch.Tensor | numpy.ndarray): Confidence scores for each box.
        cls (torch.Tensor | numpy.ndarray): Class labels for each box.
        id (torch.Tensor | numpy.ndarray, optional): Tracking IDs for each box, if available.
        xywh (torch.Tensor | numpy.ndarray): Boxes in [x, y, width, height] format, calculated on demand.
        xyxyn (torch.Tensor | numpy.ndarray): Normalized [x1, y1, x2, y2] boxes, relative to `orig_shape`.
        xywhn (torch.Tensor | numpy.ndarray): Normalized [x, y, width, height] boxes, relative to `orig_shape`.

    Methods:
        cpu(): Moves the boxes to CPU memory.
        numpy(): Converts the boxes to a numpy array format.
        cuda(): Moves the boxes to CUDA (GPU) memory.
        to(device, dtype=None): Moves the boxes to the specified device.
    """

    def __init__(self, boxes, orig_shape) -> None:
        """
        Initialize the Boxes class.

        Args:
            boxes (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the detection boxes, with
                shape (num_boxes, 6) or (num_boxes, 7). The last two columns contain confidence and class values.
                If present, the third last column contains track IDs.
            orig_shape (tuple): Original image size, in the format (height, width).
        """
        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

    @property
    def xyxy(self):
        """Return the boxes in xyxy format."""
        return self.data[:, :4]

    @property
    def conf(self):
        """Return the confidence values of the boxes."""
        return self.data[:, -2]

    @property
    def cls(self):
        """Return the class values of the boxes."""
        return self.data[:, -1]

    @property
    def id(self):
        """Return the track IDs of the boxes (if available)."""
        return self.data[:, -3] if self.is_track else None

    @property
    @lru_cache(maxsize=2)  # maxsize 1 should suffice
    def xywh(self):
        """Return the boxes in xywh format."""
        return ops.xyxy2xywh(self.xyxy)

    @property
    @lru_cache(maxsize=2)
    def xyxyn(self):
        """Return the boxes in xyxy format normalized by original image size."""
        xyxy = self.xyxy.clone() if isinstance(self.xyxy, torch.Tensor) else np.copy(self.xyxy)
        xyxy[..., [0, 2]] /= self.orig_shape[1]
        xyxy[..., [1, 3]] /= self.orig_shape[0]
        return xyxy

    @property
    @lru_cache(maxsize=2)
    def xywhn(self):
        """Return the boxes in xywh format normalized by original image size."""
        xywh = ops.xyxy2xywh(self.xyxy)
        xywh[..., [0, 2]] /= self.orig_shape[1]
        xywh[..., [1, 3]] /= self.orig_shape[0]
        return xywh

cls property

Devuelve los valores de clase de las cajas.

conf property

Devuelve los valores de confianza de las casillas.

id property

Devuelve los ID de pista de las cajas (si est谩n disponibles).

xywh cached property

Devuelve las cajas en formato xywh.

xywhn cached property

Devuelve las cajas en formato xywh normalizadas por el tama帽o original de la imagen.

xyxy property

Devuelve las casillas en formato xyxy.

xyxyn cached property

Devuelve las cajas en formato xyxy normalizadas por el tama帽o original de la imagen.

__init__(boxes, orig_shape)

Inicializa la clase Cajas.

Par谩metros:

Nombre Tipo Descripci贸n Por defecto
boxes Tensor | ndarray

Una matriz tensor o numpy que contiene las cajas de detecci贸n, con forma (num_cajas, 6) o (num_cajas, 7). Las dos 煤ltimas columnas contienen los valores de confianza y clase. Si est谩 presente, la antepen煤ltima columna contiene los ID de pista.

necesario
orig_shape tuple

Tama帽o original de la imagen, en el formato (alto, ancho).

necesario
C贸digo fuente en ultralytics/engine/results.py
def __init__(self, boxes, orig_shape) -> None:
    """
    Initialize the Boxes class.

    Args:
        boxes (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the detection boxes, with
            shape (num_boxes, 6) or (num_boxes, 7). The last two columns contain confidence and class values.
            If present, the third last column contains track IDs.
        orig_shape (tuple): Original image size, in the format (height, width).
    """
    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



ultralytics.engine.results.Masks

Bases: BaseTensor

Una clase para almacenar y manipular m谩scaras de detecci贸n.

Atributos:

Nombre Tipo Descripci贸n
xy list

Una lista de segmentos en coordenadas de p铆xel.

xyn list

Una lista de segmentos normalizados.

M茅todos:

Nombre Descripci贸n
cpu

Devuelve las m谩scaras tensor en la memoria de la CPU.

numpy

Devuelve las m谩scaras tensor como una matriz numpy.

cuda

Devuelve las m谩scaras tensor en la memoria de la GPU.

to

Devuelve las m谩scaras tensor con el dispositivo y dtype especificados.

C贸digo fuente en ultralytics/engine/results.py
class Masks(BaseTensor):
    """
    A class for storing and manipulating detection masks.

    Attributes:
        xy (list): A list of segments in pixel coordinates.
        xyn (list): A list of normalized segments.

    Methods:
        cpu(): Returns the masks tensor on CPU memory.
        numpy(): Returns the masks tensor as a numpy array.
        cuda(): Returns the masks tensor on GPU memory.
        to(device, dtype): Returns the masks tensor with the specified device and dtype.
    """

    def __init__(self, masks, orig_shape) -> None:
        """Initialize the Masks class with the given masks tensor and original image shape."""
        if masks.ndim == 2:
            masks = masks[None, :]
        super().__init__(masks, orig_shape)

    @property
    @lru_cache(maxsize=1)
    def xyn(self):
        """Return normalized segments."""
        return [
            ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=True)
            for x in ops.masks2segments(self.data)
        ]

    @property
    @lru_cache(maxsize=1)
    def xy(self):
        """Return segments in pixel coordinates."""
        return [
            ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=False)
            for x in ops.masks2segments(self.data)
        ]

xy cached property

Devuelve los segmentos en coordenadas de p铆xel.

xyn cached property

Devuelve los segmentos normalizados.

__init__(masks, orig_shape)

Inicializa la clase M谩scaras con las m谩scaras dadas tensor y la forma original de la imagen.

C贸digo fuente en ultralytics/engine/results.py
def __init__(self, masks, orig_shape) -> None:
    """Initialize the Masks class with the given masks tensor and original image shape."""
    if masks.ndim == 2:
        masks = masks[None, :]
    super().__init__(masks, orig_shape)



ultralytics.engine.results.Keypoints

Bases: BaseTensor

Una clase para almacenar y manipular puntos clave de detecci贸n.

Atributos:

Nombre Tipo Descripci贸n
xy Tensor

Una colecci贸n de puntos clave que contiene las coordenadas x, y de cada detecci贸n.

xyn Tensor

Una versi贸n normalizada de xy con coordenadas en el intervalo [0, 1].

conf Tensor

Valores de confianza asociados a los puntos clave, si est谩n disponibles; en caso contrario, Ninguno.

M茅todos:

Nombre Descripci贸n
cpu

Devuelve una copia de los puntos clave tensor en la memoria de la CPU.

numpy

Devuelve una copia de los puntos clave tensor como una matriz numpy.

cuda

Devuelve una copia de los puntos clave tensor en la memoria de la GPU.

to

Devuelve una copia de los puntos clave tensor con el dispositivo y dtype especificados.

C贸digo fuente en ultralytics/engine/results.py
class Keypoints(BaseTensor):
    """
    A class for storing and manipulating detection keypoints.

    Attributes:
        xy (torch.Tensor): A collection of keypoints containing x, y coordinates for each detection.
        xyn (torch.Tensor): A normalized version of xy with coordinates in the range [0, 1].
        conf (torch.Tensor): Confidence values associated with keypoints if available, otherwise None.

    Methods:
        cpu(): Returns a copy of the keypoints tensor on CPU memory.
        numpy(): Returns a copy of the keypoints tensor as a numpy array.
        cuda(): Returns a copy of the keypoints tensor on GPU memory.
        to(device, dtype): Returns a copy of the keypoints tensor with the specified device and dtype.
    """

    @smart_inference_mode()  # avoid keypoints < conf in-place error
    def __init__(self, keypoints, orig_shape) -> None:
        """Initializes the Keypoints object with detection keypoints and original image size."""
        if keypoints.ndim == 2:
            keypoints = keypoints[None, :]
        if keypoints.shape[2] == 3:  # x, y, conf
            mask = keypoints[..., 2] < 0.5  # points with conf < 0.5 (not visible)
            keypoints[..., :2][mask] = 0
        super().__init__(keypoints, orig_shape)
        self.has_visible = self.data.shape[-1] == 3

    @property
    @lru_cache(maxsize=1)
    def xy(self):
        """Returns x, y coordinates of keypoints."""
        return self.data[..., :2]

    @property
    @lru_cache(maxsize=1)
    def xyn(self):
        """Returns normalized x, y coordinates of keypoints."""
        xy = self.xy.clone() if isinstance(self.xy, torch.Tensor) else np.copy(self.xy)
        xy[..., 0] /= self.orig_shape[1]
        xy[..., 1] /= self.orig_shape[0]
        return xy

    @property
    @lru_cache(maxsize=1)
    def conf(self):
        """Returns confidence values of keypoints if available, else None."""
        return self.data[..., 2] if self.has_visible else None

conf cached property

Devuelve los valores de confianza de los puntos clave si est谩n disponibles, en caso contrario Ninguno.

xy cached property

Devuelve las coordenadas x, y de los puntos clave.

xyn cached property

Devuelve las coordenadas x, y normalizadas de los puntos clave.

__init__(keypoints, orig_shape)

Inicializa el objeto Puntos Clave con los puntos clave de detecci贸n y el tama帽o de la imagen original.

C贸digo fuente en ultralytics/engine/results.py
@smart_inference_mode()  # avoid keypoints < conf in-place error
def __init__(self, keypoints, orig_shape) -> None:
    """Initializes the Keypoints object with detection keypoints and original image size."""
    if keypoints.ndim == 2:
        keypoints = keypoints[None, :]
    if keypoints.shape[2] == 3:  # x, y, conf
        mask = keypoints[..., 2] < 0.5  # points with conf < 0.5 (not visible)
        keypoints[..., :2][mask] = 0
    super().__init__(keypoints, orig_shape)
    self.has_visible = self.data.shape[-1] == 3



ultralytics.engine.results.Probs

Bases: BaseTensor

Una clase para almacenar y manipular predicciones de clasificaci贸n.

Atributos:

Nombre Tipo Descripci贸n
top1 int

脥ndice de la clase 1 superior.

top5 list[int]

脥ndices de las 5 clases principales.

top1conf Tensor

Confianza de la clase 1 superior.

top5conf Tensor

Confidencias de las 5 primeras clases.

M茅todos:

Nombre Descripci贸n
cpu

Devuelve una copia de los probs tensor en la memoria de la CPU.

numpy

Devuelve una copia de las probs tensor como matriz numpy.

cuda

Devuelve una copia de los probs tensor en la memoria de la GPU.

to

Devuelve una copia de los probs tensor con el dispositivo y dtype especificados.

C贸digo fuente en ultralytics/engine/results.py
class Probs(BaseTensor):
    """
    A class for storing and manipulating classification predictions.

    Attributes:
        top1 (int): Index of the top 1 class.
        top5 (list[int]): Indices of the top 5 classes.
        top1conf (torch.Tensor): Confidence of the top 1 class.
        top5conf (torch.Tensor): Confidences of the top 5 classes.

    Methods:
        cpu(): Returns a copy of the probs tensor on CPU memory.
        numpy(): Returns a copy of the probs tensor as a numpy array.
        cuda(): Returns a copy of the probs tensor on GPU memory.
        to(): Returns a copy of the probs tensor with the specified device and dtype.
    """

    def __init__(self, probs, orig_shape=None) -> None:
        """Initialize the Probs class with classification probabilities and optional original shape of the image."""
        super().__init__(probs, orig_shape)

    @property
    @lru_cache(maxsize=1)
    def top1(self):
        """Return the index of top 1."""
        return int(self.data.argmax())

    @property
    @lru_cache(maxsize=1)
    def top5(self):
        """Return the indices of top 5."""
        return (-self.data).argsort(0)[:5].tolist()  # this way works with both torch and numpy.

    @property
    @lru_cache(maxsize=1)
    def top1conf(self):
        """Return the confidence of top 1."""
        return self.data[self.top1]

    @property
    @lru_cache(maxsize=1)
    def top5conf(self):
        """Return the confidences of top 5."""
        return self.data[self.top5]

top1 cached property

Devuelve el 铆ndice del tope 1.

top1conf cached property

Devuelve la confianza del tope 1.

top5 cached property

Devuelve los 铆ndices de los 5 primeros.

top5conf cached property

Devuelve las confidencias de los 5 primeros.

__init__(probs, orig_shape=None)

Inicializa la clase Probs con las probabilidades de clasificaci贸n y la forma original opcional de la imagen.

C贸digo fuente en ultralytics/engine/results.py
def __init__(self, probs, orig_shape=None) -> None:
    """Initialize the Probs class with classification probabilities and optional original shape of the image."""
    super().__init__(probs, orig_shape)



ultralytics.engine.results.OBB

Bases: BaseTensor

Una clase para almacenar y manipular Cajas delimitadoras orientadas (OBB).

Par谩metros:

Nombre Tipo Descripci贸n Por defecto
boxes Tensor | ndarray

Una matriz tensor o numpy que contiene las cajas de detecci贸n, con forma (num_cajas, 7) o (num_cajas, 8). Las dos 煤ltimas columnas contienen los valores de confianza y clase. Si est谩 presente, la antepen煤ltima columna contiene los ID de pista, y la quinta columna desde la izquierda contiene la rotaci贸n.

necesario
orig_shape tuple

Tama帽o original de la imagen, en el formato (alto, ancho).

necesario

Atributos:

Nombre Tipo Descripci贸n
xywhr Tensor | ndarray

Las cajas en formato [centro_x, centro_y, anchura, altura, rotaci贸n].

conf Tensor | ndarray

Los valores de confianza de las casillas.

cls Tensor | ndarray

Los valores de clase de las casillas.

id Tensor | ndarray

Los ID de pista de las cajas (si est谩n disponibles).

xyxyxyxyn Tensor | ndarray

Las cajas rotadas en formato xyxyxyxy normalizadas por el tama帽o de la imagen original.

xyxyxyxy Tensor | ndarray

Las cajas rotadas en formato xyxyxyxy.

xyxy Tensor | ndarray

Las casillas horizontales en formato xyxyxyxy.

data Tensor

El OBB en bruto tensor (alias de boxes).

M茅todos:

Nombre Descripci贸n
cpu

Mueve el objeto a la memoria de la CPU.

numpy

Convierte el objeto en una matriz numpy.

cuda

Mueve el objeto a la memoria CUDA.

to

Mueve el objeto al dispositivo especificado.

C贸digo fuente en ultralytics/engine/results.py
class OBB(BaseTensor):
    """
    A class for storing and manipulating Oriented Bounding Boxes (OBB).

    Args:
        boxes (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the detection boxes,
            with shape (num_boxes, 7) or (num_boxes, 8). The last two columns contain confidence and class values.
            If present, the third last column contains track IDs, and the fifth column from the left contains rotation.
        orig_shape (tuple): Original image size, in the format (height, width).

    Attributes:
        xywhr (torch.Tensor | numpy.ndarray): The boxes in [x_center, y_center, width, height, rotation] format.
        conf (torch.Tensor | numpy.ndarray): The confidence values of the boxes.
        cls (torch.Tensor | numpy.ndarray): The class values of the boxes.
        id (torch.Tensor | numpy.ndarray): The track IDs of the boxes (if available).
        xyxyxyxyn (torch.Tensor | numpy.ndarray): The rotated boxes in xyxyxyxy format normalized by orig image size.
        xyxyxyxy (torch.Tensor | numpy.ndarray): The rotated boxes in xyxyxyxy format.
        xyxy (torch.Tensor | numpy.ndarray): The horizontal boxes in xyxyxyxy format.
        data (torch.Tensor): The raw OBB tensor (alias for `boxes`).

    Methods:
        cpu(): Move the object to CPU memory.
        numpy(): Convert the object to a numpy array.
        cuda(): Move the object to CUDA memory.
        to(*args, **kwargs): Move the object to the specified device.
    """

    def __init__(self, boxes, orig_shape) -> None:
        """Initialize the Boxes class."""
        if boxes.ndim == 1:
            boxes = boxes[None, :]
        n = boxes.shape[-1]
        assert n in {7, 8}, f"expected 7 or 8 values but got {n}"  # xywh, rotation, track_id, conf, cls
        super().__init__(boxes, orig_shape)
        self.is_track = n == 8
        self.orig_shape = orig_shape

    @property
    def xywhr(self):
        """Return the rotated boxes in xywhr format."""
        return self.data[:, :5]

    @property
    def conf(self):
        """Return the confidence values of the boxes."""
        return self.data[:, -2]

    @property
    def cls(self):
        """Return the class values of the boxes."""
        return self.data[:, -1]

    @property
    def id(self):
        """Return the track IDs of the boxes (if available)."""
        return self.data[:, -3] if self.is_track else None

    @property
    @lru_cache(maxsize=2)
    def xyxyxyxy(self):
        """Return the boxes in xyxyxyxy format, (N, 4, 2)."""
        return ops.xywhr2xyxyxyxy(self.xywhr)

    @property
    @lru_cache(maxsize=2)
    def xyxyxyxyn(self):
        """Return the boxes in xyxyxyxy format, (N, 4, 2)."""
        xyxyxyxyn = self.xyxyxyxy.clone() if isinstance(self.xyxyxyxy, torch.Tensor) else np.copy(self.xyxyxyxy)
        xyxyxyxyn[..., 0] /= self.orig_shape[1]
        xyxyxyxyn[..., 1] /= self.orig_shape[0]
        return xyxyxyxyn

    @property
    @lru_cache(maxsize=2)
    def xyxy(self):
        """
        Return the horizontal boxes in xyxy format, (N, 4).

        Accepts both torch and numpy boxes.
        """
        x1 = self.xyxyxyxy[..., 0].min(1).values
        x2 = self.xyxyxyxy[..., 0].max(1).values
        y1 = self.xyxyxyxy[..., 1].min(1).values
        y2 = self.xyxyxyxy[..., 1].max(1).values
        xyxy = [x1, y1, x2, y2]
        return np.stack(xyxy, axis=-1) if isinstance(self.data, np.ndarray) else torch.stack(xyxy, dim=-1)

cls property

Devuelve los valores de clase de las cajas.

conf property

Devuelve los valores de confianza de las casillas.

id property

Devuelve los ID de pista de las cajas (si est谩n disponibles).

xywhr property

Devuelve las cajas giradas en formato xywhr.

xyxy cached property

Devuelve las cajas horizontales en formato xyxy, (N, 4).

Acepta las cajas torch y numpy.

xyxyxyxy cached property

Devuelve las casillas en formato xyxyxyxy, (N, 4, 2).

xyxyxyxyn cached property

Devuelve las casillas en formato xyxyxyxy, (N, 4, 2).

__init__(boxes, orig_shape)

Inicializa la clase Cajas.

C贸digo fuente en ultralytics/engine/results.py
def __init__(self, boxes, orig_shape) -> None:
    """Initialize the Boxes class."""
    if boxes.ndim == 1:
        boxes = boxes[None, :]
    n = boxes.shape[-1]
    assert n in {7, 8}, f"expected 7 or 8 values but got {n}"  # xywh, rotation, track_id, conf, cls
    super().__init__(boxes, orig_shape)
    self.is_track = n == 8
    self.orig_shape = orig_shape





Creado 2023-11-12, Actualizado 2024-05-18
Autores: glenn-jocher (5), Burhan-Q (1), Laughing-q (1)