─░├žeri─če ge├ž

Referans i├žin ultralytics/engine/results.py

Not

Bu dosya https://github.com/ultralytics/ultralytics/blob/main/ ultralytics/engine/results .py adresinde mevcuttur. Bir sorun tespit ederseniz l├╝tfen bir ├çekme ─░ste─či ­čŤá´ŞĆ ile katk─▒da bulunarak d├╝zeltilmesine yard─▒mc─▒ olun. Te┼čekk├╝rler ­čÖĆ!



ultralytics.engine.results.BaseTensor

├ťsler: SimpleClass

Kolay manip├╝lasyon ve cihaz kullan─▒m─▒ i├žin ek y├Ântemlere sahip temel tensor s─▒n─▒f─▒.

Kaynak kodu 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

Verinin ┼čeklini d├Ând├╝r tensor.

__getitem__(idx)

tensor verisinin belirtilen indeksine sahip bir BaseTensor d├Ând├╝r├╝r.

Kaynak kodu 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)

BaseTensor'u veriler ve orijinal ┼čekil ile ba┼člat─▒n.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
data Tensor | ndarray

Bbox'lar, maskeler ve anahtar noktalar gibi tahminler.

gerekli
orig_shape tuple

G├Âr├╝nt├╝n├╝n orijinal ┼čekli.

gerekli
Kaynak kodu 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__()

Verinin uzunlu─čunu d├Ând├╝r tensor.

Kaynak kodu ultralytics/engine/results.py
def __len__(self):  # override len(results)
    """Return the length of the data tensor."""
    return len(self.data)

cpu()

CPU belle─činde tensor adresinin bir kopyas─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu 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()

GPU belle─činde tensor adresinin bir kopyas─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu 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()

tensor adresinin bir kopyas─▒n─▒ numpy dizisi olarak d├Ând├╝r├╝r.

Kaynak kodu 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)

Belirtilen ayg─▒t ve dtype ile tensor adresinin bir kopyas─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu 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

├ťsler: SimpleClass

├ç─▒kar─▒m sonu├žlar─▒n─▒ saklamak ve i┼člemek i├žin bir s─▒n─▒f.

Nitelikler:

─░sim Tip A├ž─▒klama
orig_img ndarray

Bir numpy dizisi olarak orijinal g├Âr├╝nt├╝.

orig_shape tuple

(Y├╝kseklik, geni┼člik) bi├žiminde orijinal g├Âr├╝nt├╝ ┼čekli.

boxes Boxes

Alg─▒lama s─▒n─▒rlay─▒c─▒ kutular─▒ i├žeren nesne.

masks Masks

Alg─▒lama maskeleri i├žeren nesne.

probs Probs

S─▒n─▒fland─▒rma g├Ârevleri i├žin s─▒n─▒f olas─▒l─▒klar─▒n─▒ i├žeren nesne.

keypoints Keypoints

Her nesne i├žin tespit edilen anahtar noktalar─▒ i├žeren nesne.

speed dict

├ľn i┼člem, ├ž─▒kar─▒m ve son i┼člem h─▒zlar─▒ s├Âzl├╝─č├╝ (ms/g├Âr├╝nt├╝).

names dict

S─▒n─▒f adlar─▒ s├Âzl├╝─č├╝.

path str

G├Âr├╝nt├╝ dosyas─▒n─▒n yolu.

Y├Ântemler:

─░sim A├ž─▒klama
update

Nesne ├Âzniteliklerini yeni alg─▒lama sonu├žlar─▒yla g├╝nceller.

cpu

CPU belle─čindeki t├╝m tens├Ârlerle birlikte Results nesnesinin bir kopyas─▒n─▒ d├Ând├╝r├╝r.

numpy

T├╝m tens├Ârleri numpy dizileri olarak i├žeren Results nesnesinin bir kopyas─▒n─▒ d├Ând├╝r├╝r.

cuda

GPU belle─čindeki t├╝m tens├Ârlerle birlikte Results nesnesinin bir kopyas─▒n─▒ d├Ând├╝r├╝r.

to

Belirtilen ayg─▒t ve d t├╝r├╝nde tens├Ârlere sahip Results nesnesinin bir kopyas─▒n─▒ d├Ând├╝r├╝r.

new

Ayn─▒ g├Âr├╝nt├╝, yol ve adlara sahip yeni bir Results nesnesi d├Ând├╝r├╝r.

plot

Alg─▒lama sonu├žlar─▒n─▒ bir giri┼č g├Âr├╝nt├╝s├╝ ├╝zerinde ├žizer ve a├ž─▒klamal─▒ bir g├Âr├╝nt├╝ d├Ând├╝r├╝r.

show

A├ž─▒klamal─▒ sonu├žlar─▒ ekranda g├Âsterin.

save

A├ž─▒klamal─▒ sonu├žlar─▒ dosyaya kaydedin.

verbose

Her g├Ârev i├žin tespitleri ve s─▒n─▒fland─▒rmalar─▒ detayland─▒ran bir g├╝nl├╝k dizesi d├Ând├╝r├╝r.

save_txt

Alg─▒lama sonu├žlar─▒n─▒ bir metin dosyas─▒na kaydeder.

save_crop

K─▒rp─▒lm─▒┼č alg─▒lama g├Âr├╝nt├╝lerini kaydeder.

tojson

Alg─▒lama sonu├žlar─▒n─▒ JSON bi├žimine d├Ân├╝┼čt├╝r├╝r.

Kaynak kodu 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 ÔÜá´ŞĆ Classify task do not support `save_crop`.")
            return
        if self.obb is not None:
            LOGGER.warning("WARNING ÔÜá´ŞĆ 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

        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 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)

Belirtilen dizin i├žin bir Results nesnesi d├Ând├╝r├╝r.

Kaynak kodu 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)

Results s─▒n─▒f─▒n─▒ ba┼člat─▒n.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
orig_img ndarray

Bir numpy dizisi olarak orijinal g├Âr├╝nt├╝.

gerekli
path str

G├Âr├╝nt├╝ dosyas─▒n─▒n yolu.

gerekli
names dict

S─▒n─▒f adlar─▒ s├Âzl├╝─č├╝.

gerekli
boxes tensor

Her alg─▒lama i├žin s─▒n─▒rlay─▒c─▒ kutu koordinatlar─▒ndan olu┼čan bir 2D tensor .

None
masks tensor

Her maskenin bir ikili g├Âr├╝nt├╝ oldu─ču alg─▒lama maskelerinden olu┼čan bir 3D tensor .

None
probs tensor

S─▒n─▒fland─▒rma g├Ârevi i├žin her bir s─▒n─▒f─▒n olas─▒l─▒klar─▒n─▒n 1D tensor .

None
keypoints tensor

Her alg─▒lama i├žin anahtar nokta koordinatlar─▒ndan olu┼čan bir 2B tensor .

None
obb tensor

Her alg─▒lama i├žin y├Ânlendirilmi┼č s─▒n─▒rlay─▒c─▒ kutu koordinatlar─▒ndan olu┼čan bir 2D tensor .

None
Kaynak kodu 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__()

Sonu├žlar nesnesindeki alg─▒lama say─▒s─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu 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()

CPU belle─čindeki t├╝m tens├Ârlerle birlikte Results nesnesinin bir kopyas─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu 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()

GPU belle─čindeki t├╝m tens├Ârlerle birlikte Results nesnesinin bir kopyas─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu 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()

Ayn─▒ g├Âr├╝nt├╝, yol, adlar ve h─▒za sahip yeni bir Results nesnesi d├Ând├╝r├╝r.

Kaynak kodu 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()

T├╝m tens├Ârleri numpy dizileri olarak i├žeren Results nesnesinin bir kopyas─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu 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)

Alg─▒lama sonu├žlar─▒n─▒ bir giri┼č RGB g├Âr├╝nt├╝s├╝ ├╝zerinde ├žizer. Bir numpy dizisi (cv2) veya bir PIL G├Âr├╝nt├╝s├╝ kabul eder.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
conf bool

Alg─▒lama g├╝ven skorunun ├žizilip ├žizilmeyece─či.

True
line_width float

S─▒n─▒rlay─▒c─▒ kutular─▒n ├žizgi geni┼čli─či. Yok ise, g├Âr├╝nt├╝ boyutuna ├Âl├žeklendirilir.

None
font_size float

Metnin yaz─▒ tipi boyutu. Yok ise, g├Âr├╝nt├╝ boyutuna ├Âl├žeklendirilir.

None
font str

Metin i├žin kullan─▒lacak yaz─▒ tipi.

'Arial.ttf'
pil bool

G├Âr├╝nt├╝n├╝n bir PIL G├Âr├╝nt├╝s├╝ olarak d├Ând├╝r├╝l├╝p d├Ând├╝r├╝lmeyece─či.

False
img ndarray

Ba┼čka bir g├Âr├╝nt├╝ye ├žizin. de─čilse, orijinal g├Âr├╝nt├╝ye ├žizin.

None
im_gpu Tensor

Daha h─▒zl─▒ maske ├žizimi i├žin gpu'da (1, 3, 640, 640) ┼čeklinde normalle┼čtirilmi┼č g├Âr├╝nt├╝.

None
kpt_radius int

├çizilen anahtar noktalar─▒n─▒n yar─▒├žap─▒. Varsay─▒lan de─čer 5'tir.

5
kpt_line bool

Anahtar noktalar─▒ birle┼čtiren ├žizgilerin ├žizilip ├žizilmeyece─či.

True
labels bool

S─▒n─▒rlay─▒c─▒ kutular─▒n etiketinin ├žizilip ├žizilmeyece─či.

True
boxes bool

S─▒n─▒rlay─▒c─▒ kutular─▒n ├žizilip ├žizilmeyece─či.

True
masks bool

Maskelerin ├žizilip ├žizilmeyece─či.

True
probs bool

S─▒n─▒fland─▒rma olas─▒l─▒─č─▒n─▒n ├žizilip ├žizilmeyece─či

True
show bool

A├ž─▒klamal─▒ g├Âr├╝nt├╝n├╝n do─črudan g├Âr├╝nt├╝lenip g├Âr├╝nt├╝lenmeyece─či.

False
save bool

A├ž─▒klamal─▒ g├Âr├╝nt├╝n├╝n kaydedilip kaydedilmeyece─či filename.

False
filename str

Kaydet True ise g├Âr├╝nt├╝n├╝n kaydedilece─či dosya ad─▒.

None

─░ade:

Tip A├ž─▒klama
ndarray

A├ž─▒klamal─▒ g├Âr├╝nt├╝n├╝n bir numpy dizisi.

├ľrnek
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
Kaynak kodu 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)

A├ž─▒klamal─▒ sonu├ž g├Âr├╝nt├╝s├╝n├╝ kaydedin.

Kaynak kodu 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'))

K─▒rp─▒lm─▒┼č tahminleri ┼čuraya kaydet save_dir/cls/file_name.jpg.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
save_dir str | Path

Kaydetme yolu.

gerekli
file_name str | Path

Dosya ad─▒.

Path('im.jpg')
Kaynak kodu 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 ÔÜá´ŞĆ Classify task do not support `save_crop`.")
        return
    if self.obb is not None:
        LOGGER.warning("WARNING ÔÜá´ŞĆ 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)

Tahminleri txt dosyas─▒na kaydedin.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
txt_file str

txt dosya yolu.

gerekli
save_conf bool

g├╝ven puan─▒n─▒ kaydedin ya da kaydetmeyin.

False
Kaynak kodu 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)

A├ž─▒klamal─▒ sonu├ž g├Âr├╝nt├╝s├╝n├╝ g├Âsterin.

Kaynak kodu ultralytics/engine/results.py
def show(self, *args, **kwargs):
    """Show annotated results image."""
    self.plot(show=True, *args, **kwargs)

summary(normalize=False, decimals=5)

Sonu├žlar─▒ ├Âzetlenmi┼č bir bi├žime d├Ân├╝┼čt├╝r├╝n.

Kaynak kodu 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

    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

to(*args, **kwargs)

Belirtilen ayg─▒t ve d t├╝r├╝ndeki tens├Ârleri i├žeren Results nesnesinin bir kopyas─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu 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)

Sonu├žlar─▒ JSON bi├žimine d├Ân├╝┼čt├╝r├╝n.

Kaynak kodu 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)

Results nesnesinin kutular─▒n─▒, maskelerini ve probs niteliklerini g├╝ncelleyin.

Kaynak kodu 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()

Her g├Ârev i├žin g├╝nl├╝k dizesi d├Ând├╝r├╝r.

Kaynak kodu 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

├ťsler: BaseTensor

Tespit kutular─▒n─▒ y├Âneterek kutu koordinatlar─▒na, g├╝ven puanlar─▒na, s─▒n─▒flara kolay eri┼čim ve manip├╝lasyon sa─člar. tan─▒mlay─▒c─▒lar ve iste─če ba─čl─▒ izleme kimlikleri. Hem mutlak hem de mutlak olmayan koordinatlar dahil olmak ├╝zere kutu koordinatlar─▒ i├žin birden fazla bi├žimi destekler. normalle┼čtirilmi┼č formlar.

Nitelikler:

─░sim Tip A├ž─▒klama
data Tensor

Alg─▒lama kutular─▒n─▒ ve ilgili verileri i├žeren ham tensor .

orig_shape tuple

Normalle┼čtirme i├žin kullan─▒lan bir tuple (y├╝kseklik, geni┼člik) olarak orijinal g├Âr├╝nt├╝ boyutu.

is_track bool

─░zleme kimliklerinin kutu verilerine dahil edilip edilmedi─čini belirtir.

├ľzellikler

xyxy (torch.Tensor | numpy.ndarray): x1, y1, x2, y2] bi├žimindeki kutular. conf (torch.Tensor | numpy.ndarray): Her kutu i├žin g├╝ven puanlar─▒. cls (torch.Tensor | numpy.ndarray): Her kutu i├žin s─▒n─▒f etiketleri. id (torch.Tensor | numpy.ndarray, iste─če ba─čl─▒): Varsa, her kutu i├žin izleme kimlikleri. xywh (torch.Tensor | numpy.ndarray): ─░ste─če ba─čl─▒ olarak hesaplanan [x, y, geni┼člik, y├╝kseklik] bi├žimindeki kutular. xyxyn (torch.Tensor | numpy.ndarray): Normalize edilmi┼č [x1, y1, x2, y2] kutular─▒, g├Âre orig_shape. xywhn (torch.Tensor | numpy.ndarray): Normalle┼čtirilmi┼č [x, y, geni┼člik, y├╝kseklik] kutular─▒, g├Âre orig_shape.

Y├Ântemler:

─░sim A├ž─▒klama
cpu

Kutular─▒ CPU belle─čine ta┼č─▒r.

numpy

Kutular─▒ bir numpy dizisi bi├žimine d├Ân├╝┼čt├╝r├╝r.

cuda

Kutular─▒ CUDA (GPU) belle─čine ta┼č─▒r.

to

Kutular─▒ belirtilen cihaza ta┼č─▒r.

Kaynak kodu 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

Kutular─▒n s─▒n─▒f de─čerlerini d├Ând├╝r├╝r.

conf property

Kutular─▒n g├╝ven de─čerlerini d├Ând├╝r├╝r.

id property

Kutular─▒n par├ža kimliklerini d├Ând├╝r├╝r (varsa).

xywh cached property

Kutular─▒ xywh bi├žiminde d├Ând├╝r├╝r.

xywhn cached property

Kutular─▒ orijinal g├Âr├╝nt├╝ boyutuna g├Âre normalle┼čtirilmi┼č xywh bi├žiminde d├Ând├╝r├╝r.

xyxy property

Kutular─▒ xyxy bi├žiminde d├Ând├╝r├╝r.

xyxyn cached property

Kutular─▒ orijinal g├Âr├╝nt├╝ boyutuna g├Âre normalle┼čtirilmi┼č xyxy bi├žiminde d├Ând├╝r├╝r.

__init__(boxes, orig_shape)

Kutular s─▒n─▒f─▒n─▒ ba┼člat─▒n.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
boxes Tensor | ndarray

Alg─▒lama kutular─▒n─▒ i├žeren bir tensor veya numpy dizisi ile (num_boxes, 6) veya (num_boxes, 7) ┼čeklindedir. Son iki s├╝tun g├╝ven ve s─▒n─▒f de─čerlerini i├žerir. Varsa, ├╝├ž├╝nc├╝ son s├╝tun par├ža kimliklerini i├žerir.

gerekli
orig_shape tuple

Orijinal g├Âr├╝nt├╝ boyutu, (y├╝kseklik, geni┼člik) bi├žiminde.

gerekli
Kaynak kodu 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

├ťsler: BaseTensor

Alg─▒lama maskelerini saklamak ve i┼člemek i├žin bir s─▒n─▒f.

Nitelikler:

─░sim Tip A├ž─▒klama
xy list

Piksel koordinatlar─▒ndaki segmentlerin bir listesi.

xyn list

Normalle┼čtirilmi┼č segmentlerin bir listesi.

Y├Ântemler:

─░sim A├ž─▒klama
cpu

CPU belle─čindeki tensor maskelerini d├Ând├╝r├╝r.

numpy

tensor maskelerini bir numpy dizisi olarak d├Ând├╝r├╝r.

cuda

GPU belle─čindeki tensor maskelerini d├Ând├╝r├╝r.

to

Belirtilen cihaz ve dtype ile tensor maskelerini d├Ând├╝r├╝r.

Kaynak kodu 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

Segmentleri piksel koordinatlar─▒nda d├Ând├╝r├╝r.

xyn cached property

Normalle┼čtirilmi┼č segmentleri d├Ând├╝r├╝r.

__init__(masks, orig_shape)

Maskeler s─▒n─▒f─▒n─▒ verilen maskeler tensor ve orijinal g├Âr├╝nt├╝ ┼čekli ile ba┼člat─▒n.

Kaynak kodu 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

├ťsler: BaseTensor

Alg─▒lama anahtar noktalar─▒n─▒ depolamak ve i┼člemek i├žin bir s─▒n─▒f.

Nitelikler:

─░sim Tip A├ž─▒klama
xy Tensor

Her alg─▒lama i├žin x, y koordinatlar─▒n─▒ i├žeren bir anahtar nokta koleksiyonu.

xyn Tensor

Koordinatlar─▒ [0, 1] aral─▒─č─▒nda olan xy'nin normalle┼čtirilmi┼č bir s├╝r├╝m├╝.

conf Tensor

Varsa, anahtar noktalarla ili┼čkili g├╝ven de─čerleri, aksi takdirde Yok.

Y├Ântemler:

─░sim A├ž─▒klama
cpu

CPU belle─čindeki tensor anahtar noktalar─▒n─▒n bir kopyas─▒n─▒ d├Ând├╝r├╝r.

numpy

Anahtar noktalar─▒n─▒n bir kopyas─▒n─▒ tensor bir numpy dizisi olarak d├Ând├╝r├╝r.

cuda

GPU belle─činde tensor anahtar noktalar─▒n─▒n bir kopyas─▒n─▒ d├Ând├╝r├╝r.

to

Belirtilen cihaz ve dtype ile tensor anahtar noktalar─▒n─▒n bir kopyas─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu 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

Varsa anahtar noktalar─▒n─▒n g├╝ven de─čerlerini d├Ând├╝r├╝r, yoksa Yoktur.

xy cached property

Anahtar noktalar─▒n─▒n x, y koordinatlar─▒n─▒ d├Ând├╝r├╝r.

xyn cached property

Anahtar noktalar─▒n─▒n normalle┼čtirilmi┼č x, y koordinatlar─▒n─▒ d├Ând├╝r├╝r.

__init__(keypoints, orig_shape)

Anahtar Noktalar─▒ nesnesini alg─▒lama anahtar noktalar─▒ ve orijinal g├Âr├╝nt├╝ boyutu ile ba┼člat─▒r.

Kaynak kodu 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

├ťsler: BaseTensor

S─▒n─▒fland─▒rma tahminlerini saklamak ve i┼člemek i├žin bir s─▒n─▒f.

Nitelikler:

─░sim Tip A├ž─▒klama
top1 int

En ├╝st 1 s─▒n─▒f─▒n endeksi.

top5 list[int]

─░lk 5 s─▒n─▒f─▒n endeksleri.

top1conf Tensor

En iyi 1. s─▒n─▒f─▒n g├╝veni.

top5conf Tensor

─░lk 5 s─▒n─▒f─▒n s─▒rlar─▒.

Y├Ântemler:

─░sim A├ž─▒klama
cpu

CPU belle─čindeki tensor problar─▒n─▒n bir kopyas─▒n─▒ d├Ând├╝r├╝r.

numpy

tensor prob'lar─▒n─▒n bir kopyas─▒n─▒ numpy dizisi olarak d├Ând├╝r├╝r.

cuda

GPU belle─čindeki tensor problar─▒n─▒n bir kopyas─▒n─▒ d├Ând├╝r├╝r.

to

Belirtilen ayg─▒t ve dtype ile probs tensor dosyas─▒n─▒n bir kopyas─▒n─▒ d├Ând├╝r├╝r.

Kaynak kodu 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

En ├╝stteki 1'in dizinini d├Ând├╝r├╝r.

top1conf cached property

En iyi 1'in g├╝venini d├Ând├╝r├╝r.

top5 cached property

─░lk 5'in indekslerini d├Ând├╝r├╝r.

top5conf cached property

─░lk 5'in s─▒rlar─▒n─▒ d├Ând├╝r├╝n.

__init__(probs, orig_shape=None)

Probs s─▒n─▒f─▒n─▒ s─▒n─▒fland─▒rma olas─▒l─▒klar─▒ ve g├Âr├╝nt├╝n├╝n iste─če ba─čl─▒ orijinal ┼čekli ile ba┼člat─▒n.

Kaynak kodu 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

├ťsler: BaseTensor

Y├Ânlendirilmi┼č S─▒n─▒rlay─▒c─▒ Kutular─▒ (OBB) saklamak ve i┼člemek i├žin bir s─▒n─▒f.

Parametreler:

─░sim Tip A├ž─▒klama Varsay─▒lan
boxes Tensor | ndarray

Alg─▒lama kutular─▒n─▒ i├žeren bir tensor veya numpy dizisi, (num_boxes, 7) veya (num_boxes, 8) ┼čeklinde. Son iki s├╝tun g├╝ven ve s─▒n─▒f de─čerlerini i├žerir. Varsa, sondan ├╝├ž├╝nc├╝ s├╝tun par├ža kimliklerini ve soldan be┼činci s├╝tun rotasyonu i├žerir.

gerekli
orig_shape tuple

Orijinal g├Âr├╝nt├╝ boyutu, (y├╝kseklik, geni┼člik) bi├žiminde.

gerekli

Nitelikler:

─░sim Tip A├ž─▒klama
xywhr Tensor | ndarray

Kutular [x_center, y_center, width, height, rotation] bi├žimindedir.

conf Tensor | ndarray

Kutular─▒n g├╝ven de─čerleri.

cls Tensor | ndarray

Kutular─▒n s─▒n─▒f de─čerleri.

id Tensor | ndarray

Kutular─▒n par├ža kimlikleri (varsa).

xyxyxyxyn Tensor | ndarray

Xyxyxyxy format─▒nda d├Ând├╝r├╝lm├╝┼č kutular, orijinal g├Âr├╝nt├╝ boyutuna g├Âre normalle┼čtirilmi┼čtir.

xyxyxyxy Tensor | ndarray

D├Ând├╝r├╝lm├╝┼č kutular xyxyxyxy bi├žiminde.

xyxy Tensor | ndarray

xyxyxyxy bi├žimindeki yatay kutular.

data Tensor

Ham OBB tensor (i├žin takma ad boxes).

Y├Ântemler:

─░sim A├ž─▒klama
cpu

Nesneyi CPU belle─čine ta┼č─▒y─▒n.

numpy

Nesneyi bir numpy dizisine d├Ân├╝┼čt├╝r├╝n.

cuda

Nesneyi CUDA belle─čine ta┼č─▒y─▒n.

to

Nesneyi belirtilen cihaza ta┼č─▒r.

Kaynak kodu 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

Kutular─▒n s─▒n─▒f de─čerlerini d├Ând├╝r├╝r.

conf property

Kutular─▒n g├╝ven de─čerlerini d├Ând├╝r├╝r.

id property

Kutular─▒n par├ža kimliklerini d├Ând├╝r├╝r (varsa).

xywhr property

D├Ând├╝r├╝lm├╝┼č kutular─▒ xywhr bi├žiminde d├Ând├╝r├╝r.

xyxy cached property

Yatay kutular─▒ xyxy bi├žiminde d├Ând├╝r├╝r, (N, 4).

Hem torch hem de numpy kutular─▒n─▒ kabul eder.

xyxyxyxy cached property

Kutular─▒ xyxyxyxy bi├žiminde d├Ând├╝r├╝r, (N, 4, 2).

xyxyxyxyn cached property

Kutular─▒ xyxyxyxy bi├žiminde d├Ând├╝r├╝r, (N, 4, 2).

__init__(boxes, orig_shape)

Kutular s─▒n─▒f─▒n─▒ ba┼člat─▒n.

Kaynak kodu 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





Created 2023-11-12, Updated 2024-06-02
Authors: glenn-jocher (6), Burhan-Q (1), Laughing-q (1)