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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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""" |
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Ultralytics Results, Boxes and Masks classes for handling inference results. |
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Usage: See https://docs.ultralytics.com/modes/predict/ |
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""" |
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from copy import deepcopy |
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from functools import lru_cache |
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from pathlib import Path |
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import numpy as np |
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import torch |
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from ultralytics.data.augment import LetterBox |
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from ultralytics.utils import LOGGER, SimpleClass, ops |
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from ultralytics.utils.plotting import Annotator, colors, save_one_box |
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from ultralytics.utils.torch_utils import smart_inference_mode |
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class BaseTensor(SimpleClass): |
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"""Base tensor class with additional methods for easy manipulation and device handling.""" |
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def __init__(self, data, orig_shape) -> None: |
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""" |
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Initialize BaseTensor with data and original shape. |
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Args: |
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data (torch.Tensor | np.ndarray): Predictions, such as bboxes, masks and keypoints. |
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orig_shape (tuple): Original shape of image. |
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""" |
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assert isinstance(data, (torch.Tensor, np.ndarray)) |
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self.data = data |
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self.orig_shape = orig_shape |
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@property |
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def shape(self): |
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"""Return the shape of the data tensor.""" |
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return self.data.shape |
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def cpu(self): |
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"""Return a copy of the tensor on CPU memory.""" |
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return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.cpu(), self.orig_shape) |
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def numpy(self): |
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"""Return a copy of the tensor as a numpy array.""" |
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return self if isinstance(self.data, np.ndarray) else self.__class__(self.data.numpy(), self.orig_shape) |
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def cuda(self): |
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"""Return a copy of the tensor on GPU memory.""" |
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return self.__class__(torch.as_tensor(self.data).cuda(), self.orig_shape) |
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def to(self, *args, **kwargs): |
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"""Return a copy of the tensor with the specified device and dtype.""" |
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return self.__class__(torch.as_tensor(self.data).to(*args, **kwargs), self.orig_shape) |
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def __len__(self): # override len(results) |
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"""Return the length of the data tensor.""" |
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return len(self.data) |
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def __getitem__(self, idx): |
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"""Return a BaseTensor with the specified index of the data tensor.""" |
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return self.__class__(self.data[idx], self.orig_shape) |
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class Results(SimpleClass): |
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""" |
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A class for storing and manipulating inference results. |
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Args: |
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orig_img (numpy.ndarray): The original image as a numpy array. |
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path (str): The path to the image file. |
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names (dict): A dictionary of class names. |
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boxes (torch.tensor, optional): A 2D tensor of bounding box coordinates for each detection. |
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masks (torch.tensor, optional): A 3D tensor of detection masks, where each mask is a binary image. |
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probs (torch.tensor, optional): A 1D tensor of probabilities of each class for classification task. |
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keypoints (List[List[float]], optional): A list of detected keypoints for each object. |
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Attributes: |
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orig_img (numpy.ndarray): The original image as a numpy array. |
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orig_shape (tuple): The original image shape in (height, width) format. |
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boxes (Boxes, optional): A Boxes object containing the detection bounding boxes. |
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masks (Masks, optional): A Masks object containing the detection masks. |
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probs (Probs, optional): A Probs object containing probabilities of each class for classification task. |
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keypoints (Keypoints, optional): A Keypoints object containing detected keypoints for each object. |
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speed (dict): A dictionary of preprocess, inference, and postprocess speeds in milliseconds per image. |
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names (dict): A dictionary of class names. |
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path (str): The path to the image file. |
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_keys (tuple): A tuple of attribute names for non-empty attributes. |
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""" |
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def __init__(self, orig_img, path, names, boxes=None, masks=None, probs=None, keypoints=None, obb=None) -> None: |
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"""Initialize the Results class.""" |
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self.orig_img = orig_img |
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self.orig_shape = orig_img.shape[:2] |
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self.boxes = Boxes(boxes, self.orig_shape) if boxes is not None else None # native size boxes |
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self.masks = Masks(masks, self.orig_shape) if masks is not None else None # native size or imgsz masks |
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self.probs = Probs(probs) if probs is not None else None |
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self.keypoints = Keypoints(keypoints, self.orig_shape) if keypoints is not None else None |
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self.obb = OBB(obb, self.orig_shape) if obb is not None else None |
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self.speed = {"preprocess": None, "inference": None, "postprocess": None} # milliseconds per image |
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self.names = names |
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self.path = path |
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self.save_dir = None |
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self._keys = "boxes", "masks", "probs", "keypoints", "obb" |
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def __getitem__(self, idx): |
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"""Return a Results object for the specified index.""" |
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return self._apply("__getitem__", idx) |
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def __len__(self): |
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"""Return the number of detections in the Results object.""" |
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for k in self._keys: |
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v = getattr(self, k) |
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if v is not None: |
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return len(v) |
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def update(self, boxes=None, masks=None, probs=None): |
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"""Update the boxes, masks, and probs attributes of the Results object.""" |
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if boxes is not None: |
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self.boxes = Boxes(ops.clip_boxes(boxes, self.orig_shape), self.orig_shape) |
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if masks is not None: |
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self.masks = Masks(masks, self.orig_shape) |
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if probs is not None: |
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self.probs = probs |
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def _apply(self, fn, *args, **kwargs): |
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""" |
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Applies a function to all non-empty attributes and returns a new Results object with modified attributes. This |
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function is internally called by methods like .to(), .cuda(), .cpu(), etc. |
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Args: |
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fn (str): The name of the function to apply. |
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*args: Variable length argument list to pass to the function. |
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**kwargs: Arbitrary keyword arguments to pass to the function. |
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Returns: |
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Results: A new Results object with attributes modified by the applied function. |
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""" |
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r = self.new() |
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for k in self._keys: |
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v = getattr(self, k) |
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if v is not None: |
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setattr(r, k, getattr(v, fn)(*args, **kwargs)) |
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return r |
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def cpu(self): |
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"""Return a copy of the Results object with all tensors on CPU memory.""" |
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return self._apply("cpu") |
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def numpy(self): |
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"""Return a copy of the Results object with all tensors as numpy arrays.""" |
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return self._apply("numpy") |
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def cuda(self): |
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"""Return a copy of the Results object with all tensors on GPU memory.""" |
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return self._apply("cuda") |
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def to(self, *args, **kwargs): |
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"""Return a copy of the Results object with tensors on the specified device and dtype.""" |
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return self._apply("to", *args, **kwargs) |
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def new(self): |
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"""Return a new Results object with the same image, path, and names.""" |
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return Results(orig_img=self.orig_img, path=self.path, names=self.names) |
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def plot( |
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self, |
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conf=True, |
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line_width=None, |
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font_size=None, |
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font="Arial.ttf", |
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pil=False, |
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img=None, |
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im_gpu=None, |
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kpt_radius=5, |
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kpt_line=True, |
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labels=True, |
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boxes=True, |
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masks=True, |
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probs=True, |
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): |
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""" |
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Plots the detection results on an input RGB image. Accepts a numpy array (cv2) or a PIL Image. |
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Args: |
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conf (bool): Whether to plot the detection confidence score. |
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line_width (float, optional): The line width of the bounding boxes. If None, it is scaled to the image size. |
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font_size (float, optional): The font size of the text. If None, it is scaled to the image size. |
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font (str): The font to use for the text. |
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pil (bool): Whether to return the image as a PIL Image. |
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img (numpy.ndarray): Plot to another image. if not, plot to original image. |
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im_gpu (torch.Tensor): Normalized image in gpu with shape (1, 3, 640, 640), for faster mask plotting. |
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kpt_radius (int, optional): Radius of the drawn keypoints. Default is 5. |
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kpt_line (bool): Whether to draw lines connecting keypoints. |
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labels (bool): Whether to plot the label of bounding boxes. |
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boxes (bool): Whether to plot the bounding boxes. |
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masks (bool): Whether to plot the masks. |
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probs (bool): Whether to plot classification probability |
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Returns: |
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(numpy.ndarray): A numpy array of the annotated image. |
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Example: |
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```python |
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from PIL import Image |
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from ultralytics import YOLO |
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model = YOLO('yolov8n.pt') |
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results = model('bus.jpg') # results list |
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for r in results: |
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im_array = r.plot() # plot a BGR numpy array of predictions |
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im = Image.fromarray(im_array[..., ::-1]) # RGB PIL image |
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im.show() # show image |
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im.save('results.jpg') # save image |
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``` |
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""" |
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if img is None and isinstance(self.orig_img, torch.Tensor): |
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img = (self.orig_img[0].detach().permute(1, 2, 0).contiguous() * 255).to(torch.uint8).cpu().numpy() |
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names = self.names |
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is_obb = self.obb is not None |
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pred_boxes, show_boxes = self.obb if is_obb else self.boxes, boxes |
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pred_masks, show_masks = self.masks, masks |
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pred_probs, show_probs = self.probs, probs |
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annotator = Annotator( |
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deepcopy(self.orig_img if img is None else img), |
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line_width, |
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font_size, |
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font, |
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pil or (pred_probs is not None and show_probs), # Classify tasks default to pil=True |
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example=names, |
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) |
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# Plot Segment results |
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if pred_masks and show_masks: |
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if im_gpu is None: |
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img = LetterBox(pred_masks.shape[1:])(image=annotator.result()) |
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im_gpu = ( |
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torch.as_tensor(img, dtype=torch.float16, device=pred_masks.data.device) |
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.permute(2, 0, 1) |
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.flip(0) |
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.contiguous() |
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/ 255 |
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) |
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idx = pred_boxes.cls if pred_boxes else range(len(pred_masks)) |
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annotator.masks(pred_masks.data, colors=[colors(x, True) for x in idx], im_gpu=im_gpu) |
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# Plot Detect results |
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if pred_boxes is not None and show_boxes: |
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for d in reversed(pred_boxes): |
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c, conf, id = int(d.cls), float(d.conf) if conf else None, None if d.id is None else int(d.id.item()) |
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name = ("" if id is None else f"id:{id} ") + names[c] |
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label = (f"{name} {conf:.2f}" if conf else name) if labels else None |
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box = d.xyxyxyxy.reshape(-1, 4, 2).squeeze() if is_obb else d.xyxy.squeeze() |
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annotator.box_label(box, label, color=colors(c, True), rotated=is_obb) |
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# Plot Classify results |
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if pred_probs is not None and show_probs: |
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text = ",\n".join(f"{names[j] if names else j} {pred_probs.data[j]:.2f}" for j in pred_probs.top5) |
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x = round(self.orig_shape[0] * 0.03) |
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annotator.text([x, x], text, txt_color=(255, 255, 255)) # TODO: allow setting colors |
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# Plot Pose results |
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if self.keypoints is not None: |
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for k in reversed(self.keypoints.data): |
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annotator.kpts(k, self.orig_shape, radius=kpt_radius, kpt_line=kpt_line) |
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return annotator.result() |
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def verbose(self): |
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"""Return log string for each task.""" |
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log_string = "" |
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probs = self.probs |
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boxes = self.boxes |
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if len(self) == 0: |
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return log_string if probs is not None else f"{log_string}(no detections), " |
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if probs is not None: |
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log_string += f"{', '.join(f'{self.names[j]} {probs.data[j]:.2f}' for j in probs.top5)}, " |
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if boxes: |
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for c in boxes.cls.unique(): |
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n = (boxes.cls == c).sum() # detections per class |
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log_string += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " |
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return log_string |
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def save_txt(self, txt_file, save_conf=False): |
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""" |
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Save predictions into txt file. |
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Args: |
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txt_file (str): txt file path. |
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save_conf (bool): save confidence score or not. |
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""" |
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is_obb = self.obb is not None |
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boxes = self.obb if is_obb else self.boxes |
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masks = self.masks |
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probs = self.probs |
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kpts = self.keypoints |
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texts = [] |
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if probs is not None: |
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# Classify |
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[texts.append(f"{probs.data[j]:.2f} {self.names[j]}") for j in probs.top5] |
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elif boxes: |
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# Detect/segment/pose |
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for j, d in enumerate(boxes): |
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c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item()) |
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line = (c, *(d.xyxyxyxyn.view(-1) if is_obb else d.xywhn.view(-1))) |
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if masks: |
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seg = masks[j].xyn[0].copy().reshape(-1) # reversed mask.xyn, (n,2) to (n*2) |
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line = (c, *seg) |
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if kpts is not None: |
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kpt = torch.cat((kpts[j].xyn, kpts[j].conf[..., None]), 2) if kpts[j].has_visible else kpts[j].xyn |
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line += (*kpt.reshape(-1).tolist(),) |
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line += (conf,) * save_conf + (() if id is None else (id,)) |
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texts.append(("%g " * len(line)).rstrip() % line) |
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if texts: |
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Path(txt_file).parent.mkdir(parents=True, exist_ok=True) # make directory |
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with open(txt_file, "a") as f: |
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f.writelines(text + "\n" for text in texts) |
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def save_crop(self, save_dir, file_name=Path("im.jpg")): |
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""" |
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Save cropped predictions to `save_dir/cls/file_name.jpg`. |
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Args: |
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save_dir (str | pathlib.Path): Save path. |
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file_name (str | pathlib.Path): File name. |
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""" |
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if self.probs is not None: |
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LOGGER.warning("WARNING ⚠️ Classify task do not support `save_crop`.") |
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return |
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if self.obb is not None: |
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LOGGER.warning("WARNING ⚠️ OBB task do not support `save_crop`.") |
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return |
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for d in self.boxes: |
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save_one_box( |
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d.xyxy, |
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self.orig_img.copy(), |
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file=Path(save_dir) / self.names[int(d.cls)] / f"{Path(file_name)}.jpg", |
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BGR=True, |
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) |
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def tojson(self, normalize=False): |
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"""Convert the object to JSON format.""" |
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if self.probs is not None: |
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LOGGER.warning("Warning: Classify task do not support `tojson` yet.") |
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return |
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import json |
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# Create list of detection dictionaries |
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results = [] |
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data = self.boxes.data.cpu().tolist() |
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h, w = self.orig_shape if normalize else (1, 1) |
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for i, row in enumerate(data): # xyxy, track_id if tracking, conf, class_id |
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box = {"x1": row[0] / w, "y1": row[1] / h, "x2": row[2] / w, "y2": row[3] / h} |
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conf = row[-2] |
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class_id = int(row[-1]) |
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name = self.names[class_id] |
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result = {"name": name, "class": class_id, "confidence": conf, "box": box} |
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if self.boxes.is_track: |
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result["track_id"] = int(row[-3]) # track ID |
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if self.masks: |
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x, y = self.masks.xy[i][:, 0], self.masks.xy[i][:, 1] # numpy array |
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result["segments"] = {"x": (x / w).tolist(), "y": (y / h).tolist()} |
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if self.keypoints is not None: |
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x, y, visible = self.keypoints[i].data[0].cpu().unbind(dim=1) # torch Tensor |
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result["keypoints"] = {"x": (x / w).tolist(), "y": (y / h).tolist(), "visible": visible.tolist()} |
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results.append(result) |
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# Convert detections to JSON |
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return json.dumps(results, indent=2) |
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class Boxes(BaseTensor): |
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""" |
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A class for storing and manipulating detection boxes. |
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Args: |
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boxes (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the detection boxes, |
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with shape (num_boxes, 6) or (num_boxes, 7). The last two columns contain confidence and class values. |
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If present, the third last column contains track IDs. |
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orig_shape (tuple): Original image size, in the format (height, width). |
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Attributes: |
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xyxy (torch.Tensor | numpy.ndarray): The boxes in xyxy format. |
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conf (torch.Tensor | numpy.ndarray): The confidence values of the boxes. |
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cls (torch.Tensor | numpy.ndarray): The class values of the boxes. |
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id (torch.Tensor | numpy.ndarray): The track IDs of the boxes (if available). |
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xywh (torch.Tensor | numpy.ndarray): The boxes in xywh format. |
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xyxyn (torch.Tensor | numpy.ndarray): The boxes in xyxy format normalized by original image size. |
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xywhn (torch.Tensor | numpy.ndarray): The boxes in xywh format normalized by original image size. |
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data (torch.Tensor): The raw bboxes tensor (alias for `boxes`). |
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Methods: |
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cpu(): Move the object to CPU memory. |
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numpy(): Convert the object to a numpy array. |
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cuda(): Move the object to CUDA memory. |
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to(*args, **kwargs): Move the object to the specified device. |
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""" |
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def __init__(self, boxes, orig_shape) -> None: |
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"""Initialize the Boxes class.""" |
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if boxes.ndim == 1: |
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boxes = boxes[None, :] |
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n = boxes.shape[-1] |
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assert n in (6, 7), f"expected 6 or 7 values but got {n}" # xyxy, track_id, conf, cls |
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super().__init__(boxes, orig_shape) |
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self.is_track = n == 7 |
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self.orig_shape = orig_shape |
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@property |
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def xyxy(self): |
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"""Return the boxes in xyxy format.""" |
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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 |
|
|
|
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|
@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] |
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|
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] |
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|
return xywh |
|
|
|
|
|
|
|
|
class Masks(BaseTensor): |
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|
""" |
|
|
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) |
|
|
] |
|
|
|
|
|
|
|
|
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 |
|
|
|
|
|
|
|
|
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] |
|
|
|
|
|
|
|
|
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). |
|
|
xyxyxyxy (torch.Tensor | numpy.ndarray): The boxes in xyxyxyxy format normalized by original image size. |
|
|
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[1] |
|
|
return xyxyxyxyn |
|
|
|
|
|
@property |
|
|
@lru_cache(maxsize=2) |
|
|
def xyxy(self): |
|
|
"""Return the horizontal boxes in xyxy format, (N, 4).""" |
|
|
# This way to fit both torch and numpy version |
|
|
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)
|
|
|
|
