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446 lines
17 KiB
446 lines
17 KiB
# Ultralytics YOLO 🚀, GPL-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.yolo.data.augment import LetterBox
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from ultralytics.yolo.utils import LOGGER, SimpleClass, deprecation_warn, ops
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from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box
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class BaseTensor(SimpleClass):
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"""
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Attributes:
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data (torch.Tensor): Base tensor.
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orig_shape (tuple): Original image size, in the format (height, width).
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Methods:
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cpu(): Returns a copy of the tensor on CPU memory.
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numpy(): Returns a copy of the tensor as a numpy array.
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cuda(): Returns a copy of the tensor on GPU memory.
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to(): Returns a copy of the tensor with the specified device and dtype.
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"""
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def __init__(self, data, orig_shape) -> None:
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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 self.data.shape
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def cpu(self):
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return self.__class__(self.data.cpu(), self.orig_shape)
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def numpy(self):
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return self.__class__(self.data.numpy(), self.orig_shape)
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def cuda(self):
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return self.__class__(self.data.cuda(), self.orig_shape)
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def to(self, *args, **kwargs):
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return self.__class__(self.data.to(*args, **kwargs), self.orig_shape)
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def __len__(self): # override len(results)
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return len(self.data)
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def __getitem__(self, idx):
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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 (List[List[float]], optional): A list of bounding box coordinates for each detection.
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masks (numpy.ndarray, optional): A 3D numpy array of detection masks, where each mask is a binary image.
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probs (numpy.ndarray, optional): A 2D numpy array of detection probabilities for each class.
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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 (numpy.ndarray, optional): A 2D numpy array of detection probabilities for each class.
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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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keypoints (List[List[float]], optional): A list of 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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_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) -> None:
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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 if probs is not None else None
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self.keypoints = keypoints if keypoints 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._keys = ('boxes', 'masks', 'probs', 'keypoints')
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def pandas(self):
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pass
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# TODO masks.pandas + boxes.pandas + cls.pandas
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def __getitem__(self, idx):
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r = self.new()
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for k in self.keys:
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setattr(r, k, getattr(self, k)[idx])
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return r
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def update(self, boxes=None, masks=None, probs=None):
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if boxes is not None:
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self.boxes = Boxes(boxes, 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 cpu(self):
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r = self.new()
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for k in self.keys:
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setattr(r, k, getattr(self, k).cpu())
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return r
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def numpy(self):
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r = self.new()
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for k in self.keys:
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setattr(r, k, getattr(self, k).numpy())
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return r
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def cuda(self):
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r = self.new()
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for k in self.keys:
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setattr(r, k, getattr(self, k).cuda())
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return r
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def to(self, *args, **kwargs):
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r = self.new()
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for k in self.keys:
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setattr(r, k, getattr(self, k).to(*args, **kwargs))
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return r
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def __len__(self):
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for k in self.keys:
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return len(getattr(self, k))
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def new(self):
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return Results(orig_img=self.orig_img, path=self.path, names=self.names)
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@property
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def keys(self):
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return [k for k in self._keys if getattr(self, k) is not None]
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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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img_gpu=None,
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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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**kwargs # deprecated args TODO: remove support in 8.2
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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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img_gpu (torch.Tensor): Normalized image in gpu with shape (1, 3, 640, 640), for faster mask plotting.
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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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"""
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# Deprecation warn TODO: remove in 8.2
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if 'show_conf' in kwargs:
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deprecation_warn('show_conf', 'conf')
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conf = kwargs['show_conf']
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assert type(conf) == bool, '`show_conf` should be of boolean type, i.e, show_conf=True/False'
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names = self.names
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annotator = Annotator(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,
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example=names)
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pred_boxes, show_boxes = 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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keypoints = self.keypoints
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if pred_masks and show_masks:
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if img_gpu is None:
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img = LetterBox(pred_masks.shape[1:])(image=annotator.result())
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img_gpu = torch.as_tensor(img, dtype=torch.float16, device=pred_masks.data.device).permute(
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2, 0, 1).flip(0).contiguous() / 255
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annotator.masks(pred_masks.data, colors=[colors(x, True) for x in pred_boxes.cls], im_gpu=img_gpu)
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if pred_boxes 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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annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
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if pred_probs is not None and show_probs:
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n5 = min(len(names), 5)
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top5i = pred_probs.argsort(0, descending=True)[:n5].tolist() # top 5 indices
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text = f"{', '.join(f'{names[j] if names else j} {pred_probs[j]:.2f}' for j in top5i)}, "
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annotator.text((32, 32), text, txt_color=(255, 255, 255)) # TODO: allow setting colors
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if keypoints is not None:
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for k in reversed(keypoints):
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annotator.kpts(k, self.orig_shape, kpt_line=kpt_line)
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return annotator.result()
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def verbose(self):
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"""
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Return log string for each task.
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"""
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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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n5 = min(len(self.names), 5)
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top5i = probs.argsort(0, descending=True)[:n5].tolist() # top 5 indices
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log_string += f"{', '.join(f'{self.names[j]} {probs[j]:.2f}' for j in top5i)}, "
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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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"""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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boxes = 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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n5 = min(len(self.names), 5)
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top5i = probs.argsort(0, descending=True)[:n5].tolist() # top 5 indices
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[texts.append(f'{probs[j]:.2f} {self.names[j]}') for j in top5i]
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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.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 = (kpts[j][:, :2] / d.orig_shape[[1, 0]]).reshape(-1).tolist()
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line += (*kpt, )
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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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with open(txt_file, 'a') as f:
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for text in texts:
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f.write(text + '\n')
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def save_crop(self, save_dir, file_name=Path('im.jpg')):
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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 isinstance(save_dir, str):
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save_dir = Path(save_dir)
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if isinstance(file_name, str):
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file_name = Path(file_name)
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for d in self.boxes:
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save_one_box(d.xyxy,
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self.orig_img.copy(),
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file=save_dir / self.names[int(d.cls)] / f'{file_name.stem}.jpg',
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BGR=True)
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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) or (numpy.ndarray): A tensor or numpy array containing the detection boxes,
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with shape (num_boxes, 6). The last two columns should contain confidence and class values.
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orig_shape (tuple): Original image size, in the format (height, width).
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Attributes:
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boxes (torch.Tensor) or (numpy.ndarray): The detection boxes with shape (num_boxes, 6).
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orig_shape (torch.Tensor) or (numpy.ndarray): Original image size, in the format (height, width).
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is_track (bool): True if the boxes also include track IDs, False otherwise.
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Properties:
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xyxy (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format.
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conf (torch.Tensor) or (numpy.ndarray): The confidence values of the boxes.
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cls (torch.Tensor) or (numpy.ndarray): The class values of the boxes.
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id (torch.Tensor) or (numpy.ndarray): The track IDs of the boxes (if available).
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xywh (torch.Tensor) or (numpy.ndarray): The boxes in xywh format.
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xyxyn (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format normalized by original image size.
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xywhn (torch.Tensor) or (numpy.ndarray): The boxes in xywh format normalized by original image size.
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data (torch.Tensor): The raw bboxes tensor
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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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pandas(): Convert the object to a pandas DataFrame (not yet implemented).
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"""
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def __init__(self, boxes, orig_shape) -> None:
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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 `n` in [6, 7], 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 = torch.as_tensor(orig_shape, device=boxes.device) if isinstance(boxes, torch.Tensor) \
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else np.asarray(orig_shape)
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@property
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def xyxy(self):
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return self.data[:, :4]
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@property
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def conf(self):
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return self.data[:, -2]
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@property
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def cls(self):
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return self.data[:, -1]
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@property
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def id(self):
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return self.data[:, -3] if self.is_track else None
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@property
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@lru_cache(maxsize=2) # maxsize 1 should suffice
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def xywh(self):
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return ops.xyxy2xywh(self.xyxy)
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@property
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@lru_cache(maxsize=2)
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def xyxyn(self):
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return self.xyxy / self.orig_shape[[1, 0, 1, 0]]
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@property
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@lru_cache(maxsize=2)
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def xywhn(self):
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return self.xywh / self.orig_shape[[1, 0, 1, 0]]
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def pandas(self):
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LOGGER.info('results.pandas() method not yet implemented')
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@property
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def boxes(self):
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LOGGER.warning("WARNING ⚠️ 'Boxes.boxes' is deprecated. Use 'Boxes.data' instead.")
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return self.data
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class Masks(BaseTensor):
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"""
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A class for storing and manipulating detection masks.
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Args:
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masks (torch.Tensor): A tensor containing the detection masks, with shape (num_masks, height, width).
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orig_shape (tuple): Original image size, in the format (height, width).
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Attributes:
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masks (torch.Tensor): A tensor containing the detection masks, with shape (num_masks, height, width).
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orig_shape (tuple): Original image size, in the format (height, width).
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Properties:
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xy (list): A list of segments (pixels) which includes x, y segments of each detection.
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xyn (list): A list of segments (normalized) which includes x, y segments of each detection.
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Methods:
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cpu(): Returns a copy of the masks tensor on CPU memory.
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numpy(): Returns a copy of the masks tensor as a numpy array.
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cuda(): Returns a copy of the masks tensor on GPU memory.
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to(): Returns a copy of the masks tensor with the specified device and dtype.
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"""
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def __init__(self, masks, orig_shape) -> None:
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if masks.ndim == 2:
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masks = masks[None, :]
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super().__init__(masks, orig_shape)
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@property
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@lru_cache(maxsize=1)
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def segments(self):
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# Segments-deprecated (normalized)
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LOGGER.warning("WARNING ⚠️ 'Masks.segments' is deprecated. Use 'Masks.xyn' for segments (normalized) and "
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"'Masks.xy' for segments (pixels) instead.")
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return self.xyn
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@property
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@lru_cache(maxsize=1)
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def xyn(self):
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# Segments (normalized)
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return [
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ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=True)
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for x in ops.masks2segments(self.data)]
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@property
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@lru_cache(maxsize=1)
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def xy(self):
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# Segments (pixels)
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return [
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ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=False)
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for x in ops.masks2segments(self.data)]
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@property
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def masks(self):
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LOGGER.warning("WARNING ⚠️ 'Masks.masks' is deprecated. Use 'Masks.data' instead.")
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return self.data
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