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ultralytics 8.0.44 export and task fixes (#1088)

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Co-authored-by: Mehran Ghandehari <mehran.maps@gmail.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
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Glenn Jocher 2023-02-24 03:11:25 +01:00 • committed by GitHub
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112
ultralytics/yolo/engine/results.py
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@ -18,29 +18,33 @@ from ultralytics.yolo.utils.plotting import Annotator, colors
class Results:
"""
A class for storing and manipulating inference results.
A class for storing and manipulating inference results.
Args:
boxes (Boxes, optional): A Boxes object containing the detection bounding boxes.
masks (Masks, optional): A Masks object containing the detection masks.
probs (torch.Tensor, optional): A tensor containing the detection class probabilities.
orig_img (tuple, optional): Original image size.
Args:
orig_img (numpy.ndarray): The original image as a numpy array.
path (str): The path to the image file.
names (List[str]): A list of class names.
boxes (List[List[float]], optional): A list of bounding box coordinates for each detection.
masks (numpy.ndarray, optional): A 3D numpy array of detection masks, where each mask is a binary image.
probs (numpy.ndarray, optional): A 2D numpy array of detection probabilities for each class.
Attributes:
boxes (Boxes, optional): A Boxes object containing the detection bounding boxes.
masks (Masks, optional): A Masks object containing the detection masks.
probs (torch.Tensor, optional): A tensor containing the detection class probabilities.
orig_img (tuple, optional): Original image size.
data (torch.Tensor): The raw masks tensor
"""
Attributes:
orig_img (numpy.ndarray): The original image as a numpy array.
orig_shape (tuple): The original image shape in (height, width) format.
boxes (Boxes, optional): A Boxes object containing the detection bounding boxes.
masks (Masks, optional): A Masks object containing the detection masks.
probs (numpy.ndarray, optional): A 2D numpy array of detection probabilities for each class.
names (List[str]): A list of class names.
path (str): The path to the image file.
_keys (tuple): A tuple of attribute names for non-empty attributes.
"""
def __init__(self, orig_img, path, names, boxes=None, masks=None, probs=None) -> None:
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 if probs is not None else None
self.boxes = Boxes(boxes.cpu(), self.orig_shape) if boxes is not None else None # native size boxes
self.masks = Masks(masks.cpu(), self.orig_shape) if masks is not None else None # native size or imgsz masks
self.probs = probs.cpu() if probs is not None else None
self.names = names
self.path = path
self._keys = (k for k in ('boxes', 'masks', 'probs') if getattr(self, k) is not None)
@ -99,24 +103,22 @@ class Results:
def __getattr__(self, attr):
name = self.__class__.__name__
raise AttributeError(f"""
'{name}' object has no attribute '{attr}'. Valid '{name}' object attributes and properties are:
Attributes:
boxes (Boxes, optional): A Boxes object containing the detection bounding boxes.
masks (Masks, optional): A Masks object containing the detection masks.
probs (torch.Tensor, optional): A tensor containing the detection class probabilities.
orig_shape (tuple, optional): Original image size.
""")
raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
def plot(self, show_conf=True, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
"""
Plots the given result on an input RGB image. Accepts cv2(numpy) or PIL Image
Plots the detection results on an input RGB image. Accepts a numpy array (cv2) or a PIL Image.
Args:
show_conf (bool): Show confidence
line_width (Float): The line width of boxes. Automatically scaled to img size if not provided
font_size (Float): The font size of . Automatically scaled to img size if not provided
show_conf (bool): Whether to show the detection confidence score.
line_width (float, optional): The line width of the bounding boxes. If None, it is automatically scaled to the image size.
font_size (float, optional): The font size of the text. If None, it is automatically 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.
example (str): An example string to display in the plot. Useful for indicating the expected format of the output.
Returns:
None or PIL Image: If `pil` is True, the image will be returned as a PIL Image. Otherwise, nothing is returned.
"""
img = deepcopy(self.orig_img)
annotator = Annotator(img, line_width, font_size, font, pil, example)
@ -157,15 +159,24 @@ class Boxes:
boxes (torch.Tensor) or (numpy.ndarray): A tensor or numpy array containing the detection boxes,
with shape (num_boxes, 6).
orig_shape (torch.Tensor) or (numpy.ndarray): Original image size, in the format (height, width).
is_track (bool): True if the boxes also include track IDs, False otherwise.
Properties:
xyxy (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format.
conf (torch.Tensor) or (numpy.ndarray): The confidence values of the boxes.
cls (torch.Tensor) or (numpy.ndarray): The class values of the boxes.
id (torch.Tensor) or (numpy.ndarray): The track IDs of the boxes (if available).
xywh (torch.Tensor) or (numpy.ndarray): The boxes in xywh format.
xyxyn (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format normalized by original image size.
xywhn (torch.Tensor) or (numpy.ndarray): The boxes in xywh format normalized by original image size.
data (torch.Tensor): The raw bboxes tensor
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.
pandas(): Convert the object to a pandas DataFrame (not yet implemented).
"""
def __init__(self, boxes, orig_shape) -> None:
@ -257,22 +268,7 @@ class Boxes:
def __getattr__(self, attr):
name = self.__class__.__name__
raise AttributeError(f"""
'{name}' object has no attribute '{attr}'. Valid '{name}' object attributes and properties are:
Attributes:
boxes (torch.Tensor) or (numpy.ndarray): A tensor or numpy array containing the detection boxes,
with shape (num_boxes, 6).
orig_shape (torch.Tensor) or (numpy.ndarray): Original image size, in the format (height, width).
Properties:
xyxy (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format.
conf (torch.Tensor) or (numpy.ndarray): The confidence values of the boxes.
cls (torch.Tensor) or (numpy.ndarray): The class values of the boxes.
xywh (torch.Tensor) or (numpy.ndarray): The boxes in xywh format.
xyxyn (torch.Tensor) or (numpy.ndarray): The boxes in xyxy format normalized by original image size.
xywhn (torch.Tensor) or (numpy.ndarray): The boxes in xywh format normalized by original image size.
""")
raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")
class Masks:
@ -288,7 +284,18 @@ class Masks:
orig_shape (tuple): Original image size, in the format (height, width).
Properties:
segments (list): A list of segments which includes x,y,w,h,label,confidence, and mask of each detection masks.
segments (list): A list of segments which includes x, y, w, h, label, confidence, and mask of each detection masks.
Methods:
cpu(): Returns a copy of the masks tensor on CPU memory.
numpy(): Returns a copy of the masks tensor as a numpy array.
cuda(): Returns a copy of the masks tensor on GPU memory.
to(): Returns a copy of the masks tensor with the specified device and dtype.
__len__(): Returns the number of masks in the tensor.
__str__(): Returns a string representation of the masks tensor.
__repr__(): Returns a detailed string representation of the masks tensor.
__getitem__(): Returns a new Masks object with the masks at the specified index.
__getattr__(): Raises an AttributeError with a list of valid attributes and properties.
"""
def __init__(self, masks, orig_shape) -> None:
@ -337,13 +344,4 @@ class Masks:
def __getattr__(self, attr):
name = self.__class__.__name__
raise AttributeError(f"""
'{name}' object has no attribute '{attr}'. Valid '{name}' object attributes and properties are:
Attributes:
masks (torch.Tensor): A tensor containing the detection masks, with shape (num_masks, height, width).
orig_shape (tuple): Original image size, in the format (height, width).
Properties:
segments (list): A list of segments which includes x,y,w,h,label,confidence, and mask of each detection masks.
""")
raise AttributeError(f"'{name}' object has no attribute '{attr}'. See valid attributes below.\n{self.__doc__}")