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Update prediction Results docs (#4139)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
2023-08-03 18:44:40 +02:00
committed by GitHub
parent 8870084645
commit 11d0488bf1
107 changed files with 1451 additions and 1317 deletions
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132
ultralytics/engine/results.py
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@ -82,10 +82,10 @@ class Results(SimpleClass):
boxes (Boxes, optional): A Boxes object containing the detection bounding boxes.
masks (Masks, optional): A Masks object containing the detection masks.
probs (Probs, optional): A Probs object containing probabilities of each class for classification task.
keypoints (Keypoints, optional): A Keypoints object containing detected keypoints for each object.
speed (dict): A dictionary of preprocess, inference, and postprocess speeds in milliseconds per image.
names (dict): A dictionary of class names.
path (str): The path to the image file.
keypoints (Keypoints, optional): A Keypoints object containing detected keypoints for each object.
speed (dict): A dictionary of preprocess, inference and postprocess speeds in milliseconds per image.
_keys (tuple): A tuple of attribute names for non-empty attributes.
"""
@ -110,6 +110,11 @@ class Results(SimpleClass):
setattr(r, k, getattr(self, k)[idx])
return r
def __len__(self):
"""Return the number of detections in the Results object."""
for k in self.keys:
return len(getattr(self, k))
def update(self, boxes=None, masks=None, probs=None):
"""Update the boxes, masks, and probs attributes of the Results object."""
if boxes is not None:
@ -148,11 +153,6 @@ class Results(SimpleClass):
setattr(r, k, getattr(self, k).to(*args, **kwargs))
return r
def __len__(self):
"""Return the number of detections in the Results object."""
for k in self.keys:
return len(getattr(self, k))
def new(self):
"""Return a new Results object with the same image, path, and names."""
return Results(orig_img=self.orig_img, path=self.path, names=self.names)
@ -199,6 +199,18 @@ class Results(SimpleClass):
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 = r.plot() # BGR numpy array
Image.fromarray(im[..., ::-1]).show() # show RGB image
```
"""
if img is None and isinstance(self.orig_img, torch.Tensor):
img = np.ascontiguousarray(self.orig_img[0].permute(1, 2, 0).cpu().detach().numpy()) * 255
@ -328,10 +340,6 @@ class Results(SimpleClass):
file=save_dir / self.names[int(d.cls)] / f'{file_name.stem}.jpg',
BGR=True)
def pandas(self):
"""Convert the object to a pandas DataFrame (not yet implemented)."""
LOGGER.warning("WARNING ⚠️ 'Results.pandas' method is not yet implemented.")
def tojson(self, normalize=False):
"""Convert the object to JSON format."""
if self.probs is not None:
@ -368,15 +376,11 @@ class Boxes(BaseTensor):
Args:
boxes (torch.Tensor | numpy.ndarray): A tensor or numpy array containing the detection boxes,
with shape (num_boxes, 6). The last two columns should contain confidence and class values.
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).
Attributes:
boxes (torch.Tensor | numpy.ndarray): The detection boxes with shape (num_boxes, 6).
orig_shape (torch.Tensor | 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 | numpy.ndarray): The boxes in xyxy format.
conf (torch.Tensor | numpy.ndarray): The confidence values of the boxes.
cls (torch.Tensor | numpy.ndarray): The class values of the boxes.
@ -384,14 +388,13 @@ class Boxes(BaseTensor):
xywh (torch.Tensor | numpy.ndarray): The boxes in xywh format.
xyxyn (torch.Tensor | numpy.ndarray): The boxes in xyxy format normalized by original image size.
xywhn (torch.Tensor | numpy.ndarray): The boxes in xywh format normalized by original image size.
data (torch.Tensor): The raw bboxes tensor
data (torch.Tensor): The raw bboxes 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.
pandas(): Convert the object to a pandas DataFrame (not yet implemented).
"""
def __init__(self, boxes, orig_shape) -> None:
@ -459,27 +462,20 @@ class Masks(BaseTensor):
"""
A class for storing and manipulating detection masks.
Args:
masks (torch.Tensor | np.ndarray): A tensor containing the detection masks, with shape (num_masks, height, width).
orig_shape (tuple): Original image size, in the format (height, width).
Attributes:
masks (torch.Tensor | np.ndarray): A tensor containing the detection masks, with shape (num_masks, height, width).
orig_shape (tuple): Original image size, in the format (height, width).
Properties:
xy (list): A list of segments (pixels) which includes x, y segments of each detection.
xyn (list): A list of segments (normalized) which includes x, y segments of each detection.
segments (list): Deprecated property for segments (normalized).
xy (list): A list of segments in pixel coordinates.
xyn (list): A list of normalized segments.
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.
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."""
"""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)
@ -487,15 +483,16 @@ class Masks(BaseTensor):
@property
@lru_cache(maxsize=1)
def segments(self):
"""Return segments (deprecated; normalized)."""
LOGGER.warning("WARNING ⚠️ 'Masks.segments' is deprecated. Use 'Masks.xyn' for segments (normalized) and "
"'Masks.xy' for segments (pixels) instead.")
"""Return segments (normalized). Deprecated; use xyn property instead."""
LOGGER.warning(
"WARNING ⚠️ 'Masks.segments' is deprecated. Use 'Masks.xyn' for segments (normalized) and 'Masks.xy' for segments (pixels) instead."
)
return self.xyn
@property
@lru_cache(maxsize=1)
def xyn(self):
"""Return segments (normalized)."""
"""Return normalized segments."""
return [
ops.scale_coords(self.data.shape[1:], x, self.orig_shape, normalize=True)
for x in ops.masks2segments(self.data)]
@ -503,46 +500,36 @@ class Masks(BaseTensor):
@property
@lru_cache(maxsize=1)
def xy(self):
"""Return segments (pixels)."""
"""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)]
@property
def masks(self):
"""Return the raw masks tensor (deprecated)."""
"""Return the raw masks tensor. Deprecated; use data attribute instead."""
LOGGER.warning("WARNING ⚠️ 'Masks.masks' is deprecated. Use 'Masks.data' instead.")
return self.data
def pandas(self):
"""Convert the object to a pandas DataFrame (not yet implemented)."""
LOGGER.warning("WARNING ⚠️ 'Masks.pandas' method is not yet implemented.")
class Keypoints(BaseTensor):
"""
A class for storing and manipulating detection keypoints.
Args:
keypoints (torch.Tensor | np.ndarray): A tensor containing the detection keypoints, with shape (num_dets, num_kpts, 2/3).
orig_shape (tuple): Original image size, in the format (height, width).
Attributes:
keypoints (torch.Tensor | np.ndarray): A tensor containing the detection keypoints, with shape (num_dets, num_kpts, 2/3).
orig_shape (tuple): Original image size, in the format (height, width).
Properties:
xy (list): A list of keypoints (pixels) which includes x, y keypoints of each detection.
xyn (list): A list of keypoints (normalized) which includes x, y keypoints of each detection.
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(): Returns a copy of the keypoints tensor with the specified device and dtype.
to(device, dtype): Returns a copy of the keypoints tensor with the specified device and dtype.
"""
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, :]
super().__init__(keypoints, orig_shape)
@ -551,11 +538,13 @@ class Keypoints(BaseTensor):
@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]
@ -564,22 +553,19 @@ class Keypoints(BaseTensor):
@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 classify predictions.
Args:
probs (torch.Tensor | np.ndarray): A tensor containing the detection keypoints, with shape (num_class, ).
A class for storing and manipulating classification predictions.
Attributes:
probs (torch.Tensor | np.ndarray): A tensor containing the detection keypoints, with shape (num_class).
Properties:
top5 (list[int]): Top 1 indice.
top1 (int): Top 5 indices.
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.
@ -591,6 +577,12 @@ class Probs(BaseTensor):
def __init__(self, probs, orig_shape=None) -> None:
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):
@ -599,18 +591,12 @@ class Probs(BaseTensor):
@property
@lru_cache(maxsize=1)
def top1(self):
"""Return the indices of top 1."""
return int(self.data.argmax())
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]
@property
@lru_cache(maxsize=1)
def top1conf(self):
"""Return the confidences of top 1."""
return self.data[self.top1]