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ultralytics 8.0.71 updates and fixes (#1907)

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Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Pavel Bugneac <50273042+pavelbugneac@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2023-04-08 21:10:36 +02:00 • committed by GitHub
parent c38b17a0d8
commit 4e997013bc
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19 changed files with 103 additions and 39 deletions
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12
ultralytics/yolo/engine/results.py
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@ -70,10 +70,12 @@ class Results(SimpleClass):
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.
names (dict): A dictionary 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.
keypoints (List[List[float]], optional): A list of detected keypoints for each object.
Attributes:
orig_img (numpy.ndarray): The original image as a numpy array.
@ -81,9 +83,12 @@ 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 (numpy.ndarray, optional): A 2D numpy array of detection probabilities for each class.
names (List[str]): A list of class names.
names (dict): A dictionary of class names.
path (str): The path to the image file.
keypoints (List[List[float]], optional): A list of 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.
"""
def __init__(self, orig_img, path, names, boxes=None, masks=None, probs=None, keypoints=None) -> None:
@ -93,6 +98,7 @@ class Results(SimpleClass):
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.keypoints = keypoints if keypoints is not None else None
self.speed = {'preprocess': None, 'inference': None, 'postprocess': None} # milliseconds per image
self.names = names
self.path = path
self._keys = ('boxes', 'masks', 'probs', 'keypoints')
@ -203,7 +209,7 @@ class Results(SimpleClass):
keypoints = self.keypoints
if pred_masks and show_masks:
if img_gpu is None:
img = LetterBox(pred_masks.shape[1:])(image=annotator.im)
img = LetterBox(pred_masks.shape[1:])(image=annotator.result())
img_gpu = torch.as_tensor(img, dtype=torch.float16, device=pred_masks.masks.device).permute(
2, 0, 1).flip(0).contiguous() / 255
annotator.masks(pred_masks.data, colors=[colors(x, True) for x in pred_boxes.cls], im_gpu=img_gpu)