ultralytics 8.3.0 YOLO11 Models Release (#16539)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Laughing-q <1185102784@qq.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
GitHub
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50 changed files with 1154 additions and 407 deletions
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@ -1628,92 +1628,105 @@ class LetterBox:
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return labels
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class CopyPaste:
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class CopyPaste(BaseMixTransform):
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"""
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Implements Copy-Paste augmentation as described in https://arxiv.org/abs/2012.07177.
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CopyPaste class for applying Copy-Paste augmentation to image datasets.
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This class applies Copy-Paste augmentation on images and their corresponding instances.
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This class implements the Copy-Paste augmentation technique as described in the paper "Simple Copy-Paste is a Strong
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Data Augmentation Method for Instance Segmentation" (https://arxiv.org/abs/2012.07177). It combines objects from
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different images to create new training samples.
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Attributes:
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p (float): Probability of applying the Copy-Paste augmentation. Must be between 0 and 1.
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dataset (Any): The dataset to which Copy-Paste augmentation will be applied.
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pre_transform (Callable | None): Optional transform to apply before Copy-Paste.
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p (float): Probability of applying Copy-Paste augmentation.
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Methods:
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__call__: Applies Copy-Paste augmentation to given image and instances.
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get_indexes: Returns a random index from the dataset.
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_mix_transform: Applies Copy-Paste augmentation to the input labels.
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__call__: Applies the Copy-Paste transformation to images and annotations.
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Examples:
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>>> copypaste = CopyPaste(p=0.5)
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>>> augmented_labels = copypaste(labels)
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>>> augmented_image = augmented_labels["img"]
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>>> from ultralytics.data.augment import CopyPaste
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>>> dataset = YourDataset(...) # Your image dataset
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>>> copypaste = CopyPaste(dataset, p=0.5)
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>>> augmented_labels = copypaste(original_labels)
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"""
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def __init__(self, p=0.5) -> None:
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"""
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Initializes the CopyPaste augmentation object.
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def __init__(self, dataset=None, pre_transform=None, p=0.5, mode="flip") -> None:
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"""Initializes CopyPaste object with dataset, pre_transform, and probability of applying MixUp."""
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super().__init__(dataset=dataset, pre_transform=pre_transform, p=p)
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assert mode in {"flip", "mixup"}, f"Expected `mode` to be `flip` or `mixup`, but got {mode}."
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self.mode = mode
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This class implements the Copy-Paste augmentation as described in the paper "Simple Copy-Paste is a Strong Data
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Augmentation Method for Instance Segmentation" (https://arxiv.org/abs/2012.07177). It applies the Copy-Paste
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augmentation on images and their corresponding instances with a given probability.
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def get_indexes(self):
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"""Returns a list of random indexes from the dataset for CopyPaste augmentation."""
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return random.randint(0, len(self.dataset) - 1)
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Args:
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p (float): The probability of applying the Copy-Paste augmentation. Must be between 0 and 1.
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Attributes:
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p (float): Stores the probability of applying the augmentation.
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Examples:
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>>> augment = CopyPaste(p=0.7)
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>>> augmented_data = augment(original_data)
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"""
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self.p = p
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def _mix_transform(self, labels):
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"""Applies Copy-Paste augmentation to combine objects from another image into the current image."""
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labels2 = labels["mix_labels"][0]
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return self._transform(labels, labels2)
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def __call__(self, labels):
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"""
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Applies Copy-Paste augmentation to an image and its instances.
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"""Applies Copy-Paste augmentation to an image and its labels."""
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if len(labels["instances"].segments) == 0 or self.p == 0:
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return labels
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if self.mode == "flip":
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return self._transform(labels)
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Args:
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labels (Dict): A dictionary containing:
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- 'img' (np.ndarray): The image to augment.
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- 'cls' (np.ndarray): Class labels for the instances.
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- 'instances' (ultralytics.engine.results.Instances): Object containing bounding boxes, segments, etc.
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# Get index of one or three other images
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indexes = self.get_indexes()
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if isinstance(indexes, int):
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indexes = [indexes]
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Returns:
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(Dict): Dictionary with augmented image and updated instances under 'img', 'cls', and 'instances' keys.
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# Get images information will be used for Mosaic or MixUp
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mix_labels = [self.dataset.get_image_and_label(i) for i in indexes]
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Examples:
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>>> labels = {"img": np.random.rand(640, 640, 3), "cls": np.array([0, 1, 2]), "instances": Instances(...)}
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>>> augmenter = CopyPaste(p=0.5)
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>>> augmented_labels = augmenter(labels)
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"""
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im = labels["img"]
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cls = labels["cls"]
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if self.pre_transform is not None:
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for i, data in enumerate(mix_labels):
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mix_labels[i] = self.pre_transform(data)
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labels["mix_labels"] = mix_labels
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# Update cls and texts
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labels = self._update_label_text(labels)
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# Mosaic or MixUp
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labels = self._mix_transform(labels)
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labels.pop("mix_labels", None)
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return labels
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def _transform(self, labels1, labels2={}):
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"""Applies Copy-Paste augmentation to combine objects from another image into the current image."""
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im = labels1["img"]
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cls = labels1["cls"]
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h, w = im.shape[:2]
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instances = labels.pop("instances")
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instances = labels1.pop("instances")
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instances.convert_bbox(format="xyxy")
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instances.denormalize(w, h)
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if self.p and len(instances.segments):
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_, w, _ = im.shape # height, width, channels
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im_new = np.zeros(im.shape, np.uint8)
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# Calculate ioa first then select indexes randomly
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ins_flip = deepcopy(instances)
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ins_flip.fliplr(w)
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im_new = np.zeros(im.shape, np.uint8)
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instances2 = labels2.pop("instances", None)
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if instances2 is None:
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instances2 = deepcopy(instances)
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instances2.fliplr(w)
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ioa = bbox_ioa(instances2.bboxes, instances.bboxes) # intersection over area, (N, M)
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indexes = np.nonzero((ioa < 0.30).all(1))[0] # (N, )
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n = len(indexes)
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sorted_idx = np.argsort(ioa.max(1)[indexes])
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indexes = indexes[sorted_idx]
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for j in indexes[: round(self.p * n)]:
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cls = np.concatenate((cls, labels2.get("cls", cls)[[j]]), axis=0)
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instances = Instances.concatenate((instances, instances2[[j]]), axis=0)
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cv2.drawContours(im_new, instances2.segments[[j]].astype(np.int32), -1, (1, 1, 1), cv2.FILLED)
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ioa = bbox_ioa(ins_flip.bboxes, instances.bboxes) # intersection over area, (N, M)
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indexes = np.nonzero((ioa < 0.30).all(1))[0] # (N, )
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n = len(indexes)
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for j in random.sample(list(indexes), k=round(self.p * n)):
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cls = np.concatenate((cls, cls[[j]]), axis=0)
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instances = Instances.concatenate((instances, ins_flip[[j]]), axis=0)
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cv2.drawContours(im_new, instances.segments[[j]].astype(np.int32), -1, (1, 1, 1), cv2.FILLED)
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result = labels2.get("img", cv2.flip(im, 1)) # augment segments
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i = im_new.astype(bool)
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im[i] = result[i]
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result = cv2.flip(im, 1) # augment segments (flip left-right)
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i = cv2.flip(im_new, 1).astype(bool)
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im[i] = result[i]
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labels["img"] = im
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labels["cls"] = cls
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labels["instances"] = instances
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return labels
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labels1["img"] = im
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labels1["cls"] = cls
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labels1["instances"] = instances
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return labels1
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class Albumentations:
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@ -2259,9 +2272,9 @@ class RandomLoadText:
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def v8_transforms(dataset, imgsz, hyp, stretch=False):
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"""
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Applies a series of image transformations for YOLOv8 training.
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Applies a series of image transformations for training.
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This function creates a composition of image augmentation techniques to prepare images for YOLOv8 training.
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This function creates a composition of image augmentation techniques to prepare images for YOLO training.
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It includes operations such as mosaic, copy-paste, random perspective, mixup, and various color adjustments.
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Args:
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@ -2280,20 +2293,28 @@ def v8_transforms(dataset, imgsz, hyp, stretch=False):
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>>> transforms = v8_transforms(dataset, imgsz=640, hyp=hyp)
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>>> augmented_data = transforms(dataset[0])
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"""
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pre_transform = Compose(
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[
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Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic),
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CopyPaste(p=hyp.copy_paste),
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RandomPerspective(
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degrees=hyp.degrees,
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translate=hyp.translate,
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scale=hyp.scale,
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shear=hyp.shear,
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perspective=hyp.perspective,
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pre_transform=None if stretch else LetterBox(new_shape=(imgsz, imgsz)),
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),
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]
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mosaic = Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic)
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affine = RandomPerspective(
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degrees=hyp.degrees,
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translate=hyp.translate,
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scale=hyp.scale,
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shear=hyp.shear,
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perspective=hyp.perspective,
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pre_transform=None if stretch else LetterBox(new_shape=(imgsz, imgsz)),
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)
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pre_transform = Compose([mosaic, affine])
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if hyp.copy_paste_mode == "flip":
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pre_transform.insert(1, CopyPaste(p=hyp.copy_paste, mode=hyp.copy_paste_mode))
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else:
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pre_transform.append(
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CopyPaste(
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dataset,
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pre_transform=Compose([Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic), affine]),
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p=hyp.copy_paste,
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mode=hyp.copy_paste_mode,
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)
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)
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flip_idx = dataset.data.get("flip_idx", []) # for keypoints augmentation
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if dataset.use_keypoints:
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kpt_shape = dataset.data.get("kpt_shape", None)
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