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ultralytics 8.0.65 YOLOv8 Pose models (#1347)

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Ayush Chaurasia 2023-04-06 03:55:32 +05:30 • committed by GitHub
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43
ultralytics/yolo/data/augment.py
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@ -16,6 +16,8 @@ from ..utils.metrics import bbox_ioa
from ..utils.ops import segment2box
from .utils import polygons2masks, polygons2masks_overlap
POSE_FLIPLR_INDEX = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
# TODO: we might need a BaseTransform to make all these augments be compatible with both classification and semantic
class BaseTransform:
@ -309,27 +311,22 @@ class RandomPerspective:
"""apply affine to keypoints.
Args:
keypoints(ndarray): keypoints, [N, 17, 2].
keypoints(ndarray): keypoints, [N, 17, 3].
M(ndarray): affine matrix.
Return:
new_keypoints(ndarray): keypoints after affine, [N, 17, 2].
new_keypoints(ndarray): keypoints after affine, [N, 17, 3].
"""
n = len(keypoints)
n, nkpt = keypoints.shape[:2]
if n == 0:
return keypoints
new_keypoints = np.ones((n * 17, 3))
new_keypoints[:, :2] = keypoints.reshape(n * 17, 2) # num_kpt is hardcoded to 17
new_keypoints = new_keypoints @ M.T # transform
new_keypoints = (new_keypoints[:, :2] / new_keypoints[:, 2:3]).reshape(n, 34) # perspective rescale or affine
new_keypoints[keypoints.reshape(-1, 34) == 0] = 0
x_kpts = new_keypoints[:, list(range(0, 34, 2))]
y_kpts = new_keypoints[:, list(range(1, 34, 2))]
x_kpts[np.logical_or.reduce((x_kpts < 0, x_kpts > self.size[0], y_kpts < 0, y_kpts > self.size[1]))] = 0
y_kpts[np.logical_or.reduce((x_kpts < 0, x_kpts > self.size[0], y_kpts < 0, y_kpts > self.size[1]))] = 0
new_keypoints[:, list(range(0, 34, 2))] = x_kpts
new_keypoints[:, list(range(1, 34, 2))] = y_kpts
return new_keypoints.reshape(n, 17, 2)
xy = np.ones((n * nkpt, 3))
visible = keypoints[..., 2].reshape(n * nkpt, 1)
xy[:, :2] = keypoints[..., :2].reshape(n * nkpt, 2)
xy = xy @ M.T # transform
xy = xy[:, :2] / xy[:, 2:3] # perspective rescale or affine
out_mask = (xy[:, 0] < 0) | (xy[:, 1] < 0) | (xy[:, 0] > self.size[0]) | (xy[:, 1] > self.size[1])
visible[out_mask] = 0
return np.concatenate([xy, visible], axis=-1).reshape(n, nkpt, 3)
def __call__(self, labels):
"""
@ -415,12 +412,13 @@ class RandomHSV:
class RandomFlip:
def __init__(self, p=0.5, direction='horizontal') -> None:
def __init__(self, p=0.5, direction='horizontal', flip_idx=None) -> None:
assert direction in ['horizontal', 'vertical'], f'Support direction `horizontal` or `vertical`, got {direction}'
assert 0 <= p <= 1.0
self.p = p
self.direction = direction
self.flip_idx = flip_idx
def __call__(self, labels):
img = labels['img']
@ -437,6 +435,9 @@ class RandomFlip:
if self.direction == 'horizontal' and random.random() < self.p:
img = np.fliplr(img)
instances.fliplr(w)
# for keypoints
if self.flip_idx is not None and instances.keypoints is not None:
instances.keypoints = np.ascontiguousarray(instances.keypoints[:, self.flip_idx, :])
labels['img'] = np.ascontiguousarray(img)
labels['instances'] = instances
return labels
@ -633,7 +634,7 @@ class Format:
labels['cls'] = torch.from_numpy(cls) if nl else torch.zeros(nl)
labels['bboxes'] = torch.from_numpy(instances.bboxes) if nl else torch.zeros((nl, 4))
if self.return_keypoint:
labels['keypoints'] = torch.from_numpy(instances.keypoints) if nl else torch.zeros((nl, 17, 2))
labels['keypoints'] = torch.from_numpy(instances.keypoints)
# then we can use collate_fn
if self.batch_idx:
labels['batch_idx'] = torch.zeros(nl)
@ -672,13 +673,17 @@ def v8_transforms(dataset, imgsz, hyp):
perspective=hyp.perspective,
pre_transform=LetterBox(new_shape=(imgsz, imgsz)),
)])
flip_idx = dataset.data.get('flip_idx', None) # for keypoints augmentation
if dataset.use_keypoints and flip_idx is None and hyp.fliplr > 0.0:
hyp.fliplr = 0.0
LOGGER.warning("WARNING ⚠️ No `flip_idx` provided while training keypoints, setting augmentation 'fliplr=0.0'")
return Compose([
pre_transform,
MixUp(dataset, pre_transform=pre_transform, p=hyp.mixup),
Albumentations(p=1.0),
RandomHSV(hgain=hyp.hsv_h, sgain=hyp.hsv_s, vgain=hyp.hsv_v),
RandomFlip(direction='vertical', p=hyp.flipud),
RandomFlip(direction='horizontal', p=hyp.fliplr)]) # transforms
RandomFlip(direction='horizontal', p=hyp.fliplr, flip_idx=flip_idx)]) # transforms
# Classification augmentations -----------------------------------------------------------------------------------------