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ultralytics 8.0.100 add Mosaic9() augmentation (#2605)

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Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Tommy in Tongji <36354458+TommyZihao@users.noreply.github.com>
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Co-authored-by: xbkaishui <xxkaishui@gmail.com>
This commit is contained in:
Glenn Jocher
2023-05-14 20:43:35 +02:00
committed by GitHub
parent db1c5885d5
commit dce4efce48
23 changed files with 351 additions and 64 deletions
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92
ultralytics/yolo/data/augment.py
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@ -115,30 +115,42 @@ class BaseMixTransform:
class Mosaic(BaseMixTransform):
"""Mosaic augmentation.
Args:
imgsz (Sequence[int]): Image size after mosaic pipeline of single
image. The shape order should be (height, width).
Default to (640, 640).
"""
Mosaic augmentation.
This class performs mosaic augmentation by combining multiple (4 or 9) images into a single mosaic image.
The augmentation is applied to a dataset with a given probability.
Attributes:
dataset: The dataset on which the mosaic augmentation is applied.
imgsz (int, optional): Image size (height and width) after mosaic pipeline of a single image. Default to 640.
p (float, optional): Probability of applying the mosaic augmentation. Must be in the range 0-1. Default to 1.0.
n (int, optional): The grid size, either 4 (for 2x2) or 9 (for 3x3).
"""
def __init__(self, dataset, imgsz=640, p=1.0, border=(0, 0)):
def __init__(self, dataset, imgsz=640, p=1.0, n=9):
"""Initializes the object with a dataset, image size, probability, and border."""
assert 0 <= p <= 1.0, 'The probability should be in range [0, 1]. ' f'got {p}.'
assert 0 <= p <= 1.0, f'The probability should be in range [0, 1], but got {p}.'
assert n in (4, 9), 'grid must be equal to 4 or 9.'
super().__init__(dataset=dataset, p=p)
self.dataset = dataset
self.imgsz = imgsz
self.border = border
self.border = [-imgsz // 2, -imgsz // 2] if n == 4 else [-imgsz, -imgsz]
self.n = n
def get_indexes(self):
"""Return a list of 3 random indexes from the dataset."""
return [random.randint(0, len(self.dataset) - 1) for _ in range(3)]
"""Return a list of random indexes from the dataset."""
return [random.randint(0, len(self.dataset) - 1) for _ in range(self.n - 1)]
def _mix_transform(self, labels):
"""Apply mixup transformation to the input image and labels."""
assert labels.get('rect_shape', None) is None, 'rect and mosaic are mutually exclusive.'
assert len(labels.get('mix_labels', [])), 'There are no other images for mosaic augment.'
return self._mosaic4(labels) if self.n == 4 else self._mosaic9(labels)
def _mosaic4(self, labels):
"""Create a 2x2 image mosaic."""
mosaic_labels = []
assert labels.get('rect_shape', None) is None, 'rect and mosaic is exclusive.'
assert len(labels.get('mix_labels', [])) > 0, 'There are no other images for mosaic augment.'
s = self.imgsz
yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border) # mosaic center x, y
for i in range(4):
@ -172,7 +184,54 @@ class Mosaic(BaseMixTransform):
final_labels['img'] = img4
return final_labels
def _update_labels(self, labels, padw, padh):
def _mosaic9(self, labels):
"""Create a 3x3 image mosaic."""
mosaic_labels = []
s = self.imgsz
hp, wp = -1, -1 # height, width previous
for i in range(9):
labels_patch = labels if i == 0 else labels['mix_labels'][i - 1]
# Load image
img = labels_patch['img']
h, w = labels_patch.pop('resized_shape')
# Place img in img9
if i == 0: # center
img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
h0, w0 = h, w
c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates
elif i == 1: # top
c = s, s - h, s + w, s
elif i == 2: # top right
c = s + wp, s - h, s + wp + w, s
elif i == 3: # right
c = s + w0, s, s + w0 + w, s + h
elif i == 4: # bottom right
c = s + w0, s + hp, s + w0 + w, s + hp + h
elif i == 5: # bottom
c = s + w0 - w, s + h0, s + w0, s + h0 + h
elif i == 6: # bottom left
c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
elif i == 7: # left
c = s - w, s + h0 - h, s, s + h0
elif i == 8: # top left
c = s - w, s + h0 - hp - h, s, s + h0 - hp
padw, padh = c[:2]
x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords
# Image
img9[y1:y2, x1:x2] = img[y1 - padh:, x1 - padw:] # img9[ymin:ymax, xmin:xmax]
hp, wp = h, w # height, width previous for next iteration
labels_patch = self._update_labels(labels_patch, padw, padh)
mosaic_labels.append(labels_patch)
final_labels = self._cat_labels(mosaic_labels)
final_labels['img'] = img9
return final_labels
@staticmethod
def _update_labels(labels, padw, padh):
"""Update labels."""
nh, nw = labels['img'].shape[:2]
labels['instances'].convert_bbox(format='xyxy')
@ -195,8 +254,9 @@ class Mosaic(BaseMixTransform):
'resized_shape': (self.imgsz * 2, self.imgsz * 2),
'cls': np.concatenate(cls, 0),
'instances': Instances.concatenate(instances, axis=0),
'mosaic_border': self.border}
final_labels['instances'].clip(self.imgsz * 2, self.imgsz * 2)
'mosaic_border': self.border} # final_labels
clip_size = self.imgsz * (2 if self.n == 4 else 3)
final_labels['instances'].clip(clip_size, clip_size)
return final_labels
@ -695,7 +755,7 @@ class Format:
def v8_transforms(dataset, imgsz, hyp):
"""Convert images to a size suitable for YOLOv8 training."""
pre_transform = Compose([
Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic, border=[-imgsz // 2, -imgsz // 2]),
Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic),
CopyPaste(p=hyp.copy_paste),
RandomPerspective(
degrees=hyp.degrees,