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ultralytics 8.0.80 single-line docstring fixes (#2060)

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Glenn Jocher 2023-04-16 15:20:11 +02:00 • committed by GitHub
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34
ultralytics/yolo/data/dataloaders/v5augmentations.py
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@ -55,19 +55,19 @@ class Albumentations:
def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):
# Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std
"""Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std."""
return TF.normalize(x, mean, std, inplace=inplace)
def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):
# Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean
"""Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean."""
for i in range(3):
x[:, i] = x[:, i] * std[i] + mean[i]
return x
def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
# HSV color-space augmentation
"""HSV color-space augmentation."""
if hgain or sgain or vgain:
r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains
hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
@ -83,7 +83,7 @@ def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
def hist_equalize(im, clahe=True, bgr=False):
# Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255
"""Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255."""
yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
if clahe:
c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
@ -94,7 +94,7 @@ def hist_equalize(im, clahe=True, bgr=False):
def replicate(im, labels):
# Replicate labels
"""Replicate labels."""
h, w = im.shape[:2]
boxes = labels[:, 1:].astype(int)
x1, y1, x2, y2 = boxes.T
@ -213,7 +213,7 @@ def random_perspective(im,
xy = xy @ M.T # transform
xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine
# clip
# Clip
new[i] = segment2box(xy, width, height)
else: # warp boxes
@ -222,16 +222,16 @@ def random_perspective(im,
xy = xy @ M.T # transform
xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
# create new boxes
# Create new boxes
x = xy[:, [0, 2, 4, 6]]
y = xy[:, [1, 3, 5, 7]]
new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
# clip
# Clip
new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
# filter candidates
# Filter candidates
i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
targets = targets[i]
targets[:, 1:5] = new[i]
@ -240,13 +240,13 @@ def random_perspective(im,
def copy_paste(im, labels, segments, p=0.5):
# Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
"""Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)."""
n = len(segments)
if p and n:
h, w, c = im.shape # height, width, channels
im_new = np.zeros(im.shape, np.uint8)
# calculate ioa first then select indexes randomly
# Calculate ioa first then select indexes randomly
boxes = np.stack([w - labels[:, 3], labels[:, 2], w - labels[:, 1], labels[:, 4]], axis=-1) # (n, 4)
ioa = bbox_ioa(boxes, labels[:, 1:5]) # intersection over area
indexes = np.nonzero((ioa < 0.30).all(1))[0] # (N, )
@ -265,7 +265,7 @@ def copy_paste(im, labels, segments, p=0.5):
def cutout(im, labels, p=0.5):
# Applies image cutout augmentation https://arxiv.org/abs/1708.04552
"""Applies image cutout augmentation https://arxiv.org/abs/1708.04552."""
if random.random() < p:
h, w = im.shape[:2]
scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction
@ -273,16 +273,16 @@ def cutout(im, labels, p=0.5):
mask_h = random.randint(1, int(h * s)) # create random masks
mask_w = random.randint(1, int(w * s))
# box
# Box
xmin = max(0, random.randint(0, w) - mask_w // 2)
ymin = max(0, random.randint(0, h) - mask_h // 2)
xmax = min(w, xmin + mask_w)
ymax = min(h, ymin + mask_h)
# apply random color mask
# Apply random color mask
im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
# return unobscured labels
# Return unobscured labels
if len(labels) and s > 0.03:
box = np.array([[xmin, ymin, xmax, ymax]], dtype=np.float32)
ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h))[0] # intersection over area
@ -292,7 +292,7 @@ def cutout(im, labels, p=0.5):
def mixup(im, labels, im2, labels2):
# Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
"""Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf."""
r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
im = (im * r + im2 * (1 - r)).astype(np.uint8)
labels = np.concatenate((labels, labels2), 0)
@ -350,7 +350,7 @@ def classify_albumentations(
def classify_transforms(size=224):
# Transforms to apply if albumentations not installed
"""Transforms to apply if albumentations not installed."""
assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)'
# T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])