Ankle/yolov5_birdproject
1
0 Fork 0
Code Issues Pull Requests Actions 6 Packages Projects Releases Wiki Activity

Update Actions with Lychee and GitHub Token (#12592)

Browse Source 
* Add Ultralytics Actions

* Auto-format by Ultralytics actions

* Update format.yml

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Paula DerrengerUltralyticsAssistantGlenn Jocher
 authored and GitHub committed 2024-01-08 01:29:14 +01:00
1 parent f56355d321
commit ad97dc6a6d
47 files changed
+4217 -3462
Download Patch File Download Diff File Expand all files Collapse all files

No files matched your search

utils/augmentations.py
+36 -40
View File
@@ -1,7 +1,5 @@
# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
Image augmentation functions
"""
"""Image augmentation functions."""
import math
import random
@@ -23,10 +21,11 @@ class Albumentations:
# YOLOv5 Albumentations class (optional, only used if package is installed)
def __init__(self, size=640):
self.transform = None
prefix = colorstr('albumentations: ')
prefix = colorstr("albumentations: ")
try:
import albumentations as A
check_version(A.__version__, '1.0.3', hard=True) # version requirement
check_version(A.__version__, "1.0.3", hard=True) # version requirement
T = [
A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0),
@@ -36,19 +35,20 @@ class Albumentations:
A.CLAHE(p=0.01),
A.RandomBrightnessContrast(p=0.0),
A.RandomGamma(p=0.0),
A.ImageCompression(quality_lower=75, p=0.0)] # transforms
self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
A.ImageCompression(quality_lower=75, p=0.0),
] # transforms
self.transform = A.Compose(T, bbox_params=A.BboxParams(format="yolo", label_fields=["class_labels"]))
LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p))
except ImportError: # package not installed, skip
pass
except Exception as e:
LOGGER.info(f'{prefix}{e}')
LOGGER.info(f"{prefix}{e}")
def __call__(self, im, labels, p=1.0):
if self.transform and random.random() < p:
new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed
im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
im, labels = new["image"], np.array([[c, *b] for c, b in zip(new["class_labels"], new["bboxes"])])
return im, labels
@@ -97,7 +97,7 @@ def replicate(im, labels):
boxes = labels[:, 1:].astype(int)
x1, y1, x2, y2 = boxes.T
s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels)
for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices
for i in s.argsort()[: round(s.size * 0.5)]: # smallest indices
x1b, y1b, x2b, y2b = boxes[i]
bh, bw = y2b - y1b, x2b - x1b
yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y
@@ -141,15 +141,9 @@ def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleF
return im, ratio, (dw, dh)
def random_perspective(im,
targets=(),
segments=(),
degrees=10,
translate=.1,
scale=.1,
shear=10,
perspective=0.0,
border=(0, 0)):
def random_perspective(
im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0)
):
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
# targets = [cls, xyxy]
@@ -303,50 +297,52 @@ def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):
def classify_albumentations(
augment=True,
size=224,
scale=(0.08, 1.0),
ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33
hflip=0.5,
vflip=0.0,
jitter=0.4,
mean=IMAGENET_MEAN,
std=IMAGENET_STD,
auto_aug=False):
augment=True,
size=224,
scale=(0.08, 1.0),
ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33
hflip=0.5,
vflip=0.0,
jitter=0.4,
mean=IMAGENET_MEAN,
std=IMAGENET_STD,
auto_aug=False,
):
# YOLOv5 classification Albumentations (optional, only used if package is installed)
prefix = colorstr('albumentations: ')
prefix = colorstr("albumentations: ")
try:
import albumentations as A
from albumentations.pytorch import ToTensorV2
check_version(A.__version__, '1.0.3', hard=True) # version requirement
check_version(A.__version__, "1.0.3", hard=True) # version requirement
if augment: # Resize and crop
T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)]
if auto_aug:
# TODO: implement AugMix, AutoAug & RandAug in albumentation
LOGGER.info(f'{prefix}auto augmentations are currently not supported')
LOGGER.info(f"{prefix}auto augmentations are currently not supported")
else:
if hflip > 0:
T += [A.HorizontalFlip(p=hflip)]
if vflip > 0:
T += [A.VerticalFlip(p=vflip)]
if jitter > 0:
color_jitter = (float(jitter), ) * 3 # repeat value for brightness, contrast, satuaration, 0 hue
color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue
T += [A.ColorJitter(*color_jitter, 0)]
else: # Use fixed crop for eval set (reproducibility)
T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor
LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p))
return A.Compose(T)
except ImportError: # package not installed, skip
LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)')
LOGGER.warning(f"{prefix}⚠️ not found, install with `pip install albumentations` (recommended)")
except Exception as e:
LOGGER.info(f'{prefix}{e}')
LOGGER.info(f"{prefix}{e}")
def classify_transforms(size=224):
# Transforms to apply if albumentations not installed
assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)'
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)])
@@ -366,7 +362,7 @@ class LetterBox:
hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w
top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1)
im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype)
im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
im_out[top : top + h, left : left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
return im_out
@@ -380,7 +376,7 @@ class CenterCrop:
imh, imw = im.shape[:2]
m = min(imh, imw) # min dimension
top, left = (imh - m) // 2, (imw - m) // 2
return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)
return cv2.resize(im[top : top + m, left : left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)
class ToTensor: