import collections
import math
import random
from copy import deepcopy
import cv2
import numpy as np
import torch
import torchvision.transforms as T
from ..utils import LOGGER, colorstr
from ..utils.checks import check_version
from ..utils.instance import Instances
from ..utils.metrics import bbox_ioa
from ..utils.ops import segment2box
from .utils import IMAGENET_MEAN, IMAGENET_STD, polygons2masks, polygons2masks_overlap
# TODO: we might need a BaseTransform to make all these augments be compatible with both classification and semantic
class BaseTransform:
def __init__(self) -> None:
pass
def apply_image(self, labels):
pass
def apply_instances(self, labels):
pass
def apply_semantic(self, labels):
pass
def __call__(self, labels):
self.apply_image(labels)
self.apply_instances(labels)
self.apply_semantic(labels)
class Compose:
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, data):
for t in self.transforms:
data = t(data)
return data
def append(self, transform):
self.transforms.append(transform)
def tolist(self):
return self.transforms
def __repr__(self):
format_string = f"{self.__class__.__name__}("
for t in self.transforms:
format_string += "\n"
format_string += f" {t}"
format_string += "\n)"
return format_string
class BaseMixTransform:
"""This implementation is from mmyolo"""
def __init__(self, pre_transform=None, p=0.0) -> None:
self.pre_transform = pre_transform
self.p = p
def __call__(self, labels):
if random.uniform(0, 1) > self.p:
return labels
assert "dataset" in labels
dataset = labels.pop("dataset")
# get index of one or three other images
indexes = self.get_indexes(dataset)
if not isinstance(indexes, collections.abc.Sequence):
indexes = [indexes]
# get images information will be used for Mosaic or MixUp
mix_labels = [deepcopy(dataset.get_label_info(index)) for index in indexes]
if self.pre_transform is not None:
for i, data in enumerate(mix_labels):
# pre_transform may also require dataset
data.update({"dataset": dataset})
# before Mosaic or MixUp need to go through
# the necessary pre_transform
_labels = self.pre_transform(data)
_labels.pop("dataset")
mix_labels[i] = _labels
labels["mix_labels"] = mix_labels
# Mosaic or MixUp
labels = self._mix_transform(labels)
if "mix_labels" in labels:
labels.pop("mix_labels")
labels["dataset"] = dataset
return labels
def _mix_transform(self, labels):
raise NotImplementedError
def get_indexes(self, dataset):
raise NotImplementedError
class Mosaic(BaseMixTransform):
"""Mosaic augmentation.
Args:
img_size (Sequence[int]): Image size after mosaic pipeline of single
image. The shape order should be (height, width).
Default to (640, 640).
"""
def __init__(self, img_size=640, p=1.0, border=(0, 0)):
assert 0 <= p <= 1.0, "The probability should be in range [0, 1]. " f"got {p}."
super().__init__(pre_transform=None, p=p)
self.img_size = img_size
self.border = border
def get_indexes(self, dataset):
return [random.randint(0, len(dataset) - 1) for _ in range(3)]
def _mix_transform(self, labels):
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.img_size
yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border) # mosaic center x, y
mix_labels = labels["mix_labels"]
for i in range(4):
labels_patch = deepcopy(labels) if i == 0 else deepcopy(mix_labels[i - 1])
# Load image
img = labels_patch["img"]
h, w = labels_patch["resized_shape"]
# place img in img4
if i == 0: # top left
img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)
elif i == 1: # top right
x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
elif i == 2: # bottom left
x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
elif i == 3: # bottom right
x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
padw = x1a - x1b
padh = y1a - y1b
labels_patch = self._update_labels(labels_patch, padw, padh)
mosaic_labels.append(labels_patch)
final_labels = self._cat_labels(mosaic_labels)
final_labels["img"] = img4
return final_labels
def _update_labels(self, labels, padw, padh):
"""Update labels"""
nh, nw = labels["img"].shape[:2]
labels["instances"].convert_bbox(format="xyxy")
labels["instances"].denormalize(nw, nh)
labels["instances"].add_padding(padw, padh)
return labels
def _cat_labels(self, mosaic_labels):
if len(mosaic_labels) == 0:
return {}
cls = []
instances = []
for labels in mosaic_labels:
cls.append(labels["cls"])
instances.append(labels["instances"])
final_labels = {
"ori_shape": mosaic_labels[0]["ori_shape"],
"resized_shape": (self.img_size * 2, self.img_size * 2),
"im_file": mosaic_labels[0]["im_file"],
"cls": np.concatenate(cls, 0)}
final_labels["instances"] = Instances.concatenate(instances, axis=0)
final_labels["instances"].clip(self.img_size * 2, self.img_size * 2)
return final_labels
class MixUp(BaseMixTransform):
def __init__(self, pre_transform=None, p=0.0) -> None:
super().__init__(pre_transform=pre_transform, p=p)
def get_indexes(self, dataset):
return random.randint(0, len(dataset) - 1)
def _mix_transform(self, labels):
im = labels["img"]
labels2 = labels["mix_labels"][0]
im2 = labels2["img"]
cls2 = labels2["cls"]
# 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)
cat_instances = Instances.concatenate([labels["instances"], labels2["instances"]], axis=0)
cls = labels["cls"]
labels["img"] = im
labels["instances"] = cat_instances
labels["cls"] = np.concatenate([cls, cls2], 0)
return labels
class RandomPerspective:
def __init__(self, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, border=(0, 0)):
self.degrees = degrees
self.translate = translate
self.scale = scale
self.shear = shear
self.perspective = perspective
# mosaic border
self.border = border
def affine_transform(self, img):
# Center
C = np.eye(3)
C[0, 2] = -img.shape[1] / 2 # x translation (pixels)
C[1, 2] = -img.shape[0] / 2 # y translation (pixels)
# Perspective
P = np.eye(3)
P[2, 0] = random.uniform(-self.perspective, self.perspective) # x perspective (about y)
P[2, 1] = random.uniform(-self.perspective, self.perspective) # y perspective (about x)
# Rotation and Scale
R = np.eye(3)
a = random.uniform(-self.degrees, self.degrees)
# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
s = random.uniform(1 - self.scale, 1 + self.scale)
# s = 2 ** random.uniform(-scale, scale)
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
# Shear
S = np.eye(3)
S[0, 1] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180) # x shear (deg)
S[1, 0] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180) # y shear (deg)
# Translation
T = np.eye(3)
T[0, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * self.size[0] # x translation (pixels)
T[1, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * self.size[1] # y translation (pixels)
# Combined rotation matrix
M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
# affine image
if (self.border[0] != 0) or (self.border[1] != 0) or (M != np.eye(3)).any(): # image changed
if self.perspective:
img = cv2.warpPerspective(img, M, dsize=self.size, borderValue=(114, 114, 114))
else: # affine
img = cv2.warpAffine(img, M[:2], dsize=self.size, borderValue=(114, 114, 114))
return img, M, s
def apply_bboxes(self, bboxes, M):
"""apply affine to bboxes only.
Args:
bboxes(ndarray): list of bboxes, xyxy format, with shape (num_bboxes, 4).
M(ndarray): affine matrix.
Returns:
new_bboxes(ndarray): bboxes after affine, [num_bboxes, 4].
"""
n = len(bboxes)
if n == 0:
return bboxes
xy = np.ones((n * 4, 3))
xy[:, :2] = bboxes[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
xy = xy @ M.T # transform
xy = (xy[:, :2] / xy[:, 2:3] if self.perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
# create new boxes
x = xy[:, [0, 2, 4, 6]]
y = xy[:, [1, 3, 5, 7]]
return np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
def apply_segments(self, segments, M):
"""apply affine to segments and generate new bboxes from segments.
Args:
segments(ndarray): list of segments, [num_samples, 500, 2].
M(ndarray): affine matrix.
Returns:
new_segments(ndarray): list of segments after affine, [num_samples, 500, 2].
new_bboxes(ndarray): bboxes after affine, [N, 4].
"""
n, num = segments.shape[:2]
if n == 0:
return [], segments
xy = np.ones((n * num, 3))
segments = segments.reshape(-1, 2)
xy[:, :2] = segments
xy = xy @ M.T # transform
xy = xy[:, :2] / xy[:, 2:3]
segments = xy.reshape(n, -1, 2)
bboxes = np.stack([segment2box(xy, self.size[0], self.size[1]) for xy in segments], 0)
return bboxes, segments
def apply_keypoints(self, keypoints, M):
"""apply affine to keypoints.
Args:
keypoints(ndarray): keypoints, [N, 17, 2].
M(ndarray): affine matrix.
Return:
new_keypoints(ndarray): keypoints after affine, [N, 17, 2].
"""
n = len(keypoints)
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)
def __call__(self, labels):
"""
Affine images and targets.
Args:
img(ndarray): image.
labels(Dict): a dict of `bboxes`, `segments`, `keypoints`.
"""
img = labels["img"]
cls = labels["cls"]
instances = labels.pop("instances")
# make sure the coord formats are right
instances.convert_bbox(format="xyxy")
instances.denormalize(*img.shape[:2][::-1])
self.size = img.shape[1] + self.border[1] * 2, img.shape[0] + self.border[0] * 2 # w, h
# M is affine matrix
# scale for func:`box_candidates`
img, M, scale = self.affine_transform(img)
bboxes = self.apply_bboxes(instances.bboxes, M)
segments = instances.segments
keypoints = instances.keypoints
# update bboxes if there are segments.
if len(segments):
bboxes, segments = self.apply_segments(segments, M)
if keypoints is not None:
keypoints = self.apply_keypoints(keypoints, M)
new_instances = Instances(bboxes, segments, keypoints, bbox_format="xyxy", normalized=False)
# clip
new_instances.clip(*self.size)
# filter instances
instances.scale(scale_w=scale, scale_h=scale, bbox_only=True)
# make the bboxes have the same scale with new_bboxes
i = self.box_candidates(box1=instances.bboxes.T,
box2=new_instances.bboxes.T,
area_thr=0.01 if len(segments) else 0.10)
labels["instances"] = new_instances[i]
labels["cls"] = cls[i]
labels["img"] = img
labels["resized_shape"] = img.shape[:2]
return labels
def box_candidates(self, box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
class RandomHSV:
def __init__(self, hgain=0.5, sgain=0.5, vgain=0.5) -> None:
self.hgain = hgain
self.sgain = sgain
self.vgain = vgain
def __call__(self, labels):
img = labels["img"]
if self.hgain or self.sgain or self.vgain:
r = np.random.uniform(-1, 1, 3) * [self.hgain, self.sgain, self.vgain] + 1 # random gains
hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
dtype = img.dtype # uint8
x = np.arange(0, 256, dtype=r.dtype)
lut_hue = ((x * r[0]) % 180).astype(dtype)
lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
labels["img"] = img
return labels
class RandomFlip:
def __init__(self, p=0.5, direction="horizontal") -> 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
def __call__(self, labels):
img = labels["img"]
instances = labels.pop("instances")
instances.convert_bbox(format="xywh")
h, w = img.shape[:2]
h = 1 if instances.normalized else h
w = 1 if instances.normalized else w
# Flip up-down
if self.direction == "vertical" and random.random() < self.p:
img = np.flipud(img)
instances.flipud(h)
if self.direction == "horizontal" and random.random() < self.p:
img = np.fliplr(img)
instances.fliplr(w)
labels["img"] = np.ascontiguousarray(img)
labels["instances"] = instances
return labels
class LetterBox:
"""Resize image and padding for detection, instance segmentation, pose"""
def __init__(self, new_shape=(640, 640), auto=False, scaleFill=False, scaleup=True, stride=32):
self.new_shape = new_shape
self.auto = auto
self.scaleFill = scaleFill
self.scaleup = scaleup
self.stride = stride
def __call__(self, labels={}, image=None):
img = image or labels["img"]
shape = img.shape[:2] # current shape [height, width]
new_shape = labels.pop("rect_shape", self.new_shape)
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not self.scaleup: # only scale down, do not scale up (for better val mAP)
r = min(r, 1.0)
# Compute padding
ratio = r, r # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if self.auto: # minimum rectangle
dw, dh = np.mod(dw, self.stride), np.mod(dh, self.stride) # wh padding
elif self.scaleFill: # stretch
dw, dh = 0.0, 0.0
new_unpad = (new_shape[1], new_shape[0])
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
dw /= 2 # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT,
value=(114, 114, 114)) # add border
labels = self._update_labels(labels, ratio, dw, dh)
labels["img"] = img
labels["resized_shape"] = new_shape
return labels
def _update_labels(self, labels, ratio, padw, padh):
"""Update labels"""
labels["instances"].convert_bbox(format="xyxy")
labels["instances"].denormalize(*labels["img"].shape[:2][::-1])
labels["instances"].scale(*ratio)
labels["instances"].add_padding(padw, padh)
return labels
class CopyPaste:
def __init__(self, p=0.5) -> None:
self.p = p
def __call__(self, labels):
# Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
im = labels["img"]
cls = labels["cls"]
instances = labels.pop("instances")
instances.convert_bbox(format="xyxy")
if self.p and len(instances.segments):
n = len(instances)
h, w, _ = im.shape # height, width, channels
im_new = np.zeros(im.shape, np.uint8)
j = random.sample(range(n), k=round(self.p * n))
c, instance = cls[j], instances[j]
instance.fliplr(w)
ioa = bbox_ioa(instance.bboxes, instances.bboxes) # intersection over area, (N, M)
i = (ioa < 0.30).all(1) # (N, )
if i.sum():
cls = np.concatenate((cls, c[i]), axis=0)
instances = Instances.concatenate((instances, instance[i]), axis=0)
cv2.drawContours(im_new, instances.segments[j][i].astype(np.int32), -1, (255, 255, 255), cv2.FILLED)
result = cv2.bitwise_and(src1=im, src2=im_new)
result = cv2.flip(result, 1) # augment segments (flip left-right)
i = result > 0 # pixels to replace
# i[:, :] = result.max(2).reshape(h, w, 1) # act over ch
im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug
labels["img"] = im
labels["cls"] = cls
labels["instances"] = instances
return labels
class Albumentations:
# YOLOv5 Albumentations class (optional, only used if package is installed)
def __init__(self, p=1.0):
self.p = p
self.transform = None
prefix = colorstr("albumentations: ")
try:
import albumentations as A
check_version(A.__version__, "1.0.3", hard=True) # version requirement
T = [
A.Blur(p=0.01),
A.MedianBlur(p=0.01),
A.ToGray(p=0.01),
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"]))
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}")
def __call__(self, labels):
im = labels["img"]
cls = labels["cls"]
if len(cls):
labels["instances"].convert_bbox("xywh")
labels["instances"].normalize(*im.shape[:2][::-1])
bboxes = labels["instances"].bboxes
# TODO: add supports of segments and keypoints
if self.transform and random.random() < self.p:
new = self.transform(image=im, bboxes=bboxes, class_labels=cls) # transformed
labels["img"] = new["image"]
labels["cls"] = np.array(new["class_labels"])
labels["instances"].update(bboxes=bboxes)
return labels
# TODO: technically this is not an augmentation, maybe we should put this to another files
class Format:
def __init__(self,
bbox_format="xywh",
normalize=True,
return_mask=False,
return_keypoint=False,
mask_ratio=4,
mask_overlap=True,
batch_idx=True):
self.bbox_format = bbox_format
self.normalize = normalize
self.return_mask = return_mask # set False when training detection only
self.return_keypoint = return_keypoint
self.mask_ratio = mask_ratio
self.mask_overlap = mask_overlap
self.batch_idx = batch_idx # keep the batch indexes
def __call__(self, labels):
img = labels["img"]
h, w = img.shape[:2]
cls = labels.pop("cls")
instances = labels.pop("instances")
instances.convert_bbox(format=self.bbox_format)
instances.denormalize(w, h)
nl = len(instances)
if self.return_mask:
if nl:
masks, instances, cls = self._format_segments(instances, cls, w, h)
masks = torch.from_numpy(masks)
else:
masks = torch.zeros(1 if self.mask_overlap else nl, img.shape[0] // self.mask_ratio,
img.shape[1] // self.mask_ratio)
labels["masks"] = masks
if self.normalize:
instances.normalize(w, h)
labels["img"] = self._format_img(img)
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))
# then we can use collate_fn
if self.batch_idx:
labels["batch_idx"] = torch.zeros(nl)
return labels
def _format_img(self, img):
if len(img.shape) < 3:
img = np.expand_dims(img, -1)
img = np.ascontiguousarray(img.transpose(2, 0, 1))
img = torch.from_numpy(img)
return img
def _format_segments(self, instances, cls, w, h):
"""convert polygon points to bitmap"""
segments = instances.segments
if self.mask_overlap:
masks, sorted_idx = polygons2masks_overlap((h, w), segments, downsample_ratio=self.mask_ratio)
masks = masks[None] # (640, 640) -> (1, 640, 640)
instances = instances[sorted_idx]
cls = cls[sorted_idx]
else:
masks = polygons2masks((h, w), segments, color=1, downsample_ratio=self.mask_ratio)
return masks, instances, cls
def mosaic_transforms(img_size, hyp):
pre_transform = Compose([
Mosaic(img_size=img_size, p=hyp.mosaic, border=[-img_size // 2, -img_size // 2]),
CopyPaste(p=hyp.copy_paste),
RandomPerspective(
degrees=hyp.degrees,
translate=hyp.translate,
scale=hyp.scale,
shear=hyp.shear,
perspective=hyp.perspective,
border=[-img_size // 2, -img_size // 2],
),])
return Compose([
pre_transform,
MixUp(
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
def affine_transforms(img_size, hyp):
return Compose([
LetterBox(new_shape=(img_size, img_size)),
RandomPerspective(
degrees=hyp.degrees,
translate=hyp.translate,
scale=hyp.scale,
shear=hyp.shear,
perspective=hyp.perspective,
border=[0, 0],
),
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
# Classification augmentations -----------------------------------------------------------------------------------------
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)"
# 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)])
def classify_albumentations(
augment=True,
size=224,
scale=(0.08, 1.0),
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: ")
try:
import albumentations as A
from albumentations.pytorch import ToTensorV2
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)]
if auto_aug:
# TODO: implement AugMix, AutoAug & RandAug in albumentation
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, saturation, 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))
return A.Compose(T)
except ImportError: # package not installed, skip
pass
except Exception as e:
LOGGER.info(f"{prefix}{e}")
class ClassifyLetterBox:
# YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
def __init__(self, size=(640, 640), auto=False, stride=32):
super().__init__()
self.h, self.w = (size, size) if isinstance(size, int) else size
self.auto = auto # pass max size integer, automatically solve for short side using stride
self.stride = stride # used with auto
def __call__(self, im): # im = np.array HWC
imh, imw = im.shape[:2]
r = min(self.h / imh, self.w / imw) # ratio of new/old
h, w = round(imh * r), round(imw * r) # resized image
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)
return im_out
class CenterCrop:
# YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])
def __init__(self, size=640):
super().__init__()
self.h, self.w = (size, size) if isinstance(size, int) else size
def __call__(self, im): # im = np.array HWC
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)
class ToTensor:
# YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
def __init__(self, half=False):
super().__init__()
self.half = half
def __call__(self, im): # im = np.array HWC in BGR order
im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous
im = torch.from_numpy(im) # to torch
im = im.half() if self.half else im.float() # uint8 to fp16/32
im /= 255.0 # 0-255 to 0.0-1.0
return im