YOLOv8-16bit/ultralytics/yolo/data/augment.py

# Ultralytics YOLO 🚀, GPL-3.0 license

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, dataset, pre_transform=None, p=0.0) -> None:
        self.dataset = dataset
        self.pre_transform = pre_transform
        self.p = p

    def __call__(self, labels):
        if random.uniform(0, 1) > self.p:
            return labels

        # get index of one or three other images
        indexes = self.get_indexes()
        if isinstance(indexes, int):
            indexes = [indexes]

        # get images information will be used for Mosaic or MixUp
        mix_labels = [self.dataset.get_label_info(i) for i in indexes]

        if self.pre_transform is not None:
            for i, data in enumerate(mix_labels):
                mix_labels[i] = self.pre_transform(data)
        labels["mix_labels"] = mix_labels

        # Mosaic or MixUp
        labels = self._mix_transform(labels)
        labels.pop("mix_labels", None)
        return labels

    def _mix_transform(self, labels):
        raise NotImplementedError

    def get_indexes(self):
        raise NotImplementedError


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).
    """

    def __init__(self, dataset, imgsz=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__(dataset=dataset, p=p)
        self.dataset = dataset
        self.imgsz = imgsz
        self.border = border

    def get_indexes(self):
        return [random.randint(0, len(self.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.imgsz
        yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border)  # mosaic center x, y
        for i in range(4):
            labels_patch = (labels if i == 0 else labels["mix_labels"][i - 1]).copy()
            # Load image
            img = labels_patch["img"]
            h, w = labels_patch.pop("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 = {
            "im_file": mosaic_labels[0]["im_file"],
            "ori_shape": mosaic_labels[0]["ori_shape"],
            "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)
        return final_labels


class MixUp(BaseMixTransform):

    def __init__(self, dataset, pre_transform=None, p=0.0) -> None:
        super().__init__(dataset=dataset, pre_transform=pre_transform, p=p)

    def get_indexes(self):
        return random.randint(0, len(self.dataset) - 1)

    def _mix_transform(self, labels):
        # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
        r = np.random.beta(32.0, 32.0)  # mixup ratio, alpha=beta=32.0
        labels2 = labels["mix_labels"][0]
        labels["img"] = (labels["img"] * r + labels2["img"] * (1 - r)).astype(np.uint8)
        labels["instances"] = Instances.concatenate([labels["instances"], labels2["instances"]], axis=0)
        labels["cls"] = np.concatenate([labels["cls"], labels2["cls"]], 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),
                 pre_transform=None):
        self.degrees = degrees
        self.translate = translate
        self.scale = scale
        self.shear = shear
        self.perspective = perspective
        # mosaic border
        self.border = border
        self.pre_transform = pre_transform

    def affine_transform(self, img, border):
        # 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 (border[0] != 0) or (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:
            labels(Dict): a dict of `bboxes`, `segments`, `keypoints`.
        """
        if self.pre_transform and "mosaic_border" not in labels:
            labels = self.pre_transform(labels)
            labels.pop("ratio_pad")  # do not need ratio pad

        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])

        border = labels.pop("mosaic_border", self.border)
        self.size = img.shape[1] + border[1] * 2, img.shape[0] + border[0] * 2  # w, h
        # M is affine matrix
        # scale for func:`box_candidates`
        img, M, scale = self.affine_transform(img, border)

        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 box candidates: 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
        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=None, image=None):
        if labels is None:
            labels = {}
        img = labels.get("img") if image is None else image
        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 labels.get("ratio_pad"):
            labels["ratio_pad"] = (labels["ratio_pad"], (dw, dh))  # for evaluation

        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

        if len(labels):
            labels = self._update_labels(labels, ratio, dw, dh)
            labels["img"] = img
            labels["resized_shape"] = new_shape
            return labels
        else:
            return img

    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"]
        h, w = im.shape[:2]
        instances = labels.pop("instances")
        instances.convert_bbox(format="xyxy")
        instances.denormalize(w, h)
        if self.p and len(instances.segments):
            n = len(instances)
            _, w, _ = im.shape  # height, width, channels
            im_new = np.zeros(im.shape, np.uint8)

            # calculate ioa first then select indexes randomly
            ins_flip = deepcopy(instances)
            ins_flip.fliplr(w)

            ioa = bbox_ioa(ins_flip.bboxes, instances.bboxes)  # intersection over area, (N, M)
            indexes = np.nonzero((ioa < 0.30).all(1))[0]  # (N, )
            n = len(indexes)
            for j in random.sample(list(indexes), k=round(self.p * n)):
                cls = np.concatenate((cls, cls[[j]]), axis=0)
                instances = Instances.concatenate((instances, ins_flip[[j]]), axis=0)
                cv2.drawContours(im_new, instances.segments[[j]].astype(np.int32), -1, (1, 1, 1), cv2.FILLED)

            result = cv2.flip(im, 1)  # augment segments (flip left-right)
            i = cv2.flip(im_new, 1).astype(bool)
            im[i] = result[i]  # cv2.imwrite('debug.jpg', im)  # debug

        labels["img"] = im
        labels["cls"] = cls
        labels["instances"] = instances
        return labels


class Albumentations:
    # YOLOv8 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"])
                bboxes = np.array(new["bboxes"])
            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.pop("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)[::-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 v8_transforms(dataset, imgsz, hyp):
    pre_transform = Compose([
        Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic, border=[-imgsz // 2, -imgsz // 2]),
        CopyPaste(p=hyp.copy_paste),
        RandomPerspective(
            degrees=hyp.degrees,
            translate=hyp.translate,
            scale=hyp.scale,
            shear=hyp.shear,
            perspective=hyp.perspective,
            pre_transform=LetterBox(new_shape=(imgsz, imgsz)),
        ),])
    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


# Classification augmentations -----------------------------------------------------------------------------------------
def classify_transforms(size=224):
    # Transforms to apply if albumentations not installed
    if not isinstance(size, int):
        raise TypeError(f"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,
):
    # YOLOv8 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:
    # YOLOv8 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:
    # YOLOv8 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:
    # YOLOv8 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