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779 lines
30 KiB
779 lines
30 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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import math
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import random
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from copy import deepcopy
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import cv2
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import numpy as np
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import torch
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import torchvision.transforms as T
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from ..utils import LOGGER, colorstr
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from ..utils.checks import check_version
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from ..utils.instance import Instances
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from ..utils.metrics import bbox_ioa
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from ..utils.ops import segment2box
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from .utils import IMAGENET_MEAN, IMAGENET_STD, polygons2masks, polygons2masks_overlap
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# TODO: we might need a BaseTransform to make all these augments be compatible with both classification and semantic
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class BaseTransform:
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def __init__(self) -> None:
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pass
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def apply_image(self, labels):
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pass
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def apply_instances(self, labels):
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pass
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def apply_semantic(self, labels):
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pass
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def __call__(self, labels):
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self.apply_image(labels)
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self.apply_instances(labels)
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self.apply_semantic(labels)
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class Compose:
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def __init__(self, transforms):
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self.transforms = transforms
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def __call__(self, data):
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for t in self.transforms:
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data = t(data)
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return data
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def append(self, transform):
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self.transforms.append(transform)
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def tolist(self):
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return self.transforms
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def __repr__(self):
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format_string = f'{self.__class__.__name__}('
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for t in self.transforms:
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format_string += '\n'
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format_string += f' {t}'
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format_string += '\n)'
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return format_string
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class BaseMixTransform:
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"""This implementation is from mmyolo"""
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def __init__(self, dataset, pre_transform=None, p=0.0) -> None:
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self.dataset = dataset
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self.pre_transform = pre_transform
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self.p = p
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def __call__(self, labels):
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if random.uniform(0, 1) > self.p:
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return labels
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# get index of one or three other images
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indexes = self.get_indexes()
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if isinstance(indexes, int):
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indexes = [indexes]
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# get images information will be used for Mosaic or MixUp
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mix_labels = [self.dataset.get_label_info(i) for i in indexes]
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if self.pre_transform is not None:
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for i, data in enumerate(mix_labels):
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mix_labels[i] = self.pre_transform(data)
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labels['mix_labels'] = mix_labels
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# Mosaic or MixUp
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labels = self._mix_transform(labels)
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labels.pop('mix_labels', None)
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return labels
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def _mix_transform(self, labels):
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raise NotImplementedError
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def get_indexes(self):
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raise NotImplementedError
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class Mosaic(BaseMixTransform):
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"""Mosaic augmentation.
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Args:
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imgsz (Sequence[int]): Image size after mosaic pipeline of single
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image. The shape order should be (height, width).
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Default to (640, 640).
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"""
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def __init__(self, dataset, imgsz=640, p=1.0, border=(0, 0)):
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assert 0 <= p <= 1.0, 'The probability should be in range [0, 1]. ' f'got {p}.'
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super().__init__(dataset=dataset, p=p)
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self.dataset = dataset
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self.imgsz = imgsz
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self.border = border
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def get_indexes(self):
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return [random.randint(0, len(self.dataset) - 1) for _ in range(3)]
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def _mix_transform(self, labels):
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mosaic_labels = []
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assert labels.get('rect_shape', None) is None, 'rect and mosaic is exclusive.'
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assert len(labels.get('mix_labels', [])) > 0, 'There are no other images for mosaic augment.'
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s = self.imgsz
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yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border) # mosaic center x, y
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for i in range(4):
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labels_patch = (labels if i == 0 else labels['mix_labels'][i - 1]).copy()
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# Load image
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img = labels_patch['img']
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h, w = labels_patch.pop('resized_shape')
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# place img in img4
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if i == 0: # top left
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img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
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x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
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x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)
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elif i == 1: # top right
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x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
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x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
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elif i == 2: # bottom left
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x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
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x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
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elif i == 3: # bottom right
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x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
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x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
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img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
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padw = x1a - x1b
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padh = y1a - y1b
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labels_patch = self._update_labels(labels_patch, padw, padh)
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mosaic_labels.append(labels_patch)
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final_labels = self._cat_labels(mosaic_labels)
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final_labels['img'] = img4
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return final_labels
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def _update_labels(self, labels, padw, padh):
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"""Update labels"""
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nh, nw = labels['img'].shape[:2]
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labels['instances'].convert_bbox(format='xyxy')
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labels['instances'].denormalize(nw, nh)
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labels['instances'].add_padding(padw, padh)
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return labels
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def _cat_labels(self, mosaic_labels):
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if len(mosaic_labels) == 0:
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return {}
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cls = []
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instances = []
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for labels in mosaic_labels:
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cls.append(labels['cls'])
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instances.append(labels['instances'])
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final_labels = {
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'im_file': mosaic_labels[0]['im_file'],
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'ori_shape': mosaic_labels[0]['ori_shape'],
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'resized_shape': (self.imgsz * 2, self.imgsz * 2),
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'cls': np.concatenate(cls, 0),
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'instances': Instances.concatenate(instances, axis=0),
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'mosaic_border': self.border}
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final_labels['instances'].clip(self.imgsz * 2, self.imgsz * 2)
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return final_labels
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class MixUp(BaseMixTransform):
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def __init__(self, dataset, pre_transform=None, p=0.0) -> None:
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super().__init__(dataset=dataset, pre_transform=pre_transform, p=p)
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def get_indexes(self):
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return random.randint(0, len(self.dataset) - 1)
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def _mix_transform(self, labels):
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# Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
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r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
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labels2 = labels['mix_labels'][0]
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labels['img'] = (labels['img'] * r + labels2['img'] * (1 - r)).astype(np.uint8)
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labels['instances'] = Instances.concatenate([labels['instances'], labels2['instances']], axis=0)
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labels['cls'] = np.concatenate([labels['cls'], labels2['cls']], 0)
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return labels
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class RandomPerspective:
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def __init__(self,
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degrees=0.0,
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translate=0.1,
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scale=0.5,
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shear=0.0,
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perspective=0.0,
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border=(0, 0),
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pre_transform=None):
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self.degrees = degrees
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self.translate = translate
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self.scale = scale
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self.shear = shear
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self.perspective = perspective
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# mosaic border
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self.border = border
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self.pre_transform = pre_transform
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def affine_transform(self, img, border):
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# Center
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C = np.eye(3)
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C[0, 2] = -img.shape[1] / 2 # x translation (pixels)
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C[1, 2] = -img.shape[0] / 2 # y translation (pixels)
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# Perspective
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P = np.eye(3)
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P[2, 0] = random.uniform(-self.perspective, self.perspective) # x perspective (about y)
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P[2, 1] = random.uniform(-self.perspective, self.perspective) # y perspective (about x)
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# Rotation and Scale
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R = np.eye(3)
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a = random.uniform(-self.degrees, self.degrees)
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# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
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s = random.uniform(1 - self.scale, 1 + self.scale)
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# s = 2 ** random.uniform(-scale, scale)
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R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
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# Shear
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S = np.eye(3)
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S[0, 1] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180) # x shear (deg)
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S[1, 0] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180) # y shear (deg)
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# Translation
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T = np.eye(3)
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T[0, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * self.size[0] # x translation (pixels)
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T[1, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * self.size[1] # y translation (pixels)
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# Combined rotation matrix
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M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
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# affine image
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if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
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if self.perspective:
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img = cv2.warpPerspective(img, M, dsize=self.size, borderValue=(114, 114, 114))
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else: # affine
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img = cv2.warpAffine(img, M[:2], dsize=self.size, borderValue=(114, 114, 114))
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return img, M, s
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def apply_bboxes(self, bboxes, M):
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"""apply affine to bboxes only.
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Args:
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bboxes(ndarray): list of bboxes, xyxy format, with shape (num_bboxes, 4).
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M(ndarray): affine matrix.
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Returns:
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new_bboxes(ndarray): bboxes after affine, [num_bboxes, 4].
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"""
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n = len(bboxes)
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if n == 0:
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return bboxes
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xy = np.ones((n * 4, 3))
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xy[:, :2] = bboxes[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
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xy = xy @ M.T # transform
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xy = (xy[:, :2] / xy[:, 2:3] if self.perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
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# create new boxes
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x = xy[:, [0, 2, 4, 6]]
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y = xy[:, [1, 3, 5, 7]]
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return np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
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def apply_segments(self, segments, M):
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"""apply affine to segments and generate new bboxes from segments.
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Args:
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segments(ndarray): list of segments, [num_samples, 500, 2].
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M(ndarray): affine matrix.
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Returns:
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new_segments(ndarray): list of segments after affine, [num_samples, 500, 2].
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new_bboxes(ndarray): bboxes after affine, [N, 4].
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"""
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n, num = segments.shape[:2]
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if n == 0:
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return [], segments
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xy = np.ones((n * num, 3))
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segments = segments.reshape(-1, 2)
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xy[:, :2] = segments
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xy = xy @ M.T # transform
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xy = xy[:, :2] / xy[:, 2:3]
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segments = xy.reshape(n, -1, 2)
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bboxes = np.stack([segment2box(xy, self.size[0], self.size[1]) for xy in segments], 0)
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return bboxes, segments
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def apply_keypoints(self, keypoints, M):
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"""apply affine to keypoints.
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Args:
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keypoints(ndarray): keypoints, [N, 17, 2].
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M(ndarray): affine matrix.
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Return:
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new_keypoints(ndarray): keypoints after affine, [N, 17, 2].
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"""
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n = len(keypoints)
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if n == 0:
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return keypoints
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new_keypoints = np.ones((n * 17, 3))
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new_keypoints[:, :2] = keypoints.reshape(n * 17, 2) # num_kpt is hardcoded to 17
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new_keypoints = new_keypoints @ M.T # transform
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new_keypoints = (new_keypoints[:, :2] / new_keypoints[:, 2:3]).reshape(n, 34) # perspective rescale or affine
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new_keypoints[keypoints.reshape(-1, 34) == 0] = 0
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x_kpts = new_keypoints[:, list(range(0, 34, 2))]
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y_kpts = new_keypoints[:, list(range(1, 34, 2))]
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x_kpts[np.logical_or.reduce((x_kpts < 0, x_kpts > self.size[0], y_kpts < 0, y_kpts > self.size[1]))] = 0
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y_kpts[np.logical_or.reduce((x_kpts < 0, x_kpts > self.size[0], y_kpts < 0, y_kpts > self.size[1]))] = 0
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new_keypoints[:, list(range(0, 34, 2))] = x_kpts
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new_keypoints[:, list(range(1, 34, 2))] = y_kpts
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return new_keypoints.reshape(n, 17, 2)
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def __call__(self, labels):
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"""
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Affine images and targets.
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Args:
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labels(Dict): a dict of `bboxes`, `segments`, `keypoints`.
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"""
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if self.pre_transform and 'mosaic_border' not in labels:
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labels = self.pre_transform(labels)
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labels.pop('ratio_pad') # do not need ratio pad
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img = labels['img']
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cls = labels['cls']
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instances = labels.pop('instances')
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# make sure the coord formats are right
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instances.convert_bbox(format='xyxy')
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instances.denormalize(*img.shape[:2][::-1])
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border = labels.pop('mosaic_border', self.border)
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self.size = img.shape[1] + border[1] * 2, img.shape[0] + border[0] * 2 # w, h
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# M is affine matrix
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# scale for func:`box_candidates`
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img, M, scale = self.affine_transform(img, border)
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bboxes = self.apply_bboxes(instances.bboxes, M)
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segments = instances.segments
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keypoints = instances.keypoints
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# update bboxes if there are segments.
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if len(segments):
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bboxes, segments = self.apply_segments(segments, M)
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if keypoints is not None:
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keypoints = self.apply_keypoints(keypoints, M)
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new_instances = Instances(bboxes, segments, keypoints, bbox_format='xyxy', normalized=False)
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# clip
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new_instances.clip(*self.size)
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# filter instances
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instances.scale(scale_w=scale, scale_h=scale, bbox_only=True)
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# make the bboxes have the same scale with new_bboxes
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i = self.box_candidates(box1=instances.bboxes.T,
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box2=new_instances.bboxes.T,
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area_thr=0.01 if len(segments) else 0.10)
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labels['instances'] = new_instances[i]
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labels['cls'] = cls[i]
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labels['img'] = img
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labels['resized_shape'] = img.shape[:2]
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return labels
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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)
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# Compute box candidates: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
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w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
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w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
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ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
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return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
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class RandomHSV:
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def __init__(self, hgain=0.5, sgain=0.5, vgain=0.5) -> None:
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self.hgain = hgain
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self.sgain = sgain
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self.vgain = vgain
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def __call__(self, labels):
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img = labels['img']
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if self.hgain or self.sgain or self.vgain:
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r = np.random.uniform(-1, 1, 3) * [self.hgain, self.sgain, self.vgain] + 1 # random gains
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hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
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dtype = img.dtype # uint8
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x = np.arange(0, 256, dtype=r.dtype)
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lut_hue = ((x * r[0]) % 180).astype(dtype)
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lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
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lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
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im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
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cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
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return labels
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class RandomFlip:
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def __init__(self, p=0.5, direction='horizontal') -> None:
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assert direction in ['horizontal', 'vertical'], f'Support direction `horizontal` or `vertical`, got {direction}'
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assert 0 <= p <= 1.0
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self.p = p
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self.direction = direction
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def __call__(self, labels):
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img = labels['img']
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instances = labels.pop('instances')
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instances.convert_bbox(format='xywh')
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h, w = img.shape[:2]
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h = 1 if instances.normalized else h
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w = 1 if instances.normalized else w
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# Flip up-down
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if self.direction == 'vertical' and random.random() < self.p:
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img = np.flipud(img)
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|
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
|