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900 lines
36 KiB
900 lines
36 KiB
# Ultralytics YOLO 🚀, AGPL-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 polygons2masks, polygons2masks_overlap
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POSE_FLIPLR_INDEX = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
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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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"""Applies image transformation to labels."""
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pass
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def apply_instances(self, labels):
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"""Applies transformations to input 'labels' and returns object instances."""
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pass
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def apply_semantic(self, labels):
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"""Applies semantic segmentation to an image."""
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pass
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def __call__(self, labels):
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"""Applies label transformations to an image, instances and semantic masks."""
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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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"""Initializes the Compose object with a list of transforms."""
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self.transforms = transforms
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def __call__(self, data):
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"""Applies a series of transformations to input 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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"""Appends a new transform to the existing list of transforms."""
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self.transforms.append(transform)
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def tolist(self):
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"""Converts list of transforms to a standard Python list."""
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return self.transforms
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def __repr__(self):
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"""Return string representation of object."""
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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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"""Applies pre-processing transforms and mixup/mosaic transforms to labels data."""
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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_image_and_label(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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"""Applies MixUp or Mosaic augmentation to the label dictionary."""
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raise NotImplementedError
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def get_indexes(self):
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"""Gets a list of shuffled indexes for mosaic augmentation."""
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raise NotImplementedError
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class Mosaic(BaseMixTransform):
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"""
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Mosaic augmentation.
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This class performs mosaic augmentation by combining multiple (4 or 9) images into a single mosaic image.
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The augmentation is applied to a dataset with a given probability.
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Attributes:
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dataset: The dataset on which the mosaic augmentation is applied.
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imgsz (int, optional): Image size (height and width) after mosaic pipeline of a single image. Default to 640.
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p (float, optional): Probability of applying the mosaic augmentation. Must be in the range 0-1. Default to 1.0.
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n (int, optional): The grid size, either 4 (for 2x2) or 9 (for 3x3).
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"""
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def __init__(self, dataset, imgsz=640, p=1.0, n=4):
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"""Initializes the object with a dataset, image size, probability, and border."""
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assert 0 <= p <= 1.0, f'The probability should be in range [0, 1], but got {p}.'
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assert n in (4, 9), 'grid must be equal to 4 or 9.'
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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 = (-imgsz // 2, -imgsz // 2) # width, height
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self.n = n
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def get_indexes(self, buffer=True):
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"""Return a list of random indexes from the dataset."""
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if buffer: # select images from buffer
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return random.choices(list(self.dataset.buffer), k=self.n - 1)
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else: # select any images
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return [random.randint(0, len(self.dataset) - 1) for _ in range(self.n - 1)]
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def _mix_transform(self, labels):
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"""Apply mixup transformation to the input image and labels."""
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assert labels.get('rect_shape', None) is None, 'rect and mosaic are mutually exclusive.'
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assert len(labels.get('mix_labels', [])), 'There are no other images for mosaic augment.'
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return self._mosaic4(labels) if self.n == 4 else self._mosaic9(labels)
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def _mosaic4(self, labels):
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"""Create a 2x2 image mosaic."""
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mosaic_labels = []
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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]
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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 _mosaic9(self, labels):
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"""Create a 3x3 image mosaic."""
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mosaic_labels = []
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s = self.imgsz
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hp, wp = -1, -1 # height, width previous
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for i in range(9):
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labels_patch = labels if i == 0 else labels['mix_labels'][i - 1]
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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 img9
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if i == 0: # center
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img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
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h0, w0 = h, w
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c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates
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elif i == 1: # top
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c = s, s - h, s + w, s
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elif i == 2: # top right
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c = s + wp, s - h, s + wp + w, s
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elif i == 3: # right
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c = s + w0, s, s + w0 + w, s + h
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elif i == 4: # bottom right
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c = s + w0, s + hp, s + w0 + w, s + hp + h
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elif i == 5: # bottom
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c = s + w0 - w, s + h0, s + w0, s + h0 + h
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elif i == 6: # bottom left
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c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
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elif i == 7: # left
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c = s - w, s + h0 - h, s, s + h0
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elif i == 8: # top left
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c = s - w, s + h0 - hp - h, s, s + h0 - hp
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padw, padh = c[:2]
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x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords
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# Image
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img9[y1:y2, x1:x2] = img[y1 - padh:, x1 - padw:] # img9[ymin:ymax, xmin:xmax]
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hp, wp = h, w # height, width previous for next iteration
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# Labels assuming imgsz*2 mosaic size
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labels_patch = self._update_labels(labels_patch, padw + self.border[0], padh + self.border[1])
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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'] = img9[-self.border[0]:self.border[0], -self.border[1]:self.border[1]]
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return final_labels
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@staticmethod
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def _update_labels(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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"""Return labels with mosaic border instances clipped."""
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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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imgsz = self.imgsz * 2 # mosaic imgsz
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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': (imgsz, imgsz),
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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} # final_labels
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final_labels['instances'].clip(imgsz, imgsz)
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good = final_labels['instances'].remove_zero_area_boxes()
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final_labels['cls'] = final_labels['cls'][good]
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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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"""Get a random index from the dataset."""
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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, dtype=np.float32)
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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, dtype=np.float32)
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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, dtype=np.float32)
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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, dtype=np.float32)
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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, dtype=np.float32)
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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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"""
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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), dtype=bboxes.dtype)
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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)), dtype=bboxes.dtype).reshape(4, n).T
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def apply_segments(self, segments, M):
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"""
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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), dtype=segments.dtype)
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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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"""
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Apply affine to keypoints.
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Args:
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keypoints (ndarray): keypoints, [N, 17, 3].
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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, 3].
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"""
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n, nkpt = keypoints.shape[:2]
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if n == 0:
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return keypoints
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xy = np.ones((n * nkpt, 3), dtype=keypoints.dtype)
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visible = keypoints[..., 2].reshape(n * nkpt, 1)
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xy[:, :2] = keypoints[..., :2].reshape(n * nkpt, 2)
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xy = xy @ M.T # transform
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xy = xy[:, :2] / xy[:, 2:3] # perspective rescale or affine
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out_mask = (xy[:, 0] < 0) | (xy[:, 1] < 0) | (xy[:, 0] > self.size[0]) | (xy[:, 1] > self.size[1])
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visible[out_mask] = 0
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return np.concatenate([xy, visible], axis=-1).reshape(n, nkpt, 3)
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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)
|
|
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):
|
|
"""Applies random horizontal or vertical flip to an image with a given probability."""
|
|
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', flip_idx=None) -> 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
|
|
self.flip_idx = flip_idx
|
|
|
|
def __call__(self, labels):
|
|
"""Resize image and padding for detection, instance segmentation, pose."""
|
|
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)
|
|
# For keypoints
|
|
if self.flip_idx is not None and instances.keypoints is not None:
|
|
instances.keypoints = np.ascontiguousarray(instances.keypoints[:, self.flip_idx, :])
|
|
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):
|
|
"""Initialize LetterBox object with specific parameters."""
|
|
self.new_shape = new_shape
|
|
self.auto = auto
|
|
self.scaleFill = scaleFill
|
|
self.scaleup = scaleup
|
|
self.stride = stride
|
|
|
|
def __call__(self, labels=None, image=None):
|
|
"""Return updated labels and image with added border."""
|
|
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):
|
|
"""Initialize the transform object for YOLO bbox formatted params."""
|
|
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):
|
|
"""Generates object detections and returns a dictionary with detection results."""
|
|
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
|
|
if len(new['class_labels']) > 0: # skip update if no bbox in new im
|
|
labels['img'] = new['image']
|
|
labels['cls'] = np.array(new['class_labels'])
|
|
bboxes = np.array(new['bboxes'], dtype=np.float32)
|
|
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):
|
|
"""Return formatted image, classes, bounding boxes & keypoints to be used by 'collate_fn'."""
|
|
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)
|
|
# Then we can use collate_fn
|
|
if self.batch_idx:
|
|
labels['batch_idx'] = torch.zeros(nl)
|
|
return labels
|
|
|
|
def _format_img(self, img):
|
|
"""Format the image for YOLOv5 from Numpy array to PyTorch tensor."""
|
|
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, stretch=False):
|
|
"""Convert images to a size suitable for YOLOv8 training."""
|
|
pre_transform = Compose([
|
|
Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic),
|
|
CopyPaste(p=hyp.copy_paste),
|
|
RandomPerspective(
|
|
degrees=hyp.degrees,
|
|
translate=hyp.translate,
|
|
scale=hyp.scale,
|
|
shear=hyp.shear,
|
|
perspective=hyp.perspective,
|
|
pre_transform=None if stretch else LetterBox(new_shape=(imgsz, imgsz)),
|
|
)])
|
|
flip_idx = dataset.data.get('flip_idx', None) # for keypoints augmentation
|
|
if dataset.use_keypoints:
|
|
kpt_shape = dataset.data.get('kpt_shape', None)
|
|
if flip_idx is None and hyp.fliplr > 0.0:
|
|
hyp.fliplr = 0.0
|
|
LOGGER.warning("WARNING ⚠️ No 'flip_idx' array defined in data.yaml, setting augmentation 'fliplr=0.0'")
|
|
elif flip_idx and (len(flip_idx) != kpt_shape[0]):
|
|
raise ValueError(f'data.yaml flip_idx={flip_idx} length must be equal to kpt_shape[0]={kpt_shape[0]}')
|
|
|
|
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, flip_idx=flip_idx)]) # transforms
|
|
|
|
|
|
# Classification augmentations -----------------------------------------------------------------------------------------
|
|
def classify_transforms(size=224, mean=(0.0, 0.0, 0.0), std=(1.0, 1.0, 1.0)): # IMAGENET_MEAN, IMAGENET_STD
|
|
# 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)')
|
|
if any(mean) or any(std):
|
|
return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(mean, std, inplace=True)])
|
|
else:
|
|
return T.Compose([CenterCrop(size), ToTensor()])
|
|
|
|
|
|
def hsv2colorjitter(h, s, v):
|
|
"""Map HSV (hue, saturation, value) jitter into ColorJitter values (brightness, contrast, saturation, hue)"""
|
|
return v, v, s, h
|
|
|
|
|
|
def classify_albumentations(
|
|
augment=True,
|
|
size=224,
|
|
scale=(0.08, 1.0),
|
|
hflip=0.5,
|
|
vflip=0.0,
|
|
hsv_h=0.015, # image HSV-Hue augmentation (fraction)
|
|
hsv_s=0.7, # image HSV-Saturation augmentation (fraction)
|
|
hsv_v=0.4, # image HSV-Value augmentation (fraction)
|
|
mean=(0.0, 0.0, 0.0), # IMAGENET_MEAN
|
|
std=(1.0, 1.0, 1.0), # 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 albumentations
|
|
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 any((hsv_h, hsv_s, hsv_v)):
|
|
T += [A.ColorJitter(*hsv2colorjitter(hsv_h, hsv_s, hsv_v))] # brightness, contrast, saturation, hue
|
|
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):
|
|
"""Resizes image and crops it to center with max dimensions 'h' and 'w'."""
|
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super().__init__()
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self.h, self.w = (size, size) if isinstance(size, int) else size
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self.auto = auto # pass max size integer, automatically solve for short side using stride
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self.stride = stride # used with auto
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def __call__(self, im): # im = np.array HWC
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imh, imw = im.shape[:2]
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r = min(self.h / imh, self.w / imw) # ratio of new/old
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h, w = round(imh * r), round(imw * r) # resized image
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hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w
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top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1)
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im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype)
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im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
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return im_out
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class CenterCrop:
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# YOLOv8 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])
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def __init__(self, size=640):
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"""Converts an image from numpy array to PyTorch tensor."""
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super().__init__()
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self.h, self.w = (size, size) if isinstance(size, int) else size
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def __call__(self, im): # im = np.array HWC
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imh, imw = im.shape[:2]
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m = min(imh, imw) # min dimension
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top, left = (imh - m) // 2, (imw - m) // 2
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return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)
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class ToTensor:
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# YOLOv8 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
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def __init__(self, half=False):
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"""Initialize YOLOv8 ToTensor object with optional half-precision support."""
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super().__init__()
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self.half = half
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def __call__(self, im): # im = np.array HWC in BGR order
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im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous
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im = torch.from_numpy(im) # to torch
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im = im.half() if self.half else im.float() # uint8 to fp16/32
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im /= 255.0 # 0-255 to 0.0-1.0
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return im
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