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| # YOLOv5 🚀 by Ultralytics, AGPL-3.0 license | |
| """ | |
| Image augmentation functions | |
| """ | |
| import math | |
| import random | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import torchvision.transforms as T | |
| import torchvision.transforms.functional as TF | |
| from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy | |
| from utils.metrics import bbox_ioa | |
| IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean | |
| IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation | |
| class Albumentations: | |
| # YOLOv5 Albumentations class (optional, only used if package is installed) | |
| def __init__(self, size=640): | |
| self.transform = None | |
| prefix = colorstr('albumentations: ') | |
| try: | |
| import albumentations as A | |
| check_version(A.__version__, '1.0.3', hard=True) # version requirement | |
| T = [ | |
| A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0), | |
| 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, im, labels, p=1.0): | |
| if self.transform and random.random() < p: | |
| new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed | |
| im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])]) | |
| return im, labels | |
| def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False): | |
| # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std | |
| return TF.normalize(x, mean, std, inplace=inplace) | |
| def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD): | |
| # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean | |
| for i in range(3): | |
| x[:, i] = x[:, i] * std[i] + mean[i] | |
| return x | |
| def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): | |
| # HSV color-space augmentation | |
| if hgain or sgain or vgain: | |
| r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains | |
| hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) | |
| dtype = im.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=im) # no return needed | |
| def hist_equalize(im, clahe=True, bgr=False): | |
| # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 | |
| yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) | |
| if clahe: | |
| c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) | |
| yuv[:, :, 0] = c.apply(yuv[:, :, 0]) | |
| else: | |
| yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram | |
| return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB | |
| def replicate(im, labels): | |
| # Replicate labels | |
| h, w = im.shape[:2] | |
| boxes = labels[:, 1:].astype(int) | |
| x1, y1, x2, y2 = boxes.T | |
| s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) | |
| for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices | |
| x1b, y1b, x2b, y2b = boxes[i] | |
| bh, bw = y2b - y1b, x2b - x1b | |
| yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y | |
| x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] | |
| im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax] | |
| labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) | |
| return im, labels | |
| def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): | |
| # Resize and pad image while meeting stride-multiple constraints | |
| shape = im.shape[:2] # current shape [height, width] | |
| 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 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 auto: # minimum rectangle | |
| dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding | |
| elif scaleFill: # stretch | |
| dw, dh = 0.0, 0.0 | |
| new_unpad = (new_shape[1], new_shape[0]) | |
| ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios | |
| dw /= 2 # divide padding into 2 sides | |
| dh /= 2 | |
| if shape[::-1] != new_unpad: # resize | |
| im = cv2.resize(im, 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)) | |
| im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border | |
| return im, ratio, (dw, dh) | |
| def random_perspective(im, | |
| targets=(), | |
| segments=(), | |
| degrees=10, | |
| translate=.1, | |
| scale=.1, | |
| shear=10, | |
| perspective=0.0, | |
| border=(0, 0)): | |
| # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10)) | |
| # targets = [cls, xyxy] | |
| height = im.shape[0] + border[0] * 2 # shape(h,w,c) | |
| width = im.shape[1] + border[1] * 2 | |
| # Center | |
| C = np.eye(3) | |
| C[0, 2] = -im.shape[1] / 2 # x translation (pixels) | |
| C[1, 2] = -im.shape[0] / 2 # y translation (pixels) | |
| # Perspective | |
| P = np.eye(3) | |
| P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) | |
| P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) | |
| # Rotation and Scale | |
| R = np.eye(3) | |
| a = random.uniform(-degrees, degrees) | |
| # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations | |
| s = random.uniform(1 - scale, 1 + scale) | |
| # s = 2 ** random.uniform(-scale, scale) | |
| R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) | |
| # Shear | |
| S = np.eye(3) | |
| S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) | |
| S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) | |
| # Translation | |
| T = np.eye(3) | |
| T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) | |
| T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) | |
| # Combined rotation matrix | |
| M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT | |
| if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed | |
| if perspective: | |
| im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) | |
| else: # affine | |
| im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) | |
| # Visualize | |
| # import matplotlib.pyplot as plt | |
| # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() | |
| # ax[0].imshow(im[:, :, ::-1]) # base | |
| # ax[1].imshow(im2[:, :, ::-1]) # warped | |
| # Transform label coordinates | |
| n = len(targets) | |
| if n: | |
| use_segments = any(x.any() for x in segments) and len(segments) == n | |
| new = np.zeros((n, 4)) | |
| if use_segments: # warp segments | |
| segments = resample_segments(segments) # upsample | |
| for i, segment in enumerate(segments): | |
| xy = np.ones((len(segment), 3)) | |
| xy[:, :2] = segment | |
| xy = xy @ M.T # transform | |
| xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine | |
| # clip | |
| new[i] = segment2box(xy, width, height) | |
| else: # warp boxes | |
| xy = np.ones((n * 4, 3)) | |
| xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 | |
| xy = xy @ M.T # transform | |
| xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine | |
| # create new boxes | |
| x = xy[:, [0, 2, 4, 6]] | |
| y = xy[:, [1, 3, 5, 7]] | |
| new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T | |
| # clip | |
| new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) | |
| new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) | |
| # filter candidates | |
| i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) | |
| targets = targets[i] | |
| targets[:, 1:5] = new[i] | |
| return im, targets | |
| def copy_paste(im, labels, segments, p=0.5): | |
| # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) | |
| n = len(segments) | |
| if p and n: | |
| h, w, c = im.shape # height, width, channels | |
| im_new = np.zeros(im.shape, np.uint8) | |
| for j in random.sample(range(n), k=round(p * n)): | |
| l, s = labels[j], segments[j] | |
| box = w - l[3], l[2], w - l[1], l[4] | |
| ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area | |
| if (ioa < 0.30).all(): # allow 30% obscuration of existing labels | |
| labels = np.concatenate((labels, [[l[0], *box]]), 0) | |
| segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) | |
| cv2.drawContours(im_new, [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 | |
| return im, labels, segments | |
| def cutout(im, labels, p=0.5): | |
| # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 | |
| if random.random() < p: | |
| h, w = im.shape[:2] | |
| scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction | |
| for s in scales: | |
| mask_h = random.randint(1, int(h * s)) # create random masks | |
| mask_w = random.randint(1, int(w * s)) | |
| # box | |
| xmin = max(0, random.randint(0, w) - mask_w // 2) | |
| ymin = max(0, random.randint(0, h) - mask_h // 2) | |
| xmax = min(w, xmin + mask_w) | |
| ymax = min(h, ymin + mask_h) | |
| # apply random color mask | |
| im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] | |
| # return unobscured labels | |
| if len(labels) and s > 0.03: | |
| box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) | |
| ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area | |
| labels = labels[ioa < 0.60] # remove >60% obscured labels | |
| return labels | |
| def mixup(im, labels, im2, labels2): | |
| # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf | |
| r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 | |
| im = (im * r + im2 * (1 - r)).astype(np.uint8) | |
| labels = np.concatenate((labels, labels2), 0) | |
| return im, labels | |
| def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) | |
| # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio | |
| w1, h1 = box1[2] - box1[0], box1[3] - box1[1] | |
| w2, h2 = box2[2] - box2[0], box2[3] - box2[1] | |
| ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio | |
| return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates | |
| def classify_albumentations( | |
| augment=True, | |
| size=224, | |
| scale=(0.08, 1.0), | |
| ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33 | |
| hflip=0.5, | |
| vflip=0.0, | |
| jitter=0.4, | |
| mean=IMAGENET_MEAN, | |
| std=IMAGENET_STD, | |
| auto_aug=False): | |
| # YOLOv5 classification Albumentations (optional, only used if package is installed) | |
| prefix = colorstr('albumentations: ') | |
| try: | |
| import albumentations as A | |
| from albumentations.pytorch import ToTensorV2 | |
| check_version(A.__version__, '1.0.3', hard=True) # version requirement | |
| if augment: # Resize and crop | |
| T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)] | |
| 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, satuaration, 0 hue | |
| T += [A.ColorJitter(*color_jitter, 0)] | |
| else: # Use fixed crop for eval set (reproducibility) | |
| T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)] | |
| T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor | |
| LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) | |
| return A.Compose(T) | |
| except ImportError: # package not installed, skip | |
| LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)') | |
| except Exception as e: | |
| LOGGER.info(f'{prefix}{e}') | |
| def classify_transforms(size=224): | |
| # Transforms to apply if albumentations not installed | |
| assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)' | |
| # 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)]) | |
| class LetterBox: | |
| # YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) | |
| def __init__(self, size=(640, 640), auto=False, stride=32): | |
| super().__init__() | |
| self.h, self.w = (size, size) if isinstance(size, int) else size | |
| self.auto = auto # pass max size integer, automatically solve for short side using stride | |
| self.stride = stride # used with auto | |
| def __call__(self, im): # im = np.array HWC | |
| imh, imw = im.shape[:2] | |
| r = min(self.h / imh, self.w / imw) # ratio of new/old | |
| h, w = round(imh * r), round(imw * r) # resized image | |
| hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w | |
| top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1) | |
| im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype) | |
| im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) | |
| return im_out | |
| class CenterCrop: | |
| # YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()]) | |
| def __init__(self, size=640): | |
| super().__init__() | |
| self.h, self.w = (size, size) if isinstance(size, int) else size | |
| def __call__(self, im): # im = np.array HWC | |
| imh, imw = im.shape[:2] | |
| m = min(imh, imw) # min dimension | |
| top, left = (imh - m) // 2, (imw - m) // 2 | |
| return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) | |
| class ToTensor: | |
| # YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) | |
| def __init__(self, half=False): | |
| super().__init__() | |
| self.half = half | |
| def __call__(self, im): # im = np.array HWC in BGR order | |
| im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous | |
| im = torch.from_numpy(im) # to torch | |
| im = im.half() if self.half else im.float() # uint8 to fp16/32 | |
| im /= 255.0 # 0-255 to 0.0-1.0 | |
| return im |