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b/utils/augmentations.py |
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# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license |
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""" |
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Image augmentation functions |
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""" |
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import math |
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import random |
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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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import torchvision.transforms.functional as TF |
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from PIL import Image |
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from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy |
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from utils.metrics import bbox_ioa |
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IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean |
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IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation |
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class Albumentations: |
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# YOLOv5 Albumentations class (optional, only used if package is installed) |
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def __init__(self, size=640): |
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self.transform = None |
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prefix = colorstr('albumentations: ') |
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try: |
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import albumentations as A |
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check_version(A.__version__, '1.0.3', hard=True) # version requirement |
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T = [ |
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A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0), |
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A.Blur(p=0.01), |
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A.MedianBlur(p=0.01), |
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A.ToGray(p=0.01), |
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A.CLAHE(p=0.01), |
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A.RandomBrightnessContrast(p=0.0), |
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A.RandomGamma(p=0.0), |
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A.ImageCompression(quality_lower=75, p=0.0)] # transforms |
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self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) |
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LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) |
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except ImportError: # package not installed, skip |
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pass |
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except Exception as e: |
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LOGGER.info(f'{prefix}{e}') |
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def __call__(self, im, labels, p=1.0): |
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if self.transform and random.random() < p: |
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new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed |
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im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])]) |
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return im, labels |
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def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False): |
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# Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std |
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return TF.normalize(x, mean, std, inplace=inplace) |
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def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD): |
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# Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean |
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for i in range(3): |
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x[:, i] = x[:, i] * std[i] + mean[i] |
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return x |
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def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): |
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# HSV color-space augmentation |
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if hgain or sgain or vgain: |
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r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains |
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hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) |
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dtype = im.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=im) # no return needed |
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def hist_equalize(im, clahe=True, bgr=False): |
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# Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 |
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yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) |
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if clahe: |
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c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) |
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yuv[:, :, 0] = c.apply(yuv[:, :, 0]) |
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else: |
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yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram |
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return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB |
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def replicate(im, labels): |
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# Replicate labels |
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h, w = im.shape[:2] |
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boxes = labels[:, 1:].astype(int) |
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x1, y1, x2, y2 = boxes.T |
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s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) |
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for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices |
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x1b, y1b, x2b, y2b = boxes[i] |
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bh, bw = y2b - y1b, x2b - x1b |
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yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y |
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x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] |
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im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax] |
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labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) |
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return im, labels |
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def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): |
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# Resize and pad image while meeting stride-multiple constraints |
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shape = im.shape[:2] # current shape [height, width] |
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if isinstance(new_shape, int): |
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new_shape = (new_shape, new_shape) |
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# Scale ratio (new / old) |
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) |
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if not scaleup: # only scale down, do not scale up (for better val mAP) |
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r = min(r, 1.0) |
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# Compute padding |
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ratio = r, r # width, height ratios |
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) |
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding |
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if auto: # minimum rectangle |
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dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding |
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elif scaleFill: # stretch |
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dw, dh = 0.0, 0.0 |
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new_unpad = (new_shape[1], new_shape[0]) |
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ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios |
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dw /= 2 # divide padding into 2 sides |
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dh /= 2 |
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if shape[::-1] != new_unpad: # resize |
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im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) |
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# pil_img = Image.fromarray(im) |
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# image = pil_img.resize(new_shape, Image.LANCZOS) |
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# im = np.array(image) |
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) |
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) |
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im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border |
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# ratio = (1.0, 1.0) |
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# # Set dw and dh to 0.0 |
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# dw = 0.0 |
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# dh = 0.0 |
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return im, ratio, (dw, dh) |
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def random_perspective(im, |
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targets=(), |
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segments=(), |
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degrees=10, |
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translate=.1, |
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scale=.1, |
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shear=10, |
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perspective=0.0, |
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border=(0, 0)): |
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# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10)) |
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# targets = [cls, xyxy] |
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height = im.shape[0] + border[0] * 2 # shape(h,w,c) |
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width = im.shape[1] + border[1] * 2 |
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# Center |
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C = np.eye(3) |
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C[0, 2] = -im.shape[1] / 2 # x translation (pixels) |
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C[1, 2] = -im.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(-perspective, perspective) # x perspective (about y) |
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P[2, 1] = random.uniform(-perspective, 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(-degrees, 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 - scale, 1 + 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(-shear, shear) * math.pi / 180) # x shear (deg) |
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S[1, 0] = math.tan(random.uniform(-shear, 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 - translate, 0.5 + translate) * width # x translation (pixels) |
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T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # 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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if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed |
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if perspective: |
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im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) |
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else: # affine |
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im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) |
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# Visualize |
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# import matplotlib.pyplot as plt |
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# ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() |
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# ax[0].imshow(im[:, :, ::-1]) # base |
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# ax[1].imshow(im2[:, :, ::-1]) # warped |
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# Transform label coordinates |
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n = len(targets) |
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if n: |
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use_segments = any(x.any() for x in segments) and len(segments) == n |
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new = np.zeros((n, 4)) |
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if use_segments: # warp segments |
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segments = resample_segments(segments) # upsample |
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for i, segment in enumerate(segments): |
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xy = np.ones((len(segment), 3)) |
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xy[:, :2] = segment |
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xy = xy @ M.T # transform |
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xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine |
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# clip |
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new[i] = segment2box(xy, width, height) |
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else: # warp boxes |
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xy = np.ones((n * 4, 3)) |
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xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].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 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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new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T |
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# clip |
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new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) |
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new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) |
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# filter candidates |
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i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) |
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targets = targets[i] |
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targets[:, 1:5] = new[i] |
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return im, targets |
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def copy_paste(im, labels, segments, p=0.5): |
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# Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) |
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n = len(segments) |
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if p and n: |
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h, w, c = im.shape # height, width, channels |
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im_new = np.zeros(im.shape, np.uint8) |
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for j in random.sample(range(n), k=round(p * n)): |
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l, s = labels[j], segments[j] |
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box = w - l[3], l[2], w - l[1], l[4] |
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ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area |
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if (ioa < 0.30).all(): # allow 30% obscuration of existing labels |
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labels = np.concatenate((labels, [[l[0], *box]]), 0) |
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segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) |
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cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED) |
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result = cv2.flip(im, 1) # augment segments (flip left-right) |
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i = cv2.flip(im_new, 1).astype(bool) |
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im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug |
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return im, labels, segments |
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def cutout(im, labels, p=0.5): |
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# Applies image cutout augmentation https://arxiv.org/abs/1708.04552 |
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if random.random() < p: |
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h, w = im.shape[:2] |
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scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction |
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for s in scales: |
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mask_h = random.randint(1, int(h * s)) # create random masks |
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mask_w = random.randint(1, int(w * s)) |
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# box |
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xmin = max(0, random.randint(0, w) - mask_w // 2) |
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ymin = max(0, random.randint(0, h) - mask_h // 2) |
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xmax = min(w, xmin + mask_w) |
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ymax = min(h, ymin + mask_h) |
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# apply random color mask |
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im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] |
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# return unobscured labels |
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if len(labels) and s > 0.03: |
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box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) |
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ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area |
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labels = labels[ioa < 0.60] # remove >60% obscured labels |
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return labels |
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def mixup(im, labels, im2, labels2): |
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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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im = (im * r + im2 * (1 - r)).astype(np.uint8) |
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labels = np.concatenate((labels, labels2), 0) |
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return im, labels |
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def box_candidates(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 candidate boxes: 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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316 |
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317 |
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318 |
def classify_albumentations( |
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319 |
augment=True, |
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size=224, |
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scale=(0.08, 1.0), |
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ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33 |
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hflip=0.5, |
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vflip=0.0, |
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jitter=0.4, |
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mean=IMAGENET_MEAN, |
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std=IMAGENET_STD, |
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auto_aug=False): |
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# YOLOv5 classification Albumentations (optional, only used if package is installed) |
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prefix = colorstr('albumentations: ') |
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try: |
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import albumentations as A |
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from albumentations.pytorch import ToTensorV2 |
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check_version(A.__version__, '1.0.3', hard=True) # version requirement |
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if augment: # Resize and crop |
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T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)] |
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if auto_aug: |
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# TODO: implement AugMix, AutoAug & RandAug in albumentation |
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LOGGER.info(f'{prefix}auto augmentations are currently not supported') |
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else: |
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if hflip > 0: |
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T += [A.HorizontalFlip(p=hflip)] |
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if vflip > 0: |
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T += [A.VerticalFlip(p=vflip)] |
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if jitter > 0: |
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color_jitter = (float(jitter), ) * 3 # repeat value for brightness, contrast, satuaration, 0 hue |
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T += [A.ColorJitter(*color_jitter, 0)] |
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else: # Use fixed crop for eval set (reproducibility) |
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T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)] |
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T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor |
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LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) |
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return A.Compose(T) |
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except ImportError: # package not installed, skip |
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LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)') |
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except Exception as e: |
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LOGGER.info(f'{prefix}{e}') |
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358 |
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359 |
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def classify_transforms(size=224): |
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# Transforms to apply if albumentations not installed |
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assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)' |
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# T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) |
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return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) |
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365 |
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366 |
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367 |
class LetterBox: |
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# YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) |
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def __init__(self, size=(640, 640), auto=False, stride=32): |
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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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374 |
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def __call__(self, im): # im = np.array HWC |
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376 |
imh, imw = im.shape[:2] |
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377 |
r = min(self.h / imh, self.w / imw) # ratio of new/old |
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378 |
h, w = round(imh * r), round(imw * r) # resized image |
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379 |
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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380 |
top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1) |
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381 |
im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype) |
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382 |
im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) |
|
|
383 |
return im_out |
|
|
384 |
|
|
|
385 |
|
|
|
386 |
class CenterCrop: |
|
|
387 |
# YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()]) |
|
|
388 |
def __init__(self, size=640): |
|
|
389 |
super().__init__() |
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|
390 |
self.h, self.w = (size, size) if isinstance(size, int) else size |
|
|
391 |
|
|
|
392 |
def __call__(self, im): # im = np.array HWC |
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|
393 |
imh, imw = im.shape[:2] |
|
|
394 |
m = min(imh, imw) # min dimension |
|
|
395 |
top, left = (imh - m) // 2, (imw - m) // 2 |
|
|
396 |
return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) |
|
|
397 |
|
|
|
398 |
|
|
|
399 |
class ToTensor: |
|
|
400 |
# YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) |
|
|
401 |
def __init__(self, half=False): |
|
|
402 |
super().__init__() |
|
|
403 |
self.half = half |
|
|
404 |
|
|
|
405 |
def __call__(self, im): # im = np.array HWC in BGR order |
|
|
406 |
im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous |
|
|
407 |
im = torch.from_numpy(im) # to torch |
|
|
408 |
im = im.half() if self.half else im.float() # uint8 to fp16/32 |
|
|
409 |
im /= 255.0 # 0-255 to 0.0-1.0 |
|
|
410 |
return im |