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# YOLOv5 π by Ultralytics, GPL-3.0 license | |
""" | |
Image augmentation functions | |
""" | |
import math | |
import random | |
import cv2 | |
import numpy as np | |
from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box | |
from utils.metrics import bbox_ioa | |
class Albumentations: | |
# YOLOv5 Albumentations class (optional, only used if package is installed) | |
def __init__(self): | |
self.transform = None | |
try: | |
import albumentations as A | |
check_version(A.__version__, '1.0.3', hard=True) # version requirement | |
self.transform = A.Compose([ | |
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)], | |
bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) | |
LOGGER.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p)) | |
except ImportError: # package not installed, skip | |
pass | |
except Exception as e: | |
LOGGER.info(colorstr('albumentations: ') + f'{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 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) | |
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, (255, 255, 255), cv2.FILLED) | |
result = cv2.bitwise_and(src1=im, src2=im_new) | |
result = cv2.flip(result, 1) # augment segments (flip left-right) | |
i = result > 0 # pixels to replace | |
# i[:, :] = result.max(2).reshape(h, w, 1) # act over ch | |
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, labels[:, 1:5]) # 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 | |