# 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

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(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'])])

# 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)))

# 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

# 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)

# 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

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]

# 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

# 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

# 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)

# 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