text: # YOLOv5 image augmentation functions
  
  import logging
  import random
  
  import cv2
  import math
  import numpy as np
  
  from yolov5.utils.general import colorstr, segment2box, resample_segments, check_version
  from yolov5.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__, '1.0.0')  # version requirement
  
              self.transform = A.Compose([
                  A.Blur(p=0.1),
                  A.MedianBlur(p=0.1),
                  A.ToGray(p=0.01)],
                  bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
  
              logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms))
          except ImportError:  # package not installed, skip
              pass
          except Exception as e:
              logging.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)
  
          img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
          cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=im)  # no return needed
  
  
  def hist_equalize(im, clahe=True, bgr=False):
      # Equalize histogram on BGR image 'img' with img.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]  # img4[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 test 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=(.1, .1), scale=(.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(img[:, :, ::-1])  # base
      # ax[1].imshow(img2[:, :, ::-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, probability=0.5):
      # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
      n = len(segments)
      if probability 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(probability * 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', img)  # debug
  
      return im, labels, segments
  
  
  def cutout(im, labels):
      # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
      h, w = im.shape[:2]
  
      # create random masks
      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))
          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=20, 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
  