text: # from https://github.com/amdegroot/ssd.pytorch
  
  
  import torch
  from torchvision import transforms
  import cv2
  import numpy as np
  import types
  from numpy import random
  
  
  def intersect(box_a, box_b):
      max_xy = np.minimum(box_a[:, 2:], box_b[2:])
      min_xy = np.maximum(box_a[:, :2], box_b[:2])
      inter = np.clip((max_xy - min_xy), a_min=0, a_max=np.inf)
      return inter[:, 0] * inter[:, 1]
  
  
  def jaccard_numpy(box_a, box_b):
      """Compute the jaccard overlap of two sets of boxes.  The jaccard overlap
      is simply the intersection over union of two boxes.
      E.g.:
          A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B)
      Args:
          box_a: Multiple bounding boxes, Shape: [num_boxes,4]
          box_b: Single bounding box, Shape: [4]
      Return:
          jaccard overlap: Shape: [box_a.shape[0], box_a.shape[1]]
      """
      inter = intersect(box_a, box_b)
      area_a = ((box_a[:, 2] - box_a[:, 0]) *
                (box_a[:, 3] - box_a[:, 1]))  # [A,B]
      area_b = ((box_b[2] - box_b[0]) *
                (box_b[3] - box_b[1]))  # [A,B]
      union = area_a + area_b - inter
      return inter / union  # [A,B]
  
  
  def remove_empty_boxes(boxes, labels):
      """Removes bounding boxes of W or H equal to 0 and its labels
  
      Args:
          boxes   (ndarray): NP Array with bounding boxes as lines
                             * BBOX[x1, y1, x2, y2]
          labels  (labels): Corresponding labels with boxes
  
      Returns:
          ndarray: Valid bounding boxes
          ndarray: Corresponding labels
      """
      del_boxes = []
      for idx, box in enumerate(boxes):
          if box[0] == box[2] or box[1] == box[3]:
              del_boxes.append(idx)
  
      return np.delete(boxes, del_boxes, 0), np.delete(labels, del_boxes)
  
  
  class Compose(object):
      """Composes several augmentations together.
      Args:
          transforms (List[Transform]): list of transforms to compose.
      Example:
          >>> augmentations.Compose([
          >>>     transforms.CenterCrop(10),
          >>>     transforms.ToTensor(),
          >>> ])
      """
  
      def __init__(self, transforms):
          self.transforms = transforms
  
      def __call__(self, img, boxes=None, labels=None):
          for t in self.transforms:
              img, boxes, labels = t(img, boxes, labels)
              if boxes is not None:
                  boxes, labels = remove_empty_boxes(boxes, labels)
          return img, boxes, labels
  
  
  class Lambda(object):
      """Applies a lambda as a transform."""
  
      def __init__(self, lambd):
          assert isinstance(lambd, types.LambdaType)
          self.lambd = lambd
  
      def __call__(self, img, boxes=None, labels=None):
          return self.lambd(img, boxes, labels)
  
  
  class ConvertFromInts(object):
      def __call__(self, image, boxes=None, labels=None):
          return image.astype(np.float32), boxes, labels
  
  
  class SubtractMeans(object):
      def __init__(self, mean):
          self.mean = np.array(mean, dtype=np.float32)
          self.std = np.array((0.229 * 255, 0.224 * 255, 0.225 * 255), dtype=np.float32)
  
      def __call__(self, image, boxes=None, labels=None):
          image = image.astype(np.float32)
          image -= self.mean
          image /= self.std
          return image.astype(np.float32), boxes, labels
  
  
  class ToAbsoluteCoords(object):
      def __call__(self, image, boxes=None, labels=None):
          height, width, channels = image.shape
          boxes[:, 0] *= width
          boxes[:, 2] *= width
          boxes[:, 1] *= height
          boxes[:, 3] *= height
  
          return image, boxes, labels
  
  
  class ToPercentCoords(object):
      def __call__(self, image, boxes=None, labels=None):
          height, width, channels = image.shape
          boxes[:, 0] /= width
          boxes[:, 2] /= width
          boxes[:, 1] /= height
          boxes[:, 3] /= height
  
          return image, boxes, labels
  
  
  class Resize(object):
      def __init__(self, size=300):
          self.size = size
  
      def __call__(self, image, boxes=None, labels=None):
          image = cv2.resize(image, (self.size,
                                     self.size))
          return image, boxes, labels
  
  
  class RandomSaturation(object):
      def __init__(self, lower=0.5, upper=1.5):
          self.lower = lower
          self.upper = upper
          assert self.upper >= self.lower, "contrast upper must be >= lower."
          assert self.lower >= 0, "contrast lower must be non-negative."
  
      def __call__(self, image, boxes=None, labels=None):
          if random.randint(2):
              image[:, :, 1] *= random.uniform(self.lower, self.upper)
  
          return image, boxes, labels
  
  
  class RandomHue(object):
      def __init__(self, delta=18.0):
          assert delta >= 0.0 and delta <= 360.0
          self.delta = delta
  
      def __call__(self, image, boxes=None, labels=None):
          if random.randint(2):
              image[:, :, 0] += random.uniform(-self.delta, self.delta)
              image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0
              image[:, :, 0][image[:, :, 0] < 0.0] += 360.0
          return image, boxes, labels
  
  
  class RandomLightingNoise(object):
      def __init__(self):
          self.perms = ((0, 1, 2), (0, 2, 1),
                        (1, 0, 2), (1, 2, 0),
                        (2, 0, 1), (2, 1, 0))
  
      def __call__(self, image, boxes=None, labels=None):
          if random.randint(2):
              swap = self.perms[random.randint(len(self.perms))]
              shuffle = SwapChannels(swap)  # shuffle channels
              image = shuffle(image)
          return image, boxes, labels
  
  
  class ConvertColor(object):
      def __init__(self, current, transform):
          self.transform = transform
          self.current = current
  
      def __call__(self, image, boxes=None, labels=None):
          if self.current == 'BGR' and self.transform == 'HSV':
              image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
          elif self.current == 'RGB' and self.transform == 'HSV':
              image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
          elif self.current == 'BGR' and self.transform == 'RGB':
              image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
          elif self.current == 'HSV' and self.transform == 'BGR':
              image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
          elif self.current == 'HSV' and self.transform == "RGB":
              image = cv2.cvtColor(image, cv2.COLOR_HSV2RGB)
          else:
              raise NotImplementedError
          return image, boxes, labels
  
  
  class RandomContrast(object):
      def __init__(self, lower=0.5, upper=1.5):
          self.lower = lower
          self.upper = upper
          assert self.upper >= self.lower, "contrast upper must be >= lower."
          assert self.lower >= 0, "contrast lower must be non-negative."
  
      # expects float image
      def __call__(self, image, boxes=None, labels=None):
          if random.randint(2):
              alpha = random.uniform(self.lower, self.upper)
              image *= alpha
          return image, boxes, labels
  
  
  class RandomBrightness(object):
      def __init__(self, delta=32):
          assert delta >= 0.0
          assert delta <= 255.0
          self.delta = delta
  
      def __call__(self, image, boxes=None, labels=None):
          if random.randint(2):
              delta = random.uniform(-self.delta, self.delta)
              image += delta
          return image, boxes, labels
  
  
  class ToCV2Image(object):
      def __call__(self, tensor, boxes=None, labels=None):
          return tensor.cpu().numpy().astype(np.float32).transpose((1, 2, 0)), boxes, labels
  
  
  class ToTensor(object):
      def __call__(self, cvimage, boxes=None, labels=None):
          return torch.from_numpy(cvimage.astype(np.float32)).permute(2, 0, 1), boxes, labels
  
  
  class RandomSampleCrop(object):
      """Crop
      Arguments:
          img (Image): the image being input during training
          boxes (Tensor): the original bounding boxes in pt form
          labels (Tensor): the class labels for each bbox
          mode (float tuple): the min and max jaccard overlaps
      Return:
          (img, boxes, classes)
              img (Image): the cropped image
              boxes (Tensor): the adjusted bounding boxes in pt form
              labels (Tensor): the class labels for each bbox
      """
  
      def __init__(self):
          self.sample_options = (
              # using entire original input image
              None,
              # sample a patch s.t. MIN jaccard w/ obj in .1,.3,.4,.7,.9
              (0.1, None),
              (0.3, None),
              (0.7, None),
              (0.9, None),
              # randomly sample a patch
              (None, None),
          )
  
      def __call__(self, image, boxes=None, labels=None):
          # guard against no boxes
          if boxes is not None and boxes.shape[0] == 0:
              return image, boxes, labels
          height, width, _ = image.shape
          while True:
              # randomly choose a mode
              mode = random.choice(self.sample_options)
              if mode is None:
                  return image, boxes, labels
  
              min_iou, max_iou = mode
              if min_iou is None:
                  min_iou = float('-inf')
              if max_iou is None:
                  max_iou = float('inf')
  
              # max trails (50)
              for _ in range(50):
                  current_image = image
  
                  w = random.uniform(0.3 * width, width)
                  h = random.uniform(0.3 * height, height)
  
                  # aspect ratio constraint b/t .5 & 2
                  if h / w < 0.5 or h / w > 2:
                      continue
  
                  left = random.uniform(width - w)
                  top = random.uniform(height - h)
  
                  # convert to integer rect x1,y1,x2,y2
                  rect = np.array([int(left), int(top), int(left + w), int(top + h)])
  
                  # calculate IoU (jaccard overlap) b/t the cropped and gt boxes
                  overlap = jaccard_numpy(boxes, rect)
  
                  # is min and max overlap constraint satisfied? if not try again
                  if overlap.max() < min_iou or overlap.min() > max_iou:
                      continue
  
                  # cut the crop from the image
                  current_image = current_image[rect[1]:rect[3], rect[0]:rect[2],
                                  :]
  
                  # keep overlap with gt box IF center in sampled patch
                  centers = (boxes[:, :2] + boxes[:, 2:]) / 2.0
  
                  # mask in all gt boxes that above and to the left of centers
                  m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])
  
                  # mask in all gt boxes that under and to the right of centers
                  m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])
  
                  # mask in that both m1 and m2 are true
                  mask = m1 * m2
  
                  # have any valid boxes? try again if not
                  if not mask.any():
                      continue
  
                  # take only matching gt boxes
                  current_boxes = boxes[mask, :].copy()
  
                  # take only matching gt labels
                  current_labels = labels[mask]
  
                  # should we use the box left and top corner or the crop's
                  current_boxes[:, :2] = np.maximum(current_boxes[:, :2],
                                                    rect[:2])
                  # adjust to crop (by substracting crop's left,top)
                  current_boxes[:, :2] -= rect[:2]
  
                  current_boxes[:, 2:] = np.minimum(current_boxes[:, 2:],
                                                    rect[2:])
                  # adjust to crop (by substracting crop's left,top)
                  current_boxes[:, 2:] -= rect[:2]
  
                  return current_image, current_boxes, current_labels
  
  
  class Expand(object):
      def __init__(self, mean):
          self.mean = mean
  
      def __call__(self, image, boxes, labels):
          if random.randint(2):
              return image, boxes, labels
  
          height, width, depth = image.shape
          ratio = random.uniform(1, 4)
          left = random.uniform(0, width * ratio - width)
          top = random.uniform(0, height * ratio - height)
  
          expand_image = np.zeros(
              (int(height * ratio), int(width * ratio), depth),
              dtype=image.dtype)
          expand_image[:, :, :] = self.mean
          expand_image[int(top):int(top + height),
          int(left):int(left + width)] = image
          image = expand_image
  
          boxes = boxes.copy()
          boxes[:, :2] += (int(left), int(top))
          boxes[:, 2:] += (int(left), int(top))
  
          return image, boxes, labels
  
  
  class RandomMirror(object):
      def __call__(self, image, boxes, classes):
          _, width, _ = image.shape
          if random.randint(2):
              image = image[:, ::-1]
              boxes = boxes.copy()
              boxes[:, 0::2] = width - boxes[:, 2::-2]
          return image, boxes, classes
  
  
  class SwapChannels(object):
      """Transforms a tensorized image by swapping the channels in the order
       specified in the swap tuple.
      Args:
          swaps (int triple): final order of channels
              eg: (2, 1, 0)
      """
  
      def __init__(self, swaps):
          self.swaps = swaps
  
      def __call__(self, image):
          """
          Args:
              image (Tensor): image tensor to be transformed
          Return:
              a tensor with channels swapped according to swap
          """
          # if torch.is_tensor(image):
          #     image = image.data.cpu().numpy()
          # else:
          #     image = np.array(image)
          image = image[:, :, self.swaps]
          return image
  
  
  class PhotometricDistort(object):
      def __init__(self):
          self.pd = [
              RandomContrast(),  # RGB
              ConvertColor(current="RGB", transform='HSV'),  # HSV
              RandomSaturation(),  # HSV
              RandomHue(),  # HSV
              ConvertColor(current='HSV', transform='RGB'),  # RGB
              RandomContrast()  # RGB
          ]
          self.rand_brightness = RandomBrightness()
          self.rand_light_noise = RandomLightingNoise()
  
      def __call__(self, image, boxes, labels):
          im = image.copy()
          im, boxes, labels = self.rand_brightness(im, boxes, labels)
          if random.randint(2):
              distort = Compose(self.pd[:-1])
          else:
              distort = Compose(self.pd[1:])
          im, boxes, labels = distort(im, boxes, labels)
          return self.rand_light_noise(im, boxes, labels)
  