text: #!/usr/bin/env python3
  # -*- coding:utf-8 -*-
  # Copyright (c) Megvii, Inc. and its affiliates.
  
  import cv2
  import numpy as np
  
  from yolox.utils import adjust_box_anns
  
  import random
  
  from ..data_augment import box_candidates, random_perspective, augment_hsv
  from .datasets_wrapper import Dataset
  
  
  def get_mosaic_coordinate(mosaic_image, mosaic_index, xc, yc, w, h, input_h, input_w):
      # TODO update doc
      # index0 to top left part of image
      if mosaic_index == 0:
          x1, y1, x2, y2 = max(xc - w, 0), max(yc - h, 0), xc, yc
          small_coord = w - (x2 - x1), h - (y2 - y1), w, h
      # index1 to top right part of image
      elif mosaic_index == 1:
          x1, y1, x2, y2 = xc, max(yc - h, 0), min(xc + w, input_w * 2), yc
          small_coord = 0, h - (y2 - y1), min(w, x2 - x1), h
      # index2 to bottom left part of image
      elif mosaic_index == 2:
          x1, y1, x2, y2 = max(xc - w, 0), yc, xc, min(input_h * 2, yc + h)
          small_coord = w - (x2 - x1), 0, w, min(y2 - y1, h)
      # index2 to bottom right part of image
      elif mosaic_index == 3:
          x1, y1, x2, y2 = xc, yc, min(xc + w, input_w * 2), min(input_h * 2, yc + h)  # noqa
          small_coord = 0, 0, min(w, x2 - x1), min(y2 - y1, h)
      return (x1, y1, x2, y2), small_coord
  
  
  class MosaicDetection(Dataset):
      """Detection dataset wrapper that performs mixup for normal dataset."""
  
      def __init__(
          self, dataset, img_size, mosaic=True, preproc=None,
          degrees=10.0, translate=0.1, scale=(0.5, 1.5), mscale=(0.5, 1.5),
          shear=2.0, perspective=0.0, enable_mixup=True, *args
      ):
          """
  
          Args:
              dataset(Dataset) : Pytorch dataset object.
              img_size (tuple):
              mosaic (bool): enable mosaic augmentation or not.
              preproc (func):
              degrees (float):
              translate (float):
              scale (tuple):
              mscale (tuple):
              shear (float):
              perspective (float):
              enable_mixup (bool):
              *args(tuple) : Additional arguments for mixup random sampler.
          """
          super().__init__(img_size, mosaic=mosaic)
          self._dataset = dataset
          self.preproc = preproc
          self.degrees = degrees
          self.translate = translate
          self.scale = scale
          self.shear = shear
          self.perspective = perspective
          self.mixup_scale = mscale
          self.enable_mosaic = mosaic
          self.enable_mixup = enable_mixup
  
      def __len__(self):
          return len(self._dataset)
  
      @Dataset.resize_getitem
      def __getitem__(self, idx):
          if self.enable_mosaic:
              mosaic_labels = []
              input_dim = self._dataset.input_dim
              input_h, input_w = input_dim[0], input_dim[1]
  
              # yc, xc = s, s  # mosaic center x, y
              yc = int(random.uniform(0.5 * input_h, 1.5 * input_h))
              xc = int(random.uniform(0.5 * input_w, 1.5 * input_w))
  
              # 3 additional image indices
              indices = [idx] + [random.randint(0, len(self._dataset) - 1) for _ in range(3)]
  
              for i_mosaic, index in enumerate(indices):
                  img, _labels, _, _ = self._dataset.pull_item(index)
                  h0, w0 = img.shape[:2]  # orig hw
                  scale = min(1. * input_h / h0, 1. * input_w / w0)
                  img = cv2.resize(
                      img, (int(w0 * scale), int(h0 * scale)), interpolation=cv2.INTER_LINEAR
                  )
                  # generate output mosaic image
                  (h, w, c) = img.shape[:3]
                  if i_mosaic == 0:
                      mosaic_img = np.full((input_h * 2, input_w * 2, c), 114, dtype=np.uint8)
  
                  # suffix l means large image, while s means small image in mosaic aug.
                  (l_x1, l_y1, l_x2, l_y2), (s_x1, s_y1, s_x2, s_y2) = get_mosaic_coordinate(
                      mosaic_img, i_mosaic, xc, yc, w, h, input_h, input_w
                  )
  
                  mosaic_img[l_y1:l_y2, l_x1:l_x2] = img[s_y1:s_y2, s_x1:s_x2]
                  padw, padh = l_x1 - s_x1, l_y1 - s_y1
  
                  labels = _labels.copy()
                  # Normalized xywh to pixel xyxy format
                  if _labels.size > 0:
                      labels[:, 0] = scale * _labels[:, 0] + padw
                      labels[:, 1] = scale * _labels[:, 1] + padh
                      labels[:, 2] = scale * _labels[:, 2] + padw
                      labels[:, 3] = scale * _labels[:, 3] + padh
                  mosaic_labels.append(labels)
  
              if len(mosaic_labels):
                  mosaic_labels = np.concatenate(mosaic_labels, 0)
                  '''
                  np.clip(mosaic_labels[:, 0], 0, 2 * input_w, out=mosaic_labels[:, 0])
                  np.clip(mosaic_labels[:, 1], 0, 2 * input_h, out=mosaic_labels[:, 1])
                  np.clip(mosaic_labels[:, 2], 0, 2 * input_w, out=mosaic_labels[:, 2])
                  np.clip(mosaic_labels[:, 3], 0, 2 * input_h, out=mosaic_labels[:, 3])
                  '''
                  
                  mosaic_labels = mosaic_labels[mosaic_labels[:, 0] < 2 * input_w]
                  mosaic_labels = mosaic_labels[mosaic_labels[:, 2] > 0]
                  mosaic_labels = mosaic_labels[mosaic_labels[:, 1] < 2 * input_h]
                  mosaic_labels = mosaic_labels[mosaic_labels[:, 3] > 0]
                  
              #augment_hsv(mosaic_img)
              mosaic_img, mosaic_labels = random_perspective(
                  mosaic_img,
                  mosaic_labels,
                  degrees=self.degrees,
                  translate=self.translate,
                  scale=self.scale,
                  shear=self.shear,
                  perspective=self.perspective,
                  border=[-input_h // 2, -input_w // 2],
              )  # border to remove
  
              # -----------------------------------------------------------------
              # CopyPaste: https://arxiv.org/abs/2012.07177
              # -----------------------------------------------------------------
              if self.enable_mixup and not len(mosaic_labels) == 0:
                  mosaic_img, mosaic_labels = self.mixup(mosaic_img, mosaic_labels, self.input_dim)
              
              mix_img, padded_labels = self.preproc(mosaic_img, mosaic_labels, self.input_dim)
              img_info = (mix_img.shape[1], mix_img.shape[0])
  
              return mix_img, padded_labels, img_info, np.array([idx])
  
          else:
              self._dataset._input_dim = self.input_dim
              img, label, img_info, id_ = self._dataset.pull_item(idx)
              img, label = self.preproc(img, label, self.input_dim)
              return img, label, img_info, id_
  
      def mixup(self, origin_img, origin_labels, input_dim):
          jit_factor = random.uniform(*self.mixup_scale)
          FLIP = random.uniform(0, 1) > 0.5
          cp_labels = []
          while len(cp_labels) == 0:
              cp_index = random.randint(0, self.__len__() - 1)
              cp_labels = self._dataset.load_anno(cp_index)
          img, cp_labels, _, _ = self._dataset.pull_item(cp_index)
  
          if len(img.shape) == 3:
              cp_img = np.ones((input_dim[0], input_dim[1], 3)) * 114.0
          else:
              cp_img = np.ones(input_dim) * 114.0
          cp_scale_ratio = min(input_dim[0] / img.shape[0], input_dim[1] / img.shape[1])
          resized_img = cv2.resize(
              img,
              (int(img.shape[1] * cp_scale_ratio), int(img.shape[0] * cp_scale_ratio)),
              interpolation=cv2.INTER_LINEAR,
          ).astype(np.float32)
          cp_img[
              : int(img.shape[0] * cp_scale_ratio), : int(img.shape[1] * cp_scale_ratio)
          ] = resized_img
          cp_img = cv2.resize(
              cp_img,
              (int(cp_img.shape[1] * jit_factor), int(cp_img.shape[0] * jit_factor)),
          )
          cp_scale_ratio *= jit_factor
          if FLIP:
              cp_img = cp_img[:, ::-1, :]
  
          origin_h, origin_w = cp_img.shape[:2]
          target_h, target_w = origin_img.shape[:2]
          padded_img = np.zeros(
              (max(origin_h, target_h), max(origin_w, target_w), 3)
          ).astype(np.uint8)
          padded_img[:origin_h, :origin_w] = cp_img
  
          x_offset, y_offset = 0, 0
          if padded_img.shape[0] > target_h:
              y_offset = random.randint(0, padded_img.shape[0] - target_h - 1)
          if padded_img.shape[1] > target_w:
              x_offset = random.randint(0, padded_img.shape[1] - target_w - 1)
          padded_cropped_img = padded_img[
              y_offset: y_offset + target_h, x_offset: x_offset + target_w
          ]
  
          cp_bboxes_origin_np = adjust_box_anns(
              cp_labels[:, :4].copy(), cp_scale_ratio, 0, 0, origin_w, origin_h
          )
          if FLIP:
              cp_bboxes_origin_np[:, 0::2] = (
                  origin_w - cp_bboxes_origin_np[:, 0::2][:, ::-1]
              )
          cp_bboxes_transformed_np = cp_bboxes_origin_np.copy()
          '''
          cp_bboxes_transformed_np[:, 0::2] = np.clip(
              cp_bboxes_transformed_np[:, 0::2] - x_offset, 0, target_w
          )
          cp_bboxes_transformed_np[:, 1::2] = np.clip(
              cp_bboxes_transformed_np[:, 1::2] - y_offset, 0, target_h
          )
          '''
          cp_bboxes_transformed_np[:, 0::2] = cp_bboxes_transformed_np[:, 0::2] - x_offset
          cp_bboxes_transformed_np[:, 1::2] = cp_bboxes_transformed_np[:, 1::2] - y_offset
          keep_list = box_candidates(cp_bboxes_origin_np.T, cp_bboxes_transformed_np.T, 5)
  
          if keep_list.sum() >= 1.0:
              cls_labels = cp_labels[keep_list, 4:5].copy()
              id_labels = cp_labels[keep_list, 5:6].copy()
              box_labels = cp_bboxes_transformed_np[keep_list]
              labels = np.hstack((box_labels, cls_labels, id_labels))
              # remove outside bbox
              labels = labels[labels[:, 0] < target_w]
              labels = labels[labels[:, 2] > 0]
              labels = labels[labels[:, 1] < target_h]
              labels = labels[labels[:, 3] > 0]
              origin_labels = np.vstack((origin_labels, labels))
              origin_img = origin_img.astype(np.float32)
              origin_img = 0.5 * origin_img + 0.5 * padded_cropped_img.astype(np.float32)
  
          return origin_img, origin_labels
  