text: import cv2
  import math
  import numpy as np
  import os
  import torch
  from basicsr.archs.rrdbnet_arch import RRDBNet
  from torch.hub import download_url_to_file, get_dir
  from torch.nn import functional as F
  from urllib.parse import urlparse
  
  ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
  
  
  class RealESRGANer():
  
      def __init__(self, scale, model_path, tile=0, tile_pad=10, pre_pad=10, half=False):
          self.scale = scale
          self.tile_size = tile
          self.tile_pad = tile_pad
          self.pre_pad = pre_pad
          self.mod_scale = None
          self.half = half
  
          # initialize model
          self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
          model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=scale)
  
          if model_path.startswith('https://'):
              model_path = load_file_from_url(
                  url=model_path, model_dir='realesrgan/weights', progress=True, file_name=None)
          loadnet = torch.load(model_path)
          if 'params_ema' in loadnet:
              keyname = 'params_ema'
          else:
              keyname = 'params'
          model.load_state_dict(loadnet[keyname], strict=True)
          model.eval()
          self.model = model.to(self.device)
          if self.half:
              self.model = self.model.half()
  
      def pre_process(self, img):
          img = torch.from_numpy(np.transpose(img, (2, 0, 1))).float()
          self.img = img.unsqueeze(0).to(self.device)
          if self.half:
              self.img = self.img.half()
  
          # pre_pad
          if self.pre_pad != 0:
              self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
          # mod pad
          if self.scale == 2:
              self.mod_scale = 2
          elif self.scale == 1:
              self.mod_scale = 4
          if self.mod_scale is not None:
              self.mod_pad_h, self.mod_pad_w = 0, 0
              _, _, h, w = self.img.size()
              if (h % self.mod_scale != 0):
                  self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
              if (w % self.mod_scale != 0):
                  self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
              self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
  
      def process(self):
          self.output = self.model(self.img)
  
      def tile_process(self):
          """Modified from: https://github.com/ata4/esrgan-launcher
          """
          batch, channel, height, width = self.img.shape
          output_height = height * self.scale
          output_width = width * self.scale
          output_shape = (batch, channel, output_height, output_width)
  
          # start with black image
          self.output = self.img.new_zeros(output_shape)
          tiles_x = math.ceil(width / self.tile_size)
          tiles_y = math.ceil(height / self.tile_size)
  
          # loop over all tiles
          for y in range(tiles_y):
              for x in range(tiles_x):
                  # extract tile from input image
                  ofs_x = x * self.tile_size
                  ofs_y = y * self.tile_size
                  # input tile area on total image
                  input_start_x = ofs_x
                  input_end_x = min(ofs_x + self.tile_size, width)
                  input_start_y = ofs_y
                  input_end_y = min(ofs_y + self.tile_size, height)
  
                  # input tile area on total image with padding
                  input_start_x_pad = max(input_start_x - self.tile_pad, 0)
                  input_end_x_pad = min(input_end_x + self.tile_pad, width)
                  input_start_y_pad = max(input_start_y - self.tile_pad, 0)
                  input_end_y_pad = min(input_end_y + self.tile_pad, height)
  
                  # input tile dimensions
                  input_tile_width = input_end_x - input_start_x
                  input_tile_height = input_end_y - input_start_y
                  tile_idx = y * tiles_x + x + 1
                  input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
  
                  # upscale tile
                  try:
                      with torch.no_grad():
                          output_tile = self.model(input_tile)
                  except Exception as error:
                      print('Error', error)
                  print(f'\tTile {tile_idx}/{tiles_x * tiles_y}')
  
                  # output tile area on total image
                  output_start_x = input_start_x * self.scale
                  output_end_x = input_end_x * self.scale
                  output_start_y = input_start_y * self.scale
                  output_end_y = input_end_y * self.scale
  
                  # output tile area without padding
                  output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
                  output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
                  output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
                  output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
  
                  # put tile into output image
                  self.output[:, :, output_start_y:output_end_y,
                              output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
                                                                         output_start_x_tile:output_end_x_tile]
  
      def post_process(self):
          # remove extra pad
          if self.mod_scale is not None:
              _, _, h, w = self.output.size()
              self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
          # remove prepad
          if self.pre_pad != 0:
              _, _, h, w = self.output.size()
              self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
          return self.output
  
      @torch.no_grad()
      def enhance(self, img, outscale=None, alpha_upsampler='realesrgan'):
          h_input, w_input = img.shape[0:2]
          # img: numpy
          img = img.astype(np.float32)
          if np.max(img) > 255:  # 16-bit image
              max_range = 65535
              print('\tInput is a 16-bit image')
          else:
              max_range = 255
          img = img / max_range
          if len(img.shape) == 2:  # gray image
              img_mode = 'L'
              img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
          elif img.shape[2] == 4:  # RGBA image with alpha channel
              img_mode = 'RGBA'
              alpha = img[:, :, 3]
              img = img[:, :, 0:3]
              img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
              if alpha_upsampler == 'realesrgan':
                  alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2RGB)
          else:
              img_mode = 'RGB'
              img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
  
          # ------------------- process image (without the alpha channel) ------------------- #
          self.pre_process(img)
          if self.tile_size > 0:
              self.tile_process()
          else:
              self.process()
          output_img = self.post_process()
          output_img = output_img.data.squeeze().float().cpu().clamp_(0, 1).numpy()
          output_img = np.transpose(output_img[[2, 1, 0], :, :], (1, 2, 0))
          if img_mode == 'L':
              output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2GRAY)
  
          # ------------------- process the alpha channel if necessary ------------------- #
          if img_mode == 'RGBA':
              if alpha_upsampler == 'realesrgan':
                  self.pre_process(alpha)
                  if self.tile_size > 0:
                      self.tile_process()
                  else:
                      self.process()
                  output_alpha = self.post_process()
                  output_alpha = output_alpha.data.squeeze().float().cpu().clamp_(0, 1).numpy()
                  output_alpha = np.transpose(output_alpha[[2, 1, 0], :, :], (1, 2, 0))
                  output_alpha = cv2.cvtColor(output_alpha, cv2.COLOR_BGR2GRAY)
              else:
                  h, w = alpha.shape[0:2]
                  output_alpha = cv2.resize(alpha, (w * self.scale, h * self.scale), interpolation=cv2.INTER_LINEAR)
  
              # merge the alpha channel
              output_img = cv2.cvtColor(output_img, cv2.COLOR_BGR2BGRA)
              output_img[:, :, 3] = output_alpha
  
          # ------------------------------ return ------------------------------ #
          if max_range == 65535:  # 16-bit image
              output = (output_img * 65535.0).round().astype(np.uint16)
          else:
              output = (output_img * 255.0).round().astype(np.uint8)
  
          if outscale is not None and outscale != float(self.scale):
              output = cv2.resize(
                  output, (
                      int(w_input * outscale),
                      int(h_input * outscale),
                  ), interpolation=cv2.INTER_LANCZOS4)
  
          return output, img_mode
  
  
  def load_file_from_url(url, model_dir=None, progress=True, file_name=None):
      """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py
      """
      if model_dir is None:
          hub_dir = get_dir()
          model_dir = os.path.join(hub_dir, 'checkpoints')
  
      os.makedirs(os.path.join(ROOT_DIR, model_dir), exist_ok=True)
  
      parts = urlparse(url)
      filename = os.path.basename(parts.path)
      if file_name is not None:
          filename = file_name
      cached_file = os.path.abspath(os.path.join(ROOT_DIR, model_dir, filename))
      if not os.path.exists(cached_file):
          print(f'Downloading: "{url}" to {cached_file}\n')
          download_url_to_file(url, cached_file, hash_prefix=None, progress=progress)
      return cached_file
  