content: import numpy as np
  import cv2
  import torch
  import numpy as np
  from PIL import Image
  
          
  def instantiate_from_config(config):
      if not "target" in config:
          raise KeyError("Expected key `target` to instantiate.")
      return get_obj_from_str(config["target"])(**config.get("params", dict()))
  
  
  def get_obj_from_str(string, reload=False):
      import importlib
      module, cls = string.rsplit(".", 1)
      if reload:
          module_imp = importlib.import_module(module)
          importlib.reload(module_imp)
      return getattr(importlib.import_module(module, package=None), cls)
  
          
  def tensor_detail(t):
      assert type(t) == torch.Tensor
      print(f"shape: {t.shape} mean: {t.mean():.2f}, std: {t.std():.2f}, min: {t.min():.2f}, max: {t.max():.2f}")
  
  
  
  def drawRoundRec(draw, color, x, y, w, h, r):
      drawObject = draw
  
      '''Rounds'''
      drawObject.ellipse((x, y, x + r, y + r), fill=color)
      drawObject.ellipse((x + w - r, y, x + w, y + r), fill=color)
      drawObject.ellipse((x, y + h - r, x + r, y + h), fill=color)
      drawObject.ellipse((x + w - r, y + h - r, x + w, y + h), fill=color)
  
      '''rec.s'''
      drawObject.rectangle((x + r / 2, y, x + w - (r / 2), y + h), fill=color)
      drawObject.rectangle((x, y + r / 2, x + w, y + h - (r / 2)), fill=color)
  
  
  def do_resize_content(original_image: Image, scale_rate):
      # resize image content wile retain the original image size
      if scale_rate != 1:
          # Calculate the new size after rescaling
          new_size = tuple(int(dim * scale_rate) for dim in original_image.size)
          # Resize the image while maintaining the aspect ratio
          resized_image = original_image.resize(new_size)
          # Create a new image with the original size and black background
          padded_image = Image.new("RGBA", original_image.size, (0, 0, 0, 0))
          paste_position = ((original_image.width - resized_image.width) // 2, (original_image.height - resized_image.height) // 2)
          padded_image.paste(resized_image, paste_position)
          return padded_image
      else:
          return original_image
  
  def add_stroke(img, color=(255, 255, 255), stroke_radius=3):
      # color in R, G, B format
      if isinstance(img, Image.Image):
          assert img.mode == "RGBA"
          img = cv2.cvtColor(np.array(img), cv2.COLOR_RGBA2BGRA)
      else:
          assert img.shape[2] == 4
      gray = img[:,:, 3]
      ret, binary = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
      contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)  
      res = cv2.drawContours(img, contours,-1, tuple(color)[::-1] + (255,), stroke_radius)
      return Image.fromarray(cv2.cvtColor(res,cv2.COLOR_BGRA2RGBA))
  
  def make_blob(image_size=(512, 512), sigma=0.2):
      """
      make 2D blob image with:
      I(x, y)=1-\exp \left(-\frac{(x-H / 2)^2+(y-W / 2)^2}{2 \sigma^2 HS}\right)
      """
      import numpy as np
      H, W = image_size
      x = np.arange(0, W, 1, float)
      y = np.arange(0, H, 1, float)
      x, y = np.meshgrid(x, y)
      x0 = W // 2
      y0 = H // 2
      img = 1 - np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2 * H * W))
      return (img * 255).astype(np.uint8)