old_contents: from . import pre, csr
  import imageio
  from tqdm import tqdm
  import numpy as np
  from skimage import morphology
  import pandas as pd
  
  
  def process_images(filenames, image_format, threshold_radius,
                     smooth_radius, brightness_offset, scale_metadata_path):
      image_format = None if image_format == 'auto' else image_format
      results = []
      for file in tqdm(filenames):
          image = imageio.imread(file, format=image_format)
          if scale_metadata_path is not None:
              md_path = scale_metadata_path.split(sep='/')
              meta = image.meta
              for key in md_path:
                  meta = meta[key]
              scale = float(meta)
          else:
              scale = 1  # measurements will be in pixel units
          pixel_threshold_radius = int(np.ceil(threshold_radius / scale))
          pixel_smoothing_radius = smooth_radius * pixel_threshold_radius
          thresholded = pre.threshold(image, sigma=pixel_smoothing_radius,
                                      radius=pixel_threshold_radius,
                                      offset=brightness_offset)
          skeleton = morphology.skeletonize(thresholded)
          framedata = csr.summarise(skeleton, spacing=scale)
          framedata['squiggle'] = np.log2(framedata['branch-distance'] /
                                          framedata['euclidean-distance'])
          framedata['filename'] = [file] * len(framedata)
          results.append(framedata)
      return pd.concat(results)
  
new_contents: from . import pre, csr
  import imageio
  from tqdm import tqdm
  import numpy as np
  from skimage import morphology
  import pandas as pd
  
  
  def process_images(filenames, image_format, threshold_radius,
                     smooth_radius, brightness_offset, scale_metadata_path):
      """Full pipeline from images to skeleton stats with local median threshold.
  
      Parameters
      ----------
      filenames : list of string
          The list of input filenames.
      image_format : string
          The format of the files. 'auto' is automatically determined by the
          imageio library. See imageio documentation for valid image formats.
      threshold_radius : float
          The radius for median thresholding,
      smooth_radius : float in [0, 1]
          The value of sigma with which to Gaussian-smooth the image,
          **relative to `threshold_radius`**.
      brightness_offset : float
          The standard brightness value with which to threshold is the local
          median, `m(x, y)`. Use this value to offset from there: the threshold
          used will be `m(x, y) + brightness_offset`.
      scale_metadata_path : string
          The path in the image dictionary to find the metadata on pixel scale,
          separated by forward slashes ('/').
  
      Returns
      -------
      result : pandas DataFrame
          Data frame containing all computed statistics on the skeletons found
          in the input image.
      """
      image_format = None if image_format == 'auto' else image_format
      results = []
      for file in tqdm(filenames):
          image = imageio.imread(file, format=image_format)
          if scale_metadata_path is not None:
              md_path = scale_metadata_path.split(sep='/')
              meta = image.meta
              for key in md_path:
                  meta = meta[key]
              scale = float(meta)
          else:
              scale = 1  # measurements will be in pixel units
          pixel_threshold_radius = int(np.ceil(threshold_radius / scale))
          pixel_smoothing_radius = smooth_radius * pixel_threshold_radius
          thresholded = pre.threshold(image, sigma=pixel_smoothing_radius,
                                      radius=pixel_threshold_radius,
                                      offset=brightness_offset)
          skeleton = morphology.skeletonize(thresholded)
          framedata = csr.summarise(skeleton, spacing=scale)
          framedata['squiggle'] = np.log2(framedata['branch-distance'] /
                                          framedata['euclidean-distance'])
          framedata['filename'] = [file] * len(framedata)
          results.append(framedata)
      return pd.concat(results)
  
subject: Add docstring to pipe.process_images