old_contents: from . import pre, csr
  import imageio
  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 self.image_format.get() == 'auto'
                      else self.image_format.get())
      results = []
      from skan import pre, csr
      for file in tqdm(filenames):
          image = imageio.imread(file, format=image_format)
          if self.scale_metadata_path is not None:
              md_path = self.scale_metadata_path.get().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(self.threshold_radius.get() /
                                               scale))
          pixel_smoothing_radius = (self.smooth_radius.get() *
                                    pixel_threshold_radius)
          thresholded = pre.threshold(image, sigma=pixel_smoothing_radius,
                                      radius=pixel_threshold_radius,
                                      offset=self.brightness_offset.get())
          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)
      results = pd.concat(results)
  
new_contents: from . import pre, csr
  import imageio
  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)
  
subject: Add module for start-to-finish functions