code: 
  from detect import (detect_logos, detect_text)
  import pandas as pd
  import re
  import os
  #from __future__ import print_function
  from google.cloud import vision
  
  
  images_path = "C:\\Users\\heinz\\Yagora GmbH\\Ievgen Kyrda - Crawler\\images\\foodnewsgermany_images/"
  file_names = os.listdir(os.path.dirname(images_path))
  
  file_paths = [images_path + f for f in file_names]
  
  logos = [detect_logos(f) for f in file_paths]
  
  texts = [detect_text(f)[0].description for f in file_paths]
  # remove line break symbols
  texts = [x.replace("\n", ", ") for x in texts]
  
  contained = []
  #contained[1] = "test"
  for i in range(len(logos)): # loop over future rows of df
      tmp = []
      for j in logos[i]: # for every logo-row, check if in text
          if j.lower() in texts[i].lower():
              tmp.append(logos[i])
          else:
              tmp.append(None)
      contained.append(tmp)
  
  detect_df = pd.DataFrame(
      list(zip(file_names, texts, logos, contained, file_paths)),
      columns = ["files", "texts", "logos", "probable_brand", "file_path"]
  )
  detect_df
  
  # other ideas:
  # if logo in existing logos, add logo
  
  
  
  from PIL import Image
  from io import BytesIO
  from IPython.display import HTML
  import base64
  
  
  pd.set_option('display.max_colwidth', -1)
  
  def get_thumbnail(path):
      i = Image.open(path)
      i.thumbnail((150, 150), Image.LANCZOS)
      return i
  
  
  def image_base64(im):
      if isinstance(im, str):
          im = get_thumbnail(im)
      with BytesIO() as buffer:
          im.save(buffer, 'jpeg')
          return base64.b64encode(buffer.getvalue()).decode()
  
  def image_formatter(im):
      return f'<img src="data:image/jpeg;base64,{image_base64(im)}">'
  
  #dogs['file'] = dogs.id.map(lambda id: f'../input/train/{id}.jpg')
  
  detect_df['image'] = detect_df.file_path.map(lambda f: get_thumbnail(f))
  
  HTML(detect_df.to_html(formatters={'image': image_formatter}, escape=False))
apis: pandas.set_option