Downloads and creates annotation for images from openImages dataset containing certain classes

download_classes_open_images.py

"""
  This file helps reasearchers download and process the openImages dataset.
  Author: Ioannis Athanasiadis(ath.ioannis94@gmail.com)
"""
import urllib2
import csv
import os
import shutil
from subprocess import call
import cv2


PATH_TO_IMAGES_CSV = './images.csv'
PATH_TO_ANNOTATION_CSV = './annotations-human-bbox.csv'

CLASSES = ["/m/01g317", "/m/09j2d"] # you can specify the classes here directly or use the class popularity function

DESIRED_WIDTH = 640 # always greater than any width
DESIRED_HEIGHT = 640 # always greater than any height
DESIRED_SIZE = str(DESIRED_WIDTH) + "x" + str(DESIRED_HEIGHT)
DESTINATION_FOLDER = './'
GET_CLASSES_BY_POPULARITY = False



def get_class_popularity(path_to_annotation_csv):
  """
    Args:
      path_to_annotation_csv: path to the annotation csv file.
    Returns:
      A dictionary where the each key is the LabelName and its value is the corresponding
      number of images containing this LabelName.
  """
  classes = {}
  csv_file = open(os.path.join(path_to_annotation_csv), "r")
  annotations_csv_in  = csv.DictReader(csv_file, delimiter=',')
  for t in annotations_csv_in:
    if(t["LabelName"] in classes.keys()):
      classes[t["LabelName"]] += 1
    else:
      classes[t["LabelName"]] = 1
  ret = sorted(classes.items(), key=lambda x: x[1], reverse=True)
  
  return ret


def create_annotation(image_ids_to_download, destination_folder, path_to_annotation_csv, path_to_images_csv):
  """
    Creates annotation needed for dataset to be read 
    and used for object detection algorithms.
    Args:
      image_ids_to_download: list of image ids that have been validly downloaded
      destination_folder: directory where the new annotation and the downloaded images will be placed into.
      path_to_annotation_csv: path to the annotation csv file.
      path_to_images_csv: path to the images csv file.
  """
  if(not os.path.isdir(os.path.join(destination_folder, 'train'))):
    os.makedirs(os.path.join(destination_folder, 'train'))
  images_csv_in  = csv.DictReader(open(os.path.join(path_to_images_csv), "r"), delimiter=',')
  images_csv_out = csv.DictWriter(open(os.path.join(destination_folder, "train", "images.csv"), "w"), 
                                  fieldnames=images_csv_in.fieldnames)
  images_csv_out.writeheader()
  for t in images_csv_in:
    if(t["ImageID"] in image_ids_to_download):
      images_csv_out.writerow(t)
  annotations_csv_in  = csv.DictReader(open(os.path.join(path_to_annotation_csv), "r"), delimiter=',')
  annotations_csv_out = csv.DictWriter(open(os.path.join(destination_folder, "train", "annotations-human-bbox.csv"), "w"),
                                       fieldnames=annotations_csv_in.fieldnames)
  annotations_csv_out.writeheader()
  for t in annotations_csv_in:
    if(t["LabelName"] in CLASSES):
      img = cv2.imread(os.path.join(destination_folder, 'Images', t["ImageID"] + ".jpg"))
      height, width = img.shape[:2]
      if(width < DESIRED_WIDTH and height < DESIRED_HEIGHT):
        t["XMin"] = int(width * t["XMin"] + (DESIRED_WIDTH - width))
        t["XMax"] = int(width * t["XMax"] + (DESIRED_WIDTH - width))
        t["YMin"] = int(height * t["YMin"] + (DESIRED_HEIGHT - height))
        t["YMax"] = int(height * t["YMax"] + (DESIRED_HEIGHT - height))
      else:
        # resize image if needed
        cv2.resize(img, (DESIRED_WIDTH, DESIRED_HEIGHT))
        cv2.imwrite(os.path.join(destination_folder, 'Images', t["ImageID"] + ".jpg"), img)
        t["XMin"] = int(DESIRED_WIDTH * t["XMin"])
        t["XMax"] = int(DESIRED_WIDTH * t["XMax"])
        t["YMin"] = int(DESIRED_HEIGHT * t["YMin"])
        t["YMax"] = int(DESIRED_HEIGHT * t["YMax"])
      annotations_csv_out.writerow(t)
  return

def image_index_to_download(path_to_annotation_csv, classes):
  """
    Find all the images which contain elements of classes and
    will be candidates for downloading.
    Args:
      path_to_annotation_csv: path to the annotation csv file.
      classes: The class names that will be used for indexing.
    Returns:
      Candidate image indexes to be downloaded.
  """
  image_ids_to_download = set([])
  image_set_file = os.path.join(path_to_annotation_csv)
  assert os.path.exists(image_set_file), \
        'File does not exist: {}'.format(image_set_file)
  with open(image_set_file, 'r') as csvfile:
    csv_reader = csv.DictReader(csvfile, delimiter=',')
    for t in csv_reader:
      if( t["LabelName"] in classes):
        image_ids_to_download.add(t["ImageID"])
  return image_ids_to_download

def get_image_urls(image_ids_to_download, path_to_images_csv):
  """
    Args:
      image_ids_to_download: candidate image id's to download.
      path_to_images_csv: path to the images csv file.
    Returns:
      2D array [nx2] . The first collumn contains the candidate image id's to download
      and the second contains the image urls of the candidate image id's to download.
  """
  image_urls_to_download = []
  image_url_file = os.path.join(path_to_images_csv)
  assert os.path.exists(image_url_file), \
        'File does not exist: {}'.format(image_url_file)
  with open(image_url_file, 'r') as csvfile:
    csv_reader = csv.DictReader(csvfile, delimiter=',')
    for t in csv_reader:
      if(t["ImageID"] in image_ids_to_download):
        if(t["Thumbnail300KURL"]):
          image_urls_to_download.append([t["ImageID"], t["Thumbnail300KURL"]])
        else:
          image_urls_to_download.append([t["ImageID"], t["OriginalURL"]])
  return image_urls_to_download

def download_images(image_urls_to_download, destination_folder):
  """
    Args:
      image_urls_to_download: 2D array [nx2] . The first collumn contains the candidate image id's to download
                              and the second contains the image urls of the candidate image id's to download.
      destination_folder: directory where the new annotation and the downloaded images will be placed into.
    Returns:
      2D array [mx2] . The first collumn contains the candidate image id's which have been validly downloaded
      and the second contains the image urls of the candidate image id's which have been validly downloaded.
  """
  # create a directory
  if(not os.path.isdir(os.path.join(destination_folder, 'Images'))):
    os.mkdir(os.path.join(destination_folder, 'Images'))
  image_urls_downloaded = []
  l = len(image_urls_to_download)
  i = 1
  # download jpg images
  for img_rec in image_urls_to_download:
    print "\r", (str(i) +"/" + str(len) + " ") + img_rec[1]
    try:
      req = urllib2.Request(img_rec[1])
      response = urllib2.urlopen(req)
      if(200 == response.getcode() and response.geturl() == img_rec[1]):
        with open(os.path.join(destination_folder, 'Images', img_rec[0] + '.jpg'), "wb") as output_file:
          shutil.copyfileobj(response.fp, output_file)
        image_urls_downloaded.append(img_rec)
    except urllib2.HTTPError:
      continue
    except:
      continue
  return image_urls_downloaded

if __name__ == "__main__":
  print("Starting procedure")
  # get most popular classes
  CLASS_POPULARITY = get_class_popularity(PATH_TO_ANNOTATION_CSV)
  c = filter(lambda x: (x[1] < 1000 and x[1] > 600), CLASS_POPULARITY)[0]
  CLASSES = [c]
  print("classes found")
  # find image ids
  image_ids_to_download = image_index_to_download(PATH_TO_ANNOTATION_CSV, CLASSES)
  print("ids found")
  # get image url's
  image_urls_to_download = get_image_urls(image_ids_to_download, PATH_TO_IMAGES_CSV)
  print("image urls found")
  # download all images
  image_urls_downloaded = download_images(image_urls_to_download, DESTINATION_FOLDER)
  print("jpg images downloaded")
  # create annotation
  create_annotation(image_urls_downloaded,DESTINATION_FOLDER, PATH_TO_ANNOTATION_CSV, PATH_TO_IMAGES_CSV)
  print("annotation created")
  # resize all downloaded images with black fillings to do training
  call(['mogrify', '-extent', DESIRED_SIZE, '-gravity', 'Center', '-fill', 'black', 'Images/*.jpg'])  

main currently uses class popularity to select the classes
and mogrify to resize them.
This can be changed according to user preferences.