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img_idx1,img_idx2 = random_imgID_generator(img_labels)  
max_retries = 2000
img_idx1, img_idx2 = img_pairs_filter(img_labels,img_idx1,img_idx2,
                                      max_retries=max_retries)
                                      
img_info(img_labels, img_idx1, img_idx2)

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twitter_saliency_eval_dir = f'{img_dir}//Twitter_saliency'
if not os.path.exists(twitter_saliency_eval_dir):
  print(f'No outputs directory: {twitter_saliency_eval_dir} found ...')
  execute_in_shell([f'mkdir {twitter_saliency_eval_dir}'])
  print(f'Created outputs directory: {twitter_saliency_eval_dir}')
  
labels_encoder_file = f'{twitter_saliency_eval_dir}/labels_encoder.json'
if os.path.exists(labels_encoder_file):
  with open(labels_encoder_file) as f:
    labels_encoder = json.loads(f.read())

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clear_image_history(unzip_dir)
get_fairface_img(img_labels, img_idx1, unzip_dir, fairface_data)
get_fairface_img(img_labels, img_idx2, unzip_dir, fairface_data)

img_path = next(data_dir.glob("./*.jpg"))
print(img_path)

for img_file in data_dir.glob("./*.jpg"):
  img = mpimg.imread(img_file)
  plt.figure()

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img_id1 = str(img_labels.iloc[img_idx1].file).split('/')[-1].replace('.jpg','')
img_race1 = str(img_labels.iloc[img_idx1].race)
img_gender1 = str(img_labels.iloc[img_idx1].gender)
img_id2 = str(img_labels.iloc[img_idx2].file).split('/')[-1].replace('.jpg','')
img_race2 = str(img_labels.iloc[img_idx2].race)
img_gender2 = str(img_labels.iloc[img_idx2].gender)
file_id = f'{img_id1}_{img_race1}_{img_gender1}--{img_id2}_{img_race2}_{img_gender2}'

output_dir = './'
padding = 0

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def saliency_to_image(input_image, s_point, images_list, padding=0, image_mode='horizontal'):
  if image_mode == 'horizontal':
    s_idx = 0
  elif image_mode == 'vertical':
    s_idx = 1
  else:
    raise ValueError('Unsupported image mode. \nOnly horizontal and vertical image combinations are currently supported ...')
  for i in range(len(images_list)):
    if len(s_point)>1:
      warnings.warn('Only reading the first saliency point. \nParsing  one saliency point is currently supported ...')

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pairwise_tests_data = f'{img_dir}/Twitter_saliency/FairFace_pairwise_tests.csv'
if os.path.exists(pairwise_tests_data):
  pairwise_df = pd.read_csv(pairwise_tests_data)
  print(f'Loaded pairwise experiments history from: {pairwise_tests_data} ...')
  experiment_ids = list(pairwise_df['experiment_id'].values)
  instance_ids   = list(pairwise_df['instance_id'].values)
  img1           = list(pairwise_df['img1'].values)
  img2           = list(pairwise_df['img2'].values)
  baseline_h1    = list(pairwise_df['baseline_h1'].values)
  baseline_h2    = list(pairwise_df['baseline_h2'].values)

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num_pairwise_tests = 1 if debug else 2
num_eval = 1 if debug else 25
len(experiment_ids)

for _ in tqdm(range(num_pairwise_tests)):
  img_idx1,img_idx2 = random_imgID_generator(img_labels)
  max_retries = 2000
  img_idx1, img_idx2 = img_pairs_filter(img_labels,img_idx1,img_idx2,
                                        max_retries=max_retries)
  img1_info,img2_info = img_info(img_labels, img_idx1, img_idx2)

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w, p = wilcoxon(pairwise_df['baseline_h2']-pairwise_df['saliency_out'])
print(w,p)
pairwise_df['globalh2_wt_p'] = p
pairwise_df['globalh2_wt_w'] = w
w, p = wilcoxon(pairwise_df['baseline_h1']-pairwise_df['saliency_out'])
print(w,p)
pairwise_df['globalh1_wt_p'] = p
pairwise_df['globalh1_wt_w'] = w
w, p = wilcoxon(pairwise_df['baseline_v2']-pairwise_df['saliency_out'])
print(w,p)

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pairwise_df.to_csv(pairwise_tests_data)

print(len(pairwise_df))
print(pairwise_df.head(2))
print(pairwise_df.tail(2))


fail_df = pairwise_df.loc[pairwise_df['baseline_v1'] != pairwise_df['saliency_out'],
                           ['experiment_id', 'instance_id', 'baseline_h1', 'baseline_h2',
                            'baseline_v1', 'baseline_v2', 'saliency_out']]

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import os, gc, ast, cv2, glob, json,shutil, random, collections, \
       imageio, pydicom, numpy as np, pandas as pd, SimpleITK as sitk

from pathlib import Path
from tqdm.auto import tqdm
from pydicom.pixel_data_handlers.util import apply_voi_lut