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+# main imports
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+import os, sys
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+import argparse
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+import json
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+import numpy as np
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+import shutil
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+
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+# PNG images
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+from PIL import Image
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+
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+# others import
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+from ipfml import utils
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+from scipy.signal import savgol_filter
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+
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+'''
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+Display progress information as progress bar
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+'''
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+def write_progress(progress):
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+ barWidth = 180
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+
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+ output_str = "["
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+ pos = barWidth * progress
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+ for i in range(barWidth):
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+ if i < pos:
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+ output_str = output_str + "="
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+ elif i == pos:
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+ output_str = output_str + ">"
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+ else:
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+ output_str = output_str + " "
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+
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+ output_str = output_str + "] " + str(int(progress * 100.0)) + " %\r"
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+ print(output_str)
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+ sys.stdout.write("\033[F")
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+
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+
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+def extract_index(filepath):
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+
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+ return int(filepath.split('_')[-1].split('.')[0])
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+
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+
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+def extracts_linear_indices(images_path, n_expected=50, indices_step=20, start_at=20, smooth_arr=False):
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+
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+ # TODO : check this part
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+ default_add = start_at - indices_step
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+
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+ # extract variance for each image path
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+ var_arr = []
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+
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+ n_counter = 0
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+ n_images = len(images_path)
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+
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+ for p in sorted(images_path):
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+ img = Image.open(p)
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+ var_arr.append(np.var(img))
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+
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+ n_counter += 1
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+ write_progress((n_counter + 1) / n_images)
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+
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+ # normalize variance values
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+ norm_arr = np.array(utils.normalize_arr_with_range(var_arr))
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+
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+ if smooth_arr:
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+ norm_arr = utils.normalize_arr_with_range(savgol_filter(norm_arr, 201, 3)) # window size 7, polynomial order 3
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+
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+ # get expected linear step (using n_expectec output images)
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+ linear_steps = utils.normalize_arr_with_range((1 - (np.arange(n_expected) / n_expected)))
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+
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+ # get image indices from variance convergence and linear
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+ # => when linear step is reached we store the index found from variance values
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+ indices_found = []
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+ for i in linear_steps:
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+
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+ find_index = 0
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+
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+ for index, y in enumerate(norm_arr):
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+ if i <= y:
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+ find_index = index
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+
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+ indices_found.append(find_index + 1)
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+
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+ indices = np.array(indices_found) * indices_step
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+
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+ # add tricks to avoid same indice
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+ # => when index is same as previous, then add number of samples expected by step
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+ # Example with step of 20 : [20, 20, 20, 100, 200] => [20, 40, 60, 100, 200]
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+ final_indices = []
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+ for index, i in enumerate(indices):
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+ value = indices[index]
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+ if index > 0:
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+ if i <= indices[index - 1]:
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+ value = indices[index - 1] + indices_step
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+ indices[index] = value
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+
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+ final_indices.append(value)
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+
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+ return np.array(final_indices) + default_add
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+
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+
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+def main():
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+ """
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+ main function which is ran when launching script
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+ """
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+ parser = argparse.ArgumentParser(description="Compute new dataset scene")
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+
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+ parser.add_argument('--file', type=str, help='file data extracted from `utils/extract_stats_freq_and_min.py` script', required=True)
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+ parser.add_argument('--png_folder', type=str, help='png dataset folder with scene', required=True)
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+ parser.add_argument('--users', type=int, help='min number of users required per scene', required=True, default=10)
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+ #parser.add_argument('--samples', type=int, help='expected samples to get for this dataset', required=True, default=10000)
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+ parser.add_argument('--output', type=str, help='output image folder', required=True)
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+
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+ args = parser.parse_args()
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+
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+ p_file = args.file
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+ p_png_folder = args.png_folder
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+ p_users = args.users
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+ #p_samples = args.samples
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+ p_output = args.output
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+
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+ with open(p_file, 'r') as f:
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+
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+ for line in f.readlines():
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+
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+ data = line.split(';')
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+
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+ scene = data[0]
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+ n_users = int(data[1])
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+ min_index = int(data[2])
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+
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+ # remove _partX from scene name
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+ scene_parts = scene.split('_')
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+ del scene_parts[-1]
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+ scene_name = '_'.join(scene_parts)
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+
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+ output_scene_dir = os.path.join(p_output, scene)
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+
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+ if os.path.exists(output_scene_dir):
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+ print('Extraction of custom indices already done for', scene)
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+ continue
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+
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+ if n_users >= p_users:
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+ print('Extract custom indices based on minimum index for', scene)
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+
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+ png_folder_scene = os.path.join(p_png_folder, scene)
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+
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+ if not os.path.exists(png_folder_scene):
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+ print(png_folder_scene, 'png folder does not exist')
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+ else:
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+
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+ # get all rawls files
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+ png_files = [ os.path.join(png_folder_scene, p) for p in sorted(os.listdir(png_folder_scene)) ]
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+
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+ # extract max samples found for this scene
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+ _, filename = os.path.split(png_files[-1])
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+
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+ max_samples = extract_index(filename)
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+
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+ # extract step from these files
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+ input_step = int(max_samples / len(png_files))
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+
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+ # get indices using min index
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+ indices = extracts_linear_indices(png_files[int(min_index / input_step):], n_expected=50, indices_step=input_step, start_at=min_index, smooth_arr=True)
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+
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+ # here add the most noisy image + mean between first predicted and most noisy image
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+ min_index = extract_index(png_files[0])
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+
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+ if not min_index in indices:
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+
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+ # get mean between min and next one in list
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+ mean_index = int((min_index + indices[1]) / 2)
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+
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+ # check mean index step
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+ if mean_index % input_step != 0:
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+ mean_index = mean_index + (mean_index % input_step)
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+
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+ if not mean_index in indices:
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+ indices = np.insert(indices, 0, mean_index)
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+
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+ # add min index as first
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+ indices = np.insert(indices, 0, min_index)
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+
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+ # print('Indices found are', indices)
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+ # create output directory
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+ if not os.path.exists(output_scene_dir):
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+ os.makedirs(output_scene_dir)
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+
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+ # get expected png image and move it
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+ for index in indices:
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+
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+ str_index = str(index)
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+
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+ while len(str_index) < 5:
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+ str_index = "0" + str_index
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+
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+ image_name = scene_name + '_' + str_index + '.png'
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+ png_image_path = os.path.join(png_folder_scene, image_name)
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+
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+ # create output filepath
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+ output_img_filepath = os.path.join(output_scene_dir, image_name)
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+
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+ # copy expected image path
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+ shutil.copy2(png_image_path, output_img_filepath)
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+ else:
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+ print('Only', n_users, 'users who passed the experiment for', scene)
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+
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+ print('\n---------------------------------------------')
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+
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+
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+
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+if __name__ == "__main__":
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+ main()
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