predict_seuil_expe_maxwell.py 8.6 KB

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  1. from sklearn.externals import joblib
  2. import numpy as np
  3. from ipfml import processing
  4. from PIL import Image
  5. import sys, os, argparse
  6. import subprocess
  7. import time
  8. from modules.utils import config as cfg
  9. config_filename = cfg.config_filename
  10. scenes_path = cfg.dataset_path
  11. min_max_filename = cfg.min_max_filename_extension
  12. threshold_expe_filename = cfg.seuil_expe_filename
  13. threshold_map_folder = cfg.threshold_map_folder
  14. threshold_map_file_prefix = cfg.threshold_map_folder + "_"
  15. zones = cfg.zones_indices
  16. maxwell_scenes = cfg.maxwell_scenes_names
  17. normalization_choices = cfg.normalization_choices
  18. metric_choices = cfg.metric_choices_labels
  19. tmp_filename = '/tmp/__model__img_to_predict.png'
  20. current_dirpath = os.getcwd()
  21. def main():
  22. # by default..
  23. p_custom = False
  24. parser = argparse.ArgumentParser(description="Script which predicts threshold using specific model")
  25. parser.add_argument('--interval', type=str, help='Interval value to keep from svd', default='"0, 200"')
  26. parser.add_argument('--model', type=str, help='.joblib or .json file (sklearn or keras model)')
  27. parser.add_argument('--mode', type=str, help='Kind of normalization level wished', choices=normalization_choices)
  28. parser.add_argument('--metric', type=str, help='Metric data choice', choices=metric_choices)
  29. #parser.add_argument('--limit_detection', type=int, help='Specify number of same prediction to stop threshold prediction', default=2)
  30. parser.add_argument('--custom', type=str, help='Name of custom min max file if use of renormalization of data', default=False)
  31. args = parser.parse_args()
  32. p_interval = list(map(int, args.interval.split(',')))
  33. p_model_file = args.model
  34. p_mode = args.mode
  35. p_metric = args.metric
  36. #p_limit = args.limit
  37. p_custom = args.custom
  38. scenes = os.listdir(scenes_path)
  39. scenes = [s for s in scenes if s in maxwell_scenes]
  40. # go ahead each scenes
  41. for id_scene, folder_scene in enumerate(scenes):
  42. # only take in consideration maxwell scenes
  43. if folder_scene in maxwell_scenes:
  44. print(folder_scene)
  45. scene_path = os.path.join(scenes_path, folder_scene)
  46. config_path = os.path.join(scene_path, config_filename)
  47. with open(config_path, "r") as config_file:
  48. last_image_name = config_file.readline().strip()
  49. prefix_image_name = config_file.readline().strip()
  50. start_index_image = config_file.readline().strip()
  51. end_index_image = config_file.readline().strip()
  52. step_counter = int(config_file.readline().strip())
  53. threshold_expes = []
  54. threshold_expes_detected = []
  55. threshold_expes_counter = []
  56. threshold_expes_found = []
  57. # get zones list info
  58. for index in zones:
  59. index_str = str(index)
  60. if len(index_str) < 2:
  61. index_str = "0" + index_str
  62. zone_folder = "zone"+index_str
  63. threshold_path_file = os.path.join(os.path.join(scene_path, zone_folder), threshold_expe_filename)
  64. with open(threshold_path_file) as f:
  65. threshold = int(f.readline())
  66. threshold_expes.append(threshold)
  67. # Initialize default data to get detected model threshold found
  68. threshold_expes_detected.append(False)
  69. threshold_expes_counter.append(0)
  70. threshold_expes_found.append(int(end_index_image)) # by default use max
  71. current_counter_index = int(start_index_image)
  72. end_counter_index = int(end_index_image)
  73. print(current_counter_index)
  74. check_all_done = False
  75. while(current_counter_index <= end_counter_index and not check_all_done):
  76. current_counter_index_str = str(current_counter_index)
  77. while len(start_index_image) > len(current_counter_index_str):
  78. current_counter_index_str = "0" + current_counter_index_str
  79. img_path = os.path.join(scene_path, prefix_image_name + current_counter_index_str + ".png")
  80. current_img = Image.open(img_path)
  81. img_blocks = processing.divide_in_blocks(current_img, (200, 200))
  82. check_all_done = all(d == True for d in threshold_expes_detected)
  83. for id_block, block in enumerate(img_blocks):
  84. # check only if necessary for this scene (not already detected)
  85. if not threshold_expes_detected[id_block]:
  86. tmp_file_path = tmp_filename.replace('__model__', p_model_file.split('/')[-1].replace('.joblib', '_'))
  87. block.save(tmp_file_path)
  88. python_cmd = "python predict_noisy_image_svd.py --image " + tmp_file_path + \
  89. " --interval '" + p_interval + \
  90. "' --model " + p_model_file + \
  91. " --mode " + p_mode + \
  92. " --metric " + p_metric
  93. # specify use of custom file for min max normalization
  94. if p_custom:
  95. python_cmd = python_cmd + ' --custom ' + p_custom
  96. ## call command ##
  97. p = subprocess.Popen(python_cmd, stdout=subprocess.PIPE, shell=True)
  98. (output, err) = p.communicate()
  99. ## Wait for result ##
  100. p_status = p.wait()
  101. prediction = int(output)
  102. if prediction == 0:
  103. threshold_expes_counter[id_block] = threshold_expes_counter[id_block] + 1
  104. else:
  105. threshold_expes_counter[id_block] = 0
  106. if threshold_expes_counter[id_block] == p_limit:
  107. threshold_expes_detected[id_block] = True
  108. threshold_expes_found[id_block] = current_counter_index
  109. print(str(id_block) + " : " + str(current_counter_index) + "/" + str(threshold_expes[id_block]) + " => " + str(prediction))
  110. current_counter_index += step_counter
  111. print("------------------------")
  112. print("Scene " + str(id_scene + 1) + "/" + str(len(maxwell_scenes)))
  113. print("------------------------")
  114. # end of scene => display of results
  115. # construct path using model name for saving threshold map folder
  116. model_treshold_path = os.path.join(threshold_map_folder, p_model_file.split('/')[-1].replace('.joblib', ''))
  117. # create threshold model path if necessary
  118. if not os.path.exists(model_treshold_path):
  119. os.makedirs(model_treshold_path)
  120. abs_dist = []
  121. map_filename = os.path.join(model_treshold_path, threshold_map_file_prefix + folder_scene)
  122. f_map = open(map_filename, 'w')
  123. line_information = ""
  124. # default header
  125. f_map.write('| | | | |\n')
  126. f_map.write('---|----|----|---\n')
  127. for id, threshold in enumerate(threshold_expes_found):
  128. line_information += str(threshold) + " / " + str(threshold_expes[id]) + " | "
  129. abs_dist.append(abs(threshold - threshold_expes[id]))
  130. if (id + 1) % 4 == 0:
  131. f_map.write(line_information + '\n')
  132. line_information = ""
  133. f_map.write(line_information + '\n')
  134. min_abs_dist = min(abs_dist)
  135. max_abs_dist = max(abs_dist)
  136. avg_abs_dist = sum(abs_dist) / len(abs_dist)
  137. f_map.write('\nScene information : ')
  138. f_map.write('\n- BEGIN : ' + str(start_index_image))
  139. f_map.write('\n- END : ' + str(end_index_image))
  140. f_map.write('\n\nDistances information : ')
  141. f_map.write('\n- MIN : ' + str(min_abs_dist))
  142. f_map.write('\n- MAX : ' + str(max_abs_dist))
  143. f_map.write('\n- AVG : ' + str(avg_abs_dist))
  144. f_map.write('\n\nOther information : ')
  145. f_map.write('\n- Detection limit : ' + str(p_limit))
  146. # by default print last line
  147. f_map.close()
  148. print("Scene " + str(id_scene + 1) + "/" + str(len(scenes)) + " Done..")
  149. print("------------------------")
  150. time.sleep(10)
  151. if __name__== "__main__":
  152. main()