predict_seuil_expe.py 7.9 KB

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  1. from sklearn.externals import joblib
  2. import numpy as np
  3. from ipfml import image_processing
  4. from PIL import Image
  5. import sys, os, getopt
  6. import subprocess
  7. import time
  8. current_dirpath = os.getcwd()
  9. config_filename = "config"
  10. scenes_path = './fichiersSVD_light'
  11. min_max_filename = '_min_max_values'
  12. threshold_expe_filename = 'seuilExpe'
  13. tmp_filename = '/tmp/__model__img_to_predict.png'
  14. threshold_map_folder = "threshold_map"
  15. threshold_map_file_prefix = "treshold_map_"
  16. zones = np.arange(16)
  17. def main():
  18. if len(sys.argv) <= 1:
  19. print('Run with default parameters...')
  20. print('python predict_seuil_expe.py --interval "0,20" --model path/to/xxxx.joblib --mode svdn --metric lab --limit_detection xx')
  21. sys.exit(2)
  22. try:
  23. opts, args = getopt.getopt(sys.argv[1:], "ht:m:o:l", ["help=", "interval=", "model=", "mode=", "metric=" "limit_detection="])
  24. except getopt.GetoptError:
  25. # print help information and exit:
  26. print('python predict_seuil_expe.py --interval "xx,xx" --model path/to/xxxx.joblib --mode svdn --metric lab --limit_detection xx')
  27. sys.exit(2)
  28. for o, a in opts:
  29. if o == "-h":
  30. print('python predict_seuil_expe.py --interval "xx,xx" --model path/to/xxxx.joblib --mode svdn --metric lab --limit_detection xx')
  31. sys.exit()
  32. elif o in ("-t", "--interval"):
  33. p_interval = a
  34. elif o in ("-mo", "--model"):
  35. p_model_file = a
  36. elif o in ("-o", "--mode"):
  37. p_mode = a
  38. if p_mode != 'svdn' and p_mode != 'svdne' and p_mode != 'svd':
  39. assert False, "Mode not recognized"
  40. elif o in ("-me", "--metric"):
  41. p_metric = a
  42. elif o in ("-l", "--limit_detection"):
  43. p_limit = int(a)
  44. else:
  45. assert False, "unhandled option"
  46. scenes = os.listdir(scenes_path)
  47. if min_max_filename in scenes:
  48. scenes.remove(min_max_filename)
  49. # go ahead each scenes
  50. for id_scene, folder_scene in enumerate(scenes):
  51. print(folder_scene)
  52. scene_path = os.path.join(scenes_path, folder_scene)
  53. config_path = os.path.join(scene_path, config_filename)
  54. with open(config_path, "r") as config_file:
  55. last_image_name = config_file.readline().strip()
  56. prefix_image_name = config_file.readline().strip()
  57. start_index_image = config_file.readline().strip()
  58. end_index_image = config_file.readline().strip()
  59. step_counter = int(config_file.readline().strip())
  60. threshold_expes = []
  61. threshold_expes_detected = []
  62. threshold_expes_counter = []
  63. threshold_expes_found = []
  64. # get zones list info
  65. for index in zones:
  66. index_str = str(index)
  67. if len(index_str) < 2:
  68. index_str = "0" + index_str
  69. zone_folder = "zone"+index_str
  70. threshold_path_file = os.path.join(os.path.join(scene_path, zone_folder), threshold_expe_filename)
  71. with open(threshold_path_file) as f:
  72. threshold = int(f.readline())
  73. threshold_expes.append(threshold)
  74. # Initialize default data to get detected model threshold found
  75. threshold_expes_detected.append(False)
  76. threshold_expes_counter.append(0)
  77. threshold_expes_found.append(int(end_index_image)) # by default use max
  78. current_counter_index = int(start_index_image)
  79. end_counter_index = int(end_index_image)
  80. print(current_counter_index)
  81. check_all_done = False
  82. while(current_counter_index <= end_counter_index and not check_all_done):
  83. current_counter_index_str = str(current_counter_index)
  84. while len(start_index_image) > len(current_counter_index_str):
  85. current_counter_index_str = "0" + current_counter_index_str
  86. img_path = os.path.join(scene_path, prefix_image_name + current_counter_index_str + ".png")
  87. current_img = Image.open(img_path)
  88. img_blocks = image_processing.divide_in_blocks(current_img, (200, 200))
  89. check_all_done = all(d == True for d in threshold_expes_detected)
  90. for id_block, block in enumerate(img_blocks):
  91. # check only if necessary for this scene (not already detected)
  92. if not threshold_expes_detected[id_block]:
  93. tmp_file_path = tmp_filename.replace('__model__', p_model_file.split('/')[-1].replace('.joblib', '_'))
  94. block.save(tmp_file_path)
  95. python_cmd = "python metrics_predictions/predict_noisy_image_svd_" + p_metric + ".py --image " + tmp_file_path + \
  96. " --interval '" + p_interval + \
  97. "' --model " + p_model_file + \
  98. " --mode " + p_mode
  99. ## call command ##
  100. p = subprocess.Popen(python_cmd, stdout=subprocess.PIPE, shell=True)
  101. (output, err) = p.communicate()
  102. ## Wait for result ##
  103. p_status = p.wait()
  104. prediction = int(output)
  105. if prediction == 0:
  106. threshold_expes_counter[id_block] = threshold_expes_counter[id_block] + 1
  107. else:
  108. threshold_expes_counter[id_block] = 0
  109. if threshold_expes_counter[id_block] == p_limit:
  110. threshold_expes_detected[id_block] = True
  111. threshold_expes_found[id_block] = current_counter_index
  112. print(str(id_block) + " : " + str(current_counter_index) + "/" + str(threshold_expes[id_block]) + " => " + str(prediction))
  113. current_counter_index += step_counter
  114. print("------------------------")
  115. print("Scene " + str(id_scene + 1) + "/" + str(len(scenes)))
  116. print("------------------------")
  117. # end of scene => display of results
  118. # construct path using model name for saving threshold map folder
  119. model_treshold_path = os.path.join(threshold_map_folder, p_model_file.split('/')[-1].replace('.joblib', ''))
  120. # create threshold model path if necessary
  121. if not os.path.exists(model_treshold_path):
  122. os.makedirs(model_treshold_path)
  123. abs_dist = []
  124. map_filename = os.path.join(model_treshold_path, threshold_map_file_prefix + folder_scene)
  125. f_map = open(map_filename, 'w')
  126. line_information = ""
  127. # default header
  128. f_map.write('| | | | |\n')
  129. f_map.write('---|----|----|---\n')
  130. for id, threshold in enumerate(threshold_expes_found):
  131. line_information += str(threshold) + " / " + str(threshold_expes[id]) + " | "
  132. abs_dist.append(abs(threshold - threshold_expes[id]))
  133. if (id + 1) % 4 == 0:
  134. f_map.write(line_information + '\n')
  135. line_information = ""
  136. f_map.write(line_information + '\n')
  137. min_abs_dist = min(abs_dist)
  138. max_abs_dist = max(abs_dist)
  139. avg_abs_dist = sum(abs_dist) / len(abs_dist)
  140. f_map.write('\nScene information : ')
  141. f_map.write('\n- BEGIN : ' + str(start_index_image))
  142. f_map.write('\n- END : ' + str(end_index_image))
  143. f_map.write('\n\nDistances information : ')
  144. f_map.write('\n- MIN : ' + str(min_abs_dist))
  145. f_map.write('\n- MAX : ' + str(max_abs_dist))
  146. f_map.write('\n- AVG : ' + str(avg_abs_dist))
  147. f_map.write('\n\nOther information : ')
  148. f_map.write('\n- Detection limit : ' + str(p_limit))
  149. # by default print last line
  150. f_map.close()
  151. print("Scene " + str(id_scene + 1) + "/" + str(len(scenes)) + " Done..")
  152. print("------------------------")
  153. time.sleep(10)
  154. if __name__== "__main__":
  155. main()