display_svd_area_data_scene.py 9.3 KB

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  1. # main imports
  2. import sys, os, argparse
  3. import numpy as np
  4. # image processing imports
  5. from PIL import Image
  6. from skimage import color
  7. import matplotlib.pyplot as plt
  8. from data_attributes import get_svd_data
  9. from ipfml.processing import segmentation, transform, compression
  10. from ipfml import utils
  11. import ipfml.iqa.fr as fr_iqa
  12. # modules and config imports
  13. sys.path.insert(0, '') # trick to enable import of main folder module
  14. import custom_config as cfg
  15. from modules.utils import data as dt
  16. # getting configuration information
  17. zone_folder = cfg.zone_folder
  18. min_max_filename = cfg.min_max_filename_extension
  19. # define all scenes values
  20. scenes_list = cfg.scenes_names
  21. scenes_indices = cfg.scenes_indices
  22. choices = cfg.normalization_choices
  23. path = cfg.dataset_path
  24. zones = cfg.zones_indices
  25. seuil_expe_filename = cfg.seuil_expe_filename
  26. features_choices = cfg.features_choices_labels
  27. max_nb_bits = 8
  28. integral_area_choices = ['trapz', 'simps']
  29. def get_area_under_curve(p_area, p_data):
  30. function_name = 'integral_area_' + p_area
  31. try:
  32. area_method = getattr(utils, function_name)
  33. except AttributeError:
  34. raise NotImplementedError("Error `{}` not implement `{}`".format(utils.__name__, function_name))
  35. return area_method(p_data, dx=800)
  36. def display_svd_values(p_scene, p_interval, p_indices, p_metric, p_mode, p_step, p_norm, p_area, p_ylim):
  37. """
  38. @brief Method which gives information about svd curves from zone of picture
  39. @param p_scene, scene expected to show svd values
  40. @param p_interval, interval [begin, end] of svd data to display
  41. @param p_interval, interval [begin, end] of samples or minutes from render generation engine
  42. @param p_metric, metric computed to show
  43. @param p_mode, normalization's mode
  44. @param p_norm, normalization or not of selected svd data
  45. @param p_area, area method name to compute area under curve
  46. @param p_ylim, ylim choice to better display of data
  47. @return nothing
  48. """
  49. max_value_svd = 0
  50. min_value_svd = sys.maxsize
  51. scenes = os.listdir(path)
  52. # remove min max file from scenes folder
  53. scenes = [s for s in scenes if min_max_filename not in s]
  54. begin_data, end_data = p_interval
  55. begin_index, end_index = p_indices
  56. # go ahead each scenes
  57. for folder_scene in scenes:
  58. if p_scene == folder_scene:
  59. scene_path = os.path.join(path, folder_scene)
  60. # construct each zones folder name
  61. zones_folder = []
  62. # get zones list info
  63. for index in zones:
  64. index_str = str(index)
  65. if len(index_str) < 2:
  66. index_str = "0" + index_str
  67. current_zone = "zone"+index_str
  68. zones_folder.append(current_zone)
  69. images_data = []
  70. images_indices = []
  71. threshold_learned_zones = []
  72. # get all images of folder
  73. scene_images = sorted([os.path.join(scene_path, img) for img in os.listdir(scene_path) if cfg.scene_image_extension in img])
  74. number_scene_image = len(scene_images)
  75. for id, zone_folder in enumerate(zones_folder):
  76. # get threshold information
  77. zone_path = os.path.join(scene_path, zone_folder)
  78. path_seuil = os.path.join(zone_path, seuil_expe_filename)
  79. # open treshold path and get this information
  80. with open(path_seuil, "r") as seuil_file:
  81. threshold_learned = int(seuil_file.readline().strip())
  82. threshold_learned_zones.append(threshold_learned)
  83. threshold_mean = np.mean(np.asarray(threshold_learned_zones))
  84. threshold_image_found = False
  85. svd_data = []
  86. # for each images
  87. for id_img, img_path in enumerate(scene_images):
  88. current_quality_image = dt.get_scene_image_quality(img_path)
  89. img = Image.open(img_path)
  90. svd_values = get_svd_data(p_metric, img)
  91. if p_norm:
  92. svd_values = svd_values[begin_data:end_data]
  93. # update min max values
  94. min_value = svd_values.min()
  95. max_value = svd_values.max()
  96. if min_value < min_value_svd:
  97. min_value_svd = min_value
  98. if max_value > min_value_svd:
  99. max_value_svd = max_value
  100. # keep in memory used data
  101. if current_quality_image % p_step == 0:
  102. if current_quality_image >= begin_index and current_quality_image <= end_index:
  103. images_indices.append(current_quality_image)
  104. svd_data.append(svd_values)
  105. if threshold_mean < current_quality_image and not threshold_image_found:
  106. threshold_image_found = True
  107. image_name_postfix = dt.get_scene_image_postfix(img_path)
  108. threshold_image_zone = image_name_postfix
  109. print('%.2f%%' % ((id_img + 1) / number_scene_image * 100))
  110. sys.stdout.write("\033[F")
  111. # all indices of picture to plot
  112. print(images_indices)
  113. area_data = []
  114. for id, data in enumerate(svd_data):
  115. current_data = data
  116. if not p_norm:
  117. current_data = current_data[begin_data:end_data]
  118. if p_mode == 'svdn':
  119. current_data = utils.normalize_arr(current_data)
  120. if p_mode == 'svdne':
  121. current_data = utils.normalize_arr_with_range(current_data, min_value_svd, max_value_svd)
  122. images_data.append(current_data)
  123. # not use this script for 'sub_blocks_stats'
  124. current_area = get_area_under_curve(p_area, current_data)
  125. area_data.append(current_area)
  126. # display all data using matplotlib (configure plt)
  127. gridsize = (3, 2)
  128. # fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(30, 22))
  129. fig = plt.figure(figsize=(30, 22))
  130. ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2)
  131. ax2 = plt.subplot2grid(gridsize, (2, 0), colspan=2)
  132. ax1.set_title(p_scene + ' scene interval information SVD['+ str(begin_data) +', '+ str(end_data) +'], from scenes indices [' + str(begin_index) + ', '+ str(end_index) + ']' + p_metric + ' metric, ' + p_mode + ', with step of ' + str(p_step) + ', svd norm ' + str(p_norm), fontsize=20)
  133. ax1.set_ylabel('Image samples or time (minutes) generation', fontsize=14)
  134. ax1.set_xlabel('Vector features', fontsize=16)
  135. for id, data in enumerate(images_data):
  136. p_label = p_scene + '_' + str(images_indices[id]) + " | " + p_area + ": " + str(area_data[id])
  137. if images_indices[id] == threshold_image_zone:
  138. ax1.plot(data, label=p_label, lw=4, color='red')
  139. threshold_id = id
  140. else:
  141. ax1.plot(data, label=p_label)
  142. ax1.legend(bbox_to_anchor=(0.7, 1), loc=2, borderaxespad=0.2, fontsize=14)
  143. start_ylim, end_ylim = p_ylim
  144. ax1.set_ylim(start_ylim, end_ylim)
  145. ax2.set_title(p_area + " information for whole step images")
  146. ax2.set_ylabel(p_area + ' area values')
  147. ax2.set_xlabel('Number of samples per pixels or times')
  148. ax2.set_xticks(range(len(images_indices)))
  149. ax2.set_xticklabels(list(map(int, images_indices)))
  150. ax2.plot([threshold_id, threshold_id], [np.min(area_data), np.max(area_data)], 'k-', lw=2, color='red')
  151. ax2.plot(area_data)
  152. plt.show()
  153. def main():
  154. parser = argparse.ArgumentParser(description="Display area under curve data on scene")
  155. parser.add_argument('--scene', type=str, help='scene index to use', choices=cfg.scenes_indices)
  156. parser.add_argument('--interval', type=str, help='Interval value to keep from svd', default='"0, 200"')
  157. parser.add_argument('--indices', type=str, help='Samples interval to display', default='"0, 900"')
  158. parser.add_argument('--feature', type=str, help='Feature data choice', choices=features_choices)
  159. parser.add_argument('--mode', type=str, help='Kind of normalization level wished', choices=cfg.normalization_choices)
  160. parser.add_argument('--step', type=int, help='Each step samples to display', default=10)
  161. parser.add_argument('--norm', type=int, help='If values will be normalized or not', choices=[0, 1])
  162. parser.add_argument('--area', type=int, help='Way of computing area under curve', choices=integral_area_choices)
  163. parser.add_argument('--ylim', type=str, help='ylim interval to use', default='"0, 1"')
  164. args = parser.parse_args()
  165. p_scene = scenes_list[scenes_indices.index(args.scene)]
  166. p_indices = list(map(int, args.indices.split(',')))
  167. p_interval = list(map(int, args.interval.split(',')))
  168. p_feature = args.feature
  169. p_mode = args.mode
  170. p_step = args.step
  171. p_norm = args.norm
  172. p_area = args.area
  173. p_ylim = list(map(int, args.ylim.split(',')))
  174. display_svd_values(p_scene, p_interval, p_indices, p_feature, p_mode, p_step, p_norm, p_area, p_ylim)
  175. if __name__== "__main__":
  176. main()