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@@ -0,0 +1,138 @@
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+import sys, os, getopt
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+from PIL import Image
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+
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+from ipfml import processing
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+
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+from modules.utils import config as cfg
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+from modules.utils import data_type as dt
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+from modules import noise
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+
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+import matplotlib.pyplot as plt
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+
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+noise_list = cfg.noise_labels
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+generated_folder = cfg.generated_folder
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+filename_ext = cfg.filename_ext
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+metric_choices = cfg.metric_choices_labels
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+normalization_choices = cfg.normalization_choices
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+pictures_folder = cfg.pictures_output_folder
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+
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+def main():
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+
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+ p_step = 1
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+ max_value_svd = 0
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+ min_value_svd = sys.maxsize
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+
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+ if len(sys.argv) <= 1:
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+ print('python noise_svd_visualization.py --prefix path/with/prefix --metric lab --mode svdn --n 300 --interval "0, 200" --step 30 --output filename')
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+ sys.exit(2)
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+ try:
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+ opts, args = getopt.getopt(sys.argv[1:], "h:p:m:m:n:i:s:o", ["help=", "prefix=", "metric=", "mode=", "n=", "interval=", "step=", "output="])
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+ except getopt.GetoptError:
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+ # print help information and exit:
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+ print('python noise_svd_visualization.py --prefix path/with/prefix --metric lab --mode svdn --n 300 --interval "0, 200" --step 30 --output filename')
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+ sys.exit(2)
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+ for o, a in opts:
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+ if o == "-h":
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+ print('python noise_svd_visualization.py --prefix path/with/prefix --metric lab --mode svdn --n 300 --interval "0, 200" --step 30 --output filename')
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+ sys.exit()
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+ elif o in ("-p", "--prefix"):
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+ p_prefix = a
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+ elif o in ("-m", "--mode"):
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+ p_mode = a
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+
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+ if not p_mode in normalization_choices:
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+ assert False, "Unknown normalization choice, %s" % normalization_choices
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+
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+ elif o in ("-m", "--metric"):
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+ p_metric = a
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+
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+ if not p_metric in metric_choices:
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+ assert False, "Unknown metric choice, %s" % metric_choices
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+
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+ elif o in ("-n", "--n"):
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+ p_n = int(a)
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+ elif o in ("-i", "--interval"):
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+ p_interval = list(map(int, a.split(',')))
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+ elif o in ("-s", "--step"):
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+ p_step = int(a)
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+ elif o in ("-o", "--output"):
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+ p_output = a
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+ else:
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+ assert False, "unhandled option"
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+
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+
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+ noise_name = p_prefix.split('/')[1].replace('_', '')
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+
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+ file_path = p_prefix + "{}." + filename_ext
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+
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+ begin, end = p_interval
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+ all_svd_data = []
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+
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+ svd_data = []
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+ image_indices = []
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+
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+ # get all data from images
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+ for i in range(1, p_n):
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+
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+ image_path = file_path.format(str(i))
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+ img = Image.open(image_path)
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+
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+ svd_values = dt.get_svd_data(p_metric, img)
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+ svd_values = svd_values[begin:end]
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+ all_svd_data.append(svd_values)
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+
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+ # update min max values
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+ min_value = svd_values.min()
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+ max_value = svd_values.max()
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+
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+ if min_value < min_value_svd:
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+ min_value_svd = min_value
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+
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+ if max_value > min_value_svd:
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+ max_value_svd = max_value
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+
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+ print('%.2f%%' % ((i + 1) / p_n * 100))
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+ sys.stdout.write("\033[F")
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+
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+ print("Generation of output figure...")
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+ for id, data in enumerate(all_svd_data):
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+
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+ if id % p_step == 0:
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+
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+ current_data = data
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+ if p_mode == 'svdn':
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+ current_data = processing.normalize_arr(current_data)
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+
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+ if p_mode == 'svdne':
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+ current_data = processing.normalize_arr_with_range(current_data, min_value_svd, max_value_svd)
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+
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+ svd_data.append(current_data)
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+ image_indices.append(id)
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+
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+ # display all data using matplotlib
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+
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+ plt.title(noise_name + ' noise, interval information ['+ str(begin) +', '+ str(end) +'], ' + p_metric + ' metric, step ' + str(p_step), fontsize=20)
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+ plt.ylabel('Importance of noise [1, 999]', fontsize=14)
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+ plt.xlabel('Vector features', fontsize=16)
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+
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+ for id, data in enumerate(svd_data):
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+
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+ p_label = p_prefix + str(image_indices[id])
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+ plt.plot(data, label=p_label)
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+
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+ plt.legend(bbox_to_anchor=(0.8, 1), loc=2, borderaxespad=0.2, fontsize=14)
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+ plt.ylim(0, 0.1)
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+ plt.show()
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+
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+ output_filename = noise_name + "1_to_" + str(p_n) + "_B" + str(begin) + "_E" + str(end) + "_" + p_metric + "_S" + str(p_step) + "_" + p_mode
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+ output_path = os.path.join(pictures_folder, output_filename)
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+
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+ if not os.path.exists(pictures_folder):
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+ os.makedirs(pictures_folder)
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+
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+ plt.savefig(output_path)
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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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