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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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+import numpy as np
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+
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+import matplotlib.pyplot as plt
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+plt.style.use('ggplot')
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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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+step_picture = 10
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+
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+error_data_choices = ['MAE', 'MSE']
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+
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+def compute_mae(previous_data, current_data):
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+
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+ n = len(previous_data)
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+ mae_sum = 0.
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+
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+ for id, x in enumerate(current_data):
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+ y = previous_data[id] # current data reduces error
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+ mae_sum += abs(x - y)
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+
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+ return mae_sum / n
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+
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+def compute_mse(previous_data, current_data):
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+
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+ n = len(previous_data)
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+ mse_sum = 0.
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+
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+ for id, x in enumerate(current_data):
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+ y = previous_data[id] # current data reduces error
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+ mse_sum += abs(x - y)
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+
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+ return mse_sum / n
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+
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+def main():
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+
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+ # default values
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+ p_step = 1
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+ p_color = 0
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+ p_norm = 0
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+ p_ylim = (0, 1)
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+
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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_mae_visualization.py --prefix generated/prefix/noise --metric lab --mode svdn --n 300 --interval "0, 200" --step 30 --color 1 --norm 1 --ylim "0, 1" --error MAE')
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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:c:n:y:e", ["help=", "prefix=", "metric=", "mode=", "n=", "interval=", "step=", "color=", "norm=", "ylim=", "error="])
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+ except getopt.GetoptError:
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+ # print help information and exit:
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+ print('python noise_svd_mae_visualization.py --prefix generated/prefix/noise --metric lab --mode svdn --n 300 --interval "0, 200" --step 30 --color 1 --norm 1 --ylim "0, 1" --error MAE')
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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_mae_visualization.py --prefix generated/prefix/noise --metric lab --mode svdn --n 300 --interval "0, 200" --step 30 --color 1 --norm 1 --ylim "0, 1" --error MAE')
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+ sys.exit()
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+ elif o in ("-p", "--prefix"):
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+ p_path = 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 ("-n", "--norm"):
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+ p_norm = int(a)
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+ elif o in ("-c", "--color"):
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+ p_color = 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 ("-y", "--ylim"):
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+ p_ylim = list(map(float, a.split(',')))
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+ elif o in ("-e", "--error"):
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+ p_error = a
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+
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+ if p_error not in error_data_choices:
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+ assert False, "Unknow error choice to display %s" % error_data_choices
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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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+ p_prefix = p_path.split('/')[1].replace('_', '')
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+ noise_name = p_path.split('/')[2]
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+
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+ if p_color:
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+ file_path = os.path.join(p_path, p_prefix + "_" + noise_name + "_color_{}." + filename_ext)
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+ else:
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+ file_path = os.path.join(p_path, p_prefix + "_" + noise_name + "_{}." + 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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+ noise_indices = range(1, p_n)[::-1]
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+
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+ # get all data from images
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+ for i in noise_indices:
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+
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+ if i % step_picture == 0:
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+
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+ image_path = file_path.format(str(i))
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+
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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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+
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+ if p_norm:
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+ svd_values = svd_values[begin:end]
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+
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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%%' % ((p_n - i + 1) / p_n * 100))
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+ sys.stdout.write("\033[F")
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+
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+ previous_data = []
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+ error_data = [0.]
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+
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+ for id, data in enumerate(all_svd_data):
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+
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+ current_id = (p_n - ((id + 1) * 10))
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+
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+ if current_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(current_id)
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+
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+ if len(previous_data) > 0:
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+ current_mae = compute_mae(previous_data, current_data)
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+ error_data.append(current_mae)
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+
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+ if len(previous_data) == 0:
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+ previous_data = current_data
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+
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+ # display all data using matplotlib (configure plt)
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+ gridsize = (3, 2)
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+
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+ # fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(30, 22))
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+ fig = plt.figure(figsize=(30, 22))
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+ ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2)
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+ ax2 = plt.subplot2grid(gridsize, (2, 0), colspan=2)
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+
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+ ax1.set_title(p_prefix + ' noise, interval information ['+ str(begin) +', '+ str(end) +'], ' + p_metric + ' metric, step ' + str(p_step) + ' normalization ' + p_mode)
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+ ax1.set_label('Importance of noise [1, 999]')
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+ ax1.set_xlabel('Vector features')
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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]) + " | MAE : " + str(error_data[id])
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+ ax1.plot(data, label=p_label)
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+
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+ ax1.legend(bbox_to_anchor=(0, 1), loc=2, borderaxespad=0.2, fontsize=12)
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+
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+ if not p_norm:
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+ ax1.set_xlim(begin, end)
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+
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+ # adapt ylim
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+ y_begin, y_end = p_ylim
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+ ax1.set_ylim(y_begin, y_end)
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+
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+ output_filename = p_prefix + "_" + noise_name + "_1_to_" + str(p_n) + "_B" + str(begin) + "_E" + str(end) + "_" + p_metric + "_S" + str(p_step) + "_norm" + str(p_norm )+ "_" + p_mode + "_" + p_error
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+
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+ if p_color:
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+ output_filename = output_filename + '_color'
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+
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+ ax2.set_title(p_error + " information for : " + p_prefix + ', ' + noise_name + ' noise, interval information ['+ str(begin) +', '+ str(end) +'], ' + p_metric + ' metric, step ' + str(p_step) + ', normalization ' + p_mode)
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+ ax2.set_ylabel('Mean Squared Error')
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+ ax2.set_xlabel('Number of samples per pixels')
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+ ax2.set_xticks(range(len(image_indices)))
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+ ax2.set_xticklabels(image_indices)
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+ ax2.plot(error_data)
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+
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+ print("Generation of output figure... %s" % output_filename)
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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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+ fig.savefig(output_path, dpi=(200))
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+
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+if __name__== "__main__":
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+ main()
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