import sys, os, getopt from PIL import Image from ipfml import processing, utils import ipfml.iqa.fr as fr_iqa from modules.utils import config as cfg from modules.utils import data_type as dt from modules import noise import numpy as np import matplotlib.pyplot as plt plt.style.use('ggplot') noise_list = cfg.noise_labels generated_folder = cfg.generated_folder filename_ext = cfg.filename_ext metric_choices = cfg.metric_choices_labels normalization_choices = cfg.normalization_choices pictures_folder = cfg.pictures_output_folder step_picture = 10 error_data_choices = ['mae', 'mse', 'ssim', 'psnr'] def get_error_distance(p_error, y_true, y_test): noise_method = None function_name = p_error try: error_method = getattr(fr_iqa, function_name) except AttributeError: raise NotImplementedError("Error method `{}` not implement `{}`".format(fr_iqa.__name__, function_name)) return error_method(y_true, y_test) def main(): # default values p_step = 1 p_color = 0 p_norm = 0 p_ylim = (0, 1) max_value_svd = 0 min_value_svd = sys.maxsize if len(sys.argv) <= 1: print('python noise_svd_tend_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') sys.exit(2) try: 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="]) except getopt.GetoptError: # print help information and exit: print('python noise_svd_tend_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') sys.exit(2) for o, a in opts: if o == "-h": print('python noise_svd_tend_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') sys.exit() elif o in ("-p", "--prefix"): p_path = a elif o in ("-m", "--mode"): p_mode = a if not p_mode in normalization_choices: assert False, "Unknown normalization choice, %s" % normalization_choices elif o in ("-m", "--metric"): p_metric = a if not p_metric in metric_choices: assert False, "Unknown metric choice, %s" % metric_choices elif o in ("-n", "--n"): p_n = int(a) elif o in ("-n", "--norm"): p_norm = int(a) elif o in ("-c", "--color"): p_color = int(a) elif o in ("-i", "--interval"): p_interval = list(map(int, a.split(','))) elif o in ("-s", "--step"): p_step = int(a) elif o in ("-y", "--ylim"): p_ylim = list(map(float, a.split(','))) elif o in ("-e", "--error"): p_error = a if p_error not in error_data_choices: assert False, "Unknow error choice to display %s" % error_data_choices else: assert False, "unhandled option" p_prefix = p_path.split('/')[1].replace('_', '') noise_name = p_path.split('/')[2] if p_color: file_path = os.path.join(p_path, p_prefix + "_" + noise_name + "_color_{}." + filename_ext) else: file_path = os.path.join(p_path, p_prefix + "_" + noise_name + "_{}." + filename_ext) begin, end = p_interval all_svd_data = [] svd_data = [] image_indices = [] noise_indices = range(1, p_n)[::-1] # get all data from images for i in noise_indices: if i % step_picture == 0: image_path = file_path.format(str(i)) img = Image.open(image_path) svd_values = dt.get_svd_data(p_metric, img) if p_norm: svd_values = svd_values[begin:end] all_svd_data.append(svd_values) # update min max values min_value = svd_values.min() max_value = svd_values.max() if min_value < min_value_svd: min_value_svd = min_value if max_value > max_value_svd: max_value_svd = max_value print('%.2f%%' % ((p_n - i + 1) / p_n * 100)) sys.stdout.write("\033[F") previous_data = [] error_data = [0.] for id, data in enumerate(all_svd_data): current_id = (p_n - ((id + 1) * 10)) if current_id % p_step == 0: current_data = data if p_mode == 'svdn': current_data = utils.normalize_arr(current_data) if p_mode == 'svdne': current_data = utils.normalize_arr_with_range(current_data, min_value_svd, max_value_svd) svd_data.append(current_data) image_indices.append(current_id) # use of whole image data for computation of ssim or psnr if p_error == 'ssim' or p_error == 'psnr': image_path = file_path.format(str(current_id)) current_data = np.asarray(Image.open(image_path)) if len(previous_data) > 0: current_error = get_error_distance(p_error, previous_data, current_data) error_data.append(current_error) if len(previous_data) == 0: previous_data = current_data # display all data using matplotlib (configure plt) gridsize = (3, 2) # fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(30, 22)) fig = plt.figure(figsize=(30, 22)) ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2) ax2 = plt.subplot2grid(gridsize, (2, 0), colspan=2) ax1.set_title(p_prefix + ', ' + noise_name + ' noise, interval information ['+ str(begin) +', '+ str(end) +'], ' + p_metric + ' metric, step ' + str(p_step) + ' normalization ' + p_mode) ax1.set_label('Importance of noise [1, 999]') ax1.set_xlabel('Vector features') for id, data in enumerate(svd_data): p_label = p_prefix + str(image_indices[id]) + " | " + p_error + ": " + str(error_data[id]) ax1.plot(data, label=p_label) ax1.legend(bbox_to_anchor=(0.8, 1), loc=2, borderaxespad=0.2, fontsize=12) if not p_norm: ax1.set_xlim(begin, end) # adapt ylim y_begin, y_end = p_ylim ax1.set_ylim(y_begin, y_end) 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 if p_color: output_filename = output_filename + '_color' 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) ax2.set_ylabel(p_error + ' error') ax2.set_xlabel('Number of samples per pixels') ax2.set_xticks(range(len(image_indices))) ax2.set_xticklabels(image_indices) ax2.plot(error_data) print("Generation of output figure... %s" % output_filename) output_path = os.path.join(pictures_folder, output_filename) if not os.path.exists(pictures_folder): os.makedirs(pictures_folder) fig.savefig(output_path, dpi=(200)) if __name__== "__main__": main()