# main imports import sys, os, argparse import numpy as np # image processing imports from PIL import Image import matplotlib.pyplot as plt from ipfml import utils import ipfml.iqa.fr as fr_iqa # modules and config imports sys.path.insert(0, '') # trick to enable import of main folder module import custom_config as cfg from data_attributes import get_image_features # others variables noise_list = cfg.noise_labels generated_folder = cfg.generated_folder filename_ext = cfg.filename_ext feature_choices = cfg.features_choices_labels normalization_choices = cfg.normalization_choices pictures_folder = cfg.pictures_output_folder error_data_choices = cfg.error_data_choices steparam_picture = 10 def get_error_distance(param_error, y_true, y_test): function_name = param_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(): max_value_svd = 0 min_value_svd = sys.maxsize parser = argparse.ArgumentParser(description="Display svd tend of images with noise level") parser.add_argument('--prefix', type=str, help='Generated noise folder prefix (ex: `generated/prefix/noise`)') parser.add_argument('--mode', type=str, help='Kind of normalization', default=normalization_choices) parser.add_argument('--feature', type=str, help='feature choice', default=feature_choices) parser.add_argument('--n', type=int, help='Number of images') parser.add_argument('--color', type=int, help='Use of color or grey level', default=0) parser.add_argument('--norm', type=int, help='Use of normalization from interval or whole data vector', default=0) parser.add_argument('--interval', type=str, help='Interval data choice (ex: `0, 200`)', default="0, 200") parser.add_argument('--step', type=int, help='Step of image indices to keep', default=1) parser.add_argument('--ylim', type=str, help='Limite to display data (ex: `0, 1`)', default="0, 1") parser.add_argument('--error', type=str, help='Error used for information data', default=error_data_choices) args = parser.parse_args() param_prefix = args.prefix param_mode = args.mode param_feature = args.feature param_n = args.n param_color = args.color param_norm = args.norm param_interval = list(map(int, args.interval.split(','))) param_step = args.step param_ylim = list(map(float, args.ylim.split(','))) param_error = args.error param_prefix = param_prefix.split('/')[1].replace('_', '') noise_name = param_prefix.split('/')[2] if param_color: file_path = os.path.join(param_prefix, param_prefix + "_" + noise_name + "_color_{}." + filename_ext) else: file_path = os.path.join(param_prefix, param_prefix + "_" + noise_name + "_{}." + filename_ext) begin, end = param_interval all_svd_data = [] svd_data = [] image_indices = [] noise_indices = range(1, param_n)[::-1] # get all data from images for i in noise_indices: if i % steparam_picture == 0: image_path = file_path.format(str(i)) img = Image.open(image_path) svd_values = get_image_features(param_feature, img) if param_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%%' % ((param_n - i + 1) / param_n * 100)) sys.stdout.write("\033[F") previous_data = [] error_data = [0.] for id, data in enumerate(all_svd_data): current_id = (param_n - ((id + 1) * 10)) if current_id % param_step == 0: current_data = data if param_mode == 'svdn': current_data = utils.normalize_arr(current_data) if param_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 param_error == 'ssim' or param_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(param_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(param_prefix + ', ' + noise_name + ' noise, interval information ['+ str(begin) +', '+ str(end) +'], ' + param_feature + ' feature, step ' + str(param_step) + ' normalization ' + param_mode) ax1.set_label('Importance of noise [1, 999]') ax1.set_xlabel('Vector features') for id, data in enumerate(svd_data): param_label = param_prefix + str(image_indices[id]) + " | " + param_error + ": " + str(error_data[id]) ax1.plot(data, label=param_label) ax1.legend(bbox_to_anchor=(0.75, 1), loc=2, borderaxespad=0.2, fontsize=12) if not param_norm: ax1.set_xlim(begin, end) # adapt ylim y_begin, y_end = param_ylim ax1.set_ylim(y_begin, y_end) output_filename = param_prefix + "_" + noise_name + "_1_to_" + str(param_n) + "_B" + str(begin) + "_E" + str(end) + "_" + param_feature + "_S" + str(param_step) + "_norm" + str(param_norm )+ "_" + param_mode + "_" + param_error if param_color: output_filename = output_filename + '_color' ax2.set_title(param_error + " information for : " + param_prefix + ', ' + noise_name + ' noise, interval information ['+ str(begin) +', '+ str(end) +'], ' + param_feature + ' feature, step ' + str(param_step) + ', normalization ' + param_mode) ax2.set_ylabel(param_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()