# main imports import sys, os, argparse import numpy as np # image processing imports from PIL import Image from ipfml import processing, utils import matplotlib.pyplot as plt # 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 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 steparam_picture = 10 class ThresholdData(): """ A simple class to store threshold data """ def __init__(self, noise, threshold, color): self.noise = noise self.threshold = threshold self.color = color def get_noise(self): return self.noise def get_threshold(self): return self.threshold def isColor(self): return self.color def main(): parser = argparse.ArgumentParser(description="Display threshold svd data") parser.add_argument('--prefix', type=str, help='Generated noise folder prefix (ex: `generated/prefix/noise`)') parser.add_argument('--file', type=str, help='Threshold file to use') 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('--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") args = parser.parse_args() param_prefix = args.prefix param_file = args.file 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_prefix = param_prefix.split('/')[1].replace('_', '') if param_color: file_path = param_prefix + "{}/" + param_prefix + "_{}_color_{}." + filename_ext else: file_path = param_prefix + "{}/" + param_prefix + "_{}_{}." + filename_ext begin, end = param_interval svd_data = [] final_svd_data = [] image_indices = [] min_max_list = {} threshold_data = [] # read data threshold file with open(param_file, 'r') as f: lines = f.readlines() for line in lines: data = line.replace('\n', '').split(';') print(data) threshold = ThresholdData(data[0], float(data[1]), int(data[2])) threshold_data.append(threshold) # filter data if color or not threshold_data = [t for t in threshold_data if t.isColor() == param_color] for id, threshold in enumerate(threshold_data): current_noise = threshold.get_noise() current_threshold = threshold.get_threshold() min_max_list[current_noise] = (sys.maxsize, 0) threshold_found = False # get all data from images for i in range(1, param_n): if i % steparam_picture == 0: image_path = file_path.format(current_noise, current_noise, str(i)) img = Image.open(image_path) svd_values = get_image_features(param_feature, img) if param_norm: svd_values = svd_values[begin:end] # only append data once if not threshold_found and current_threshold < i: svd_data.append(svd_values) image_indices.append(i) if current_threshold < i: threshold_found = True # update min max values min_value = svd_values.min() max_value = svd_values.max() # update of min max values for noise current_min, current_max = min_max_list[current_noise] if min_value < current_min: current_min = min_value if max_value > current_max: current_max = max_value min_max_list[current_noise] = (current_min, current_max) print('%.2f%%' % (((i + 1) * 100 + (id * param_n * 100)) / (param_n * len(threshold_data)))) sys.stdout.write("\033[F") for id, data in enumerate(svd_data): current_data = data threshold = threshold_data[id] min_value_svd, max_value_svd = min_max_list[threshold.get_noise()] 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) final_svd_data.append(current_data) # display all data using matplotlib (configure plt) plt.rcParams['figure.figsize'] = (25, 18) plt.title(param_prefix + ' noise, interval information ['+ str(begin) +', '+ str(end) +'], ' + param_feature + ' feature, step ' + str(param_step) + ' normalization ' + param_mode, fontsize=20) plt.ylabel('Importance of noise [1, 999]', fontsize=14) plt.xlabel('Vector features', fontsize=16) for id, data in enumerate(final_svd_data): param_label = param_prefix + '_' + threshold_data[id].get_noise() + str(image_indices[id]) plt.plot(data, label=param_label) plt.legend(bbox_to_anchor=(0.8, 1), loc=2, borderaxespad=0.2, fontsize=14) if not param_norm: plt.xlim(begin, end) # adapt ylim y_begin, y_end = param_ylim plt.ylim(y_begin, y_end) output_filename = param_prefix + "_threshold_1_to_" + str(param_n) + "_B" + str(begin) + "_E" + str(end) + "_" + param_feature + "_S" + str(param_step) + "_norm" + str(param_norm )+ "_" + param_mode if param_color: output_filename = output_filename + '_color' 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) plt.savefig(output_path, dpi=(200)) if __name__== "__main__": main()