import numpy as np import pandas as pd import matplotlib.pyplot as plt import os, sys, argparse from modules.utils.data import get_svd_data from modules.utils import config as cfg learned_zones_folder = cfg.learned_zones_folder models_name = cfg.models_names_list label_freq = 6 def display_curves(folder_path, model_name): """ @brief Method used to display simulation given .csv files @param folder_path, folder which contains all .csv files obtained during simulation @param model_name, current name of model @return nothing """ for name in models_name: if name in model_name: data_filename = model_name learned_zones_folder_path = os.path.join(learned_zones_folder, data_filename) data_files = [x for x in os.listdir(folder_path) if '.png' not in x] scene_names = [f.split('_')[3] for f in data_files] for id, f in enumerate(data_files): print(scene_names[id]) path_file = os.path.join(folder_path, f) scenes_zones_used_file_path = os.path.join(learned_zones_folder_path, scene_names[id] + '.csv') zones_used = [] with open(scenes_zones_used_file_path, 'r') as f: zones_used = [int(x) for x in f.readline().split(';') if x != ''] print(zones_used) df = pd.read_csv(path_file, header=None, sep=";") fig=plt.figure(figsize=(35, 22)) fig.suptitle("Detection simulation for " + scene_names[id] + " scene", fontsize=20) for index, row in df.iterrows(): row = np.asarray(row) threshold = row[2] start_index = row[3] step_value = row[4] counter_index = 0 current_value = start_index while(current_value < threshold): counter_index += 1 current_value += step_value fig.add_subplot(4, 4, (index + 1)) plt.plot(row[5:]) if index in zones_used: ax = plt.gca() ax.set_facecolor((0.9, 0.95, 0.95)) # draw vertical line from (70,100) to (70, 250) plt.plot([counter_index, counter_index], [-2, 2], 'k-', lw=2, color='red') if index % 4 == 0: plt.ylabel('Not noisy / Noisy', fontsize=20) if index >= 12: plt.xlabel('Samples per pixel', fontsize=20) x_labels = [id * step_value + start_index for id, val in enumerate(row[5:]) if id % label_freq == 0] x = [v for v in np.arange(0, len(row[5:])+1) if v % label_freq == 0] plt.xticks(x, x_labels, rotation=45) plt.ylim(-1, 2) plt.savefig(os.path.join(folder_path, scene_names[id] + '_simulation_curve.png')) #plt.show() def main(): parser = argparse.ArgumentParser(description="Display simulations curves from simulation data") parser.add_argument('--folder', type=str, help='Folder which contains simulations data for scenes') parser.add_argument('--model', type=str, help='Name of the model used for simulations') args = parser.parse_args() p_folder = args.folder if args.model: p_model = args.model else: # find p_model from folder if model arg not given (folder path need to have model name) if p_folder.split('/')[-1]: p_model = p_folder.split('/')[-1] else: p_model = p_folder.split('/')[-2] print(p_model) display_curves(p_folder, p_model) print(p_folder) if __name__== "__main__": main()