#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Sep 14 21:02:42 2018 @author: jbuisine """ from __future__ import print_function import sys, os, getopt import numpy as np import random import time import json from PIL import Image from ipfml import image_processing from ipfml import metrics from skimage import color import matplotlib.pyplot as plt from modules.utils.data_type import get_svd_data from modules.utils import config as cfg # getting configuration information config_filename = cfg.config_filename zone_folder = cfg.zone_folder min_max_filename = cfg.min_max_filename_extension # define all scenes values scenes_list = cfg.scenes_names scenes_indexes = cfg.scenes_indices choices = cfg.normalization_choices path = cfg.dataset_path zones = cfg.zones_indices seuil_expe_filename = cfg.seuil_expe_filename metric_choices = cfg.metric_choices_labels max_nb_bits = 8 def display_svd_values(p_scene, p_interval, p_zone, p_metric, p_mode, p_step): """ @brief Method which gives information about svd curves from zone of picture @param p_scene, scene expected to show svd values @param p_interval, interval [begin, end] of samples or minutes from render generation engine @param p_zone, zone's identifier of picture @param p_metric, metric computed to show @param p_mode, normalization's mode @return nothing """ scenes = os.listdir(path) # remove min max file from scenes folder scenes = [s for s in scenes if min_max_filename not in s] begin, end = p_interval data_min_max_filename = os.path.join(path, p_metric + min_max_filename) # go ahead each scenes for id_scene, folder_scene in enumerate(scenes): if p_scene == folder_scene: print(folder_scene) scene_path = os.path.join(path, folder_scene) config_file_path = os.path.join(scene_path, config_filename) with open(config_file_path, "r") as config_file: last_image_name = config_file.readline().strip() prefix_image_name = config_file.readline().strip() start_index_image = config_file.readline().strip() end_index_image = config_file.readline().strip() step_counter = int(config_file.readline().strip()) # construct each zones folder name zones_folder = [] # get zones list info for index in zones: index_str = str(index) if len(index_str) < 2: index_str = "0" + index_str current_zone = "zone"+index_str zones_folder.append(current_zone) zones_images_data = [] images_indexes = [] zone_folder = zones_folder[p_zone] zone_path = os.path.join(scene_path, zone_folder) current_counter_index = int(start_index_image) end_counter_index = int(end_index_image) # get threshold information path_seuil = os.path.join(zone_path, seuil_expe_filename) # open treshold path and get this information with open(path_seuil, "r") as seuil_file: seuil_learned = int(seuil_file.readline().strip()) threshold_image_found = False while(current_counter_index <= end_counter_index): current_counter_index_str = str(current_counter_index) while len(start_index_image) > len(current_counter_index_str): current_counter_index_str = "0" + current_counter_index_str if current_counter_index % p_step == 0: if current_counter_index >= begin and current_counter_index <= end: images_indexes.append(current_counter_index_str) if seuil_learned < int(current_counter_index) and not threshold_image_found: threshold_image_found = True threshold_image_zone = current_counter_index_str current_counter_index += step_counter # all indexes of picture to plot print(images_indexes) for index in images_indexes: img_path = os.path.join(scene_path, prefix_image_name + str(index) + ".png") current_img = Image.open(img_path) img_blocks = image_processing.divide_in_blocks(current_img, (200, 200)) # getting expected block id block = img_blocks[p_zone] # get data from mode # Here you can add the way you compute data data = get_svd_data(p_metric, block) ################## # Data mode part # ################## if p_mode == 'svdne': # getting max and min information from min_max_filename with open(data_min_max_filename, 'r') as f: min_val = float(f.readline()) max_val = float(f.readline()) data = image_processing.normalize_arr_with_range(data, min_val, max_val) if p_mode == 'svdn': data = image_processing.normalize_arr(data) zones_images_data.append(data) plt.title(p_scene + ' scene interval information ['+ str(begin) +', '+ str(end) +'], ' + p_metric + ' metric, ' + p_mode, fontsize=20) plt.ylabel('Image samples or time (minutes) generation', fontsize=14) plt.xlabel('Vector features', fontsize=16) for id, data in enumerate(zones_images_data): p_label = p_scene + "_" + images_indexes[id] if images_indexes[id] == threshold_image_zone: plt.plot(data, label=p_label, lw=4, color='red') else: plt.plot(data, label=p_label) plt.legend(bbox_to_anchor=(0.8, 1), loc=2, borderaxespad=0.2, fontsize=14) plt.ylim(0, 0.1) plt.show() def main(): # by default p_step value is 10 to enable all photos p_step = 10 if len(sys.argv) <= 1: print('Run with default parameters...') print('python display_svd_zone_scene.py --scene A --interval "0,200" --zone 3 --metric lab --mode svdne --step 50') sys.exit(2) try: opts, args = getopt.getopt(sys.argv[1:], "hs:i:z:l:m:s", ["help=", "scene=", "interval=", "zone=", "metric=", "mode=", "step="]) except getopt.GetoptError: # print help information and exit: print('python display_svd_zone_scene.py --scene A --interval "0,200" --zone 3 --metric lab --mode svdne --step 50') sys.exit(2) for o, a in opts: if o == "-h": print('python display_svd_zone_scene.py --scene A --interval "0,200" --zone 3 --metric lab --mode svdne --step 50') sys.exit() elif o in ("-s", "--scene"): p_scene = a if p_scene not in scenes_indexes: assert False, "Invalid scene choice" else: p_scene = scenes_list[scenes_indexes.index(p_scene)] elif o in ("-i", "--interval"): p_interval = list(map(int, a.split(','))) elif o in ("-z", "--zone"): p_zone = int(a) elif o in ("-m", "--metric"): p_metric = a if p_metric not in metric_choices: assert False, "Invalid metric choice" elif o in ("-m", "--mode"): p_mode = a if p_mode not in choices: assert False, "Invalid normalization choice, expected ['svd', 'svdn', 'svdne']" elif o in ("-s", "--step"): p_step = int(a) else: assert False, "unhandled option" display_svd_values(p_scene, p_interval, p_zone, p_metric, p_mode, p_step) if __name__== "__main__": main()