#!/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 processing, metrics, utils from skimage import color import matplotlib.pyplot as plt from modules.utils import config as cfg 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_data_scenes(p_scene, p_bits, p_shifted): """ @brief Method which generates all .csv files from scenes photos @param p_scene, scene we want to show values @param nb_bits, number of bits expected @param p_shifted, number of bits expected to be shifted @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] # 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 = [] threshold_info = [] for id_zone, zone_folder in enumerate(zones_folder): 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 and not threshold_image_found): if seuil_learned < int(current_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 threshold_image_found = True threshold_image_zone = current_counter_index_str threshold_info.append(threshold_image_zone) current_counter_index += step_counter # all indexes of picture to plot images_indexes = [start_index_image, threshold_image_zone, end_index_image] images_data = [] print(images_indexes) for index in images_indexes: img_path = os.path.join(scene_path, prefix_image_name + index + ".png") current_img = Image.open(img_path) img_blocks = processing.divide_in_blocks(current_img, (200, 200)) # getting expected block id block = img_blocks[id_zone] # get data from mode # Here you can add the way you compute data low_bits_block = processing.rgb_to_LAB_L_bits(block, (p_shifted + 1, p_shifted + p_bits + 1)) data = metrics.get_SVD_s(low_bits_block) ################## # Data mode part # ################## # modify data depending mode data = utils.normalize_arr(data) images_data.append(data) zones_images_data.append(images_data) fig=plt.figure(figsize=(8, 8)) fig.suptitle('Lab SVD ' + str(p_bits) + ' bits shifted by ' + str(p_shifted) + " for " + p_scene + " scene", fontsize=20) for id, data in enumerate(zones_images_data): fig.add_subplot(4, 4, (id + 1)) plt.plot(data[0], label='Noisy_' + start_index_image) plt.plot(data[1], label='Threshold_' + threshold_info[id]) plt.plot(data[2], label='Reference_' + end_index_image) plt.ylabel('Lab SVD ' + str(p_bits) + ' bits shifted by ' + str(p_shifted) + ', ZONE_' + str(id + 1), fontsize=14) plt.xlabel('Vector features', fontsize=16) plt.legend(bbox_to_anchor=(0.5, 1), loc=2, borderaxespad=0.2, fontsize=14) plt.ylim(0, 0.1) plt.show() def main(): if len(sys.argv) <= 1: print('Run with default parameters...') print('python generate_all_data.py --scene A --bits 3 --shifted 3') sys.exit(2) try: opts, args = getopt.getopt(sys.argv[1:], "hs:b:s", ["help=", "scene=", "bits=", "shifted="]) except getopt.GetoptError: # print help information and exit: print('python generate_all_data.py --scene A --bits 3 --shifted 3') sys.exit(2) for o, a in opts: if o == "-h": print('python generate_all_data.py --scene A --bits 3 --shifted 3') sys.exit() elif o in ("-b", "--bits"): p_bits = int(a) elif o in ("-s", "--scene"): p_scene = a if p_scene not in scenes_indexes: assert False, "Invalid metric choice" else: p_scene = scenes_list[scenes_indexes.index(p_scene)] elif o in ("-f", "--shifted"): p_shifted = int(a) else: assert False, "unhandled option" if p_bits + p_shifted > max_nb_bits: assert False, "Invalid parameters, cannot have bits greater than 8 after shift move" display_data_scenes(p_scene, p_bits, p_shifted) if __name__== "__main__": main()