#!/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 utils.data_type_module import get_svd_data from PIL import Image from ipfml import image_processing from ipfml import metrics from skimage import color config_filename = "config" zone_folder = "zone" min_max_filename = "_min_max_values" generic_output_file_svd = '_random.csv' output_data_folder = 'data' # define all scenes values scenes = ['Appart1opt02', 'Bureau1', 'Cendrier', 'Cuisine01', 'EchecsBas', 'PNDVuePlongeante', 'SdbCentre', 'SdbDroite', 'Selles'] scenes_indexes = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'] choices = ['svd', 'svdn', 'svdne'] path = './fichiersSVD_light' zones = np.arange(16) seuil_expe_filename = 'seuilExpe' metric_choices = ['lab', 'mscn', 'mscn_revisited', 'low_bits_2', 'low_bits_3', 'low_bits_4', 'low_bits_5', 'low_bits_6','low_bits_4_shifted_2'] picture_step = 10 def generate_data_svd(data_type, mode): """ @brief Method which generates all .csv files from scenes photos @param path - path of scenes folder information @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] # keep in memory min and max data found from data_type min_val_found = sys.maxsize max_val_found = 0 data_min_max_filename = os.path.join(path, data_type + min_max_filename) # go ahead each scenes for id_scene, folder_scene in enumerate(scenes): 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()) # getting output filename output_svd_filename = data_type + "_" + mode + generic_output_file_svd # construct each zones folder name zones_folder = [] svd_output_files = [] # 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) zone_path = os.path.join(scene_path, current_zone) svd_file_path = os.path.join(zone_path, output_svd_filename) # add writer into list svd_output_files.append(open(svd_file_path, 'w')) current_counter_index = int(start_index_image) end_counter_index = int(end_index_image) while(current_counter_index <= end_counter_index): if current_counter_index % picture_step == 0: 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 img_path = os.path.join(scene_path, prefix_image_name + current_counter_index_str + ".png") current_img = Image.open(img_path) img_blocks = image_processing.divide_in_blocks(current_img, (200, 200)) for id_block, block in enumerate(img_blocks): ########################### # Metric computation part # ########################### data = get_svd_data(data_type, block) ################## # Data mode part # ################## # modify data depending mode if 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 mode == 'svdn': data = image_processing.normalize_arr(data) # save min and max found from dataset in order to normalize data using whole data known if mode == 'svd': current_min = data.min() current_max = data.max() if current_min < min_val_found: min_val_found = current_min if current_max > max_val_found: max_val_found = current_max # now write data into current writer current_file = svd_output_files[id_block] # add of index current_file.write(current_counter_index_str + ';') for val in data: current_file.write(str(val) + ";") current_file.write('\n') start_index_image_int = int(start_index_image) print(data_type + "_" + mode + "_" + folder_scene + " - " + "{0:.2f}".format((current_counter_index - start_index_image_int) / (end_counter_index - start_index_image_int)* 100.) + "%") sys.stdout.write("\033[F") current_counter_index += step_counter for f in svd_output_files: f.close() print('\n') # save current information about min file found if mode == 'svd': with open(data_min_max_filename, 'w') as f: f.write(str(min_val_found) + '\n') f.write(str(max_val_found) + '\n') print("%s : end of data generation\n" % _mode) def main(): # default value of p_step p_step = 10 if len(sys.argv) <= 1: print('Run with default parameters...') print('python generate_all_data.py --metric all') print('python generate_all_data.py --metric lab') print('python generate_all_data.py --metric lab --step 10') sys.exit(2) try: opts, args = getopt.getopt(sys.argv[1:], "hms", ["help=", "metric=", "step="]) except getopt.GetoptError: # print help information and exit: print('python generate_all_data.py --metric all --step 10') sys.exit(2) for o, a in opts: if o == "-h": print('python generate_all_data.py --metric all --step 10') sys.exit() elif o in ("-s", "--step"): p_step = int(a) elif o in ("-m", "--metric"): p_metric = a if p_metric != 'all' and p_metric not in metric_choices: assert False, "Invalid metric choice" else: assert False, "unhandled option" global picture_step picture_step = p_step if picture_step % 10 != 0: assert False, "Picture step variable needs to be divided by ten" # generate all or specific metric data if p_metric == 'all': for m in metric_choices: generate_data_svd(m, 'svd') generate_data_svd(m, 'svdn') generate_data_svd(m, 'svdne') else: generate_data_svd(p_metric, 'svd') generate_data_svd(p_metric, 'svdn') generate_data_svd(p_metric, 'svdne') if __name__== "__main__": main()