#!/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 config_filename = "config" zone_folder = "zone" min_max_filename = "_min_max_values" # define all scenes values scenes_list = ['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'] def display_data_scenes(data_type, p_scene): """ @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] # 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 = image_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 if data_type == 'lab': block_file_path = '/tmp/lab_img.png' block.save(block_file_path) data = image_processing.get_LAB_L_SVD_s(Image.open(block_file_path)) if data_type == 'mscn_revisited': img_mscn_revisited = image_processing.rgb_to_mscn(block) # save tmp as img img_output = Image.fromarray(img_mscn_revisited.astype('uint8'), 'L') mscn_revisited_file_path = '/tmp/mscn_revisited_img.png' img_output.save(mscn_revisited_file_path) img_block = Image.open(mscn_revisited_file_path) # extract from temp image data = metrics.get_SVD_s(img_block) if data_type == 'mscn': img_gray = np.array(color.rgb2gray(np.asarray(block))*255, 'uint8') img_mscn = image_processing.calculate_mscn_coefficients(img_gray, 7) img_mscn_norm = image_processing.normalize_2D_arr(img_mscn) img_mscn_gray = np.array(img_mscn_norm*255, 'uint8') data = metrics.get_SVD_s(img_mscn_gray) if data_type == 'low_bits_6': low_bits_6 = image_processing.rgb_to_LAB_L_low_bits(block, 63) # extract from temp image data = metrics.get_SVD_s(low_bits_6) if data_type == 'low_bits_5': low_bits_5 = image_processing.rgb_to_LAB_L_low_bits(block, 31) # extract from temp image data = metrics.get_SVD_s(low_bits_5) if data_type == 'low_bits_4': low_bits_4 = image_processing.rgb_to_LAB_L_low_bits(block) # extract from temp image data = metrics.get_SVD_s(low_bits_4) if data_type == 'low_bits_3': low_bits_3 = image_processing.rgb_to_LAB_L_low_bits(block, 7) # extract from temp image data = metrics.get_SVD_s(low_bits_3) if data_type == 'low_bits_2': low_bits_2 = image_processing.rgb_to_LAB_L_low_bits(block, 3) # extract from temp image data = metrics.get_SVD_s(low_bits_2) ################## # Data mode part # ################## # modify data depending mode data = image_processing.normalize_arr(data) images_data.append(data) zones_images_data.append(images_data) fig=plt.figure(figsize=(8, 8)) fig.suptitle(data_type + " values 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(data_type + ' SVD, 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 --metric all --scene A') print('python generate_all_data.py --metric lab --scene A') sys.exit(2) try: opts, args = getopt.getopt(sys.argv[1:], "hm", ["help=", "metric=", "scene="]) except getopt.GetoptError: # print help information and exit: print('python generate_all_data.py --metric all --scene A') sys.exit(2) for o, a in opts: if o == "-h": print('python generate_all_data.py --metric all --scene A') sys.exit() elif o in ("-m", "--metric"): p_metric = a if p_metric != 'all' and p_metric not in metric_choices: assert False, "Invalid metric choice" 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)] else: assert False, "unhandled option" display_data_scenes(p_metric, p_scene) if __name__== "__main__": main()