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+#!/usr/bin/env python3
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+# -*- coding: utf-8 -*-
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+"""
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+Created on Fri Sep 14 21:02:42 2018
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+
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+@author: jbuisine
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+"""
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+
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+from __future__ import print_function
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+import sys, os, getopt
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+
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+import numpy as np
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+import random
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+import time
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+import json
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+
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+from PIL import Image
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+from ipfml import processing, metrics, utils
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+import ipfml.iqa.fr as fr_iqa
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+
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+from skimage import color
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+
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+import matplotlib.pyplot as plt
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+from modules.utils.data import get_svd_data
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+
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+from modules.utils import config as cfg
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+
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+# getting configuration information
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+config_filename = cfg.config_filename
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+zone_folder = cfg.zone_folder
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+min_max_filename = cfg.min_max_filename_extension
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+
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+# define all scenes values
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+scenes_list = cfg.scenes_names
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+scenes_indices = cfg.scenes_indices
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+choices = cfg.normalization_choices
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+path = cfg.dataset_path
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+zones = cfg.zones_indices
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+seuil_expe_filename = cfg.seuil_expe_filename
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+
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+metric_choices = cfg.metric_choices_labels
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+
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+max_nb_bits = 8
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+
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+integral_area_choices = ['trapz', 'simps']
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+
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+def get_area_under_curve(p_area, p_data):
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+
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+ noise_method = None
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+ function_name = 'integral_area_' + p_area
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+
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+ try:
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+ area_method = getattr(utils, function_name)
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+ except AttributeError:
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+ raise NotImplementedError("Error `{}` not implement `{}`".format(utils.__name__, function_name))
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+
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+ return area_method(p_data, dx=800)
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+
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+
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+def display_svd_values(p_scene, p_interval, p_indices, p_metric, p_mode, p_step, p_norm, p_area, p_ylim):
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+ """
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+ @brief Method which gives information about svd curves from zone of picture
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+ @param p_scene, scene expected to show svd values
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+ @param p_interval, interval [begin, end] of svd data to display
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+ @param p_interval, interval [begin, end] of samples or minutes from render generation engine
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+ @param p_metric, metric computed to show
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+ @param p_mode, normalization's mode
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+ @param p_norm, normalization or not of selected svd data
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+ @param p_area, area method name to compute area under curve
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+ @param p_ylim, ylim choice to better display of data
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+ @return nothing
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+ """
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+
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+ max_value_svd = 0
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+ min_value_svd = sys.maxsize
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+
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+ image_indices = []
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+
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+ scenes = os.listdir(path)
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+ # remove min max file from scenes folder
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+ scenes = [s for s in scenes if min_max_filename not in s]
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+
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+ begin_data, end_data = p_interval
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+ begin_index, end_index = p_indices
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+
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+ data_min_max_filename = os.path.join(path, p_metric + min_max_filename)
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+
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+ # go ahead each scenes
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+ for id_scene, folder_scene in enumerate(scenes):
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+
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+ if p_scene == folder_scene:
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+ scene_path = os.path.join(path, folder_scene)
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+
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+ config_file_path = os.path.join(scene_path, config_filename)
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+
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+ with open(config_file_path, "r") as config_file:
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+ last_image_name = config_file.readline().strip()
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+ prefix_image_name = config_file.readline().strip()
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+ start_index_image = config_file.readline().strip()
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+ end_index_image = config_file.readline().strip()
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+ step_counter = int(config_file.readline().strip())
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+
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+ # construct each zones folder name
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+ zones_folder = []
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+
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+ # get zones list info
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+ for index in zones:
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+ index_str = str(index)
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+ if len(index_str) < 2:
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+ index_str = "0" + index_str
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+
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+ current_zone = "zone"+index_str
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+ zones_folder.append(current_zone)
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+
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+ images_data = []
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+ images_indices = []
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+
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+ threshold_learned_zones = []
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+
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+ for id, zone_folder in enumerate(zones_folder):
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+
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+ # get threshold information
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+ zone_path = os.path.join(scene_path, zone_folder)
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+ path_seuil = os.path.join(zone_path, seuil_expe_filename)
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+
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+ # open treshold path and get this information
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+ with open(path_seuil, "r") as seuil_file:
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+ threshold_learned = int(seuil_file.readline().strip())
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+ threshold_learned_zones.append(threshold_learned)
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+
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+ current_counter_index = int(start_index_image)
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+ end_counter_index = int(end_index_image)
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+
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+ threshold_mean = np.mean(np.asarray(threshold_learned_zones))
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+ threshold_image_found = False
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+
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+ file_path = os.path.join(scene_path, prefix_image_name + "{}.png")
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+
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+ svd_data = []
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+
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+ while(current_counter_index <= end_counter_index):
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+
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+ current_counter_index_str = str(current_counter_index)
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+
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+ while len(start_index_image) > len(current_counter_index_str):
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+ current_counter_index_str = "0" + current_counter_index_str
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+
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+ image_path = file_path.format(str(current_counter_index_str))
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+ img = Image.open(image_path)
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+
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+ svd_values = get_svd_data(p_metric, img)
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+
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+ if p_norm:
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+ svd_values = svd_values[begin_data:end_data]
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+
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+ # update min max values
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+ min_value = svd_values.min()
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+ max_value = svd_values.max()
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+
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+ if min_value < min_value_svd:
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+ min_value_svd = min_value
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+
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+ if max_value > min_value_svd:
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+ max_value_svd = max_value
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+
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+ # keep in memory used data
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+ if current_counter_index % p_step == 0:
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+ if current_counter_index >= begin_index and current_counter_index <= end_index:
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+ images_indices.append(current_counter_index_str)
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+ svd_data.append(svd_values)
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+
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+ if threshold_mean < int(current_counter_index) and not threshold_image_found:
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+
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+ threshold_image_found = True
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+ threshold_image_zone = current_counter_index_str
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+
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+ current_counter_index += step_counter
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+ print('%.2f%%' % (current_counter_index / end_counter_index * 100))
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+ sys.stdout.write("\033[F")
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+
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+
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+ # all indices of picture to plot
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+ print(images_indices)
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+
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+ previous_data = []
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+ area_data = []
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+
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+ for id, data in enumerate(svd_data):
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+
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+ current_data = data
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+
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+ if not p_norm:
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+ current_data = current_data[begin_data:end_data]
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+
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+ if p_mode == 'svdn':
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+ current_data = utils.normalize_arr(current_data)
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+
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+ if p_mode == 'svdne':
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+ current_data = utils.normalize_arr_with_range(current_data, min_value_svd, max_value_svd)
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+
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+ images_data.append(current_data)
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+
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+ # not use this script for 'sub_blocks_stats'
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+ current_area = get_area_under_curve(p_area, current_data)
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+ area_data.append(current_area)
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+
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+ # display all data using matplotlib (configure plt)
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+ gridsize = (3, 2)
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+
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+ # fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(30, 22))
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+ fig = plt.figure(figsize=(30, 22))
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+ ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2)
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+ ax2 = plt.subplot2grid(gridsize, (2, 0), colspan=2)
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+
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+
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+ ax1.set_title(p_scene + ' scene interval information SVD['+ str(begin_data) +', '+ str(end_data) +'], from scenes indices [' + str(begin_index) + ', '+ str(end_index) + ']' + p_metric + ' metric, ' + p_mode + ', with step of ' + str(p_step) + ', svd norm ' + str(p_norm), fontsize=20)
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+ ax1.set_ylabel('Image samples or time (minutes) generation', fontsize=14)
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+ ax1.set_xlabel('Vector features', fontsize=16)
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+
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+ for id, data in enumerate(images_data):
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+
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+ p_label = p_scene + '_' + str(images_indices[id]) + " | " + p_area + ": " + str(area_data[id])
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+
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+ if images_indices[id] == threshold_image_zone:
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+ ax1.plot(data, label=p_label, lw=4, color='red')
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+ else:
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+ ax1.plot(data, label=p_label)
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+
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+ ax1.legend(bbox_to_anchor=(0.7, 1), loc=2, borderaxespad=0.2, fontsize=14)
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+
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+ start_ylim, end_ylim = p_ylim
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+ ax1.set_ylim(start_ylim, end_ylim)
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+
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+ ax2.set_title(p_area + " information for whole step images")
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+ ax2.set_ylabel(p_area + ' area values')
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+ ax2.set_xlabel('Number of samples per pixels or times')
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+ ax2.set_xticks(range(len(images_indices)))
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+ ax2.set_xticklabels(list(map(int, images_indices)))
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+ ax2.plot(area_data)
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+
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+ plt.show()
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+
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+def main():
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+
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+
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+ # by default p_step value is 10 to enable all photos
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+ p_step = 10
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+ p_ylim = (0, 1)
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+
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+ if len(sys.argv) <= 1:
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+ print('Run with default parameters...')
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+ print('python display_svd_area_data_scene.py --scene A --interval "0,800" --indices "0, 900" --metric lab --mode svdne --step 50 --norm 0 --area simps --ylim "0, 0.1"')
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+ sys.exit(2)
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+ try:
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+ opts, args = getopt.getopt(sys.argv[1:], "hs:i:i:z:l:m:s:n:a:y", ["help=", "scene=", "interval=", "indices=", "metric=", "mode=", "step=", "norm=", "area=", "ylim="])
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+ except getopt.GetoptError:
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+ # print help information and exit:
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+ print('python display_svd_area_data_scene.py --scene A --interval "0,800" --indices "0, 900" --metric lab --mode svdne --step 50 --norm 0 --area simps --ylim "0, 0.1"')
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+ sys.exit(2)
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+ for o, a in opts:
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+ if o == "-h":
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+ print('python display_svd_area_data_scene.py --scene A --interval "0,800" --indices "0, 900" --metric lab --mode svdne --step 50 --norm 0 --area simps --ylim "0, 0.1"')
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+ sys.exit()
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+ elif o in ("-s", "--scene"):
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+ p_scene = a
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+
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+ if p_scene not in scenes_indices:
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+ assert False, "Invalid scene choice"
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+ else:
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+ p_scene = scenes_list[scenes_indices.index(p_scene)]
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+ elif o in ("-i", "--interval"):
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+ p_interval = list(map(int, a.split(',')))
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+
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+ elif o in ("-i", "--indices"):
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+ p_indices = list(map(int, a.split(',')))
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+
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+ elif o in ("-m", "--metric"):
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+ p_metric = a
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+
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+ if p_metric not in metric_choices:
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+ assert False, "Invalid metric choice"
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+
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+ elif o in ("-m", "--mode"):
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+ p_mode = a
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+
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+ if p_mode not in choices:
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+ assert False, "Invalid normalization choice, expected ['svd', 'svdn', 'svdne']"
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+
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+ elif o in ("-s", "--step"):
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+ p_step = int(a)
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+
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+ elif o in ("-n", "--norm"):
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+ p_norm = int(a)
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+
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+ elif o in ("-a", "--area"):
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+ p_area = a
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+
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+ if p_area not in integral_area_choices:
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+ assert False, "Invalid area computation choices : %s " % integral_area_choices
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+
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+ elif o in ("-y", "--ylim"):
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+ p_ylim = list(map(float, a.split(',')))
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+
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+ else:
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+ assert False, "unhandled option"
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+
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+ display_svd_values(p_scene, p_interval, p_indices, p_metric, p_mode, p_step, p_norm, p_area, p_ylim)
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+
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+if __name__== "__main__":
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+ main()
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