Display script updates; Add of new metric

SVDAnalysis/svd_roration_view.py

@@ -21,6 +21,7 @@ def get_svd_mean_and_image_rotations(img_path):
     for i in range(4):
         rotations.append(processing.rotate_image(img, (i+1)*90, pil=False))
         svd_data_rotation.append(processing.get_LAB_L_SVD_s(rotations[i]))
+        Image.fromarray(rotations[i]).show()
 
     mean_image = processing.fusion_images(rotations, pil=False)
     mean_data = processing.get_LAB_L_SVD_s(mean_image)

display_simulation_curves.py

@@ -26,7 +26,6 @@ def display_curves(folder_path):
 
         df = pd.read_csv(path_file, header=None, sep=";")
 
-
         fig=plt.figure(figsize=(35, 22))
         fig.suptitle("Detection simulation for " + scene_names[id] + " scene", fontsize=20)
 

display_svd_area_data_scene.py

@@ -0,0 +1,310 @@
+#!/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
+import ipfml.iqa.fr as fr_iqa
+
+from skimage import color
+
+import matplotlib.pyplot as plt
+from modules.utils.data 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_indices      = 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
+
+integral_area_choices = ['trapz', 'simps']
+
+def get_area_under_curve(p_area, p_data):
+
+    noise_method = None
+    function_name = 'integral_area_' + p_area
+
+    try:
+        area_method = getattr(utils, function_name)
+    except AttributeError:
+        raise NotImplementedError("Error `{}` not implement `{}`".format(utils.__name__, function_name))
+
+    return area_method(p_data, dx=800)
+
+
+def display_svd_values(p_scene, p_interval, p_indices, p_metric, p_mode, p_step, p_norm, p_area, p_ylim):
+    """
+    @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 svd data to display
+    @param p_interval, interval [begin, end] of samples or minutes from render generation engine
+    @param p_metric, metric computed to show
+    @param p_mode, normalization's mode
+    @param p_norm, normalization or not of selected svd data
+    @param p_area, area method name to compute area under curve
+    @param p_ylim, ylim choice to better display of data
+    @return nothing
+    """
+
+    max_value_svd = 0
+    min_value_svd = sys.maxsize
+
+    image_indices = []
+
+    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_data, end_data = p_interval
+    begin_index, end_index = p_indices
+
+    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:
+            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)
+
+            images_data = []
+            images_indices = []
+
+            threshold_learned_zones = []
+
+            for id, zone_folder in enumerate(zones_folder):
+
+                # get threshold information
+                zone_path = os.path.join(scene_path, zone_folder)
+                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:
+                    threshold_learned = int(seuil_file.readline().strip())
+                    threshold_learned_zones.append(threshold_learned)
+
+            current_counter_index = int(start_index_image)
+            end_counter_index = int(end_index_image)
+
+            threshold_mean = np.mean(np.asarray(threshold_learned_zones))
+            threshold_image_found = False
+
+            file_path = os.path.join(scene_path, prefix_image_name + "{}.png")
+
+            svd_data = []
+
+            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
+
+                image_path = file_path.format(str(current_counter_index_str))
+                img = Image.open(image_path)
+
+                svd_values = get_svd_data(p_metric, img)
+
+                if p_norm:
+                    svd_values = svd_values[begin_data:end_data]
+
+                # update min max values
+                min_value = svd_values.min()
+                max_value = svd_values.max()
+
+                if min_value < min_value_svd:
+                    min_value_svd = min_value
+
+                if max_value > min_value_svd:
+                    max_value_svd = max_value
+
+                # keep in memory used data
+                if current_counter_index % p_step == 0:
+                    if current_counter_index >= begin_index and current_counter_index <= end_index:
+                        images_indices.append(current_counter_index_str)
+                        svd_data.append(svd_values)
+
+                    if threshold_mean < 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
+                print('%.2f%%' % (current_counter_index / end_counter_index * 100))
+                sys.stdout.write("\033[F")
+
+
+            # all indices of picture to plot
+            print(images_indices)
+
+            previous_data = []
+            area_data = []
+
+            for id, data in enumerate(svd_data):
+
+                current_data = data
+
+                if not p_norm:
+                    current_data = current_data[begin_data:end_data]
+
+                if p_mode == 'svdn':
+                    current_data = utils.normalize_arr(current_data)
+
+                if p_mode == 'svdne':
+                    current_data = utils.normalize_arr_with_range(current_data, min_value_svd, max_value_svd)
+
+                images_data.append(current_data)
+
+                # not use this script for 'sub_blocks_stats'
+                current_area = get_area_under_curve(p_area, current_data)
+                area_data.append(current_area)
+
+            # display all data using matplotlib (configure plt)
+            gridsize = (3, 2)
+
+            # fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(30, 22))
+            fig = plt.figure(figsize=(30, 22))
+            ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2)
+            ax2 = plt.subplot2grid(gridsize, (2, 0), colspan=2)
+
+
+            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)
+            ax1.set_ylabel('Image samples or time (minutes) generation', fontsize=14)
+            ax1.set_xlabel('Vector features', fontsize=16)
+
+            for id, data in enumerate(images_data):
+
+                p_label = p_scene + '_' + str(images_indices[id]) + " | " + p_area + ": " + str(area_data[id])
+
+                if images_indices[id] == threshold_image_zone:
+                    ax1.plot(data, label=p_label, lw=4, color='red')
+                else:
+                    ax1.plot(data, label=p_label)
+
+            ax1.legend(bbox_to_anchor=(0.7, 1), loc=2, borderaxespad=0.2, fontsize=14)
+
+            start_ylim, end_ylim = p_ylim
+            ax1.set_ylim(start_ylim, end_ylim)
+
+            ax2.set_title(p_area + " information for whole step images")
+            ax2.set_ylabel(p_area + ' area values')
+            ax2.set_xlabel('Number of samples per pixels or times')
+            ax2.set_xticks(range(len(images_indices)))
+            ax2.set_xticklabels(list(map(int, images_indices)))
+            ax2.plot(area_data)
+
+            plt.show()
+
+def main():
+
+
+    # by default p_step value is 10 to enable all photos
+    p_step = 10
+    p_ylim = (0, 1)
+
+    if len(sys.argv) <= 1:
+        print('Run with default parameters...')
+        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"')
+        sys.exit(2)
+    try:
+        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="])
+    except getopt.GetoptError:
+        # print help information and exit:
+        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"')
+        sys.exit(2)
+    for o, a in opts:
+        if o == "-h":
+            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"')
+            sys.exit()
+        elif o in ("-s", "--scene"):
+            p_scene = a
+
+            if p_scene not in scenes_indices:
+                assert False, "Invalid scene choice"
+            else:
+                p_scene = scenes_list[scenes_indices.index(p_scene)]
+        elif o in ("-i", "--interval"):
+            p_interval = list(map(int, a.split(',')))
+
+        elif o in ("-i", "--indices"):
+            p_indices = list(map(int, a.split(',')))
+
+        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)
+
+        elif o in ("-n", "--norm"):
+            p_norm = int(a)
+
+        elif o in ("-a", "--area"):
+            p_area = a
+
+            if p_area not in integral_area_choices:
+                assert False, "Invalid area computation choices : %s " % integral_area_choices
+
+        elif o in ("-y", "--ylim"):
+            p_ylim = list(map(float, a.split(',')))
+
+        else:
+            assert False, "unhandled option"
+
+    display_svd_values(p_scene, p_interval, p_indices, p_metric, p_mode, p_step, p_norm, p_area, p_ylim)
+
+if __name__== "__main__":
+    main()

display_svd_data_scene.py

@@ -153,7 +153,7 @@ def display_svd_values(p_scene, p_interval, p_indices, p_metric, p_mode, p_step,
                 svd_values = get_svd_data(p_metric, img)
 
                 if p_norm:
-                    svd_values = svd_values[begin:end]
+                    svd_values = svd_values[begin_data:end_data]
 
                 # update min max values
                 min_value = svd_values.min()
@@ -191,6 +191,9 @@ def display_svd_values(p_scene, p_interval, p_indices, p_metric, p_mode, p_step,
 
                 current_data = data
 
+                if not p_norm:
+                    current_data = current_data[begin_data:end_data]
+
                 if p_mode == 'svdn':
                     current_data = utils.normalize_arr(current_data)
 

display_svd_zone_scene.py

@@ -201,6 +201,7 @@ def display_svd_values(p_scene, p_interval, p_indices, p_zone, p_metric, p_mode,
                 # Here you can add the way you compute data
                 data = get_svd_data(p_metric, block)
 
+                # TODO : improve part of this code to get correct min / max values
                 if p_norm:
                     data = data[begin_data:end_data]
 

modules/utils/config.py

@@ -33,4 +33,4 @@ cycle_scenes_indices            = ['E', 'I']
 normalization_choices           = ['svd', 'svdn', 'svdne']
 zones_indices                   = np.arange(16)
 
-metric_choices_labels           = ['lab', 'mscn_revisited', 'low_bits_2', 'low_bits_3', 'low_bits_4', 'low_bits_5', 'low_bits_6','low_bits_4_shifted_2']
+metric_choices_labels           = ['lab', 'mscn_revisited', 'low_bits_2', 'low_bits_3', 'low_bits_4', 'low_bits_5', 'low_bits_6','low_bits_4_shifted_2', 'sub_blocks_stats']

modules/utils/data.py

@@ -1,4 +1,4 @@
-from ipfml import processing, metrics
+from ipfml import processing, metrics, utils
 from modules.utils.config import *
 
 from PIL import Image
@@ -77,6 +77,35 @@ def get_svd_data(data_type, block):
 
         data = metrics.get_SVD_s(processing.rgb_to_LAB_L_bits(block, (3, 6)))
 
+    if data_type == 'sub_blocks_stats':
+
+        block = np.asarray(block)
+        width, height, _= block.shape
+        sub_width, sub_height = int(width / 4), int(height / 4)
+
+        sub_blocks = processing.divide_in_blocks(block, (sub_width, sub_height))
+
+        data = []
+
+        for sub_b in sub_blocks:
+
+            # by default use the whole lab L canal
+            l_svd_data = np.array(processing.get_LAB_L_SVD_s(sub_b))
+
+            # get information we want from svd
+            data.append(np.mean(l_svd_data))
+            data.append(np.median(l_svd_data))
+            data.append(np.percentile(l_svd_data, 25))
+            data.append(np.percentile(l_svd_data, 75))
+            data.append(np.var(l_svd_data))
+
+            area_under_curve = utils.integral_area_trapz(l_svd_data, dx=100)
+            data.append(area_under_curve)
+
+        # convert into numpy array after computing all stats
+        data = np.asarray(data)
+
+
     return data
 
 def get_renderer_scenes_indices(renderer_name):