Add of svd threshold curves script generation

README.md

@@ -83,4 +83,17 @@ Parameters :
 - **color** : specify if we use 3 chanels with different noise or with same noise
 - **ylim** : ylim to use in order to display curves
 
+### noise_svd_tend_visualization.py
+
+Display information about tend of svd values for specific scene
+
+### noise_svd_threshold.py
+
+Display threshold information about scene for each noise perceived. It's necessary to have in scene folder one of this file :
+- threshold_data_mean.csv
+- threshold_data_median.csv
+
+These files contains threshold information about a noise such that each row are written like that :
+- <noise>;<threshold>;<color(0, 1)>
+
 

noise_svd_tend_visualization.py

@@ -135,7 +135,7 @@ def main():
             if min_value < min_value_svd:
                 min_value_svd = min_value
 
-            if max_value > min_value_svd:
+            if max_value > max_value_svd:
                 max_value_svd = max_value
 
         print('%.2f%%' % ((p_n - i + 1) / p_n * 100))

noise_svd_threshold.py

@@ -0,0 +1,223 @@
+import sys, os, getopt
+from PIL import Image
+
+from ipfml import processing, utils
+
+from modules.utils import config as cfg
+from modules.utils import data_type as dt
+from modules import noise
+
+import matplotlib.pyplot as plt
+
+noise_list            = cfg.noise_labels
+generated_folder      = cfg.generated_folder
+filename_ext          = cfg.filename_ext
+metric_choices        = cfg.metric_choices_labels
+normalization_choices = cfg.normalization_choices
+pictures_folder       = cfg.pictures_output_folder
+
+step_picture          = 10
+
+class ThresholdData():
+    """
+    A simple class to store threshold data
+    """
+
+    def __init__(self, noise, threshold, color):
+        self.noise = noise
+        self.threshold = threshold
+        self.color = color
+
+    def get_noise(self):
+        return self.noise
+
+    def get_threshold(self):
+        return self.threshold
+
+    def isColor(self):
+        return self.color
+
+
+def main():
+
+    # default values
+    p_step = 1
+    p_color = 0
+    p_norm = 0
+    p_ylim = (0, 1)
+    p_n = 1000
+
+    if len(sys.argv) <= 1:
+        print('python noise_svd_threshold.py --prefix generated/scene --file threshold_file --metric lab --mode svdn --interval "0, 200" --step 30 --color 1 --norm 1 --ylim "0, 1"')
+        sys.exit(2)
+    try:
+        opts, args = getopt.getopt(sys.argv[1:], "h:p:f:m:m:i:s:c:n:y", ["help=", "prefix=", "file=", "metric=", "mode=", "interval=", "step=", "color=", "norm=", "ylim="])
+    except getopt.GetoptError:
+        # print help information and exit:
+        print('python noise_svd_threshold.py --prefix generated/scene --file threshold_file --metric lab --mode svdn --interval "0, 200" --step 30 --color 1 --norm 1 --ylim "0, 1"')
+        sys.exit(2)
+    for o, a in opts:
+        if o == "-h":
+            print('python noise_svd_threshold.py --prefix generated/scene --file threshold_file --metric lab --mode svdn --interval "0, 200" --step 30 --color 1 --norm 1 --ylim "0, 1"')
+            sys.exit()
+        elif o in ("-p", "--prefix"):
+            p_path = a
+        elif o in ("-f", "--file"):
+            p_data_file = a
+
+        elif o in ("-m", "--mode"):
+            p_mode = a
+
+            if not p_mode in normalization_choices:
+                assert False, "Unknown normalization choice, %s" % normalization_choices
+
+        elif o in ("-m", "--metric"):
+            p_metric = a
+
+            if not p_metric in metric_choices:
+                assert False, "Unknown metric choice, %s" % metric_choices
+
+        elif o in ("-n", "--norm"):
+            p_norm = int(a)
+        elif o in ("-c", "--color"):
+            p_color = int(a)
+        elif o in ("-i", "--interval"):
+            p_interval = list(map(int, a.split(',')))
+        elif o in ("-s", "--step"):
+            p_step = int(a)
+        elif o in ("-y", "--ylim"):
+            p_ylim = list(map(float, a.split(',')))
+        else:
+            assert False, "unhandled option"
+
+
+    p_prefix = p_path.split('/')[1].replace('_', '')
+
+    if p_color:
+        file_path = p_path + "{}/" + p_prefix + "_{}_color_{}." + filename_ext
+    else:
+        file_path = p_path + "{}/" + p_prefix + "_{}_{}." + filename_ext
+
+    begin, end = p_interval
+
+    svd_data = []
+    final_svd_data = []
+    image_indices = []
+    min_max_list = {}
+
+    threshold_data = []
+
+    # read data threshold file
+    with open(p_data_file, 'r') as f:
+        lines = f.readlines()
+
+        for line in lines:
+            data = line.replace('\n', '').split(';')
+            print(data)
+
+            threshold = ThresholdData(data[0], float(data[1]), int(data[2]))
+            threshold_data.append(threshold)
+
+    # filter data if color or not
+    threshold_data = [t for t in threshold_data if t.isColor() == p_color]
+
+    for id, threshold in enumerate(threshold_data):
+
+        current_noise = threshold.get_noise()
+        current_threshold = threshold.get_threshold()
+
+        min_max_list[current_noise] = (sys.maxsize, 0)
+        threshold_found = False
+
+        # get all data from images
+        for i in range(1, p_n):
+
+            if i % step_picture == 0:
+                image_path = file_path.format(current_noise, current_noise, str(i))
+                img = Image.open(image_path)
+
+                svd_values = dt.get_svd_data(p_metric, img)
+
+                if p_norm:
+                    svd_values = svd_values[begin:end]
+
+                # only append data once
+                if not threshold_found and current_threshold < i:
+                    svd_data.append(svd_values)
+                    image_indices.append(i)
+
+                if current_threshold < i:
+                    threshold_found = True
+
+                # update min max values
+                min_value = svd_values.min()
+                max_value = svd_values.max()
+
+                # update of min max values for noise
+                current_min, current_max = min_max_list[current_noise]
+
+                if min_value < current_min:
+                    current_min = min_value
+
+                if max_value > current_max:
+                    current_max = max_value
+
+                min_max_list[current_noise] = (current_min, current_max)
+
+            print('%.2f%%' % (((i + 1) * 100 + (id * p_n * 100)) / (p_n * len(threshold_data))))
+            sys.stdout.write("\033[F")
+
+    for id, data in enumerate(svd_data):
+
+        current_data = data
+
+        threshold = threshold_data[id]
+        min_value_svd, max_value_svd = min_max_list[threshold.get_noise()]
+
+        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)
+
+        final_svd_data.append(current_data)
+
+    # display all data using matplotlib (configure plt)
+
+    plt.rcParams['figure.figsize'] = (25, 18)
+
+    plt.title(p_prefix  + ' noise, interval information ['+ str(begin) +', '+ str(end) +'], ' + p_metric + ' metric, step ' + str(p_step) + ' normalization ' + p_mode, fontsize=20)
+    plt.ylabel('Importance of noise [1, 999]', fontsize=14)
+    plt.xlabel('Vector features', fontsize=16)
+
+    for id, data in enumerate(final_svd_data):
+
+        p_label = p_prefix + '_' + threshold_data[id].get_noise() + str(image_indices[id])
+        plt.plot(data, label=p_label)
+
+    plt.legend(bbox_to_anchor=(0.8, 1), loc=2, borderaxespad=0.2, fontsize=14)
+
+    if not p_norm:
+        plt.xlim(begin, end)
+
+    # adapt ylim
+    y_begin, y_end = p_ylim
+    plt.ylim(y_begin, y_end)
+
+    output_filename = p_prefix + "_threshold_1_to_" + str(p_n) + "_B" + str(begin) + "_E" + str(end) + "_" + p_metric + "_S" + str(p_step) + "_norm" + str(p_norm )+  "_" + p_mode
+
+    if p_color:
+        output_filename = output_filename + '_color'
+
+    print("Generation of output figure... %s" % output_filename)
+    output_path = os.path.join(pictures_folder, output_filename)
+
+    if not os.path.exists(pictures_folder):
+        os.makedirs(pictures_folder)
+
+    plt.savefig(output_path, dpi=(200))
+
+
+
+if __name__== "__main__":
+    main()

noise_svd_visualization.py

@@ -108,11 +108,11 @@ def main():
             if min_value < min_value_svd:
                 min_value_svd = min_value
 
-            if max_value > min_value_svd:
+            if max_value > max_value_svd:
                 max_value_svd = max_value
 
-        print('%.2f%%' % ((i + 1) / p_n * 100))
-        sys.stdout.write("\033[F")
+            print('%.2f%%' % ((i + 1) / p_n * 100))
+            sys.stdout.write("\033[F")
 
     for id, data in enumerate(all_svd_data):