Python scripts updated caused by new version of IPFML

display_scenes_zones.py

@@ -14,8 +14,7 @@ import time
 import json
 
 from PIL import Image
-from ipfml import processing
-from ipfml import metrics
+from ipfml import processing, metrics, utils
 from skimage import color
 import matplotlib.pyplot as plt
 
@@ -150,7 +149,7 @@ def display_data_scenes(data_type, p_scene, p_kind):
 
                         img_gray = np.array(color.rgb2gray(np.asarray(block))*255, 'uint8')
                         img_mscn = processing.calculate_mscn_coefficients(img_gray, 7)
-                        img_mscn_norm = processing.normalize_2D_arr(img_mscn)
+                        img_mscn_norm = utils.normalize_2D_arr(img_mscn)
 
                         img_mscn_gray = np.array(img_mscn_norm*255, 'uint8')
 
@@ -200,7 +199,7 @@ def display_data_scenes(data_type, p_scene, p_kind):
                     # modify data depending mode
 
                     if p_kind == 'svdn':
-                        data = processing.normalize_arr(data)
+                        data = utils.normalize_arr(data)
 
                     if p_kind == 'svdne':
                         path_min_max = os.path.join(path, data_type + min_max_filename)
@@ -209,7 +208,7 @@ def display_data_scenes(data_type, p_scene, p_kind):
                             min_val = float(f.readline())
                             max_val = float(f.readline())
 
-                        data = processing.normalize_arr_with_range(data, min_val, max_val)
+                        data = utils.normalize_arr_with_range(data, min_val, max_val)
 
                     # append of data
                     images_data.append(data)

display_scenes_zones_shifted.py

@@ -14,8 +14,7 @@ import time
 import json
 
 from PIL import Image
-from ipfml import processing
-from ipfml import metrics
+from ipfml import processing, metrics, utils
 from skimage import color
 import matplotlib.pyplot as plt
 
@@ -136,7 +135,7 @@ def display_data_scenes(p_scene, p_bits, p_shifted):
                     ##################
 
                     # modify data depending mode
-                    data = processing.normalize_arr(data)
+                    data = utils.normalize_arr(data)
                     images_data.append(data)
 
                 zones_images_data.append(images_data)

display_simulation_curves.py

@@ -27,7 +27,7 @@ def display_curves(folder_path):
         df = pd.read_csv(path_file, header=None, sep=";")
 
 
-        fig=plt.figure(figsize=(8, 8))
+        fig=plt.figure(figsize=(20, 20))
         fig.suptitle("Detection simulation for " + scene_names[id] + " scene", fontsize=20)
 
         for index, row in df.iterrows():
@@ -62,6 +62,7 @@ def display_curves(folder_path):
             plt.ylim(-1, 2)
 
         plt.show()
+        plt.savefig(os.path.join(folder_path, scene_names[id] + '_simulation_curve.png'))
 
 def main():
 

display_svd_zone_scene.py

@@ -15,8 +15,7 @@ import time
 import json
 
 from PIL import Image
-from ipfml import processing
-from ipfml import metrics
+from ipfml import processing, metrics, utils
 from skimage import color
 
 import matplotlib.pyplot as plt
@@ -153,10 +152,10 @@ def display_svd_values(p_scene, p_interval, p_zone, p_metric, p_mode, p_step):
                         min_val = float(f.readline())
                         max_val = float(f.readline())
 
-                    data = processing.normalize_arr_with_range(data, min_val, max_val)
+                    data = utils.normalize_arr_with_range(data, min_val, max_val)
 
                 if p_mode == 'svdn':
-                    data = processing.normalize_arr(data)
+                    data = utils.normalize_arr(data)
 
                 zones_images_data.append(data)
 

generate_all_data.py

@@ -15,8 +15,7 @@ import json
 
 from modules.utils.data import get_svd_data
 from PIL import Image
-from ipfml import processing
-from ipfml import metrics
+from ipfml import processing, metrics, utils
 from skimage import color
 
 from modules.utils import config as cfg
@@ -131,10 +130,10 @@ def generate_data_svd(data_type, mode):
                         min_val = float(f.readline())
                         max_val = float(f.readline())
 
-                    data = processing.normalize_arr_with_range(data, min_val, max_val)
+                    data = utils.normalize_arr_with_range(data, min_val, max_val)
 
                 if mode == 'svdn':
-                    data = processing.normalize_arr(data)
+                    data = utils.normalize_arr(data)
 
                 # save min and max found from dataset in order to normalize data using whole data known
                 if mode == 'svd':

generate_data_model.py

@@ -14,7 +14,7 @@ import time
 import json
 
 from PIL import Image
-from ipfml import processing, metrics
+from ipfml import processing, metrics, utils
 
 from modules.utils import config as cfg
 
@@ -53,7 +53,7 @@ def construct_new_line(path_seuil, interval, line, norm, sep, index):
 
     # TODO : check if it's always necessary to do that (loss of information for svd)
     if norm:
-        metrics = processing.normalize_arr_with_range(metrics, min_value_interval, max_value_interval)
+        metrics = utils.normalize_arr_with_range(metrics, min_value_interval, max_value_interval)
 
     with open(path_seuil, "r") as seuil_file:
         seuil_learned = int(seuil_file.readline().strip())

generate_data_model_random.py

@@ -14,7 +14,7 @@ import time
 import json
 
 from PIL import Image
-from ipfml import processing, metrics
+from ipfml import processing, metrics, utils
 
 from modules.utils import config as cfg
 from modules.utils import data as dt
@@ -57,9 +57,9 @@ def construct_new_line(path_seuil, interval, line, choice, norm):
     if norm:
 
         if choice == 'svdne':
-            metrics = processing.normalize_arr_with_range(metrics, min_value_interval, max_value_interval)
+            metrics = utils.normalize_arr_with_range(metrics, min_value_interval, max_value_interval)
         if choice == 'svdn':
-            metrics = processing.normalize_arr(metrics)
+            metrics = utils.normalize_arr(metrics)
 
     with open(path_seuil, "r") as seuil_file:
         seuil_learned = int(seuil_file.readline().strip())

predict_noisy_image_svd.py

@@ -2,7 +2,7 @@ from sklearn.externals import joblib
 
 import numpy as np
 
-from ipfml import processing
+from ipfml import processing, utils
 from PIL import Image
 
 import sys, os, getopt
@@ -84,10 +84,10 @@ def main():
                 min_val = float(f.readline().replace('\n', ''))
                 max_val = float(f.readline().replace('\n', ''))
 
-            test_data = processing.normalize_arr_with_range(test_data, min_val, max_val)
+            test_data = utils.normalize_arr_with_range(test_data, min_val, max_val)
 
         if p_mode == 'svdn':
-            test_data = processing.normalize_arr(test_data)
+            test_data = utils.normalize_arr(test_data)
 
     else:
 
@@ -103,10 +103,10 @@ def main():
                 min_val = float(f.readline().replace('\n', ''))
                 max_val = float(f.readline().replace('\n', ''))
 
-            l_values = processing.normalize_arr_with_range(data, min_val, max_val)
+            l_values = utils.normalize_arr_with_range(data, min_val, max_val)
 
         elif p_mode == 'svdn':
-            l_values = processing.normalize_arr(data)
+            l_values = utils.normalize_arr(data)
         else:
             l_values = data
 

predict_seuil_expe.py

@@ -2,7 +2,7 @@ from sklearn.externals import joblib
 
 import numpy as np
 
-from ipfml import processing
+from ipfml import processing, utils
 from PIL import Image
 
 import sys, os, getopt