final push

Projet/Bootstrap.py

@@ -1,6 +1,7 @@
 import matplotlib.pyplot as plt 
 import matplotlib.image as mpimg
 import numpy as np
+import re
 from PIL import Image
 
 from Utils.ArgParser import ArgParser
@@ -41,7 +42,9 @@ def run(self):
 
             for i in descs:
                 nw_letter = i[0]
-                nw_desc = [str(j) for j in range(1, len(i))]
+                nw_desc = [float(j) for j in re.findall(r"[\d.]+", i)]
+                print(desc_i)
+                print(nw_desc)
                 dist_actu = DescriptorGenerator.distance(desc_i, nw_desc)
                 if dist_actu < min_diff:
                     min_diff = dist_actu

Projet/BoundaryTracing/BoudaryTracing.py

@@ -127,7 +127,7 @@ def trace(self):
             if self.UD != old_ud and self.UD == -1:
                 count_up = 0
 
-            if br == 1200:
+            if br == 2000:
                 print('hello')
                 break
         # test = np.asarray(self.image)

Projet/Classifier/DescriptorGenerator.py

@@ -24,7 +24,6 @@ def generate_descriptor(data):
 
         for i in range(0, len(new_xs)):
             new_element = f(new_xs[i])
-            print(new_element)
             new_ys.append(complex(new_element[0], new_element[1]))
 
         new_ys = np.absolute(np.array(new_ys)).tolist()

Projet/Pipeline/Pipeline.py

@@ -19,9 +19,5 @@ def __init__(self):
     def run(self, input):
         edges_image = self.edgeDetec.detect_edges(input)
         clipped_image = self.clip.clip(edges_image)
-        '''test = np.asarray(clipped_image)
-        t = Image.fromarray(test)
-        plot = plt.imshow(t)
-        plt.show()'''
         bound_image_data = self.boundTrac.run(clipped_image)
         return bound_image_data

Projet/TrainedModel/model.txt

@@ -0,0 +1,8 @@
+w1.0 0.9870267806503344 0.9735362460720288 0.9592011029025258 0.9444022989991473 0.9297016926464909 0.9151825506371228 0.9006585487464958 0.886078303648402 0.8716498276384175 0.8572174514383347 0.8427728456855207 0.8283871042253792 0.8139705129568293 0.7997401909060731 0.7854649036910545 0.7713265261892785 0.7571625616602837 0.7431998005953263 0.7293491527161432 3.0
+u1.0 0.990458439758131 0.9809945324129336 0.9715460351773836 0.9620734074294379 0.9526160991975022 0.9432082954380068 0.9337661627924588 0.9243103782572862 0.9148578948608076 0.9053949760523091 0.8959455305382876 0.8865070039223456 0.8771391752180615 0.8677121618699647 0.8582783900101427 0.8488472646547812 0.8394453664053069 0.8300278380951315 0.8206303887905347 2.6666666666666665
+i1.0 0.9900330805393018 0.9799267684402521 0.969930105294399 0.959881692786049 0.9499156444225191 0.9398973109387505 0.9299774093264649 0.9200253729792116 0.9100629703796151 0.9001260117635439 0.8901920518403313 0.8801535582036658 0.8700370433409732 0.86009749990915 0.850073645335427 0.8399486477413898 0.8298637313668864 0.8198221593662403 0.8097006638519503 1.2
+k1.0 0.9890627882870118 0.9782829326972156 0.9674863521904538 0.956866694995331 0.9461545671250953 0.9355640241069237 0.9249872258319893 0.9144955222106349 0.9039873220128651 0.8934687466801673 0.8829877804977714 0.8725835462755951 0.8622110014706869 0.851791511046461 0.8414312611982354 0.8311457952825035 0.8209161550728566 0.8107271068871501 0.8005378277464835 2.6666666666666665
+v1.0 0.9908625429770358 0.9812518791129468 0.9712776041163959 0.9608201778717635 0.9504642891241538 0.9400845283761637 0.9296254565969266 0.9192627027758454 0.9089493279242049 0.8988023964646481 0.8885606365705078 0.8783739810800189 0.8682272127224749 0.8580528867725095 0.8479810197435752 0.8378830644394221 0.8278841577993532 0.8177546513048264 0.8077600172155925 3.3333333333333335
+l1.0 0.9886313261973856 0.9772047725728248 0.9657746123152318 0.9544108668081015 0.9430958457951536 0.9317388494016104 0.920402854335435 0.9089975231231584 0.8976785449994307 0.8864096334358293 0.8750702218313482 0.8637831231836044 0.8525010664051405 0.8412772553760819 0.8300644250778888 0.8188846300851933 0.8077280304571433 0.7966288375514582 0.7854463833800481 5.0
+f1.0 0.9909766928148457 0.981913793867278 0.9729159200132932 0.9639032400225703 0.9548842260070639 0.9458745952659744 0.9368670203501704 0.9278850208101751 0.918888478304557 0.9099011066950535 0.9009319996927229 0.8919469882340932 0.8829966311604174 0.8740314694311492 0.8650507613342873 0.8560921775643651 0.8471387147037911 0.8381668533120411 0.8292185503444749 2.0
+b1.0 0.9907304352236308 0.9810954099217086 0.9714609406327653 0.9619155900916428 0.9523781397271012 0.9428540324598401 0.9332770587485351 0.9237059365225558 0.9142210614543377 0.9046273708905108 0.8950912494060923 0.8855549966533088 0.8760235850325813 0.8665141183845557 0.8569448773155468 0.8474593414751496 0.8379623097120816 0.8284928692949312 0.8190007298770767 2.8

Projet/Training/Trainer.py

@@ -1,5 +1,6 @@
 from PIL import Image
 from os import listdir, walk
+from operator import add
 import numpy as np
 
 from Utils.ReadWrite import ReadWrite
@@ -26,14 +27,17 @@ def train(self):
             if not(actu_letter in descs):
                 descs[actu_letter] = [DescriptorGenerator.generate_origin_descriptor(), 0]
 
+
             img = np.asarray(Image.open(self.path_data + training_data_files[i]).convert('L'))
             data = Pipeline().run(img)
             nw_desc = DescriptorGenerator.generate_descriptor(data)
 
-            descs[actu_letter] = np.array(descs[actu_letter]) + np.array([nw_desc, 1])
+            print(descs[actu_letter])
+
+            descs[actu_letter] = [list(map(add, descs[actu_letter][0], nw_desc)), descs[actu_letter][1] + 1]
 
         for key in descs:
             data_to_add = np.array(descs[key][0]) / descs[key][1]
-            ReadWrite.write(self.path_model, key + " ".join(str(x) for x in data_to_add.list()))
+            ReadWrite.write(self.path_model, key + " ".join(str(x) for x in data_to_add.tolist()))
 
 

Projet/Utils/ReadWrite.py

@@ -7,7 +7,7 @@ def read(path):
     @staticmethod
     def write(path, data):
         file = open(path, 'a+')
-        file.write(data)
+        file.write(data + '\n')
         file.close()