added files

clustering_precomputed_dbscan.py

@@ -95,14 +95,14 @@ def dist(rectangle1, rectangle2):
                 distance = dist
         if rectangle1[4] != rectangle2[4]:
             distance = dist + 100
-        print(intersects(rectangle1,rectangle2))
+        #print(intersects(rectangle1,rectangle2))
         if intersects(rectangle1, rectangle2):
             distance = 0
             #print(rectangle1)
     return distance
 
 def clustering(distance_matrix):
-    db = DBSCAN(eps=0.001, min_samples=1, metric="precomputed").fit(dm)  ##3.93 until now, bei 5 shon mehr erkannt, 7 noch mehr erkannt aber auch schon zu viel; GV12 ist 4.5 gut für LH zu wenig
+    db = DBSCAN(eps=4, min_samples=1, metric="precomputed").fit(dm)  ##3.93 until now, bei 5 shon mehr erkannt, 7 noch mehr erkannt aber auch schon zu viel; GV12 ist 4.5 gut für LH zu wenig
     #db = OPTICS(min_samples=1,xi=0.1, metric="precomputed").fit(dm)
     labels = db.labels_
     # Number of clusters in labels
@@ -112,7 +112,7 @@ def clustering(distance_matrix):
     data_df = pandas.read_csv("/home/bscheibel/PycharmProjects/dxf_reader/temporary/list_to_csv_with_corner_points.csv",
                            sep=";")
     data_df["cluster"] = labels
-    data_df.groupby('cluster')['element'].apply(' '.join).reset_index().to_csv("values_clusteredfrom_precomputed_dbscan.csv",sep=";")
+    data_df.groupby('cluster')['element'].apply(','.join).reset_index().to_csv("values_clusteredfrom_precomputed_dbscan.csv",sep=";", header=False, index=False)
 
 
 #file = "/home/bscheibel/PycharmProjects/dxf_reader/drawings/5152166_Rev04.html"

organize_drawing_according_to_details.py

@@ -14,7 +14,6 @@ def get_details(result):
             details_.append(element)
 
 
-
     for elem in details_:
         #print(elem)
         ymin = 100000000

read_from_clustered_merged.py

@@ -6,9 +6,29 @@ with open("/home/bscheibel/PycharmProjects/dxf_reader/values_clusteredfrom_preco
     reg_search = []
     for row in reader:
         reg = r",\s*'\(*(\w*\W*.,*\d*)\)*'\]*\]"
-        reg_search.append(re.findall(reg, row[2]))
+        #print(row[1])
+        row3 = row[1]
+        row3 = eval(row3)
+        for blub in row3:
+            #print(len(row3),row3)
+            if len(row3) == 1:
+                print(blub[4])
+            else:
+
+                if isinstance(blub[0],list):
+                    for blubi in blub:
+                        print(blubi[4])
+                else:
+
+                    print(blub[4])
+
+
+        print("\n")
+"""      reg_search.append(re.findall(reg, row[1]))
         #for reg in reg_search:
     for reg in reg_search:
             reg_new = reg
-            print(reg_new)
-    ##print(data[labels == 0])
+            #print(reg_new)
+    ##print(data[labels == 0])"""
+
+####TO DO: beim auslesen nach x-Koordinaten sortieren