compute statistic train, eval and testing

compute_features.py

@@ -17,12 +17,16 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
                                 [0.0000000, 0.0000000, -1.0000000],
                                 [1.0000000, 0.0000000, 0.0000000]])
     
+    scenario = "Worlds"
+    destination_folders = [f"Generated_{scenario}_Testing", f"Generated_{scenario}_Evaluating", f"Generated_{scenario}_Training" ]
+
     list_count_categories = {}
+    for destination_folder_loop in destination_folders : # [f"Generated_{scenario}_Testing", f"Generated_{scenario}_Evaluating", f"Generated_{scenario}_Training"] :
+        list_count_categories[destination_folder_loop] = {}
 
 
     for i in range(World_begin, World_begin + Nb_world): # worlds
 
-        scenario = "Worlds"
 
         if i > 4 :
             destination_folder = f"Generated_{scenario}_Testing"
@@ -56,25 +60,23 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
             
             if os.path.isfile(f'{data_name}/{destination_folder}/Count_{p-1}.json'):
                 with open(f'{data_name}/{destination_folder}/Count_{p-1}.json') as f:
-                    list_count_categories = json.load(f)
+                    list_count_categories[destination_folder] = json.load(f)
 
             for categories in list_categories:
                 if categories in catergories_occ_array.keys():
                     Nb_instance = len(catergories_occ_array[categories])
 
                     meta = {}
-                    if categories in list_count_categories.keys():
-                        print("hello")
-                    else:
+                    if not categories in list_count_categories[destination_folder].keys():
                         #list_count_categories[categories] = {categories}
-                        list_count_categories[categories] = {}
+                        list_count_categories[destination_folder][categories] = {}
 
-                    if Nb_instance in list_count_categories[categories].keys() : 
-                        list_count_categories[categories][Nb_instance] += 1
+                    if Nb_instance in list_count_categories[destination_folder][categories].keys() : 
+                        list_count_categories[destination_folder][categories][Nb_instance] += 1
                     else : 
-                        list_count_categories[categories][Nb_instance] = 1
+                        list_count_categories[destination_folder][categories][Nb_instance] = 1
 
-                    meta['id_generated'] = list_count_categories[categories][Nb_instance]
+                    meta['id_generated'] = list_count_categories[destination_folder][categories][Nb_instance]
                     meta['id_original'] = p
                     meta['id_category'] = catergories_label_to_id[categories] 
                     meta['id_instance'] = catergories_occ_array[categories]
@@ -166,9 +168,12 @@ def process_compute(data_name, camera, camera_resized, new_size, Nb_camera, Worl
                             out_resized.append(y_resized)
                             ind_resized += 2
                         np.savetxt(f'{data_name}/{destination_folder}/{categories}/FPS_resized/{p}.txt',  np.array(out_resized).reshape(1, len(out_resized)))
-        
-    with open(f'{data_name}/{destination_folder}/Count_{p}.json', mode='w') as f:
-        f.write(json.dumps(list_count_categories, indent=4))
+    
+
+    for destination_folder_loop in destination_folders : # [f"Generated_{scenario}_Testing", f"Generated_{scenario}_Evaluating", f"Generated_{scenario}_Training"] :
+        with open(f'{data_name}/{destination_folder_loop}/Count_{p}.json', mode='w') as f:
+            f.write(json.dumps(list_count_categories[destination_folder], indent=4))
+        print(list_count_categories[destination_folder])
     print(list_count_categories)