Firsh Push at 20241207

Load_process/Load_Indepentend.py

@@ -0,0 +1,78 @@
+from Read_and_process_image.ReadAndProcess import Read_image_and_Process_image
+from model_data_processing.processing import shuffle_data
+from merge_class.merge import merge
+from Read_and_process_image.ReadAndProcess import Read_image_and_Process_image
+from Load_process.LoadData import Load_Data_Prepare, Load_Data_Tools
+
+class Load_Indepentend_Data():
+    def __init__(self, Labels, OneHot_Encording):
+        '''
+        影像切割物件
+        label有2類,會將其轉成one-hot-encoding的形式
+            [0, 1] = NPC_negative
+            [1, 0] = NPC_positive
+        '''
+        self.merge = merge()
+        self.Labels = Labels
+        self.OneHot_Encording = OneHot_Encording
+        pass
+
+    def process_main(self, Test_data_root, Validation_data_root):
+        self.test, self.test_label = self.get_Independent_image(Test_data_root)
+        print("\ntest_labels有" + str(len(self.test_label)) + "筆資料\n")
+
+        self.validation, self.validation_label = self.get_Independent_image(Validation_data_root)
+        print("validation_labels有 " + str(len(self.validation_label)) + " 筆資料\n")
+
+    def get_Independent_image(self, independent_DataRoot):
+        image_processing = Read_image_and_Process_image()
+
+        classify_image = []
+        Total_Dict_Data_Root = self.Get_Independent_data_Root(independent_DataRoot) # 讀取測試資料集的資料
+        Total_Dict_Data_Root = self.Specified_Amount_Of_Data(Total_Dict_Data_Root) # 打亂並取出指定資料筆數的資料
+        Total_List_Data_Root = [Total_Dict_Data_Root[self.Labels[0]], Total_Dict_Data_Root[self.Labels[1]]]            
+        
+        test_label, Classify_Label = [], []
+        i = 0 # 計算classify_image的counter，且計算總共有幾筆資料
+        for test_title in Total_List_Data_Root: # 藉由讀取所有路徑來進行讀檔
+            test_label = image_processing.make_label_list(len(test_title), self.OneHot_Encording[i]) # 製作對應圖片數量的label出來+    
+            print(self.Labels[i] + " 有 " + str(len(test_label)) + " 筆資料 ")
+
+            classify_image.append(test_title)
+            Classify_Label.append(test_label)
+            i += 1
+
+        original_test_root = self.merge.merge_data_main(classify_image, 0, 2)
+        original_test_label = self.merge.merge_data_main(Classify_Label, 0, 2)
+
+        test = []
+        test = image_processing.Data_Augmentation_Image(original_test_root)
+        test, test_label = image_processing.image_data_processing(test, original_test_label)
+        test = image_processing.normalization(test)
+
+        return test, test_label
+    
+    
+    def Get_Independent_data_Root(self, load_data_root):
+        Prepare = Load_Data_Prepare()
+        Load_Tool = Load_Data_Tools()
+
+        Prepare.Set_Data_Content([], len(self.Labels))
+        Prepare.Set_Data_Dictionary(self.Labels, Prepare.Get_Data_Content(), 2)
+        Get_Data_Dict_Content = Prepare.Get_Data_Dict()
+        Total_Data_Roots = Load_Tool.get_data_root(load_data_root, Get_Data_Dict_Content, self.Labels)
+
+        return Total_Data_Roots
+    
+    def Specified_Amount_Of_Data(self, Data): # 打亂資料後重新處理
+        Data = shuffle_data(Data, self.Labels, 2)
+        tmp = []
+        if len(Data[self.Labels[0]]) >= len(Data[self.Labels[1]]):
+            for i in range(len(Data[self.Labels[1]])):
+                tmp.append(Data[self.Labels[0]][i])
+            Data[self.Labels[0]] = tmp
+        else:
+            for i in range(len(Data[self.Labels[0]])):
+                tmp.append(Data[self.Labels[1]][i])
+            Data[self.Labels[1]] = tmp
+        return Data