The Stage is Xception+U-Net+Xception and U-Net estimates Convolution

.python-version

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+3.11

Read_and_process_image/ReadAndProcess.py

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-import cv2
-import numpy as np
-import torch
-
-class Read_image_and_Process_image:
-    def __init__(self, Image_Size) -> None:
-        self.Image_Size = Image_Size
-        pass
-    def get_data(self, path):
-        '''讀檔'''
-        try:
-            img_arr = cv2.imread(path, cv2.IMREAD_COLOR) # 讀檔(彩色)
-            # img_arr = cv2.imread(path, cv2.IMREAD_GRAYSCALE) # 讀檔(灰階)
-            resized_arr = cv2.resize(img_arr, (self.Image_Size, self.Image_Size)) # 濤整圖片大小
-        except Exception as e:
-            print(e)
-        
-        return resized_arr
-    
-    def Data_Augmentation_Image(self, path):
-        resized_arr = []
-
-        for p in path:
-            try:
-                img_arr = cv2.imread(p, cv2.IMREAD_COLOR) # 讀檔(彩色)
-                # img_arr = cv2.imread(path, cv2.IMREAD_GRAYSCALE) # 讀檔(灰階)
-                resized_arr.append(cv2.resize(img_arr, (self.Image_Size, self.Image_Size))) # 調整圖片大小
-            except Exception as e:
-                print(e)
-        
-        return np.array(resized_arr)
-
-    def image_data_processing(self, data, label):
-        '''讀檔後處理圖片'''
-        data = np.asarray(data).astype(np.float32) # 將圖list轉成np.array
-        data = data.reshape(-1, self.Image_Size, self.Image_Size, 3)  # 更改陣列形狀
-        label = np.array(label)                         # 將label從list型態轉成 numpy array
-        return data, label
-    
-    def normalization(self, images):
-        imgs = []
-        for img in images:
-            img = np.asarray(img).astype(np.float32) # 將圖list轉成np.array
-            img = img / 255                     # 標準化影像資料
-            imgs.append(img)
-
-        return torch.as_tensor(imgs)
-    
-    # def load_numpy_data(self, file_names):
-    #     '''載入numpy圖檔，並執行影像處理提高特徵擷取'''
-    #     i = 0
-    #     numpy_image = []
-    #     original_image = []
-    #     for file_name in file_names:
-    #         compare = str(file_name).split(".")
-    #         if compare[-1] == "npy":
-    #             image = np.load(file_name) # 讀圖片檔
-    #             numpy_image.append(image) # 合併成一個陣列
-    #         else:
-    #             original_image.append(file_name)
-
-    #     original_image = self.get_data(original_image)
-
-    #     for file in original_image:
-    #         numpy_image.append(file)
-            
-    #     return numpy_image
-
-    def make_label_list(self, length, content):
-        '''製作label的列表'''
-        label_list = []
-        for i in range(length):
-            label_list.append(content)
-        return label_list