Firsh Push at 20241207

Read_and_process_image/ReadAndProcess.py

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+import cv2
+import numpy as np
+
+class Read_image_and_Process_image:
+    def __init__(self) -> None:
+        pass
+    def get_data(self, path):
+        '''讀檔'''
+        img_size = 512 # 縮小後的影像
+        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, (img_size, img_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:
+            img_size = 512 # 縮小後的影像
+            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, (img_size, img_size))) # 濤整圖片大小
+            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'''
+        img_size = 512
+        data = np.asarray(data).astype(np.float32) # 將圖list轉成np.array
+        data = data.reshape(-1, img_size, img_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 np.array(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