75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
import cv2
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import numpy as np
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class Read_image_and_Process_image:
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def __init__(self) -> None:
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pass
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def get_data(self, path):
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'''讀檔'''
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img_size = 512 # 縮小後的影像
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try:
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img_arr = cv2.imread(path, cv2.IMREAD_COLOR) # 讀檔(彩色)
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# img_arr = cv2.imread(path, cv2.IMREAD_GRAYSCALE) # 讀檔(灰階)
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resized_arr = cv2.resize(img_arr, (img_size, img_size)) # 濤整圖片大小
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except Exception as e:
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print(e)
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return resized_arr
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def Data_Augmentation_Image(self, path):
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resized_arr = []
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for p in path:
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img_size = 512 # 縮小後的影像
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try:
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img_arr = cv2.imread(p, cv2.IMREAD_COLOR) # 讀檔(彩色)
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# img_arr = cv2.imread(path, cv2.IMREAD_GRAYSCALE) # 讀檔(灰階)
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resized_arr.append(cv2.resize(img_arr, (img_size, img_size))) # 濤整圖片大小
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except Exception as e:
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print(e)
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return np.array(resized_arr)
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def image_data_processing(self, data, label):
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'''讀檔後處理圖片'''
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img_size = 512
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data = np.asarray(data).astype(np.float32) # 將圖list轉成np.array
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data = data.reshape(-1, img_size, img_size, 3) # 更改陣列形狀
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label = np.array(label) # 將label從list型態轉成 numpy array
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return data, label
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def normalization(self, images):
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imgs = []
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for img in images:
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img = np.asarray(img).astype(np.float32) # 將圖list轉成np.array
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img = img / 255 # 標準化影像資料
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imgs.append(img)
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return np.array(imgs)
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# def load_numpy_data(self, file_names):
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# '''載入numpy圖檔，並執行影像處理提高特徵擷取'''
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# i = 0
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# numpy_image = []
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# original_image = []
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# for file_name in file_names:
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# compare = str(file_name).split(".")
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# if compare[-1] == "npy":
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# image = np.load(file_name) # 讀圖片檔
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# numpy_image.append(image) # 合併成一個陣列
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# else:
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# original_image.append(file_name)
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# original_image = self.get_data(original_image)
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# for file in original_image:
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# numpy_image.append(file)
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# return numpy_image
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def make_label_list(self, length, content):
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'''製作label的列表'''
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label_list = []
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for i in range(length):
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label_list.append(content)
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return label_list |