67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
import random
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from merge_class.merge import merge
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def calculate_confusion_matrix(predict, result):
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'''計算並畫出混淆矩陣'''
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tp, fp, tn, fn = 0
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for i in range(len(predict)):
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if predict[i] == [1., 0., 0.] and result[i] == [1., 0., 0.]:
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pass
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def shuffle_data(image, label, mode = 1):
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'''
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## 被用來做資料打亂的用途
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### 有兩種不同的需求
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1. 打亂影像資料(讀完檔後的影像) => 回傳Label與Image Root兩個List
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2. 打亂路徑資料(影像的路徑資料，還沒讀檔前) => 回傳打亂後的Dict
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'''
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if mode == 1:
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shuffle_image, shuffle_label = [], []
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total = list(zip(image, label))
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random.shuffle(total)
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for total_data in total:
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shuffle_image.append(total_data[0])
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shuffle_label.append(total_data[1])
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return shuffle_image, shuffle_label
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else:
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shuffle_image = {
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label[0] : []
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}
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for i in range(1, len(label)):
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shuffle_image.update({label[i] : []})
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for Label in label:
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shuffle_image[Label] = image[Label]
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random.shuffle(shuffle_image[Label])
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return shuffle_image
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def Balance_Process(Datas, Size_List):
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# Data_Dict_Data = shuffle_data(Data_Content, Labels, 2)
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Train_Size, start = 0, 0
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Image_List = []
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Images, Labels = [], []
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Merge = merge()
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Train_Size = min(Size_List[0], Size_List[1])
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for i in range(2, len(Size_List), 1):
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Train_Size = min(Train_Size, Size_List[i])
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for i in Size_List:
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Image_List.append(Datas[start : Train_Size])
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start = Train_Size
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Train_Size += Train_Size
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Image_List = Merge.merge_data_main(Image_List, 0, len(Image_List))
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for Image in Image_List:
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Images.append(Image[0])
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Labels.append(Image[1])
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return Images, Labels |