日常報錯（累~）：

　　小編最近用yolact對BraTS資料集做預測，驗證結果如下：

　　發現ET對於Dice和PPV太小了，根據公式，我一開始以為是模型預測的區域過多導致的。

後面生成圖片觀察：

　　忽然之間，意識到，是自己的target生成錯了。筆者對這個三個區域，首先是採用邊緣提取，獲取邊緣的座標，之後進行一次判斷，只保留點數大於20的區域，所以就可能出現上述圖片的情況。我們真是target是由ET區域（黃色區域），但我最後生成的target是沒有ET區域的。

　　所以再模型預測的階段，也不會預測出ET，造成的PPV值偏小，進而Dice偏小。

如下是我生成邊緣座標（改進後，每個if價格else：continue）的部分程式碼：

f=open('F:/DATA/train_mask4.txt','w')

for r in tqdm(range(len(image_traintest_paths))):
# for r in tqdm(range(4)):
    # 獲取路徑
    a=0
    b=0
    c=0
#     fold_path=mask_train_paths[r]
    fold_path=mask_traintest_paths[r]
    npmask=np.load(fold_path)
    #print(npmask.shape)
    txt_line = ''
    vertices_list_WT
 
=[]
    vertices_list_ET=[]
    vertices_list_TC=[]
    # 獲取邊緣
    WT_Label = npmask.copy()
    WT_Label[npmask == 1] = 1.
    WT_Label[npmask == 2] = 1.
    WT_Label[npmask == 4] = 1.
    WT_Label=WT_Label.astype(np.uint8)
    WT_Label=WT_Label*255
    TC_Label = npmask.copy()
    TC_Label[npmask == 1] = 1.
    TC_Label[npmask 
 
== 2] = 0.
    TC_Label[npmask == 4] = 1.
    TC_Label=TC_Label.astype(np.uint8)
    TC_Label=TC_Label*255
    ET_Label = npmask.copy()
    ET_Label[npmask == 1] = 0.
    ET_Label[npmask == 2] = 0.
    ET_Label[npmask == 4] = 1
    ET_Label=ET_Label.astype(np.uint8)
    ET_Label=ET_Label*255
    # 寫入到text line
    path_forward=fold_path.split('\\')[-1]
    txt_line=txt_line+path_forward+" "
    # 獲取WT邊緣座標
    detected_edges = cv2.Canny(WT_Label, lowThreshold, lowThreshold+10 * ratio, apertureSize=kernel_size)
#     print(detected_edges.shape)
    if SumNum(detected_edges)>20:
        a=1
        for i in range(detected_edges.shape[0]):
            for j in range(detected_edges.shape[1]):
                if detected_edges[i,j] !=0:
                    vertices_list_WT.append([j,i])
        vertices_list_WT=np.array(vertices_list_WT)
        # 獲取WTbounding box
        max_x_WT=np.max(vertices_list_WT[:,0])
        max_y_WT=np.max(vertices_list_WT[:,1])
        min_x_WT=np.min(vertices_list_WT[:,0])
        min_y_WT=np.min(vertices_list_WT[:,1])
        # 新增WT bounding box , vertices
        txt_line=txt_line+ str(min_x_WT)+','+str(min_y_WT)+','+str(max_x_WT)+','+str(max_y_WT)+','+'1'+','
        for i in range(len(vertices_list_WT)):
            txt_line=txt_line+str(vertices_list_WT[i][0])+','
            txt_line=txt_line+str(vertices_list_WT[i][1])+','
        txt_line=txt_line[:-1]+" "
    else:continue
    # 獲取TC邊緣座標
    detected_edges = cv2.Canny(TC_Label, lowThreshold, lowThreshold+10 * ratio, apertureSize=kernel_size)
    if SumNum(detected_edges)>20:
        b=1
        for i in range(detected_edges.shape[0]):
            for j in range(detected_edges.shape[1]):
                if detected_edges[i,j] !=0:
                    vertices_list_TC.append([j,i])
        vertices_list_TC=np.array(vertices_list_TC)
        max_x_TC=np.max(vertices_list_TC[:,0])
        max_y_TC=np.max(vertices_list_TC[:,1])
        min_x_TC=np.min(vertices_list_TC[:,0])
        min_y_TC=np.min(vertices_list_TC[:,1])
        # 新增TC bounding box, vertices
        txt_line=txt_line+ str(min_x_TC)+','+str(min_y_TC)+','+str(max_x_TC)+','+str(max_y_TC)+','+'2'+','
        for i in range(len(vertices_list_TC)):
            txt_line=txt_line+str(vertices_list_TC[i][0])+','
            txt_line=txt_line+str(vertices_list_TC[i][1])+','
        txt_line=txt_line[:-1]+" "
    else:continue
    # 獲取ET邊緣座標
    detected_edges = cv2.Canny(ET_Label, lowThreshold, lowThreshold+10 * ratio, apertureSize=kernel_size)
    if SumNum(detected_edges)>20:
        c=1
        for i in range(detected_edges.shape[0]):
            for j in range(detected_edges.shape[1]):
                if detected_edges[i,j] !=0:
                    vertices_list_ET.append([j,i])
        vertices_list_ET=np.array(vertices_list_ET)
        max_x_ET=np.max(vertices_list_ET[:,0])
        max_y_ET=np.max(vertices_list_ET[:,1])
        min_x_ET=np.min(vertices_list_ET[:,0])
        min_y_ET=np.min(vertices_list_ET[:,1])
        # 新增ET bounding box, vertices
        txt_line=txt_line+ str(min_x_ET)+','+str(min_y_ET)+','+str(max_x_ET)+','+str(max_y_ET)+','+'3'+','
        for i in range(len(vertices_list_ET)):
            txt_line=txt_line+str(vertices_list_ET[i][0])+','
            txt_line=txt_line+str(vertices_list_ET[i][1])+','
    else:continue
    
    txt_line=txt_line[:-1]+'\n'
    if (a==b==c==0):
        continue

    f.write(txt_line)

f.close()

　　不知再次訓練結果會怎樣。。。