分割数据预处理
作者:互联网
日常报错(累~):
小编最近用yolact对BraTS数据集做预测,验证结果如下:
发现ET对于Dice和PPV太小了,根据公式,我一开始以为是模型预测的区域过多导致的。
后面生成图片观察:
忽然之间,意识到,是自己的target生成错了。笔者对这个三个区域,首先是采用边缘提取,获取边缘的坐标,之后进行一次判断,只保留点数大于20的区域,所以就可能出现上述图片的情况。我们真是target是由ET区域(黄色区域),但我最后生成的target是没有ET区域的。
所以再模型预测的阶段,也不会预测出ET,造成的PPV值偏小,进而Dice偏小。
如下是我生成边缘坐标(改进后,每个if价格else:continue)的部分代码:
f=open('F:/DATA/train_mask4.txt','w') for r in tqdm(range(len(image_traintest_paths))): # for r in tqdm(range(4)): # 获取路径 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=[] # 获取边缘 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 # 写入到text line path_forward=fold_path.split('\\')[-1] txt_line=txt_line+path_forward+" " # 获取WT边缘坐标 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) # 获取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]) # 添加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 # 获取TC边缘坐标 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]) # 添加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 # 获取ET边缘坐标 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]) # 添加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()
不知再次训练结果会怎样。。。
标签:分割,list,vertices,TC,WT,ET,txt,数据,预处理 来源: https://www.cnblogs.com/a-runner/p/14878741.html