diff --git a/comparison.py b/comparison.py
new file mode 100644
index 0000000000000000000000000000000000000000..34ab468b336c27214c8e31a14cffe7315d8c991c
--- /dev/null
+++ b/comparison.py
@@ -0,0 +1,139 @@
+import os
+from unittest import result
+import pandas as pd
+import json
+
+dirname = '人工标注'
+outputname = '人工标注比对结果'
+# dirname = 'AI辅助标注'
+# outputname='AI辅助标注比对结果'
+# dirname = '纯AI'
+# outputname='纯AI比对结果'
+studyID_out = []
+seriesID_out = []
+labelout = []
+gold_labelout = []
+posout = []
+gold_posout = []
+cnt = []
+cntid = []
+marker_out=[]
+
+def iou_3d(box1, box2):
+    '''
+        box [x1,y1,z1,x2,y2,z2]   分别是两对角定点的坐标
+    '''
+    area1 = (box1[3]-box1[0])*(box1[4]-box1[1])*(box1[5]-box1[2])
+    area2 = (box2[3]-box2[0])*(box2[4]-box2[1])*(box2[5]-box2[2])
+    area_sum = area1 + area2
+
+    # 计算重叠部分 设重叠box坐标为 [x1,y1,z1,x2,y2,z2]
+    x1 = max(box1[0], box2[0])
+    y1 = max(box1[1], box2[1])
+    z1 = max(box1[2], box2[2])
+    x2 = min(box1[3], box2[3])
+    y2 = min(box1[4], box2[4])
+    z2 = min(box1[5], box2[5])
+    if x1 >= x2 or y1 >= y2 or z1 >= z2:
+        return 0
+    else:
+        inter_area = (x2-x1)*(y2-y1)*(z2-z1)
+
+    return inter_area/(area_sum-inter_area)
+
+
+gold = pd.read_excel("原始数据/金标准.xlsx", encoding='gbk', header=0)
+
+num = 0
+for root, dirs, files in os.walk(dirname):
+    for file in files:
+        num = num+1
+        print(str(num)+"/"+str(len(files)))
+        filename = root+"/"+file
+        df = pd.read_csv(filename, encoding='gbk', header=0)
+        for indexs in df.index:
+            data = df.loc[indexs].values
+            marker=data[1]
+            studyID = data[2]
+            seriesID = data[3]
+            startTime = data[4]
+            endTime = data[5]
+            label = data[6]
+            label = json.loads(label)
+            # posAI=label['position']
+            leftTopPoint = label['rectangle']["leftTopPoint"]
+            rightBottomPoint = label['rectangle']["rightBottomPoint"]
+            pos = [leftTopPoint['x'], leftTopPoint['y'], leftTopPoint['z'],
+                   rightBottomPoint['x'], rightBottomPoint['y'], rightBottomPoint['z']]
+            # pos=[posAI[0],posAI[1],posAI[2],posAI[3],posAI[4],posAI[5]]
+            typeID = data[7]
+            typeNmae = data[8]
+            flag = 0
+            try:
+                gold_place = gold.loc[gold['序列号'] == seriesID].index[0]
+            except:
+                print("没有找到金标准病例")
+                break
+            for gold_indexs in range(gold_place, gold.shape[0]):
+                gold_data = gold.loc[gold_indexs].values
+                if gold_data[1] == seriesID:
+                    flag = 1
+                    gold_label = gold_data[2]
+                    gold_label = json.loads(gold_label)
+                    gold_leftTopPoint = gold_label['rectangle']["leftTopPoint"]
+                    gold_rightBottomPoint = gold_label['rectangle']["rightBottomPoint"]
+                    gold_pos = [gold_leftTopPoint['x'], gold_leftTopPoint['y'], gold_leftTopPoint['z'],
+                                gold_rightBottomPoint['x'], gold_rightBottomPoint['y'], gold_rightBottomPoint['z']]
+                    # gold_pos=[gold_label['position'][0],gold_label['position'][1],gold_label['position'][2],gold_label['position'][3],gold_label['position'][4],gold_label['position'][5]]
+                    iou = iou_3d(pos, gold_pos)
+                    gold_type = gold_data[3]
+                    gold_typename = gold_data[4]
+                    if iou == 1 and typeID == gold_type:
+                        marker_out.append(marker)
+                        studyID_out.append(studyID)
+                        seriesID_out.append(seriesID)
+                        labelout.append(label)
+                        gold_labelout.append(gold_label)
+                        posout.append(typeNmae)
+                        gold_posout.append(gold_typename)
+                        cnt.append("完全正确")
+                        cntid.append(1)
+                        break
+                    if iou == 1 and typeID != gold_type:
+                        marker_out.append(marker)
+                        studyID_out.append(studyID)
+                        seriesID_out.append(seriesID)
+                        labelout.append(label)
+                        gold_labelout.append(gold_label)
+                        posout.append(typeNmae)
+                        gold_posout.append(gold_typename)
+                        cnt.append("位置正确，分类错误")
+                        cntid.append(1)
+                        break
+                    elif iou < 1 and typeID != gold_type and iou > 0.5:
+                        marker_out.append(marker)
+                        studyID_out.append(studyID)
+                        seriesID_out.append(seriesID)
+                        labelout.append(label)
+                        gold_labelout.append(gold_label)
+                        posout.append(typeNmae)
+                        gold_posout.append(gold_typename)
+                        cnt.append("位置调整")
+                        cntid.append(0)
+                        break
+                elif gold_data[1] != seriesID and flag == 1:
+                    marker_out.append(marker)
+                    studyID_out.append(studyID)
+                    seriesID_out.append(seriesID)
+                    labelout.append(label)
+                    gold_labelout.append("")
+                    posout.append(typeNmae)
+                    gold_posout.append("")
+                    cnt.append("位置错误")
+                    cntid.append(0)
+                    break
+
+
+output = pd.DataFrame({"检查号": studyID_out, "序列号": seriesID_out, "标注医生":marker_out,"标注位置": labelout,
+                       "金标准位置": gold_labelout, "标注分类": posout, "金标准分类": gold_posout, "比对结果": cnt})
+output.to_csv(outputname+".csv", encoding='gbk')