update voc_annotation.py

voc_annotation.py

@@ -2,6 +2,8 @@
 import random
 import xml.etree.ElementTree as ET
 
+import numpy as np
+
 from utils.utils import get_classes
 
 #--------------------------------------------------------------------------------------------------------------------------------#
@@ -35,6 +37,10 @@
 VOCdevkit_sets  = [('2007', 'train'), ('2007', 'val')]
 classes, _      = get_classes(classes_path)
 
+#-------------------------------------------------------#
+#   统计目标数量
+#-------------------------------------------------------#,
+nums = np.zeros(len(classes))
 def convert_annotation(year, image_id, list_file):
     in_file = open(os.path.join(VOCdevkit_path, 'VOC%s/Annotations/%s.xml'%(year, image_id)), encoding='utf-8')
     tree=ET.parse(in_file)
@@ -52,6 +58,8 @@ def convert_annotation(year, image_id, list_file):
         b = (int(float(xmlbox.find('xmin').text)), int(float(xmlbox.find('ymin').text)), int(float(xmlbox.find('xmax').text)), int(float(xmlbox.find('ymax').text)))
         list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id))
 
+        nums[classes.index(cls)] = nums[classes.index(cls)] + 1
+
 if __name__ == "__main__":
     random.seed(0)
     if annotation_mode == 0 or annotation_mode == 1:
@@ -107,3 +115,29 @@ def convert_annotation(year, image_id, list_file):
                 list_file.write('\n')
             list_file.close()
         print("Generate 2007_train.txt and 2007_val.txt for train done.")
+
+    def printTable(List1, List2):
+        for i in range(len(List1[0])):
+            print("|", end=' ')
+            for j in range(len(List1)):
+                print(List1[j][i].rjust(int(List2[j])), end=' ')
+                print("|", end=' ')
+            print()
+
+    str_nums = [str(int(x)) for x in nums]
+    tableData = [
+        classes, str_nums
+    ]
+    colWidths = [0]*len(tableData)
+    len1 = 0
+    for i in range(len(tableData)):
+        for j in range(len(tableData[i])):
+            if len(tableData[i][j]) > colWidths[i]:
+                colWidths[i] = len(tableData[i][j])
+    printTable(tableData, colWidths)
+
+    if np.sum(nums) == 0:
+        print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！")
+        print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！")
+        print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！")
+        print("（重要的事情说三遍）。")