update readme: how to use this code

README.md

@@ -3,6 +3,7 @@
 
 * author is leilei(CSU)
 * reflective-clothes-detect qq群: 980489677
+* 如果此项目对您有所帮助，请给个star，您的star是对我的鼓励！
 
 ## Some details
 * reflective-clothes-detect-dataset (with xml annotations 1083) download: [BaiDuYunPan](https://pan.baidu.com/s/1_Ei9bYmUpa-8q-hXZk1u8w) 提取码->(dooh) 
@@ -15,6 +16,10 @@
     ```
     python detect.py --source ***/aaa.jpg --weights ./best.pt
     ```
+3. convert VOC2021 to YOLO format：
+    ```
+    call yolov5's voc_label_Re.py
+    ```
 
 ## How to use dataset?
 * We annotate the reflective-clothes-detect-dataset as Pascal VOC format:
@@ -34,7 +39,7 @@
         --label_name: reflective_clothes、other_clothes
     ```
 
-* if you want to crawl some images
+* If you want to crawl some images
     ```
     Please refer to this crawler code on github:
     https://github.com/gengyanlei/fire-detect-yolov4 -> test_baidu.py | test_google.py
@@ -44,6 +49,7 @@
 * ./result: 
 
 |![demo1](https://github.com/gengyanlei/reflective-clothes-detect/blob/master/result/test02.jpg)|
+|----|
 |![demo2](https://github.com/gengyanlei/reflective-clothes-detect/blob/master/result/test05.jpg)|
 |----|
 

yolov5/voc_label_Re.py

@@ -0,0 +1,67 @@
+import xml.etree.ElementTree as ET
+import pickle
+import os
+from collections import OrderedDict
+from os import listdir, getcwd
+from os.path import join
+
+'''
+    工作服检测 VOC2021 
+'''
+sets = [('2021', 'train')]
+classes = ["reflective_clothes", "other_clothes"]  # 0-反光衣 1-其它
+print(classes)
+
+data_root = r'/home/helmet_dataset/'
+
+# voc的训练txt 验证txt 必须在VOC*** 以及目录下  不能在Main目录下面；它是在统计目录下
+def convert(size, box):
+    dw = 1./(size[0])
+    dh = 1./(size[1])
+    x = (box[0] + box[1])/2.0 - 1
+    y = (box[2] + box[3])/2.0 - 1
+    w = box[1] - box[0]
+    h = box[3] - box[2]
+    x = x*dw
+    w = w*dw
+    y = y*dh
+    h = h*dh
+    return (x,y,w,h)  # 中心点坐标 wh
+
+def convert_annotation(year, image_id):
+    global data_root
+    in_file = open(data_root + 'VOC%s/Annotations/%s.xml'%(year, image_id), encoding='utf-8')
+    out_file = open(data_root + 'VOC%s/labels/%s.txt'%(year, image_id), 'w')
+    tree=ET.parse(in_file)
+    root = tree.getroot()
+    size = root.find('size')
+    w = int(size.find('width').text)
+    h = int(size.find('height').text)
+
+    for obj in root.iter('object'):
+        difficult = obj.find('difficult').text
+        cls = obj.find('name').text
+        if cls not in classes:  # or int(difficult)==1 不关心difficult
+            continue
+        cls_id = classes.index(cls)
+        xmlbox = obj.find('bndbox')
+        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
+        bb = convert((w,h), b)
+        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
+
+# wd = getcwd()
+
+for year, image_set in sets:
+    if not os.path.exists(data_root + 'VOC%s/labels/'%(year)):
+        os.makedirs(data_root + 'VOC%s/labels/'%(year))
+    image_ids = open(data_root + 'VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()#有空格的就不行了
+    list_file = open(data_root + 'VOC%s/%s_%s.txt'%(year, year, image_set), 'w')
+    for image_id in image_ids:
+        print(image_id)
+        list_file.write(data_root + 'VOC%s/JPEGImages/%s.jpg\n'%(year, image_id))
+        convert_annotation(year, image_id)
+    list_file.close()
+
+# os.system("cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt > train.txt")
+# os.system("cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt > train.all.txt")
+