to yolo labels

maketxt.py

@@ -0,0 +1,31 @@
+import os
+import random
+trainval_percent = 0.1
+train_percent = 0.9
+xmlfilepath = 'data/xml'
+txtsavepath = 'data/ImageSets'
+total_xml = os.listdir(xmlfilepath)
+num = len(total_xml)
+list = range(num)
+tv = int(num * trainval_percent)
+tr = int(tv * train_percent)
+trainval = random.sample(list, tv)
+train = random.sample(trainval, tr)
+ftrainval = open('data/ImageSets/trainval.txt', 'w')
+ftest = open('data/ImageSets/test.txt', 'w')
+ftrain = open('data/ImageSets/train.txt', 'w')
+fval = open('data/ImageSets/val.txt', 'w')
+for i in list:
+    name = total_xml[i][:-4] + '\n'
+    if i in trainval:
+        ftrainval.write(name)
+        if i in train:
+            ftest.write(name)
+        else:
+            fval.write(name)
+    else:
+        ftrain.write(name)
+ftrainval.close()
+ftrain.close()
+fval.close()
+ftest.close()

voc_label_coco.py

@@ -0,0 +1,49 @@
+import xml.etree.ElementTree as ET
+import pickle
+import os
+from os import listdir, getcwd
+from os.path import join
+sets = ['train', 'test', 'val']
+classes = ['car', 'bus', 'truck', 'van']
+def convert(size, box):
+    dw = 1. / size[0]
+    dh = 1. / size[1]
+    x = (box[0] + box[1]) / 2.0
+    y = (box[2] + box[3]) / 2.0
+    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)
+def convert_annotation(image_id):
+    in_file = open('data/xml/%s.xml' % (image_id))
+    out_file = open('data/labels/%s.txt' % (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:
+            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()
+print(wd)
+for image_set in sets:
+    if not os.path.exists('data/labels/'):
+        os.makedirs('data/labels/')
+    image_ids = open('data/ImageSets/%s.txt' % (image_set)).read().strip().split()
+    list_file = open('data/%s.txt' % (image_set), 'w')
+    for image_id in image_ids:
+        list_file.write('data/images/%s.jpg\n' % (image_id))
+        convert_annotation(image_id)
+    list_file.close()