train
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68
voc_label.py
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68
voc_label.py
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# 标签xml转txt
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import xml.etree.ElementTree as ET
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import os
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from os import getcwd
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sets = ['train', 'val', 'test']
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classes = ['6925303773908','6924743915848','6920152471616','6920005772716','6902227018162','6920459905012','6972194461407','6935284412918','6921489033706','6904012526494','6923644272159','6924882486100','6956511907458']
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abs_path = os.getcwd()
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print(abs_path)
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def convert(size, box):
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dw = 1. / (size[0])
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dh = 1. / (size[1])
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x = (box[0] + box[1]) / 2.0 - 1
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y = (box[2] + box[3]) / 2.0 - 1
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w = box[1] - box[0]
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h = box[3] - box[2]
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x = x * dw
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y = y * dh
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w = w * dw
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h = h * dh
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return x, y, w, h
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def convert_annotation(image_id):
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in_file = open('paper_data/Annotations/%s.xml' % (image_id), encoding='UTF-8')
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out_file = open('paper_data/labels/%s.txt' % (image_id), 'w')
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tree = ET.parse(in_file)
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root = tree.getroot()
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size = root.find('size')
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w = int(size.find('width').text)
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h = int(size.find('height').text)
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for obj in root.iter('object'):
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# difficult = obj.find('difficult').text
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# difficult = obj.find('Difficult').text
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cls = obj.find('name').text
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cls = cls[:13]
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print("cls:",cls,len(cls))
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# if cls not in classes or int(difficult) == 1:
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# continue
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cls_id = classes.index(cls)
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xmlbox = obj.find('bndbox')
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b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
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float(xmlbox.find('ymax').text))
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b1, b2, b3, b4 = b
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# 标注越界修正
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if b2 > w:
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b2 = w
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if b4 > h:
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b4 = h
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b = (b1, b2, b3, b4)
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bb = convert((w, h), b)
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out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
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wd = getcwd()
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for image_set in sets:
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if not os.path.exists('paper_data/labels'):
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os.makedirs('paper_data/labels')
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image_ids = open('paper_data/ImageSets/Main/%s.txt' % (image_set)).read().strip().split()
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list_file = open('paper_data/%s.txt' % (image_set), 'w')
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for image_id in image_ids:
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list_file.write(abs_path + '/paper_data/images/%s.jpg\n' % (image_id))
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print(image_id, "Converting...")
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convert_annotation(image_id)
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list_file.close()
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