"""

将xml文件解析成字典形式，参考tensorflow的recursive_parse_xml_to_dict

Args：

xml: xml tree obtained by parsing XML file contents using lxml.etree

Returns:

Python dictionary holding XML contents.

"""

if len(xml) == 0: # 遍历到底层，直接返回tag对应的信息

return {xml.tag: xml.text}

result = {}

for child in xml:

child_result = parse_xml_to_dict(child) # 递归遍历标签信息

if child.tag != 'object':

result[child.tag] = child_result[child.tag]

else:

if child.tag not in result: # 因为object可能有多个，所以需要放入列表里

result[child.tag] = []

result[child.tag].append(child_result[child.tag])

"""

将对应xml文件信息转为yolo中使用的txt文件信息

:param file_names:

:param save_root:

:param class_dict:

:param type_str:

:return:

"""

# 在Kaist_YOLO/train或Kaist_YOLO/val目录下创建/打开lables和images目录

save_txt_path = os.path.join(save_root, type_str, "labels")

if os.path.exists(save_txt_path) is False:

os.makedirs(save_txt_path)

save_images_path = os.path.join(save_root, type_str, "images")

if os.path.exists(save_images_path) is False:

os.makedirs(save_images_path)

for file in tqdm(file_names, desc="translate {} files...".format(type_str)):

# 检查下图像文件是否存在

visible_img_path = os.path.join(voc_images_path, "visible", file + ".jpg")

assert os.path.exists(visible_img_path), "file: '{}' not exist...".format(visible_img_path)

lwir_img_path = os.path.join(voc_images_path, "lwir", file + ".jpg")

assert os.path.exists(lwir_img_path), "file: '{}' not exist...".format(lwir_img_path)

# 检查xml文件是否存在

xml_path = os.path.join(voc_xml_path, file + ".xml")

assert os.path.exists(xml_path), "file: '{}' not exist...".format(xml_path)

# read xml

with open(xml_path) as fid:

xml_str = fid.read()

xml = etree.fromstring(xml_str)

data = parse_xml_to_dict(xml)["annotation"]

img_height = int(data["size"]["height"])

img_width = int(data["size"]["width"])

# write object info into txt

assert "object" in data.keys(), "file: '{}' lack of object key.".format(xml_path)

if len(data["object"]) == 0:

# 如果xml文件中没有目标就直接忽略该样本

print("Warning: in '{}' xml, there are no objects.".format(xml_path))

continue

with open(os.path.join(save_txt_path, file + ".txt"), "w") as f:

for index, obj in enumerate(data["object"]):

# 获取每个object的box信息

xmin = float(obj["bndbox"]["xmin"])

xmax = float(obj["bndbox"]["xmax"])

ymin = float(obj["bndbox"]["ymin"])

ymax = float(obj["bndbox"]["ymax"])

class_name = obj["name"]

class_index = class_dict[class_name] - 1 # 目标id从0开始

# 进一步检查数据，有的标注信息中可能有w或h为0的情况，这样的数据会导致计算回归loss为nan

if xmax <= xmin or ymax <= ymin:

print("Warning: in '{}' xml, there are some bbox w/h <=0".format(xml_path))

continue

# 将box信息转换到yolo格式

xcenter = xmin + (xmax - xmin) / 2

ycenter = ymin + (ymax - ymin) / 2

w = xmax - xmin

h = ymax - ymin

# 绝对坐标转相对坐标，保存6位小数

xcenter = round(xcenter / img_width, 6)

ycenter = round(ycenter / img_height, 6)

w = round(w / img_width, 6)

h = round(h / img_height, 6)

info = [str(i) for i in [class_index, xcenter, ycenter, w, h]]

if index == 0:

f.write(" ".join(info))

else:

f.write("\n" + " ".join(info))

# copy image into save_images_path

# 复制可见光图像到指定目录

visible_path_copy_to = os.path.join(save_images_path, file + "_visible.jpg")

if not os.path.exists(visible_path_copy_to):

shutil.copyfile(visible_img_path, visible_path_copy_to)

# 复制红外光图像到指定目录

lwir_path_copy_to = os.path.join(save_images_path, file + "_lwir.jpg")

if not os.path.exists(lwir_path_copy_to):

keys = class_dict.keys()

with open("data/kaist_data_label.names", "w") as w:

for index, k in enumerate(keys):

if index + 1 == len(keys):

w.write(k)

else:

# 读取类别索引映射文件

json_file = open(label_json_path, 'r')

class_dict = json.load(json_file)

# 指定VOC数据集txt文件路径

voc_txt_names = ['train', 'val', 'test', 'day_test', 'night_test']

voc_txt_files = [tn + '.txt' for tn in voc_txt_names]

txt_paths = [os.path.join(kaist_voc_root, "ImageSets/Main", x) for x in voc_txt_files]

for tp in txt_paths:

assert os.path.exists(tp), f"VOC image set txt file '{tp}' not exist!"

# 将VOC数据集转换成YOLO数据集格式

for i, (tp, tn) in zip(txt_paths, voc_txt_names):

# 读取.txt中的所有行信息，删除空行

with open(tp, "r") as r:

train_file_names = [i for i in r.read().splitlines() if len(i.strip()) > 0]

# 将voc信息转yolo，并将图像文件复制到相应文件夹

translate_info(train_file_names, yolo_root, class_dict, tn)

# 创建kaist_data_label.names文件