完美解决labelimg xml转可视化中文乱码问题，不用matplotlib

背景简述

我们有一批标注项目要转可视化，因为之前没有做过，然后网上随意找了一段代码测试完美（并没有）搞定，开始疯狂标注，当真正要转的时候傻眼了，因为测试的时候用的是英文标签，实际标注的是中文标签，结果都是一大堆？？？？？？，

结果瞬间让我满脑袋？？？？？？，赶紧找资料解决，各种方法试了个遍，网上大多数都是用cv2+matplotlib实现的计算和渲染，所以解决的主要思想就是集中在各种显示的设置matplotlib字体，然并卵；最后找到一种另辟蹊径的办法使用PIL+cv2实现，最后完美解决，

贴上解决代码：

python 复制代码

import cv2
import os
import numpy as np  
from PIL import Image, ImageDraw, ImageFont
import xml.etree.ElementTree as ET

data_path = 'E:\\test\\tianjingulou'
imgs_path = os.path.join(data_path, "img")
anns_path = os.path.join(data_path, "xml")
result_path = os.path.join(data_path)

img_names = set([os.path.splitext(i)[0] for i in os.listdir(imgs_path)])
ann_names = set([os.path.splitext(i)[0] for i in os.listdir(anns_path)])
img_names = list(img_names)
ann_names = list(ann_names)

for i in range(len(img_names)):
    img_path = os.path.join(imgs_path, img_names[i] + ".jpg")
    img_bgr = cv2.imread(img_path)

    xml_path = os.path.join(anns_path, ann_names[i] + ".xml")

    xml_inf = open(xml_path, encoding='utf-8')
    tree = ET.parse(xml_inf)
    root = tree.getroot()

    bbox_color = (0, 129, 255)
    bbox_thickness = 2
    # 把rgb转成16进制'#0081FF'
    bbox_color_str = "#{:02x}{:02x}{:02x}".format(*bbox_color)

    # 把rgb转成bgr再转16进制'#FF8100'
    # bbox_color_rgb = bbox_color[::-1]
    # bbox_color_str = "#{:02x}{:02x}{:02x}".format(*bbox_color_rgb)

    bbox_labelstr = {
        'font_size': 16,
        'font_thickness': 2,
        'offset_x': 0,
        'offset_y': -20,
    }

    # 创建一个空白图像
    img_pil = Image.fromarray(cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB))
    draw = ImageDraw.Draw(img_pil)

    # 设置字体  SimHei.ttf黑体，msyh.ttf微软雅黑
    # 打开命令行窗口或者Anaconda Prompt，输入python，进入python解释器窗口，
    #   输入import matplotlib;引入可视化库；
    #   然后输入print(matplotlib.matplotlib_fname())打印出当前库所在位置；
    #   进入到上面打印出的路径下字体目录：mpl-data\\fonts\\ttf，下载中文字体放进去
    font_path = "D:\\ProgramData\\anaconda3\\Lib\\site-packages\\matplotlib\\mpl-data\\fonts\\ttf\\msyh.ttf"  # 请替换为实际路径
    font = ImageFont.truetype(font_path, bbox_labelstr['font_size'])

    # 画框和文字
    for obj in root.iter('object'):
        bbox_label = obj.find('name').text
        bbox_top_left_x = int(obj.find('bndbox').find('xmin').text)
        bbox_top_left_y = int(obj.find('bndbox').find('ymin').text)
        bbox_bottom_right_x = int(obj.find('bndbox').find('xmax').text)
        bbox_bottom_right_y = int(obj.find('bndbox').find('ymax').text)

        draw.rectangle([(bbox_top_left_x, bbox_top_left_y), (bbox_bottom_right_x, bbox_bottom_right_y)],
                       outline=bbox_color, width=bbox_thickness)

        draw.text((bbox_top_left_x + bbox_labelstr['offset_x'], bbox_top_left_y + bbox_labelstr['offset_y']),
                  bbox_label, font=font, fill=bbox_color_str)

    img_bgr = cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR)

    # 保存图像
    cv2.imwrite(result_path + "\\{}.jpg".format(img_names[i]), img_bgr)

下面是matplotlib+cv2版代码

python 复制代码

# 数据集可视化
import cv2
import os
import matplotlib.pyplot as plt
import xml.etree.ElementTree as ET

# 设置 Matplotlib 使用的字体为黑体
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False 

imgs_path = 'E:\\test\\tianjingulou\\img'
anns_path = 'E:\\test\\tianjingulou\\xml'
 
img_names = set([os.path.splitext(i)[0] for i in os.listdir(imgs_path)])
ann_names = set([os.path.splitext(i)[0] for i in os.listdir(anns_path)])
img_names = list(img_names)
ann_names = list(ann_names)

for i in range(len(img_names)):
    img_path = os.path.join(imgs_path, img_names[i] + ".jpg")
    img_bgr = cv2.imread(img_path)
 
    xml_path = os.path.join(anns_path, ann_names[i] + ".xml")
 
    xml_inf = open(xml_path, encoding='utf-8')
    tree = ET.parse(xml_inf)
    root = tree.getroot()
 
# 框可视化配置
    bbox_color = (255, 129, 0)  # 框的颜色
    bbox_thickness = 2  # 框的线宽
 
# 框类别文字
    bbox_labelstr = {
        'font_size': 1,  # 字体大小
        'font_thickness': 2,  # 字体粗细
        'offset_x': 0,  # X 方向，文字偏移距离，向右为正
        'offset_y': -10,  # Y 方向，文字偏移距离，向下为正
    }
# 画框
    for obj in root.iter('object'):  # 一个object代表一个标注物体
 
        # 框的类别
        bbox_label = obj.find('name').text
 
        # 框的两点坐标
        # 左上角坐标
        bbox_top_left_x = int(obj.find('bndbox').find('xmin').text)
        bbox_top_left_y = int(obj.find('bndbox').find('ymin').text)
        # 右下角坐标
        bbox_bottom_right_x = int(obj.find('bndbox').find('xmax').text)
        bbox_bottom_right_y = int(obj.find('bndbox').find('ymax').text)
 
        # 画矩形：画框
        img_bgr = cv2.rectangle(img_bgr, (bbox_top_left_x, bbox_top_left_y), (bbox_bottom_right_x, bbox_bottom_right_y),
                            bbox_color, bbox_thickness)

        # 写框类别文字：图片，文字字符串，文字左上角坐标，字体，字体大小，颜色，字体粗细
        img_bgr = cv2.putText(img_bgr, bbox_label, (
            bbox_top_left_x + bbox_labelstr['offset_x'],
            bbox_top_left_y + bbox_labelstr['offset_y']),
                          cv2.FONT_HERSHEY_SIMPLEX, bbox_labelstr['font_size'], bbox_color,
                          bbox_labelstr['font_thickness'])
 
    cv2.imwrite("E:\\test\\tianjingulou\\{}.jpg".format(img_names[i]), img_bgr)

写在最后，matplotlib的方式应该也有解决的办法，也可能是我的环境问题，提供这两种方式大家各取所需，下面这种方式是我从一位博主那里拷贝来稍加改动的，但是我找不到出处了，如有侵权请联系我删除。

----------------------------------------------华丽分割-------------------------------------------------

追加一种类似的写法，这个是宋体，字体可以酌情替换，亲测可用

python 复制代码

import cv2
import os
import matplotlib.pyplot as plt
import xml.etree.ElementTree as ET
import numpy as np


# 导入 PIL 库
import PIL.Image
import PIL.ImageDraw
import PIL.ImageFont


data_path = os.path.join("E:\\test\\tianjingulou")
imgs_path = os.path.join(data_path, "img")
anns_path = os.path.join(data_path, "xml")

# 获取图像名称和标注名称
img_names = set(os.path.splitext(i)[0] for i in os.listdir(imgs_path))
ann_names = set(os.path.splitext(i)[0] for i in os.listdir(anns_path))
img_names = list(img_names)
ann_names = list(ann_names)

# 遍历所有图像
for i, img_name in enumerate(img_names):

    # 读取图像
    img_bgr = cv2.imread(os.path.join(imgs_path, img_name + ".jpg"))

    # 读取标注
    xml_path = os.path.join(anns_path, img_name + ".xml")
    xml_inf = open(xml_path, encoding='utf-8')
    tree = ET.parse(xml_inf)
    root = tree.getroot()

    # 画框
    for obj in root.iter('object'):

        # 获取框的类别
        bbox_label = obj.find('name').text

        # 获取框的两点坐标
        bbox_top_left_x = int(obj.find('bndbox').find('xmin').text)
        bbox_top_left_y = int(obj.find('bndbox').find('ymin').text)
        bbox_bottom_right_x = int(obj.find('bndbox').find('xmax').text)
        bbox_bottom_right_y = int(obj.find('bndbox').find('ymax').text)

        # 画矩形
        img_bgr = cv2.rectangle(img_bgr, (bbox_top_left_x, bbox_top_left_y), (bbox_bottom_right_x, bbox_bottom_right_y),
                               (255, 129, 0), 2)

        # 写框类别文字
        # 转换为 PIL 图像
        img_pil = PIL.Image.fromarray(img_bgr)

        # 使用 PIL 绘制文本
        font = PIL.ImageFont.truetype("simsun.ttc", 16)
        draw = PIL.ImageDraw.Draw(img_pil)
        draw.text((bbox_top_left_x, bbox_top_left_y - 18), bbox_label, font=font, fill=(255, 129, 0))

        # 直接使用 PIL 图像
        img_bgr = np.array(img_pil)

    # 保存图像
    cv2.imwrite(data_path + "\\{}.jpg".format(img_name), img_bgr)