本文主要是介绍完美解决labelimg xml转可视化中文乱码问题，不用matplotlib，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
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：
import cv2
import os
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import xml.etree.ElementTree as ETdata_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').textbbox_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
# 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。
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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
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.ImageFontdata_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)这篇关于完美解决labelimg xml转可视化中文乱码问题，不用matplotlib的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
