【目标检测】YOLOv5：标签中文显示/自定义颜色
前言
本篇主要用来实现将YOLOv5输出的标签转成中文，并且自定义标签颜色的需求。
我所使用的是YOLOv5-5.0版本。
源码逻辑分析
在detect.py中，这两行代码设置标签名字和颜色。
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
可以发现，类别名字并不是在运行检测时导入的，而是内嵌在保存的模型参数文件中。
新建一个load_model.py文件，加载训练好的模型：
import torch
ckpt1 = torch.load('runs/train/exp21/weights/best.pt')
print("Done")
启动断点调试：
可以看到，类别名称包含在了模型内部：
而至于颜色，每次运行，程序会随机生成RGB三个数值，并不稳定。
思路分析
了解了上面的加载逻辑之后，为了实现中文显示的需求，主要有两种思路。
思路一
思路一：直接在data.yaml中，将names改成中文。
这种思路需要注意，文件默认打开并不是UTF-8编码，需要对文件读取编码进行修改。
在train.py中，将
with open(opt.data) as f:
改为
with open(opt.data, encoding='UTF-8') as f:
在test.py中，将
with open(data) as f:
改为
with open(data, encoding='UTF-8') as f:
这种思路意味着模型需要重新训练，并且后面还是会存在一些小问题。
思路二
思路二：直接在渲染标签的时候进行文字转换。
但是opencv默认不支持中文，因此需要下列步骤：
- 将opencv图片格式转换成PIL的图片格式；
- 使用PIL绘制文字；
- PIL图片格式转换成oepncv的图片格式；
思路实现
采用思路二进行操作。
下载字体
首先是下载支持中文的字体，我所采用的是SimHei这款字体，下载链接：
http://www.font5.com.cn/ziti_xiazai.php?id=151&part=1237887120&address=0
混淆矩阵字体修改
在utils/metrics.py文件中，开头添加代码：
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
之后，将这段代码
sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size
改为
sn.set(font='SimHei', font_scale=1.0 if self.nc < 50 else 0.8) # for label size
中文标签/颜色修改
在detect.py的Write results中，添加这部分
# Write results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or view_img: # Add bbox to image
# label = f'{names[int(cls)]} {conf:.2f}'
# label = None # 修改隐藏标签
# plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
# 增加中文标签
label = '%s %.2f' % (names[int(cls)], conf)
# 设置固定颜色
color_dict = {'1': [0, 131, 252], '2': [190, 90, 92], '3': [142, 154, 78], '4': [2, 76, 82], '5': [119, 80, 5], '6': [189, 163, 234]}
# 中文输出
if names[int(cls)] == 'truck':
ch_text = '%s %.2f' % ('卡车', conf)
color_single = color_dict['1']
elif names[int(cls)] == 'SUV':
ch_text = '%s %.2f' % ('越野车', conf)
color_single = color_dict['2']
im0 = plot_one_box(xyxy, im0, label=label, ch_text=ch_text, color=color_single, line_thickness=3)
其中，颜色我根据自己整理的调色盘进行吸取筛选。
之后，在utils/plots.py中导入库
from PIL import Image, ImageDraw, ImageFont
修改plot_one_box这个函数：
def cv2ImgAddText(img, text, left, top, textColor=(0, 255, 0), textSize=25):
# 图像从OpenCV格式转换成PIL格式
if (isinstance(img, np.ndarray)): # 判断是否OpenCV图片类型
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(img)
fontText = ImageFont.truetype("Font/simhei.ttf", textSize, encoding="utf-8")
draw.text((left, top - 2), text, textColor, font=fontText)
return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
def plot_one_box(x, img, color=None, label=None, ch_text=None, line_thickness=None):
# Plots one bounding box on image img
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
if label:
tf = max(tl - 1, 1) # font thickness
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
c2 = c1[0] + t_size[0], c1[1] - t_size[1]
cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
# cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
img_text = cv2ImgAddText(img, ch_text, c1[0], c2[1], (255, 255, 255), 25)
return img_text
查看效果
修改之前：
修改之后：
结果能够成功显示，不过存在标签宽度过长的小问题，后续有空再进行优化。
2022.8.9更
自定义宽度优化
又研究了下这个绘图函数的逻辑，下面这个函数getTextSize中是用来计算字符的尺寸，似乎也并不支持中文，于是我想根据不同类别的名称长度来自定义标签宽度调整。
下面的函数中有两个画框操作cv2.rectangle，第一个绘制的是目标的矩形框，第二个是用来填充标签的背景。主要修改的地方在第二个框的填充参数上，可以根据不同的类别，调整缩减长度，改进代码如下：
def plot_one_box(x, img, color=None, label=None, ch_text=None, line_thickness=None):
# Plots one bounding box on image img
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) # 绘制目标框
if label:
tf = max(tl - 1, 1) # font thickness
t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
if label.split()[0] == "类别一":
sublength = 50 # 缩减方框的长度
c2 = c1[0] + t_size[0] - sublength, c1[1] - t_size[1]
elif label.split()[0] == "类别二":
sublength = 30 # 缩减方框的长度
c2 = c1[0] + t_size[0] - sublength, c1[1] - t_size[1]
cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # 填充方框背景色
img_text = cv2ImgAddText(img, ch_text, c1[0], c2[1], (255, 255, 255), 25)
return img_text
2022.8.13更
YOLOv5-6.x版本添加中文
有读者问道TPH-YOLOv5如何添加中文标签，TPH-YOLOv5的绘图代码和YOLOv5-6.0版本一样。
先来看绘图源码，同样位于utils/plots.py文件中：
class Annotator:
if RANK in (-1, 0):
check_font() # download TTF if necessary
# YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
self.pil = pil or not is_ascii(example) or is_chinese(example)
if self.pil: # use PIL
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
self.draw = ImageDraw.Draw(self.im)
self.font = check_font(font='Arial.Unicode.ttf' if is_chinese(example) else font,
size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
else: # use cv2
self.im = im
self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
# Add one xyxy box to image with label
if self.pil or not is_ascii(label):
self.draw.rectangle(box, width=self.lw, outline=color) # box
if label:
w, h = self.font.getsize(label) # text width, height
outside = box[1] - h >= 0 # label fits outside box
self.draw.rectangle([box[0],
box[1] - h if outside else box[1],
box[0] + w + 1,
box[1] + 1 if outside else box[1] + h + 1], fill=color)
self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
else: # cv2
p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
if label:
tf = max(self.lw - 1, 1) # font thickness
w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
outside = p1[1] - h - 3 >= 0 # label fits outside box
p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
cv2.putText(self.im, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, self.lw / 3, txt_color,
thickness=tf, lineType=cv2.LINE_AA)
可以看到，在6.x版本中，作者进行了一个绘图优化，即输入的标签会先进行判断，如果是中文，则走self.pil or not is_ascii(label)这个分支，调用PIL进行绘制，如果是英文，则走下面这个分支，也就是说，如果模型训练时标签是中文，它默认是支持的。
那么如果训练时标签是英文，想把它变成中文显示怎么做呢？也很容易。
将第一个分支的绘制代码复制到第二分支即可，翻译判断在后面的分支进行。注意这里的字体仍和上面一样，需要下载修改。
修改后的代码：
class Annotator:
if RANK in (-1, 0):
check_font() # download TTF if necessary
# YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
def __init__(self, im, line_width=None, font_size=None, font='Font/simhei.ttf', pil=False, example='abc'):
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
self.pil = pil or not is_ascii(example) or is_chinese(example)
if self.pil: # use PIL
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
self.draw = ImageDraw.Draw(self.im)
self.font = check_font(font='Arial.Unicode.ttf' if is_chinese(example) else font,
size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
else: # use cv2
# 补充
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
self.draw = ImageDraw.Draw(self.im)
self.font = check_font(font='Arial.Unicode.ttf' if is_chinese(example) else font,
size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
# self.im = im
self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
# Add one xyxy box to image with label
if self.pil or not is_ascii(label):
self.draw.rectangle(box, width=self.lw, outline=color) # box
if label:
w, h = self.font.getsize(label) # text width, height
outside = box[1] - h >= 0 # label fits outside box
self.draw.rectangle([box[0],
box[1] - h if outside else box[1],
box[0] + w + 1,
box[1] + 1 if outside else box[1] + h + 1], fill=color)
# self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
# print(label)
self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
else: # cv2
# p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
# cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
# if label:
# tf = max(self.lw - 1, 1) # font thickness
# w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
# outside = p1[1] - h - 3 >= 0 # label fits outside box
# p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
# cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
# self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
# cv2.putText(self.im, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, self.lw / 3, txt_color,
# thickness=tf, lineType=cv2.LINE_AA)
self.draw.rectangle(box, width=self.lw, outline=color) # box
if label:
w, h = self.font.getsize(label) # text width, height
outside = box[1] - h >= 0 # label fits outside box
self.draw.rectangle([box[0],
box[1] - h if outside else box[1],
box[0] + w + 1,
box[1] + 1 if outside else box[1] + h + 1], fill=color)
# self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
# print(label.split()[0])
if label.split()[0] == "person":
label = "行人" + label.split()[1]
elif label.split()[0] == "bus":
label = "公交车" + label.split()[1]
self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
修改前后效果对比：
注：这里的颜色框宽度没有再去调，调整方式和上一节类似。
References
[1]https://blog.csdn.net/bu_fo/article/details/114668184
[2]https://blog.csdn.net/oJiWuXuan/article/details/109337713
2022.10.15更
后记
在项目中又遇到了添加中文这个问题，回看两个月前的方法觉得太蠢了，一个个修改标签宽度存在的一个问题是，如果换一台电脑，在不同分辨率下，宽度显示仍然是不同的。问题的根源在于OpenCV无法计算中文的宽度，但是，用PIL中的font.getsize，可以计算中文的宽度，而YOLOv5-6.0版本开始，就用该方式对中文进行了适配。
下面是6.0版本的核心方法，5.0版本的YOLOv5直接将绘制标签的函数替换成6.0即可。
6.0版本在plots.py中将绘图函数封装成了一个类：
class Annotator:
if RANK in (-1, 0):
check_font() # download TTF if necessary
# YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
self.pil = pil or not is_ascii(example) or is_chinese(example)
if self.pil: # use PIL
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
self.draw = ImageDraw.Draw(self.im)
self.font = check_font(font='Font/simhei.ttf' if is_chinese(example) else font,
size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
else: # use cv2
self.im = im
self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
# Add one xyxy box to image with label
if self.pil or not is_ascii(label):
self.draw.rectangle(box, width=self.lw, outline=color) # box
if label:
w, h = self.font.getsize(label) # text width, height
outside = box[1] - h >= 0 # label fits outside box
self.draw.rectangle([box[0],
box[1] - h if outside else box[1],
box[0] + w + 1,
box[1] + 1 if outside else box[1] + h + 1], fill=color)
# self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
else: # cv2
p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
if label:
tf = max(self.lw - 1, 1) # font thickness
w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
outside = p1[1] - h - 3 >= 0 # label fits outside box
p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
cv2.putText(self.im, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, self.lw / 3, txt_color,
thickness=tf, lineType=cv2.LINE_AA)
def rectangle(self, xy, fill=None, outline=None, width=1):
# Add rectangle to image (PIL-only)
self.draw.rectangle(xy, fill, outline, width)
def text(self, xy, text, txt_color=(255, 255, 255)):
# Add text to image (PIL-only)
w, h = self.font.getsize(text) # text width, height
self.draw.text((xy[0], xy[1] - h + 1), text, fill=txt_color, font=self.font)
def result(self):
# Return annotated image as array
return np.asarray(self.im)
可以看到，代码中，对于label进行了一个中文判断 is_chinese(example)，如果是中文，就走上面的PIL分支。因此，只需要在检测之前，将英文的label转换成中文即可。
修改detect.py部分如下：
# names读取的是模型的英文names，这里强制改成中文，直接定义自己的类别
# names = model.module.names if hasattr(model, 'module') else model.names
names = ['类别一', '类别二']
# 在len前面添加
annotator = Annotator(im0, line_width=3, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if save_img:
c = int(cls)
label = f'{names[int(cls)]} {conf:.2f}'
# print(label)
annotator.box_label(xyxy, label, color=colors(c, True))
print(f'{s}')
# 不要忘了在保存图片前添加这一句
im0 = annotator.result()
# Save results (image with detections)
if save_img:
这样就能完美得将英文替换成中文了。