python+yolov3 输出中文标签

利用yolo进行目标识别时，输出的是英文标签，如下图，不便于用户阅读，因此需要转换为中文标签，我采取的办法是对yolo predefined的80类进行了“汉化”，以实现输出中文标签。
不知道是否有更便捷的方法，只是提供了一种思路，供大家参考。

首先获取LOYO中label名称

打开LOYO文件coco.nameslabel，获取这80类label名称，并译为中文

    predefined_En=['person','bicycle','car','motorbike','aeroplane','bus','train','truck','boat','traffic light',
                   'fire hydrant','stop sign','parking meter','bench','bird','cat','dog','horse','sheep','cow',
                   'elephant','bear','zebra','giraffe','backpack','umbrella','handbag','tie','suitcase','frisbee',
                   'skis','snowboard','sports ball','kite','baseball bat','baseball glove','skateboard','surfboard','tennis racket','bottle',
                   'wine glass','cup','fork','knife','spoon','bowl','banana','apple','sandwich','orange',
                   'broccoli','carrot','hot dog','pizza','donut','cake','chair','sofa','pottedplant','bed',
                   'diningtable','toilet','tvmonitor','laptop','mouse','remote','keyboard','cell phone','microwave','oven',
                   'toaster','sink','refrigerator','book','clock','vase','scissors','teddy bear','hair drier','toothbrush']
    predefined_CN=['人','自行车','汽车','摩托车','飞机','公共汽车','火车','卡车','船','红绿灯',
                   '消防栓','停止标志','停车收费表','板凳','鸟','猫','狗','马','羊','牛',
                   '大象','熊','斑马','长颈鹿','背包','雨伞','手提包','领带','手提箱','飞盘',
                   '滑雪板','单板滑雪','运动球类','风筝','棒球棒','棒球手套','滑板','冲浪板','网球拍','瓶子',
                   '红酒杯','杯子','叉子','刀','勺子','碗','香蕉','苹果','三明治','橘子',
                   '西兰花','胡萝卜','热狗','比萨','甜甜圈','蛋糕','椅子','沙发','盆栽','床',
                   '餐桌','厕所','电视监视器','笔记本电脑','老鼠','遥控器','键盘','手机','微波炉','烤箱',
                   '烤面包机','水槽','冰箱','书','时钟','花瓶','剪刀','泰迪熊','吹风机','牙刷']		

输出中文标签

OpenCV中可以利用putText()在图片上添加英文，但是添加中文则会出现乱码，因此需要：
1）将OpenCV图片格式转换成PIL的图片格式；
2）使用PIL绘制文字；
3）PIL图片格式转换成OpenCV的图片格式；

def cv2ImgAddText(img, text, left, top, textColor=(255, 0, 0), textSize=20):
    if (isinstance(img, np.ndarray)):  # 判断是否OpenCV图片类型
        img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    # 创建一个可以在给定图像上绘图的对象
    draw = ImageDraw.Draw(img)
    # 字体的格式
    fontStyle = ImageFont.truetype(
        "font/simsun.ttc", textSize, encoding="utf-8")
    # 绘制文本
    draw.text((left, top), text, textColor, font=fontStyle)
    # 转换回OpenCV格式
    result=cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
    return result
    

此方法转载自：https://blog.youkuaiyun.com/baidu_37366055/article/details/81627185

完整代码

from test import video_demo 
import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont

def E2C(label):
    predefined_En=['person','bicycle','car','motorbike','aeroplane','bus','train','truck','boat','traffic light',
                   'fire hydrant','stop sign','parking meter','bench','bird','cat','dog','horse','sheep','cow',
                   'elephant','bear','zebra','giraffe','backpack','umbrella','handbag','tie','suitcase','frisbee',
                   'skis','snowboard','sports ball','kite','baseball bat','baseball glove','skateboard','surfboard','tennis racket','bottle',
                   'wine glass','cup','fork','knife','spoon','bowl','banana','apple','sandwich','orange',
                   'broccoli','carrot','hot dog','pizza','donut','cake','chair','sofa','pottedplant','bed',
                   'diningtable','toilet','tvmonitor','laptop','mouse','remote','keyboard','cell phone','microwave','oven',
                   'toaster','sink','refrigerator','book','clock','vase','scissors','teddy bear','hair drier','toothbrush']
    predefined_CN=['人','自行车','汽车','摩托车','飞机','公共汽车','火车','卡车','船','红绿灯',
                   '消防栓','停止标志','停车收费表','板凳','鸟','猫','狗','马','羊','牛',
                   '大象','熊','斑马','长颈鹿','背包','雨伞','手提包','领带','手提箱','飞盘',
                   '滑雪板','单板滑雪','运动球类','风筝','棒球棒','棒球手套','滑板','冲浪板','网球拍','瓶子',
                   '红酒杯','杯子','叉子','刀','勺子','碗','香蕉','苹果','三明治','橘子',
                   '西兰花','胡萝卜','热狗','比萨','甜甜圈','蛋糕','椅子','沙发','盆栽','床',
                   '餐桌','厕所','电视监视器','笔记本电脑','老鼠','遥控器','键盘','手机','微波炉','烤箱',
                   '烤面包机','水槽','冰箱','书','时钟','花瓶','剪刀','泰迪熊','吹风机','牙刷']
    #找到英文label名称在list中的位置
    loc = predefined_En.index(label)
    #显示对应位置的中文名称
    label_CN=predefined_CN[loc]
    return label_CN


def cv2ImgAddText(img, text, left, top, textColor=(255, 0, 0), textSize=20):
    if (isinstance(img, np.ndarray)):  # 判断是否OpenCV图片类型
        img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
    # 创建一个可以在给定图像上绘图的对象
    draw = ImageDraw.Draw(img)
    # 字体的格式
    fontStyle = ImageFont.truetype(
        "font/simsun.ttc", textSize, encoding="utf-8")
    # 绘制文本
    draw.text((left, top), text, textColor, font=fontStyle)
    # 转换回OpenCV格式
    result=cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
    return result


if __name__ == '__main__':
    #待检测的图片
    file='H:/dog.jpg'
    #进行LOYO识别
    image, info = video_demo(file)
    imgA=cv2.imread(file)
    #YOLO返回的坐标值和标签、置信度
    locx1 = [q1['xtop'] for q1 in info]  # 左上角x坐标
    locx2 = [q2['ytop'] for q2 in info]  # 左上角y坐标
    locy1 = [q3['xbottom'] for q3 in info]  # 右下角x坐标
    locy2 = [q4['ybottom'] for q4 in info]  # 右下角y坐标
    lable_str = [q5['label'] for q5 in info]  # 标签
    confidence = [q6['confidence'] for q6 in info]  # 置信度

    B = len(locx1)
    for l in range(0, B):
        x2 = (locx1[l], locx2[l])
        y2 = (locy1[l], locy2[l])
        #英文标签转换为中文标签
        label_CN = E2C(lable_str[l])
        #输出中文标签和置信度
        text = "{}: {}".format(label_CN, confidence[l])
        #在imgA上画出矩形
        cv2.rectangle(imgA, x2, y2, (0, 0, 255), 2)
        #在imgA上显示中文标签+置信度
        imgA = cv2ImgAddText(imgA, text, locx1[l], locx2[l])

    cv2.imshow('Image', imgA)
    cv2.waitKey(0)
    

运行结果