yolo视频检测时,检测框上显示中文名字

python 复制代码
from ultralytics import YOLO
import numpy as np
import cv2
from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO("yolov8n.pt")#我加载的是官方权重

# Define path to video file
video_path = r"D:\daye_input.mp4"
cap = cv2.VideoCapture(video_path)
if not cap.isOpened():
    print("Cannot open camera")
    exit()

fps = cap.get(cv2.CAP_PROP_FPS)#输入视频帧率
print(f"输入视频帧率为:Frames per second: {fps}")

# 保存视频的一些设置
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# width = int(1920)
# height = int(1080)
# fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # 保存视频的编码格式
# output_video_path = 'daye.mp4'
# fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # 保存视频的编码格式
# output_video_path = 'daye.mp4'

fourcc = cv2.VideoWriter_fourcc(*'XVID')  # 保存视频的编码格式
output_video_path = 'daye.avi'


out = cv2.VideoWriter(output_video_path, fourcc, fps, (width, height))

from PIL import Image, ImageDraw, ImageFont

# 设置字体
font = ImageFont.truetype(r'C:\Windows\Fonts\SimHei.ttf', 60)  # 替换为你的字体路径
# 循环遍历视频帧
while cap.isOpened():

    # start_time = time.time() #记录开始时间

    # 从视频读取一帧
    success, frame = cap.read()

    if success:
        # 在帧上运行YOLOv8追踪,持续追踪帧间的物体
        #     results = model(frame,  conf=0.3,iou=0.5,imgsz=(640,640))
            results = model.track(frame, persist=True, conf=0.3, iou=0.5, tracker="ultralytics/cfg/trackers/bytetrack.yaml",
                              imgsz=(1920,1080))
            img_pil = Image.fromarray(frame)
            draw = ImageDraw.Draw(img_pil)  # 创建Draw对象

            if results[0].boxes and results[0].boxes.id is not None:
               boxes = results[0].boxes.xyxy.cpu()
               clss = results[0].boxes.cls
               track_ids = results[0].boxes.id.int().cpu().tolist()
               for id, cls, boxxyxy in zip( track_ids, clss, boxes ):
                   class_id = cls.item()
                   x1, y1, x2, y2 = boxxyxy
                   tracker_id = id
                   if class_id == 0.0:
                       # class_id = 'DaYe'

                       draw.text((int(x1), int(y1) - 50), str(tracker_id)+"号大爷", font=font, fill=(0, 0,255))# 在图片上绘制中文
                       # cv2.rectangle(img_pil, (int(x1), int(y1)), (int(x2), int(y2)), (0, 69, 255), 2)  # 橙红

                       draw.rectangle([(int(x1), int(y1)), (int(x2), int(y2))], outline="blue", width=4)  # 红色边框,宽度5

                   # else:
                   #     draw.text((int(x1), int(y1) - 50),  "不像大爷", font=font,
                   #               fill=(255, 0, 0))  # 在图片上绘制中文
                   #     # cv2.rectangle(img_pil, (int(x1), int(y1)), (int(x2), int(y2)), (0, 69, 255), 2)  # 橙红
                   #
                   #     draw.rectangle([(int(x1), int(y1)), (int(x2), int(y2))], outline="red", width=4)  # 红色边框,宽度5

            # img = cv2.cvtColor(np.array(img_pil), cv2.COLOR_BGR2RGB)
            img = np.array(img_pil)
            # cv2.imshow('frame_pil', img)
            # img = cv2.resize( img, (1920, 1080))
            out.write(img)

            cv2.imshow('frame_pil', img)
            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
            # cv2.waitKey(0)
            # cv2.destroyAllWindows()
    else:
        break
cap.release()
out.release()
cv2.destroyAllWindows()

检测效果见我b站视频【yolo检测框显示中文】 https://www.bilibili.com/video/BV1ih2wYWEcM/?share_source=copy_web\&vd_source=84543f4291e70cc3c31e5db4f6cabde8

相关推荐
@Mr_LiuYang15 小时前
用自己的数据训练yolov11目标检测
yolo·目标检测·实例分割·yolov11
子正19 小时前
yolo11s rknn无法detect的bugfix - step by step
yolo·机器学习·ai
子正4 天前
pt->onnx->rknn(量化) step by step & FAQ
python·深度学习·yolo·ai
欣然~4 天前
使用 YOLOv8 模型分析摄像头的图像
yolo
Hello Mr.Z4 天前
在香橙派5 NPU上使用Yolov5
yolo·npu·香橙派5
goomind6 天前
YOLOv11实战海洋动物图像识别
pytorch·深度学习·yolo·计算机视觉·cnn·海洋动物
前网易架构师-高司机7 天前
木材表面缺陷检测数据集,支持YOLO+COCO JSON+PASICAL VOC XML+DARKNET格式标注信息,平均正确识别率95.0%
yolo·数据集·木材缺陷识别·木材表面缺陷
goomind8 天前
深度卷积神经网络实战海洋动物图像识别
深度学习·神经网络·yolo·计算机视觉·cnn·pyqt5·海洋动物识别
糖炒狗子8 天前
基于YoloV11和驱动级鼠标模拟实现Ai自瞄
人工智能·yolo·计算机外设
飞瀑8 天前
计算机视觉核心任务
yolo