使用YOLOv11进行视频目标检测

使用YOLOv11进行视频目标检测

完整代码

bash 复制代码
import cv2
from ultralytics import YOLO

def predict(chosen_model, img, classes=[], conf=0.5):
    if classes:
        results = chosen_model.predict(img, classes=classes, conf=conf)
    else:
        results = chosen_model.predict(img, conf=conf)

    return results

def predict_and_detect(chosen_model, img, classes=[], conf=0.5, rectangle_thickness=2, text_thickness=1):
    results = predict(chosen_model, img, classes, conf=conf)
    for result in results:
        for box in result.boxes:
            cv2.rectangle(img, (int(box.xyxy[0][0]), int(box.xyxy[0][1])),
                          (int(box.xyxy[0][2]), int(box.xyxy[0][3])), (255, 0, 0), rectangle_thickness)
            cv2.putText(img, f"{result.names[int(box.cls[0])]}",
                        (int(box.xyxy[0][0]), int(box.xyxy[0][1]) - 10),
                        cv2.FONT_HERSHEY_PLAIN, 1, (255, 0, 0), text_thickness)
    return img, results

# defining function for creating a writer (for mp4 videos)
def create_video_writer(video_cap, output_filename):
    # grab the width, height, and fps of the frames in the video stream.
    frame_width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    frame_height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = int(video_cap.get(cv2.CAP_PROP_FPS))
    # initialize the FourCC and a video writer object
    fourcc = cv2.VideoWriter_fourcc(*'MP4V')
    writer = cv2.VideoWriter(output_filename, fourcc, fps,
                             (frame_width, frame_height))
    return writer

model = YOLO("yolo11x.pt")

output_filename = "YourFilename.mp4"

video_path = r"YourVideoPath.mp4"
cap = cv2.VideoCapture(video_path)
writer = create_video_writer(cap, output_filename)
while True:
    success, img = cap.read()
    if not success:
        break
    result_img, _ = predict_and_detect(model, img, classes=[], conf=0.5)
    writer.write(result_img)
    cv2.imshow("Image", result_img)
    
    cv2.waitKey(1)
writer.release()

参考资料:

1.https://blog.csdn.net/qq_42589613/article/details/142729428

2.https://blog.csdn.net/java1314777/article/details/142665078

相关推荐
联犀1 天前
联犀 Go SIMD 实战:视频转码从 16.5ms 到 5.46ms
开发语言·golang·音视频
9527华安1 天前
FPGA纯verilog实现DP视频接口,支持2K60帧分辨率,提供3套工程源码和技术支持
fpga开发·音视频·dp
MetaEnchanter2 天前
视频章节怎么整理:填好时间点,导出前检查这几处
音视频
小豆庄2 天前
测试视频里的人脸车牌敏感信息自动化脱敏方案
计算机视觉·目标跟踪·ffmpeg·音视频·边缘计算·视频·敏捷流程
光泽雨2 天前
WPF 音视频四大核心方案对比
wpf·音视频
揽秀亭长2 天前
音频转乐谱如何实现?四类AI 扒谱工具的流程
音视频
微尘hjx3 天前
【Deep-OC-SORT 01】多轨迹算法Deep-OC-SORT源码分析
深度学习·yolo·目标跟踪·ultralytics·轨迹算法·deep-oc-sort·车流统计
敢敢のwings3 天前
SimpleMemVLA 深度解读:把原生视频上下文变成机器人的记忆
数码相机·机器人·音视频
AI日报派送佬3 天前
高效目标检测利器:X-AnyLabeling 加载自定义 YOLO 模型自动标注实操指南
人工智能·yolo·目标检测
微尘hjx3 天前
【Deep-OC-SORT 02】多轨迹算法Deep-OC-SORT-相对 OC-SORT 的改进点
人工智能·算法·yolo·目标跟踪·视觉检测·ultralytics·deep-oc-sort