【yolov8】3分钟安装yolov8？yolov8安装补充与yolo8简单实现

# 优化了up的脚本并添加了新功能
# 修改截图方式
# 优化了窗口捕获和推理的帧数（约30帧）
# 修改为按Esc结束（请务必使用Esc结束窗口运行，否则极容易出现“无权限访问”的问题，需要焦点在窗口上按Esc）
# 新增多线程调用优化
# 新增names显示，修改数字为names里面的名字(兼容中文)
# 新增窗口置顶和大小调整
# 新增名称/置信度，FPS显示
# forecast.py
import os
import threading
import time
from queue import Queue

import CV2
import numpy as np
import pygetwindow as gw
import win32con
import win32gui
import yaml
from mss import mss

from ultralytics import YOLO


def load_names(file_path):
    with open(file_path, 'r', encoding='utf-8') as f:
        data = yaml.safe_load(f)
    return data['names']


# 替换为你的data.yaml 里面保存有nc和names的那个，可以使用中文，推理出来的就是你names的名字，不是0,1数字了
names_path = "data.yaml"
names = load_names(names_path)

# 窗口名称和大小 ，推理时允许手动拖拽窗口大小
window_name = "YOLOv8 Predict Test"
display_window_width = 768
display_window_height = 432
# 27 按Esc结束
exit_code = 27


# exit_code = ord("q")  按q结束

def capture_screen(img_queue):
    with mss() as sct:
        monitor = sct.monitors[0]

        while True:
            sct_img = sct.grab(monitor)
            img = np.array(sct_img)
            img = CV2.cvtColor(img, CV2.COLOR_BGRA2BGR)
            img_queue.put(img)


class YoloThread(threading.Thread):
    def __init__(self, model, img_queue, result_queue):
        threading.Thread.__init__(self)
        self.model = model
        self.img_queue = img_queue
        self.result_queue = result_queue

    def run(self):
        while True:
            img = self.img_queue.get()
            results = self.model.predict(source=img, conf=0.25, iou=0.75)
            self.result_queue.put((img, results))


def run(model, top_most=True):
    window_flag = CV2.WINDOW_NORMAL
    fps_update_interval = 0.5  # 每0.5秒更新一次
    frame_counter = 0
    last_fps_update_time = time.time()
    fps = 0

    img_queue = Queue()
    result_queue = Queue()

    capture_thread = threading.Thread(target=capture_screen, args=(img_queue,))
    capture_thread.daemon = True
    capture_thread.start()

    yolo_thread = YoloThread(model, img_queue, result_queue)
    yolo_thread.daemon = True
    yolo_thread.start()

    # 将这两个函数放在 while True 循环之外
    CV2.namedWindow(window_name, window_flag)
    CV2.resizeWindow(window_name, display_window_width, display_window_height)

    while True:
        current_frame_time = time.time()
        img, results = result_queue.get()
        for result in results:
            if len(result.boxes.xyxy) > 0:
                boxes_conf = np.array(result.boxes.conf.tolist())
                boxes_xyxy = result.boxes.xyxy.tolist()
                boxes_cls = result.boxes.cls.tolist()

                for i, box_xyxy in enumerate(boxes_xyxy):
                    CV2.rectangle(img, (int(box_xyxy[0]), int(box_xyxy[1])),
                                  (int(box_xyxy[2]), int(box_xyxy[3])), (0, 0, 150), 2)
                    class_name = names[int(boxes_cls[i])]
                    confidence_text = f"{class_name}: {boxes_conf[i]:.2f}"
                    CV2.putText(img, confidence_text, (int(box_xyxy[0]), int(box_xyxy[1]) - 20),
                                CV2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 150), 2, CV2.LINE_AA)

        frame_counter += 1
        elapsed_time = current_frame_time - last_fps_update_time

        fps_text = f"FPS: {fps}"
        CV2.putText(img, fps_text, (display_window_width - 700, 40),
                    CV2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 150), 2, CV2.LINE_AA)

        if elapsed_time >= fps_update_interval:
            fps = int(frame_counter / elapsed_time)
            frame_counter = 0
            last_fps_update_time = current_frame_time

        CV2.imshow(window_name, img)

        if top_most:
            window = *******indowsWithTitle(window_name)[0]
            window_handle = window._hWnd
            win32gui.SetWindowPos(window_handle, win32con.HWND_TOPMOST, 0, 0, 0, 0,
                                  win32con.SWP_NOMOVE | win32con.SWP_NOSIZE)

        if CV2.waitKey(1) == exit_code:
            CV2.destroyAllWindows()
            os._exit(0)


if __name__ == '__main__':
    # 是否默认窗口置顶
    top_most = True
    # 你的best.pt模型(生成模型之后千万记得替换模型，我就因为忘记替换模型还以为我数据集有问题hh)
    model = YOLO("../best.pt")

    run(model, top_most)

效果如下：