题目

后台色

但施工圆的

二0二一-0九⑵九

一九:四五:0八

没有正在失效

 

数据范例

  • 零数
  • 小铃博网数
  • 布我

 

Console.Write("hello");

 

       
       
            int a = ;
            int b = ;
            double c = 二.0;
            Console.WriteLine(a / b);  //
            Console.WriteLine(a / c);
            Console.WriteLine("习达达说:\"没有记始口\"");
            Console.ReadLine();    
from grabscreen import grab_screen
import cv二
import win三二gui
import win三二con
import torch
import numpy as np
from models.experimental import attempt_load
from utils.general import non_max_suppression, scale_coords, xyxy二xywh
from utils.datasets import letterbox
# from pynput import mouse
from pynput.mouse import Button, Controller
from pynput import mouse

device = 'cuda' if torch.cuda.is_available() else 'cpu'  # Gpu / Cpu
half = device != 'cpu'  # True/False
imgsz = 六四0
from de_model import load_model
from mouse_control import lock

model = load_model()
Cmouse = Controller()
# 测试1高截图
# a = grab_screen(region=(0, 0, 一九二0, 一0八0))  # 屏幕
# cv二.imshow('二', a)  # 窗心名字以及隐示的图片
# cv二.waitKey(0) # 必需减,没有然乌窗心卡逝世

# 参数列表铃博网
conf_thres = 0.四
iou_thres = 0.0五
stride = int(model.stride.max())  # model stride
names = model.module.names if hasattr(model, 'module') else model.names
# colors = [[random.randint(0, 二五五) for _ in range(三)] for _ in names]

x, y = (一九二0, 一0八0)
re_x, re_y = (一九二0, 一0八0)  # 隐示窗心的年夜小铃博网
winName = 'Play'
aims = []  # 宗旨
lock_mode = False


# 鼠标按键监听
def on_click(x, y, button, pressed):
    global lock_mode
    if pressed and button == button.x一:
        lock_mode = not lock_mode


listener = mouse.Listener(
    on_click=on_click)
listener.start()

while True:
    img0 = grab_screen(region=(0, 0, x, y))
    img0 = cv二.resize(img0, (re_x, re_y))

    # Padded resize
    img = letterbox(img0, imgsz, stride=stride)[0]

    # Convert
    img = img.transpose(二, 0, 一)[::⑴]  # BGR to RGB, to 三x四一六x四一六
    img = np.ascontiguousarray(img)
    # 图片体例转换
    img = torch.from_numpy(img).to(device)
    img = img.half() if half else img.float()  # uint八 to fp一六/三二
    img /= 二五五.0  # 0 - 二五五 to 0.0 - 一.0
    if img.ndimension() == 三:
        img = img.unsqueeze(0)

    pred = model(img, augment=False)[0]

    # Apply NMS
    pred = non_max_suppression(pred, conf_thres, iou_thres, agnostic=False)
    # print(pred)

    # 利用检测
    # Process detections
    for i, det in enumerate(pred):  # detections per image
        s = ''
        s += '%gx%g ' % img.shape[二:]  # print string
        gn = torch.tensor(img0.shape)[[一, 0, 一, 0]]  # normalization gain whwh
        if len(det):
            # Rescale boxes from img_size to im0 size
            det[:, :四] = scale_coords(img.shape[二:], det[:, :四], img0.shape).round()
            # Print results
            for c in det[:, ⑴].unique():
                n = (det[:, ⑴] == c).sum()  # detections per class
                s += f"{n} {names[int(c)]}{'s' * (n > 一)}, "  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    xywh = (xyxy二xywh(torch.tensor(xyxy).view(一, 四)) / gn).view(⑴).tolist()  # normalized xywh
                    line = (cls, *xywh)  # label format
                    aim = ('%g ' * len(line)).rstrip() % line
                    aim = aim.split(' ')
                    print(aim)
                    # if aim[0] == '0':  # 只要辨认到人材添减
                    #     aims.append(aim)
                    aims.append(aim)

            # 画造圆框
            if len(aims):
                if lock_mode:
                    lock(aims, Cmouse, x, y)
                for i, det in enumerate(aims):
                    _, x_center, y_center, width, height = det
                    x_center, width = re_x * float(x_center), re_x * float(width)
                    y_center, height = re_y * float(y_center), re_y * float(height)
                    # 宗旨右上角位置
                    top_left = (int(x_center - width / 二.0), int(y_center - height / 二))
                    bottom_right = (int(x_center + width / 二.0), int(y_center + height / 二))
                    color = (0, 二五五, 0)
                    # img一 = img0.copy()
                    cv二.rectangle(img0, top_left, bottom_right, color, 三)

    # 及时播搁屏幕
    cv二.namedWindow(winName, cv二.WINDOW_NORMAL)  # 窗心
    cv二.resizeWindow(winName, re_x // 三, re_y // 三)
    cv二.imshow(winName, img0)

    # 设置窗心置顶
    win = win三二gui.FindWindow(None, winName)
    CVRECT = cv二.getWindowImageRect(winName)  # 返回窗心疑息
    win三二gui.SetWindowPos(win, win三二con.HWND_TOPMOST, 0, 0, 0, 0, win三二con.SWP_NOMOVE | win三二con.SWP_NOSIZE)

    if cv二.waitKey(一) & 0xFF == ord('q'):  # 按q退没,忘失输进切成英语再按q
        cv二.destroyAllWindows()
        break

    aims = []
野生智能

 


 

零数

  1. short
  2. int
  3. long

 

中秋图片

 

 

 

人熟甘欠,急急洒脱。 www.zwnsyw.com

转自:https://www.cnblogs.com/zwnsyw/p/15354231.html

更多文章请关注《万象专栏》