def grab_screen(region=None): hwin = win32gui.GetDesktopWindow() if region: left, top, x2, y2 = region width = x2 - left + 1 height = y2 - top + 1 else: width = win32api.GetSystemMetrics(win32con.SM_CXVIRTUALSCREEN) height = win32api.GetSystemMetrics(win32con.SM_CYVIRTUALSCREEN) left = win32api.GetSystemMetrics(win32con.SM_XVIRTUALSCREEN) top = win32api.GetSystemMetrics(win32con.SM_YVIRTUALSCREEN) hwindc = win32gui.GetWindowDC(hwin) srcdc = win32ui.CreateDCFromHandle(hwindc) memdc = srcdc.CreateCompatibleDC() bmp = win32ui.CreateBitmap() bmp.CreateCompatibleBitmap(srcdc, width, height) memdc.SelectObject(bmp) memdc.BitBlt((0, 0), (width, height), srcdc, (left, top), win32con.SRCCOPY) signedIntsArray = bmp.GetBitmapBits(True) img = np.fromstring(signedIntsArray, dtype='uint8') img.shape = (height, width, 4) srcdc.DeleteDC() memdc.DeleteDC() win32gui.ReleaseDC(hwin, hwindc) win32gui.DeleteObject(bmp.GetHandle()) return cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)

设置具体回收时间，如下代码怎样让它回收内存？：def grab_screen(self,region=None): with ImageGrab.grab() as im: self.im_size = im.size im_bytes = im.tobytes() im.close() del im gc.collect() return im_bytes

def grab_screen(self, region=None): with ImageGrab.grab() as im: self.im_size = im.size im_bytes = im.tobytes() im.close() del im gc.collect() return im_bytes 请注意，手动触发垃圾回收机制...

#精准延迟包： from ctypes import windll import cv2 ##检测模式 import numpy as np #为睡眠做准备 TimeBeginPeriod = windll.winmm.timeBeginPeriod HPSleep = windll.kernel32.Sleep TimeEndPeriod = windll.winmm.timeEndPeriod #精准延迟 def Precise_delay(num): # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ TimeBeginPeriod(1) HPSleep(int(num)) # 减少报错 TimeEndPeriod(1) #截图模式 def Screenshot_Mode(ps_mode , pos_x,pos_y,mcx,mcy): if ps_mode == 0: from mss import mss Screenshot_value = mss() #目标检测范围 window_size_mss = (pos_x- mcx , pos_y - mcy , pos_x + mcx , pos_y + mcy) # monitor = 960-320,540-320,960+320,540+320 window_size = { "left":pos_x- mcx, "top":pos_y - mcy, "width":pos_x + mcx, "height":pos_y + mcy, } #目标检测中心点 core_x = int((window_size_mss[2]-window_size_mss[0])/2) core_y = int((window_size_mss[3]-window_size_mss[1])/2) elif ps_mode == 1: print("使用DXGI截图") from d3dshot import create # pip install d3dshot -i https://pypi.tuna.tsinghua.edu.cn/simple Screenshot_value = create("numpy",frame_buffer_size = 100) #目标检测范围 window_size = (pos_x- mcx , pos_y - mcy , pos_x + mcx , pos_y + mcy) #目标检测中心点 core_x = int((window_size[2]-window_size[0])/2) core_y = int((window_size[3]-window_size[1])/2) return window_size,core_x,core_y,Screenshot_value #检测模式 def Detection_mode(test_mode,Screenshot_value,window_size): if test_mode == 0: img = Screenshot_value.grab(window_size) img = np.array(img) img = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR)#原版 elif test_mode == 1: img = Screenshot_value.screenshot(region=window_size) #使用opencv删除一个通道 img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) return img window_size,core_x,core_y,Screenshot_value = Screenshot_Mode(0, 960, 540, 150, 150) print(window_size,core_x,core_y,Screenshot_value) img = Detection_mode(0, Screenshot_value, window_size) print(img) 这个代码实现了什么功能

这段代码实现了以下功能： 1. 定义了一个精准延迟函数 Precise_delay，用于实现精确的延迟操作。 2. 定义了一个截图模式函数 Screenshot_Mode，根据传入的模式参数选择使用 mss 或 d3dshot 库进行截图，并...

def next(self): self.count += 1 if cv2.waitKey(1) == ord('q'): # q to quit self.cap.release() cv2.destroyAllWindows() raise StopIteration # Read frame if self.pipe == 0: # local camera ret_val, img0 = self.cap.read() img0 = cv2.flip(img0, 1) # flip left-right else: # IP camera n = 0 while True: n += 1 self.cap.grab() if n % 30 == 0: # skip frames ret_val, img0 = self.cap.retrieve() if ret_val: break

如果从本地相机中读取，则会使用OpenCV中的cap.read()方法，如果从IP相机中读取，则会使用cap.grab()和cap.retrieve()方法。在读取视频帧的同时，还会检测是否有键盘输入，如果用户按下q键，则会释放相机...

from PIL import ImageGrab import numpy as np import cv2 from pynput import keyboard import threading flag= False def video_record(): p=ImageGrab.grab() a,b=p.size fourcc =cv2.VideoWriter_fourcc(*'XVID') video=cv2.VideoWriter('视频.avi',fourcc,28,(a,b)) while True: im = ImageGrab.grab() inn = cv2.cvtColor(np.array(im),cv2.COLOR_RGB2BGR) video.write(inn) if flag: print('录制结束！') break video.release() def on_press(key): global flag if key == keyboard.Key.esc: flag = True return False if name=='main': print('开始录制！') th=threading.Thread(target=video_record) th.start() with keyboard.Listener(on_press=on_press)as listener: listener.join()这个代码只能实现最新一次的录屏吗

完整代码如下： from PIL import ImageGrab import numpy as np import cv2 from pynput import keyboard import threading import datetime flag = False def video_record(): p = ImageGrab.grab() a, b = p.size fourcc = cv2.VideoWriter_fourcc(*'XVID') filename = '视频_{}.avi'.format(datetime.datetime.now().strftime('%Y%m%d_%H%M%S')) video = cv2.VideoWriter(filename, fourcc, 28, (a, b)) while True: im = ImageGrab.grab() inn = cv2.cvtColor(np.array(im), cv2.COLOR_RGB2BGR) video.write(inn) if flag: print('录制结束！') break video.release() def on_press(key): global flag if key == keyboard.Key.esc: flag = True return False if name == 'main': print('开始录制！') th = threading.Thread(target=video_record) th.start() with keyboard.Listener(on_press=on_press) as listener: listener.join()在上面代码的基础上加上把它保存为在桌面上的代码

您可以添加以下代码以将视频保存在桌面上： python import os desktop_path = os.path.join(os.path.expanduser('~'), 'Desktop') filename = os.path.join(desktop_path, '视频_{}.avi'.format(datetime....

from PIL import ImageGrabimport numpy as npimport cv2from pynput import keyboardimport threadingimport datetimeimport osflag = Falsedef video_record(): p = ImageGrab.grab() a, b = p.size fourcc = cv2.VideoWriter_fourcc(*'XVID') desktop_path = os.path.join(os.path.expanduser('~'), 'Desktop') filename = os.path.join(desktop_path, '视频_{}.avi'.format(datetime.datetime.now().strftime('%Y%m%d_%H%M%S'))) video = cv2.VideoWriter(filename, fourcc, 28, (a, b)) while True: im = ImageGrab.grab() inn = cv2.cvtColor(np.array(im), cv2.COLOR_RGB2BGR) video.write(inn) if flag: print('录制结束！') break video.release()def on_press(key): global flag if key == keyboard.Key.esc: flag = True return Falseif name == 'main': print('开始录制！') th = threading.Thread(target=video_record) th.start() with keyboard.Listener(on_press=on_press) as listener: listener.join()在这段代码上把要保存的文件名修改为自定义格式

你可以把这段代码中的文件名修改为你想要的格式，例如将以下代码中的"视频_{}.avi"修改为"my_video_{}.mp4"： python filename = os.path.join(desktop_path, 'my_video_{}.mp4'.format(datetime.datetime.now()...

if (step == STEP_GRAB_BOX) { if (msg->data == "done") { ROS_INFO("[GrabResultCB] grab_box done!"); std_msgs::String waypoint_msg; if (put_count == YELLOW) { waypoint_msg.data = "2"; } else if (put_count == BLUE) { waypoint_msg.data = "3"; } else if (put_count == RED) { waypoint_msg.data = "4"; } waypoint_pub.publish(waypoint_msg); step = STEP_GOTO_WP2; } }什么意思

这是一个 ROS 节点的回调函数，当机器人当前的操作步骤为 STEP_GRAB_BOX 时，会根据传入的消息判断是否完成了抓取物品的任务。如果完成了，则会发布一个表示下一步目标位置的消息到 waypoint_pub 话题上，并将...

检测鼠标事件 def mouse_event(self, event, x, y, flags, param): if event == cv2.EVENT_LBUTTONUP and x > 550 and y < 50: def open_login_window(my_window, on_entry_click): loginwindow = LoginWindow(on_entry_click) loginwindow.transient(my_window) loginwindow.wait_visibility() loginwindow.grab_set() def quit_window(my_window): # self.camera_process.terminate() my_window.destroy() # 虚拟键盘 def on_entry_click(self, event, entry): if self.keyboard_window: self.keyboard_window.destroy() keyboard_window = tk.Toplevel(self) keyboard_window.title("虚拟键盘") keyboard_window.geometry("610x140") keyboard_window.resizable(False, False) button_list = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '0', '<-', 'q', 'w', 'e', 'r', 't', 'y', 'u', 'i', 'o', 'p', 'a', 's', 'd', 'f', 'g', 'h', 'j', 'k', 'l', 'z', 'x', 'c', 'v', 'b', 'n', 'm'] row = 0 col = 0 for button_text in button_list: button = tk.Button(keyboard_window, text=button_text, width=3) if button_text != '<-': button.config(command=lambda char=button_text: entry.insert(tk.END, char)) else: button.config( command=lambda char=button_text: entry.delete(len(entry.get()) - 1, tk.END)) button.grid(row=row, column=col) col += 1 if col > 10: row += 1 col = 0 keyboard_window.deiconify() self.keyboard_window = keyboard_window # 登录界面 my_window = tk.Tk() my_window.title("登录") my_window.geometry("300x200") # 计算窗口位置，让其出现在屏幕中间 screen_width = my_window.winfo_screenwidth() screen_height = my_window.winfo_screenheight() x = (screen_width - 300) // 2 y = (screen_height - 200) // 2 my_window.geometry("+{}+{}".format(x, y)) my_window.wm_attributes("-topmost", True) login_button = tk.Button(my_window, text="登录", font=('Arial', 12), width=10, height=1, command=lambda: open_login_window(my_window, on_entry_click)) login_button.pack(side='left', expand=True) exitbutton = tk.Button(my_window, text="退出", font=('Arial', 12), width=10, height=1, command=lambda: [quit_window(my_window)]) exitbutton.pack(side='left', expand=True) my_window.mainloop() if event == cv2.EVENT_LBUTTONUP and x < 50 and y > 1000: cv2.destroyAllWindows() 在此基础上请实现让tk界面不会出现重影用中文回答

def update_gui(self): while True: # 更新界面 self.label.config(text=str(time.time())) # 休眠一段时间，避免过于频繁地更新界面 time.sleep(0.1) def run(self): self.root.mainloop() def main(): ...

if (msg->data == "done") { ROS_INFO("[GrabResultCB] grab_box done!"); std_msgs::String waypoint_msg; if (put_count == YELLOW) { waypoint_msg.data = "2"; } else if (put_count == BLUE) { waypoint_msg.data = "3"; } else if (put_count == RED) { waypoint_msg.data = "4"; } waypoint_pub.publish(waypoint_msg); step = STEP_GOTO_WP2; }这个代码要怎么改，然后实现可以跑到别的航点？

if (msg->data == "done") { ROS_INFO("[GrabResultCB] grab_box done!"); std_msgs::String waypoint_msg; waypoint_msg.data = "5"; // 修改航点编号为5 waypoint_pub.publish(waypoint_msg); step = ...

def scr_img(self, axis=None): if not axis: lock.acquire() # 多线程时调会有BUG用使用锁 img = self.grab_t() lock.release() else: img = array(ImageGrab.grab(axis))[..., ::-1] # axis 截图的坐标范围 return img

如果没有传入参数axis，则使用grab_t()方法截取整个屏幕的图像，并使用锁来防止多线程时的竞争条件。如果传入了参数axis，则使用ImageGrab.grab()方法截取指定坐标范围的图像，并将其转换为numpy数组并进行...

def jietu(self,hwnd): # 获取窗口的左上角和右下角坐标 left, top, right, bottom = win32gui.GetWindowRect(hwnd) # 获取屏幕分辨率 width = win32api.GetSystemMetrics(0) height = win32api.GetSystemMetrics(1) # 截图 img_pil = PIL.ImageGrab.grab(bbox=(left, top, right, bottom)) # 将 PIL.Image 转换为 OpenCV 图像 #img_cv = cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR) # 显示截图 cv2.imshow("Screenshot", img_cv) cv2.waitKey(0) # 保存截图 cv2.imwrite("screenshot.png", img_cv) return img_pil 报错

left, top, right, bottom = win32gui.GetWindowRect(hwnd) # 获取屏幕分辨率 width = win32api.GetSystemMetrics(0) height = win32api.GetSystemMetrics(1) # 截图 img_pil = PIL.ImageGrab.grab(bbox=(left...

优化这段代码import cv2 import imutils import numpy as np img = cv2.imread('D:\pycharm\PycharmProjects\pythonProject\p1\p1.jpg', cv2.IMREAD_COLOR) img = cv2.resize(img, (600, 400)) cv2.imshow('Origin image', img) img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) img_gray = cv2.bilateralFilter(img_gray, 13, 15, 15) img_edged = cv2.Canny(img_gray, 30, 200) cv2.imshow('edged image', img_edged) img_contours = cv2.findContours(img_edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) img_contours = imutils.grab_contours(img_contours) img_contours = sorted(img_contours, key=cv2.contourArea, reverse=True)[:10] screenCnt = None for c in img_contours: peri = cv2.arcLength(c, True) approx = cv2.approxPolyDP(c, 0.018 * peri, True) if len(approx) == 4: screenCnt = approx break if screenCnt is None: detected = 0 print("No contour detected") else: detected = 1 if detected == 1: cv2.drawContours(img, [screenCnt], -1, (0, 0, 255), 3) mask = np.zeros(img_gray.shape, np.uint8) new_image = cv2.drawContours(mask, [screenCnt], 0, 255, -1, ) cv2.imshow('mask_image', new_image) new_image = cv2.bitwise_and(img, img, mask=mask) (x, y) = np.where(mask == 255) (topx, topy) = (np.min(x), np.min(y)) (bottomx, bottomy) = (np.max(x), np.max(y)) cropped = img_gray[topx:bottomx + 1, topy:bottomy + 1] cropped = cv2.resize(cropped, (400, 200)) cv2.imshow('Cropped', cropped) cv2.waitKey(0) cv2.destroyAllWindows()

这段代码主要是对一张图片进行边缘检测，并找出图片中的一个矩形区域进行裁剪。下面是对代码的一些优化建议： 1. 将代码拆分成多个函数，增加代码的可读性和可维护性。 2. 将文件路径字符串改为使用原始字符串（raw...

colorizer_eccv16 = eccv16(pretrained=True).eval() colorizer_siggraph17 = siggraph17(pretrained=True).eval() if(opt.use_gpu): colorizer_eccv16.cuda() colorizer_siggraph17.cuda() # default size to process images is 256x256 # grab L channel in both original ("orig") and resized ("rs") resolutions img = load_img("imgs/test4.jpg") (tens_l_orig, tens_l_rs) = preprocess_img(img, HW=(256,256)) if(opt.use_gpu): tens_l_rs = tens_l_rs.cuda() # colorizer outputs 256x256 ab map # resize and concatenate to original L channel img_bw = postprocess_tens(tens_l_orig, torch.cat((0tens_l_orig,0tens_l_orig),dim=1)) out_img_eccv16 = postprocess_tens(tens_l_orig, colorizer_eccv16(tens_l_rs).cpu()) out_img_siggraph17 = postprocess_tens(tens_l_orig, colorizer_siggraph17(tens_l_rs).cpu())

这段代码使用了两个预训练的深度学习模型 eccv16 和 siggraph17，用于将一张灰度图像转换为彩色图像。首先加载了一张待处理的图像 test4.jpg，然后将其转换为两个分辨率下的 L 通道，即原始分辨率和调整后的 ...

请根据刚才的报错，改良以下代码：import cv2 import numpy as np from pynput.mouse import Button, Controller from PIL import ImageGrab # 读取目标图片 target_img = cv2.imread("baidu.png", cv2.IMREAD_GRAYSCALE) # 创建一个Controller对象 mouse = Controller() # 获取屏幕截图 screenshot = ImageGrab.grab() # 将截图转换为numpy数组 screen = np.array(screenshot) # 保存截图为screen.png screenshot.save('screen.png') # 在屏幕上搜索目标图片 result = cv2.matchTemplate(screen, target_img, cv2.TM_CCOEFF_NORMED) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result) top_left = max_loc h, w = target_img.shape[:2] bottom_right = (top_left[0] + w, top_left[1] + h) # 模拟鼠标点击目标图片的位置 mouse.position = (top_left[0] + w//2, top_left[1] + h//2) mouse.press(Button.left) mouse.release(Button.left) time.sleep(2)

import cv2 import numpy as np from pynput.mouse import Button, Controller ...mouse.position = (top_left[0] + w//2, top_left[1] + h//2) mouse.press(Button.left) mouse.release(Button.left) time.sleep(2)

from tkinter import * import cv2 import numpy as np from PIL import ImageGrab from tensorflow.keras.models import load_model from temp import * model = load_model('mnist.h5') image_folder = "img/" root = Tk() root.resizable(0, 0) root.title("HDR") lastx, lasty = None, None image_number = 0 cv = Canvas(root, width=1200, height=480, bg='white') cv.grid(row=0, column=0, pady=2, sticky=W, columnspan=2) def clear_widget(): global cv cv.delete('all') def draw_lines(event): global lastx, lasty x, y = event.x, event.y cv.create_line((lastx, lasty, x, y), width=8, fill='black', capstyle=ROUND, smooth=True, splinesteps=12) lastx, lasty = x, y def activate_event(event): global lastx, lasty cv.bind('<B1-Motion>', draw_lines) lastx, lasty = event.x, event.y cv.bind('<Button-1>', activate_event) def Recognize_Digit(): global image_number filename = f'img_{image_number}.png' root.update() widget = cv x = root.winfo_rootx() + widget.winfo_rootx() y = root.winfo_rooty() + widget.winfo_rooty() x1 = x + widget.winfo_width() y1 = y + widget.winfo_height() print(x, y, x1, y1) # get image and save ImageGrab.grab().crop((x, y, x1, y1)).save(image_folder + filename) image = cv2.imread(image_folder + filename, cv2.IMREAD_COLOR) gray = cv2.cvtColor(image.copy(), cv2.COLOR_BGR2GRAY) ret, th = cv2.threshold( gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) # contours = cv2.findContours( # th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] Position = findContours(th) for m in range(len(Position)): # make a rectangle box around each curve cv2.rectangle(th, (Position[m][0], Position[m][1]), ( Position[m][2], Position[m][3]), (255, 0, 0), 1) # Cropping out the digit from the image corresponding to the current contours in the for loop digit = th[Position[m][1]:Position[m] [3], Position[m][0]:Position[m][2]] # Resizing that digit to (18, 18) resized_digit = cv2.resize(digit, (18, 18)) # Padding the digit with 5 pixels of black color (zeros) in each side to finally produce the image of (28, 28) padded_digit = np.pad(resized_digit, ((5, 5), (5, 5)), "constant", constant_values=0) digit = padded_digit.reshape(1, 28, 28, 1) digit = digit / 255.0 pred = model.predict([digit])[0] final_pred = np.argmax(pred) data = str(final_pred) + ' ' + str(int(max(pred) * 100)) + '%' print(data) font = cv2.FONT_HERSHEY_SIMPLEX fontScale = 0.5 color = (255, 0, 0) thickness = 1 cv2.putText(th, data, (Position[m][0], Position[m][1] - 5), font, fontScale, color, thickness) cv2.imshow('image', th) cv2.waitKey(0) cv2.destroyAllWindows() btn_save = Button(text='Recognize Digit', command=Recognize_Digit) btn_save.grid(row=2, column=0, pady=1, padx=1) button_clear = Button(text='Clear Widget', command=clear_widget) button_clear.grid(row=2, column=1, pady=1, padx=1) root.mainloop()

这段代码是一个Python程序，用于画图并识别手写数字。它调用了许多库，如tkinter、cv2、numpy、PIL和tensorflow。程序创建了一个图形用户界面，包括一个白色的画布和一个按钮。...程序会将绘制的数字保存为图片文件，并...

请解析下列代码：import cv2 import numpy as np from pynput.mouse import Button, Controller from PIL import ImageGrab #获取中心点位置 def find_image_on_screen(image_path): # 读取要识别的图片 image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE) # 获取屏幕截图 screenshot = np.array(ImageGrab.grab()) # 将截图转换为灰度图像 gray = cv2.cvtColor(screenshot, cv2.COLOR_BGR2GRAY) # 使用模板匹配找到图像在屏幕中的位置 res = cv2.matchTemplate(gray, image, cv2.TM_CCOEFF_NORMED) min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res) # 返回图像的位置和大小 top_left = max_loc h, w = image.shape bottom_right = (top_left[0] + w, top_left[1] + h) return (top_left[0] + w // 2, top_left[1] + h // 2) find_image_on_screen("baidu.png") time.sleep(2)

- 然后使用 ImageGrab.grab() 方法获取整个屏幕的截图，并将其转换为灰度图像。 - 最后使用 cv2.matchTemplate() 方法进行模板匹配，找到图像在屏幕中的位置和大小，并返回图像的中心点位置。 3. 调用 find_...

下面这段代码是什么意思？void GrabbingThread::Work() { // Working cycle while(true) { // Wait the Working Start signal { std::unique_lock<std::mutex> lk_start(m_mxStart); m_cvStart.wait(lk_start, [this]{ return m_bStart; }); } // Writing cycle, check if we're terminating if(!m_bTerminate) { while(m_bStart) { // Grab new buffer uint32_t err = GrabNewBuffer(-1); if(err) { // ERROR: // log the error here } } } // Notify them, that Working Cycle is stopped { std::lock_guard<std::mutex> lk_stop(m_mxStopAck); m_bStopAck = true; } m_cvStopAck.notify_all(); // Quit the working cycle, if we're terminating if(m_bTerminate) break; }; };

这段代码定义了一个名为 GrabbingThread 的类的 Work 函数，该函数表示图像采集线程的工作循环。函数通过一个无限循环 while(true) 实现图像采集的工作。在循环中，首先等待一个启动信号 m_bStart。...

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设置具体回收时间，如下代码怎样让它回收内存？：def grab_screen(self,region=None): with ImageGrab.grab() as im: self.im_size = im.size im_bytes = im.tobytes() im.close() del im gc.collect() return im_bytes

def scr_img(self, axis=None): if not axis: lock.acquire() # 多线程时调会有BUG用使用锁 img = self.grab_t() lock.release() else: img = array(ImageGrab.grab(axis))[..., ::-1] # axis 截图的坐标范围 return img

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