目前，我的代码运行良好，只是对于我的使用来说运行有点慢。我正在使用 OpenCV 捕获屏幕，然后使用一些 if 语句检查这些结果。我相信当前的瓶颈在于 if 语句。
pyautogui.PAUSE = 0.005
Location1 = range(90, 289)
Location2 = range(290, 400) # 394, 403f
Location3 = range(450, 630)# 551, 565
Location4 = range(640, 800) # 730, 695
Location5 = range(801, 1000)
empty = []
# Change the working directory to the folder this script is in.
# Doing this because I'll be putting the files from each video in their own folder on GitHub
os.chdir(os.path.dirname(os.path.abspath(__file__)))
# initialize the WindowCapture class
wincap = WindowCapture(None)
# initialize the Vision class
vision_limestone = Vision('Festivalnote.jpg')
keys = []
inputs = []
loop_time = time()
while 1:
# get an updated image of the game
screenshot = wincap.get_screenshot()
#haystack_img = vision_limestone.apply_hsv_filter(screenshot)
# display the processed image
points = vision_limestone.find(screenshot, 0.5)#, 'rectangles')
#cv.imshow('Matches', haystack_img)
#length = len(keys)
if points == empty:
points = [(0, 0, 0)]
if len(points) < 3:
points.append((0, 0))
points.append ((0, 0))
first, sec, thd, *others = points
#print(points)
y_thresh = 175
if first [0] in Location1 or sec[0] in Location1 or thd[0] in Location1:
#print('d')
pyautogui.keyDown("d")
pyautogui.keyUp("d")
#keys.append('d')
#inputs.append('d')
if first [0] in Location2 or sec[0] in Location2 or thd[0] in Location2:
pyautogui.keyDown("f")
pyautogui.keyUp("f")
#print('f')
#keys.append('f')
#inputs.append("f")
if first [0] in Location3 or sec[0] in Location3 or thd[0] in Location3:
pyautogui.keyDown("j")
pyautogui.keyUp("j")
#print('j')
#keys.append('j')
#inputs.append('j')
if first [0] in Location4 or sec[0] in Location4 or thd[0] in Location4:
pyautogui.keyDown("k")
pyautogui.keyUp("k")
#print("k")
#keys.append('k')
#inputs.append('k')
if first [0] in Location5 or sec[0] in Location5 or thd[0] in Location5:
pyautogui.keyDown("l")
pyautogui.keyUp("l")
#print("l")
#keys.append('l')
#inputs.append('l')
#if length == len(keys):
#inputs.append(time() - loop_time)
#print(inputs)
# debug the loop rate
print('FPS {}'.format(1 / (time() - loop_time)))
loop_time = time()
# press 'q' with the output window focused to exit.
# waits 1 ms every loop to process key presses
if cv2.waitKey(1) == ord('q'):
cv2.destroyAllWindows()
break
我真的不知道这是否可以加快，因为我是一个较新的编码器，但是我查看了 numpy 并以这种方式进行循环，但我没有找到使用 numpy 执行此操作的方法（如果我错了，请纠正我） ）。我还研究了多处理，但据我了解，我无法从线程内回调屏幕截图，因此必须为每个线程拍摄屏幕截图，这似乎会更慢。请告诉我您认为我可以加快速度的任何方法。完整代码。
def func():
import cv2
import numpy as np
import os
from time import time
import pyautogui
import win32gui, win32ui, win32con
class WindowCapture:
# properties
w = 0
h = 0
hwnd = None
cropped_x = 800
cropped_y = 950
offset_x = 0
offset_y = 0
# constructor
def __init__(self, window_name=None):
# find the handle for the window we want to capture.
# if no window name is given, capture the entire screen
if window_name is None:
self.hwnd = win32gui.GetDesktopWindow()
else:
self.hwnd = win32gui.FindWindow(None, window_name)
if not self.hwnd:
raise Exception('Window not found: {}'.format(window_name))
# get the window size
window_rect = win32gui.GetWindowRect(self.hwnd)
self.w = 1000 #window_rect[2] - window_rect[0]
self.h = 300 #window_rect[3] - window_rect[1]
# account for the window border and titlebar and cut them off
#border_pixels = 8
#titlebar_pixels = 30
#self.w = self.w - (border_pixels * 2)
#self.h = self.h - titlebar_pixels - border_pixels
#self.cropped_x = border_pixels
#self.cropped_y = titlebar_pixels
# set the cropped coordinates offset so we can translate screenshot
# images into actual screen positions
self.offset_x = window_rect[0] + self.cropped_x
self.offset_y = window_rect[1] + self.cropped_y
def get_screenshot(self):
# get the window image data
wDC = win32gui.GetWindowDC(self.hwnd)
dcObj = win32ui.CreateDCFromHandle(wDC)
cDC = dcObj.CreateCompatibleDC()
dataBitMap = win32ui.CreateBitmap()
dataBitMap.CreateCompatibleBitmap(dcObj, self.w, self.h)
cDC.SelectObject(dataBitMap)
cDC.BitBlt((0, 0), (self.w, self.h), dcObj, (self.cropped_x, self.cropped_y), win32con.SRCCOPY)
# convert the raw data into a format opencv can read
#dataBitMap.SaveBitmapFile(cDC, 'debug.bmp')
signedIntsArray = dataBitMap.GetBitmapBits(True)
img = np.fromstring(signedIntsArray, dtype='uint8')
img.shape = (self.h, self.w, 4)
# free resources
dcObj.DeleteDC()
cDC.DeleteDC()
win32gui.ReleaseDC(self.hwnd, wDC)
win32gui.DeleteObject(dataBitMap.GetHandle())
# drop the alpha channel, or cv.matchTemplate() will throw an error like:
# error: (-215:Assertion failed) (depth == CV_8U || depth == CV_32F) && type == _templ.type()
# && _img.dims() <= 2 in function 'cv::matchTemplate'
img = img[...,:3]
# make image C_CONTIGUOUS to avoid errors that look like:
# File ... in draw_rectangles
# TypeError: an integer is required (got type tuple)
# see the discussion here:
# https://github.com/opencv/opencv/issues/14866#issuecomment-580207109
img = np.ascontiguousarray(img)
return img
# find the name of the window you're interested in.
# once you have it, update window_capture()
# https://stackoverflow.com/questions/55547940/how-to-get-a-list-of-the-name-of-every-open-window
@staticmethod
def list_window_names():
def winEnumHandler(hwnd, ctx):
if win32gui.IsWindowVisible(hwnd):
print(hex(hwnd), win32gui.GetWindowText(hwnd))
win32gui.EnumWindows(winEnumHandler, None)
# translate a pixel position on a screenshot image to a pixel position on the screen.
# pos = (x, y)
# WARNING: if you move the window being captured after execution is started, this will
# return incorrect coordinates, because the window position is only calculated in
#the __init__ constructor.
def get_screen_position(self, pos):
return (pos[0] + self.offset_x, pos[1] + self.offset_y)
class Vision:
# properties
needle_img = None
needle_w = 0
needle_h = 0
method = None
# constructor
def __init__(self, needle_img_path, method=cv2.TM_CCOEFF_NORMED):
# load the image we're trying to match
# https://docs.opencv.org/4.2.0/d4/da8/group__imgcodecs.html
self.needle_img = cv2.imread(needle_img_path, cv2.IMREAD_UNCHANGED)
# Save the dimensions of the needle image
self.needle_w = self.needle_img.shape[1]
self.needle_h = self.needle_img.shape[0]
# There are 6 methods to choose from:
# TM_CCOEFF, TM_CCOEFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_SQDIFF, TM_SQDIFF_NORMED
self.method = method
def find(self, haystack_img, threshold=0.5, debug_mode=None):
# run the OpenCV algorithm
result = cv2.matchTemplate(haystack_img, self.needle_img, self.method)
# Get the all the positions from the match result that exceed our threshold
locations = np.where(result >= threshold)
locations = list(zip(*locations[::-1]))
#print(locations)
# You'll notice a lot of overlapping rectangles get drawn. We can eliminate those redundant
# locations by using groupRectangles().
# First we need to create the list of [x, y, w, h] rectangles
rectangles = []
for loc in locations:
rect = [int(loc[0]), int(loc[1]), self.needle_w, self.needle_h]
# Add every box to the list twice in order to retain single (non-overlapping) boxes
rectangles.append(rect)
rectangles.append(rect)
# Apply group rectangles.
# The groupThreshold parameter should usually be 1. If you put it at 0 then no grouping is
# done. If you put it at 2 then an object needs at least 3 overlapping rectangles to appear
# in the result. I've set eps to 0.5, which is:
# "Relative difference between sides of the rectangles to merge them into a group."
rectangles, weights = cv2.groupRectangles(rectangles, groupThreshold=1, eps=0.5)
#print(rectangles)
points = []
if len(rectangles):
#print('Found needle.')
#line_color = (0, 255, 0)
#line_type = cv2.LINE_4
#marker_color = (0, 255, 0)
#marker_type = cv2.MARKER_CROSS
# Loop over all the rectangles
for (x, y, w, h) in rectangles:
# Determine the center position
center_x = x + int(w/2)
center_y = y + int(h/2)
# Save the points
points.append((center_x, center_y))
#if debug_mode == 'rectangles':
# Determine the box position
#top_left = (x, y)
#bottom_right = (x + w, y + h)
# Draw the box
#cv2.rectangle(haystack_img, top_left, bottom_right, color=line_color,
#lineType=line_type, thickness=2)
#elif debug_mode == 'points':
# Draw the center point
#cv.drawMarker(haystack_img, (center_x, center_y),
#color=marker_color, markerType=marker_type,
#markerSize=40, thickness=2)
if debug_mode:
cv2.imshow('Matches', haystack_img)
#cv.waitKey()
#cv.imwrite('result_click_point.jpg', haystack_img)
return points
pyautogui.PAUSE = 0.005
Location1 = range(90, 289)
Location2 = range(290, 400) # 394, 403f
Location3 = range(450, 630)# 551, 565
Location4 = range(640, 800) # 730, 695
Location5 = range(801, 1000)
empty = []
# Change the working directory to the folder this script is in.
# Doing this because I'll be putting the files from each video in their own folder on GitHub
os.chdir(os.path.dirname(os.path.abspath(__file__)))
# initialize the WindowCapture class
wincap = WindowCapture(None)
# initialize the Vision class
vision_limestone = Vision('Festivalnote.jpg')
keys = []
inputs = []
loop_time = time()
while 1:
# get an updated image of the game
screenshot = wincap.get_screenshot()
#haystack_img = vision_limestone.apply_hsv_filter(screenshot)
# display the processed image
points = vision_limestone.find(screenshot, 0.5)#, 'rectangles')
#cv.imshow('Matches', haystack_img)
#length = len(keys)
if points == empty:
points = [(0, 0, 0)]
if len(points) < 3:
points.append((0, 0))
points.append ((0, 0))
first, sec, thd, *others = points
#print(points)
y_thresh = 175
if first [0] in Location1 or sec[0] in Location1 or thd[0] in Location1:
#print('d')
pyautogui.keyDown("d")
pyautogui.keyUp("d")
#keys.append('d')
#inputs.append('d')
if first [0] in Location2 or sec[0] in Location2 or thd[0] in Location2:
pyautogui.keyDown("f")
pyautogui.keyUp("f")
#print('f')
#keys.append('f')
#inputs.append("f")
if first [0] in Location3 or sec[0] in Location3 or thd[0] in Location3:
pyautogui.keyDown("j")
pyautogui.keyUp("j")
#print('j')
#keys.append('j')
#inputs.append('j')
if first [0] in Location4 or sec[0] in Location4 or thd[0] in Location4:
pyautogui.keyDown("k")
pyautogui.keyUp("k")
#print("k")
#keys.append('k')
#inputs.append('k')
if first [0] in Location5 or sec[0] in Location5 or thd[0] in Location5:
pyautogui.keyDown("l")
pyautogui.keyUp("l")
#print("l")
#keys.append('l')
#inputs.append('l')
#if length == len(keys):
#inputs.append(time() - loop_time)
#print(inputs)
# debug the loop rate
print('FPS {}'.format(1 / (time() - loop_time)))
loop_time = time()
# press 'q' with the output window focused to exit.
# waits 1 ms every loop to process key presses
if cv2.waitKey(1) == ord('q'):
cv2.destroyAllWindows()
break
#with open("inputs.txt", "w") as output:
#output.write(",".join(str(i) for i in inputs))
print('Done.')
func()
要加快 if 语句的速度，请考虑以下提示：
优先考虑条件：首先放置更可能的真实条件。 切换到字典/映射查找：如果可能，使用字典或映射而不是 if-else 语句链。 降低复杂性：简化条件并将复杂的检查分解为更小、更易于管理的部分。