尝试制作一个用于钓鱼脚本，关于阿尔比恩的钓鱼脚本，问题是在这个游戏中指针非常细，并且有许多不同的纹理和颜色，可以进行简单的灰度匹配在滑块的一个部分（例如，水纹理）上效果很好，有时在其他部分（树纹理）上效果，而在第三个元素上效果不佳。如果我更改为较低的阈值，它将经常激活而没有匹配。

# import the necessary packagesfrom collections import dequefrom imutils.video import VideoStreamimport numpy as npimport argparseimport cv2import imutilsimport timefrom mss.linux import MSS as mssfrom PIL import Imageimport mssimport numpyimport pyautogui# construct the argument parse and parse the argumentsap = argparse.ArgumentParser()ap.add_argument("-v", "--video", help="path to the (optional) video file")ap.add_argument("-b", "--buffer", type=int, default=64, help="max buffer size")args = vars(ap.parse_args())# define the lower and upper boundaries of the "green"# ball in the HSV color space, then initialize the# list of tracked pointsgreenLower = (42, 84, 211)greenUpper = (69, 130, 255)blueLower = (88, 76, 255)blueUpper = (151, 76, 255)pts = deque(maxlen=args["buffer"])# grab video from screen(monitor area)with mss.mss() as sct: monitor = {"top": 325, "left": 4423, "width": 136, "height": 662} while "Screen capturing": #last_time = time.time() #vs = numpy.array(sct.grab(monitor)) #print("fps: {}".format(1 / (time.time() - last_time))) vs = sct.grab(monitor) # grab the current frame #frame = vs frame = np.array(vs) # resize the frame, blur it, and convert it to the HSV # color space blurred = cv2.GaussianBlur(frame, (11, 11), 0) hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV) # construct a mask for the color "green", then perform # a series of dilations and erosions to remove any small # blobs left in the mask mask = cv2.inRange(hsv, greenLower, greenUpper) mask = cv2.erode(mask, None, iterations=2) mask = cv2.dilate(mask, None, iterations=2) mask2 = cv2.inRange(hsv, blueLower, blueUpper) mask2 = cv2.erode(mask2, None, iterations=2) mask2 = cv2.dilate(mask2, None, iterations=2) # find contours in the mask and initialize the current # (x, y) center of the ball cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts = imutils.grab_contours(cnts) center = None cnts2 = cv2.findContours(mask2.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) cnts2 = imutils.grab_contours(cnts2) center2 = None # only proceed if at least one contour was found if len(cnts) > 0: # find the largest contour in the mask, then use # it to compute the minimum enclosing rectangle and # centroid c = max(cnts, key=cv2.contourArea) (x, y, w, h) = cv2.boundingRect(c) M = cv2.moments(c) center = (int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"])) c2 = max(cnts2, key=cv2.contourArea) (x2, y2, w2, h2) = cv2.boundingRect(c2) M2 = cv2.moments(c2) center2 = (int(M2["m10"] / M2["m00"]), int(M2["m01"] / M2["m00"])) # draw the rectangle and centroid on the frame, # then update the list of tracked points cv2.rectangle(frame, (int(x), int(y)), (int(x+w), int(y+h)),(0, 255, 255), 2) cv2.circle(frame, center, 5, (0, 0, 255), -1) cv2.rectangle(frame, (int(x2), int(y2)), (int(x2+w2), int(y2+h2)),(0, 255, 255), 2) cv2.circle(frame, center2, 5, (0, 0, 255), -1) # update the points queue pts.appendleft(center) if y-15 < y2 < y+15: pyautogui.click(4908, 984) time.sleep(2) y2 = 0 cv2.imshow("frame", frame) key = cv2.waitKey(1) if cv2.waitKey(25) & 0xFF == ord("q"): cv2.destroyAllWindows() break