网站建站制作,重庆市建设工程信息网招标,北京通州网站制作公司,跨境电商为什么招人难大漠图色是一种自动化测试工具#xff0c;可以用于识别屏幕上的图像并执行相应的操作。在Python中#xff0c;可以使用第三方库pyautogui来实现大漠图色功能。具体步骤如下#xff1a;
安装pyautogui库#xff1a;在命令行中输入pip install pyautogui。导入pyautogui库可以用于识别屏幕上的图像并执行相应的操作。在Python中可以使用第三方库pyautogui来实现大漠图色功能。具体步骤如下
安装pyautogui库在命令行中输入pip install pyautogui。导入pyautogui库在Python脚本中使用import pyautogui语句导入pyautogui库。使用pyautogui.locateOnScreen()函数来查找屏幕上的图像并返回图像的位置坐标。使用pyautogui.click()函数来模拟鼠标点击操作。使用pyautogui.typewrite()函数来模拟键盘输入操作。
实现如标题废话不多说直接看代码
# 更新StressShow命令import numpy as np
import pyautogui
import copy
import cv2
from sklearn import clusterclass TuSe:def __init__(self):print(欢迎使用)def GetCapture(self, stax, stay, endx, endy):w endx - staxh endy - stayim pyautogui.screenshot(region(stax, stay, w, h))# im cv2.cvtColor(np.array(im), cv2.COLOR_BGR2RGB)return np.array(im)def FindPic(self, x1, y1, x2, y2, path, thd):找图:param x1: 起点X:param y1: 起点Y:param x2: 终点X:param y2: 终点Y:param path: 图片路径:param thd: 相似度:return: 图片中心坐标img self.GetCapture(x1, y1, x2, y2)img cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)template cv2.imread(path, 0)th, tw template.shape[::]rv cv2.matchTemplate(img, template, 1)minVal, maxVal, minLoc, maxLoc cv2.minMaxLoc(rv)if 1 - minVal thd:return minLoc[0] tw / 2 x1, minLoc[1] th / 2 y1else:return -1, -1def Hex_to_Rgb(self, hex):十六进制转RGB:param hex: 十六进制颜色值:return: RGBreturn np.array(tuple(int(hex[i:i 2], 16) for i in (0, 2, 4)))def CmpColor(self, x, y, color, sim: float):比色:param x: X坐标:param y: Y坐标:param color: 十六进制颜色可以从大漠直接获取:param sim: 相似偏移:return: 真或加img self.GetCapture(x - 1, y - 1, x 1, y 1)img np.array(img)img img[1][1]color self.Hex_to_Rgb(color)res np.absolute(color - img)sim int((1 - sim) * 255)return True if np.amax(res) sim else Falsedef FindColor(self, x1, y1, x2, y2, des, sim: float):找色:param x1: 起点X:param y1: 起点Y:param x2: 终点X:param y2: 终点Y:param des: 十六进制颜色可以从大漠直接获取:param sim: 相似偏移:return:img self.GetCapture(x1, y1, x2, y2)img np.array(img)res np.absolute(img - self.Hex_to_Rgb(des))sim int((1 - sim) * 255)res np.argwhere(np.all(res sim, axis2))res res (y1, x1)return res[:, [1, 0]]def GetColorNum(self, x1, y1, x2, y2, des, sim: float):获取颜色数量:param x1: 起点X:param y1: 起点Y:param x2: 终点X:param y2: 终点Y:param des: 十六进制颜色可以从大漠直接获取:param sim: 相似偏移:return:return len(self.FindColor(x1, y1, x2, y2, des, sim))def FindMultColor(self, stax, stay, endx, endy, des):多点找色:param stax::param stay::param endx::param endy::param des: 大漠获取到的多点找色数据偏色必须写上:return:w endx - staxh endy - stayimg pyautogui.screenshot(region(stax, stay, w, h))img np.array(img)rgby []ps []a 0firstXY []res np.empty([0, 2])for i in des.split(,):rgb_y i[-13:]r int(rgb_y[0:2], 16)g int(rgb_y[2:4], 16)b int(rgb_y[4:6], 16)y int(rgb_y[-2:])rgby.append([r, g, b, y])for i in range(1, len(des.split(,))):ps.append([int(des.split(,)[i].split(|)[0]), int(des.split(,)[i].split(|)[1])])for i in rgby:result np.logical_and(abs(img[:, :, 0:1] - i[0]) i[3], abs(img[:, :, 1:2] - i[1]) i[3],abs(img[:, :, 2:3] - i[2]) i[3])results np.argwhere(np.all(result True, axis2)).tolist()if a 0:firstXY copy.deepcopy(results)else:nextnextXY copy.deepcopy(results)for index in nextnextXY:index[0] int(index[0]) - ps[a - 1][1]index[1] int(index[1]) - ps[a - 1][0]q set([tuple(t) for t in firstXY])w set([tuple(t) for t in nextnextXY])matched np.array(list(q.intersection(w)))res np.append(res, matched, axis0)a 1unique, counts np.unique(res, return_countsTrue, axis0)index np.argmax(counts)re unique[index] (stay, stax)if np.max(counts) len(des.split(,)) - 1:return np.flipud(re)return np.array([-1, -1])def FindPicEx(self, x1, y1, x2, y2, path, thd0.9, MIN_MATCH_COUNT8):全分辨率找图:param x1::param y1::param x2::param y2::param path::param thd: 相似度:param MIN_MATCH_COUNT: 特征点数量:return:thd thd - 0.2template cv2.imread(path, 0) # queryImage# target cv2.imread(target.jpg, 0) # trainImagetarget self.GetCapture(x1, y1, x2, y2)target cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)# Initiate SIFT detector创建sift检测器sift cv2.xfeatures2d.SIFT_create()# find the keypoints and descriptors with SIFTkp1, des1 sift.detectAndCompute(template, None)kp2, des2 sift.detectAndCompute(target, None)# 创建设置FLANN匹配FLANN_INDEX_KDTREE 0index_params dict(algorithmFLANN_INDEX_KDTREE, trees5)search_params dict(checks50)flann cv2.FlannBasedMatcher(index_params, search_params)matches flann.knnMatch(des1, des2, k2)# store all the good matches as per Lowes ratio test.good []for m, n in matches:if m.distance thd * n.distance:good.append(m)if len(good) MIN_MATCH_COUNT:# 获取关键点的坐标src_pts np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)dst_pts np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)# 计算变换矩阵和MASKM, mask cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)h, w template.shape# 使用得到的变换矩阵对原图像的四个角进行变换获得在目标图像上对应的坐标pts np.float32([[0, 0], [0, h - 1], [w - 1, h - 1], [w - 1, 0]]).reshape(-1, 1, 2)dst cv2.perspectiveTransform(pts, M)res (dst[0] dst[2]) / 2 # [[[ 39.11337 147.11575 ]] [[135.06624 255.12143 ]]return int(res[0][0]) x1, int(res[0][1]) y1else:return -1, -1def _FilterRec(self, res, loc): 对同一对象的多个框按位置聚类后按置信度选最大的一个进行保留。:param res: 是 cv2.matchTemplate 返回值:param loc: 是 cv2.np.argwhere(resthreshold) 返回值:return: 返回保留的点的列表 ptsmodel cluster.AffinityPropagation(damping0.5, max_iter100, convergence_iter10, preference-50).fit(loc)y_pred model.labels_pts []for i in set(y_pred):argj loc[y_pred i]argi argj.Tpt argj[np.argmax(res[tuple(argi)])]pts.append(pt[::-1])return np.array(pts)def FindMultPic(self, x1, y1, x2, y2, path, thd):多目标找图:param x1::param y1::param x2::param y2::param path::param thd: 相似度:return:target self.GetCapture(x1, y1, x2, y2)target cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)template cv2.imread(path, 0)w, h template.shape[:2]res cv2.matchTemplate(target, template, cv2.TM_CCOEFF_NORMED)loc np.argwhere(res thd)if len(loc):resc self._FilterRec(res, loc)return resc (h / 2 x1, w / 2 y1)else:return [[-1, -1]]def FindPic_TM(self, x1, y1, x2, y2, path, thd):找透明图透明色为黑色:param x1: 起点X:param y1: 起点Y:param x2: 终点X:param y2: 终点Y:param path: 图片路径:param thd: 相似度:return: 图片中心坐标img self.GetCapture(x1, y1, x2, y2)img cv2.cvtColor(img, cv2.COLOR_BGR2RGB)template cv2.imread(path)template2 cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)ret, mask cv2.threshold(template2, 20, 255, cv2.THRESH_BINARY)th, tw template.shape[:2]rv cv2.matchTemplate(img, template, 1, maskmask)minVal, maxVal, minLoc, maxLoc cv2.minMaxLoc(rv)if 1 - minVal thd:return minLoc[0] tw / 2 x1, minLoc[1] th / 2 y1else:return -1, -1def StressShow(self, stax, stay, endx, endy, des, type0):保留选中颜色其他为黑色相似度根据偏色调整:param stax::param stay::param endx::param endy::param des: 大漠的色彩描述:param type: 0为原来颜色1为白色:return:# des e81010-101010|f9ad08-000000dess des.split(|)des [i[0:6] for i in dess]des [np.array(self.Hex_to_Rgb(d)) for d in des]pds [i[-6:] for i in dess]pds tuple(tuple(int(item[i:i 2]) for i in range(0, len(item), 2)) for item in pds)img self.GetCapture(stax, stay, endx, endy)mask np.zeros(img.shape[:2], dtypenp.bool_)for i, color in enumerate(des):mask np.all(np.abs(img - color) pds[i], axis-1)new_img np.where(mask[..., None], [255, 255, 255], [0, 0, 0]) if type else np.where(mask[..., None], img,[0, 0,0]) # 修改这里将选中的颜色设为白色img_converted cv2.convertScaleAbs(new_img)img_converted cv2.cvtColor(np.array(img_converted), cv2.COLOR_BGR2RGB)return img_converteda TuSe()
b a.StressShow(0, 0, 1920, 1080, e81010-101010|36659e-101010, 0)
cv2.imshow(13, b)
cv2.waitKey(0)
cv2.destroyAllWindows()
