纯python实现大漠图色功能

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大漠图色是一种自动化测试工具，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：

1. 安装pyautogui库：在命令行中输入pip install pyautogui。
2. 导入pyautogui库：在Python脚本中使用import pyautogui语句导入pyautogui库。
3. 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。
4. 使用pyautogui.click()函数来模拟鼠标点击操作。
5. 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: RGB'''return 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, axis=2))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, axis=2)).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, axis=0)a += 1unique, counts = np.unique(res, return_counts=True, axis=0)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, thd=0.9, MIN_MATCH_COUNT=8):'''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(algorithm=FLANN_INDEX_KDTREE, trees=5)search_params = dict(checks=50)flann = cv2.FlannBasedMatcher(index_params, search_params)matches = flann.knnMatch(des1, des2, k=2)# store all the good matches as per Lowe's 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(res>threshold) 返回值:return: 返回保留的点的列表 pts"""model = cluster.AffinityPropagation(damping=0.5, max_iter=100, convergence_iter=10, 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, mask=mask)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, type=0):'''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|f9ad08-000000'dess = 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], dtype=np.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()

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