使用kociemba模块可以快速地得到3阶魔方的还原步骤，例如：
U = '红绿黄白红白白蓝白'R = '红蓝绿绿绿蓝黄红红'F = '绿橙绿黄白红蓝橙红'D = '橙黄绿红橙蓝橙黄黄'L = '白橙橙绿蓝红白白黄'B = '橙白蓝黄黄橙蓝绿蓝'cubdict = {U[4]: 'U', R[4]: 'R', F[4]: 'F', D[4]: 'D', L[4]: 'L', B[4]: 'B'}UC = ''for s in [U, R, F, D, L, B]:for i in range(9):UC = UC + cubdict[s[i]]print(UC)print(kc.solve(UC))
指定面向自己的一面为Front（F），相对的一面即为Back（B），左边的一面为Left（L），右边的一面为Right（R），上面为Up（U），下面为Down（D），将魔方的6个面的颜色分别写到U R F D L B中，最后就会打印出还原魔方所有的步骤，按照步骤操作即可还原。
但是，每次都要自己输颜色会显得自己比较low，所以可以借用opencv的图像处理功能来进行魔方颜色的自动识别，例如：
import kociemba as kcimport osimport cv2import numpy as npfrom copy import deepcopyimport mathdef imgcheck(frame_raw):hsv_table = [[[0, 10], [43, 255], [46, 255], '红'],[[156, 180], [43, 255], [46, 255], '红'],[[11, 20], [43, 255], [46, 255], '橙'],[[20, 34], [43, 255], [46, 255], '黄'],[[35, 80], [43, 255], [46, 255], '绿'],[[80, 99], [43, 255], [46, 255], '青'],[[100, 124], [43, 255], [46, 255], '蓝'],[[125, 155], [43, 255], [46, 255], '紫'],[[0, 180], [0, 30], [166, 255], '白'],[[0, 180], [0, 43], [46, 166], '灰'],[[0, 180], [0, 255], [0, 46], '黑']]cube_list = []frame = frame_raw.copy()index = 0center = []candidates = []hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)for process_ind in range(2):hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)#cv2.imshow("image", hsv)#cv2.waitKey(0)gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)blurred = cv2.GaussianBlur(gray, (3, 3), 0)canny = cv2.Canny(blurred, 20, 40)#cv2.imshow("image", canny)#cv2.waitKey(0)if process_ind == 0:kernel = np.ones((3, 3), np.uint8)dilated = cv2.dilate(canny, kernel, iterations=12)else:kernel = np.ones((6, 6), np.uint8)dilated = cv2.dilate(canny, kernel, iterations=3)if process_ind == 1 or process_ind == 0:cv2.imshow("image", dilated)cv2.waitKey(0)(contours, hierarchy) = cv2.findContours(dilated.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)hierarchy = hierarchy[0]pre_cX = 0pre_cY = 0area_arr = []for component in zip(contours, hierarchy):contour = component[0]peri = cv2.arcLength(contour, True)approx = cv2.approxPolyDP(contour, 0.1 * peri, True)area = cv2.contourArea(contour)corners = len(approx)# compute the center of the contourM = cv2.moments(contour)if M["m00"]:cX = int(M["m10"] / M["m00"])cY = int(M["m01"] / M["m00"])else:cX = NonecY = Noneif cX is not None:if process_ind == 0:tmp = {'area': area, 'contour': contour}area_arr.append(tmp)elif 60000 > area > 1000:tmp = {'index': index, 'cx': cX, 'cy': cY, 'contour': contour}center.append(tmp)index += 1if process_ind == 0:area_arr.sort(key=lambda k: (k.get('area', 0)), reverse=True)mx,my,mw,mh = cv2.boundingRect(area_arr[0].get('contour'))cv2.rectangle(frame, (mx,my), (mx+mw, my+mh), (0, 255, 0), 2)#cv2.imshow("image", frame)#cv2.waitKey(0)frame = frame[my-5:my+mh+5, mx-5:mx+mw+5]#cv2.imshow("image", frame)#cv2.waitKey(0)frame = cv2.resize(frame, (320, 320))#if index < 9:# returnprint(str(index))'''center.sort(key=lambda k: (k.get('cx', 0)))center.sort(key=lambda k: (k.get('cy', 0)))'''center.sort(key=lambda k: (k.get('cy', 0)))row1 = center[0:3]row1.sort(key=lambda k: (k.get('cx', 0)))row2 = center[3:6]row2.sort(key=lambda k: (k.get('cx', 0)))row3 = center[6:9]row3.sort(key=lambda k: (k.get('cx', 0)))center.clear()center = row1 + row2 + row3for component in center:candidates.append(component.get('contour'))x,y,w,h = cv2.boundingRect(component.get('contour'))if abs(w - h) < 10:cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)#cv2.imshow("image", frame)#cv2.waitKey(0)h_ = 0s_ = 0v_ = 0ss = w * hfor i in range(w):for j in range(h):h_ = h_ + hsv[y+j][x+i][0]s_ = s_ + hsv[y+j][x+i][1]v_ = v_ + hsv[y+j][x+i][2]h_ = h_ / sss_ = s_ / ssv_ = v_ / ssprint(str(h_) + ',' + str(s_) + ',' + str(v_))for k in hsv_table:if k[0][0] < h_ < k[0][1] and k[1][0] < s_ < k[1][1] and k[2][0] < v_ < k[2][1]:# print(k[3])cube_list.append(k[3])breakprint(str(len(cube_list)))#if len(cube_list) == 9:print(cube_list)#cv2.drawContours(frame, candidates, -1, (0, 0, 255), 3)cv2.imshow("image", frame)cv2.waitKey(0)if __name__ == "__main__":webcam = cv2.VideoCapture(0)if not webcam.isOpened():print("can't open the camera!!!")while True:ret, frame = webcam.read()rec_w = 200rec_h = 200rec_y = int((frame.shape[0] - rec_h)/2)rec_x = int((frame.shape[1] - rec_w) / 2)cv2.rectangle(frame, (rec_x, rec_y), (rec_x + rec_w, rec_y + rec_h), (0, 255, 0), 2)imgcheck(frame)cv2.imshow("video", frame)if cv2.waitKey(1) & 0xFF == ord('q'):breakwebcam.release()cv2.destroyAllWindows()
hsv_table的各个颜色的范围可能需要根据实际情况进行调试。
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