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How to use mask_kaze method in Airtest
Best Python code snippet using Airtest
keypoint_base.py
Source:keypoint_base.py 
...22 self.im_source = im_source23 self.im_search = im_search24 self.threshold = threshold25 self.rgb = rgb26 def mask_kaze(self):27 """åºäºkazeæ¥æ¾å¤ä¸ªç®æ åºåçæ¹æ³."""28 # æ±åºç¹å¾ç¹åï¼self.im_sourceä¸è·å¾matchçé£äºç¹è¿è¡èç±»29 raise NotImplementedError30 def find_all_results(self):31 """åºäºkazeæ¥æ¾å¤ä¸ªç®æ åºåçæ¹æ³."""32 # æ±åºç¹å¾ç¹åï¼self.im_sourceä¸è·å¾matchçé£äºç¹è¿è¡èç±»33 raise NotImplementedError34 @print_run_time35 def find_best_result(self):36 """åºäºkazeè¿è¡å¾åè¯å«ï¼åªçéåºæä¼åºå."""37 # 第ä¸æ¥ï¼æ£éªå¾åæ¯å¦æ£å¸¸ï¼38 if not check_image_valid(self.im_source, self.im_search):39 return None40 # 第äºæ¥ï¼è·åç¹å¾ç¹é并å¹é
åºç¹å¾ç¹å¯¹: è¿åå¼ good, pypts, kp_sch, kp_src...
VisualOdometry.py
Source:VisualOdometry.py
1import os2import glob3import numpy as np4import math5import cv2 as cv6from matplotlib import pyplot as plt78##################plot graph#######################9position_figure = plt.figure()10position_axes = position_figure.add_subplot(1, 1, 1)11# error_figure = plt.figure()12# rotation_error_axes = error_figure.add_subplot(1, 1, 1)13# rotation_error_list = []14# frame_index_list = []1516position_axes.set_aspect('equal', adjustable='box')17####################################################18###############ground truth###################################19gt_file = glob.glob('./poses/01.txt')20ground_truth_exist = True21ground_truth = []22with open(*gt_file) as f:23 gt_lines = f.readlines()2425 for gt_line in gt_lines:26 pose = np.array(gt_line.split()).reshape((3, 4)).astype(np.float32)27 ground_truth.append(pose)2829#######################################################################30#######################################################################31# parameters for lucas kanade optical flow32lk_params = dict(winSize=(21, 21),criteria=(cv.TERM_CRITERIA_EPS |cv.TERM_CRITERIA_COUNT, 30, 0.03))33#######################################################################34#####################camera setting information ###############################35camera_matrix = np.array([[718.8560, 0.0, 607.1928],36 [0.0, 718.8560, 185.2157],37 [0.0, 0.0, 1.0]])38f_l = camera_matrix[0][0]39f_r = camera_matrix[0][0]40c_x_l = camera_matrix[0][2]41c_y_l = camera_matrix[1][2]42c_x_r = camera_matrix[0][2]43c_y_r = camera_matrix[1][2]44baseline = -3.861448000000e+0245##################################################################################4647prev_img = None4849current_pos_akaze = np.zeros((3, 1))50current_rot_akaze = np.eye(3)51current_pos_orb = np.zeros((3, 1))52current_rot_orb = np.eye(3)53current_pos_kaze = np.zeros((3, 1))54current_rot_kaze = np.eye(3)55current_pos_sift = np.zeros((3, 1))56current_rot_sift = np.eye(3)5758camera_matrix = np.array([[718.8560, 0.0, 607.1928],59 [0.0, 718.8560, 185.2157],60 [0.0, 0.0, 1.0]])6162output = len(glob.glob('./image_0/*.png')) # ì¬ì§ ê°ì6364for index in range(output):65 img_file = os.path.join('./image_0/', '{:06d}.png').format(index)66 img = cv.imread('./image_0/{}'.format(img_file))67 # print("check: {} , img : {} ".format(index, img))68 # cvtColorë ìí¨ ì´ë¯¸ ë³íëì´ ìì¼ëê¹6970 # keypoint detection and feature description71 # 1. AKAZE72 akaze = cv.AKAZE_create()73 kp_akaze = akaze.detect(img, None) # find keypoints74 kp_akaze, des_akaze = akaze.compute(img, kp_akaze) # compute descriptors with AKAZE75 # kp_akaze, des_akaze =akaze.detectAndCompute(img, None)76 # print("Keypoints AKAZE : {}".format(len(kp_akaze)))7778 # 2, ORB79 orb = cv.ORB_create()80 kp_orb = orb.detect(img, None) # find keypoints81 kp_orb, des_orb = orb.compute(img, kp_orb) # compute descriptors with ORB82 # kp_orb, des_orb =orb.detectAndCompute(img, None)83 # print("Keypoints ORB : {}".format(len(kp_orb)))8485 # 3. KAZE86 kaze = cv.KAZE_create()87 kp_kaze = kaze.detect(img, None)88 kp_kaze, des_kaze = kaze.compute(img, kp_kaze)89 # kp_kaze, des_kaze =kaze.detectAndCompute(img, None)90 # print("Keypoints KAZE : {}".format(len(kp_kaze)))9192 # 4. SIFT93 sift = cv.SIFT_create()94 kp_sift = sift.detect(img, None)95 kp_sift, des_sift = sift.compute(img, kp_sift)96 # print("Keypoints SIFT : {}".format(len(kp_sift)))97 # kp_sift, des_sift =sift.detectAndCompute(img, None)9899 if prev_img is None:100 prev_img = img101 prev_keypoint_akaze = kp_akaze102 prev_keypoint_orb = kp_orb103 prev_keypoint_kaze = kp_kaze104 prev_keypoint_sift = kp_sift105 #106 prev_des_akaze = des_akaze107 prev_des_orb = des_orb108 prev_des_kaze = des_kaze109 prev_des_sift = des_sift110 continue111112 points_akaze = np.array(list(map(lambda x: [x.pt], prev_keypoint_akaze)), dtype=np.float32).squeeze()113 points_orb = np.array(list(map(lambda x: [x.pt], prev_keypoint_orb)), dtype=np.float32).squeeze()114 points_kaze = np.array(list(map(lambda x: [x.pt], prev_keypoint_kaze)), dtype=np.float32).squeeze()115 points_sift = np.array(list(map(lambda x: [x.pt], prev_keypoint_sift)), dtype=np.float32).squeeze()116117 #############################points matching ##################################118 # # BFMatcher with default params119 # # brute-force matcher120 # # ì¬ì´ì¦ê° 커ì§ë©´ ìë ì í121 # bf = cv.BFMatcher()122 # matches_akaze = bf.knnMatch(prev_des_akaze, des_akaze, k=2)123 # matches_orb = bf.knnMatch(prev_des_orb, des_orb, k=2)124 # matches_kaze = bf.knnMatch(prev_des_kaze, des_kaze, k=2)125 # matches_sift = bf.knnMatch(prev_des_sift, des_sift, k=2)126 #127 # # Apply ratio test128 # good_matches_akaze = []129 # good_matches_orb = []130 # good_matches_kaze = []131 # good_matches_sift = []132 #133 # pt1_akaze, pt2_akaze = [], []134 # pt1_orb, pt2_orb = [],[]135 # pt1_kaze, pt2_kaze = [],[]136 # pt1_sift, pt2_sift = [],[]137 #138 # for m, n in matches_akaze:139 # if m.distance < 0.75 * n.distance:140 # good_matches_akaze.append([m])141 # pt1_akaze.append(prev_keypoint_akaze[m.queryIdx].pt)142 # pt2_akaze.append(kp_akaze[m.trainIdx].pt)143 # for m,n in matches_orb:144 # if m.distance < 0.75*n.distance:145 # good_matches_orb.append([m])146 # pt1_orb.append(prev_keypoint_orb[m.queryIdx].pt)147 # pt2_orb.append(kp_orb[m.trainIdx].pt)148 # for m,n in matches_kaze:149 # if m.distance < 0.75*n.distance:150 # good_matches_kaze.append([m])151 # pt1_kaze.append(prev_keypoint_kaze[m.queryIdx].pt)152 # pt2_kaze.append(kp_kaze[m.trainIdx].pt)153 # for m,n in matches_sift:154 # if m.distance < 0.75*n.distance:155 # good_matches_sift.append([m])156 # pt1_sift.append(prev_keypoint_sift[m.queryIdx].pt)157 # pt2_sift.append(kp_sift[m.trainIdx].pt)158 #159 # pt1_akaze, pt2_akaze = np.float32(pt1_akaze), np.float32(pt2_akaze)160 # pt1_orb, pt2_orb = np.float32(pt1_orb), np.float32(pt2_orb)161 # pt1_kaze, pt2_kaze = np.float32(pt1_kaze), np.float32(pt2_kaze)162 # pt1_sift, pt2_sift = np.float32(pt1_sift), np.float32(pt2_sift)163 #################################################################################164165 pt1_akaze, st, err = cv.calcOpticalFlowPyrLK(prev_img, img, points_akaze, None, **lk_params)166 pt2_akaze = points_akaze167 pt1_orb, st, err = cv.calcOpticalFlowPyrLK(prev_img, img, points_orb, None, **lk_params)168 pt2_orb = points_orb169 pt1_kaze, st, err = cv.calcOpticalFlowPyrLK(prev_img, img, points_kaze, None, **lk_params)170 pt2_kaze = points_kaze171 pt1_sift, st, err = cv.calcOpticalFlowPyrLK(prev_img, img, points_sift, None, **lk_params)172 pt2_sift = points_sift173174 # Essenstial Matrix175 E_akaze, mask_akaze = cv.findEssentialMat(pt1_akaze, pt2_akaze, camera_matrix, cv.RANSAC, 0.999, 1.0, None)176 E_orb, mask_orb = cv.findEssentialMat(pt1_orb, pt2_orb, camera_matrix, cv.RANSAC, 0.999, 1.0, None)177 E_kaze, mask_kaze = cv.findEssentialMat(pt1_kaze, pt2_kaze, camera_matrix, cv.RANSAC, 0.999, 1.0, None)178 E_sift, mask_sift = cv.findEssentialMat(pt1_sift, pt2_sift, camera_matrix, cv.RANSAC, 0.999, 1.0, None)179180 pt1_akaze, R_akaze, t_akaze, mask_akaze = cv.recoverPose(E_akaze, pt2_akaze, pt1_akaze, camera_matrix)181 pt1_orb, R_orb, t_orb, mask_orb = cv.recoverPose(E_orb, pt2_orb, pt1_orb, camera_matrix)182 pt1_kaze, R_kaze, t_kaze, mask_kaze = cv.recoverPose(E_kaze, pt2_kaze, pt1_kaze, camera_matrix)183 pt1_sift, R_sift, t_sift, mask_sift = cv.recoverPose(E_sift, pt2_sift, pt1_sift, camera_matrix)184185 scale = 1.0186187 # ground truth를 기ë°ì¼ë¡ scale ê³ì°188 if ground_truth_exist:189 gt_pose = [ground_truth[index][0, 3], ground_truth[index][2, 3]]190 pre_gt_pose = [ground_truth[index - 1][0, 3], ground_truth[index - 1][2, 3]]191 scale = math.sqrt(math.pow((gt_pose[0] - pre_gt_pose[0]), 2.0) + math.pow((gt_pose[1] - pre_gt_pose[1]), 2.0))192193 # ì°ì ground truthê° ìë¤ë©´ scale ê³ì°ì íì§ ìê³ 194 current_pos_akaze += current_rot_akaze.dot(t_akaze) * scale195 current_rot_akaze = R_akaze.dot(current_rot_akaze)196197 current_pos_orb += current_rot_orb.dot(t_orb) * scale198 current_rot_orb = R_orb.dot(current_rot_orb)199200 current_pos_kaze += current_rot_kaze.dot(t_kaze) * scale201 current_rot_kaze = R_kaze.dot(current_rot_kaze)202203 current_pos_sift += current_rot_sift.dot(t_sift) * scale204 current_rot_sift = R_sift.dot(current_rot_sift)205206 # ground truth plot207 position_axes.scatter(ground_truth[index][0, 3], ground_truth[index][2, 3],s=2 , c='red')208209 # odometry plot210 position_axes.scatter(-current_pos_akaze[0][0], -current_pos_akaze[2][0], s=2, c='gray')211 position_axes.scatter(-current_pos_orb[0][0], -current_pos_orb[2][0], s=2, c='purple')212 position_axes.scatter(-current_pos_kaze[0][0], -current_pos_kaze[2][0], s=2, c='blue')213 position_axes.scatter(-current_pos_sift[0][0], -current_pos_sift[2][0], s=2, c='black')214 plt.pause(.01)215216 plot_img = cv.drawKeypoints(img, kp_akaze, None)217218 # cv.imshow('feature', plot_img)219 # cv.waitKey(1)220221 prev_img = img222 prev_keypoint_akaze = kp_akaze223 prev_keypoint_orb = kp_orb224 prev_keypoint_kaze = kp_kaze225 prev_keypoint_sift = kp_sift226227 prev_des_akaze = des_akaze228 prev_des_orb = des_orb229 prev_des_kaze = des_kaze230 prev_des_sift = des_sift231
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