def main(scene_idx=0): #scene_idx = 0 mapper_scene2z = get_mapper() mapper_scene2points = get_mapper_scene2points() Train_Scenes, Test_Scenes = get_train_test_scenes() scene_name = Test_Scenes[scene_idx] num_startPoints = len(mapper_scene2points[scene_name]) num_steps = 50 ## create test folder test_folder = '/home/reza/Datasets/GibsonEnv/my_code/visual_servoing/test_IBVS' #approach_folder = '{}/gtCorrespondence_interMatrix_gtDepth_Vz_OmegaY'.format(test_folder) #create_folder(approach_folder) #scene_folder = '{}/{}'.format(approach_folder, scene_name) #create_folder(scene_folder) f = open('{}/{}_{}.txt'.format(test_folder, perception_rep, depth_method), 'a') f.write('scene_name = {}\n'.format(scene_name)) list_count_correct = [] list_count_runs = [] ## rrt functions ## first figure out how to sample points from rrt graph rrt_directory = '/home/reza/Datasets/GibsonEnv/gibson/assets/dataset/{}_for_rrt'.format( scene_name) path_finder = rrt.PathFinder(rrt_directory) path_finder.load() num_nodes = len(path_finder.nodes_x) free = cv2.imread( '/home/reza/Datasets/GibsonEnv/gibson/assets/dataset/{}_for_rrt/free.png' .format(scene_name), 0) ## GibsonEnv setup ## For Gibson Env import gym, logging from mpi4py import MPI from gibson.envs.husky_env import HuskyNavigateEnv from baselines import logger import skimage.io from transforms3d.euler import euler2quat config_file = os.path.join( '/home/reza/Datasets/GibsonEnv/my_code/CVPR_workshop', 'env_yamls', '{}_navigate.yaml'.format(scene_name)) env = HuskyNavigateEnv(config=config_file, gpu_count=1) obs = env.reset( ) ## this line is important otherwise there will be an error like 'AttributeError: 'HuskyNavigateEnv' object has no attribute 'potential'' def get_obs(current_pose): pos, orn = func_pose2posAndorn(current_pose, mapper_scene2z[scene_name]) env.robot.reset_new_pose(pos, orn) obs, _, _, _ = env.step(4) obs_rgb = obs['rgb_filled'] obs_depth = obs['depth'] return obs_rgb.copy(), obs_depth.copy() base_folder = '/home/reza/Datasets/GibsonEnv/my_code/visual_servoing/sample_image_pairs_{}'.format( 'test') ## go through each point folder for point_idx in range(0, num_startPoints): #for point_idx in range(0, 1): print('point_idx = {}'.format(point_idx)) #point_folder = '{}/point_{}'.format(scene_folder, point_idx) #create_folder(point_folder) ## read in start img and start pose point_image_folder = '{}/{}/point_{}'.format(base_folder, scene_name, point_idx) point_pose_npy_file = np.load('{}/{}/point_{}_poses.npy'.format( base_folder, scene_name, point_idx)) start_img = cv2.imread('{}/{}.png'.format( point_image_folder, point_pose_npy_file[0]['img_name']))[:, :, ::-1] start_pose = point_pose_npy_file[0]['pose'] ## index 0 is the left image, so right_img_idx starts from index 1 count_correct = 0 list_correct_img_names = [] list_whole_stat = [] for right_img_idx in range(1, len(point_pose_npy_file)): #for right_img_idx in range(10, 11): flag_correct = False print('right_img_idx = {}'.format(right_img_idx)) count_steps = 0 current_pose = start_pose right_img_name = point_pose_npy_file[right_img_idx]['img_name'] goal_pose = point_pose_npy_file[right_img_idx]['pose'] goal_img, goal_depth = get_obs(goal_pose) list_result_poses = [current_pose] num_matches = 0 flag_broken = False while count_steps < num_steps: current_img, current_depth = get_obs(current_pose) try: kp1, kp2 = sample_gt_correspondences_relativelyDense( current_depth, goal_depth, current_pose, goal_pose) if count_steps == 0: start_depth = current_depth.copy() except: print('run into error') break num_matches = kp1.shape[1] print('num_matches = {}'.format(num_matches)) vx, vz, omegay, flag_stop = compute_velocity_through_correspondences_and_depth( kp1, kp2, current_depth) previous_pose = current_pose current_pose, _, _, flag_stop_goToPose = goToPose_one_step( current_pose, vx, vz, omegay) ## check if there is collision during the action left_pixel = path_finder.point_to_pixel( (previous_pose[0], previous_pose[1])) right_pixel = path_finder.point_to_pixel( (current_pose[0], current_pose[1])) # rrt.line_check returns True when there is no obstacle if not rrt.line_check(left_pixel, right_pixel, free): flag_broken = True #print('run into an obstacle ...') break ## check if we should stop or not if flag_stop or flag_stop_goToPose: #print('flag_stop = {}, flag_stop_goToPose = {}'.format(flag_stop, flag_stop_goToPose)) #print('break') break count_steps += 1 list_result_poses.append(current_pose) ## sample current_img again to save in list_obs current_img, current_depth = get_obs(current_pose) #assert 1==2 ## decide if this run is successful or not flag_correct, dist, theta_change = similar_location_under_certainThreshold( goal_pose, list_result_poses[count_steps]) #print('dist = {}, theta = {}'.format(dist, theta_change)) #print('start_pose = {}, final_pose = {}, goal_pose = {}'.format(start_pose, list_result_poses[-1], goal_pose)) if flag_correct: count_correct += 1 list_correct_img_names.append(right_img_name[10:]) if flag_correct: str_succ = 'Success' print('str_succ = {}'.format(str_succ)) else: str_succ = 'Failure' print('str_succ = {}'.format(str_succ)) ## =================================================================================================================== ## plot the pose graph ''' img_name = 'goTo_{}.jpg'.format(right_img_name[10:]) print('img_name = {}'.format(img_name)) ## plot the poses free2 = cv2.imread('/home/reza/Datasets/GibsonEnv/gibson/assets/dataset/{}_for_rrt/free.png'.format(scene_name), 1) rows, cols, _ = free2.shape plt.imshow(free2) for m in range(len(list_result_poses)): pose = list_result_poses[m] x, y = path_finder.point_to_pixel((pose[0], pose[1])) theta = pose[2] plt.arrow(x, y, cos(theta), sin(theta), color='y', \ overhang=1, head_width=0.1, head_length=0.15, width=0.001) ## draw goal pose x, y = path_finder.point_to_pixel((goal_pose[0], goal_pose[1])) theta = goal_pose[2] plt.arrow(x, y, cos(theta), sin(theta), color='r', \ overhang=1, head_width=0.1, head_length=0.15, width=0.001) plt.axis([0, cols, 0, rows]) plt.xticks([]) plt.yticks([]) plt.title('{}, start point_{}, goal viewpoint {}, {}\n dist = {:.4f} meter, theta = {:.4f} degree\n'.format(scene_name, point_idx, right_img_name[10:], str_succ, dist, theta_change)) plt.savefig('{}/{}'.format(point_folder, img_name), bbox_inches='tight', dpi=(400)) plt.close() ## ====================================================================================================================== ## save stats current_test_dict = {} current_test_dict['img_name'] = right_img_name current_test_dict['success_flag'] = flag_correct current_test_dict['dist'] = dist current_test_dict['theta'] = theta_change current_test_dict['steps'] = count_steps current_test_dict['collision'] = flag_broken list_whole_stat.append(current_test_dict) ''' ''' np.save('{}/runs_statistics.npy'.format(point_folder), list_whole_stat) success_rate = 1.0 * count_correct / (len(point_pose_npy_file)-1) print('count_correct/num_right_images = {}/{} = {}'.format(count_correct, len(point_pose_npy_file)-1, success_rate)) ## write correctly run target image names to file f = open('{}/successful_runs.txt'.format(point_folder), 'w') f.write('count_correct/num_right_images = {}/{} = {}\n'.format(count_correct, len(point_pose_npy_file)-1, success_rate)) for i in range(len(list_correct_img_names)): f.write('{}\n'.format(list_correct_img_names[i])) f.close() print('writing correct run image names to txt ...') ''' f.write('point {} : {}/{}\n'.format(point_idx, count_correct, len(point_pose_npy_file) - 1)) f.flush() list_count_correct.append(count_correct) list_count_runs.append(len(point_pose_npy_file) - 1) f.write('In total : {}/{}\n'.format(sum(list_count_correct), sum(list_count_runs))) f.write( '-------------------------------------------------------------------------------------\n' )
previous_pose = current_pose if depth_method == 'void': current_pose = update_current_pose(current_pose, 0.0, 0.1, omegay) flag_stop = flag_stop else: current_pose, _, _, flag_stop_goToPose = goToPose_one_step( current_pose, vx, vz, omegay) ## check if there is collision during the action left_pixel = path_finder.point_to_pixel( (previous_pose[0], previous_pose[1])) right_pixel = path_finder.point_to_pixel( (current_pose[0], current_pose[1])) # rrt.line_check returns True when there is no obstacle if not rrt.line_check(left_pixel, right_pixel, free): flag_broken = True print('run into an obstacle ...') break ## check if we should stop or not if flag_stop or flag_stop_goToPose: print('flag_stop = {}, flag_stop_goToPose = {}'.format( flag_stop, flag_stop_goToPose)) print('break') break count_steps += 1 list_result_poses.append(current_pose) ## sample current_img again to save in list_obs current_img, current_depth = get_obs(current_pose)
right_pose_list = [] for i in range(len(theta_list)): for j in range(len(dist_list)): #temp_theta = plus_theta_fn(theta0, theta_list[random.randint(0, 6)]) #temp_dist = dist_list[random.randint(0, 6)] location_theta = plus_theta_fn(theta0, theta_list[i]) location_dist = dist_list[j] x1 = x0 + location_dist * math.cos(location_theta) y1 = y0 + location_dist * math.sin(location_theta) left_pixel = path_finder.point_to_pixel(left_pose) right_pixel = path_finder.point_to_pixel((x1, y1)) # check the line flag = rrt.line_check(left_pixel, right_pixel, free) if not flag: print('j = {}, obstacle'.format(j)) else: diff_theta_idx = random.randint(0, len(diff_theta_list)-1) diff_theta = diff_theta_list[diff_theta_idx] theta1 = plus_theta_fn(theta0, diff_theta) right_pose = [x1, y1, theta1] current_pose = right_pose pos, orn = func_pose2posAndorn(current_pose, mapper_scene2z[scene_name]) env.robot.reset_new_pose(pos, orn) obs, _, _, _ = env.step(4) obs_rgb = obs['rgb_filled'] cv2.imwrite('{}/right_img_dist_{}_theta_{}.png'.format(file_addr, j, diff_theta_idx), obs_rgb[:,:,::-1])
def main(scene_idx=0, point_a=0): #scene_idx = 1 ## necessary constants mapper_scene2points = get_mapper_scene2points() num_episodes = 200000 batch_size = 64 lambda_action = 0.25 action_table = buildActionMapper(flag_fewer_actions=True) seq_len = 50 Train_Scenes, Test_Scenes = get_train_test_scenes() if mode == 'Test': scene_name = Test_Scenes[scene_idx] elif mode == 'Train': scene_name = Train_Scenes[scene_idx] num_startPoints = len(mapper_scene2points[scene_name]) model_weights_save_path = '{}/{}'.format('/home/reza/Datasets/GibsonEnv/my_code/vs_controller/trained_dqn', approach) action_space = action_table.shape[0] ##============================================================================================================= ## rrt functions ## first figure out how to sample points from rrt graph rrt_directory = '/home/reza/Datasets/GibsonEnv/gibson/assets/dataset/{}_for_rrt'.format(scene_name) path_finder = rrt.PathFinder(rrt_directory) path_finder.load() num_nodes = len(path_finder.nodes_x) free = cv2.imread('/home/reza/Datasets/GibsonEnv/gibson/assets/dataset/{}_for_rrt/free.png'.format(scene_name), 0) ##------------------------------------------------------------------------------------------------------------ ## setup environment import gym, logging from mpi4py import MPI from gibson.envs.husky_env import HuskyNavigateEnv from baselines import logger import skimage.io from transforms3d.euler import euler2quat config_file = os.path.join('/home/reza/Datasets/GibsonEnv/my_code/CVPR_workshop', 'env_yamls', '{}_navigate.yaml'.format(scene_name)) env = HuskyNavigateEnv(config=config_file, gpu_count = 1) obs = env.reset() ## this line is important otherwise there will be an error like 'AttributeError: 'HuskyNavigateEnv' object has no attribute 'potential'' mapper_scene2z = get_mapper() def get_obs(current_pose): pos, orn = func_pose2posAndorn(current_pose, mapper_scene2z[scene_name]) env.robot.reset_new_pose(pos, orn) obs, _, _, _ = env.step(4) obs_rgb = obs['rgb_filled'] obs_depth = obs['depth'] #obs_normal = obs['normal'] return obs_rgb, obs_depth#, obs_normal def close_to_goal(pose1, pose2, thresh=0.20): L2_dist = math.sqrt((pose1[0] - pose2[0])**2 + (pose1[1] - pose2[1])**2) thresh_L2_dist = thresh theta_change = abs(pose1[2] - pose2[2])/math.pi * 180 return (L2_dist <= thresh_L2_dist) #and (theta_change <= 30) ##============================================================================================================ if mode == 'Test': base_folder = '/home/reza/Datasets/GibsonEnv/my_code/visual_servoing/sample_image_pairs_{}'.format('test') elif mode == 'Train': base_folder = '/home/reza/Datasets/GibsonEnv/my_code/visual_servoing/sample_image_pairs_{}'.format('train') import torch import torch.nn as nn import torch.nn.functional as F device = torch.device('cuda:0') ## Default CUDA device num_epochs = 200000 ## same as # of trajs sampled num_actions = action_table.shape[0] if input_type == 'both': perception = Perception_overlap(4).to(device) elif input_type == 'siamese': perception = Perception_siamese(4).to(device) elif input_type == 'optical_flow': perception = Perception_overlap(2).to(device) elif input_type == 'optical_flow_depth': perception = Perception_overlap(3).to(device) elif input_type == 'optical_flow_depth_normalized': perception = Perception_overlap(3).to(device) elif input_type == 'optical_flow_depth_unnormalized_mask': perception = Perception_overlap(3).to(device) elif input_type == 'optical_flow_depth_siamese': perception = Perception_siamese_fusion_new(3).to(device) elif input_type == 'optical_flow_memory': perception = Preception_overlap_resnet(4).to(device) else: perception = Perception_overlap(2).to(device) if input_type == 'siamese': model = DQN_OVERLAP_Controller(perception, num_actions, input_size=512).to(device) elif input_type == 'optical_flow_memory': model = DQN_OVERLAP_RESNET_Controller(perception, num_actions, input_size=512).to(device) else: model = DQN_OVERLAP_Controller(perception, num_actions, input_size=256).to(device) model.load_state_dict(torch.load('{}/dqn_epoch_{}_Uvalda.pt'.format(model_weights_save_path, num_epochs))) #model.eval() list_succ = [] list_collision = [] ## go through each point folder if mode == 'Test': a, b = 0, 1 elif mode == 'Train': a, b = 7, 8 #a, b = 0, 1 #for point_idx in range(0, num_startPoints): #for point_idx in range(a, b): for point_idx in range(point_a, point_a+1): print('point_idx = {}'.format(point_idx)) task_folder = '{}/{}/point_{}'.format('/home/reza/Datasets/GibsonEnv/my_code/vs_controller/for_video', scene_name, point_idx) create_folder(task_folder) ## read in start img and start pose point_image_folder = '{}/{}/point_{}'.format(base_folder, scene_name, point_idx) point_pose_npy_file = np.load('{}/{}/point_{}_poses.npy'.format(base_folder, scene_name, point_idx)) #start_img = cv2.imread('{}/{}.png'.format(point_image_folder, point_pose_npy_file[0]['img_name']))[:, :, ::-1] start_pose = point_pose_npy_file[0]['pose'] start_img, start_depth = get_obs(start_pose) start_depth = start_depth.copy() count_succ = 0 count_collision = 0 count_short_runs = 0 count_short_runs_collision = 0 count_short_runs_succ = 0 ## index 0 is the left image, so right_img_idx starts from index 1 #for right_img_idx in range(1, len(point_pose_npy_file)): for right_img_idx in range(1, 101): print('right_img_idx = {}'.format(right_img_idx)) run_folder = '{}/run_{}'.format(task_folder, right_img_idx) create_folder(run_folder) current_pose = start_pose right_img_name = point_pose_npy_file[right_img_idx]['img_name'] goal_pose = point_pose_npy_file[right_img_idx]['pose'] #goal_img = cv2.imread('{}/{}.png'.format(point_image_folder, right_img_name), 1)[:,:,::-1] goal_img, goal_depth = get_obs(goal_pose) goal_depth = goal_depth.copy() current_depth = start_depth.copy() episode_reward = 0 flag_succ = False poses_list = [] poses_list.append(start_pose) poses_list.append(goal_pose) poses_list.append([current_pose]) for i_step in range(seq_len): if input_type == 'both' or input_type == 'siamese': overlapArea_currentView = genOverlapAreaOnCurrentView(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2] overlapArea_goalView = genOverlapAreaOnGoalView(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2] overlapArea = np.concatenate((overlapArea_currentView, overlapArea_goalView), axis=2) elif input_type == 'optical_flow': overlapArea = genGtDenseCorrespondenseFlowMap(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2] overlapArea = removeCorrespondenceRandomly(overlapArea, keep_prob=1.0) elif input_type == 'optical_flow_depth': opticalFlow = genGtDenseCorrespondenseFlowMap(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2] overlapArea = np.concatenate((opticalFlow, current_depth), axis=2) elif input_type == 'optical_flow_depth_normalized': opticalFlow = genGtDenseCorrespondenseFlowMap(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2] normalized_opticalFlow = normalize_opticalFlow(opticalFlow) normalized_depth = normalize_depth(current_depth) #normalized_depth = np.ones((256, 256, 1), np.float32) overlapArea = np.concatenate((normalized_opticalFlow, normalized_depth), axis=2) elif input_type == 'optical_flow_depth_unnormalized_mask': opticalFlow, mask_flow = genGtDenseCorrespondenseFlowMapAndMask(current_depth, goal_depth, current_pose, goal_pose) opticalFlow = opticalFlow[:, :, :2] normalized_depth = current_depth * mask_flow #normalized_opticalFlow = normalize_opticalFlow(opticalFlow) normalized_depth = normalize_depth(normalized_depth) overlapArea = np.concatenate((opticalFlow, normalized_depth), axis=2) elif input_type == 'optical_flow_depth_siamese': opticalFlow = genGtDenseCorrespondenseFlowMap(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2] normalized_depth = normalize_depth(current_depth) #normalized_depth = np.ones((256, 256, 1), np.float32) overlapArea = np.concatenate((opticalFlow, normalized_depth), axis=2) #print('overlapArea.shape = {}'.format(overlapArea.shape)) elif input_type == 'optical_flow_memory': opticalFlow = genGtDenseCorrespondenseFlowMap(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2] if i_step == 0: overlapArea = np.concatenate((opticalFlow, opticalFlow), axis=2) else: overlapArea = np.concatenate((old_opticalFlow, opticalFlow), axis=2) else: overlapArea = genOverlapAreaOnCurrentView(current_depth, goal_depth, current_pose, goal_pose)[:,:,:2] tensor_left = torch.tensor(overlapArea, dtype=torch.float32).to(device).unsqueeze(0).permute(0, 3, 1, 2) Qvalue_table = model(tensor_left) pred = Qvalue_table.max(1)[1].view(1, 1).detach().cpu().numpy().item() ## batch_size x 3 #print('Qvalue_table: {}'.format(Qvalue_table)) #print('pred = {}'.format(pred)) ## update current_pose vz, omegay = action_table[pred] #print('vz = {:.2f}, omegay = {:.2f}'.format(vz, omegay)) vx = 0.0 vx = vx * lambda_action vz = vz * lambda_action omegay = omegay * pi * lambda_action #print('actual velocity = {:.2f}, {:.2f}, {:.2f}'.format(vx, vz, omegay)) previous_pose = current_pose current_pose = update_current_pose(current_pose, vx, vz, omegay) poses_list[2].append(current_pose) flag_broken = False left_pixel = path_finder.point_to_pixel((previous_pose[0], previous_pose[1])) right_pixel = path_finder.point_to_pixel((current_pose[0], current_pose[1])) ## rrt.line_check returns True when there is no obstacle if not rrt.line_check(left_pixel, right_pixel, free): print('run into something') flag_broken = True break if close_to_goal(current_pose, goal_pose): print('success run') flag_succ = True break ## compute new_state current_img, current_depth = get_obs(current_pose) current_depth = current_depth.copy() #old_opticalFlow = opticalFlow.copy() np.save('{}/{}_waypoint_pose_list.npy'.format(run_folder, right_img_name[10:]), poses_list) #assert 1==2 if flag_succ: count_succ += 1 list_succ.append(point_pose_npy_file[right_img_idx]['img_name']) if findShortRangeImageName(right_img_name): count_short_runs_succ += 1 if flag_broken: count_collision += 1 list_collision.append(point_pose_npy_file[right_img_idx]['img_name']) if findShortRangeImageName(right_img_name): count_short_runs_collision += 1 if findShortRangeImageName(right_img_name): count_short_runs += 1 print('count_succ = {}'.format(count_succ)) print('count_collision = {}'.format(count_collision)) print('count_short_runs_succ = {}'.format(count_short_runs_succ)) print('count_short_runs_collision = {}'.format(count_short_runs_collision)) print('num_succ = {}, num_run = {}, count_short_runs_succ = {}, count_short_runs = {}'.format(count_succ, len(point_pose_npy_file), count_short_runs_succ, count_short_runs))
def refine_path_worker(self, ): path_nodes = self.solution.path node_map = [node for node in self.solution.path] num_nodes = len(path_nodes) nodes_x = np.array([self.pf.nodes_x[node] for node in path_nodes]) nodes_y = np.array([self.pf.nodes_y[node] for node in path_nodes]) points = [ self.pf.pc.point_to_pixel((nodes_x[i], nodes_y[i])) for i in range(num_nodes) ] last_cost = self.solution.cost edges = defaultdict(dict) for idx, node in enumerate(path_nodes): if idx: node0, node1 = idx - 1, idx distance = math.sqrt((nodes_x[node0] - nodes_x[node1])**2 + (nodes_y[node0] - nodes_y[node1])**2) edges[node0][node1] = distance edges[node1][node0] = distance solution = None last_solution = None max_edge_tries = num_nodes max_iters = 1000 tolerance = 0.0000001 max_zero_diffs = 100 n_zero_diffs = 0 iters = 0 mesg = "" # pdb.set_trace() while True: iters += 1 node0 = None node1 = None found = False for i in range(max_edge_tries): # if not last_solution: # nodes = list(range(num_nodes)) # else: # nodes = last_solution.path nodes = list(range(num_nodes)) node0 = nodes[int(random.random() * len(nodes))] node1 = nodes[int(random.random() * len(nodes))] if (node0 != node1 and node0 not in edges[node1] and rrt.line_check( points[node0], points[node1], self.pf.free, skip=5)): found = True break if found: distance = math.sqrt((nodes_x[node0] - nodes_x[node1])**2 + (nodes_y[node0] - nodes_y[node1])**2) edges[node0][node1] = distance edges[node1][node0] = distance # self.pf.edges[node_map[node0]][node_map[node1]] = distance # self.pf.edges[node_map[node1]][node_map[node0]] = distance # n_edges = self.pf.edges_idx.shape[0] # edges_idx = np.zeros((n_edges + 1, 2), dtype=np.int64) # edges_idx[:n_edges,:] = self.pf.edges_idx # edges_idx[n_edges] = (node_map[node0], node_map[node1]) # self.pf.edges_idx = edges_idx # self.send_redraw() pf = rrt.PathFinder(free=self.pf.free, pc=self.pf.pc, nodes_x=nodes_x, nodes_y=nodes_y, edges=edges) solution, _ = pf.find(self.path_start_point[0], self.path_start_point[1], self.path_end_point[0], self.path_end_point[1]) if not solution: mesg = "Didn't get back a path solution" solution = last_soluton or None break cost = solution.cost delta = last_cost - cost if delta < 0: last_cost = cost last_solution = solution continue if delta < tolerance: mesg = "Got a diff {} < which is less than {}".format( delta, tolerance) n_zero_diffs += 1 if n_zero_diffs == max_zero_diffs: break else: continue n_zero_diffs = 0 last_cost = cost last_solution = solution if iters > max_iters: mesg = "Ran out of iterations {}".format(max_iters) break else: mesg = "Couldn't find a new edge after {} tries.".format( max_edge_tries) break self.send_status_message(mesg) if solution: # Add new edges in path to RRT edges_to_add = [] for i, node1 in enumerate(solution.path): if i: node0 = solution.path[i - 1] if node_map[node1] not in self.pf.edges[node_map[node0]]: edges_to_add.append((node0, node1)) for node0, node1 in edges_to_add: self.pf.edges[node_map[node0]][ node_map[node1]] = edges[node0][node1] self.pf.edges[node_map[node1]][ node_map[node0]] = edges[node1][node0] self.pf.edges_idx = np.vstack((self.pf.edges_idx, np.array([(node_map[i], node_map[j]) for i, j in edges_to_add], dtype=np.int64))) # Copy to path to main path for i, node in enumerate(solution.path): solution.path[i] = node_map[node] self.solution = solution self.send_redraw()
def main(scene_idx): scene_name = Test_Scenes[scene_idx] scene_file_addr = '/home/reza/Datasets/GibsonEnv/my_code/visual_servoing/sample_image_pairs_test/{}'.format( scene_name) #scene_name = Train_Scenes[scene_idx] #scene_file_addr = '/home/reza/Datasets/GibsonEnv/my_code/visual_servoing/sample_image_pairs_train/{}'.format(scene_name) create_folder(scene_file_addr) ## rrt functions ## first figure out how to sample points from rrt graph rrt_directory = '/home/reza/Datasets/GibsonEnv/gibson/assets/dataset/{}_for_rrt'.format( scene_name) path_finder = rrt.PathFinder(rrt_directory) path_finder.load() num_nodes = len(path_finder.nodes_x) free = cv2.imread( '/home/reza/Datasets/GibsonEnv/gibson/assets/dataset/{}_for_rrt/free.png' .format(scene_name), 0) ## GibsonEnv setup config_file = os.path.join( '/home/reza/Datasets/GibsonEnv/my_code/CVPR_workshop', 'env_yamls', '{}_navigate.yaml'.format(scene_name)) env = HuskyNavigateEnv(config=config_file, gpu_count=1) obs = env.reset( ) ## this line is important otherwise there will be an error like 'AttributeError: 'HuskyNavigateEnv' object has no attribute 'potential'' def get_obs(current_pose): pos, orn = func_pose2posAndorn(current_pose, mapper_scene2z[scene_name]) env.robot.reset_new_pose(pos, orn) obs, _, _, _ = env.step(4) obs_rgb = obs['rgb_filled'] obs_depth = obs['depth'] return obs_rgb.copy(), obs_depth.copy() left_pose_list = mapper_scene2points[scene_name] right_pose_list = [] for p_idx, p in enumerate(left_pose_list): #for p_idx in range(0, 1): #p = left_pose_list[p_idx] list_whole = [] x0, y0, theta0 = p left_pose = [x0, y0, theta0] point_file_addr = '{}/point_{}'.format(scene_file_addr, p_idx) create_folder(point_file_addr) current_pose = left_pose left_rgb, left_depth = get_obs(current_pose) cv2.imwrite('{}/left_img.png'.format(point_file_addr), left_rgb[:, :, ::-1]) np.save('{}/left_img_depth.npy'.format(point_file_addr), left_depth) ## add left_img to list_whole current_dict = {} current_dict['img_name'] = 'left_img' current_dict['pose'] = left_pose list_whole.append(current_dict) for i in range(len(theta_list)): if i == 0 or i == 6: len_dist_list = 2 elif i == 1 or i == 5: len_dist_list = 3 elif i == 2 or i == 4: len_dist_list = 4 elif i == 3: len_dist_list = 6 print('len_dist_list = {}'.format(len_dist_list)) for j in range(len_dist_list): location_theta = plus_theta_fn(theta0, theta_list[i]) location_dist = dist_list[j] x1 = x0 + location_dist * math.cos(location_theta) y1 = y0 + location_dist * math.sin(location_theta) left_pixel = path_finder.point_to_pixel(left_pose) right_pixel = path_finder.point_to_pixel((x1, y1)) # check the line flag = rrt.line_check(left_pixel, right_pixel, free) if not flag: print('j = {}, obstacle'.format(j)) else: for diff_theta_idx in range(len(diff_theta_list)): diff_theta = diff_theta_list[diff_theta_idx] theta1 = plus_theta_fn(theta0, diff_theta) right_pose = [x1, y1, theta1] current_pose = right_pose right_rgb, right_depth = get_obs(current_pose) ## check if there is common space between left img and right img kp1, kp2 = sample_gt_dense_correspondences( left_depth, right_depth, left_pose, right_pose, gap=32, focal_length=128, resolution=256, start_pixel=31) if kp1.shape[1] > 2: cv2.imwrite( '{}/right_img_dist_{}_theta_{}_heading_{}.png'. format(point_file_addr, mapper_dist[j], mapper_theta[i], mapper_theta[diff_theta_idx]), right_rgb[:, :, ::-1]) np.save( '{}/right_img_dist_{}_theta_{}_heading_{}_depth.npy' .format(point_file_addr, mapper_dist[j], mapper_theta[i], mapper_theta[diff_theta_idx]), right_depth) right_pose_list.append(right_pose) ## add right_img to list_whole current_dict = {} current_dict[ 'img_name'] = 'right_img_dist_{}_theta_{}_heading_{}'.format( mapper_dist[j], mapper_theta[i], mapper_theta[diff_theta_idx]) current_dict['pose'] = right_pose list_whole.append(current_dict) else: print('No common space') ## save list_whole np.save('{}/point_{}_poses.npy'.format(scene_file_addr, p_idx), list_whole) # plot the pose graph pose_file_addr = '{}'.format(scene_file_addr) img_name = '{}_sampled_poses.jpg'.format(scene_name) print('img_name = {}'.format(img_name)) ## plot the poses free = cv2.imread( '/home/reza/Datasets/GibsonEnv/gibson/assets/dataset/{}_for_rrt/free.png' .format(scene_name), 1) rows, cols, _ = free.shape plt.imshow(free) for m in range(len(left_pose_list)): pose = left_pose_list[m] x, y = path_finder.point_to_pixel((pose[0], pose[1])) theta = pose[2] plt.arrow(x, y, cos(theta), sin(theta), color='r', \ overhang=1, head_width=0.1, head_length=0.15, width=0.001) for m in range(len(right_pose_list)): pose = right_pose_list[m] x, y = path_finder.point_to_pixel((pose[0], pose[1])) theta = pose[2] plt.arrow(x, y, cos(theta), sin(theta), color='b', \ overhang=1, head_width=0.1, head_length=0.15, width=0.001) plt.axis([0, cols, 0, rows]) plt.xticks([]) plt.yticks([]) plt.savefig('{}/{}'.format(pose_file_addr, img_name), bbox_inches='tight', dpi=(400)) plt.close()