import rrt
import random
from util import action2pose, func_pose2posAndorn, similar_location_under_certainThreshold, plus_theta_fn, minus_theta_fn
from util_vscontroller import gt_goToPose, genOverlapAreaOnCurrentView, genOverlapAreaOnGoalView, genErrorOverlapAreaOnCurrentView
from utils_ddpg import buildActionMapper, update_current_pose
from dqn_vs import DQN_vs_triplet
import torch
np.set_printoptions(precision=2, suppress=True)
## necessary constants
mapper_scene2points = get_mapper_scene2points()
num_episodes = 200000
batch_size = 128
lambda_action = 0.25
action_table = buildActionMapper(flag_fewer_actions=True)
seq_len = 50
def main(scene_idx=0, actual_episodes=1):
#scene_idx = 0
#actual_episodes=2
Train_Scenes, Test_Scenes = get_train_test_scenes()
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')
action_space = action_table.shape[0]
##=============================================================================================================
## rrt functions
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))
