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 = 35
## create test folder
test_folder = '/home/reza/Datasets/GibsonEnv/my_code/visual_servoing/test_IBVS'
'''
approach_folder = '{}/learnedCorrespondence_interMatrix_noDepth_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 = []
list_count_steps = []
## 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 = []
list_steps = []
for right_img_idx in range(1, len(point_pose_npy_file)):
#for right_img_idx in range(1, 10):
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 = kpNet.detect_learned_correspondences(
current_img, goal_img)
if count_steps == 0:
start_depth = current_depth.copy()
except:
print('run into error')
break
num_matches = kp1.shape[1]
omegay, flag_stop = compute_angular_velocity_through_correspondences(
kp1, kp2, 100)
previous_pose = current_pose
current_pose = update_current_pose(current_pose, 0.0, 0.1,
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:
print('flag_stop = {}'.format(flag_stop))
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))
list_steps.append(len(list_result_poses))
## ===================================================================================================================
'''
## 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 ...')
'''
avg_steps = 1.0 * sum(list_steps) / len(list_steps)
f.write('point {} : {}/{}, {}\n'.format(point_idx, count_correct,
len(point_pose_npy_file) - 1,
avg_steps))
list_count_correct.append(count_correct)
list_count_runs.append(len(point_pose_npy_file) - 1)
list_count_steps.append(avg_steps)
f.flush()
avg_count_steps = 1.0 * sum(list_count_steps) / len(list_count_steps)
f.write('In total : {}/{}, {}\n'.format(sum(list_count_correct),
sum(list_count_runs),
avg_count_steps))
f.write(
'-------------------------------------------------------------------------------------\n'
)
'/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=0.15):
L2_dist = math.sqrt((pose1[0] - pose2[0])**2 + (pose1[1] - pose2[1])**2)
import argparse
import os
import numpy as np
import matplotlib.pyplot as plt
config_file = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..',
'configs', 'test_control.yaml')
print(config_file)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default=config_file)
args = parser.parse_args()
env = HuskyNavigateEnv(config=args.config)
env.reset()
base_action_omage = np.array([-0.001, 0.001, -0.001, 0.001])
base_action_v = np.array([0.001, 0.001, 0.001, 0.001])
control_signal = -0.5
control_signal_omega = 0.5
v = 0
omega = 0
kp = 100
ki = 0.1
kd = 25
ie = 0
de = 0
olde = 0
ie_omega = 0
def main(scene_idx=0, sigma=0.0, keep_prob=1.0):
#scene_idx = 0
#keep_prob = 0.95
#sigma = 5.0
#print('keep_prob = {}'.format(keep_prob))
#print('sigma = {}'.format(sigma))
## 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]
## create test folder
test_folder = '/home/reza/Datasets/GibsonEnv/my_code/vs_controller/test_LBVS'
f = open('{}/{}.txt'.format(test_folder, method_description), 'a')
f.write('sigma = {}, keep_prob = {}\n'.format(sigma, keep_prob))
f.write('scene_name = {}\n'.format(scene_name))
list_count_correct = []
list_count_runs = []
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_siamese_fusion_new(3).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)
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)))
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):
print('point_idx = {}'.format(point_idx))
## 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_correct = 0
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, 4):
print('right_img_idx = {}'.format(right_img_idx))
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
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]
## add gaussian noise
overlapArea = addGaussianNoise(overlapArea, sigma=sigma)
#overlapArea = removeCorrespondenceRandomly(overlapArea, keep_prob=keep_prob)
overlapArea = removeCorrespondenceRandomly_withSmoothing(overlapArea, keep_prob=keep_prob)
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)
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()
if flag_succ:
count_succ += 1
count_correct += 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('sigma = {}, keep_prob = {}, num_succ = {}, num_run = {}, count_short_runs_succ = {}, count_short_runs = {}'.format(sigma, keep_prob, count_succ, len(point_pose_npy_file)-1, count_short_runs_succ, count_short_runs))
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')
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
## 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=0.15):
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)
def compute_distance_old(left_pose, right_pose, lamb=0.5):
x1, y1 = left_pose[0], left_pose[1]
a1, b1 = cos(left_pose[2]), sin(left_pose[2])
x2, y2 = right_pose[0], right_pose[1]
a2, b2 = cos(right_pose[2]), sin(right_pose[2])
x_y_dist = math.sqrt((x1-x2)**2 + (y1-y2)**2)
theta_dist = math.sqrt((a1-a2)**2 + (b1-b2)**2)
return x_y_dist + lamb * theta_dist
def compute_distance(left_pose, right_pose, lamb_alpha=0.5, lamb_beta=0.2):
x1, y1 = left_pose[0], left_pose[1]
x2, y2 = right_pose[0], right_pose[1]
pho_dist = math.sqrt((x1-x2)**2 + (y1-y2)**2)
left_pose_heading = left_pose[2]
right_pose_heading = right_pose[2]
location_angle = atan2(y2-y1, x2-x1)
#print('left_pose_heading = {}, right_pose_heading = {}, location_angle = {}'.format(left_pose_heading, right_pose_heading, location_angle))
if pho_dist >= 0.05:
## alpha angle in goToPose is the difference between location angle and left_pose_heading
a1, b1 = cos(location_angle), sin(location_angle)
a2, b2 = cos(left_pose_heading), sin(left_pose_heading)
alpha_dist = math.sqrt((a1-a2)**2 + (b1-b2)**2)
## beta angle in goToPose is the difference between right_pose_heading and location angle
a1, b1 = cos(right_pose_heading), sin(right_pose_heading)
a2, b2 = cos(location_angle), sin(location_angle)
beta_dist = math.sqrt((a1-a2)**2 + (b1-b2)**2)
else:
## when pho_dist is close to zero, alpha_dist is not important
alpha_dist = 0.0
## beta angle becomes the anlge between left and right poses
a1, b1 = cos(right_pose_heading), sin(right_pose_heading)
a2, b2 = cos(left_pose_heading), sin(left_pose_heading)
beta_dist = math.sqrt((a1-a2)**2 + (b1-b2)**2)
#print('pho_dist = {:.2f}, alpha_dist = {:.2f}, beta_dist = {:.2f}'.format(pho_dist, alpha_dist, beta_dist))
return pho_dist + lamb_alpha * alpha_dist + lamb_beta * beta_dist
def decide_reward_and_done(previous_pose, current_pose, goal_pose, start_pose):
## check if the new step is on free space or not
reward = 0.0
done = 0
## check if current_pose is closer to goal_pose than previous_pose
'''
L2_dist_current = math.sqrt((current_pose[0] - goal_pose[0])**2 + (current_pose[1] - goal_pose[1])**2)
L2_dist_previous = math.sqrt((previous_pose[0] - goal_pose[0])**2 + (previous_pose[1] - goal_pose[1])**2)
if L2_dist_current < L2_dist_previous:
reward += 0.25
print('L2_dist_current = {:.2f}, L2_dist_previous = {:.2f}, reward = {}'.format(L2_dist_current, L2_dist_previous, reward))
'''
## following Fereshteh's DiVIs paper
dist_init = compute_distance(start_pose, goal_pose, lamb_alpha=0.2)
dist_current = compute_distance(current_pose, goal_pose, lamb_alpha=0.2)
reward = max(0, 1 - min(dist_init, dist_current)/(dist_init+0.0001))
#print('dist_init = {:.2f}, dist_current = {:.2f}, reward = {:.2f}'.format(dist_init, dist_current, reward))
## check if current_pose is close to goal
## goal reward should be larger than all the previously accumulated reward
flag_close_to_goal = close_to_goal(current_pose, goal_pose)
if flag_close_to_goal:
reward = 50.0
done = 1
#print('current_pose = {}, goal_pose = {}, flag_close_to_goal = {}, reward = {}'.format(current_pose, goal_pose, flag_close_to_goal, reward))
#collision_done = 0
## if there is a collision, reward is -1 and the episode is done
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('bumped into obstacle ....')
reward = 0.0
#collision_done = 1
done=1
print('final reward = {}'.format(reward))
return reward, done, 0 #, collision_done
##============================================================================================================
base_folder = '/home/reza/Datasets/GibsonEnv/my_code/visual_servoing/sample_image_pairs_{}'.format('train')
#agent = DQN_vs_overlap_resnet(trained_model_path=None, num_actions=action_space, input_channels=4)
agent = DQN_vs_overlap_resnet(trained_model_path=model_weights_save_path, num_actions=action_space, input_channels=4)
rewards = []
avg_rewards = []
for i_epoch in range(actual_episodes):
## 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))
## 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()
## 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(3, 4):
#print('right_img_idx = {}'.format(right_img_idx))
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_img, goal_depth = goal_img.copy(), goal_depth.copy()
opticalFlow = genGtDenseCorrespondenseFlowMap(start_depth, goal_depth, start_pose, goal_pose)[:,:,:2]
memory_opticalFlow = np.concatenate((opticalFlow, opticalFlow), axis=2)
state = [memory_opticalFlow]
episode_reward = 0
for i_step in range(seq_len):
action = agent.select_action(state)
print('action = {}'.format(action))
## update current_pose
vz, omegay = action_table[action]
#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)
## compute new_state
current_img, current_depth = get_obs(current_pose)
next_left_img, next_left_depth = current_img.copy(), current_depth.copy()
new_opticalFlow = genGtDenseCorrespondenseFlowMap(next_left_depth, goal_depth, current_pose, goal_pose)[:,:,:2]
memory_new_opticalFlow = np.concatenate((state[0][:,:,2:4], new_opticalFlow), axis=2)
'''
fig = plt.figure(figsize=(15, 5)) #cols, rows
r, c = 1, 6
ax = fig.add_subplot(r, c, 1)
ax.imshow(memory_new_opticalFlow[:, :, 0])
ax = fig.add_subplot(r, c, 2)
ax.imshow(memory_new_opticalFlow[:, :, 1])
ax = fig.add_subplot(r, c, 3)
ax.imshow(memory_new_opticalFlow[:, :, 2])
ax = fig.add_subplot(r, c, 4)
ax.imshow(memory_new_opticalFlow[:, :, 3])
plt.show()
'''
#print('memory_new_opticalFlow.shape = {}'.format(memory_new_opticalFlow.shape))
new_state = [memory_new_opticalFlow]
## visualize the state
'''
fig = plt.figure(figsize=(15, 10))
r, c, = 2, 2
ax = fig.add_subplot(r, c, 1)
ax.imshow(next_left_img)
ax = fig.add_subplot(r, c, 2)
ax.imshow(goal_img)
ax = fig.add_subplot(r, c, 3)
start_mask = np.concatenate((new_overlapArea, np.zeros((256, 256, 1), dtype=np.uint8)), axis=2)
ax.imshow(start_mask)
plt.show()
'''
## collision done only stops continuing the sequence, but won't affect reward computing
reward, done, collision_done = decide_reward_and_done(previous_pose, current_pose, goal_pose, start_pose)
print('done = {}, collision_done = {}'.format(done, collision_done))
if i_step == seq_len-1:
print('used up all the steps ...')
done = 1
agent.memory.push(state, action, reward, new_state, done)
if len(agent.memory) > batch_size:
agent.update(batch_size)
state = new_state
episode_reward += reward
print('---------------- end of a action ------------------ ')
if done or collision_done:
break
print('---------------- end of a sequence ------------------ ')
rewards.append(episode_reward)
avg_rewards.append(np.mean(rewards[-10:]))
sys.stdout.write("------------------------------------epoch = {}, point = {}, traj = {}, reward: {}, average_reward: {} #_steps: {}\n".format(i_epoch, point_idx, right_img_idx, np.round(episode_reward, decimals=2), np.round(avg_rewards[-1], decimals=2), i_step))
if right_img_idx % 10 == 0:
agent.update_critic()
## plot the running_loss
plt.plot(rewards, label='reward')
plt.plot(avg_rewards, label='avg_reward')
plt.xlabel('Episode')
plt.ylabel('Reward')
plt.grid(True)
plt.legend(loc='upper right')
plt.yscale('linear')
plt.title('change of reward and avg_reward')
plt.savefig('{}/Reward_episode_{}_{}.jpg'.format(
model_weights_save_path, num_episodes, scene_name), bbox_inches='tight')
plt.close()
torch.save(agent.actor.state_dict(), '{}/dqn_epoch_200000_{}.pt'.format(model_weights_save_path, scene_name))
torch.save(agent.actor.state_dict(), '{}/dqn_epoch_200000.pt'.format(model_weights_save_path))
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_file_addr = '/home/reza/Datasets/GibsonEnv/my_code/visual_servoing/sample_image_pairs_test_longer_dist/{}'.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 = 10 #4
elif i == 3:
len_dist_list = 10 #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()