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Copy pathpy3_process_rope_cpc.py
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py3_process_rope_cpc.py
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import sys
sys.path.insert(1, '/home/owen/anaconda2/envs/softlearning/lib/python3.6/site-packages/cv2/')
import zmq
import pickle
import zlib
import os
import matplotlib.pyplot as plt
import cv2
from datetime import datetime
import torch
from constants import *
from vision_utils import *
COLOR = WHITE
def init_socket():
context = zmq.Context()
socket = context.socket(zmq.REP)
socket.bind("tcp://*:{}".format(PORT))
return socket
def init_policy(path):
checkpoint = torch.load(path, map_location='cuda')
encoder, trans = checkpoint['encoder'], checkpoint['trans']
return encoder, trans
def update_image(image, location, delta):
image, location, delta = image.copy(), location.copy(), delta.copy()
image = preprocess_image(image, resize=False)
# Image to label pick (yellow) and place (red) positions
start_loc, end_loc = location, location + delta * MAX_IMAGE_DELTA
start_loc, end_loc = coord_image_to_robot(start_loc), coord_image_to_robot(end_loc)
start_loc = (start_loc - b).dot(np.linalg.inv(A))
end_loc = (end_loc - b).dot(np.linalg.inv(A))
start_loc, end_loc = start_loc - IMAGE_ORIGIN, end_loc - IMAGE_ORIGIN
start_loc, end_loc = start_loc.astype('int32'), end_loc.astype('int32')
sr, sc = start_loc
er, ec = end_loc
radius = 4
start_goal_image = image.copy()
h, w = start_goal_image.shape[:2]
start_goal_image[max(0, sr - radius):min(h, sr + radius), max(0, sc - radius):min(w, sc + radius)] = [255, 255, 0]
start_goal_image[max(0, er - radius):min(h, er + radius), max(0, ec - radius):min(w, ec + radius)] = [255, 0, 0]
ims[0].set_data(start_goal_image)
# Image showing segmentation
seg_image = cv2.resize(image.copy(), (IMAGE_INPUT_SIZE, IMAGE_INPUT_SIZE))
idxs = get_seg_idxs(seg_image, COLOR).astype('int32')
seg_image[:] = 0
for r, c in idxs:
seg_image[r, c, :] = 255
seg_image = cv2.resize(seg_image, (IMAGE_SIZE, IMAGE_SIZE))
ims[1].set_data(seg_image)
# Image showing down-sampled version
downsampled_image = cv2.resize(image.copy(), (IMAGE_INPUT_SIZE, IMAGE_INPUT_SIZE))
ims[2].set_data(downsampled_image)
# Update all plots
fig.canvas.draw()
plt.savefig(os.path.join(folder, 'observations', '{}.png'.format(time_step)))
return seg_image
def run_single(model, *args):
return model(*[a.unsqueeze(0) for a in args]).squeeze(0)
def sample_actions(locations, n):
locs = locations[np.random.randint(len(locations), size=(n,))]
deltas = 2 * np.random.rand(n, 2) - 1
return np.concatenate((locs, deltas), axis=1)
def generate_action(encoder, trans, current_image, goal_image):
locations = 2 * (get_seg_idxs(preprocess_image(current_image), COLOR) / 63.) - 1
if False:
action = sample_actions(locations, 1)[0]
else:
current_image = preprocess_image(current_image, to_torch=True)
z_current, z_goal = run_single(encoder, current_image), run_single(encoder, goal_image)
z_current, z_goal = z_current.unsqueeze(0), z_goal.unsqueeze(0)
n_trials = 1000
with torch.no_grad():
actions = torch.FloatTensor(sample_actions(locations, n_trials)).cuda()
zs = trans(z_current.repeat(n_trials, 1), actions)
dists = torch.norm((zs - z_goal).view(n_trials, -1), dim=-1)
idx = torch.argmin(dists)
action = actions[idx].cpu().numpy()
location, delta = action[:2], action[2:]
location = (location * 0.5 + 0.5) * 63
delta[1] = -delta[1]
delta = delta[[1, 0]]
# action[3] = -action[3]
# action[[2, 3]] = action[[3, 2]]
# print("z current:", z_current)
# print("z_goal:", z_goal)
# print("z next:", zs[idx])
# print("action:", action)
return location, delta
if __name__ == '__main__':
# name = datetime.now().isoformat() + '_{}'.format(sys.argv[1])
folder = os.path.join('images', EXPERIMENT_NAME, sys.argv[1])
if not os.path.exists(folder):
os.makedirs(folder)
os.makedirs(os.path.join(folder, 'full_observations'))
os.makedirs(os.path.join(folder, 'raw_observations'))
os.makedirs(os.path.join(folder, 'observations'))
os.makedirs(os.path.join(folder, 'segmentations'))
os.makedirs(os.path.join(folder, 'rewards'))
socket = init_socket()
encoder, trans = init_policy(POLICY_PATH)
goal_image = cv2.resize(cv2.cvtColor(cv2.imread(GOAL_IMAGE), cv2.COLOR_BGR2RGB), (IMAGE_INPUT_SIZE, IMAGE_INPUT_SIZE))
dummy_img = np.zeros((IMAGE_SIZE, IMAGE_SIZE, 3), dtype=np.uint8)
plt.ion()
fig, axs = plt.subplots(2, 2, figsize=(12, 9))
ims = []
ims.append(axs[0, 0].imshow(dummy_img.copy()))
ims.append(axs[1, 0].imshow(dummy_img.copy()))
ims.append(axs[0, 1].imshow(dummy_img.copy()))
ims.append(axs[1, 1].imshow(goal_image))
fig.canvas.draw()
goal_image = 2 * torch.FloatTensor(goal_image.astype('float32') / 255.).permute(2, 0, 1).cuda() - 1
print('py3::Starting...')
time_step = 0
r1 = []
while True:
print('py3::Waiting for image...')
data = socket.recv()
data = zlib.decompress(data)
image = pickle.loads(data, encoding='latin1')
print('py3::Received image, executing policy')
location, delta = generate_action(encoder, trans, image, goal_image)
binary_image = update_image(image, location, delta)
cv2.imwrite(os.path.join(folder, 'full_observations', '{}.png'.format(time_step)),
cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
cv2.imwrite(os.path.join(folder, 'raw_observations', '{}.png'.format(time_step)),
cv2.cvtColor(preprocess_image(image, resize=False), cv2.COLOR_BGR2RGB))
cv2.imwrite(os.path.join(folder, 'segmentations', '{}.png'.format(time_step)), binary_image)
np.save(os.path.join(folder, 'rewards', 'intersection.npy'), r1)
print('py3::Sending action')
two_hand = False
picks = np.expand_dims(location, axis=0)
deltas = np.expand_dims(delta, axis=0)
data = pickle.dumps((picks, deltas, two_hand), protocol=2)
data = zlib.compress(data)
socket.send(data)
time_step += 1