def find_cool_images(filename):
"""
:param filenames: one string or a list of strings
:return:
"""
print('Opening file: %s' % filename)
f = h5py.File(filename, "r")
times = np.array(f['/timestamp'].value)
time_post2_grasping = float(f["time_post2_grasping"].value)
index_time_post2_grasping = np.argmin(
np.abs(times -
time_post2_grasping)) # Find index corresponding to timestamp
compressed_kinectA = f['/color_image_KinectA'].value[
index_time_post2_grasping]
KinectA_color_time_post2_grasping = np.array(
ig.uncompress(compressed_kinectA))
compressed_kinectB = f['/color_image_KinectB'].value[
index_time_post2_grasping]
KinectB_color_time_post2_grasping = np.array(
ig.uncompress(compressed_kinectB))
compressed_GelSightA = f['/GelSightA_image'].value[
index_time_post2_grasping]
GelSightA_image_time_post2_grasping = np.array(
ig.uncompress(compressed_GelSightA))
compressed_GelSightB = f['/GelSightB_image'].value[
index_time_post2_grasping]
GelSightB_image_time_post2_grasping = np.array(
ig.uncompress(compressed_GelSightB))
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
ax1.imshow(KinectA_color_time_post2_grasping)
ax2.imshow(KinectB_color_time_post2_grasping)
ax3.imshow(GelSightA_image_time_post2_grasping)
ax4.imshow(GelSightB_image_time_post2_grasping)
ax1.axis('off')
ax2.axis('off')
ax3.axis('off')
ax4.axis('off')
plt.draw()
# plt.ion()
plt.show()
def set_label(path, filenames):
"""
:param filenames: one string or a list of strings
:return:
"""
if isinstance(filenames, basestring):
filename = [filenames] # only a string is convert to a list
for filename in filenames:
path_to_file = path + filename
print('Opening file: %s' % path_to_file)
f = h5py.File(path_to_file, "r+")
times = np.array(f['/timestamp'].value)
time_pre_grasping = float(f["time_pre_grasping"].value)
index_time_pre_grasping = np.argmin(
np.abs(times -
time_pre_grasping)) # Find index corresponding to timestamp
GelSightA_image_time_pre_grasping = np.array(
ig.uncompress(
f['/GelSightA_image'].value[index_time_pre_grasping]))
GelSightB_image_time_pre_grasping = np.array(
ig.uncompress(
f['/GelSightB_image'].value[index_time_pre_grasping]))
KinectA_color_time_pre_grasping = np.array(
ig.uncompress(
f['/color_image_KinectA'].value[index_time_pre_grasping]))
KinectB_color_time_pre_grasping = np.array(
ig.uncompress(
f['/color_image_KinectB'].value[index_time_pre_grasping]))
plt.imshow(KinectA_color_time_pre_grasping)
plt.axis('off')
plt.draw()
plt.ion()
plt.show()
str(f['object_name'].value)
print(
"Does the name of the object correspond to the one printed below? [y/n]"
)
print(str(f['object_name'].value))
done = False
while not done:
c = grip_and_record.getch.getch()
if c:
if c in ['n']:
name = input("Enter the correct name: ")
data = f['object_name']
data[...] = np.asarray([name])
done = True
elif c in ['y']:
done = True
f.flush()
f.close()
plt.close()
def find_cool_images(path, filenames):
"""
:param filenames: one string or a list of strings
:return:
"""
if isinstance(filenames, basestring):
filename = [filenames] # only a string is convert to a list
for filename in filenames:
path_to_file = path + filename
print('Opening file: %s' % path_to_file)
f = h5py.File(path_to_file, "r")
times = np.array(f['/timestamp'].value)
time_post2_grasping = float(f["time_at_grasping"].value)
index_time_post2_grasping = np.argmin(
np.abs(
times -
time_post2_grasping)) # Find index corresponding to timestamp
compressed_kinectA = f['/color_image_KinectA'].value[
index_time_post2_grasping]
KinectA_color_time_post2_grasping = np.array(
ig.uncompress(compressed_kinectA))
compressed_kinectB = f['/color_image_KinectB'].value[
index_time_post2_grasping]
KinectB_color_time_post2_grasping = np.array(
ig.uncompress(compressed_kinectB))
kinectA_depth = f['/depth_image_KinectA'].value[
index_time_post2_grasping]
KinectA_depth_time_post2_grasping = np.array(kinectA_depth).squeeze()
kinectB_depth = f['/depth_image_KinectB'].value[
index_time_post2_grasping]
KinectB_depth_time_post2_grasping = np.array(kinectB_depth).squeeze()
compressed_GelSightA = f['/GelSightA_image'].value[
index_time_post2_grasping]
GelSightA_image_time_post2_grasping = np.array(
ig.uncompress(compressed_GelSightA))
compressed_GelSightB = f['/GelSightB_image'].value[
index_time_post2_grasping]
GelSightB_image_time_post2_grasping = np.array(
ig.uncompress(compressed_GelSightB))
# fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
# ax1.imshow(KinectA_color_time_at_grasping)
# ax2.imshow(KinectB_color_time_at_grasping)
# ax3.imshow(GelSightA_image_time_at_grasping)
# ax4.imshow(GelSightB_image_time_at_grasping)
# ax1.axis('off')
# ax2.axis('off')
# ax3.axis('off')
# ax4.axis('off')
# plt.draw()
# plt.ion()
# plt.show()
#
# print("Is it a cool image? [y/n]")
# done = False
# cool = False
# while not done:
# c = grip_and_record.getch.getch()
# if c:
# if c in ['n']:
# cool = False
# done = True
# elif c in ['y']:
# cool = True
# done = True
path_cool = "/home/manu/ros_ws/src/manu_research/data/test_figure/"
file = open(path_cool + filename[:-5] + '_GelSightA_at.jpeg', 'w')
file.write(str(compressed_GelSightA))
file.close()
time.sleep(0.5)
file = open(path_cool + filename[:-5] + '_GelSightB_at.jpeg', 'w')
file.write(str(compressed_GelSightB))
file.close()
time.sleep(0.5)
file = open(path_cool + filename[:-5] + '_kinectA_at.jpeg', 'w')
file.write(str(compressed_kinectA))
file.close()
time.sleep(0.5)
file = open(path_cool + filename[:-5] + '_kinectB_at.jpeg', 'w')
file.write(str(compressed_kinectB))
file.close()
fig = plt.figure()
print(KinectA_depth_time_post2_grasping.shape)
KinectA_depth_time_post2_grasping = 1024 * (
KinectA_depth_time_post2_grasping >
1024) + KinectA_depth_time_post2_grasping * (
KinectA_depth_time_post2_grasping < 1024)
plt.imshow(KinectA_depth_time_post2_grasping, cmap='gray')
plt.show()
import scipyplot as spp
spp.save2file(fig=fig, nameFile='depth_image_KinectA_at')
fig = plt.figure()
KinectB_depth_time_post2_grasping = 1024 * (
KinectB_depth_time_post2_grasping >
1024) + KinectB_depth_time_post2_grasping * (
KinectB_depth_time_post2_grasping < 1024)
plt.imshow(KinectB_depth_time_post2_grasping, cmap='gray')
plt.show()
spp.save2file(fig=fig, nameFile='depth_image_KinectB_at')
def set_label(path, filenames):
"""
:param filenames: one string or a list of strings
:return:
"""
if isinstance(filenames, basestring):
filename = [filenames] # only a string is convert to a list
for filename in filenames:
path_to_file = path + filename
print('Opening file: %s' % path_to_file)
f = h5py.File(path_to_file, "r")
n_steps = int(f['n_steps'].value)
# times = []
# for i in tqdm(range(1, n_steps)):
# id = '/step_%012d' % i
# times.append(f[id + '/timestamp'].value)
# times = np.array(times)
times = np.array(f['/timestamp'].value)
time_pre_grasping = float(f["time_pre_grasping"].value)
index_time_pre_grasping = np.argmin(
np.abs(times - time_pre_grasping)) # Find index corresponding to timestamp
GelSightA_image_time_pre_grasping = np.array(
ig.uncompress(f['/GelSightA_image'].value[index_time_pre_grasping]))
GelSightB_image_time_pre_grasping = np.array(
ig.uncompress(f['/GelSightB_image'].value[index_time_pre_grasping]))
time_post2_grasping = float(f["time_post2_grasping"].value)
index_time_post2_grasping = np.argmin(
np.abs(times - time_post2_grasping)) # Find index corresponding to timestamp
KinectA_color_time_post2_grasping = np.array(
ig.uncompress(f['/color_image_KinectA'].value[index_time_post2_grasping]))
KinectB_color_time_post2_grasping = np.array(
ig.uncompress(f['/color_image_KinectB'].value[index_time_post2_grasping]))
GelSightA_image_time_post2_grasping = np.array(
ig.uncompress(f['/GelSightA_image'].value[index_time_post2_grasping]))
GelSightB_image_time_post2_grasping = np.array(
ig.uncompress(f['/GelSightB_image'].value[index_time_post2_grasping]))
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
ax1.imshow(KinectA_color_time_post2_grasping)
ax2.imshow(KinectB_color_time_post2_grasping)
ax3.imshow(GelSightA_image_time_post2_grasping)
ax4.imshow(GelSightB_image_time_post2_grasping)
ax1.axis('off')
ax2.axis('off')
ax3.axis('off')
ax4.axis('off')
plt.draw()
plt.ion()
plt.show()
print("Is the robot holding the object in its gripper? [y/n]")
done = False
while not done:
c = grip_and_record.getch.getch()
if c:
if c in ['n']:
is_gripping = False
done = True
elif c in ['y']:
is_gripping = True
done = True
# TODO: check if folder exists, if not create
path_0 = "/home/manu/ros_ws/src/manu_research/data/"
file = open(path_0 + 'labels/' + filename[:-4] + 'txt', 'w')
file.write(str(is_gripping))
file.close()
file = h5py.File(path_0 + "for_Andrew/" + filename, "w")
file.create_dataset("GelSightA_image_pre_gripping", data=GelSightA_image_time_pre_grasping)
file.create_dataset("GelSightB_image_pre_gripping", data=GelSightB_image_time_pre_grasping)
file.create_dataset("GelSightA_image_post_gripping", data=GelSightA_image_time_post2_grasping)
file.create_dataset("GelSightB_image_post_gripping", data=GelSightB_image_time_post2_grasping)
file.create_dataset("is_gripping", data=np.asarray([is_gripping]))
file.close()
f.close()
plt.close()
def find_cool_images(path, filenames):
"""
:param filenames: one string or a list of strings
:return:
"""
if isinstance(filenames, basestring):
filename = [filenames] # only a string is convert to a list
for filename in filenames:
path_to_file = path + filename
print('Opening file: %s' % path_to_file)
f = h5py.File(path_to_file, "r")
times = np.array(f['/timestamp'].value)
time_at_grasping = float(f["time_at_grasping"].value)
index_time_at_grasping = np.argmin(
np.abs(times -
time_at_grasping)) # Find index corresponding to timestamp
compressed_kinectA = f['/color_image_KinectA'].value[
index_time_at_grasping]
KinectA_color_time_at_grasping = np.array(
ig.uncompress(compressed_kinectA))
compressed_kinectB = f['/color_image_KinectB'].value[
index_time_at_grasping]
KinectB_color_time_at_grasping = np.array(
ig.uncompress(compressed_kinectB))
compressed_GelSightA = f['/GelSightA_image'].value[
index_time_at_grasping]
GelSightA_image_time_at_grasping = np.array(
ig.uncompress(compressed_GelSightA))
compressed_GelSightB = f['/GelSightB_image'].value[
index_time_at_grasping]
GelSightB_image_time_at_grasping = np.array(
ig.uncompress(compressed_GelSightB))
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2)
ax1.imshow(KinectA_color_time_at_grasping)
ax2.imshow(KinectB_color_time_at_grasping)
ax3.imshow(GelSightA_image_time_at_grasping)
ax4.imshow(GelSightB_image_time_at_grasping)
ax1.axis('off')
ax2.axis('off')
ax3.axis('off')
ax4.axis('off')
plt.draw()
plt.ion()
plt.show()
print("Is it a cool image? [y/n]")
done = False
cool = False
while not done:
c = grip_and_record.getch.getch()
if c:
if c in ['n']:
cool = False
done = True
elif c in ['y']:
cool = True
done = True
if cool:
path_cool = "/home/manu/ros_ws/src/manu_research/data/cool_images/"
file = open(path_cool + filename[:-5] + '_GelSightA.jpeg', 'w')
file.write(str(compressed_GelSightA))
file.close()
time.sleep(0.5)
file = open(path_cool + filename[:-5] + '_GelSightB.jpeg', 'w')
file.write(str(compressed_GelSightB))
file.close()
plt.close()