def _append_images(self, imgs):
if self.cache_img is None:
out_bgr = jsk_recognition_utils.get_tile_image(imgs)
self.cache_img = out_bgr
else:
out_bgr = jsk_recognition_utils.get_tile_image(imgs, tile_shape=None, result_img=self.cache_img)
bridge = cv_bridge.CvBridge()
imgmsg = bridge.cv2_to_imgmsg(out_bgr, encoding="bgr8")
self.pub_img.publish(imgmsg)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--tile-shape', default='5x8',
help='Default: 5x8')
args = parser.parse_args()
tile_shape = map(int, args.tile_shape.split('x'))
with open(osp.join(PKG_DIR, 'config/label_names.yaml')) as f:
obj_names = yaml.load(f)['label_names']
obj_names = obj_names[1:-1]
imgs = []
data_dir = osp.join(PKG_DIR, 'data')
for i, obj in enumerate(obj_names):
obj_id = i + 1
img_file = osp.join(data_dir, 'objects', obj, 'top.jpg')
img = cv2.imread(img_file)
# put obj_id
height, width = img.shape[:2]
x1, y1, x2, y2 = 10, 10, width - 10, height - 10
img = img[y1:y2, x1:x2]
cv2.putText(img, '%2d' % obj_id, (0, 60),
cv2.cv.CV_FONT_HERSHEY_SIMPLEX, 1.8, (255, 255, 255), 3)
imgs.append(img)
img_viz = jsk_recognition_utils.get_tile_image(imgs, tile_shape=tile_shape)
out_file = osp.join(PKG_DIR, 'data/others',
'object_list_{0}x{1}.jpg'.format(*tile_shape))
cv2.imwrite(out_file, img_viz)
print('==> Wrote file: %s' % out_file)
def get_gallery():
content = []
for pkg in PACKAGES:
# TODO(wkentaro): Scrape pacakge.xml only once by creating a class.
from bs4 import BeautifulSoup
with open(osp.join(pkg, 'package.xml')) as f:
soup = BeautifulSoup(f.read(), 'lxml')
website_url = soup.find('url', type='website')
imgs = []
for ext in ['*.png', '*.jpg']:
pattern = osp.join(here, 'doc', pkg, 'nodes/images', ext)
for fname in glob.glob(pattern):
img = cv2.imread(fname)
scale = math.sqrt(1. * 200 * 200 / img.shape[0] / img.shape[1])
img = cv2.resize(img, None, None, fx=scale, fy=scale)
imgs.append(img)
if len(imgs) >= 15:
break
if not imgs:
continue
cols = 5
rows = max(len(imgs) // cols, 1)
tiled = jsk_recognition_utils.get_tile_image(
imgs, tile_shape=(cols, rows), margin_color=[255, 255, 255])
fname = osp.join(here, '.readme/gallery_%s.jpg' % pkg)
cv2.imwrite(fname, tiled)
content.append('### [%s](%s)' % (pkg, website_url.text))
content.append('')
content.append('[](%s)' %
(osp.relpath(fname, here), website_url.text))
content.append('')
return '\n'.join(content)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-s',
'--tile-shape',
default='5x8',
help='Default: 5x8')
args = parser.parse_args()
tile_shape = map(int, args.tile_shape.split('x'))
obj_names = jsk_arc2017_common.get_object_names()
imgs = []
data_dir = osp.join(PKG_DIR, 'data')
for i, obj in enumerate(obj_names):
obj_id = i + 1
img_file = osp.join(data_dir, 'objects', obj, 'top.jpg')
img = cv2.imread(img_file)
# put obj_id
height, width = img.shape[:2]
x1, y1, x2, y2 = 10, 10, width - 10, height - 10
img = img[y1:y2, x1:x2]
cv2.putText(img, '%2d' % obj_id, (0, 60),
cv2.cv.CV_FONT_HERSHEY_SIMPLEX, 1.8, (255, 255, 255), 3)
imgs.append(img)
img_viz = jsk_recognition_utils.get_tile_image(imgs, tile_shape=tile_shape)
out_file = osp.join(PKG_DIR, 'data/others',
'object_list_{0}x{1}.jpg'.format(*tile_shape))
cv2.imwrite(out_file, img_viz)
print('==> Wrote file: %s' % out_file)
def _append_images(self, imgs):
if not imgs:
return
if self.cache_img is None:
out_bgr = jsk_recognition_utils.get_tile_image(imgs)
self.cache_img = out_bgr
else:
try:
out_bgr = jsk_recognition_utils.get_tile_image(
imgs, tile_shape=None, result_img=self.cache_img)
except ValueError: # cache miss
out_bgr = jsk_recognition_utils.get_tile_image(imgs)
self.cache_img = out_bgr
bridge = cv_bridge.CvBridge()
imgmsg = bridge.cv2_to_imgmsg(out_bgr, encoding='bgr8')
self.pub_img.publish(imgmsg)
def get_gallery():
content = []
for pkg in PACKAGES:
# TODO(wkentaro): Scrape pacakge.xml only once by creating a class.
from bs4 import BeautifulSoup
with open(osp.join(pkg, 'package.xml')) as f:
soup = BeautifulSoup(f.read(), 'lxml')
website_url = soup.find('url', type='website')
imgs = []
for ext in ['*.png', '*.jpg']:
pattern = osp.join(here, 'doc', pkg, 'nodes/images', ext)
for fname in glob.glob(pattern):
img = cv2.imread(fname)
scale = math.sqrt(1. * 200 * 200 / img.shape[0] / img.shape[1])
img = cv2.resize(img, None, None, fx=scale, fy=scale)
imgs.append(img)
if len(imgs) >= 15:
break
if not imgs:
continue
cols = 5
rows = max(len(imgs) // cols, 1)
tiled = jsk_recognition_utils.get_tile_image(
imgs, tile_shape=(cols, rows), margin_color=[255, 255, 255])
fname = osp.join(here, '.readme/gallery_%s.jpg' % pkg)
cv2.imwrite(fname, tiled)
content.append('### [%s](%s)' % (pkg, website_url.text))
content.append('')
content.append('[](%s)' % (osp.relpath(fname, here),
website_url.text))
content.append('')
return '\n'.join(content)
def _apply(self, *msgs):
bridge = cv_bridge.CvBridge()
imgs = []
for msg in msgs:
img = bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
imgs.append(img)
out_bgr = jsk_recognition_utils.get_tile_image(imgs)
imgmsg = bridge.cv2_to_imgmsg(out_bgr, encoding='bgr8')
self.pub_img.publish(imgmsg)
def _apply(self, *msgs):
bridge = cv_bridge.CvBridge()
imgs = []
for msg in msgs:
img = bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
imgs.append(img)
out_bgr = jsk_recognition_utils.get_tile_image(imgs)
imgmsg = bridge.cv2_to_imgmsg(out_bgr, encoding='bgr8')
self.pub_img.publish(imgmsg)
def _apply(self, *msgs):
bridge = cv_bridge.CvBridge()
imgs = []
for msg, topic in zip(msgs, self.input_topics):
img = bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
if self.draw_topic_name:
cv2.putText(img, topic, (0, 50), cv2.FONT_HERSHEY_PLAIN, 4, (0, 255, 0), 4)
imgs.append(img)
out_bgr = jsk_recognition_utils.get_tile_image(imgs)
imgmsg = bridge.cv2_to_imgmsg(out_bgr, encoding='bgr8')
self.pub_img.publish(imgmsg)
def _append_images(self, imgs):
if not imgs:
return
# convert tile shape: (Y, X) -> (X, Y)
# if None, shape is automatically decided to be square AMAP.
shape_xy = self._shape[::-1] if self._shape else None
if self.cache_img is None:
out_bgr = jsk_recognition_utils.get_tile_image(
imgs, tile_shape=shape_xy)
self.cache_img = out_bgr
else:
try:
out_bgr = jsk_recognition_utils.get_tile_image(
imgs, tile_shape=shape_xy, result_img=self.cache_img)
except ValueError: # cache miss
out_bgr = jsk_recognition_utils.get_tile_image(
imgs, tile_shape=shape_xy)
self.cache_img = out_bgr
bridge = cv_bridge.CvBridge()
imgmsg = bridge.cv2_to_imgmsg(out_bgr, encoding='bgr8')
self.pub_img.publish(imgmsg)
def _append_images(self, imgs):
if not imgs:
return
# convert tile shape: (Y, X) -> (X, Y)
# if None, shape is automatically decided to be square AMAP.
shape_xy = self._shape[::-1] if self._shape else None
if self.cache_img is None:
out_bgr = jsk_recognition_utils.get_tile_image(
imgs, tile_shape=shape_xy)
self.cache_img = out_bgr
else:
try:
out_bgr = jsk_recognition_utils.get_tile_image(
imgs, tile_shape=shape_xy, result_img=self.cache_img)
except ValueError: # cache miss
out_bgr = jsk_recognition_utils.get_tile_image(
imgs, tile_shape=shape_xy)
self.cache_img = out_bgr
bridge = cv_bridge.CvBridge()
imgmsg = bridge.cv2_to_imgmsg(out_bgr, encoding='bgr8')
self.pub_img.publish(imgmsg)
def _apply_tile(self, img_msg, label_msg):
bridge = cv_bridge.CvBridge()
img = bridge.imgmsg_to_cv2(img_msg)
label_img = bridge.imgmsg_to_cv2(label_msg)
imgs = []
labels = np.unique(label_img)
for label in labels:
if label == 0:
# should be skipped 0, because
# 0 is to label image as black region to mask image
continue
img_tmp = img.copy()
mask = label_img == label
img_tmp[~mask] = 0
img_tmp = bounding_rect_of_mask(img_tmp, mask)
imgs.append(img_tmp)
tile_img = get_tile_image(imgs)
tile_msg = bridge.cv2_to_imgmsg(tile_img, encoding='bgr8')
tile_msg.header = img_msg.header
self.pub_tile.publish(tile_msg)
def _apply_tile(self, img_msg, label_msg):
bridge = cv_bridge.CvBridge()
img = bridge.imgmsg_to_cv2(img_msg)
label_img = bridge.imgmsg_to_cv2(label_msg)
imgs = []
labels = np.unique(label_img)
for label in labels:
if label == 0:
# should be skipped 0, because
# 0 is to label image as black region to mask image
continue
img_tmp = img.copy()
mask = label_img == label
img_tmp[~mask] = 0
img_tmp = bounding_rect_of_mask(img_tmp, mask)
imgs.append(img_tmp)
tile_img = get_tile_image(imgs)
tile_msg = bridge.cv2_to_imgmsg(tile_img, encoding='bgr8')
tile_msg.header = img_msg.header
self.pub_tile.publish(tile_msg)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('dataset_dir', help='Dataset dir path')
parser.add_argument('-s', '--start',
help='Start timestamp (ex. 2017-06-10T10:00:23)')
args = parser.parse_args()
dataset_dir = args.dataset_dir
start = args.start
if not osp.exists(dataset_dir):
print('Please install dataset to: %s' % dataset_dir)
quit(1)
label_files = []
for stamp_dir in os.listdir(dataset_dir):
stamp_dir = osp.join(dataset_dir, stamp_dir)
label_file = osp.join(stamp_dir, 'label.npz')
if osp.exists(label_file):
label_files.append(label_file)
else:
label_files.append(None)
print('==> Size of dataset: All: %d, Annotated: %d.' %
(len(label_files), len(list(filter(None, label_files)))))
PKG_DIR = rospkg.RosPack().get_path('jsk_arc2017_common')
with open(osp.join(PKG_DIR, 'config/label_names.yaml')) as f:
object_names = yaml.load(f)
object_names.append('__unlabeled__')
print('==> Press keys: [q] to quit, [n] to go next, [p] to go previous')
stamp_dirs = list(sorted(os.listdir(dataset_dir)))
i = 0
while True:
stamp = datetime.datetime.fromtimestamp(int(stamp_dirs[i]) / 1e9)
if start and stamp < dateutil.parser.parse(start):
i += 1
continue
start = None
stamp_dir = osp.join(dataset_dir, stamp_dirs[i])
print('%s: %s' % (stamp.isoformat(), stamp_dir))
img_file = osp.join(stamp_dir, 'image.jpg')
img = skimage.io.imread(img_file)
depth_file = osp.join(stamp_dir, 'depth.npz')
depth = np.load(depth_file)['arr_0']
depth_viz = colorize_depth(depth, min_value=0.4, max_value=1.0)
label_file = osp.join(stamp_dir, 'label.npz')
if osp.exists(label_file):
label = np.load(label_file)['arr_0']
mask_unlabeled = label == -1
label[mask_unlabeled] = object_names.index('__unlabeled__')
img_labeled = img.copy()
img_labeled[mask_unlabeled] = \
np.random.randint(0, 255, (mask_unlabeled.sum(), 3))
label_viz = label2rgb(
lbl=label, img=img_labeled,
label_names=dict(enumerate(object_names)))
else:
label_viz = np.zeros_like(img)
viz = jsk_recognition_utils.get_tile_image([img, label_viz, depth_viz])
cv2.imshow('view_jsk_v1', viz[:, :, ::-1])
key = cv2.waitKey(0)
if key == ord('q'):
break
elif key == ord('n'):
if i == len(stamp_dirs) - 1:
print('Reached the end edge of the dataset')
continue
i += 1
elif key == ord('p'):
if i == 0:
print('Reached the start edge of the dataset')
continue
i -= 1
else:
continue
def main():
parser = argparse.ArgumentParser()
parser.add_argument('dataset_dir', help='Dataset dir path')
parser.add_argument('-s',
'--start',
help='Start timestamp (ex. 2017-06-10T10:00:23)')
args = parser.parse_args()
dataset_dir = args.dataset_dir
start = args.start
if not osp.exists(dataset_dir):
print('Please install dataset to: %s' % dataset_dir)
quit(1)
label_files = []
for stamp_dir in os.listdir(dataset_dir):
stamp_dir = osp.join(dataset_dir, stamp_dir)
label_file = osp.join(stamp_dir, 'label.npz')
if osp.exists(label_file):
label_files.append(label_file)
else:
label_files.append(None)
print('==> Size of dataset: All: %d, Annotated: %d.' %
(len(label_files), len(list(filter(None, label_files)))))
PKG_DIR = rospkg.RosPack().get_path('jsk_arc2017_common')
with open(osp.join(PKG_DIR, 'config/label_names.yaml')) as f:
object_names = yaml.load(f)
object_names.append('__unlabeled__')
print('==> Press keys: [q] to quit, [n] to go next, [p] to go previous')
stamp_dirs = list(sorted(os.listdir(dataset_dir)))
i = 0
while True:
stamp = datetime.datetime.fromtimestamp(int(stamp_dirs[i]) / 1e9)
if start and stamp < dateutil.parser.parse(start):
i += 1
continue
start = None
stamp_dir = osp.join(dataset_dir, stamp_dirs[i])
print('%s: %s' % (stamp.isoformat(), stamp_dir))
img_file = osp.join(stamp_dir, 'image.jpg')
img = skimage.io.imread(img_file)
depth_file = osp.join(stamp_dir, 'depth.npz')
depth = np.load(depth_file)['arr_0']
depth_viz = colorize_depth(depth, min_value=0.4, max_value=1.0)
label_file = osp.join(stamp_dir, 'label.npz')
if osp.exists(label_file):
label = np.load(label_file)['arr_0']
mask_unlabeled = label == -1
label[mask_unlabeled] = object_names.index('__unlabeled__')
img_labeled = img.copy()
img_labeled[mask_unlabeled] = \
np.random.randint(0, 255, (mask_unlabeled.sum(), 3))
label_viz = label2rgb(lbl=label,
img=img_labeled,
label_names=dict(enumerate(object_names)))
else:
label_viz = np.zeros_like(img)
viz = jsk_recognition_utils.get_tile_image([img, label_viz, depth_viz])
cv2.imshow('view_jsk_v1', viz[:, :, ::-1])
key = cv2.waitKey(0)
if key == ord('q'):
break
elif key == ord('n'):
if i == len(stamp_dirs) - 1:
print('Reached the end edge of the dataset')
continue
i += 1
elif key == ord('p'):
if i == 0:
print('Reached the start edge of the dataset')
continue
i -= 1
else:
continue
