def visualize_stow_contents(work_order):
from jsk_apc2015_common.util import rescale
tote_img = cv2.imread(osp.join(PKG_PATH, 'models/tote/image.jpg'))
object_list = jsk_apc2015_common.get_object_list()
object_imgs = {}
for obj in object_list:
img_path = osp.join(OLD_PKG_PATH, 'models/{obj}/image.jpg'.format(obj=obj))
img = cv2.imread(img_path)
h, w = img.shape[:2]
if h > w:
img = np.rollaxis(img, 1)
object_imgs[obj] = img
# draw object images on tote image
tote_region = [[190,230],[1080,790]]
region_h = tote_region[1][1] - tote_region[0][1]
region_w = tote_region[1][0] - tote_region[0][0]
max_obj_h,max_obj_w = region_h / 3, region_w / 4
tote_x_min,tote_y_min = tote_region[0][0], tote_region[0][1]
x_min, y_min = tote_x_min, tote_y_min
for obj in work_order:
obj_img = object_imgs[obj]
scale_h = 1. * max_obj_h / obj_img.shape[0]
scale_w = 1. * max_obj_w / obj_img.shape[1]
scale = min([scale_h, scale_w])
obj_img = rescale(obj_img, scale)
obj_h, obj_w = obj_img.shape[:2]
x_max, y_max = x_min + obj_w, y_min + obj_h
tote_img[y_min:y_max, x_min:x_max] = obj_img
x_min += max_obj_w
if x_max >= region_w :
x_min = tote_x_min
y_min += max_obj_h
return tote_img
def visualize_stow_contents(work_order):
"""Visualize stow contents with passed work order.
Args:
work_order (list): objects in the stow.
Returns:
tote_img (~numpy.ndarray): image of objects over the tote.
"""
from jsk_apc2015_common.util import rescale
rp = rospkg.RosPack()
pkg_path = rp.get_path(PKG)
tote_img = cv2.imread(osp.join(pkg_path, 'models/tote/image.jpg'))
object_list = jsk_apc2015_common.get_object_list()
object_imgs = {}
pkg_path = rp.get_path('jsk_apc2015_common')
for obj in object_list:
img_path = osp.join(pkg_path, 'models/{obj}/image.jpg'.format(obj=obj))
img = cv2.imread(img_path)
h, w = img.shape[:2]
if h > w:
img = np.rollaxis(img, 1)
object_imgs[obj] = img
# draw object images on tote image
tote_region = [[190, 230], [1080, 790]]
region_h = tote_region[1][1] - tote_region[0][1]
region_w = tote_region[1][0] - tote_region[0][0]
max_obj_h, max_obj_w = region_h / 3, region_w / 4
tote_x_min, tote_y_min = tote_region[0][0], tote_region[0][1]
x_min, y_min = tote_x_min, tote_y_min
for obj in work_order:
obj_img = object_imgs[obj]
scale_h = 1. * max_obj_h / obj_img.shape[0]
scale_w = 1. * max_obj_w / obj_img.shape[1]
scale = min([scale_h, scale_w])
obj_img = rescale(obj_img, scale)
obj_h, obj_w = obj_img.shape[:2]
x_max, y_max = x_min + obj_w, y_min + obj_h
tote_img[y_min:y_max, x_min:x_max] = obj_img
x_min += max_obj_w
if x_max >= region_w:
x_min = tote_x_min
y_min += max_obj_h
return tote_img
def visualize_stow_contents(work_order):
"""Visualize stow contents with passed work order.
Args:
work_order (list): objects in the stow.
Returns:
tote_img (~numpy.ndarray): image of objects over the tote.
"""
from jsk_apc2015_common.util import rescale
rp = rospkg.RosPack()
pkg_path = rp.get_path(PKG)
tote_img = cv2.imread(osp.join(pkg_path, 'models/tote/image.jpg'))
object_list = jsk_apc2015_common.get_object_list()
object_imgs = {}
pkg_path = rp.get_path('jsk_apc2015_common')
for obj in object_list:
img_path = osp.join(pkg_path, 'models/{obj}/image.jpg'.format(obj=obj))
img = cv2.imread(img_path)
h, w = img.shape[:2]
if h > w:
img = np.rollaxis(img, 1)
object_imgs[obj] = img
# draw object images on tote image
tote_region = [[190, 230], [1080, 790]]
region_h = tote_region[1][1] - tote_region[0][1]
region_w = tote_region[1][0] - tote_region[0][0]
max_obj_h, max_obj_w = region_h / 3, region_w / 4
tote_x_min, tote_y_min = tote_region[0][0], tote_region[0][1]
x_min, y_min = tote_x_min, tote_y_min
for obj in work_order:
obj_img = object_imgs[obj]
scale_h = 1. * max_obj_h / obj_img.shape[0]
scale_w = 1. * max_obj_w / obj_img.shape[1]
scale = min([scale_h, scale_w])
obj_img = rescale(obj_img, scale)
obj_h, obj_w = obj_img.shape[:2]
x_max, y_max = x_min + obj_w, y_min + obj_h
tote_img[y_min:y_max, x_min:x_max] = obj_img
x_min += max_obj_w
if x_max >= region_w:
x_min = tote_x_min
y_min += max_obj_h
return tote_img
def visualize_bin_contents(bin_contents, work_order=None,
extra_img_paths=None):
"""Returns visualized image of bin contents.
Args:
bin_contents (dict): contents of each bin.
work_order (dict): target objects for each bin (default: ``None``).
extra_img_paths (dict): {object_name: img_path}
Returns:
kiva_pod_img (~numpy.ndarray):
visualized image of listed objects over the Kiva Pod image.
"""
from jsk_apc2015_common.util import rescale
pkg_path = rospkg.RosPack().get_path(PKG)
object_img_paths = {
obj: osp.join(pkg_path, 'models/{0}/image.jpg'.format(obj))
for obj in get_object_list()
}
if extra_img_paths is not None:
object_img_paths.update(extra_img_paths)
# initialize variables
kiva_pod_img = cv2.imread(osp.join(pkg_path, 'models/kiva_pod/image.jpg'))
BIN_REGION = {
'a': ((0, 50), (640, 610)),
'b': ((640, 50), (1410, 610)),
'c': ((1410, 50), (2060, 610)),
'd': ((0, 680), (640, 1200)),
'e': ((640, 680), (1410, 1200)),
'f': ((1410, 680), (2060, 1200)),
'g': ((0, 1280), (640, 1770)),
'h': ((640, 1280), (1410, 1770)),
'i': ((1410, 1280), (2060, 1770)),
'j': ((0, 1850), (640, 2430)),
'k': ((640, 1850), (1410, 2430)),
'l': ((1410, 1850), (2060, 2430)),
}
# get object images
object_imgs = {}
for obj, img_path in object_img_paths.items():
img = cv2.imread(img_path)
h, w = img.shape[:2]
if h > w:
img = np.rollaxis(img, 1)
object_imgs[obj] = img
# draw objects
for bin, contents in bin_contents.items():
if not contents:
continue # skip empty bin
bin_pt1, bin_pt2 = BIN_REGION[bin]
bin_region = kiva_pod_img[bin_pt1[1]:bin_pt2[1], bin_pt1[0]:bin_pt2[0]]
x_num, y_num = _get_tile_shape(len(contents))
bin_h, bin_w = bin_region.shape[:2]
max_obj_h, max_obj_w = bin_h // y_num, bin_w // x_num
for i_y in xrange(y_num):
y_min = int(1. * bin_h / y_num * i_y)
for i_x in xrange(x_num):
x_min = int(1. * bin_w / x_num * i_x)
if contents:
obj = contents.pop()
obj_img = object_imgs[obj]
scale_h = 1. * max_obj_h / obj_img.shape[0]
scale_w = 1. * max_obj_w / obj_img.shape[1]
scale = min([scale_h, scale_w])
obj_img = rescale(obj_img, scale)
obj_h, obj_w = obj_img.shape[:2]
x_max, y_max = x_min + obj_w, y_min + obj_h
bin_region[y_min:y_max, x_min:x_max] = obj_img
# highlight work order
if work_order and work_order[bin] == obj:
pt1 = (x_min + 10, y_min + 10)
pt2 = (x_max - 10, y_max - 10)
cv2.rectangle(bin_region, pt1, pt2, (0, 255, 0), 3)
# draw bin regions
for bin, region in BIN_REGION.items():
bin_pt1, bin_pt2 = region
bin_h, bin_w = bin_pt2[1] - bin_pt1[1], bin_pt2[0] - bin_pt1[0]
cv2.rectangle(kiva_pod_img, bin_pt1, bin_pt2, (0, 0, 255), 2)
text_pos = (bin_pt2[0] - bin_h//2 - 100, bin_pt2[1])
cv2.putText(kiva_pod_img, bin.upper(), text_pos,
cv2.FONT_HERSHEY_PLAIN, 10, (0, 0, 255), 3)
return kiva_pod_img
def visualize_bin_contents(bin_contents, work_order=None,
extra_img_paths=None):
"""Returns visualized image of bin contents.
Args:
bin_contents (dict): contents of each bin.
work_order (dict): target objects for each bin (default: ``None``).
extra_img_paths (dict): {object_name: img_path}
Returns:
kiva_pod_img (~numpy.ndarray):
visualized image of listed objects over the Kiva Pod image.
"""
from jsk_apc2015_common.util import rescale
pkg_path = rospkg.RosPack().get_path(PKG)
object_img_paths = {
obj: osp.join(pkg_path, 'models/{0}/image.jpg'.format(obj))
for obj in get_object_list()
}
if extra_img_paths is not None:
object_img_paths.update(extra_img_paths)
# initialize variables
kiva_pod_img = cv2.imread(osp.join(pkg_path, 'models/kiva_pod/image.jpg'))
BIN_REGION = {
'a': ((0, 50), (640, 610)),
'b': ((640, 50), (1410, 610)),
'c': ((1410, 50), (2060, 610)),
'd': ((0, 680), (640, 1200)),
'e': ((640, 680), (1410, 1200)),
'f': ((1410, 680), (2060, 1200)),
'g': ((0, 1280), (640, 1770)),
'h': ((640, 1280), (1410, 1770)),
'i': ((1410, 1280), (2060, 1770)),
'j': ((0, 1850), (640, 2430)),
'k': ((640, 1850), (1410, 2430)),
'l': ((1410, 1850), (2060, 2430)),
}
# get object images
object_imgs = {}
for obj, img_path in object_img_paths.items():
img = cv2.imread(img_path)
h, w = img.shape[:2]
if h > w:
img = np.rollaxis(img, 1)
object_imgs[obj] = img
# draw objects
for bin, contents in bin_contents.items():
if not contents:
continue # skip empty bin
bin_pt1, bin_pt2 = BIN_REGION[bin]
bin_region = kiva_pod_img[bin_pt1[1]:bin_pt2[1], bin_pt1[0]:bin_pt2[0]]
x_num, y_num = _get_tile_shape(len(contents))
bin_h, bin_w = bin_region.shape[:2]
max_obj_h, max_obj_w = bin_h // y_num, bin_w // x_num
for i_y in xrange(y_num):
y_min = int(1. * bin_h / y_num * i_y)
for i_x in xrange(x_num):
x_min = int(1. * bin_w / x_num * i_x)
if contents:
obj = contents.pop()
obj_img = object_imgs[obj]
scale_h = 1. * max_obj_h / obj_img.shape[0]
scale_w = 1. * max_obj_w / obj_img.shape[1]
scale = min([scale_h, scale_w])
obj_img = rescale(obj_img, scale)
obj_h, obj_w = obj_img.shape[:2]
x_max, y_max = x_min + obj_w, y_min + obj_h
bin_region[y_min:y_max, x_min:x_max] = obj_img
# highlight work order
if work_order and work_order[bin] == obj:
pt1 = (x_min + 10, y_min + 10)
pt2 = (x_max - 10, y_max - 10)
cv2.rectangle(bin_region, pt1, pt2, (0, 255, 0), 8)
# draw bin regions
for bin, region in BIN_REGION.items():
bin_pt1, bin_pt2 = region
bin_h, bin_w = bin_pt2[1] - bin_pt1[1], bin_pt2[0] - bin_pt1[0]
cv2.rectangle(kiva_pod_img, bin_pt1, bin_pt2, (0, 0, 255), 2)
text_pos = (bin_pt2[0] - bin_h//2 - 100, bin_pt2[1])
cv2.putText(kiva_pod_img, bin.upper(), text_pos,
cv2.FONT_HERSHEY_PLAIN, 10, (0, 0, 255), 6)
return kiva_pod_img
def visualize_bin_contents(bin_contents, work_order=None):
from jsk_apc2015_common.util import rescale
# initialize variables
kiva_pod_img = cv2.imread(osp.join(PKG_PATH, 'models/kiva_pod/image.jpg'))
BIN_REGION = {
'a': ((0, 50), (640, 610)),
'b': ((640, 50), (1410, 610)),
'c': ((1410, 50), (2060, 610)),
'd': ((0, 680), (640, 1200)),
'e': ((640, 680), (1410, 1200)),
'f': ((1410, 680), (2060, 1200)),
'g': ((0, 1280), (640, 1770)),
'h': ((640, 1280), (1410, 1770)),
'i': ((1410, 1280), (2060, 1770)),
'j': ((0, 1850), (640, 2430)),
'k': ((640, 1850), (1410, 2430)),
'l': ((1410, 1850), (2060, 2430)),
}
# get object images
object_list = get_object_list()
object_imgs = {}
for obj in object_list:
img_path = osp.join(PKG_PATH, 'models/{obj}/image.jpg'.format(obj=obj))
img = cv2.imread(img_path)
h, w = img.shape[:2]
if h > w:
img = np.rollaxis(img, 1)
object_imgs[obj] = img
# draw objects
for bin, contents in bin_contents.items():
bin_pt1, bin_pt2 = BIN_REGION[bin]
bin_region = kiva_pod_img[bin_pt1[1]:bin_pt2[1], bin_pt1[0]:bin_pt2[0]]
x_num, y_num = _get_tile_shape(len(contents))
bin_h, bin_w = bin_region.shape[:2]
max_obj_h, max_obj_w = bin_h // y_num, bin_w // x_num
for i_y in xrange(y_num):
y_min = int(1. * bin_h / y_num * i_y)
for i_x in xrange(x_num):
x_min = int(1. * bin_w / x_num * i_x)
if contents:
obj = contents.pop()
obj_img = object_imgs[obj]
scale_h = 1. * max_obj_h / obj_img.shape[0]
scale_w = 1. * max_obj_w / obj_img.shape[1]
scale = min([scale_h, scale_w])
obj_img = rescale(obj_img, scale)
obj_h, obj_w = obj_img.shape[:2]
x_max, y_max = x_min + obj_w, y_min + obj_h
bin_region[y_min:y_max, x_min:x_max] = obj_img
# highlight work order
if work_order and work_order[bin] == obj:
pt1 = (x_min + 10, y_min + 10)
pt2 = (x_max - 10, y_max - 10)
cv2.rectangle(bin_region, pt1, pt2, (0, 255, 0), 3)
# draw bin regions
for bin, region in BIN_REGION.items():
bin_pt1, bin_pt2 = region
bin_h, bin_w = bin_pt2[1] - bin_pt1[1], bin_pt2[0] - bin_pt1[0]
cv2.rectangle(kiva_pod_img, bin_pt1, bin_pt2, (0, 0, 255), 2)
text_pos = (bin_pt2[0] - bin_h//2 - 100, bin_pt2[1])
cv2.putText(kiva_pod_img, bin.upper(), text_pos,
cv2.FONT_HERSHEY_PLAIN, 10, (0, 0, 255), 3)
return kiva_pod_img
