)
# debug_cloud([ clean_cloud_aligned_color ], world=world)
# debug_cloud([ shelf_cloud, clean_cloud, clean_cloud_aligned ])
# pcd.write(clean_cloud_aligned_color, open('/tmp/{}_{}.pcd'.format(self.knowledge_base.target_object, self.knowledge_base.target_bin), 'w'))
# dilate the object mask
from scipy.misc import imsave
from scipy.ndimage.morphology import binary_dilation
object_mask_dilated = binary_dilation(object_mask, iterations=2)
# label connected components
from scipy.ndimage.measurements import label
# (object_labeled, label_count) = label(object_mask_dilated)
(object_labeled, label_count) = distance_label(cloud, object_mask_dilated)
logger.info("found {} connected components".format(label_count))
if not label_count:
logger.warning("found no connected components")
mask = object_mask
else:
object_blobs = sorted(
[object_labeled == (l + 1) for l in range(label_count)], key=lambda b: -numpy.count_nonzero(b)
)
# for (i, blob) in enumerate(object_blobs):
# logger.info('blob {}: {} points'.format(i, numpy.count_nonzero(blob)))
# filter out blobs with fewer than 1000 points
object_blobs = filter(lambda blob: numpy.count_nonzero(blob[clean_mask]) >= 1000, object_blobs)
def localizeSpecificObject(self, bin, object):
SHELF_DIMS_PATH = os.path.join('kb', 'shelf_dims.json')
SHELF_CLOUD_PATH = os.path.join('perception', 'segmentation', 'models', 'shelf_thin_10000.npy')
# acquire the camera image
address = { 'left': '192.168.0.103', 'right': '192.168.0.104' }[self.knowledge_base.active_limb]
#address = { 'left': '192.168.0.103', 'right': '192.168.0.103' }[self.knowledge_base.active_limb]
camera = RemoteCamera(address, xform=self.camera_xform)
camera.read()
camera.close()
# load the shelf subtraction data
bin_bounds = json.load(open(SHELF_DIMS_PATH))[bin]
# load and transform the shelf point cloud
shelf_cloud = numpy.load(open(SHELF_CLOUD_PATH))
shelf_cloud = shelf_cloud.dot(numpy.array(self.knowledge_base.shelf_xform[0]).reshape((3,3))) + self.knowledge_base.shelf_xform[1]
# load and transform the shelf model
world = WorldModel()
shelf = world.loadRigidObject(os.path.join('klampt_models', 'north_shelf', 'shelf_with_bins.obj'))
shelf.setTransform(*self.knowledge_base.shelf_xform)
# clean up the point cloud
(clean_mask, clean_cloud) = camera.clean()
_, clean_cloud_aligned, _, clean_object_mask = shelf_subtract(shelf_cloud, clean_cloud, shelf, bin_bounds, downsample=1000)
object_mask = numpy.zeros(camera.cloud.shape[:2], dtype=numpy.bool)
object_mask[clean_mask] = clean_object_mask
#clean_cloud_aligned_color = map(lambda x: x[0] + [ x[1] ], zip(clean_cloud_aligned[clean_object_mask].tolist(), camera.colorize()[clean_mask][clean_object_mask].tolist()))
#debug_cloud([ clean_cloud_aligned_color ], [ self.base_xform, self.camera_xform ])
#debug_cloud([ shelf_cloud, clean_cloud, clean_cloud_aligned ])
#pcd.write(clean_cloud_aligned_color, open('/tmp/{}_{}.pcd'.format(self.knowledge_base.target_object, self.knowledge_base.target_bin), 'w'))
(target_bin, target_object) = (self.knowledge_base.target_bin, self.knowledge_base.target_object)
bin_contents = self.knowledge_base.bin_contents[target_bin] if target_bin in self.knowledge_base.bin_contents else []
# perform special handling
if target_object == 'rollodex_mesh_collection_jumbo_pencil_cup':
logger.info('running specialized rollodex cups detection')
return self._localizeRollodexCups(world, camera.color, clean_cloud_aligned, camera.depth_uv, clean_mask, object_mask)
# dilate the object mask
# from scipy.misc import imsave
from scipy.ndimage.morphology import binary_dilation
object_mask_dilated = binary_dilation(object_mask, iterations=2)
# imsave('/tmp/test.png', object_mask)
# label connected components
(object_labeled, label_count) = distance_label(camera.cloud, object_mask_dilated, threshold=0.01)
logger.info('found {} connected components'.format(label_count))
#KH: added to help debugging without changing order bin contents
if object not in bin_contents:
logger.warning("Warning, trying to detect object "+object+" not in bin contents, adding a temporary entry")
bin_contents = bin_contents + [object]
bin_contents_with_shelf = bin_contents + [ 'shelf' ]
if not label_count:
logger.warning('found no connected components')
mask = object_mask
bin_contents = self.knowledge_base.bin_contents[self.knowledge_base.target_bin]
confusion_row = dict(zip(bin_contents, [0]*len(bin_contents)))
else:
object_blobs = sorted([ object_labeled == (l+1) for l in range(label_count) ], key=lambda b: -numpy.count_nonzero(b))
#for (i, blob) in enumerate(object_blobs):
# logger.debug('blob {}: {} points'.format(i, numpy.count_nonzero(blob)))
# filter out blobs with fewer than 1000 points
min_point_count = 1000
object_blobs = filter(lambda blob: numpy.count_nonzero(blob[clean_mask]) >= min_point_count, object_blobs)
#debug_cloud([ clean_cloud_aligned[(object_mask & blob_mask)[clean_mask]] for blob_mask in object_blobs ], [ self.base_xform, self.camera_xform ], wait=False)
known_object_count = len(bin_contents)
if known_object_count == 0:
# return all large blobs
n = len(object_blobs)
# take only the largest n blobs
blob_mask = reduce(numpy.bitwise_or, object_blobs[:n])
mask = object_mask & blob_mask
logger.warning('no objects in bin... -> returning all blobs')
else:
# load the matching statistics
match_stats = json.load(open(os.path.join('perception', 'segmentation', 'match_stats.json')))
# load the object hue histograms
known_histograms = {}
for obj in bin_contents_with_shelf:
#array = numpy.load(os.path.join('perception', 'hue_array', '{}.npz'.format(obj)))['arr_0']
#known_histograms[obj] = numpy.histogram(array, bins=range(0,181), density=True)[0].reshape(-1,1).astype(numpy.float32)
known_histograms[obj] = numpy.load(os.path.join('perception', 'uv_hist2', '{}.npz'.format(obj)))['arr_0']
# compute the color validity mask
color_valid_mask = ~invalid_mask(camera.depth_uv)
#KH addition 5/23: test to see whether blobs should be broken up
if len(object_blobs)==1 and len(known_histograms) > 2:
logger.info("Trying KMeans segmentation to break up large blob")
blob_mask = reduce(numpy.bitwise_or, object_blobs)
mask = object_mask & blob_mask & color_valid_mask
labels,k,score = xyzrgb_segment_kmeans(camera.cloud[mask],camera.colorize()[mask],known_histograms.values())
labels = numpy.array(labels)
object_blobs = []
for i in xrange(len(known_histograms)):
blank = numpy.zeros_like(object_mask,dtype=numpy.bool)
blank[mask] = (labels==i)
object_blobs.append(blank)
# score each blob for each object in the bin
blob_scores = []
blob_accepts = []
blob_rejects = []
matched_blobs = {}
confusion_matrix = []
for (i, blob) in enumerate(object_blobs):
# compute the blob histogram
blob_uv = [ rgb2yuv(*rgb)[1:3] for rgb in camera.colorize()[object_mask & blob & color_valid_mask] ]
blob_histogram = make_uv_hist(blob_uv)
# compare the blob to each possible object
scores = dict([ (obj, 2 * numpy.minimum(blob_histogram, histogram).sum() - numpy.maximum(blob_histogram, histogram).sum()) for (obj, histogram) in known_histograms.items() ])
logger.debug('blob {}:'.format(i))
blob_scores.append(scores)
logger.debug(' scores: {}'.format([ '{}={}'.format(*x) for x in scores.items() ]))
# apply the cutoff to each score and associate with positive confidence
accepts = dict([ (obj, match_stats[obj]['ppv']) for obj in bin_contents_with_shelf if scores[obj] >= match_stats[obj]['cutoff'] ])
blob_accepts.append(accepts)
logger.debug(' accepts: {}'.format([ '{}={}'.format(*x) for x in accepts.items() ]))
# apply the cutoff to each score and associate with negative confidence
rejects = dict([ (obj, match_stats[obj]['npv']) for obj in bin_contents_with_shelf if scores[obj] < match_stats[obj]['cutoff'] ])
blob_rejects.append(rejects)
logger.debug(' rejects: {}'.format([ '{}={}'.format(*x) for x in rejects.items() ]))
# populate the confusion matrix
shelf_probability = 0.1
S = lambda o: match_stats[o]['specificity']
iS = lambda o: 1 - S(o)
total_probability = sum(map(S, accepts)) + sum(map(iS, rejects)) + shelf_probability
confusion_matrix.append([ [ iS(o), S(o) ][ o in accepts ] / total_probability for o in bin_contents ])
# resolve multiple assignments
if len(accepts) > 1:
# choose the object with the highest matched confidence
best_match_object = sorted(accepts.items(), key=lambda a: -a[1])[0][0]
# remove all other objects
for (obj, confidence) in accepts.items():
if obj != best_match_object:
del accepts[obj]
logger.warn('blob {} multiple accept ignored: {}={}'.format(i, obj, confidence))
if len(accepts) == 1:
# a single match so record it
matched_blobs.setdefault(accepts.keys()[0], []).append(i)
logger.info('blob {} assigned to {} by single match'.format(i, accepts.keys()[0]))
confusion_matrix = numpy.array(confusion_matrix)
logger.debug('confusion matrix:\n{}'.format(numpy.array(confusion_matrix)))
# assign blobs by least threshold difference until all objects are assigned (excluding shelf) or all blobs are assigned
while len([ k for k in matched_blobs.keys() if k != 'shelf']) < len(bin_contents) and len(sum(matched_blobs.values(), [])) < len(object_blobs):
best_match_objects = []
for (i, scores) in enumerate(blob_scores):
# only consider unmatched blobs
if i not in sum(matched_blobs.values(), []):
threshold_differences = [ [obj, match_stats[obj]['cutoff'] - score] for (obj, score) in scores.items() ]
best_match_objects.append([ i ] + sorted(threshold_differences, key=lambda d: d[1])[0])
# choose the least threshold difference across all blobs and objects
best_blob, best_object, _ = sorted(best_match_objects, key=lambda b: b[2])[0]
matched_blobs.setdefault(best_object, []).append(best_blob)
logger.warn('blob {} assigned to {} by least threshold difference'.format(best_blob, best_object))
# report unmatched blobs and objects (excluding shelf)
for obj in bin_contents:
if obj not in matched_blobs:
logger.warn('no blobs matched {}'.format(obj))
for i in range(len(object_blobs)):
if i not in sum(matched_blobs.values(), []):
logger.warn('blob {} is unassigned'.format(i))
for obj in bin_contents_with_shelf:
if obj in matched_blobs:
print obj
mask = reduce(numpy.bitwise_or, [ object_blobs[i] for i in matched_blobs[obj] ], numpy.zeros_like(object_mask))
object_cloud = clean_cloud_aligned[mask[clean_mask]]
xform = (so3.identity(), object_cloud.mean(axis=0).tolist())
object_cloud_color = map(lambda x: x[0] + [ x[1] ], zip(object_cloud.tolist(), camera.colorize()[mask].tolist()))
#debug_cloud([ object_cloud_color ], [ self.base_xform, self.camera_xform, xform ])
# check that the target object was found
if target_object not in matched_blobs:
logger.error('no blobs assigned to target object')
return None
# return blob for the selected object
mask = reduce(numpy.bitwise_or, [ object_blobs[i] for i in matched_blobs[target_object] ], numpy.zeros_like(object_mask))
# return the confusion matrix rows for the selected object
confusion_rows = confusion_matrix[matched_blobs[target_object], :]
confusion_row = dict([ (obj, confusion_rows[:, i].mean()) for (i, obj) in enumerate(bin_contents) ])
logger.info('confusion row for {}: {}'.format(target_object, confusion_row))
object_cloud = clean_cloud_aligned[mask[clean_mask]]
#xform = (so3.identity(), object_cloud.mean(axis=0).tolist())
xform = se3.identity()
object_cloud_color = map(lambda x: x[0] + [ x[1] ], zip(object_cloud.tolist(), camera.colorize()[mask].tolist()))
#debug_cloud([ object_cloud_color ], [ self.base_xform, self.camera_xform, xform ])
# downsample the cloud to about 1000 points
ratio = int(len(object_cloud) / 1000)
if ratio > 1:
object_cloud_color = object_cloud_color[::ratio]
pcd.write(object_cloud_color, open('/tmp/{}_{}.pcd'.format(self.knowledge_base.target_object, self.knowledge_base.target_bin), 'w'))
return (xform, object_cloud_color, confusion_row)
