def draw_annotation_images(self,
plate,
training_set,
container,
learner,
rid=""):
cldir = dict([(cname, join(learner.samples_dir, cname)) \
for cname in learner.class_names.values()])
# create dir per class name
for dir_ in cldir.values():
makedirs(dir_)
for obj in training_set.itervalues():
rgb_value = ccore.RGBValue(*hex2rgb(obj.strHexColor))
file_ = 'PL%s___P%s___T%05d___X%04d___Y%04d' \
%(plate, self.P, self._iT,
obj.oCenterAbs[0], obj.oCenterAbs[1])
obj.file = file_
file_ = join(cldir[obj.strClassName],
'%s___%s.png' % (file_, rid + "_%s"))
container.exportObject(obj.iId, file_ % "img", file_ % "msk")
container.markObjects([obj.iId], rgb_value, False, True)
ccore.drawFilledCircle(ccore.Diff2D(*obj.oCenterAbs), 3,
container.img_rgb, rgb_value)
def render_tracks(self, frame, size, n=5, thick=True, radius=3):
img_conn = ccore.Image(*size)
img_split = ccore.Image(*size)
if n < 0 or frame - n + 1 < self.start_frame:
current = self.start_frame
n = frame - current + 1
else:
current = frame - n + 1
found = False
for i in range(n):
col = int(max(255. * (i + 1) / n, 255))
if current in self._frame_data:
preframe = self.closest_preceding_frame(current)
if preframe is not None:
found = True
for objIdP in self._frame_data[preframe]:
nodeIdP = self.node_id(preframe, objIdP)
objP = self.graph.node_data(nodeIdP)
if self.graph.out_degree(nodeIdP) > 1:
img = img_split
else:
img = img_conn
for edgeId in self.graph.out_arcs(nodeIdP):
nodeIdC = self.graph.tail(edgeId)
objC = self.graph.node_data(nodeIdC)
ccore.drawLine(ccore.Diff2D(*objP.oCenterAbs),
ccore.Diff2D(*objC.oCenterAbs),
img,
col,
thick=thick)
ccore.drawFilledCircle(
ccore.Diff2D(*objC.oCenterAbs), radius,
img_conn, col)
current += 1
if not found and frame in self._frame_data:
for objId in self._frame_data[frame]:
nodeId = self.node_id(frame, objId)
obj = self.graph.node_data(nodeId)
ccore.drawFilledCircle(ccore.Diff2D(*obj.oCenterAbs), radius,
img_conn, col)
return img_conn, img_split
def render_tracks(self, frame, size, n=5, thick=True, radius=3):
img_conn = ccore.Image(*size)
img_split = ccore.Image(*size)
if n < 0 or frame-n+1 < self.start_frame:
current = self.start_frame
n = frame-current+1
else:
current = frame-n+1
found = False
for i in range(n):
col = int(max(255.*(i+1)/n, 255))
if current in self._frame_data:
preframe = self.closest_preceding_frame(current)
if preframe is not None:
found = True
for objIdP in self._frame_data[preframe]:
nodeIdP = self.node_id(preframe, objIdP)
objP = self.graph.node_data(nodeIdP)
if self.graph.out_degree(nodeIdP) > 1:
img = img_split
else:
img = img_conn
for edgeId in self.graph.out_arcs(nodeIdP):
nodeIdC = self.graph.tail(edgeId)
objC = self.graph.node_data(nodeIdC)
ccore.drawLine(ccore.Diff2D(*objP.oCenterAbs),
ccore.Diff2D(*objC.oCenterAbs),
img, col,
thick=thick)
ccore.drawFilledCircle(ccore.Diff2D(*objC.oCenterAbs),
radius, img_conn, col)
current += 1
if not found and frame in self._frame_data:
for objId in self._frame_data[frame]:
nodeId = self.node_id(frame, objId)
obj = self.graph.node_data(nodeId)
ccore.drawFilledCircle(ccore.Diff2D(*obj.oCenterAbs),
radius, img_conn, col)
return img_conn, img_split
def draw_annotation_images(self, plate, training_set, container, learner, rid=""):
cldir = dict([(cname, join(learner.samples_dir, cname)) \
for cname in learner.class_names.values()])
# create dir per class name
for dir_ in cldir.values():
makedirs(dir_)
for obj in training_set.itervalues():
rgb_value = ccore.RGBValue(*hex2rgb(obj.strHexColor))
file_ = 'PL%s___P%s___T%05d___X%04d___Y%04d' \
%(plate, self.P, self._iT,
obj.oCenterAbs[0], obj.oCenterAbs[1])
obj.file = file_
file_ = join(cldir[obj.strClassName],
'%s___%s.png' %(file_, rid+"_%s"))
container.exportObject(obj.iId, file_ %"img", file_ %"msk")
container.markObjects([obj.iId], rgb_value, False, True)
ccore.drawFilledCircle(ccore.Diff2D(*obj.oCenterAbs),
3, container.img_rgb, rgb_value)
def collectObjects(self, plate_id, P, lstReader, oLearner, byTime=True):
#channel_name = oLearner.strChannelId
strRegionId = oLearner.strRegionId
img_rgb = None
self._oLogger.debug('* collecting samples...')
# bSuccess = True
# channels = sorted(self._channel_registry.values())
# primary_cChannel = None
# for channel2 in lstChannels:
#
# self.time_holder.prepare_raw_image(channel)
# self.time_holder.apply_segmentation(oChannel2, oPrimaryChannel)
#
# if oPrimaryChannel is None:
# assert oChannel2.RANK == 1
# oPrimaryChannel = oChannel2
self.process(apply = False, extract_features = False)
# self._channel_registry
oChannel = self._channel_registry[oLearner.channel_name]
oContainer = oChannel.get_container(strRegionId)
objects = oContainer.getObjects()
object_lookup = {}
for oReader in lstReader:
lstCoordinates = None
if (byTime and P == oReader.getPosition() and self._iT in oReader):
lstCoordinates = oReader[self._iT]
elif (not byTime and P in oReader):
lstCoordinates = oReader[P]
#print "moo", P, oReader.getPosition(), byTime, self._iT in oReader
#print lstCoordinates, byTime, self.P, oReader.keys()
if not lstCoordinates is None:
#print self.iP, self._iT, lstCoordinates
for dctData in lstCoordinates:
label = dctData['iClassLabel']
if (label in oLearner.dctClassNames and
dctData['iPosX'] >= 0 and
dctData['iPosX'] < oContainer.width and
dctData['iPosY'] >= 0 and
dctData['iPosY'] < oContainer.height):
center1 = ccore.Diff2D(dctData['iPosX'],
dctData['iPosY'])
# test for obj_id "under" annotated pixel first
obj_id = oContainer.img_labels[center1]
# if not background: valid obj_id found
if obj_id > 0:
dict_append_list(object_lookup, label, obj_id)
# otherwise try to find nearest object in a search
# radius of 30 pixel (compatibility with CellCounter)
else:
dists = []
for obj_id, obj in objects.iteritems():
diff = obj.oCenterAbs - center1
dist_sq = diff.squaredMagnitude()
# limit to 30 pixel radius
if dist_sq < 900:
dists.append((obj_id, dist_sq))
if len(dists) > 0:
dists.sort(lambda a,b: cmp(a[1], b[1]))
obj_id = dists[0][0]
dict_append_list(object_lookup, label, obj_id)
object_ids = set(flatten(object_lookup.values()))
objects_del = set(objects.keys()) - object_ids
for obj_id in objects_del:
oContainer.delObject(obj_id)
self.time_holder.apply_features(oChannel)
region = oChannel.get_region(strRegionId)
learner_objects = []
for label, object_ids in object_lookup.iteritems():
class_name = oLearner.dctClassNames[label]
hex_color = oLearner.dctHexColors[class_name]
rgb_value = ccore.RGBValue(*hexToRgb(hex_color))
for obj_id in object_ids:
obj = region[obj_id]
obj.iLabel = label
obj.strClassName = class_name
obj.strHexColor = hex_color
if (obj.oRoi.upperLeft[0] >= 0 and
obj.oRoi.upperLeft[1] >= 0 and
obj.oRoi.lowerRight[0] < oContainer.width and
obj.oRoi.lowerRight[1] < oContainer.height):
iCenterX, iCenterY = obj.oCenterAbs
strPathOutLabel = os.path.join(oLearner.dctEnvPaths['samples'],
oLearner.dctClassNames[label])
safe_mkdirs(strPathOutLabel)
strFilenameBase = 'PL%s___P%s___T%05d___X%04d___Y%04d' % (plate_id, self.P, self._iT, iCenterX, iCenterY)
obj.sample_id = strFilenameBase
learner_objects.append(obj)
strFilenameImg = os.path.join(strPathOutLabel, '%s___img.png' % strFilenameBase)
strFilenameMsk = os.path.join(strPathOutLabel, '%s___msk.png' % strFilenameBase)
# FIXME: export Objects is segfaulting for objects
# where its bounding box is touching the border
# i.e. one corner point equals zero!
oContainer.exportObject(obj_id,
strFilenameImg,
strFilenameMsk)
oContainer.markObjects([obj_id], rgb_value, False, True)
#print obj_id, obj.oCenterAbs, iCenterX, iCenterY
print '*** CSdebug: drawFilledCircle', iCenterX, iCenterY
ccore.drawFilledCircle(ccore.Diff2D(iCenterX, iCenterY),
3, oContainer.img_rgb, rgb_value)
if len(learner_objects) > 0:
oLearner.applyObjects(learner_objects)
# we don't want to apply None for feature names
oLearner.setFeatureNames(oChannel.lstFeatureNames)
strPathOut = os.path.join(oLearner.dctEnvPaths['controls'])
safe_mkdirs(strPathOut)
oContainer.exportRGB(os.path.join(strPathOut,
"P%s_T%05d_C%s_R%s.jpg" %\
(self.P, self._iT, oLearner.strChannelId, oLearner.strRegionId)),
'90')
img_rgb = oContainer.img_rgb
return img_rgb
