def draw(uim):
'''Tells the HotSpotterAPI to draw the current selection in the current
mode. It will automatically switch tabs to the current view.'''
cm, gm = uim.hs.get_managers('cm','gm')
#current_tab = uim.hsgui.main_skel.tablesTabWidget.currentIndex
if uim.state in ['splash_view']:
uim.hs.dm.show_splash()
elif uim.state in ['annotate']:
if gm.is_valid(uim.sel_gid):
uim.hs.dm.show_image(gm.gx(uim.sel_gid))
elif uim.state in ['chip_view']:
if cm.is_valid(uim.sel_cid):
uim.hs.dm.show_chip(cm.cx(uim.sel_cid))
uim.select_tab('chip', block_draw=True)
elif uim.state in ['image_view']:
if gm.is_valid(uim.sel_gid):
uim.hs.dm.show_image(gm.gx(uim.sel_gid))
uim.select_tab('image', block_draw=True)
elif uim.state in ['result_view']:
if uim.sel_res != None:
logdbg('Drawing Query Results')
uim.hs.dm.show_query(uim.sel_res)
uim.select_tab('result', block_draw=True)
else:
logerr('I dont know how to draw in state: '+str(uim.state))
def chip_alloc(cm, nAlloc):
'Allocate room for nAlloc more chips'
logdbg('Allocating room for %d more chips' % nAlloc)
cm.cx2_cid = np.append(cm.cx2_cid, np.zeros(nAlloc, dtype=np.uint32))
# Implicit Data Local Identifiers
cm.cx2_nx = np.append(cm.cx2_nx, np.zeros(nAlloc, dtype=np.uint32))
cm.cx2_gx = np.append(cm.cx2_gx, np.zeros(nAlloc, dtype=np.uint32))
# Explicit Data
cm.cx2_roi = np.append(cm.cx2_roi,
np.zeros((nAlloc, 4), dtype=np.uint32),
axis=0)
cm.cx2_theta = np.append(cm.cx2_theta,
np.zeros(nAlloc, dtype=np.float32),
axis=0)
cm.cx2_fpts = np.append(cm.cx2_fpts, np.empty(nAlloc, dtype=object))
cm.cx2_fdsc = np.append(cm.cx2_fdsc, np.empty(nAlloc, dtype=object))
# Feature Representation
cm.cx2_dirty_bit = np.append(cm.cx2_dirty_bit,
np.ones(nAlloc, dtype=np.bool))
# Reverse Index
idAlloc = len(cm.cid2_cx) - len(cm.cx2_cid)
if idAlloc > 0:
cm.cid2_cx = np.append(cm.cid2_cx,
np.zeros(idAlloc, dtype=np.uint32))
# User Properties
for prop in cm.user_props.iterkeys():
cm.user_props[prop] = np.append(cm.user_props[prop],
np.empty(nAlloc, dtype=object))
def cx2_rr(qm, qcx, qhs=None):
hs = qm.hs
qhs = hs if qhs is None else qhs
rr = RawResults(qcx, hs, qhs)
qm.rr = rr
# Save in query database.
rr_dpath = qhs.iom.ensure_computed_directory('query_results')
# Filename points to result database
depends = ['chiprep', 'preproc', 'model', 'query']
algo_suffix = hs.am.get_algo_suffix(depends)
samp_suffix = hs.vm.get_samp_suffix()
rr_suffix = algo_suffix + samp_suffix
# Populate raw results
force_recompute = False #TODO PREFERENCE
if rr.has_result(rr_dpath, rr_suffix) and not force_recompute:
logdbg('Loading query.')
rr.load_result(rr_dpath, rr_suffix)
else:
logmsg('Recomputing query.')
(qfpts, qfdsc) = qhs.cm.get_feats(qcx)
rr.qfpts = qfpts
rr.qfdsc = qfdsc
rr.qchip_size = qhs.cm.cx2_chip_size(qcx)
qm.repopulate_raw_results(rr, hs, qhs)
rr.save_result(rr_dpath, rr_suffix)
return rr
def add_images(dm, img_list, title_list=[]):
fig = dm.get_figure()
fig.clf()
num_images = len(img_list)
#
dm.ax_list = [None] * num_images
title_list = title_list + ["NoTitle"] * (num_images - len(title_list))
# Fit Images into a grid
max_columns = min(num_images, max(1, dm.draw_prefs.num_result_cols))
if max_columns == 0:
max_columns = 1
nr = int(ceil(float(num_images) / max_columns))
nc = max_columns if num_images >= max_columns else 1
#
gs = gridspec.GridSpec(nr, nc)
for i in xrange(num_images):
# logdbg(' Adding the '+str(i)+'th Image')
# logdbg(' * type(img_list[i]): %s'+str(type(img_list[i])))
# logdbg(' * img_list[i].shape: %s'+str(img_list[i].shape))
dm.ax_list[i] = fig.add_subplot(gs[i])
imgplot = dm.ax_list[i].imshow(img_list[i])
imgplot.set_cmap("gray")
dm.ax_list[i].get_xaxis().set_ticks([])
dm.ax_list[i].get_yaxis().set_ticks([])
dm.ax_list[i].set_title(title_list[i])
# transData: data coordinates -> display coordinates
# transAxes: axes coordinates -> display coordinates
# transLimits: data - > axes
#
# gs.tight_layout(fig)
logdbg("Added " + str(num_images) + " images/axes")
def draw(uim):
'''Tells the HotSpotterAPI to draw the current selection in the current
mode. It will automatically switch tabs to the current view.'''
cm, gm = uim.hs.get_managers('cm', 'gm')
#current_tab = uim.hsgui.main_skel.tablesTabWidget.currentIndex
if uim.state in ['splash_view']:
uim.hs.dm.show_splash()
elif uim.state in ['annotate']:
if gm.is_valid(uim.sel_gid):
uim.hs.dm.show_image(gm.gx(uim.sel_gid))
elif uim.state in ['chip_view']:
if cm.is_valid(uim.sel_cid):
uim.hs.dm.show_chip(cm.cx(uim.sel_cid))
uim.select_tab('chip', block_draw=True)
elif uim.state in ['image_view']:
if gm.is_valid(uim.sel_gid):
uim.hs.dm.show_image(gm.gx(uim.sel_gid))
uim.select_tab('image', block_draw=True)
elif uim.state in ['result_view']:
if uim.sel_res != None:
logdbg('Drawing Query Results')
uim.hs.dm.show_query(uim.sel_res)
uim.select_tab('result', block_draw=True)
else:
logerr('I dont know how to draw in state: ' + str(uim.state))
def cx2_rr(qm, qcx, qhs=None):
hs = qm.hs
qhs = hs if qhs is None else qhs
rr = RawResults(qcx, hs, qhs)
qm.rr = rr
# Save in query database.
rr_dpath = qhs.iom.ensure_computed_directory('query_results')
# Filename points to result database
depends = ['chiprep', 'preproc', 'model', 'query']
algo_suffix = hs.am.get_algo_suffix(depends)
samp_suffix = hs.vm.get_samp_suffix()
rr_suffix = algo_suffix + samp_suffix
# Populate raw results
force_recompute = False #TODO PREFERENCE
if rr.has_result(rr_dpath, rr_suffix) and not force_recompute:
logdbg('Loading query.')
rr.load_result(rr_dpath, rr_suffix)
else:
logmsg('Recomputing query.')
(qfpts, qfdsc) = qhs.cm.get_feats(qcx)
rr.qfpts = qfpts
rr.qfdsc = qfdsc
rr.qchip_size = qhs.cm.cx2_chip_size(qcx)
qm.repopulate_raw_results(rr, hs, qhs)
rr.save_result(rr_dpath, rr_suffix)
return rr
def __image_csv_func(iom, csv_data, csv_headers=None):
""" A function which reads a single line of csv image data """
"""
gid = None
gname = None
aif = None
if csv_headers != None: pass"""
if len(csv_data) == 3:
# Format where extension is part of name
gid = int(csv_data[0])
gname = csv_data[1]
aif = csv_data[2]
logdbg("Adding Image")
elif len(csv_data) == 4:
# Format where extension is its own field
gid = int(csv_data[0])
gnameext = csv_data[2]
gname_noext = csv_data[1]
if gname_noext.find(".") == -1 and gnameext.find(".") == -1:
gname = gname_noext + "." + gnameext
else:
gname = gname_noext + gnameext
aif = csv_data[3]
logdbg("Adding Image (old way)")
iom.hs.gm.add_img(gid, gname, aif)
def _check_altfname(iom, alt_names=None):
"Checks for a legacy data table"
alt_dirs = [
iom.get_internal_dpath(),
iom.hs.db_dpath,
join(iom.hs.db_dpath, "data"),
join(iom.hs.db_dpath, "data", "..", "data", ".."),
]
for adir in iter(alt_dirs):
for aname in iter(alt_names):
alt_fpath = normpath(join(adir, aname))
logdbg("Checking: " + alt_fpath)
if exists(alt_fpath):
logwarn("Using Alternative Datatable " + alt_fpath)
timestamp = str(time.time())
backup_fpath = normpath(alt_fpath + "." + timestamp + ".bak")
logwarn("Creating Backup: " + backup_fpath)
shutil.copyfile(alt_fpath, backup_fpath)
return alt_fpath
if iom.hs.db_dpath.find(iom.internal_dname) >= 0:
# Disallow Hotspotter directories inside HotSpotter directories
new_db_path = iom.hs.db_dpath[0 : iom.hs.db_dpath.find(iom.internal_dname)]
logwarn("Changing this data dir " + iom.hs.db_dpath)
logwarn("To that data dir " + new_db_path)
iom.hs.db_dpath = new_db_path
return "CSV_Name_not_found"
def save_model(vm):
# See if the model is savable
if not vm.model_prefs.save_load_model:
logdbg('Can NOT save the visual model due to preferences')
return False
if vm.isDirty:
raise Exception('Can NOT save the visual model due to dirty index')
if vm.flann is None:
raise Exception('Can NOT save the visual model without a flann index')
logdbg('Building dictionary to save')
# TODO: This dictionary should just exist and not be
# directly tied to this class.
# Build a dictionary of savable model terms
to_save_dict = {key : vm.__dict__[key] \
for key in vm.savable_model_fields }
# Get the save paths
model_fpath = vm.hs.iom.get_model_fpath()
flann_index_fpath = vm.hs.iom.get_flann_index_fpath()
# Save the Model
logio('Saving model to: '+model_fpath)
np.savez(model_fpath, **to_save_dict)
# Save the Index
logio('Saving index to: '+flann_index_fpath)
vm.flann.save_index(flann_index_fpath)
logio('Model save was sucessfull')
return True
def restart(hs, db_dpath=None, autoload=True, save_pref_bit=True):
hs.data_loaded_bit = False
if hs.db_dpath != None and db_dpath == None:
db_dpath = hs.db_dpath
db_dpath = hs.smartget_db_dpath(db_dpath)
# --
hs.db_dpath = None
if hs.is_valid_db_dpath(db_dpath):
hs.db_dpath = db_dpath
if save_pref_bit:
logdbg('Setting db_dpath = '+str(db_dpath))
hs.core_prefs.update('database_dpath',db_dpath)
if hs.db_dpath is None:
logerr('Invalid Database. '+\
'Select an existing HotSpotter, StripeSpotter database. '+\
'To create a new database, select and empty directory. ')
hs.gm = ImageManager(hs)
hs.nm = NameManager(hs)
hs.cm = ChipManager(hs)
hs.vm = VisualModel(hs)
hs.qm = QueryManager(hs)
hs.em = ExperimentManager(hs)
if autoload == True:
hs.load_tables()
else:
logdbg('autoload is false.')
def _check_altfname(iom, alt_names=None):
'Checks for a legacy data table'
alt_dirs = [iom.get_internal_dpath(),
iom.hs.db_dpath,
join(iom.hs.db_dpath,'data'),
join(iom.hs.db_dpath,'data','..','data','..')]
for adir in iter(alt_dirs):
for aname in iter(alt_names):
alt_fpath = normpath(join(adir,aname))
logdbg('Checking: '+alt_fpath)
if exists(alt_fpath):
logwarn('Using Alternative Datatable '+alt_fpath)
timestamp = str(time.time())
backup_fpath = normpath(alt_fpath+'.'+timestamp+'.bak')
logwarn('Creating Backup: '+backup_fpath)
shutil.copyfile(alt_fpath, backup_fpath)
return alt_fpath
if iom.hs.db_dpath.find(iom.internal_dname) >= 0:
# Disallow Hotspotter directories inside HotSpotter directories
new_db_path = iom.hs.db_dpath[0:iom.hs.db_dpath.find(iom.internal_dname)]
logwarn('Changing this data dir '+iom.hs.db_dpath)
logwarn('To that data dir '+new_db_path)
iom.hs.db_dpath = new_db_path
return 'CSV_Name_not_found'
def __image_csv_func(iom, csv_data, csv_headers=None):
""" A function which reads a single line of csv image data """
'''
gid = None
gname = None
aif = None
if csv_headers != None: pass'''
if len(csv_data) == 3:
# Format where extension is part of name
gid = int(csv_data[0])
gname = csv_data[1]
aif = csv_data[2]
logdbg('Adding Image')
elif len(csv_data) == 4:
# Format where extension is its own field
gid = int(csv_data[0])
gnameext = csv_data[2]
gname_noext = csv_data[1]
if gname_noext.find('.') == -1 and gnameext.find('.') == -1:
gname = gname_noext + '.' + gnameext
else:
gname = gname_noext + gnameext
aif = csv_data[3]
logdbg('Adding Image (old way)')
iom.hs.gm.add_img(gid, gname, aif)
def add_images(dm, img_list, title_list=[]):
fig = dm.get_figure(); fig.clf()
num_images = len(img_list)
#
dm.ax_list = [None]*num_images
title_list = title_list + ['NoTitle']*(num_images-len(title_list))
# Fit Images into a grid
max_columns = min(num_images, max(1,dm.draw_prefs.num_result_cols))
if max_columns == 0: max_columns = 1
nr = int( ceil( float(num_images)/max_columns) )
nc = max_columns if num_images >= max_columns else 1
#
gs = gridspec.GridSpec( nr, nc )
for i in xrange(num_images):
#logdbg(' Adding the '+str(i)+'th Image')
#logdbg(' * type(img_list[i]): %s'+str(type(img_list[i])))
#logdbg(' * img_list[i].shape: %s'+str(img_list[i].shape))
dm.ax_list[i] = fig.add_subplot(gs[i])
imgplot = dm.ax_list[i].imshow(img_list[i])
imgplot.set_cmap('gray')
dm.ax_list[i].get_xaxis().set_ticks([])
dm.ax_list[i].get_yaxis().set_ticks([])
dm.ax_list[i].set_title(title_list[i])
# transData: data coordinates -> display coordinates
# transAxes: axes coordinates -> display coordinates
# transLimits: data - > axes
#
#gs.tight_layout(fig)
logdbg('Added '+str(num_images)+' images/axes')
def populatePrefTreeSlot(epw, pref_struct):
'Populates the Preference Tree Model'
logdbg('Bulding Preference Model of: '+repr(pref_struct))
epw.pref_model = pref_struct.createQPreferenceModel()
logdbg('Built: '+repr(epw.pref_model))
epw.pref_skel.prefTreeView.setModel(epw.pref_model)
epw.pref_skel.prefTreeView.header().resizeSection(0,250)
def select_tab(uim, tabname, block_draw=False):
logdbg('Selecting the ' + tabname + ' Tab')
if block_draw:
prevBlock = uim.hsgui.main_skel.tablesTabWidget.blockSignals(True)
tab_index = uim.tab_order.index(tabname)
uim.selectTabSignal.emit(tab_index)
if block_draw:
uim.hsgui.main_skel.tablesTabWidget.blockSignals(prevBlock)
def select_tab(uim, tabname, block_draw=False):
logdbg('Selecting the '+tabname+' Tab')
if block_draw:
prevBlock = uim.hsgui.main_skel.tablesTabWidget.blockSignals(True)
tab_index = uim.tab_order.index(tabname)
uim.selectTabSignal.emit(tab_index)
if block_draw:
uim.hsgui.main_skel.tablesTabWidget.blockSignals(prevBlock)
def __init__(iom, hs):
super( IOManager, iom ).__init__( hs )
logdbg('Creating IOManager')
iom.hs = hs
iom._hsroot = None
iom.settings_dpath = normpath(join(expanduser('~'),'.hotspotter'))
iom.internal_dname = '.hs_internals';
iom.dummy_delete = False #Dont actually delete things
iom.find_hotspotter_root_dir()
def _cut_out_roi(cm, img, roi):
logdbg('Image shape is: ' + str(img.shape))
[gh, gw] = [x - 1 for x in img.shape[0:2]]
[rx1, ry1, rw, rh] = [max(0, x) for x in roi]
rx2 = min(gw, rx1 + rw)
ry2 = min(gh, ry1 + rh)
logdbg('Cutting out chip using: ' + str((ry1, ry2, rx1, rx2)))
raw_chip = img[ry1:ry2, rx1:rx2, :]
return raw_chip
def delete_model(vm):
logdbg('Deleting Sample Index')
if vm.flann != None:
try:
vm.flann.delete_index()
vm.flann = None
except WindowsError:
logwarn('WARNING: FLANN is not deleting correctly')
vm.reset()
def __init__(iom, hs):
super(IOManager, iom).__init__(hs)
logdbg("Creating IOManager")
iom.hs = hs
iom._hsroot = None
iom.settings_dpath = normpath(join(expanduser("~"), ".hotspotter"))
iom.internal_dname = ".hs_internals"
iom.dummy_delete = False # Dont actually delete things
iom.find_hotspotter_root_dir()
def _cut_out_roi(cm, img, roi):
logdbg('Image shape is: '+str(img.shape))
[gh, gw] = [ x-1 for x in img.shape[0:2] ]
[rx1,ry1,rw,rh] = [ max(0,x) for x in roi]
rx2 = min(gw, rx1+rw)
ry2 = min(gh, ry1+rh)
logdbg('Cutting out chip using: '+str((ry1,ry2,rx1,rx2)))
raw_chip = img[ ry1:ry2, rx1:rx2, : ]
return raw_chip
def setPlotWidgetVisibleSlot(hsgui, bit=None): #None = toggle
if hsgui.plotWidget != None:
logdbg('Disabling Plot Widget')
if bit is None: bit = not hsgui.plotWidget.isVisible()
was_visible = hsgui.plotWidget.setVisible(bit)
if was_visible != bit:
if bit:
hsgui.main_skel.fignumSPIN.setValue(0)
else:
hsgui.main_skel.fignumSPIN.setValue(1)
def remove_file(iom, fpath):
if iom.dummy_delete:
logdbg("DummyDelete: %s" % fpath)
return False
logdbg("Deleting: %s" % fpath)
try:
os.remove(fpath)
except OSError as e:
logwarn("OSError: %s,\n Could not delete %s" % (str(e), fpath))
return False
return True
def remove_file(iom, fpath):
if iom.dummy_delete:
logdbg('DummyDelete: %s' % fpath)
return False
logdbg('Deleting: %s' % fpath)
try:
os.remove(fpath)
except OSError as e:
logwarn('OSError: %s,\n Could not delete %s' % (str(e), fpath))
return False
return True
def unload_features(cm, cxs):
if not np.iterable(cxs):
cxs = [cxs]
nRequest = len(cxs)
nUnload = nRequest - np.sum(cm.cx2_dirty_bit[cxs])
# Print unloaded cxs unless there are more than 3
logdbg('Unloading features: %r' % cxs)
logmsg('Unloading %d/%d features: ' % (nUnload, nRequest))
cm.cx2_fpts[cxs] = np.empty(nUnload, dtype=object)
cm.cx2_fdsc[cxs] = np.empty(nUnload, dtype=object)
cm.cx2_fdsc[cxs] = np.empty(nUnload, dtype=object)
cm.cx2_dirty_bit[cxs] = True
def bilateral_filter(img):
try:
import skimage.filter
img_uint8 = img
img_float = float32(img_uint8) / 255
#mode = Points outside the boundaries of the input are filled according to the given mode (?constant?, ?nearest?, ?reflect? or ?wrap?). Default is ?constant?.
img_bilat = skimage.filter.denoise_bilateral(img_float, win_size=20, sigma_range=1.6, sigma_spatial=1.6, bins=256, mode='reflect', cval='reflect')
return img_bilat
except Exception as ex:
logdbg('Scikits not found: %s' % str(ex))
logwarn('Scikits not found: %s' % str(ex))
return img
def unload_features(cm, cxs):
if not np.iterable(cxs):
cxs = [cxs]
nRequest = len(cxs)
nUnload = nRequest - np.sum(cm.cx2_dirty_bit[cxs])
# Print unloaded cxs unless there are more than 3
logdbg('Unloading features: %r' % cxs)
logmsg('Unloading %d/%d features: ' % (nUnload, nRequest))
cm.cx2_fpts[cxs] = np.empty(nUnload,dtype=object)
cm.cx2_fdsc[cxs] = np.empty(nUnload,dtype=object)
cm.cx2_fdsc[cxs] = np.empty(nUnload,dtype=object)
cm.cx2_dirty_bit[cxs] = True
def histeq(pil_img):
img = asarray(pil_img)
try:
from skimage import exposure
'Local histogram equalization'
# Equalization
img_eq_float64 = exposure.equalize_hist(img)
return Image.fromarray(uint8(np.round(img_eq_float64 * 255)))
except Exception as ex:
from hotspotter.tpl.other import imtools
logdbg('Scikits not found: %s' % str(ex))
logdbg('Using fallback histeq')
return Image.fromarray(imtools.histeq(img)).convert('L')
def remove_files_with_pattern(iom, dpath, fname_pattern, recursive_bit=True):
logdbg("Removing files in directory %r %s" % (dpath, ["", ", Recursively"][recursive_bit]))
logdbg("Removing files with pattern: %r" % fname_pattern)
num_removed = 0
num_matched = 0
for root, dname_list, fname_list in os.walk(dpath):
for fname in fnmatch.filter(fname_list, fname_pattern):
num_matched += 1
num_removed += iom.remove_file(join(root, fname))
if not recursive_bit:
break
logmsg("Removed %d/%d files" % (num_removed, num_matched))
return True
def remove_files_with_pattern(iom, dpath, fname_pattern, recursive_bit=True):
logdbg('Removing files in directory %r %s' % (dpath, ['', ', Recursively'][recursive_bit]))
logdbg('Removing files with pattern: %r' % fname_pattern)
num_removed = 0
num_matched = 0
for root, dname_list, fname_list in os.walk(dpath):
for fname in fnmatch.filter(fname_list, fname_pattern):
num_matched += 1
num_removed += iom.remove_file(join(root, fname))
if not recursive_bit:
break
logmsg('Removed %d/%d files' % (num_removed, num_matched))
return True
def histeq(pil_img):
img = asarray(pil_img)
try:
from skimage import exposure
'Local histogram equalization'
# Equalization
img_eq_float64 = exposure.equalize_hist(img)
return Image.fromarray(uint8(np.round(img_eq_float64*255)))
except Exception as ex:
from hotspotter.tpl.other import imtools
logdbg('Scikits not found: %s' % str(ex))
logdbg('Using fallback histeq')
return Image.fromarray(imtools.histeq(img)).convert('L')
def load_features(cm, _cxs=None, force_recomp=False):
if _cxs is None:
cxs = cm.get_valid_cxs()
elif type(_cxs) is types.ListType:
cxs = np.array(_cxs)
elif type(_cxs) in [types.IntType, types.LongType, np.uint32]:
cxs = np.array([_cxs])
else:
cxs = _cxs
count_feat = 0
is_dirty = np.bitwise_or(cm.cx2_dirty_bit[cxs], force_recomp)
num_samp = cxs.size
num_dirty = np.sum(is_dirty)
# HACKS
if not np.iterable(is_dirty):
is_dirty = np.array([is_dirty])
if not np.iterable(cxs):
cxs = np.array([cxs])
load_cx = cxs[is_dirty]
num_clean = num_samp - num_dirty
#logdbg('Loading Features: Dirty=%d ; #Clean=%d' % (num_dirty, num_clean))
if num_dirty == 0:
return
logio('Loading %d Feature Reps' % num_dirty)
am = cm.hs.am
for cx in iter(load_cx):
cid = cm.cx2_cid[cx]
if cid <= 0:
logwarn('WARNING: IX=' + str(cx) + ' is invalid')
continue
chiprep_fpath = cm.hs.iom.get_chiprep_fpath(cid)
# Ensure that the features exists
if force_recomp or not os.path.exists(chiprep_fpath):
logio('Computing and saving features of cid=' + str(cid))
hotspotter.ChipFunctions.precompute_chipreps(
cm.hs, [cx], num_procs=1, force_recompute=force_recomp)
# Load the features
logdbg('Loading features in ' + chiprep_fpath)
npz = np.load(chiprep_fpath)
fpts = npz['arr_0']
fdsc = npz['arr_1']
npz.close()
cm.cx2_fpts[cx] = fpts
cm.cx2_fdsc[cx] = fdsc
cm.cx2_dirty_bit[cx] = False
count_feat += len(fpts)
logdbg('* Loaded ' + str(count_feat) + ' keypoints and fdscriptors')
return True
def add_image_list(hs, image_list):
num_add = len(image_list)
logmsg('Selected '+str(num_add)+' images to import')
prev_g = hs.gm.num_g
logdbg('Prev #g=%d' % prev_g)
for src_img in image_list:
hs.gm.add_img(gid=None, gname=None, aif=False, src_img=src_img)
post_g = hs.gm.num_g
num_new = (post_g - prev_g)
num_old = num_add - num_new
logdbg('Post #g=%d' % post_g)
logmsg('Imported '+str(num_new)+' new images')
if num_old != 0:
logmsg('%d Images had already been copied into the image directory' % num_old)
def load_model(vm):
# See if the model is loadable
if not vm.model_prefs.save_load_model:
logdbg('Can NOT load the visual model')
return False
if not vm.flann is None:
raise Exception('Cannot load a model when FLANN already exists')
logdbg('Trying to load visual model')
# Check to see if new model on disk
model_fpath = vm.hs.iom.get_model_fpath()
if not os.path.exists(model_fpath):
logdbg(' * A saved model data file was missing: '+
model_fpath); return False
flann_index_fpath = vm.hs.iom.get_flann_index_fpath()
if not os.path.exists(flann_index_fpath):
logdbg(' * A saved flann index file was missing: '+
flann_index_fpath); return False
# Model and Flann Exist on disk
# Load the model data first
# Read model into dictionary
logmsg('Loading visual model data: ' + model_fpath)
npz = np.load(model_fpath)
for _key in npz.files:
vm.__dict__[_key] = npz[_key]
npz.close()
# Read FLANN index
logmsg('Loading FLANN index: '+ flann_index_fpath)
vm.flann = FLANN()
vm.flann.load_index(flann_index_fpath, vm.wx2_fdsc)
vm.isDirty = False
logmsg('The model was sucessfully loaded')
return True
def update_state(uim, new_state):
'Updates the state of the UI'
old_state = uim.state
logdbg('State Change: from: '+str(old_state)+', to: '+str(new_state))
if old_state == 'annotate':
if new_state != 'annotate_done':
uim.state = 'annotate_done'
logerr('Cannot enter new state while selecting an ROI. Attempting to recover')
elif old_state == 'querying':
if new_state != 'done_querying':
uim.state = 'done_querying'
logerr('Cannot enter new state while querying. Attempting to recover')
uim.state = new_state
uim.updateStateLabelSignal.emit(new_state)
return old_state
def load_features(cm, _cxs=None, force_recomp=False):
if _cxs is None:
cxs = cm.get_valid_cxs()
elif type(_cxs) is types.ListType:
cxs = np.array(_cxs)
elif type(_cxs) in [types.IntType, types.LongType, np.uint32]:
cxs = np.array([_cxs])
else:
cxs = _cxs
count_feat = 0
is_dirty = np.bitwise_or(cm.cx2_dirty_bit[cxs], force_recomp)
num_samp = cxs.size
num_dirty = np.sum(is_dirty)
# HACKS
if not np.iterable(is_dirty):
is_dirty = np.array([is_dirty])
if not np.iterable(cxs):
cxs = np.array([cxs])
load_cx = cxs[is_dirty]
num_clean = num_samp - num_dirty
#logdbg('Loading Features: Dirty=%d ; #Clean=%d' % (num_dirty, num_clean))
if num_dirty == 0:
return
logio('Loading %d Feature Reps' % num_dirty)
am = cm.hs.am
for cx in iter(load_cx):
cid = cm.cx2_cid[cx]
if cid <= 0:
logwarn('WARNING: IX='+str(cx)+' is invalid'); continue
chiprep_fpath = cm.hs.iom.get_chiprep_fpath(cid)
# Ensure that the features exists
if force_recomp or not os.path.exists(chiprep_fpath):
logio('Computing and saving features of cid='+str(cid))
hotspotter.ChipFunctions.precompute_chipreps(cm.hs, [cx], num_procs=1, force_recompute=force_recomp)
# Load the features
logdbg('Loading features in '+chiprep_fpath)
npz = np.load(chiprep_fpath)
fpts = npz['arr_0']
fdsc = npz['arr_1']
npz.close()
cm.cx2_fpts[cx] = fpts
cm.cx2_fdsc[cx] = fdsc
cm.cx2_dirty_bit[cx] = False
count_feat += len(fpts)
logdbg('* Loaded '+str(count_feat)+' keypoints and fdscriptors' )
return True
def end_draw(dm):
#gray()
logdbg('Finalizing Draw with '+str(len(dm.ax_list))+' axes')
fig = dm.get_figure()
#fig.subplots_adjust(hspace=0.2, wspace=0.2)
#fig.tight_layout(pad=.3, h_pad=None, w_pad=None)
#fig.tight_layout()
if dm.draw_prefs.in_qtc_bit:
try:
from IPython.back.display import display
display(fig)
except:
logwarn('Cannot Draw in QTConsole')
fig.show()
dm.hs.uim.redraw_gui()
fig.canvas.draw()
def batch_rename(hs, name1, name2):
logmsg('Batch Renaming %s to %s' % (name1, name2))
cm, nm = hs.get_managers('cm','nm')
if name1 == nm.UNIDEN_NAME():
logerr('Cannot batch rename '+str(name1)+'. It is UNIDENTIFIED and has special meaning')
if name1 not in nm.name2_nx.keys():
logerr('Cannot batch rename. '+str(name1)+' does not exist')
cx_list = nm.name2_cx_list(name1)[:] # COPY BEFORE YOU CHANGE. Man, sneaky errors
num_chips = len(cx_list)
if num_chips == 0:
logerr('Cannot batch rename. '+str(name1)+' has no chips')
logmsg('Renaming '+str(num_chips)+' chips: '+str(cx_list))
for cx in cx_list:
logdbg('Batch Rename '+str(cx))
cm.rename_chip(cx, name2)
return True
def compute_scores(rr, hs):
' Aggregates the votes for chips into votes for animal names'
logdbg('Aggregating Feature Scores ')
# get managers
nm = hs.nm
vm = hs.vm
cm = hs.cm
# get intermediate results
cx2_fm = rr.cx2_fm
cx2_fs = rr.cx2_fs
cx2_nscore = -np.ones(cm.max_cx + 1, dtype=np.float32)
cx2_cscore = compute_cscore(cx2_fs)
nx2_fx2_scores = {}
# Analyze the freq of a keypoint matching a name later
nx2_fx2_freq = {}
for (cx, fm) in enumerate(cx2_fm):
# Each keypoint votes for the highest scoring match
# it had to a particular name. (it can vote for multiple names)
fs = cx2_fs[cx]
nx = cm.cx2_nx[cx]
if nx == nm.UNIDEN_NX(): # UNIDEN is a unique name
nx = -int(cx)
if not nx in nx2_fx2_scores.keys():
nx2_fx2_scores[nx] = {}
nx2_fx2_freq[nx] = {}
for qfs, (qfx, fx) in iter(zip(fs, fm)):
if qfx in nx2_fx2_scores[nx].keys():
nx2_fx2_scores[nx][qfx] = max(nx2_fx2_scores[nx][qfx], qfs)
nx2_fx2_freq[nx][qfx] += 1
else:
nx2_fx2_scores[nx][qfx] = qfs
nx2_fx2_freq[nx][qfx] = 1
for nx in nx2_fx2_scores.keys():
fx2_scores = nx2_fx2_scores[nx]
scores = np.array(fx2_scores.values())
nscore = scores[scores > 0].sum()
if nx < 0: # UNIDEN HACK. See -int(cx) above
cx2_nscore[-nx] = nscore
else:
for cx in nm.nx2_cx_list[nx]:
cx2_nscore[cx] = nscore
rr.cx2_cscore = cx2_cscore
rr.cx2_nscore = cx2_nscore
def compute_scores(rr, hs):
' Aggregates the votes for chips into votes for animal names'
logdbg('Aggregating Feature Scores ')
# get managers
nm = hs.nm
vm = hs.vm
cm = hs.cm
# get intermediate results
cx2_fm = rr.cx2_fm
cx2_fs = rr.cx2_fs
cx2_nscore = -np.ones(cm.max_cx + 1, dtype=np.float32)
cx2_cscore = compute_cscore(cx2_fs)
nx2_fx2_scores = {}
# Analyze the freq of a keypoint matching a name later
nx2_fx2_freq = {}
for (cx, fm) in enumerate(cx2_fm):
# Each keypoint votes for the highest scoring match
# it had to a particular name. (it can vote for multiple names)
fs = cx2_fs[cx]
nx = cm.cx2_nx[cx]
if nx == nm.UNIDEN_NX(): # UNIDEN is a unique name
nx = -int(cx)
if not nx in nx2_fx2_scores.keys():
nx2_fx2_scores[nx] = {}
nx2_fx2_freq[nx] = {}
for qfs, (qfx, fx) in iter(zip(fs, fm)):
if qfx in nx2_fx2_scores[nx].keys():
nx2_fx2_scores[nx][qfx] = max(nx2_fx2_scores[nx][qfx], qfs)
nx2_fx2_freq[nx][qfx] += 1
else:
nx2_fx2_scores[nx][qfx] = qfs
nx2_fx2_freq[nx][qfx] = 1
for nx in nx2_fx2_scores.keys():
fx2_scores = nx2_fx2_scores[nx]
scores = np.array(fx2_scores.values())
nscore = scores[scores > 0].sum()
if nx < 0: # UNIDEN HACK. See -int(cx) above
cx2_nscore[-nx] = nscore
else:
for cx in nm.nx2_cx_list[nx]:
cx2_nscore[cx] = nscore
rr.cx2_cscore = cx2_cscore
rr.cx2_nscore = cx2_nscore
def __init__(res, hs, rr, qhs=None):
super(QueryResult, res).__init__(['hs','qhs'])
logdbg('Constructing Query Result')
res.rr = rr
res.hs = hs
res.qhs = hs if qhs is None else qhs
# Set Result Settings based on preferences
result_prefs = hs.am.algo_prefs.results
res.score_type = result_prefs.score
res.one_result_per_name = result_prefs.one_result_per_name
# Return all matches higher than this threshold. Subject to...
res.top_thresh = result_prefs.match_threshold
# And return between this many results
res.num_top_min = result_prefs.min_num_results
res.num_top_max = result_prefs.max_num_results
# And add extra runners up for context
res.num_extra_return = result_prefs.extra_num_results
def start_gui(uim, fac): # Currently needs facade access
logdbg('Creating the GUI')
uim.hsgui = HotspotterMainWindow(fac)
uim.hsgui.connectSignals(fac)
logdbg('Connecting Facade >> to >> GUI')
uim.updateDBStatsSignal.connect( uim.hsgui.updateDBStatsSlot )
uim.populateChipTblSignal.connect( uim.hsgui.populateChipTblSlot )
uim.populateImageTblSignal.connect( uim.hsgui.populateImageTblSlot )
uim.populateResultTblSignal.connect( uim.hsgui.populateResultTblSlot )
uim.updateStateLabelSignal.connect( uim.hsgui.updateStateLabelSlot )
uim.selectionSignal.connect( uim.hsgui.updateSelSpinsSlot )
uim.redrawGuiSignal.connect( uim.hsgui.redrawGuiSlot )
uim.populatePrefTreeSignal.connect( uim.hsgui.epw.populatePrefTreeSlot )
uim.selectTabSignal.connect( uim.hsgui.main_skel.tablesTabWidget.setCurrentIndex )
uim.setfignumSignal.connect( uim.hsgui.main_skel.fignumSPIN.setValue )
uim.populate_algo_settings()
def adapt_histeq(img):
try:
from skimage import exposure
# input uint8, output uint16
img_uint8 = img
img_uint16 = uint16(img)*2**8
img_adapteq_uint16 = exposure.equalize_adapthist(img_uint16,\
ntiles_x=8,\
ntiles_y=8,\
clip_limit=0.01,\
nbins=256)
img_adapteq_cropped_uint8 = uint8(img_adapteq_uint16[5:-5][5:-5] / uint16(2)**8 )
return img_adapteq_cropped_uint8
except Exception as ex:
logdbg('Scikits not found: %s' % str(ex))
logwarn('Scikits not found: %s' % str(ex))
return img
def end_draw(dm):
# gray()
logdbg("Finalizing Draw with " + str(len(dm.ax_list)) + " axes")
fig = dm.get_figure()
# fig.subplots_adjust(hspace=0.2, wspace=0.2)
# fig.tight_layout(pad=.3, h_pad=None, w_pad=None)
# fig.tight_layout()
if dm.draw_prefs.in_qtc_bit:
try:
from IPython.back.display import display
display(fig)
except:
logwarn("Cannot Draw in QTConsole")
fig.show()
dm.hs.uim.redraw_gui()
fig.canvas.draw()
def __init__(res, hs, rr, qhs=None):
super(QueryResult, res).__init__(['hs', 'qhs'])
logdbg('Constructing Query Result')
res.rr = rr
res.hs = hs
res.qhs = hs if qhs is None else qhs
# Set Result Settings based on preferences
result_prefs = hs.am.algo_prefs.results
res.score_type = result_prefs.score
res.one_result_per_name = result_prefs.one_result_per_name
# Return all matches higher than this threshold. Subject to...
res.top_thresh = result_prefs.match_threshold
# And return between this many results
res.num_top_min = result_prefs.min_num_results
res.num_top_max = result_prefs.max_num_results
# And add extra runners up for context
res.num_extra_return = result_prefs.extra_num_results
def annotate_orientation(dm):
logmsg('Please select an orientation of the torso (Click Two Points on the Image)')
try:
# Compute an angle from user interaction
sys.stdout.flush()
fig = dm.get_figure()
pts = np.array(fig.ginput(2))
logdbg('GInput Points are: '+str(pts))
# Get reference point to origin
refpt = pts[0] - pts[1]
#theta = np.math.atan2(refpt[1], refpt[0])
theta = np.math.atan(refpt[1]/refpt[0])
logmsg('The angle in radians is: '+str(theta))
return theta
except Exception as ex:
logmsg('Annotate Orientation Failed'+str(ex))
return None
def annotate_orientation(dm):
logmsg("Please select an orientation of the torso (Click Two Points on the Image)")
try:
# Compute an angle from user interaction
sys.stdout.flush()
fig = dm.get_figure()
pts = np.array(fig.ginput(2))
logdbg("GInput Points are: " + str(pts))
# Get reference point to origin
refpt = pts[0] - pts[1]
# theta = np.math.atan2(refpt[1], refpt[0])
theta = np.math.atan(refpt[1] / refpt[0])
logmsg("The angle in radians is: " + str(theta))
return theta
except Exception as ex:
logmsg("Annotate Orientation Failed" + str(ex))
return None
def adapt_histeq(img):
try:
from skimage import exposure
# input uint8, output uint16
img_uint8 = img
img_uint16 = uint16(img) * 2**8
img_adapteq_uint16 = exposure.equalize_adapthist(img_uint16,\
ntiles_x=8,\
ntiles_y=8,\
clip_limit=0.01,\
nbins=256)
img_adapteq_cropped_uint8 = uint8(img_adapteq_uint16[5:-5][5:-5] /
uint16(2)**8)
return img_adapteq_cropped_uint8
except Exception as ex:
logdbg('Scikits not found: %s' % str(ex))
logwarn('Scikits not found: %s' % str(ex))
return img
def bilateral_filter(img):
try:
import skimage.filter
img_uint8 = img
img_float = float32(img_uint8) / 255
#mode = Points outside the boundaries of the input are filled according to the given mode (?constant?, ?nearest?, ?reflect? or ?wrap?). Default is ?constant?.
img_bilat = skimage.filter.denoise_bilateral(img_float,
win_size=20,
sigma_range=1.6,
sigma_spatial=1.6,
bins=256,
mode='reflect',
cval='reflect')
return img_bilat
except Exception as ex:
logdbg('Scikits not found: %s' % str(ex))
logwarn('Scikits not found: %s' % str(ex))
return img
def is_valid_db_dpath(hs, db_dpath):
'Checks to see if database conforms to expected conventions'
if not os.path.exists(db_dpath):
logwarn('db_dpath \"'+str(db_dpath)+'\" doesnt exist')
return False
db_dpath_files = os.listdir(db_dpath)
if hs.iom.internal_dname in db_dpath_files:
logmsg('Opening a HotSpotter database: '+db_dpath)
elif 'images' in db_dpath_files or\
'data' in db_dpath_files:
logmsg('Opening a StripSpotter database: '+db_dpath)
elif len(db_dpath_files) == 0:
logmsg('Creating a new database: '+db_dpath)
else:
logwarn('Unknown database type: '+db_dpath)
logdbg('Files in dir: '+str(db_dpath_files))
return False
return True
def start_gui(uim, fac): # Currently needs facade access
logdbg('Creating the GUI')
uim.hsgui = HotspotterMainWindow(fac)
uim.hsgui.connectSignals(fac)
logdbg('Connecting Facade >> to >> GUI')
uim.updateDBStatsSignal.connect(uim.hsgui.updateDBStatsSlot)
uim.populateChipTblSignal.connect(uim.hsgui.populateChipTblSlot)
uim.populateImageTblSignal.connect(uim.hsgui.populateImageTblSlot)
uim.populateResultTblSignal.connect(uim.hsgui.populateResultTblSlot)
uim.updateStateLabelSignal.connect(uim.hsgui.updateStateLabelSlot)
uim.selectionSignal.connect(uim.hsgui.updateSelSpinsSlot)
uim.redrawGuiSignal.connect(uim.hsgui.redrawGuiSlot)
uim.populatePrefTreeSignal.connect(uim.hsgui.epw.populatePrefTreeSlot)
uim.selectTabSignal.connect(
uim.hsgui.main_skel.tablesTabWidget.setCurrentIndex)
uim.setfignumSignal.connect(uim.hsgui.main_skel.fignumSPIN.setValue)
uim.populate_algo_settings()
def annotate_roi(dm):
logmsg('Please Select a Rectangular Region of Interest (Click Two Points on the Image)')
try:
sys.stdout.flush()
fig = dm.get_figure()
pts = fig.ginput(2)
logdbg('GInput Points are: '+str(pts))
(x1, y1, x2, y2) = (pts[0][0], pts[0][1], pts[1][0], pts[1][1])
xm = min(x1,x2)
xM = max(x1,x2)
ym = min(y1,y2)
yM = max(y1,y2)
(x, y, w, h) = (xm, ym, xM-xm, yM-ym)
roi = array(round([x,y,w,h]),dtype=uint32)
logmsg('The new ROI is: '+str(roi))
return roi
except Exception as ex:
logmsg('Annotate ROI Failed'+str(ex))
return None
def update_state(uim, new_state):
'Updates the state of the UI'
old_state = uim.state
logdbg('State Change: from: ' + str(old_state) + ', to: ' +
str(new_state))
if old_state == 'annotate':
if new_state != 'annotate_done':
uim.state = 'annotate_done'
logerr(
'Cannot enter new state while selecting an ROI. Attempting to recover'
)
elif old_state == 'querying':
if new_state != 'done_querying':
uim.state = 'done_querying'
logerr(
'Cannot enter new state while querying. Attempting to recover'
)
uim.state = new_state
uim.updateStateLabelSignal.emit(new_state)
return old_state
def rename_chip(cm, cx, new_name):
nm = cm.hs.nm
cid = cm.cid(cx)
old_nx = cm.cx2_nx[cx]
old_name = nm.nx2_name[old_nx]
if old_name == new_name:
logdbg('new_name == old_name')
return
logmsg('Renaming cid=' + str(cid) + ' from ' + str(old_name) + ' to ' +
new_name)
if not new_name in nm.name2_nx.keys():
nm.add_name(-1, new_name)
old_nx = cm.cx2_nx[cx]
new_nx = nm.name2_nx[new_name]
#Debug
old_nid = nm.nx2_nid[old_nx]
new_nid = nm.nx2_nid[new_nx]
logdbg('Old Name Info: cid=%d cx=%d, nid=%d, nx=%d, name=%s' %
(cid, cx, old_nid, old_nx, old_name))
logdbg('New Name Info: cid=%d cx=%d, nid=%d, nx=%d, name=%s' %
(cid, cx, new_nid, new_nx, new_name))
#EndDebug
nm.nx2_cx_list[old_nx].remove(cx)
nm.nx2_cx_list[new_nx].append(cx)
cm.cx2_nx[cx] = new_nx