def test_delete_predictions_dialog(centered_pair_predictions, qtbot):
context = CommandContext.from_labels(centered_pair_predictions)
context.state["frame_idx"] = 123
context.state["video"] = centered_pair_predictions.videos[0]
context.state["has_frame_range"] = True
context.state["frame_range"] = (10, 20)
win = DeleteDialog(context=context)
assert len(win.get_frames_instances("user", "current video", "any")) == 0
assert len(win.get_frames_instances("predicted", "current video",
"any")) == 2274
assert len(win.get_frames_instances("predicted", "selected clip",
"any")) == 20
lf_inst_list = win.get_frames_instances(
"predicted", "current video except for selected clip", "any")
assert len(lf_inst_list) == 2274 - 20
win.tracks_menu.setCurrentIndex(3)
assert len(win.get_frames_instances("predicted", "selected clip",
"")) == 10
assert len(win.get_frames_instances("predicted", "selected clip",
"no")) == 0
def __init__(self, labels_path: Optional[str] = None, *args, **kwargs):
"""Initialize the app.
Args:
labels_path: Path to saved :class:`Labels` dataset.
Returns:
None.
"""
super(MainWindow, self).__init__(*args, **kwargs)
self.state = GuiState()
self.labels = Labels()
self.commands = CommandContext(state=self.state,
app=self,
update_callback=self.on_data_update)
self._menu_actions = dict()
self._buttons = dict()
self._child_windows = dict()
self.overlays = dict()
self.state.connect("filename", self.setWindowTitle)
self.state["skeleton"] = Skeleton()
self.state["labeled_frame"] = None
self.state["filename"] = None
self.state["show labels"] = True
self.state["show edges"] = True
self.state["edge style"] = "Line"
self.state["fit"] = False
self.state["color predicted"] = prefs["color predicted"]
self._initialize_gui()
if labels_path:
self.loadProjectFile(labels_path)
def test_delete_user_dialog(centered_pair_predictions):
context = CommandContext.from_labels(centered_pair_predictions)
context.state["labeled_frame"] = centered_pair_predictions.find(
centered_pair_predictions.videos[0], frame_idx=123)[0]
# No user instances, just predicted
assert len(context.state["labeled_frame"].user_instances) == 0
assert len(context.state["labeled_frame"].predicted_instances) == 2
context.addUserInstancesFromPredictions()
# Make sure we now have user instances
assert len(context.state["labeled_frame"].user_instances) == 2
def test_delete_all(centered_pair_predictions, qtbot):
context = CommandContext.from_labels(centered_pair_predictions)
context.state["frame_idx"] = 123
context.state["video"] = centered_pair_predictions.videos[0]
context.state["has_frame_range"] = True
context.state["frame_range"] = (10, 20)
win = DeleteDialog(context=context)
# Get all instances
lf_inst_list = win.get_frames_instances("all", "current video", "any")
assert len(lf_inst_list) == 2274
# Delete them
win._delete(lf_inst_list)
# Make sure the all empty frames were also deleted
assert centered_pair_predictions.labeled_frames == []
def test_delete_user_dialog(centered_pair_labels, qtbot):
context = CommandContext.from_labels(centered_pair_labels)
context.state["frame_idx"] = 123
context.state["video"] = centered_pair_labels.videos[0]
context.state["has_frame_range"] = True
context.state["frame_range"] = (10, 20)
win = DeleteDialog(context=context)
assert len(win.get_frames_instances("user", "current video", "any")) == 140
assert len(win.get_frames_instances("predicted", "current video",
"any")) == 0
assert len(win.get_frames_instances("user", "selected clip", "any")) == 2
lf_inst_list = win.get_frames_instances(
"user", "current video except for selected clip", "any")
assert len(lf_inst_list) == 138
self.context.labels.remove_instance(lf, inst, in_transaction=True)
if not lf.instances:
self.context.labels.remove(lf)
# Update caches since we skipped doing this after each deletion
self.context.labels.update_cache()
# Log update
self.context.changestack_push("delete instances")
if __name__ == "__main__":
app = QtWidgets.QApplication([])
from sleap import Labels
from sleap.gui.commands import CommandContext
labels = Labels.load_file(
"tests/data/json_format_v2/centered_pair_predictions.json")
context = CommandContext.from_labels(labels)
context.state["frame_idx"] = 123
context.state["video"] = labels.videos[0]
context.state["has_frame_range"] = True
context.state["frame_range"] = (10, 20)
win = DeleteDialog(context=context)
win.show()
app.exec_()
class MainWindow(QMainWindow):
"""The SLEAP GUI application.
Each project (`Labels` dataset) that you have loaded in the GUI will
have its own `MainWindow` object.
Attributes:
labels: The :class:`Labels` dataset. If None, a new, empty project
(i.e., :class:`Labels` object) will be created.
state: Object that holds GUI state, e.g., current video, frame,
whether to show node labels, etc.
"""
def __init__(self, labels_path: Optional[str] = None, *args, **kwargs):
"""Initialize the app.
Args:
labels_path: Path to saved :class:`Labels` dataset.
Returns:
None.
"""
super(MainWindow, self).__init__(*args, **kwargs)
self.state = GuiState()
self.labels = Labels()
self.commands = CommandContext(state=self.state,
app=self,
update_callback=self.on_data_update)
self._menu_actions = dict()
self._buttons = dict()
self._child_windows = dict()
self.overlays = dict()
self.state.connect("filename", self.setWindowTitle)
self.state["skeleton"] = Skeleton()
self.state["labeled_frame"] = None
self.state["filename"] = None
self.state["show labels"] = True
self.state["show edges"] = True
self.state["edge style"] = "Line"
self.state["fit"] = False
self.state["color predicted"] = prefs["color predicted"]
self._initialize_gui()
if labels_path:
self.loadProjectFile(labels_path)
def setWindowTitle(self, value):
"""Sets window title (if value is not None)."""
if value is not None:
super(MainWindow, self).setWindowTitle(
f"{value} - SLEAP Label v{sleap.version.__version__}")
def event(self, e: QEvent) -> bool:
"""Custom event handler.
We use this to ignore events that would clear status bar.
Args:
e: The event.
Returns:
True if we ignore event, otherwise returns whatever the usual
event handler would return.
"""
if e.type() == QEvent.StatusTip:
if e.tip() == "":
return True
return super().event(e)
def closeEvent(self, event):
"""Closes application window, prompting for saving as needed."""
if not self.state["has_changes"]:
# No unsaved changes, so accept event (close)
event.accept()
else:
msgBox = QMessageBox()
msgBox.setText("Do you want to save the changes to this project?")
msgBox.setInformativeText(
"If you don't save, your changes will be lost.")
msgBox.setStandardButtons(QMessageBox.Save | QMessageBox.Discard
| QMessageBox.Cancel)
msgBox.setDefaultButton(QMessageBox.Save)
ret_val = msgBox.exec_()
if ret_val == QMessageBox.Cancel:
# cancel close by ignoring event
event.ignore()
elif ret_val == QMessageBox.Discard:
# don't save, just close
event.accept()
elif ret_val == QMessageBox.Save:
# save
self.commands.saveProject()
# accept event (closes window)
event.accept()
@property
def labels(self):
return self.state["labels"]
@labels.setter
def labels(self, value):
self.state["labels"] = value
def _initialize_gui(self):
"""Creates menus, dock windows, starts timers to update gui state."""
self._create_color_manager()
self._create_video_player()
self.statusBar()
self._create_menus()
self._create_dock_windows()
self.load_overlays()
# Create timer to update state of gui at regular intervals
self.update_gui_timer = QtCore.QTimer()
self.update_gui_timer.timeout.connect(self._update_gui_state)
self.update_gui_timer.start(1)
def _create_video_player(self):
"""Creates and connects :class:`QtVideoPlayer` for gui."""
self.player = QtVideoPlayer(color_manager=self.color_manager,
state=self.state,
context=self.commands)
self.player.changedPlot.connect(self._after_plot_update)
self.player.view.instanceDoubleClicked.connect(
self.doubleClickInstance)
self.player.seekbar.selectionChanged.connect(
lambda: self.updateStatusMessage())
self.setCentralWidget(self.player)
def switch_frame(video):
# Jump to last labeled frame
last_label = self.labels.find_last(video)
if last_label is not None:
self.state["frame_idx"] = last_label.frame_idx
else:
self.state["frame_idx"] = 0
self.state.connect(
"video",
callbacks=[switch_frame, lambda x: self.updateSeekbarMarks()])
def _create_color_manager(self):
self.color_manager = ColorManager(self.labels)
self.color_manager.palette = self.state.get("palette",
default="standard")
def _create_menus(self):
"""Creates main application menus."""
shortcuts = Shortcuts()
# add basic menu item
def add_menu_item(menu, key: str, name: str, action: Callable):
menu_item = menu.addAction(name, action, shortcuts[key])
self._menu_actions[key] = menu_item
# set menu checkmarks
def connect_check(key):
self._menu_actions[key].setCheckable(True)
self._menu_actions[key].setChecked(self.state[key])
self.state.connect(key, self._menu_actions[key].setChecked)
# add checkable menu item connected to state variable
def add_menu_check_item(menu, key: str, name: str):
add_menu_item(menu, key, name, lambda: self.state.toggle(key))
connect_check(key)
# check and uncheck submenu items
def _menu_check_single(menu, item_text):
"""Helper method to select exactly one submenu item."""
for menu_item in menu.children():
if menu_item.text() == str(item_text):
menu_item.setChecked(True)
else:
menu_item.setChecked(False)
# add submenu with checkable items
def add_submenu_choices(menu, title, options, key):
submenu = menu.addMenu(title)
self.state.connect(key, lambda x: _menu_check_single(submenu, x))
for option in options:
submenu_item = submenu.addAction(
f"{option}", lambda x=option: self.state.set(key, x))
submenu_item.setCheckable(True)
self.state.emit(key)
### File Menu ###
fileMenu = self.menuBar().addMenu("File")
add_menu_item(fileMenu, "new", "New Project", self.commands.newProject)
add_menu_item(fileMenu, "open", "Open Project...",
self.commands.openProject)
import_types_menu = fileMenu.addMenu("Import...")
add_menu_item(
import_types_menu,
"import_coco",
"COCO dataset...",
self.commands.importCoco,
)
add_menu_item(
import_types_menu,
"import_dlc",
"DeepLabCut dataset...",
self.commands.importDLC,
)
add_menu_item(
import_types_menu,
"import_dpk",
"DeepPoseKit dataset...",
self.commands.importDPK,
)
add_menu_item(
import_types_menu,
"import_leap",
"LEAP Matlab dataset...",
self.commands.importLEAP,
)
add_menu_item(
import_types_menu,
"import_analysis",
"SLEAP Analysis HDF5...",
self.commands.importAnalysisFile,
)
add_menu_item(
fileMenu,
"import predictions",
"Merge into Project...",
self.commands.importPredictions,
)
fileMenu.addSeparator()
add_menu_item(fileMenu, "add videos", "Add Videos...",
self.commands.addVideo)
add_menu_item(fileMenu, "replace videos", "Replace Videos...",
self.commands.replaceVideo)
fileMenu.addSeparator()
add_menu_item(fileMenu, "save", "Save", self.commands.saveProject)
add_menu_item(fileMenu, "save as", "Save As...",
self.commands.saveProjectAs)
fileMenu.addSeparator()
add_menu_item(
fileMenu,
"export analysis",
"Export Analysis HDF5...",
self.commands.exportAnalysisFile,
)
fileMenu.addSeparator()
add_menu_item(fileMenu, "close", "Quit", self.close)
### Go Menu ###
goMenu = self.menuBar().addMenu("Go")
add_menu_item(
goMenu,
"goto next labeled",
"Next Labeled Frame",
self.commands.nextLabeledFrame,
)
add_menu_item(
goMenu,
"goto prev labeled",
"Previous Labeled Frame",
self.commands.previousLabeledFrame,
)
add_menu_item(
goMenu,
"goto next user",
"Next User Labeled Frame",
self.commands.nextUserLabeledFrame,
)
add_menu_item(
goMenu,
"goto next suggestion",
"Next Suggestion",
self.commands.nextSuggestedFrame,
)
add_menu_item(
goMenu,
"goto prev suggestion",
"Previous Suggestion",
self.commands.prevSuggestedFrame,
)
add_menu_item(
goMenu,
"goto next track spawn",
"Next Track Spawn Frame",
self.commands.nextTrackFrame,
)
goMenu.addSeparator()
def next_vid():
self.state.increment_in_list("video", self.labels.videos)
def prev_vid():
self.state.increment_in_list("video",
self.labels.videos,
reverse=True)
add_menu_item(goMenu, "next video", "Next Video", next_vid)
add_menu_item(goMenu, "prev video", "Previous Video", prev_vid)
goMenu.addSeparator()
add_menu_item(goMenu, "goto frame", "Go to Frame...",
self.commands.gotoFrame)
add_menu_item(goMenu, "select to frame", "Select to Frame...",
self.commands.selectToFrame)
### View Menu ###
viewMenu = self.menuBar().addMenu("View")
self.viewMenu = viewMenu # store as attribute so docks can add items
viewMenu.addSeparator()
add_menu_check_item(viewMenu, "color predicted",
"Color Predicted Instances")
add_submenu_choices(
menu=viewMenu,
title="Color Palette",
options=self.color_manager.palette_names,
key="palette",
)
distinctly_color_options = ("instances", "nodes", "edges")
add_submenu_choices(
menu=viewMenu,
title="Apply Distinct Colors To",
options=distinctly_color_options,
key="distinctly_color",
)
self.state["palette"] = prefs["palette"]
self.state["distinctly_color"] = "instances"
viewMenu.addSeparator()
add_menu_check_item(viewMenu, "show labels", "Show Node Names")
add_menu_check_item(viewMenu, "show edges", "Show Edges")
add_submenu_choices(
menu=viewMenu,
title="Edge Style",
options=("Line", "Wedge"),
key="edge style",
)
add_submenu_choices(
menu=viewMenu,
title="Trail Length",
options=(0, 10, 20, 50, 100, 200, 500),
key="trail_length",
)
viewMenu.addSeparator()
add_menu_check_item(viewMenu, "fit", "Fit Instances to View")
viewMenu.addSeparator()
seekbar_header_options = (
"None",
"Point Displacement (sum)",
"Point Displacement (max)",
"Primary Point Displacement (sum)",
"Primary Point Displacement (max)",
"Instance Score (sum)",
"Instance Score (min)",
"Point Score (sum)",
"Point Score (min)",
"Number of predicted points",
"Min Centroid Proximity",
)
add_submenu_choices(
menu=viewMenu,
title="Seekbar Header",
options=seekbar_header_options,
key="seekbar_header",
)
self.state["seekbar_header"] = "None"
self.state.connect("seekbar_header", self.setSeekbarHeader)
viewMenu.addSeparator()
### Label Menu ###
instance_adding_methods = dict(
best="Best",
template="Average Instance",
force_directed="Force Directed",
random="Random",
prior_frame="Copy prior frame",
prediction="Copy predictions",
)
def new_instance_menu_action():
method_key = [
key for (key, val) in instance_adding_methods.items()
if val == self.state["instance_init_method"]
]
if method_key:
self.commands.newInstance(init_method=method_key[0])
labelMenu = self.menuBar().addMenu("Labels")
add_menu_item(labelMenu, "add instance", "Add Instance",
new_instance_menu_action)
add_submenu_choices(
menu=labelMenu,
title="Instance Placement Method",
options=instance_adding_methods.values(),
key="instance_init_method",
)
self.state["instance_init_method"] = instance_adding_methods["best"]
add_menu_item(
labelMenu,
"delete instance",
"Delete Instance",
self.commands.deleteSelectedInstance,
)
labelMenu.addSeparator()
self.track_menu = labelMenu.addMenu("Set Instance Track")
add_menu_item(
labelMenu,
"transpose",
"Transpose Instance Tracks",
self.commands.transposeInstance,
)
add_menu_item(
labelMenu,
"delete track",
"Delete Instance and Track",
self.commands.deleteSelectedInstanceTrack,
)
labelMenu.addSeparator()
add_menu_item(
labelMenu,
"custom delete",
"Custom Instance Delete...",
self.commands.deleteDialog,
)
labelMenu.addSeparator()
add_menu_item(
labelMenu,
"select next",
"Select Next Instance",
lambda: self.state.increment_in_list(
"instance", self.state["labeled_frame"].instances_to_show),
)
add_menu_item(
labelMenu,
"clear selection",
"Clear Selection",
lambda: self.state.set("instance", None),
)
labelMenu.addSeparator()
### Predict Menu ###
predictionMenu = self.menuBar().addMenu("Predict")
add_menu_item(
predictionMenu,
"training",
"Run Training...",
lambda: self.showLearningDialog("training"),
)
add_menu_item(
predictionMenu,
"inference",
"Run Inference...",
lambda: self.showLearningDialog("inference"),
)
predictionMenu.addSeparator()
add_menu_item(
predictionMenu,
"show metrics",
"Evaluation Metrics for Trained Models...",
self.showMetricsDialog,
)
add_menu_item(
predictionMenu,
"visualize models",
"Visualize Model Outputs...",
self.visualizeOutputs,
)
predictionMenu.addSeparator()
add_menu_item(
predictionMenu,
"add instances from all frame predictions",
"Add Instances from All Predictions on Current Frame",
self.commands.addUserInstancesFromPredictions,
)
predictionMenu.addSeparator()
add_menu_item(
predictionMenu,
"delete frame predictions",
"Delete Predictions on Current Frame",
self.commands.deleteFramePredictions,
)
add_menu_item(
predictionMenu,
"delete all predictions",
"Delete All Predictions...",
self.commands.deletePredictions,
)
add_menu_item(
predictionMenu,
"delete clip predictions",
"Delete Predictions from Clip...",
self.commands.deleteClipPredictions,
)
add_menu_item(
predictionMenu,
"delete area predictions",
"Delete Predictions from Area...",
self.commands.deleteAreaPredictions,
)
add_menu_item(
predictionMenu,
"delete score predictions",
"Delete Predictions with Low Score...",
self.commands.deleteLowScorePredictions,
)
add_menu_item(
predictionMenu,
"delete frame limit predictions",
"Delete Predictions beyond Frame Limit...",
self.commands.deleteFrameLimitPredictions,
)
predictionMenu.addSeparator()
add_menu_item(
predictionMenu,
"export frames",
"Export Training Package...",
self.commands.exportDatasetWithImages,
)
add_menu_item(
predictionMenu,
"export clip",
"Export Video with Visual Annotations...",
self.commands.exportLabeledClip,
)
############
helpMenu = self.menuBar().addMenu("Help")
helpMenu.addAction("Keyboard Shortcuts", self.openKeyRef)
def process_events_then(self, action: Callable):
"""Decorates a function with a call to first process events."""
def wrapped_function(*args):
QApplication.instance().processEvents()
action(*args)
return wrapped_function
def _create_dock_windows(self):
"""Create dock windows and connects them to gui."""
def _make_dock(name, widgets=[], tab_with=None):
dock = QDockWidget(name)
dock.setAllowedAreas(Qt.LeftDockWidgetArea
| Qt.RightDockWidgetArea)
dock_widget = QWidget()
layout = QVBoxLayout()
for widget in widgets:
layout.addWidget(widget)
dock_widget.setLayout(layout)
dock.setWidget(dock_widget)
key = f"hide {name.lower()} dock"
if key in prefs and prefs[key]:
dock.hide()
self.addDockWidget(Qt.RightDockWidgetArea, dock)
self.viewMenu.addAction(dock.toggleViewAction())
if tab_with is not None:
self.tabifyDockWidget(tab_with, dock)
return layout
def _add_button(to, label, action, key=None):
key = key or label.lower()
btn = QPushButton(label)
btn.clicked.connect(action)
to.addWidget(btn)
self._buttons[key] = btn
return btn
####### Videos #######
videos_layout = _make_dock("Videos")
self.videosTable = GenericTableView(
state=self.state,
row_name="video",
is_activatable=True,
model=VideosTableModel(items=self.labels.videos,
context=self.commands),
)
videos_layout.addWidget(self.videosTable)
hb = QHBoxLayout()
_add_button(hb, "Show Video", self.videosTable.activateSelected)
_add_button(hb, "Add Videos", self.commands.addVideo)
_add_button(hb, "Remove Video", self.commands.removeVideo)
hbw = QWidget()
hbw.setLayout(hb)
videos_layout.addWidget(hbw)
####### Skeleton #######
skeleton_layout = _make_dock("Skeleton",
tab_with=videos_layout.parent().parent())
gb = QGroupBox("Nodes")
vb = QVBoxLayout()
self.skeletonNodesTable = GenericTableView(
state=self.state,
row_name="node",
model=SkeletonNodesTableModel(items=self.state["skeleton"],
context=self.commands),
)
vb.addWidget(self.skeletonNodesTable)
hb = QHBoxLayout()
_add_button(hb, "New Node", self.commands.newNode)
_add_button(hb, "Delete Node", self.commands.deleteNode)
hbw = QWidget()
hbw.setLayout(hb)
vb.addWidget(hbw)
gb.setLayout(vb)
skeleton_layout.addWidget(gb)
def _update_edge_src():
self.skeletonEdgesDst.model().skeleton = self.state["skeleton"]
gb = QGroupBox("Edges")
vb = QVBoxLayout()
self.skeletonEdgesTable = GenericTableView(
state=self.state,
row_name="edge",
model=SkeletonEdgesTableModel(items=self.state["skeleton"],
context=self.commands),
)
vb.addWidget(self.skeletonEdgesTable)
hb = QHBoxLayout()
self.skeletonEdgesSrc = QComboBox()
self.skeletonEdgesSrc.setEditable(False)
self.skeletonEdgesSrc.currentIndexChanged.connect(_update_edge_src)
self.skeletonEdgesSrc.setModel(
SkeletonNodeModel(self.state["skeleton"]))
hb.addWidget(self.skeletonEdgesSrc)
hb.addWidget(QLabel("to"))
self.skeletonEdgesDst = QComboBox()
self.skeletonEdgesDst.setEditable(False)
hb.addWidget(self.skeletonEdgesDst)
self.skeletonEdgesDst.setModel(
SkeletonNodeModel(self.state["skeleton"],
lambda: self.skeletonEdgesSrc.currentText()))
def new_edge():
src_node = self.skeletonEdgesSrc.currentText()
dst_node = self.skeletonEdgesDst.currentText()
self.commands.newEdge(src_node, dst_node)
_add_button(hb, "Add Edge", new_edge)
_add_button(hb, "Delete Edge", self.commands.deleteEdge)
hbw = QWidget()
hbw.setLayout(hb)
vb.addWidget(hbw)
gb.setLayout(vb)
skeleton_layout.addWidget(gb)
hb = QHBoxLayout()
_add_button(hb, "Load Skeleton", self.commands.openSkeleton)
_add_button(hb, "Save Skeleton", self.commands.saveSkeleton)
hbw = QWidget()
hbw.setLayout(hb)
skeleton_layout.addWidget(hbw)
####### Instances #######
instances_layout = _make_dock("Instances")
self.instancesTable = GenericTableView(
state=self.state,
row_name="instance",
name_prefix="",
model=LabeledFrameTableModel(items=self.state["labeled_frame"],
context=self.commands),
)
instances_layout.addWidget(self.instancesTable)
hb = QHBoxLayout()
_add_button(hb, "New Instance", lambda x: self.commands.newInstance())
_add_button(hb, "Delete Instance",
self.commands.deleteSelectedInstance)
hbw = QWidget()
hbw.setLayout(hb)
instances_layout.addWidget(hbw)
####### Suggestions #######
suggestions_layout = _make_dock("Labeling Suggestions")
self.suggestionsTable = GenericTableView(
state=self.state,
is_sortable=True,
model=SuggestionsTableModel(items=self.labels.suggestions,
context=self.commands),
)
suggestions_layout.addWidget(self.suggestionsTable)
hb = QHBoxLayout()
_add_button(
hb,
"Prev",
self.process_events_then(self.commands.prevSuggestedFrame),
"goto previous suggestion",
)
self.suggested_count_label = QLabel()
hb.addWidget(self.suggested_count_label)
_add_button(
hb,
"Next",
self.process_events_then(self.commands.nextSuggestedFrame),
"goto next suggestion",
)
hbw = QWidget()
hbw.setLayout(hb)
suggestions_layout.addWidget(hbw)
self.suggestions_form_widget = YamlFormWidget.from_name(
"suggestions",
title="Generate Suggestions",
)
self.suggestions_form_widget.mainAction.connect(
self.process_events_then(self.commands.generateSuggestions))
suggestions_layout.addWidget(self.suggestions_form_widget)
def goto_suggestion(*args):
selected_frame = self.suggestionsTable.getSelectedRowItem()
self.commands.gotoVideoAndFrame(selected_frame.video,
selected_frame.frame_idx)
self.suggestionsTable.doubleClicked.connect(goto_suggestion)
self.state.connect("suggestion_idx", self.suggestionsTable.selectRow)
def load_overlays(self):
"""Load all standard video overlays."""
self.overlays["track_labels"] = TrackListOverlay(
self.labels, self.player)
self.overlays["trails"] = TrackTrailOverlay(self.labels, self.player)
self.overlays["instance"] = InstanceOverlay(self.labels, self.player,
self.state)
# When gui state changes, we also want to set corresponding attribute
# on overlay (or color manager shared by overlays) so that they can
# update/redraw as needed.
def overlay_state_connect(overlay, state_key, overlay_attribute=None):
overlay_attribute = overlay_attribute or state_key
self.state.connect(
state_key,
callbacks=[
lambda x: setattr(overlay, overlay_attribute, x),
self.plotFrame,
],
)
overlay_state_connect(self.overlays["trails"], "trail_length")
overlay_state_connect(self.color_manager, "palette")
overlay_state_connect(self.color_manager, "distinctly_color")
overlay_state_connect(self.color_manager, "color predicted",
"color_predicted")
self.state.connect("palette", lambda x: self.updateSeekbarMarks())
# update the skeleton tables since we may want to redraw colors
for state_var in ("palette", "distinctly_color", "edge style"):
self.state.connect(
state_var,
lambda x: self.on_data_update([UpdateTopic.skeleton]))
# Set defaults
self.state["trail_length"] = prefs["trail length"]
# Emit signals for default that may have been set earlier
self.state.emit("palette")
self.state.emit("distinctly_color")
self.state.emit("color predicted")
def _update_gui_state(self):
"""Enable/disable gui items based on current state."""
has_selected_instance = self.state["instance"] is not None
has_selected_video = self.state["selected_video"] is not None
has_selected_node = self.state["selected_node"] is not None
has_selected_edge = self.state["selected_edge"] is not None
has_frame_range = bool(self.state["has_frame_range"])
has_unsaved_changes = bool(self.state["has_changes"])
has_multiple_videos = self.labels is not None and len(
self.labels.videos) > 1
has_labeled_frames = self.labels is not None and any(
(lf.video == self.state["video"] for lf in self.labels))
has_suggestions = self.labels is not None and bool(
self.labels.suggestions)
has_tracks = self.labels is not None and (len(self.labels.tracks) > 0)
has_multiple_instances = (
self.state["labeled_frame"] is not None
and len(self.state["labeled_frame"].instances) > 1)
# todo: exclude predicted instances from count
has_nodes_selected = (self.skeletonEdgesSrc.currentIndex() > -1
and self.skeletonEdgesDst.currentIndex() > -1)
control_key_down = QApplication.queryKeyboardModifiers(
) == Qt.ControlModifier
# Update menus
self.track_menu.setEnabled(has_selected_instance)
self._menu_actions["clear selection"].setEnabled(has_selected_instance)
self._menu_actions["delete instance"].setEnabled(has_selected_instance)
self._menu_actions["delete clip predictions"].setEnabled(
has_frame_range)
# self._menu_actions["export clip"].setEnabled(has_frame_range)
self._menu_actions["transpose"].setEnabled(has_multiple_instances)
self._menu_actions["save"].setEnabled(has_unsaved_changes)
self._menu_actions["next video"].setEnabled(has_multiple_videos)
self._menu_actions["prev video"].setEnabled(has_multiple_videos)
self._menu_actions["goto next labeled"].setEnabled(has_labeled_frames)
self._menu_actions["goto prev labeled"].setEnabled(has_labeled_frames)
self._menu_actions["goto next suggestion"].setEnabled(has_suggestions)
self._menu_actions["goto prev suggestion"].setEnabled(has_suggestions)
self._menu_actions["goto next track spawn"].setEnabled(has_tracks)
# Update buttons
self._buttons["add edge"].setEnabled(has_nodes_selected)
self._buttons["delete edge"].setEnabled(has_selected_edge)
self._buttons["delete node"].setEnabled(has_selected_node)
self._buttons["show video"].setEnabled(has_selected_video)
self._buttons["remove video"].setEnabled(has_selected_video)
self._buttons["delete instance"].setEnabled(has_selected_instance)
# Update overlays
self.overlays["track_labels"].visible = (control_key_down
and has_selected_instance)
def on_data_update(self, what: List[UpdateTopic]):
def _has_topic(topic_list):
if UpdateTopic.all in what:
return True
for topic in topic_list:
if topic in what:
return True
return False
if _has_topic([
UpdateTopic.frame,
UpdateTopic.skeleton,
UpdateTopic.project_instances,
UpdateTopic.tracks,
]):
self.plotFrame()
if _has_topic([
UpdateTopic.frame,
UpdateTopic.project_instances,
UpdateTopic.tracks,
UpdateTopic.suggestions,
]):
self.updateSeekbarMarks()
if _has_topic([
UpdateTopic.frame, UpdateTopic.project_instances,
UpdateTopic.tracks
]):
self.updateTrackMenu()
if _has_topic([UpdateTopic.video]):
self.videosTable.model().items = self.labels.videos
if _has_topic([UpdateTopic.skeleton]):
self.skeletonNodesTable.model().items = self.state["skeleton"]
self.skeletonEdgesTable.model().items = self.state["skeleton"]
self.skeletonEdgesSrc.model().skeleton = self.state["skeleton"]
self.skeletonEdgesDst.model().skeleton = self.state["skeleton"]
if self.labels.skeletons:
self.suggestions_form_widget.set_field_options(
"node", self.labels.skeletons[0].node_names)
if _has_topic([UpdateTopic.project, UpdateTopic.on_frame]):
self.instancesTable.model().items = self.state["labeled_frame"]
if _has_topic([UpdateTopic.suggestions]):
self.suggestionsTable.model().items = self.labels.suggestions
if _has_topic([UpdateTopic.project_instances,
UpdateTopic.suggestions]):
# update count of suggested frames w/ labeled instances
suggestion_status_text = ""
suggestion_list = self.labels.get_suggestions()
if suggestion_list:
suggestion_label_counts = [
self.labels.instance_count(item.video, item.frame_idx)
for item in suggestion_list
]
labeled_count = len(
suggestion_list) - suggestion_label_counts.count(0)
suggestion_status_text = (
f"{labeled_count}/{len(suggestion_list)} labeled")
self.suggested_count_label.setText(suggestion_status_text)
def plotFrame(self, *args, **kwargs):
"""Plots (or replots) current frame."""
if self.state["video"] is None:
return
self.player.plot()
def _after_plot_update(self, player, frame_idx, selected_inst):
"""Called each time a new frame is drawn."""
# Store the current LabeledFrame (or make new, empty object)
self.state["labeled_frame"] = self.labels.find(self.state["video"],
frame_idx,
return_new=True)[0]
# Show instances, etc, for this frame
for overlay in self.overlays.values():
overlay.add_to_scene(self.state["video"], frame_idx)
# Select instance if there was already selection
if selected_inst is not None:
player.view.selectInstance(selected_inst)
if self.state["fit"]:
player.zoomToFit()
# Update related displays
self.updateStatusMessage()
self.on_data_update([UpdateTopic.on_frame])
# Trigger event after the overlays have been added
player.view.updatedViewer.emit()
def updateStatusMessage(self, message: Optional[str] = None):
"""Updates status bar."""
current_video = self.state["video"]
frame_idx = self.state["frame_idx"] or 0
spacer = " "
if message is None:
message = f"Frame: {frame_idx+1:,}/{len(current_video):,}"
if self.player.seekbar.hasSelection():
start, end = self.state["frame_range"]
message += f" (selection: {start+1:,}-{end:,})"
if len(self.labels.videos) > 1:
message += f" of video {self.labels.videos.index(current_video)+1}"
message += f"{spacer}Labeled Frames: "
if current_video is not None:
message += str(
self.labels.get_labeled_frame_count(current_video, "user"))
if len(self.labels.videos) > 1:
message += " in video, "
if len(self.labels.videos) > 1:
project_user_frame_count = self.labels.get_labeled_frame_count(
filter="user")
message += f"{project_user_frame_count} in project"
if current_video is not None:
pred_frame_count = self.labels.get_labeled_frame_count(
current_video, "predicted")
if pred_frame_count:
message += f"{spacer}Predicted Frames: {pred_frame_count:,}"
message += (
f" ({pred_frame_count/current_video.num_frames*100:.2f}%)"
)
message += " in video"
self.statusBar().showMessage(message)
def loadProjectFile(self, filename: Optional[str] = None):
"""
Loads given labels file into GUI.
Args:
filename: The path to the saved labels dataset. If None,
then don't do anything.
Returns:
None:
"""
if len(filename) == 0:
return
gui_video_callback = Labels.make_gui_video_callback(
search_paths=[os.path.dirname(filename)])
has_loaded = False
labels = None
if type(filename) == Labels:
labels = filename
filename = None
has_loaded = True
else:
try:
labels = Labels.load_file(filename,
video_search=gui_video_callback)
has_loaded = True
except ValueError as e:
print(e)
QMessageBox(text=f"Unable to load {filename}.").exec_()
if has_loaded:
self.loadLabelsObject(labels, filename)
def loadLabelsObject(self, labels: Labels, filename: Optional[str] = None):
"""
Loads a `Labels` object into the GUI, replacing any currently loaded.
Args:
labels: The `Labels` object to load.
filename: The filename where this file is saved, if any.
Returns:
None.
"""
self.state["labels"] = labels
self.state["filename"] = filename
self.commands.changestack_clear()
self.color_manager.labels = self.labels
self.color_manager.set_palette(self.state["palette"])
self.load_overlays()
if len(self.labels.skeletons):
self.state["skeleton"] = self.labels.skeletons[0]
# Load first video
if len(self.labels.videos):
self.state["video"] = self.labels.videos[0]
self.on_data_update([UpdateTopic.project, UpdateTopic.all])
def updateTrackMenu(self):
"""Updates track menu options."""
self.track_menu.clear()
for track in self.labels.tracks:
key_command = ""
if self.labels.tracks.index(track) < 9:
key_command = Qt.CTRL + Qt.Key_0 + self.labels.tracks.index(
track) + 1
self.track_menu.addAction(
f"{track.name}",
lambda x=track: self.commands.setInstanceTrack(x),
key_command,
)
self.track_menu.addAction("New Track", self.commands.addTrack,
Qt.CTRL + Qt.Key_0)
def updateSeekbarMarks(self):
"""Updates marks on seekbar."""
set_slider_marks_from_labels(self.player.seekbar, self.labels,
self.state["video"], self.color_manager)
def setSeekbarHeader(self, graph_name):
"""Updates graph shown in seekbar header."""
data_obj = StatisticSeries(self.labels)
header_functions = {
"Point Displacement (sum)": data_obj.get_point_displacement_series,
"Point Displacement (max)": data_obj.get_point_displacement_series,
"Primary Point Displacement (sum)":
data_obj.get_primary_point_displacement_series,
"Primary Point Displacement (max)":
data_obj.get_primary_point_displacement_series,
"Instance Score (sum)": data_obj.get_instance_score_series,
"Instance Score (min)": data_obj.get_instance_score_series,
"Point Score (sum)": data_obj.get_point_score_series,
"Point Score (min)": data_obj.get_point_score_series,
"Number of predicted points": data_obj.get_point_count_series,
"Min Centroid Proximity":
data_obj.get_min_centroid_proximity_series,
}
if graph_name == "None":
self.player.seekbar.clearHeader()
else:
if graph_name in header_functions:
kwargs = dict(video=self.state["video"])
reduction_name = re.search("\((sum|max|min)\)", graph_name)
if reduction_name is not None:
kwargs["reduction"] = reduction_name.group(1)
series = header_functions[graph_name](**kwargs)
self.player.seekbar.setHeaderSeries(series)
else:
print(f"Could not find function for {header_functions}")
def _frames_for_prediction(self):
"""Builds options for frames on which to run inference.
Args:
None.
Returns:
Dictionary, keys are names of options (e.g., "clip", "random"),
values are {video: list of frame indices} dictionaries.
"""
user_labeled_frames = self.labels.user_labeled_frames
def remove_user_labeled(video, frame_idxs):
if len(frame_idxs) == 0:
return frame_idxs
video_user_labeled_frame_idxs = {
lf.frame_idx
for lf in user_labeled_frames if lf.video == video
}
return list(set(frame_idxs) - video_user_labeled_frame_idxs)
current_video = self.state["video"]
selection = dict()
selection["frame"] = {current_video: [self.state["frame_idx"]]}
# Use negative number in list for range (i.e., "0,-123" means "0-123")
# The ranges should be [X, Y) like standard Python ranges
def encode_range(a: int, b: int) -> Tuple[int, int]:
return a, -b
clip_range = self.state.get("frame_range", default=(0, 0))
selection["clip"] = {current_video: encode_range(*clip_range)}
selection["video"] = {
current_video: encode_range(0, current_video.num_frames)
}
selection["suggestions"] = {
video:
remove_user_labeled(video,
self.labels.get_video_suggestions(video))
for video in self.labels.videos
}
selection["random"] = {
video: remove_user_labeled(
video, random.sample(range(video.frames),
min(20, video.frames)))
for video in self.labels.videos
}
if len(self.labels.videos) > 1:
selection["random_video"] = {
current_video:
remove_user_labeled(
current_video,
random.sample(range(current_video.frames),
min(20, current_video.frames)),
)
}
if user_labeled_frames:
selection["user"] = {
video: [
lf.frame_idx for lf in user_labeled_frames
if lf.video == video
]
for video in self.labels.videos
}
return selection
def showLearningDialog(self, mode: str):
"""Helper function to show learning dialog in given mode.
Args:
mode: A string representing mode for dialog, which could be:
* "training"
* "inference"
Returns:
None.
"""
from sleap.gui.learning.dialog import LearningDialog
if "inference" in self.overlays:
QMessageBox(
text="In order to use this function you must first quit and "
"re-open SLEAP to release resources used by visualizing "
"model outputs.").exec_()
return
if not self.state["filename"] or self.state["has_changes"]:
QMessageBox(
text=
"You have unsaved changes. Please save before running training or inference."
).exec_()
return
if self._child_windows.get(mode, None) is None:
self._child_windows[mode] = LearningDialog(
mode,
self.state["filename"],
self.labels,
)
self._child_windows[mode].learningFinished.connect(
self.learningFinished)
self._child_windows[mode].update_file_lists()
self._child_windows[
mode].frame_selection = self._frames_for_prediction()
self._child_windows[mode].open()
def learningFinished(self, new_count: int):
"""Called when inference finishes."""
# we ran inference so update display/ui
self.on_data_update([UpdateTopic.all])
if new_count > 0:
self.commands.changestack_push("new predictions")
def showMetricsDialog(self):
self._child_windows["metrics"] = MetricsTableDialog(
self.state["filename"])
self._child_windows["metrics"].show()
def visualizeOutputs(self):
"""Gui for adding overlay with live visualization of predictions."""
filters = ["Model (*.json)"]
# Default to opening from models directory from project
models_dir = None
if self.state["filename"] is not None:
models_dir = os.path.join(os.path.dirname(self.state["filename"]),
"models/")
# Show dialog
filename, selected_filter = FileDialog.open(
self,
dir=models_dir,
caption="Import model outputs...",
filter=";;".join(filters),
)
if len(filename) == 0:
return
# Model as overlay datasource
# This will show live inference results
from sleap.gui.overlays.base import DataOverlay
predictor = DataOverlay.make_predictor(filename)
show_pafs = False
# If multi-head model with both confmaps and pafs,
# ask user which to show.
if (predictor.confidence_maps_key_name
and predictor.part_affinity_fields_key_name):
results = FormBuilderModalDialog(
form_name="head_type_form").get_results()
show_pafs = "Part Affinity" in results["head_type"]
overlay = DataOverlay.from_predictor(
predictor=predictor,
video=self.state["video"],
player=self.player,
show_pafs=show_pafs,
)
self.overlays["inference"] = overlay
self.plotFrame()
def doubleClickInstance(self,
instance: Instance,
event: QtGui.QMouseEvent = None):
"""
Handles when the user has double-clicked an instance.
If prediction, then copy to new user-instance.
If already user instance, then add any missing nodes (in case
skeleton has been changed after instance was created).
Args:
instance: The :class:`Instance` that was double-clicked.
"""
# When a predicted instance is double-clicked, add a new instance
if hasattr(instance, "score"):
mark_complete = False
# Mark the nodes as "complete" if shift-key is down
if event is not None and event.modifiers() & Qt.ShiftModifier:
mark_complete = True
self.commands.newInstance(copy_instance=instance,
mark_complete=mark_complete)
# When a regular instance is double-clicked, add any missing points
else:
self.commands.completeInstanceNodes(instance)
def openKeyRef(self):
"""Shows gui for viewing/modifying keyboard shortucts."""
ShortcutDialog().exec_()