Merge branch 'develop'

.conda/bld.bat

@@ -13,7 +13,7 @@ rem # this out myself, ughhh.
 set PIP_NO_INDEX=False
 set PIP_NO_DEPENDENCIES=False
 set PIP_IGNORE_INSTALLED=False
-pip install cattrs==1.0.0rc0 opencv-python-headless==4.2.0.34 "PySide2>=5.12.0,<=5.14.1" imgaug==0.3.0 qimage2ndarray==1.8 imgstore==0.2.9 jsmin seaborn scikit-video
+pip install scipy==1.4.1 cattrs==1.0.0rc0 opencv-python-headless==4.2.0.34 "PySide2>=5.12.0,<=5.14.1" imgaug==0.3.0 qimage2ndarray==1.8 imgstore==0.2.9 jsmin seaborn scikit-video pykalman==0.9.5
 pip install
 
 rem # Use and update environment.yml call to install pip dependencies. This is slick.

.conda/build.sh

@@ -11,7 +11,7 @@ export PIP_NO_INDEX=False
 export PIP_NO_DEPENDENCIES=False
 export PIP_IGNORE_INSTALLED=False
 
-pip install cattrs==1.0.0rc0 opencv-python-headless==4.2.0.34 "PySide2>=5.12.0,<=5.14.1" imgaug==0.3.0 qimage2ndarray==1.8 imgstore==0.2.9 jsmin seaborn scikit-video
+pip install scipy==1.4.1 cattrs==1.0.0rc0 opencv-python-headless==4.2.0.34 "PySide2>=5.12.0,<=5.14.1" imgaug==0.3.0 qimage2ndarray==1.8 imgstore==0.2.9 jsmin seaborn scikit-video pykalman==0.9.5
 #pip install tensorflow==2.1
 
 pip install setuptools-scm

.conda/meta.yaml

@@ -25,6 +25,7 @@ requirements:
   run:
     - python=3.6
     - numpy>=1.16
+    - scipy<=1.4.1
     - attrs=19.3
     - jsonpickle=1.2
     - networkx

environment.yml

@@ -19,6 +19,7 @@ dependencies:
 - python-rapidjson
 - pip
 - pip:
+    - scipy==1.4.1
     - opencv-python-headless==4.2.0.34
     - PySide2>=5.12.0,<=5.14.1
     - imgaug==0.3.0
@@ -28,5 +29,6 @@ dependencies:
     - jsmin
     - seaborn
     - scikit-video
+    - pykalman==0.9.5
     - "--editable=./"
     - "--requirement=./dev_requirements.txt"

requirements.txt

@@ -13,10 +13,12 @@ PySide2>=5.12.0,<=5.14.1
 pyzmq
 pyyaml
 imgaug==0.3.0
+scipy<=1.4.1
 scikit-image
 scikit-learn
 scikit-video
 imgstore==0.2.9
 qimage2ndarray==1.8
 jsmin
 seaborn
+pykalman==0.9.5

sleap/__init__.py

@@ -7,7 +7,7 @@
 # Import submodules we want available at top-level
 from sleap.io.dataset import Labels
 from sleap.io.video import Video
-from sleap.instance import LabeledFrame, Instance, PredictedInstance
+from sleap.instance import LabeledFrame, Instance, PredictedInstance, Track
 from sleap.skeleton import Skeleton
 import sleap.nn
 

sleap/config/pipeline_form.yaml

@@ -235,6 +235,28 @@ inference:
   none:
 
   flow:
+    - type: text
+      text: '<b>Pre-tracker data cleaning</b>:'
+    - name: tracking.target_instance_count
+      label: Target Number of Instances Per Frame
+      type: optional_int
+      none_label: No target
+      default_disabled: true
+      range: 1,10
+      default: 1
+    - name: tracking.pre_cull_to_target
+      label: Cull to Target Instance Count
+      type: bool
+      default: false
+    - name: tracking.pre_cull_iou_threshold
+      label: Cull using IoU Threshold
+      type: double
+      default: 0.8
+    - type: text
+      text: '<b>Tracking with optical flow</b>:<br />
+      This tracker "shifts" instances from previous frames using optical flow
+      before matching instances in each frame to the <i>shifted</i> instances from
+      prior frames.'
     - name: tracking.similarity
       label: Similarity Method
       type: list
@@ -249,12 +271,50 @@ inference:
       label: Elapsed Frame Window
       type: int
       default: 5
-    - name: tracking.clean_instance_count
-      label: Target Number of Tracks (0 for all)
-      type: int
-      default: 0
+    - type: text
+      text: '<b>Kalman filter-based tracking</b>:<br />
+      Uses the above tracking options to track instances for an initial
+      number of frames, then initializes Kalman filters which are used to track
+      instances on subsequent frames.'
+    - name: tracking.kf_init_frame_count
+      label: Enable Filters after Initial Frames
+      type: optional_int
+      none_label: Don't use filters
+      default_disabled: true
+      default: 10
+    - name: tracking.kf_node_indices
+      label: Nodes to use for Tracking
+      type: string
+      default: 0,1,2
+    - type: text
+      text: '<b>Post-tracker data cleaning</b>:'
+    - name: tracking.post_connect_single_breaks
+      label: Connect Single Track Breaks
+      type: bool
+      default: false
 
   simple:
+    - type: text
+      text: '<b>Pre-tracker data cleaning</b>:'
+    - name: tracking.target_instance_count
+      label: Target Number of Instances Per Frame
+      type: optional_int
+      none_label: No target
+      default_disabled: true
+      range: 1,10
+      default: 1
+    - name: tracking.pre_cull_to_target
+      label: Cull to Target Instance Count
+      type: bool
+      default: false
+    - name: tracking.pre_cull_iou_threshold
+      label: Cull using IoU Threshold
+      type: double
+      default: 0.8
+    - type: text
+      text: '<b>Tracking</b>:<br />
+        This tracker assigns track identities by matching instances from prior
+        frames to instances on subsequent frames.'
     - name: tracking.similarity
       label: Similarity Method
       type: list
@@ -269,10 +329,30 @@ inference:
       label: Elapsed Frame Window
       type: int
       default: 5
-    - name: tracking.clean_instance_count
-      label: Target Number of Tracks (0 for all)
-      type: int
-      default: 0
+    - type: text
+      text: '<b>Kalman filter-based tracking</b>:<br />
+        Uses the above tracking options to track instances for an initial
+        number of frames, then initializes Kalman filters which are used to track
+        instances on subsequent frames.'
+    - name: tracking.kf_init_frame_count
+      label: Enable Filters after Initial Frames
+      type: optional_int
+      none_label: Don't use filters
+      default_disabled: true
+      default: 10
+    - name: tracking.kf_node_indices
+      label: Nodes to use for Tracking
+      type: string
+      default: 0,1,2
+    - type: text
+      text: '<b>Post-tracker data cleaning</b>:'
+    - name: tracking.post_connect_single_breaks
+      label: Connect Single Track Breaks
+      type: bool
+      default: false
+
+
+
 
 - name: _predict_frames
   label: Predict On

sleap/gui/app.py

@@ -7,7 +7,7 @@
 import os
 import random
 
-from typing import Callable, List, Optional
+from typing import Callable, List, Optional, Tuple
 
 from PySide2 import QtCore, QtGui
 from PySide2.QtCore import Qt, QEvent
@@ -104,7 +104,8 @@ 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__}")
+                f"{value} - SLEAP Label v{sleap.version.__version__}"
+            )
 
     def event(self, e: QEvent) -> bool:
         """Custom event handler.
@@ -433,6 +434,7 @@ def prev_vid():
             "Point Score (sum)",
             "Point Score (min)",
             "Number of predicted points",
+            "Min Centroid Proximity",
         )
 
         add_submenu_choices(
@@ -1186,6 +1188,7 @@ def setSeekbarHeader(self, graph_name):
             "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":
@@ -1228,11 +1231,13 @@ def remove_user_labeled(video, frame_idxs):
 
         # 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))
-        if clip_range[1] > 0:
-            clip_range = (clip_range[0], -clip_range[1])
-        selection["clip"] = {current_video: clip_range}
-        selection["video"] = {current_video: (0, -current_video.num_frames)}
+
+        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))

sleap/gui/color.py

@@ -5,6 +5,7 @@
 from sleap.util import get_config_file
 from sleap.instance import Instance, Track, Node
 from sleap.io.dataset import Labels
+from sleap.prefs import prefs
 
 
 class ColorManager(object):
@@ -36,9 +37,14 @@ def __init__(self, labels: Labels = None, palette: str = "standard"):
         self.set_palette(palette)
 
         self.uncolored_prediction_color = (250, 250, 10)
-        self.default_pen_width = 1
-        self.medium_pen_width = 1.5
-        self.thick_pen_width = 3
+
+        if prefs["bold lines"]:
+            self.thick_pen_width = 6
+        else:
+            self.thick_pen_width = 3
+
+        self.medium_pen_width = self.thick_pen_width // 2
+        self.default_pen_width = max(1, self.thick_pen_width // 4)
 
     @property
     def labels(self):

sleap/gui/dialogs/formbuilder.py

@@ -409,7 +409,11 @@ def add_item(self, item: Dict[str, Any]):
             elif item["default"] is not None and item["default"] > 100:
                 min, max = 0, item["default"] * 10
                 field.setRange(min, max)
+
             field.setValue(item["default"])
+            if item.get("default_disabled", False):
+                field.setToNone()
+
             field.valueChanged.connect(lambda: self.valueChanged.emit())
 
         # bool: show checkbox
@@ -684,15 +688,19 @@ def isNoneVal(self, val) -> bool:
                     return True
         return False
 
+    @property
     def noneVal(self):
         if self.none_string.lower() in ("", "none"):
             return None
         else:
             return self.none_string
 
+    def setToNone(self):
+        self.setValue(self.noneVal)
+
     def value(self):
         if self.check_widget.isChecked():
-            return self.noneVal()
+            return self.noneVal
         return self.spin_widget.value()
 
     def setValue(self, val):