[tracks, classifier] include neighborhood classifier, not yet doing
anything
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@ -1,15 +1,17 @@
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import logging
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import numpy as np
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import pyqtgraph as pg
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from PySide6.QtWidgets import QWidget, QVBoxLayout, QTabWidget,QPushButton
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from PySide6.QtWidgets import QWidget, QVBoxLayout, QTabWidget,QPushButton, QGraphicsView
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from PySide6.QtCore import Signal
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from PySide6.QtGui import QBrush, QColor
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import pyqtgraph as pg
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from fixtracks.utils.trackingdata import TrackingData
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class SizeClassifier(QWidget):
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apply = Signal()
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name = "SizeClassifier"
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def __init__(self, parent=None):
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super().__init__(parent)
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@ -29,17 +31,16 @@ class SizeClassifier(QWidget):
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def setupGraph(self):
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track1_brush = QBrush(QColor.fromString("orange"))
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track1_brush.color().setAlphaF(0.5)
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track2_brush = QBrush(QColor.fromString("green"))
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pg.setConfigOptions(antialias=True)
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plot_widget = pg.GraphicsLayoutWidget(show=False)
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self._t1_selection = pg.LinearRegionItem([100, 200])
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self._t1_selection.setZValue(-10) # what is that?
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self._t1_selection.setZValue(-10)
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self._t1_selection.setBrush(track1_brush)
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self._t2_selection = pg.LinearRegionItem([300,400])
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self._t2_selection.setZValue(-10) # what is that?
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self._t2_selection.setZValue(-10)
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self._t2_selection.setBrush(track2_brush)
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return plot_widget
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@ -47,8 +48,8 @@ class SizeClassifier(QWidget):
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if bodyaxis is None:
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bodyaxis = [0, 1, 2, 5]
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bodycoords = coords[:, bodyaxis, :]
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dists = np.sum(np.sqrt(np.sum(np.diff(bodycoords, axis=1)**2, axis=2)), axis=1)
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return dists
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lengths = np.sum(np.sqrt(np.sum(np.diff(bodycoords, axis=1)**2, axis=2)), axis=1)
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return lengths
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def estimate_histogram(self, dists, min_threshold=1., max_threshold=99.):
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min_length = np.percentile(dists, min_threshold)
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@ -83,6 +84,112 @@ class SizeClassifier(QWidget):
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tracks[(self._sizes >= t2lower) & (self._sizes < t2upper)] = 2
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return tracks
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class NeighborhoodValidator(QWidget):
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apply = Signal()
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name = "Neighborhood Validator"
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def __init__(self, parent = None):
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super().__init__(parent)
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self._threshold = None
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self._positions = None
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self._distances = None
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self._tracks = None
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self._plot = None
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self._plot_widget = self.setupGraph()
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self._apply_btn = QPushButton("apply")
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self._apply_btn.clicked.connect(lambda: self.apply.emit())
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layout = QVBoxLayout()
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print(isinstance(self._plot_widget, QGraphicsView))
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layout.addWidget(self._plot_widget)
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layout.addWidget(self._apply_btn)
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self.setLayout(layout)
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def setupGraph(self):
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pg.setConfigOptions(antialias=True)
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plot_widget = pg.GraphicsLayoutWidget(show=False)
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self._threshold = pg.LineSegmentROI([[10, 64], [120,64]], pen='r')
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self._threshold.setZValue(-10) # what is that?
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return plot_widget
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def estimate_histogram(self, dists, min_threshold=1., max_threshold=99., bin_count=100, log=False):
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min_dist = np.percentile(dists, min_threshold)
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max_dist = np.percentile(dists, max_threshold)
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if log:
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bins = np.logspace(min_dist, max_dist, bin_count, base=10)
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bins = np.linspace(min_dist, max_dist, bin_count)
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hist, edges = np.histogram(dists, bins=bins, density=True)
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return hist, edges
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def neighborDistances(self, x, frames, n=5, symmetric=True):
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logging.debug("classifier:NeighborhoodValidator neighborDistance")
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pad_shape = list(x.shape)
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pad_shape[0] = n
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pad = np.atleast_2d(np.zeros(pad_shape))
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if symmetric:
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padded_x = np.vstack((pad, x, pad))
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dists = np.zeros((x.shape[0]-1, 2*n))
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else:
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padded_x = np.vstack((pad, x))
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dists = np.zeros((x.shape[0]-1, n))
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count = 0
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r = range(-n, n+1) if symmetric else range(-n, 0)
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for i in r:
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if i == 0:
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continue
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shifted_x = np.roll(padded_x, i, axis=0)
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dist = np.sqrt(np.sum((padded_x - shifted_x)**2, axis=1))
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dists[:, count] = dist[n+1:]/np.diff(frames)
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count += 1
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return dists
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def setData(self, positions, tracks, frames):
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"""Set the data, the classifier/should be working on.
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Parameters
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----------
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positions : np.ndarray
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The position estimates, e.g. the center of gravity for each detection
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tracks : np.ndarray
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The current track assignment.
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frames : np.ndarray
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respective frame.
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"""
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def mouseClicked(self, event):
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print("mouse clicked at", event.pos())
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track2_brush = QBrush(QColor.fromString("green"))
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track1_brush = QBrush(QColor.fromString("orange"))
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self._positions = positions
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self._tracks = tracks
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self._frames = frames
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t1_positions = self._positions[self._tracks == 1]
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t1_frames = self._frames[self._tracks == 1]
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t1_distances = self.neighborDistances(t1_positions, t1_frames, 1, False)
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t2_positions = self._positions[self._tracks == 2]
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t2_frames = self._frames[self._tracks == 2]
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t2_distances = self.neighborDistances(t2_positions, t2_frames, 1, False)
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n, e = self.estimate_histogram(t1_distances[1:], bin_count=100, log=False)
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self._plot = self._plot_widget.addPlot()
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bgi1 = pg.BarGraphItem(x0=e[:-1], x1=e[1:], height=n, pen='w', brush=track1_brush)
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self._plot.addItem(bgi1)
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n, e = self.estimate_histogram(t2_distances[1:], bin_count=100, log=False)
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bgi2 = pg.BarGraphItem(x0=e[:-1], x1=e[1:], height=n, pen='w', brush=track2_brush)
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self._plot.addItem(bgi2)
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self._plot.scene().sigMouseClicked.connect(mouseClicked)
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self._plot.setLogMode(x=False, y=True)
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# plot.setXRange(np.min(t1_distances), np.max(t1_distances))
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self._plot.setLabel('left', "prob. density")
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self._plot.setLabel('bottom', "distance", units="px/frame")
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# plot.addItem(self._threshold)
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vLine = pg.InfiniteLine(pos=10, angle=90, movable=False)
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self._plot.addItem(vLine, ignoreBounds=True)
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vb = self._plot.vb
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class ClassifierWidget(QTabWidget):
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apply_sizeclassifier = Signal(np.ndarray)
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@ -90,7 +197,9 @@ class ClassifierWidget(QTabWidget):
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def __init__(self, parent=None):
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super().__init__(parent)
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self._size_classifier = SizeClassifier()
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self.addTab(self._size_classifier, "Size classifier")
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self._neigborhood_validator = NeighborhoodValidator()
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self.addTab(self._size_classifier, SizeClassifier.name)
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self.addTab(self._neigborhood_validator, NeighborhoodValidator.name)
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self._size_classifier.apply.connect(self._on_applySizeClassifier)
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def _on_applySizeClassifier(self):
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@ -101,48 +210,51 @@ class ClassifierWidget(QTabWidget):
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def size_classifier(self):
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return self._size_classifier
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@property
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def neighborhood_validator(self):
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return self._neigborhood_validator
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def test_sizeClassifier(coords):
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app = QApplication([])
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window = QWidget()
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window.setMinimumSize(200, 200)
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layout = QVBoxLayout()
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win = SizeClassifier()
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win.setCoordinates(coords)
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def as_dict(df):
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d = {c: df[c].values for c in df.columns}
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d["index"] = df.index.values
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return d
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layout.addWidget(win)
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window.setLayout(layout)
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window.show()
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app.exec()
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def main():
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test_size = False
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import pickle
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from fixtracks.info import PACKAGE_ROOT
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datafile = PACKAGE_ROOT / "data/merged_small.pkl"
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with open(datafile, "rb") as f:
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df = pickle.load(f)
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data = TrackingData()
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data.setData(as_dict(df))
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positions = data.centerOfGravity()
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tracks = data["track"]
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frames = data["frame"]
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coords = data.coordinates()
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def test_neighborhoodClassifier(coords):
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app = QApplication([])
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window = QWidget()
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window.setMinimumSize(200, 200)
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# if test_size:
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# win = SizeClassifier()
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# win.setCoordinates(coords)
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# else:
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w = ClassifierWidget()
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w.neighborhood_validator.setData(positions, tracks, frames)
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layout = QVBoxLayout()
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win = SizeClassifier()
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win.setCoordinates(coords)
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layout.addWidget(win)
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layout.addWidget(w)
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window.setLayout(layout)
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window.show()
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app.exec()
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def main():
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import pickle
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from fixtracks.info import PACKAGE_ROOT
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from PySide6.QtWidgets import QApplication
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datafile = PACKAGE_ROOT / "data/merged_small.pkl"
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print(datafile)
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with open(datafile, "rb") as f:
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df = pickle.load(f)
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coords = np.stack(df.keypoints.values,).astype(np.float32)
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frames = df.frame.values
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test_sizeClassifier(coords)
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if __name__ == "__main__":
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from PySide6.QtWidgets import QApplication
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main()
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@ -378,7 +378,11 @@ class FixTracks(QWidget):
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rel_width = self._windowspinner.value() / maxframes
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self._timeline.setWindowWidth(rel_width)
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coordinates = self._data.coordinates()
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positions = self._data.centerOfGravity()
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tracks = self._data["track"]
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frames = self._data["frame"]
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self._classifier.size_classifier.setCoordinates(coordinates)
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self._classifier.neighborhood_validator.setData(positions, tracks, frames)
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self.update()
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self._saveBtn.setEnabled(True)
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