217 lines
8.5 KiB
Python
217 lines
8.5 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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from skimage.draw import disk
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from IPython import embed
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from util import RegionShape, AnalysisType, Illumination
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class Region(object):
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def __init__(self, origin, extent, inverted_y=True, name="", region_shape=RegionShape.Rectangular, parent=None) -> None:
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assert len(origin) == 2
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self._origin = origin
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self._extent = extent
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self._inverted_y = inverted_y
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self._name = name
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self._shape_type = region_shape
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self._check_extent(extent)
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self._parent = parent
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@staticmethod
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def circular_mask(width, height, center, radius):
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assert center[1] + radius < width and center[1] - radius > 0
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assert center[0] + radius < height and center[0] - radius > 0
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mask = np.zeros((height, width), dtype=np.uint8)
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rr, cc = disk(reversed(center), radius)
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mask[rr, cc] = 1
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return mask
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@property
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def _max_extent(self):
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if self._shape_type == RegionShape.Rectangular:
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max_extent = (self._origin[0] + self._extent[0], self._origin[1] + self._extent[1])
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else:
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max_extent = (self._origin[0] + self._extent, self._origin[1] + self._extent)
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return max_extent
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@property
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def _min_extent(self):
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if self._shape_type == RegionShape.Rectangular:
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min_extent = self._origin
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else:
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min_extent = (self._origin[0] - self._extent, self._origin[1] - self._extent)
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return min_extent
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@property
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def position(self):
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"""Returns the position and extent of the region as 4-tuple, (x, y, width, height)
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"""
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x = self._min_extent[0]
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y = self._min_extent[1]
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width = self._max_extent[0] - self._min_extent[0]
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height = self._max_extent[1] - self._min_extent[1]
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return x, y, width, height
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def _check_extent(self, ext):
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"""Checks whether the extent matches the shape. i.e. if the shape is Rectangular, extent must be a length 2 list, tuple, otherwise, if the region is circular, extent must be a single numerical value.
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Parameters
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----------
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ext : tuple, or numeric scalar
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"""
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if self._shape_type == RegionShape.Rectangular:
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if not isinstance(ext, (list, tuple, np.ndarray)) and len(ext) != 2:
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raise ValueError("Extent must be a length 2 list or tuple for rectangular regions!")
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elif self._shape_type == RegionShape.Circular:
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if not isinstance(ext, (int, float)):
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raise ValueError("Extent must be a numerical scalar for circular regions!")
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else:
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raise ValueError(f"Invalid ShapeType, {self._shape_type}!")
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def fits(self, other) -> bool:
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"""
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Returns true if the other region fits inside this region!
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"""
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assert isinstance(other, Region)
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does_fit = all((other._min_extent[0] >= self._min_extent[0], other._min_extent[1] >= self._min_extent[1],
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other._max_extent[0] <= self._max_extent[0], other._max_extent[1] <= self._max_extent[1]))
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return does_fit
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@property
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def is_child(self):
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return self._parent is not None
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def points_in_region(self, x, y, analysis_type=AnalysisType.Full):
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"""returns the indices of the points specified by 'x' and 'y' that fall into this region.
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Parameters
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----------
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x : np.ndarray
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the x positions
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y : np.ndarray
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the y positions
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analysis_type : AnalysisType, optional
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defines how the positions are evaluated, by default AnalysisType.Full
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FIXME: some of this can probably be solved using linear algebra, what with multiple exact same points?
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"""
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if self._shape_type == RegionShape.Rectangular or (self._shape_type == RegionShape.Circular and analysis_type != AnalysisType.Full):
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x_indices = np.where((x >= self._min_extent[0]) & (x <= self._max_extent[0] ))[0]
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y_indices = np.where((y >= self._min_extent[1]) & (y <= self._max_extent[1] ))[0]
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if analysis_type == AnalysisType.Full:
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indices = np.array(list(set(x_indices).intersection(set(y_indices))), dtype=int)
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elif analysis_type == AnalysisType.CollapseX:
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indices = np.asarray(x_indices, dtype=int)
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else:
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indices = np.asarray(y_indices, dtype=int)
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else:
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if self.is_child:
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mask = self.circular_mask(self._parent.position[2], self._parent.position[3], self._origin, self._extent)
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else:
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mask = self.circular_mask(self.position[2], self.position[3], self._origin, self._extent)
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img = np.zeros_like(mask)
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img[np.asarray(y, dtype=int), np.asarray(x, dtype=int)] = 1
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temp = np.where(img & mask)
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indices = []
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for i, j in zip(list(temp[1]), list(temp[0])):
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matches = np.where((x == i) & (y == j))
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if len(matches[0]) == 0:
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continue
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indices.append(matches[0][0])
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indices = np.array(indices)
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return indices
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def patch(self, **kwargs):
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if "fc" not in kwargs:
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kwargs["fc"] = None
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kwargs["fill"] = False
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if self._shape_type == RegionShape.Rectangular:
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w = self.position[2]
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h = self.position[3]
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return patches.Rectangle(self._origin, w, h, **kwargs)
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else:
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return patches.Circle(self._origin, self._extent, **kwargs)
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def __repr__(self):
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return f"Region: '{self._name}' of {self._shape_type} shape."
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class Arena(Region):
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def __init__(self, origin, extent, inverted_y=True, name="", arena_shape=RegionShape.Rectangular,
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illumination=Illumination.Backlight) -> None:
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super().__init__(origin, extent, inverted_y, name, arena_shape)
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self._illumination = illumination
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self.regions = []
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def add_region(self, origin, extent, name="", shape_type=RegionShape.Rectangular, region=None):
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if region is None:
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region = Region(origin, extent, name=name, region_shape=shape_type, parent=self)
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else:
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region._parent = self
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if self.fits(region):
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self.regions.append(region)
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else:
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raise Warning(f"Region {region} fits not! Not added to the list of regions!")
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def __repr__(self):
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return f"Arena: '{self._name}' of {self._shape_type} shape."
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def plot(self, axis=None):
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if axis is None:
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fig = plt.figure()
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axis = fig.add_subplot(111)
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axis.add_patch(self.patch())
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axis.set_xlim([self._origin[0], self._max_extent[0]])
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axis.set_ylim([self._origin[1], self._max_extent[1]])
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for r in self.regions:
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axis.add_patch(r.patch())
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return axis
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if __name__ == "__main__":
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a = Arena((0, 0), (1024, 768), name="arena", arena_shape=RegionShape.Rectangular)
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a.add_region((0, 0), (100, 300), name="small rect1")
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a.add_region((150, 0), (100, 300), name="small rect2")
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a.add_region((300, 0), (100, 300), name="small rect3")
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a.add_region((600, 400), 150, name="circ", shape_type=RegionShape.Circular)
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axis = a.plot()
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x = np.linspace(a.position[0], a.position[0] + a.position[2] - 1, 100, dtype=int)
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y = np.asarray((np.sin(x*0.01) + 1) * a.position[3] / 2 + a.position[1] -1, dtype=int)
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#y = np.linspace(a.position[1], a.position[1] + a.position[3] - 1, 100, dtype=int)
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axis.scatter(x, y, c="k", s=2)
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ind = a.regions[3].points_in_region(x, y)
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if len(ind) > 0:
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axis.scatter(x[ind], y[ind], label="circ full")
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ind = a.regions[3].points_in_region(x, y, AnalysisType.CollapseX)
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if len(ind) > 0:
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axis.scatter(x[ind], y[ind] - 10, label="circ collapseX")
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ind = a.regions[3].points_in_region(x, y, AnalysisType.CollapseY)
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if len(ind) > 0:
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axis.scatter(x[ind], y[ind] + 10, label="circ collapseY")
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ind = a.regions[0].points_in_region(x, y, AnalysisType.CollapseX)
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if len(ind) > 0:
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axis.scatter(x[ind], y[ind]-10, label="rect collapseX")
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ind = a.regions[1].points_in_region(x, y, AnalysisType.CollapseY)
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if len(ind) > 0:
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axis.scatter(x[ind], y[ind] + 10, label="rect collapseY")
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ind = a.regions[2].points_in_region(x, y, AnalysisType.Full)
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if len(ind) > 0:
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axis.scatter(x[ind], y[ind]+20, label="rect full")
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axis.legend()
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plt.show()
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a.plot()
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plt.show() |