Docos updates. (e594ba08) · Commits · aflab / astrogeology / Autocnet

autocnet/matcher/outlier_detector.py

+42 −13

Original line number	Diff line number	Diff line
		@@ -7,6 +7,29 @@ import pandas as pd

		class DistanceRatio(object):

		"""
		A stateful object to store ratio test results and provenance.

		Attributes
		----------

		nvalid : int
		The number of valid entries in the mask

		mask : series
		Pandas boolean series indexed by the match id

		matches : dataframe
		The matches dataframe from an edge. This dataframe
		must have 'source_idx' and 'distance' columns.

		single : bool
		If True, then single entries in the distance ratio
		mask are assumed to have passed the ratio test. Else
		False.

		"""

		def __init__(self, matches):

		self._action_stack = deque(maxlen=10)
		@@ -19,10 +42,10 @@ class DistanceRatio(object):
		def nvalid(self):
		return self.mask.sum()

		def compute(self, ratio, mask=None, mask_name=None):
		def compute(self, ratio, mask=None, mask_name=None, single=False):
		"""
		Compute and return a mask for a matches dataframe
		using Lowe's ratio test.
		using Lowe's ratio test. If keypoints have a single
		Lowe (2004) [Lowe2004]_

		Parameters
		@@ -31,22 +54,27 @@ class DistanceRatio(object):
		the ratio between the first and second-best match distances
		for each keypoint to use as a bound for marking the first keypoint
		as "good". Default: 0.8
		Returns
		-------
		mask : ndarray
		Intended to mask the matches dataframe. Rows are True if the associated keypoint passes
		the ratio test and false otherwise. Keypoints without more than one match are True by
		default, since the ratio test will not work for them.

		mask : series
		A pandas boolean series to initially mask the matches array

		mask_name : list or str
		An arbitrary mask name for provenance tracking

		single : bool
		If True, points with only a single entry are included (True)
		in the result mask, else False.
		"""
		def func(group):
		res = [False] * len(group)
		if len(res) == 1:
		return res
		return [single]
		if group.iloc[0] < group.iloc[1] * ratio:
		res[0] = True
		return res

		self.single = single

		if mask is not None:
		self.mask = mask.copy()
		new_mask = self.matches[mask].groupby('source_idx')['distance'].transform(func).astype('bool')
		@@ -57,7 +85,8 @@ class DistanceRatio(object):

		state_package = {'ratio': ratio,
		'mask': self.mask.copy(),
		'clean_keys': mask_name
		'clean_keys': mask_name,
		'single': single
		}
		self._action_stack.append(state_package)
		self._current_action_stack = len(self._action_stack) - 1
		@@ -98,6 +127,7 @@ class DistanceRatio(object):
		setattr(self, 'mask', state['mask'])
		setattr(self, 'ratio', state['ratio'])
		setattr(self, 'clean_keys', state['clean_keys'])
		setattr(self, 'single', state['single'])
		# Reset attributes (could also cache)
		self._notify_subscribers(self)

		@@ -118,9 +148,11 @@ class DistanceRatio(object):
		setattr(self, 'mask', state['mask'])
		setattr(self, 'ratio', state['ratio'])
		setattr(self, 'clean_keys', state['clean_keys'])
		setattr(self, 'single', state['single'])
		# Reset attributes (could also cache)
		self._notify_subscribers(self)


		def self_neighbors(matches):
		"""
		Returns a pandas data series intended to be used as a mask. Each row
		@@ -143,9 +175,6 @@ def self_neighbors(matches):
		return matches.source_image != matches.destination_image





		def mirroring_test(matches):
		"""
		Compute and return a mask for the matches dataframe on each edge of the graph which