Loading autocnet/matcher/outlier_detector.py +2 −7 Original line number Diff line number Diff line Loading @@ -180,15 +180,10 @@ class SpatialSuppression(Observable): if self.k > len(self.df): warnings.warn('Only {} valid points, but {} points requested'.format(len(self.df), self.k)) self.k = len(self.df) <<<<<<< HEAD search_space = np.linspace(self.min_radius, self.max_radius, 250) cell_sizes = search_space / math.sqrt(2) ======= result = self.df.index process = False search_space = np.linspace(self.min_radius, self.max_radius / 16, 250) cell_sizes = (search_space / math.sqrt(2)).astype(np.int) >>>>>>> dd5bb1a3b9810162d4ece48163e4db74b5bb3ac8 search_space = np.linspace(self.min_radius, self.max_radius, 250) cell_sizes = search_space / math.sqrt(2) min_idx = 0 max_idx = len(search_space) - 1 Loading Loading
autocnet/matcher/outlier_detector.py +2 −7 Original line number Diff line number Diff line Loading @@ -180,15 +180,10 @@ class SpatialSuppression(Observable): if self.k > len(self.df): warnings.warn('Only {} valid points, but {} points requested'.format(len(self.df), self.k)) self.k = len(self.df) <<<<<<< HEAD search_space = np.linspace(self.min_radius, self.max_radius, 250) cell_sizes = search_space / math.sqrt(2) ======= result = self.df.index process = False search_space = np.linspace(self.min_radius, self.max_radius / 16, 250) cell_sizes = (search_space / math.sqrt(2)).astype(np.int) >>>>>>> dd5bb1a3b9810162d4ece48163e4db74b5bb3ac8 search_space = np.linspace(self.min_radius, self.max_radius, 250) cell_sizes = search_space / math.sqrt(2) min_idx = 0 max_idx = len(search_space) - 1 Loading