def __init__(self,block_size = 7, aperture_size=3, k=0.04, **kwargs): pass DetectorROI.__init__(self,**kwargs) self.block_size = block_size self.aperture_size = aperture_size self.k = k
def __init__(self, sigma=DEFAULT_SIGMA, scales=DOG_SCALES, min_size=20, min_contrast=0.03, max_curvature_ratio=10, **kwargs): ''' min_size - Image pyramid terminates when one of the image demensions reaches this size. ''' DetectorROI.__init__(self,**kwargs) self.min_size = min_size self.scales = scales self.sigma = sigma self.min_contrast = min_contrast self.max_curvature_ratio = max_curvature_ratio
def __init__(self, mask=[[-1, 0, 1]], radius=9, sigma=0.7, k=0.04, **kwargs): ''' Corner Detector mask - first dirivitive filter radius - radius of the max filter sigma - sigma of the smoothing gaussian. k - not sure what this parameter means. Passed to superclass: n - is the approximate number of points requested. bin_size - the width and height of each bin in pixels. corner_selector ('all', 'bins', or 'best') - stratagy for point selection. When corner_selector is set to bins, the image is subdivided in to bins of size <bin_size>X<bin_size> pixels and an equal number of points will be taken from each of those bins. This insures that points are found in all parts of the image not just where the corners are strongest. This code is based on a function originally written for matlab. Original matlab code by: Jingyu Yan and Marc Pollefeys Department of Computer Science University of North Carolina at Chapel Hill Converted to Python by: David Bolme Department of Computer Science Colorado State Univerisity ''' DetectorROI.__init__(self, **kwargs) self.mask = mask self.radius = radius self.sigma = sigma self.k = k
def __init__(self,filter = [[-1,0,1]], radius=9, sigma=0.7, k=0.04, **kwargs): ''' Corner Detector filter - first dirivitive filter radius - radius of the max filter sigma - sigma of the smoothing gaussian. k - not sure what this parameter means. Passed to superclass: n - is the approximate number of points requested. bin_size - the width and height of each bin in pixels. corner_selector ('all', 'bins', or 'best') - stratagy for point selection. When corner_selector is set to bins, the image is subdivided in to bins of size <bin_size>X<bin_size> pixels and an equal number of points will be taken from each of those bins. This insures that points are found in all parts of the image not just where the corners are strongest. This code is based on a function originally written for matlab. Original matlab code by: Jingyu Yan and Marc Pollefeys Department of Computer Science University of North Carolina at Chapel Hill Converted to Python by: David Bolme Department of Computer Science Colorado State Univerisity ''' DetectorROI.__init__(self,**kwargs) self.filter = filter self.radius = radius self.sigma = sigma self.k = k
def __init__(self, min_hessian=400.0, **kwargs): ''' ''' self.min_hessian = min_hessian DetectorROI.__init__(self, **kwargs)
def __init__(self, min_hessian=400.0, **kwargs): """ """ self.min_hessian = min_hessian DetectorROI.__init__(self, **kwargs)