Source Code for Module pyvision.point.DetectorROI

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 33   
 34  import pyvision as pv 
 35  import numpy as np 
 36   
 37 -class DetectorROI: 
 38      ''' 
 39      This class defines an interface to a Region Of Interest (ROI) detector. 
 40      ''' 
 41 -    def __init__(self,n=250,selector='bins',bin_size=50): 
 42          ''' 
 43          n        - is the approximate number of points requested. 
 44          bin_size - the width and height of each bin in pixels. 
 45          selector - ('all', 'bins', or 'best') stratagy for point selection. 
 46           
 47          When corner_selector is set to bins, the image is subdivided in to bins of 
 48          size <bin_size>X<bin_size> pixels and an equal number of points will be taken 
 49          from each of those bins.  This insures that points are found in all parts of the 
 50          image not just where the corners are strongest. 
 51          ''' 
 52   
 53          self.n = n 
 54          self.selector = selector 
 55          self.bin_size = bin_size 
 56          pass 
 57       
 58 -    def detect(self,image,**kwargs): 
 59          ''' 
 60          Returns a list of region of interest. Each element in the list is a  
 61          tuple of (score,centerpoint,radius). Radius of "None" is used for point  
 62          detectors.  Higher scores are better and scores of "None" indicate no  
 63          score is avalible. 
 64          ''' 
 65          # TODO: Call subclass 
 66          A = None 
 67          if isinstance(image,pv.Image): 
 68              A = image.asMatrix2D() 
 69          elif isinstance(image,np.array) and len(image.shape)==2: 
 70              A = image 
 71          else: 
 72              raise TypeError("ERROR Unknown Type (%s) - Only arrays and pyvision images supported."%type(image)) 
 73       
 74          L = self._detect(image,**kwargs) 
 75           
 76          L.sort() 
 77          L.reverse() 
 78           
 79          if self.selector == 'best': 
 80              L=L[:self.n] 
 81          elif self.selector == 'bins': 
 82              nbins = A.shape[0]/self.bin_size*A.shape[1]/self.bin_size 
 83              npts = self.n / nbins + 1 
 84       
 85              corners = [] 
 86              for xmin in range(0,A.shape[0],self.bin_size): 
 87                  xmax = xmin + self.bin_size 
 88                  for ymin in range(0,A.shape[1],self.bin_size): 
 89                      bin_data = [] 
 90                      ymax = ymin + self.bin_size 
 91                      for each in L: 
 92                          #print each 
 93                          if xmin <= each[1] and each[1] < xmax and ymin <= each[2] and each[2] < ymax: 
 94                              bin_data.append(each) 
 95                              if len(bin_data) >= npts: 
 96                                  break 
 97                      corners += bin_data 
 98              L = corners 
 99          else: # TODO: assume all 
100              pass 
101               
102          roi = []                    
103          for each in L: 
104              roi.append([each[0],pv.Point(each[1],each[2]),each[3]]) 
105               
106          #L = concatenate((L.transpose,ones((1,L.shape[0])))) 
107          return roi 
108   
109 -    def _detect(self): 
110          raise NotImplementedError("This method should be overridden in a sub class.") 
111