def histogram_equalize(self, use_bands, **kwargs):
        ''' Equalize and the histogram and normalize value range
            Equalization is on all three bands, not per-band'''
        data = self._read(self[use_bands,...], **kwargs)
        data = np.rollaxis(data.astype(np.float32), 0, 3)
        flattened = data.flatten()
        if 0 in data:
            masked = np.ma.masked_values(data, 0).compressed()
            image_histogram, bin_edges = np.histogram(masked, 256)
        else:
            image_histogram, bin_edges = np.histogram(flattened, 256)
        bins = (bin_edges[:-1] + bin_edges[1:]) / 2.0
        cdf = image_histogram.cumsum() 
        cdf = cdf / float(cdf[-1])
        image_equalized = np.interp(flattened, bins, cdf).reshape(data.shape)
        if 'stretch' in kwargs or 'gamma' in kwargs:
            return self._histogram_stretch(image_equalized, **kwargs)
        else:
            return image_equalized
def histogram_equalize(self, use_bands, **kwargs):
        ''' Equalize and the histogram and normalize value range
            Equalization is on all three bands, not per-band'''
        data = self._read(self[use_bands,...], **kwargs)
        data = np.rollaxis(data.astype(np.float32), 0, 3)
        flattened = data.flatten()
        if 0 in data:
            masked = np.ma.masked_values(data, 0).compressed()
            image_histogram, bin_edges = np.histogram(masked, 256)
        else:
            image_histogram, bin_edges = np.histogram(flattened, 256)
        bins = (bin_edges[:-1] + bin_edges[1:]) / 2.0
        cdf = image_histogram.cumsum() 
        cdf = cdf / float(cdf[-1])
        image_equalized = np.interp(flattened, bins, cdf).reshape(data.shape)
        if 'stretch' in kwargs or 'gamma' in kwargs:
            return self._histogram_stretch(image_equalized, **kwargs)
        else:
            return image_equalized
def histogram_equalize ( self , use_bands , * * kwargs ) : data = self . _read ( self [ use_bands , ... ] , * * kwargs ) data = np . rollaxis ( data . astype ( np . float32 ) , 0 , 3 ) flattened = data . flatten ( ) if 0 in data : masked = np . ma . masked_values ( data , 0 ) . compressed ( ) image_histogram , bin_edges = np . histogram ( masked , 256 ) else : image_histogram , bin_edges = np . histogram ( flattened , 256 ) bins = ( bin_edges [ : - 1 ] + bin_edges [ 1 : ] ) / 2.0 cdf = image_histogram . cumsum ( ) cdf = cdf / float ( cdf [ - 1 ] ) image_equalized = np . interp ( flattened , bins , cdf ) . reshape ( data . shape ) if 'stretch' in kwargs or 'gamma' in kwargs : return self . _histogram_stretch ( image_equalized , * * kwargs ) else : return image_equalized
Equalize and the histogram and normalize value range
            Equalization is on all three bands, not per-band
Equalize and the histogram and normalize value range Equalization is on all three bands not per - band