CNTK/GeneralizedHoughTransform/generalized_ht.py
def build_r_table(image, origin):
    '''
    Build the R-table from the given shape image and a reference point
    '''
    edges = canny(image, low_threshold=MIN_CANNY_THRESHOLD,
                  high_threshold=MAX_CANNY_THRESHOLD)
    gradient = gradient_orientation(edges)

    r_table = defaultdict(list)
    for (i,j),value in np.ndenumerate(edges):
        if value:
            r_table[gradient[i,j]].append((origin[0]-i, origin[1]-j))

    return r_table

    Build the R-table from the given shape image and a reference point
    
Build the R-table from the given shape image and a reference point
Build the R - table from the given shape image and a reference point
def build_r_table(image, origin):
    edges = canny(image, low_threshold=MIN_CANNY_THRESHOLD,
                  high_threshold=MAX_CANNY_THRESHOLD)
    gradient = gradient_orientation(edges)
    r_table = defaultdict(list)
    for (i,j),value in np.ndenumerate(edges):
        if value:
            r_table[gradient[i,j]].append((origin[0]-i, origin[1]-j))
    return r_table
def build_r_table ( image , origin ) : edges = canny ( image , low_threshold = MIN_CANNY_THRESHOLD , high_threshold = MAX_CANNY_THRESHOLD ) gradient = gradient_orientation ( edges ) r_table = defaultdict ( list ) for ( i , j ) , value in np . ndenumerate ( edges ) : if value : r_table [ gradient [ i , j ] ] . append ( ( origin [ 0 ] - i , origin [ 1 ] - j ) ) return r_table
Build the R-table from the given shape image and a reference point
Build the R - table from the given shape image and a reference point
'''
    Build the R-table from the given shape image and a reference point
    '''
image
origin