malib.py 文件源码

def get_edgemask(a, edge_env=False, convex=False, dilate=False):
    a = checkma(a)
    #Need to deal with RGB images here
    #Need to be careful, probably want to take minimum value from masks
    if a.ndim == 3:
        #Assume that the same mask applies to each band
        #Only create edges for one band
        b = a[:,:,0]
        #Could convert to HSL and do edges for L channel
        #b = a[:,:,0].mask + a[:,:,1].mask + a[:,:,2].mask
    else:
        b = a

    #Get pixel locations of edges
    edges0, edges1, edges = get_edges(b, convex)

    #Compute min/max indices
    #minrow, maxrow, mincol, maxcol
    #edge_bbox = [edges0[0][0].min(), edges0[1][0].max() + 1, edges0[0][1].min(), edges0[1][1].max() + 1]
    edge_bbox = [edges[0].min(), edges[0].max() + 1, edges[1].min(), edges[1].max() + 1]

    #Initialize new mask arrays
    #Need both to deal with undercuts
    colmask = np.empty_like(b.mask)
    colmask[:] = True
    rowmask = np.empty_like(b.mask)
    rowmask[:] = True

    #Loop through each item in the edge list
    #i is index, col is the column number listed at index i
    #unmask pixels between specified row numbers at index i
    for i,col in enumerate(edges0[0][1]):
        colmask[edges0[0][0][i]:edges0[1][0][i], col] = False

    for j,row in enumerate(edges1[0][0]):
        rowmask[row, edges1[0][1][j]:edges1[1][1][j]] = False

    #Combine the two masks with or operator
    newmask = np.logical_or(colmask, rowmask)

    if dilate:
        print("Dilating edgemask")
        import scipy.ndimage as ndimage 
        n = 3
        #Note: this results in unmasked elements near image corners
        #This will erode True values, which correspond to masked elements
        #Effect is to expand the unmasked area
        #newmask = ndimage.morphology.binary_erosion(newmask, iterations=n)
        #Now dilate to return to original size
        #newmask = ndimage.morphology.binary_dilation(newmask, iterations=n)
        #This is a more logical approach, dilating unmasked areas
        newmask = ~ndimage.morphology.binary_dilation(~newmask, iterations=n)
        newmask = ~ndimage.morphology.binary_erosion(~newmask, iterations=n)

    if edge_env:
        return newmask, edge_bbox
    else:
        return newmask

#This will update the mask to remove interior holes from unmasked region