segmenting.py 文件源码

def findObjects(clip,rem_thr=500, preview=False):
    """ 
    Returns a list of ImageClips representing each a separate object on
    the screen.

    rem_thr : all objects found with size < rem_Thr will be
         considered false positives and will be removed

    """

    image = clip.get_frame(0)
    if clip.mask is None:
        clip = clip.add_mask()

    mask = clip.mask.get_frame(0)
    labelled, num_features = ndi.measurements.label(image[:,:,0])

    #find the objects
    slices = ndi.find_objects(labelled)
    # cool trick to remove letter holes (in o,e,a, etc.)
    slices = [e for e in slices if  mask[e[0],e[1]].mean() >0.2]
    # remove very small slices
    slices = [e for e in slices if  image[e[0],e[1]].size > rem_thr]
    # Sort the slices from left to right
    islices = sorted(enumerate(slices), key = lambda s : s[1][1].start)

    letters = []
    for i,(ind,(sy,sx)) in enumerate(islices):
        """ crop each letter separately """
        sy = slice(sy.start-1,sy.stop+1)
        sx = slice(sx.start-1,sx.stop+1)
        letter = image[sy,sx]
        labletter = labelled[sy,sx]
        maskletter = (labletter==(ind+1))*mask[sy,sx]
        letter = ImageClip(image[sy,sx])
        letter.mask = ImageClip( maskletter,ismask=True)
        letter.screenpos = np.array((sx.start,sy.start))
        letters.append(letter)

    if preview:
        import matplotlib.pyplot as plt
        print( "found %d objects"%(num_features) )
        fig,ax = plt.subplots(2)
        ax[0].axis('off')
        ax[0].imshow(labelled)
        ax[1].imshow([range(num_features)],interpolation='nearest')
        ax[1].set_yticks([])
        plt.show()

    return letters