60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
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import numpy as np
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import scipy.ndimage as ndi
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from moviepy.video.VideoClip import ImageClip
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def findObjects(clip,rem_thr=500, preview=False):
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"""
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Returns a list of ImageClips representing each a separate object on
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the screen.
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rem_thr : all objects found with size < rem_Thr will be
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considered false positives and will be removed
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"""
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image = clip.get_frame(0)
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if not clip.mask:
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clip = clip.add_mask()
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mask = clip.mask.get_frame(0)
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labelled, num_features = ndi.measurements.label(image[:,:,0])
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#find the objects
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slices = []
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for e in ndi.find_objects(labelled):
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if mask[e[0],e[1]].mean() <= 0.2:
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# remove letter holes (in o,e,a, etc.)
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continue
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if image[e[0],e[1]].size <= rem_thr:
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# remove very small slices
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continue
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slices.append(e)
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islices = sorted(enumerate(slices), key = lambda s : s[1][1].start)
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letters = []
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for i,(ind,(sy,sx)) in enumerate(islices):
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""" crop each letter separately """
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sy = slice(sy.start-1,sy.stop+1)
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sx = slice(sx.start-1,sx.stop+1)
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letter = image[sy,sx]
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labletter = labelled[sy,sx]
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maskletter = (labletter==(ind+1))*mask[sy,sx]
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letter = ImageClip(image[sy,sx])
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letter.mask = ImageClip( maskletter,ismask=True)
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letter.screenpos = np.array((sx.start,sy.start))
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letters.append(letter)
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if preview:
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import matplotlib.pyplot as plt
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print( "found %d objects"%(num_features) )
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fig,ax = plt.subplots(2)
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ax[0].axis('off')
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ax[0].imshow(labelled)
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ax[1].imshow([range(num_features)],interpolation='nearest')
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ax[1].set_yticks([])
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plt.show()
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return letters
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