ai-content-maker/.venv/Lib/site-packages/moviepy/video/tools/segmenting.py

60 lines
1.8 KiB
Python

import numpy as np
import scipy.ndimage as ndi
from moviepy.video.VideoClip import ImageClip
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 not clip.mask:
clip = clip.add_mask()
mask = clip.mask.get_frame(0)
labelled, num_features = ndi.measurements.label(image[:,:,0])
#find the objects
slices = []
for e in ndi.find_objects(labelled):
if mask[e[0],e[1]].mean() <= 0.2:
# remove letter holes (in o,e,a, etc.)
continue
if image[e[0],e[1]].size <= rem_thr:
# remove very small slices
continue
slices.append(e)
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