def find_objects(clip, size_threshold=500, preview=False):
"""
Returns a list of ImageClips representing each a separate object on
the screen.
size_threshold : all objects found with size < size_threshold 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 obj in ndi.find_objects(labelled):
if mask[obj[0], obj[1]].mean() <= 0.2:
# remove letter holes (in o,e,a, etc.)
continue
if image[obj[0], obj[1]].size <= size_threshold:
# remove very small slices
continue
slices.append(obj)
indexed_slices = sorted(enumerate(slices),
key=lambda slice: slice[1][1].start)
letters = []
for i, (sy, sx) in indexed_slices:
""" 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 == (i + 1)) * mask[sy, sx]
letter = ImageClip(image[sy, sx])
letter.mask = ImageClip(maskletter, is_mask=True)
letter.screenpos = np.array((sx.start, sy.start))
letters.append(letter)
if preview:
import matplotlib.pyplot as plt
print(f"Found {num_features} objects")
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
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 == 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
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
def freeze_at_start(clip, freeze_duration=None, total_duration=None):
""" Momentarily freeze the clip on its first frame.
With ``duration``you can specify the duration of the freeze.
With ``total_duration`` you can specify the total duration of
the clip and the freeze (i.e. the duration of the freeze is
automatically calculated). If neither is provided, the freeze
will have an infinite length.
"""
freezed_clip = ImageClip(clip.get_frame(0))
if clip.mask:
freezed_clip.mask = ImageClip(clip.mask.get_frame(0))
if total_duration:
freeze_duration = total_duration - clip.duration
if freeze_duration:
freezed_clip = freezed_clip.set_duration(freeze_duration)
return concatenate_videoclips([freezed_clip, clip])
def freeze_at_start(clip, freeze_duration=None, total_duration=None):
""" Momentarily freeze the clip on its first frame.
With ``duration``you can specify the duration of the freeze.
With ``total_duration`` you can specify the total duration of
the clip and the freeze (i.e. the duration of the freeze is
automatically calculated). If neither is provided, the freeze
will have an infinite length.
"""
freezed_clip = ImageClip(clip.get_frame(0))
if clip.mask:
freezed_clip.mask = ImageClip(clip.mask.get_frame(0))
if total_duration:
freeze_duration = total_duration - clip.duration
if freeze_duration:
freezed_clip = freezed_clip.set_duration(freeze_duration)
return concatenate([freezed_clip, clip])
def find_objects(clip, size_threshold=500, preview=False):
"""Returns a list of ImageClips representing each a separate object on
the screen.
Parameters
----------
clip : video.VideoClip.ImageClip
MoviePy video clip where the objects will be searched.
size_threshold : float, optional
Minimum size of what is considered an object. All objects found with
``size < size_threshold`` will be considered false positives and will
be removed.
preview : bool, optional
Previews with matplotlib the different objects found in the image before
applying the size threshold. Requires matplotlib installed.
Examples
--------
>>> clip = ImageClip("media/afterimage.png")
>>> objects = find_objects(clip)
>>>
>>> print(len(objects))
>>> print([obj_.screenpos for obj_ in objects])
"""
image = clip.get_frame(0)
if not clip.mask: # pragma: no cover
clip = clip.add_mask()
mask = clip.mask.get_frame(0)
labelled, num_features = ndi.measurements.label(image[:, :, 0])
# find the objects
slices = []
for obj in ndi.find_objects(labelled):
if mask[obj[0], obj[1]].mean() <= 0.2:
# remove letter holes (in o,e,a, etc.)
continue
if image[obj[0], obj[1]].size <= size_threshold:
# remove very small slices
continue
slices.append(obj)
indexed_slices = sorted(enumerate(slices), key=lambda slice: slice[1][1].start)
letters = []
for i, (sy, sx) in indexed_slices:
# 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 == (i + 1)) * mask[sy, sx]
letter = ImageClip(image[sy, sx])
letter.mask = ImageClip(maskletter, is_mask=True)
letter.screenpos = np.array((sx.start, sy.start))
letters.append(letter)
if preview: # pragma: no cover
import matplotlib.pyplot as plt
print(f"Found {num_features} objects")
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
