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 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
