def test_movie(tmpdir):
"""Test saving a movie of an MEG inverse solution."""
import imageio
if sys.version_info < (3,):
raise SkipTest('imageio ffmpeg requires Python 3')
# create and setup the Brain instance
_set_backend()
brain = Brain(*std_args)
stc_fname = os.path.join(data_dir, 'meg_source_estimate-lh.stc')
stc = io.read_stc(stc_fname)
data = stc['data']
time = np.arange(data.shape[1]) * stc['tstep'] + stc['tmin']
brain.add_data(data, colormap='hot', vertices=stc['vertices'],
smoothing_steps=10, time=time, time_label='time=%0.2f ms')
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
# save movies with different options
dst = str(tmpdir.join('test.mov'))
# test the number of frames in the movie
brain.save_movie(dst)
frames = imageio.mimread(dst)
assert len(frames) == 2
brain.save_movie(dst, time_dilation=10)
frames = imageio.mimread(dst)
assert len(frames) == 7
brain.save_movie(dst, tmin=0.081, tmax=0.102)
frames = imageio.mimread(dst)
assert len(frames) == 2
brain.close()
def test_different_read_modes():
dname1, dname2, fname1, fname2 = _prepare()
for fname, dname, n in [(fname1, dname1, 1), (fname2, dname2, 2)]:
# Test imread()
im = imageio.imread(fname)
assert isinstance(im, np.ndarray)
assert im.shape == (512, 512)
# Test mimread()
ims = imageio.mimread(fname)
assert isinstance(ims, list)
assert ims[0].shape == im.shape
assert len(ims) > 1
#
ims2 = imageio.mimread(dname)
assert len(ims) == len(ims2)
# Test volread()
vol = imageio.volread(dname)
assert vol.ndim == 3
assert vol.shape[0] > 10
assert vol.shape[1:] == (512, 512)
#
vol2 = imageio.volread(fname) # fname works as well
assert (vol == vol2).all()
# Test mvolread()
vols = imageio.mvolread(dname)
assert isinstance(vols, list)
assert len(vols) == n
assert vols[0].shape == vol.shape
assert sum([v.shape[0] for v in vols]) == len(ims)
def test_movie():
"""Test saving a movie of an MEG inverse solution."""
import imageio
# create and setup the Brain instance
_set_backend()
brain = Brain(*std_args)
stc_fname = os.path.join(data_dir, 'meg_source_estimate-lh.stc')
stc = io.read_stc(stc_fname)
data = stc['data']
time = np.arange(data.shape[1]) * stc['tstep'] + stc['tmin']
brain.add_data(data, colormap='hot', vertices=stc['vertices'],
smoothing_steps=10, time=time, time_label='time=%0.2f ms')
brain.scale_data_colormap(fmin=13, fmid=18, fmax=22, transparent=True)
# save movies with different options
tempdir = mkdtemp()
try:
dst = os.path.join(tempdir, 'test.mov')
# test the number of frames in the movie
brain.save_movie(dst)
frames = imageio.mimread(dst)
assert_equal(len(frames), 2)
brain.save_movie(dst, time_dilation=10)
frames = imageio.mimread(dst)
assert_equal(len(frames), 7)
brain.save_movie(dst, tmin=0.081, tmax=0.102)
frames = imageio.mimread(dst)
assert_equal(len(frames), 2)
finally:
# clean up
if not (sys.platform == 'win32' and
os.getenv('APPVEYOR', 'False') == 'True'): # cleanup problems
shutil.rmtree(tempdir)
brain.close()
def test_singleton():
im1 = imageio.imread("imageio:chelsea.png")
fname = os.path.join(test_dir, "chelsea.bsdf")
imageio.imsave(fname, im1)
# Does it look alright if we open it in bsdf without extensions?
raw = bsdf.load(fname, [])
assert isinstance(raw, dict)
assert set(raw.keys()) == set(["meta", "array"])
assert isinstance(raw["meta"], dict)
assert isinstance(raw["array"], dict)
assert raw["array"]["shape"] == list(im1.shape)
assert isinstance(raw["array"]["data"], bytes)
# Read singleton image as singleton
im2 = imageio.imread(fname)
assert np.all(im1 == im2)
# Read singleton image as series
ims = imageio.mimread(fname)
assert len(ims) == 1 and np.all(im1 == ims[0])
# Read + write back without image extensions
bsdf.save(fname, bsdf.load(fname))
im3 = imageio.mimread(fname)
assert np.all(im1 == im3)
def test_fits_reading():
""" Test reading fits """
need_internet() # We keep the fits files in the imageio-binary repo
if IS_PYPY:
return # no support for fits format :(
simple = get_remote_file("images/simple.fits")
multi = get_remote_file("images/multi.fits")
# One image
im = imageio.imread(simple)
ims = imageio.mimread(simple)
assert (im == ims[0]).all()
assert len(ims) == 1
# Multiple images
ims = imageio.mimread(multi)
assert len(ims) == 3
R = imageio.read(multi)
assert R.format.name == "FITS"
ims = list(R) # == [im for im in R]
assert len(ims) == 3
# Fail
raises = pytest.raises
raises(IndexError, R.get_data, -1)
raises(IndexError, R.get_data, 3)
raises(RuntimeError, R.get_meta_data, None) # no meta data support
raises(RuntimeError, R.get_meta_data, 0) # no meta data support
def test_series():
im1 = imageio.imread("imageio:chelsea.png")
ims1 = [im1, im1 * 0.8, im1 * 0.5]
fname = os.path.join(test_dir, "chelseam.bsdf")
imageio.mimsave(fname, ims1)
# Does it look alright if we open it in bsdf without extensions?
raw = bsdf.load(fname, [])
assert isinstance(raw, list) and len(raw) == 3
for r in raw:
assert set(r.keys()) == set(["meta", "array"])
assert isinstance(r["meta"], dict)
assert isinstance(r["array"], dict)
assert r["array"]["shape"] == list(im1.shape)
assert isinstance(r["array"]["data"], bytes)
# Read multi-image as singleton
im2 = imageio.imread(fname)
assert np.all(im1 == im2)
# Read multi-image as series
ims2 = imageio.mimread(fname)
assert len(ims2) == 3 and all(np.all(ims1[i] == ims2[i]) for i in range(3))
# Read + write back without image extensions
bsdf.save(fname, bsdf.load(fname))
ims3 = imageio.mimread(fname)
assert len(ims3) == 3 and all(np.all(ims1[i] == ims3[i]) for i in range(3))
def test_npz_reading_writing():
""" Test reading and saveing npz """
if IS_PYPY:
return # no support for npz format :(
im2 = np.ones((10, 10), np.uint8) * 2
im3 = np.ones((10, 10, 10), np.uint8) * 3
im4 = np.ones((10, 10, 10, 10), np.uint8) * 4
filename1 = os.path.join(test_dir, 'test_npz.npz')
# One image
imageio.imsave(filename1, im2)
im = imageio.imread(filename1)
ims = imageio.mimread(filename1)
assert (im == im2).all()
assert len(ims) == 1
# Multiple images
imageio.mimsave(filename1, [im2, im2, im2])
im = imageio.imread(filename1)
ims = imageio.mimread(filename1)
assert (im == im2).all()
assert len(ims) == 3
# Volumes
imageio.mvolsave(filename1, [im3, im3])
im = imageio.volread(filename1)
ims = imageio.mvolread(filename1)
assert (im == im3).all()
assert len(ims) == 2
# Mixed
W = imageio.save(filename1)
assert W.format.name == 'NPZ'
W.append_data(im2)
W.append_data(im3)
W.append_data(im4)
raises(RuntimeError, W.set_meta_data, {}) # no meta data support
W.close()
#
R = imageio.read(filename1)
assert R.format.name == 'NPZ'
ims = list(R) # == [im for im in R]
assert (ims[0] == im2).all()
assert (ims[1] == im3).all()
assert (ims[2] == im4).all()
# Fail
raises(IndexError, R.get_data, -1)
raises(IndexError, R.get_data, 3)
raises(RuntimeError, R.get_meta_data, None) # no meta data support
raises(RuntimeError, R.get_meta_data, 0) # no meta data support
def test_tifffile_reading_writing():
""" Test reading and saving tiff """
need_internet() # We keep a test image in the imageio-binary repo
im2 = np.ones((10, 10, 3), np.uint8) * 2
filename1 = os.path.join(test_dir, 'test_tiff.tiff')
# One image
imageio.imsave(filename1, im2)
im = imageio.imread(filename1)
ims = imageio.mimread(filename1)
assert (im == im2).all()
assert len(ims) == 1
# Multiple images
imageio.mimsave(filename1, [im2, im2, im2])
im = imageio.imread(filename1)
ims = imageio.mimread(filename1)
assert (im == im2).all()
assert len(ims) == 3, ims[0].shape
# remote multipage rgb file
filename2 = get_remote_file('images/multipage_rgb.tif')
img = imageio.mimread(filename2)
assert len(img) == 2
assert img[0].shape == (3, 10, 10)
# Mixed
W = imageio.save(filename1)
W.set_meta_data({'planarconfig': 'planar'})
assert W.format.name == 'TIFF'
W.append_data(im2)
W.append_data(im2)
W.close()
#
R = imageio.read(filename1)
assert R.format.name == 'TIFF'
ims = list(R) # == [im for im in R]
assert (ims[0] == im2).all()
meta = R.get_meta_data()
assert meta['orientation'] == 'top_left'
# Fail
raises(IndexError, R.get_data, -1)
raises(IndexError, R.get_data, 3)
# Ensure imwrite write works round trip
filename3 = os.path.join(test_dir, 'test_tiff2.tiff')
R = imageio.imread(filename1)
imageio.imwrite(filename3, R)
R2 = imageio.imread(filename3)
assert (R == R2).all()
def test_write_not_contiguous():
R = imageio.read(get_remote_file("images/cockatoo.mp4"), "ffmpeg")
assert R.format is imageio.formats["ffmpeg"]
fname1 = get_remote_file("images/cockatoo.mp4", test_dir)
fname2 = fname1[:-4] + ".out.mp4"
# Read
ims1 = []
with imageio.read(fname1, "ffmpeg") as R:
for i in range(10):
im = R.get_next_data()
ims1.append(im)
# Save non contiguous data
with imageio.save(fname2, "ffmpeg") as W:
for im in ims1:
# DOn't slice the first dimension since it won't be
# a multiple of 16. This will cause the writer to expand
# the data to make it fit, we won't be able to compare
# the difference between the saved and the original images.
im = im[:, ::2]
assert not im.flags.c_contiguous
W.append_data(im)
ims2 = imageio.mimread(fname2, "ffmpeg")
# Check
for im1, im2 in zip(ims1, ims2):
diff = np.abs(im1[:, ::2].astype(np.float32) - im2.astype(np.float32))
if IS_PYPY:
assert (diff.sum() / diff.size) < 100
else:
assert diff.mean() < 2.5
def test_ico():
for float in (False, True):
for crop in (0, 1, 2):
for colors in (0, 1, 3, 4):
fname = fnamebase + '%i.%i.%i.ico' % (float, crop, colors)
rim = get_ref_im(colors, crop, float)
imageio.imsave(fname, rim)
im = imageio.imread(fname)
mul = 255 if float else 1
assert_close(rim * mul, im, 0.1) # lossless
# Meta data
R = imageio.read(fnamebase + '0.0.0.ico')
assert isinstance(R.get_meta_data(0), dict)
assert isinstance(R.get_meta_data(None), dict) # But this print warning
writer = imageio.save(fnamebase + 'I.ico')
writer.set_meta_data({})
writer.close()
# Parameters. Note that with makealpha, RGBA images are read in incorrectly
im = imageio.imread(fnamebase + '0.0.0.ico', makealpha=True)
assert im.ndim == 3 and im.shape[-1] == 4
# Parameter fail
raises(TypeError, imageio.imread, fname, notavalidkwarg=True)
raises(TypeError, imageio.imsave, fnamebase + '1.gif', im, notavalidk=True)
# Multiple images
im = get_ref_im(4, 0, 0)
ims1 = im, np.column_stack([im, im]), np.row_stack([im, im])
imageio.mimsave(fnamebase + 'I.ico', ims1)
ims2 = imageio.mimread(fnamebase + 'I.ico')
for im1, im2 in zip(ims1, ims2):
assert_close(im1, im2, 0.1)
def test_types():
need_internet()
fname1 = get_remote_file("images/stent.swf", test_dir)
fname2 = fname1[:-4] + ".out3.swf"
for dtype in [
np.uint8,
np.uint16,
np.uint32,
np.uint64,
np.int8,
np.int16,
np.int32,
np.int64,
np.float16,
np.float32,
np.float64,
]:
for shape in [(100, 1), (100, 3)]:
# Repeats an identity matrix, just for testing
im1 = np.dstack((np.identity(shape[0], dtype=dtype),) * shape[1])
imageio.mimsave(fname2, [im1], "swf")
im2 = imageio.mimread(fname2, "swf")[0]
assert im2.shape == (100, 100, 4)
assert im2.dtype == np.uint8
if len(shape) == 3 and dtype == np.uint8:
assert (im1[:, :, 0] == im2[:, :, 0]).all()
def MakeGlitchGifVSH(image, len_=60, blockSize=16, sigma=10, iterations=300, random_=True, Glitch_=False):
im = Image.open(image)
VSH = imageio.mimread('vsh.gif')
VSH = extendgif(VSH, len_)
nFrames = []
glitchVar = 0
path = '/'.join(image.split('/')[:-1])
pathT = '/'.join(image.split('/')[:-1])
name = image.split('/')[-1]
fname = name.split('.')[0]
path += '/glitch_' + fname + '.gif'
frames = [im.copy() for a in range(len_)]
i = 0
for frame in frames:
i += 1
if random.randint(0, 15) >= 10 and glitchVar == 0:
glitchVar = random.randint(1, sigma)
if glitchVar != 0:
frame = GlitchRet(frame.convert('RGB'), Glitch_=Glitch_, sigma=glitchVar, blockSize=blockSize,
iterations=iterations, random_=random_)
glitchVar -= 1
frame = ImageChops.multiply(frame, Image.fromarray(VSH[i]).resize(frame.size).convert('RGB'))
nFrames.append(np.asarray(frame.convert('RGB')))
i = 0
imageio.mimwrite(path, nFrames, )
return path
def test_simpleitk_reading_writing():
""" Test reading and saveing tiff """
im2 = np.ones((10, 10, 3), np.uint8) * 2
filename1 = os.path.join(test_dir, 'test_tiff.tiff')
# One image
imageio.imsave(filename1, im2, 'itk')
im = imageio.imread(filename1, 'itk')
ims = imageio.mimread(filename1, 'itk')
assert (im == im2).all()
assert len(ims) == 1
# Mixed
W = imageio.save(filename1, 'itk')
raises(RuntimeError, W.set_meta_data, 1)
assert W.format.name == 'ITK'
W.append_data(im2)
W.append_data(im2)
W.close()
#
R = imageio.read(filename1, 'itk')
assert R.format.name == 'ITK'
ims = list(R) # == [im for im in R]
assert (ims[0] == im2).all()
# Fail
raises(IndexError, R.get_data, -1)
raises(IndexError, R.get_data, 3)
raises(RuntimeError, R.get_meta_data)
def test_animated_gif():
# Get images
im = get_ref_im(4, 0, 0)
ims = []
for i in range(10):
im = im.copy()
im[:, -5:, 0] = i * 20
ims.append(im)
# Store - animated GIF always poops out RGB
for float in (False, True):
for colors in (3, 4):
ims1 = ims[:]
if float:
ims1 = [x.astype(np.float32) / 256 for x in ims1]
ims1 = [x[:, :, :colors] for x in ims1]
fname = fnamebase + '.animated.%i.gif' % colors
imageio.mimsave(fname, ims1, duration=0.2)
# Retrieve
ims2 = imageio.mimread(fname)
ims1 = [x[:, :, :3] for x in ims] # fresh ref
ims2 = [x[:, :, :3] for x in ims2] # discart alpha
for im1, im2 in zip(ims1, ims2):
assert_close(im1, im2, 1.1)
# We can also store grayscale
fname = fnamebase + '.animated.%i.gif' % 1
imageio.mimsave(fname, [x[:, :, 0] for x in ims], duration=0.2)
imageio.mimsave(fname, [x[:, :, :1] for x in ims], duration=0.2)
# Irragular duration. You probably want to check this manually (I did)
duration = [0.1 for i in ims]
for i in [2, 5, 7]:
duration[i] = 0.5
imageio.mimsave(fnamebase + '.animated_irr.gif', ims, duration=duration)
# Other parameters
imageio.mimsave(fnamebase + '.animated.loop2.gif', ims, loop=2)
R = imageio.read(fnamebase + '.animated.loop2.gif')
W = imageio.save(fnamebase + '.animated.palettes100.gif', palettesize=100)
assert W._palettesize == 128
# Fail
raises(IndexError, R.get_meta_data, -1)
raises(ValueError, imageio.mimsave, fname, ims, palettesize=300)
raises(ValueError, imageio.mimsave, fname, ims, quantizer='foo')
raises(ValueError, imageio.mimsave, fname, ims, duration='foo')
# Test subrectangles
imageio.mimsave(fnamebase + '.subno.gif', ims, subrectangles=False)
imageio.mimsave(fnamebase + '.subyes.gif', ims, subrectangles=True)
s1 = os.stat(fnamebase + '.subno.gif').st_size
s2 = os.stat(fnamebase + '.subyes.gif').st_size
assert s2 < s1
# Meta (dummy, because always {}
assert isinstance(imageio.read(fname).get_meta_data(), dict)
def test_read_from_url():
need_internet()
burl = 'https://raw.githubusercontent.com/imageio/imageio-binaries/master/'
url = burl + 'images/stent.swf'
ims = imageio.mimread(url)
assert len(ims) == 10
def test_tifffile_reading_writing():
""" Test reading and saveing tiff """
im2 = np.ones((10, 10, 3), np.uint8) * 2
filename1 = os.path.join(test_dir, "test_tiff.tiff")
# One image
imageio.imsave(filename1, im2)
im = imageio.imread(filename1)
ims = imageio.mimread(filename1)
assert (im == im2).all()
assert len(ims) == 1
# Multiple images
imageio.mimsave(filename1, [im2, im2, im2])
im = imageio.imread(filename1)
ims = imageio.mimread(filename1)
assert (im == im2).all()
assert len(ims) == 3, ims[0].shape
# remote multipage rgb file
filename2 = get_remote_file("images/multipage_rgb.tif")
img = imageio.mimread(filename2)
assert len(img) == 2
assert img[0].shape == (3, 10, 10)
# Mixed
W = imageio.save(filename1)
W.set_meta_data({"planarconfig": "planar"})
assert W.format.name == "TIFF"
W.append_data(im2)
W.append_data(im2)
W.close()
#
R = imageio.read(filename1)
assert R.format.name == "TIFF"
ims = list(R) # == [im for im in R]
assert (ims[0] == im2).all()
meta = R.get_meta_data()
assert meta["is_rgb"]
# Fail
raises(IndexError, R.get_data, -1)
raises(IndexError, R.get_data, 3)
def from_file(cls, path):
"""
This function ...
:param path:
:return:
"""
frames = imageio.mimread(path)
return cls(frames)
def from_file(cls, path):
"""
This function ...
:param path:
:return:
"""
frames = imageio.mimread(path)
animation = cls(frames)
animation.path = path
return animation
def test_types():
fname1 = get_remote_file('images/stent.swf', test_dir)
fname2 = fname1[:-4] + '.out3.swf'
for dtype in [np.uint8, np.float32]:
for shape in [(100, 100), (100, 100, 1), (100, 100, 3)]:
im1 = np.empty(shape, dtype) # empty is nice for testing nan
imageio.mimsave(fname2, [im1], 'swf')
im2 = imageio.mimread(fname2, 'swf')[0]
assert im2.shape == (100, 100, 4)
assert im2.dtype == np.uint8
if len(shape) == 3 and dtype == np.uint8:
assert (im1[:, :, 0] == im2[:, :, 0]).all()
def test_spe_reading():
need_internet()
fname = get_remote_file("images/test_000_.SPE")
fr1 = np.zeros((32, 32), np.uint16)
fr2 = np.ones_like(fr1)
# Test imread
im = imageio.imread(fname)
ims = imageio.mimread(fname)
np.testing.assert_equal(im, fr1)
np.testing.assert_equal(ims, [fr1, fr2])
# Test volread
vol = imageio.volread(fname)
vols = imageio.mvolread(fname)
np.testing.assert_equal(vol, [fr1, fr2])
np.testing.assert_equal(vols, [[fr1, fr2]])
# Test get_reader
r = imageio.get_reader(fname)
np.testing.assert_equal(r.get_data(1), fr2)
np.testing.assert_equal(list(r), [fr1, fr2])
pytest.raises(IndexError, r.get_data, -1)
pytest.raises(IndexError, r.get_data, 2)
# check metadata
md = r.get_meta_data()
assert md["ROIs"] == [
{"top_left": [238, 187], "bottom_right": [269, 218], "bin": [1, 1]}
]
cmt = [
"OD 1.0 in r, g "
" ",
"000200000000000004800000000000000000000000000000000000000000000000"
"0002000001000X",
" "
" ",
" "
" ",
"ACCI2xSEQU-1---10000010001600300EA SW"
"0218COMVER0500",
]
assert md["comments"] == cmt
np.testing.assert_equal(md["frame_shape"], fr1.shape)
def test_read_and_write():
fname1 = get_remote_file('images/cockatoo.mp4', test_dir)
fname2 = fname1[:-4] + '.out.mp4'
# Read
ims1 = []
with imageio.read(fname1, 'ffmpeg') as R:
for i in range(10):
im = R.get_next_data()
ims1.append(im)
assert im.shape == (720, 1280, 3)
assert im.sum() > 0
# Seek
im = R.get_data(120)
assert im.shape == (720, 1280, 3)
# Save
with imageio.save(fname2, 'ffmpeg') as W:
for im in ims1:
W.append_data(im)
# Read the result
ims2 = imageio.mimread(fname2, 'ffmpeg')
assert len(ims1) == len(ims2)
# Check
for im1, im2 in zip(ims1, ims2):
diff = np.abs(im1.astype(np.float32) - im2.astype(np.float32))
assert diff.mean() < 2.0
# Check loop
R = imageio.read(fname2, 'ffmpeg', loop=True)
im1 = R.get_next_data()
for i in range(1, len(R)):
R.get_next_data()
im2 = R.get_next_data()
im3 = R.get_data(0)
im4 = R.get_data(2) # touch skipping frames
assert (im1 == im2).all()
assert (im1 == im3).all()
assert not (im1 == im4).all()
R.close()
def test_ico():
if os.getenv("TRAVIS", "") == "true" and sys.version_info >= (3, 4):
skip("Freeimage ico is unstable for this Travis build")
for float in (False, True):
for crop in (0,):
for colors in (1, 3, 4):
fname = fnamebase + "%i.%i.%i.ico" % (float, crop, colors)
rim = get_ref_im(colors, crop, float)
rim = rim[:32, :32] # ico needs nice size
imageio.imsave(fname, rim)
im = imageio.imread(fname)
mul = 255 if float else 1
assert_close(rim * mul, im, 0.1) # lossless
# Meta data
R = imageio.read(fnamebase + "0.0.1.ico")
assert isinstance(R.get_meta_data(0), dict)
assert isinstance(R.get_meta_data(None), dict) # But this print warning
R.close()
writer = imageio.save(fnamebase + "I.ico")
writer.set_meta_data({})
writer.close()
# Parameters. Note that with makealpha, RGBA images are read in incorrectly
im = imageio.imread(fnamebase + "0.0.1.ico", makealpha=True)
assert im.ndim == 3 and im.shape[-1] == 4
# Parameter fail
raises(TypeError, imageio.imread, fname, notavalidkwarg=True)
raises(TypeError, imageio.imsave, fnamebase + "1.gif", im, notavalidk=True)
if sys.platform.startswith("win"): # issue #21
skip("Windows has a known issue with multi-icon files")
# Multiple images
im = get_ref_im(4, 0, 0)[:32, :32]
ims = [np.repeat(np.repeat(im, i, 1), i, 0) for i in (1, 2)] # SegF on win
ims = im, np.column_stack((im, im)), np.row_stack((im, im)) # error on win
imageio.mimsave(fnamebase + "I2.ico", ims)
ims2 = imageio.mimread(fnamebase + "I2.ico")
for im1, im2 in zip(ims, ims2):
assert_close(im1, im2, 0.1)
def test_not_an_image():
fname = os.path.join(test_dir, "notanimage.bsdf")
# Not an image not a list
bsdf.save(fname, 1)
with raises(RuntimeError):
imageio.imread(fname)
# A list with non-images
bsdf.save(fname, [1])
with raises(RuntimeError):
imageio.imread(fname)
# An empty list could work though
bsdf.save(fname, [])
with raises(IndexError):
imageio.imread(fname)
assert imageio.mimread(fname) == []
def test_ico():
for float in (False, True):
for crop in (0, 1, 2):
for colors in (1, 3, 4):
fname = fnamebase + '%i.%i.%i.ico' % (float, crop, colors)
rim = get_ref_im(colors, crop, float)
imageio.imsave(fname, rim)
im = imageio.imread(fname)
mul = 255 if float else 1
assert_close(rim * mul, im, 0.1) # lossless
# Meta data
R = imageio.read(fnamebase + '0.0.1.ico')
assert isinstance(R.get_meta_data(0), dict)
assert isinstance(R.get_meta_data(None), dict) # But this print warning
R.close()
writer = imageio.save(fnamebase + 'I.ico')
writer.set_meta_data({})
writer.close()
# Parameters. Note that with makealpha, RGBA images are read in incorrectly
im = imageio.imread(fnamebase + '0.0.1.ico', makealpha=True)
assert im.ndim == 3 and im.shape[-1] == 4
# Parameter fail
raises(TypeError, imageio.imread, fname, notavalidkwarg=True)
raises(TypeError, imageio.imsave, fnamebase + '1.gif', im, notavalidk=True)
if sys.platform.startswith('win'): # issue #21
skip('Windows has a known issue with multi-icon files')
# Multiple images
im = get_ref_im(4, 0, 0)
ims = [np.repeat(np.repeat(im, i, 1), i, 0) for i in (1, 2)] # SegF on win
ims = im, np.column_stack((im, im)), np.row_stack((im, im)) # error on win
imageio.mimsave(fnamebase + 'I2.ico', ims)
ims2 = imageio.mimread(fnamebase + 'I2.ico')
for im1, im2 in zip(ims, ims2):
assert_close(im1, im2, 0.1)
def test_read_and_write():
need_internet()
R = imageio.read(get_remote_file('images/cockatoo.mp4'), 'ffmpeg')
assert R.format is imageio.formats['ffmpeg']
fname1 = get_remote_file('images/cockatoo.mp4', test_dir)
fname2 = fname1[:-4] + '.out.mp4'
# Read
ims1 = []
with imageio.read(fname1, 'ffmpeg') as R:
for i in range(10):
im = R.get_next_data()
ims1.append(im)
assert im.shape == (720, 1280, 3)
assert (im.sum() / im.size) > 0 # pypy mean is broken
assert im.sum() > 0
# Seek
im = R.get_data(120)
assert im.shape == (720, 1280, 3)
# Save
with imageio.save(fname2, 'ffmpeg') as W:
for im in ims1:
W.append_data(im)
# Read the result
ims2 = imageio.mimread(fname2, 'ffmpeg')
assert len(ims1) == len(ims2)
# Check
for im1, im2 in zip(ims1, ims2):
diff = np.abs(im1.astype(np.float32) - im2.astype(np.float32))
if IS_PYPY:
assert (diff.sum() / diff.size) < 100
else:
assert diff.mean() < 2.5
def recursive_copy_files(source_path, destination_path, override=False):
"""
Recursive copies files from source to destination directory.
:param source_path: source directory
:param destination_path: destination directory
:param override if True all files will be overridden otherwise skip if file exist
:return: count of copied files
"""
startTime = time.time()
totalFiles = sum([len(files) for r, d, files in os.walk(source_path)])
files_count = 0
if not os.path.exists(destination_path):
os.mkdir(destination_path)
items = glob.glob(source_path + SEPARATOR + '*')
for item in items:
try:
if os.path.isdir(item):
path = os.path.join(destination_path,
item.split(SEPARATOR)[-1])
files_count += recursive_copy_files(source_path=item,
destination_path=path,
override=override)
else:
file = os.path.join(destination_path,
item.split(SEPARATOR)[-1])
if not os.path.exists(file) or override:
print("Input " + item)
print("Output " + file)
base_file, ext = os.path.splitext(file)
if ext == ".gif":
gif = imageio.mimread(item)
#for number, image in enumerate(gif):
img = Image.fromarray(gif[len(gif) - 1])
outputFileName = base_file + ".png"
img.save(outputFileName)
img = loadImageToArray(outputFileName)
img = rescaleImage(img)
img = Image.fromarray(img)
os.remove(outputFileName)
img.save(base_file + ".png")
if ext == ".mp4":
vid = imageio.get_reader(item, 'ffmpeg')
vid_length = vid.get_length()
nums = [
int(vid_length / 8),
int(vid_length / 4),
int(vid_length / 2),
int(vid_length / 1.5)
]
for num in nums:
image = vid.get_data(num)
outputFileName = base_file + str(num) + ".png"
imageio.imwrite(outputFileName, image)
img = loadImageToArray(outputFileName)
img = rescaleImage(img)
img = Image.fromarray(img)
os.remove(outputFileName)
#print(base_file, ext)
img.save(base_file + "_" + str(num) + ".png")
else:
img = loadImageToArray(item)
img = rescaleImage(img)
img = Image.fromarray(img)
#print(base_file, ext)
img.save(base_file + ".png")
#shutil.copyfile(item, file)
except Exception as e:
print("Exception in recursive_copy_files(): " + str(e))
files_count += 1
timeElapsed = time.time() - startTime
timeLeft = (totalFiles / files_count * timeElapsed) - timeElapsed
sys.stdout.write("\rTotal time left - " + str(timeLeft).split(".")[0] +
" s\n")
sys.stdout.flush()
return files_count
import imageio
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from skimage.transform import resize
#from IPython.display import HTML
import warnings
warnings.filterwarnings("ignore")
source_image = imageio.imread('C:/Users/Sharan Babu/Desktop/02.png')
driving_video = imageio.mimread('C:/Users/Sharan Babu/Desktop/00.mp4')
#Resize image and video to 256x256
source_image = resize(source_image, (256, 256))[..., :3]
driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_video]
def display(source, driving, generated=None):
fig = plt.figure(figsize=(8 + 4 * (generated is not None), 6))
ims = []
for i in range(len(driving)):
cols = [source]
cols.append(driving[i])
if generated is not None:
cols.append(generated[i])
im = plt.imshow(np.concatenate(cols, axis=1), animated=True)
plt.axis('off')
ims.append([im])
newImg = np.zeros((row, col), np.uint8)
for x in range(0, 255):
H = (cdf[x] * 255 - cdf[np.min(img)] * 255) / (cdf[254] -
cdf[np.min(img)])
H = round(H)
newImg[img == x] = H
return newImg
"""
--------------------------------------------------------------------------------------------
"""
img1 = cv2.imread('1.tiff', 0)
img2 = cv2.imread('2.png', 0)
gif = imageio.mimread('3.gif')
img3 = cv2.cvtColor(gif[0], cv2.COLOR_BGR2GRAY)
img4 = cv2.imread('4.jpg', 0)
img5 = cv2.imread('5.bmp', 0)
"""
showing image
"""
plt.subplot(151), plt.imshow(
img1, cmap="gray"), plt.axis("off"), plt.title("format:TIFF")
plt.subplot(152), plt.imshow(
img2, cmap="gray"), plt.axis("off"), plt.title("format:PNG")
plt.subplot(153), plt.imshow(
img3, cmap="gray"), plt.axis("off"), plt.title("format:GIF")
plt.subplot(154), plt.imshow(
img4, cmap="gray"), plt.axis("off"), plt.title("format:JPG")
plt.subplot(155), plt.imshow(
def test_functions():
""" Test the user-facing API functions """
# Test help(), it prints stuff, so we just check whether that goes ok
imageio.help() # should print overview
imageio.help('PNG') # should print about PNG
fname1 = get_remote_file('images/chelsea.png', test_dir)
fname2 = fname1[:-3] + 'jpg'
fname3 = fname1[:-3] + 'notavalidext'
open(fname3, 'wb')
# Test read()
R1 = imageio.read(fname1)
R2 = imageio.read(fname1, 'png')
assert R1.format is R2.format
# Fail
raises(ValueError, imageio.read, fname3) # existing but not readable
raises(IOError, imageio.read, 'notexisting.barf')
raises(IndexError, imageio.read, fname1, 'notexistingformat')
# Test save()
W1 = imageio.save(fname2)
W2 = imageio.save(fname2, 'JPG')
assert W1.format is W2.format
# Fail
raises(ValueError, imageio.save, 'wtf.notexistingfile')
# Test imread()
im1 = imageio.imread(fname1)
im2 = imageio.imread(fname1, 'png')
assert im1.shape[2] == 3
assert np.all(im1 == im2)
# Test imsave()
if os.path.isfile(fname2):
os.remove(fname2)
assert not os.path.isfile(fname2)
imageio.imsave(fname2, im1[:, :, 0])
imageio.imsave(fname2, im1)
assert os.path.isfile(fname2)
# Test mimread()
fname3 = get_remote_file('images/newtonscradle.gif', test_dir)
ims = imageio.mimread(fname3)
assert isinstance(ims, list)
assert len(ims) > 1
assert ims[0].ndim == 3
assert ims[0].shape[2] in (1, 3, 4)
if IS_PYPY:
return # no support for npz format :(
# Test mimsave()
fname5 = fname3[:-4] + '2.npz'
if os.path.isfile(fname5):
os.remove(fname5)
assert not os.path.isfile(fname5)
imageio.mimsave(fname5, [im[:, :, 0] for im in ims])
imageio.mimsave(fname5, ims)
assert os.path.isfile(fname5)
# Test volread()
fname4 = get_remote_file('images/stent.npz', test_dir)
vol = imageio.volread(fname4)
assert vol.ndim == 3
assert vol.shape[0] == 256
assert vol.shape[1] == 128
assert vol.shape[2] == 128
# Test volsave()
volc = np.zeros((10, 10, 10, 3), np.uint8) # color volume
fname6 = fname4[:-4] + '2.npz'
if os.path.isfile(fname6):
os.remove(fname6)
assert not os.path.isfile(fname6)
imageio.volsave(fname6, volc)
imageio.volsave(fname6, vol)
assert os.path.isfile(fname6)
# Test mvolread()
vols = imageio.mvolread(fname4)
assert isinstance(vols, list)
assert len(vols) == 1
assert vols[0].shape == vol.shape
# Test mvolsave()
if os.path.isfile(fname6):
os.remove(fname6)
assert not os.path.isfile(fname6)
imageio.mvolsave(fname6, [volc, volc])
imageio.mvolsave(fname6, vols)
assert os.path.isfile(fname6)
# Fail for save functions
raises(ValueError, imageio.imsave, fname2, np.zeros((100, 100, 5)))
raises(ValueError, imageio.imsave, fname2, 42)
raises(ValueError, imageio.mimsave, fname5, [np.zeros((100, 100, 5))])
raises(ValueError, imageio.mimsave, fname5, [42])
raises(ValueError, imageio.volsave, fname4, np.zeros((100, 100, 100, 40)))
raises(ValueError, imageio.volsave, fname4, 42)
raises(ValueError, imageio.mvolsave, fname4, [np.zeros((90, 90, 90, 40))])
raises(ValueError, imageio.mvolsave, fname4, [42])
def test_tifffile_reading_writing():
""" Test reading and saving tiff """
need_internet() # We keep a test image in the imageio-binary repo
im2 = np.ones((10, 10, 3), np.uint8) * 2
filename1 = os.path.join(test_dir, 'test_tiff.tiff')
# One image
imageio.imsave(filename1, im2)
im = imageio.imread(filename1)
ims = imageio.mimread(filename1)
assert im.shape == im2.shape
assert (im == im2).all()
assert len(ims) == 1
# Multiple images
imageio.mimsave(filename1, [im2, im2, im2])
im = imageio.imread(filename1)
ims = imageio.mimread(filename1)
assert im.shape == im2.shape
assert (im == im2).all() # note: this does not imply that the shape match!
assert len(ims) == 3
for i in range(3):
assert ims[i].shape == im2.shape
assert (ims[i] == im2).all()
# Read all planes as one array - we call it a volume for clarity
vol = imageio.volread(filename1)
vols = imageio.mvolread(filename1)
assert vol.shape == (3, ) + im2.shape
assert len(vols) == 1 and vol.shape == vols[0].shape
for i in range(3):
assert (vol[i] == im2).all()
# remote multipage rgb file
filename2 = get_remote_file('images/multipage_rgb.tif')
img = imageio.mimread(filename2)
assert len(img) == 2
assert img[0].shape == (3, 10, 10)
# Mixed
W = imageio.save(filename1)
W.set_meta_data({'planarconfig': 'SEPARATE'}) # was "planar"
assert W.format.name == 'TIFF'
W.append_data(im2)
W.append_data(im2)
W.close()
#
R = imageio.read(filename1)
assert R.format.name == 'TIFF'
ims = list(R) # == [im for im in R]
assert (ims[0] == im2).all()
# meta = R.get_meta_data()
# assert meta['orientation'] == 'top_left' # not there in later version
# Fail
raises(IndexError, R.get_data, -1)
raises(IndexError, R.get_data, 3)
# Ensure imread + imwrite works round trip
filename3 = os.path.join(test_dir, 'test_tiff2.tiff')
im1 = imageio.imread(filename1)
imageio.imwrite(filename3, im1)
im3 = imageio.imread(filename3)
assert im1.ndim == 3
assert im1.shape == im3.shape
assert (im1 == im3).all()
# Ensure imread + imwrite works round trip - volume like
filename3 = os.path.join(test_dir, 'test_tiff2.tiff')
im1 = imageio.volread(filename1)
imageio.volwrite(filename3, im1)
im3 = imageio.volread(filename3)
assert im1.ndim == 4
assert im1.shape == im3.shape
assert (im1 == im3).all()
# Read metadata
md = imageio.get_reader(filename2).get_meta_data()
assert md['is_imagej'] is None
assert md['description'] == 'shape=(2,3,10,10)'
assert md['description1'] == ''
assert md['datetime'] == datetime.datetime(2015, 5, 9, 9, 8, 29)
assert md['software'] == 'tifffile.py'
# Write metadata
dt = datetime.datetime(2018, 8, 6, 15, 35, 5)
w = imageio.get_writer(filename1, software='testsoftware')
w.append_data(np.zeros((10, 10)), meta={'description': 'test desc',
'datetime': dt})
w.close()
r = imageio.get_reader(filename1)
md = r.get_meta_data()
assert 'datetime' in md
assert md['datetime'] == dt
assert 'software' in md
assert md['software'] == 'testsoftware'
assert 'description' in md
assert md['description'] == 'test desc'
def test_animated_gif():
# Read newton's cradle
ims = imageio.mimread("imageio:newtonscradle.gif")
assert len(ims) == 36
for im in ims:
assert im.shape == (150, 200, 4)
assert im.min() > 0
assert im.max() <= 255
# Get images
im = get_ref_im(4, 0, 0)
ims = []
for i in range(10):
im = im.copy()
im[:, -5:, 0] = i * 20
ims.append(im)
# Store - animated GIF always poops out RGB
for isfloat in (False, True):
for colors in (3, 4):
ims1 = ims[:]
if isfloat:
ims1 = [x.astype(np.float32) / 256 for x in ims1]
ims1 = [x[:, :, :colors] for x in ims1]
fname = fnamebase + ".animated.%i.gif" % colors
imageio.mimsave(fname, ims1, duration=0.2)
# Retrieve
print("fooo", fname, isfloat, colors)
ims2 = imageio.mimread(fname)
ims1 = [x[:, :, :3] for x in ims] # fresh ref
ims2 = [x[:, :, :3] for x in ims2] # discart alpha
for im1, im2 in zip(ims1, ims2):
assert_close(im1, im2, 1.1)
# We can also store grayscale
fname = fnamebase + ".animated.%i.gif" % 1
imageio.mimsave(fname, [x[:, :, 0] for x in ims], duration=0.2)
imageio.mimsave(fname, [x[:, :, :1] for x in ims], duration=0.2)
# Irragular duration. You probably want to check this manually (I did)
duration = [0.1 for i in ims]
for i in [2, 5, 7]:
duration[i] = 0.5
imageio.mimsave(fnamebase + ".animated_irr.gif", ims, duration=duration)
# Other parameters
imageio.mimsave(fnamebase + ".animated.loop2.gif", ims, loop=2, fps=20)
R = imageio.read(fnamebase + ".animated.loop2.gif")
W = imageio.save(fnamebase + ".animated.palettes100.gif", palettesize=100)
assert W._writer.opt_palette_size == 128
# Fail
assert raises(IndexError, R.get_meta_data, -1)
assert raises(ValueError, imageio.mimsave, fname, ims, palettesize=300)
assert raises(ValueError, imageio.mimsave, fname, ims, quantizer="foo")
assert raises(ValueError, imageio.mimsave, fname, ims, duration="foo")
# Add one duplicate image to ims to touch subractangle with not change
ims.append(ims[-1])
# Test subrectangles
imageio.mimsave(fnamebase + ".subno.gif", ims, subrectangles=False)
imageio.mimsave(fnamebase + ".subyes.gif", ims, subrectangles=True)
s1 = os.stat(fnamebase + ".subno.gif").st_size
s2 = os.stat(fnamebase + ".subyes.gif").st_size
assert s2 < s1
# Meta (dummy, because always {})
imageio.mimsave(fname, [x[:, :, 0] for x in ims], duration=0.2)
assert isinstance(imageio.read(fname).get_meta_data(), dict)
parser.set_defaults(adapt_scale=False)
opt = parser.parse_args()
print("TEST!!")
print(opt.relative)
print(opt.adapt_scale)
print(opt.cpu)
print(opt.find_best_frame)
print(opt.best_frame)
print(opt.result_video)
source_image = imageio.imread(opt.source_image)
reader = imageio.get_reader(opt.driving_video)
fps = reader.get_meta_data()['fps']
reader.close()
driving_video = imageio.mimread(opt.driving_video, memtest=False)
source_image = resize(source_image, (256, 256))[..., :3]
driving_video = [
resize(frame, (256, 256))[..., :3] for frame in driving_video
]
generator, kp_detector = load_checkpoints(config_path=opt.config,
checkpoint_path=opt.checkpoint,
cpu=opt.cpu)
if opt.find_best_frame or opt.best_frame is not None:
i = opt.best_frame if opt.best_frame is not None else find_best_frame(
source_image, driving_video, cpu=opt.cpu)
print("Best frame: " + str(i))
driving_forward = driving_video[i:]
driving_backward = driving_video[:(i + 1)][::-1]
Z_ = W * (1/(2+rho)*(2*X + rho*temp)) + (1-W)*temp
converged_2 = torch.norm(Z-Z_)/torch.norm(Z) < eps or j>limit
print((torch.norm(Z-Z_)/torch.norm(Z)).item())
Z = Z_[:]
j+=1
temp = (Z - U @ V.transpose(0,1))
print(torch.norm(temp))
Y = Y + rho*temp
rho = min(rho*mu, 1e20)
converged = torch.norm(temp)<eps or i>limit
i+=1
imshow(U @ V.transpose(0,1))
imshow(Z)
# %%
gif = imageio.mimread('mit_logo_2.gif')
gif = gif[:75]
imageio.mimwrite('mit_logo_2_short.mp4', gif)
# %%
# with open('netflix-data/combined_data_1.txt') as f:
# txt = f.read()
#
# lines = txt.split("\n")
# user_id_to_idx = {}
# idx = 0
# t = tqdm(lines)
# for line in t:
# if len(line)==0:
# continue
def test_reading_saving():
need_internet()
fname1 = get_remote_file("images/stent.swf", test_dir)
fname2 = fname1[:-4] + ".out.swf"
fname3 = fname1[:-4] + ".compressed.swf"
fname4 = fname1[:-4] + ".out2.swf"
# Read
R = imageio.read(fname1)
assert len(R) == 10
assert R.get_meta_data() == {} # always empty dict
ims1 = []
for im in R:
assert im.shape == (657, 451, 4)
assert mean(im) > 0
ims1.append(im)
# Seek
assert (R.get_data(3) == ims1[3]).all()
# Fails
raises(IndexError, R.get_data, -1) # No negative index
raises(IndexError, R.get_data, 10) # Out of bounds
R.close()
# Test loop
R = imageio.read(fname1, loop=True)
assert (R.get_data(10) == ims1[0]).all()
# setting meta data is ignored
W = imageio.save(fname2)
W.set_meta_data({"foo": 3})
W.close()
# Just make sure mimread works
assert len(imageio.mimread(fname1)) == 10
# I'm not sure why, but the below does not work on pypy, which is weird,
# because the file *is* closed, but somehow it's not flushed? Ah well ...
if IS_PYPY:
return
# Write and re-read, now without loop, and with html page
imageio.mimsave(fname2, ims1, loop=False, html=True)
ims2 = imageio.mimread(fname2)
# Check images. We can expect exact match, since
# SWF is lossless.
assert len(ims1) == len(ims2)
for im1, im2 in zip(ims1, ims2):
assert (im1 == im2).all()
# Test compressed
imageio.mimsave(fname3, ims2, compress=True)
ims3 = imageio.mimread(fname3)
assert len(ims1) == len(ims3)
for im1, im3 in zip(ims1, ims3):
assert (im1 == im3).all()
# Test conventional, Bonus, we don't officially support this.
_swf = imageio.plugins.swf.load_lib()
_swf.write_swf(fname4, ims1)
ims4 = _swf.read_swf(fname4)
assert len(ims1) == len(ims4)
for im1, im4 in zip(ims1, ims4):
assert (im1 == im4).all()
# We want to manually validate that this file plays in 3d party tools
# So we write a small HTML5 doc that we can load
html = """<!DOCTYPE html>
<html>
<body>
Original:
<embed src="%s">
<br ><br >
Written:
<embed src="%s">
<br ><br >
Compressed:
<embed src="%s">
<br ><br >
Written 2:
<embed src="%s">
</body>
</html>
""" % (
fname1,
fname2,
fname3,
fname4,
)
with open(os.path.join(test_dir, "test_swf.html"), "wb") as f:
for line in html.splitlines():
f.write(line.strip().encode("utf-8") + b"\n")
def test_read_and_write():
R = imageio.read(get_remote_file("images/cockatoo.mp4"), "ffmpeg")
assert R.format is imageio.formats["ffmpeg"]
fname1 = get_remote_file("images/cockatoo.mp4", test_dir)
fname2 = fname1[:-4] + ".out.mp4"
frame1, frame2, frame3 = 41, 131, 227
# Read
ims1 = []
with imageio.read(fname1, "ffmpeg") as R:
for i in range(10):
im = R.get_next_data()
ims1.append(im)
assert im.shape == (720, 1280, 3)
assert (im.sum() / im.size) > 0 # pypy mean is broken
assert im.sum() > 0
# Seek to reference frames in steps. OUR code will skip steps
im11 = R.get_data(frame1)
im12 = R.get_data(frame2)
im13 = R.get_data(frame3)
# Now go backwards, seek will kick in
R.get_next_data()
im23 = R.get_data(frame3)
im22 = R.get_data(frame2)
im21 = R.get_data(frame1)
# Also use set_image_index
R.set_image_index(frame2)
im32 = R.get_next_data()
R.set_image_index(frame3)
im33 = R.get_next_data()
R.set_image_index(frame1)
im31 = R.get_next_data()
for im in (im11, im12, im13, im21, im22, im23, im31, im32, im33):
assert im.shape == (720, 1280, 3)
assert (im11 == im21).all() and (im11 == im31).all()
assert (im12 == im22).all() and (im12 == im32).all()
assert (im13 == im23).all() and (im13 == im33).all()
assert not (im11 == im12).all()
assert not (im11 == im13).all()
# Save
with imageio.save(fname2, "ffmpeg") as W:
for im in ims1:
W.append_data(im)
# Read the result
ims2 = imageio.mimread(fname2, "ffmpeg")
assert len(ims1) == len(ims2)
for im in ims2:
assert im.shape == (720, 1280, 3)
# Check
for im1, im2 in zip(ims1, ims2):
diff = np.abs(im1.astype(np.float32) - im2.astype(np.float32))
if IS_PYPY:
assert (diff.sum() / diff.size) < 100
else:
assert diff.mean() < 2.5
parser.add_argument("--swap_index", default="1,2,5", type=lambda x: list(map(int, x.split(','))),
help='index of swaped parts')
parser.add_argument("--hard", action="store_true", help="use hard segmentation labels for blending")
parser.add_argument("--use_source_segmentation", action="store_true", help="use source segmentation for swaping")
parser.add_argument("--first_order_motion_model", action="store_true", help="use first order model for alignment")
parser.add_argument("--supervised", action="store_true",
help="use supervised segmentation labels for blending. Only for faces.")
parser.add_argument("--cpu", action="store_true", help="cpu mode")
opt = parser.parse_args()
source_image = imageio.imread(opt.source_image)
target_video = imageio.mimread(opt.target_video, memtest=False)
source_image = resize(source_image, (256, 256))[..., :3]
target_video = [resize(frame, (256, 256))[..., :3] for frame in target_video]
blend_scale = (256 / 4) / 512 if opt.supervised else 1
reconstruction_module, segmentation_module = load_checkpoints(opt.config, opt.checkpoint, blend_scale=blend_scale,
first_order_motion_model=opt.first_order_motion_model, cpu=opt.cpu)
if opt.supervised:
face_parser = load_face_parser(opt.cpu)
else:
face_parser = None
predictions = make_video(opt.swap_index, source_image, target_video, reconstruction_module, segmentation_module,
face_parser, hard=opt.hard, use_source_segmentation=opt.use_source_segmentation, cpu=opt.cpu)
# Read fps of the target video and save result with the same fps
def test_animated_gif():
if sys.platform.startswith('darwin'):
skip('On OSX quantization of freeimage is unstable')
# Get images
im = get_ref_im(4, 0, 0)
ims = []
for i in range(10):
im = im.copy()
im[:, -5:, 0] = i * 20
ims.append(im)
# Store - animated GIF always poops out RGB
for isfloat in (False, True):
for colors in (3, 4):
ims1 = ims[:]
if isfloat:
ims1 = [x.astype(np.float32) / 256 for x in ims1]
ims1 = [x[:, :, :colors] for x in ims1]
fname = fnamebase + '.animated.%i.gif' % colors
imageio.mimsave(fname, ims1, duration=0.2)
# Retrieve
ims2 = imageio.mimread(fname)
ims1 = [x[:, :, :3] for x in ims] # fresh ref
ims2 = [x[:, :, :3] for x in ims2] # discart alpha
for im1, im2 in zip(ims1, ims2):
assert_close(im1, im2, 1.1)
# We can also store grayscale
fname = fnamebase + '.animated.%i.gif' % 1
imageio.mimsave(fname, [x[:, :, 0] for x in ims], duration=0.2)
imageio.mimsave(fname, [x[:, :, :1] for x in ims], duration=0.2)
# Irragular duration. You probably want to check this manually (I did)
duration = [0.1 for i in ims]
for i in [2, 5, 7]:
duration[i] = 0.5
imageio.mimsave(fnamebase + '.animated_irr.gif', ims, duration=duration)
# Other parameters
imageio.mimsave(fnamebase + '.animated.loop2.gif', ims, loop=2, fps=20)
R = imageio.read(fnamebase + '.animated.loop2.gif')
W = imageio.save(fnamebase + '.animated.palettes100.gif', palettesize=100)
assert W._palettesize == 128
# Fail
raises(IndexError, R.get_meta_data, -1)
raises(ValueError, imageio.mimsave, fname, ims, palettesize=300)
raises(ValueError, imageio.mimsave, fname, ims, quantizer='foo')
raises(ValueError, imageio.mimsave, fname, ims, duration='foo')
# Add one duplicate image to ims to touch subractangle with not change
ims.append(ims[-1])
# Test subrectangles
imageio.mimsave(fnamebase + '.subno.gif', ims, subrectangles=False)
imageio.mimsave(fnamebase + '.subyes.gif', ims, subrectangles=True)
s1 = os.stat(fnamebase + '.subno.gif').st_size
s2 = os.stat(fnamebase + '.subyes.gif').st_size
assert s2 < s1
# Meta (dummy, because always {}
assert isinstance(imageio.read(fname).get_meta_data(), dict)
# GAME = 'MsPacman-v0' # mspacman_color = np.array([210, 164, 74]).mean() # # # Preprocess the image input # def preprocess(obs): # img = obs[1:176:2, ::2] # crop and downsize # img = img.sum(axis=2) # to greyscale # img[img == mspacman_color] = 0 # Improve contrast # img = (img // 3 - 128).astype(np.int64) # img = img.reshape(88, 80, 1) # return img # # # env = gym.make(GAME) # s = env.reset() # prev_img = preprocess(s) # s_, r, done, _ = env.step(1) # img = preprocess(s_) # im = Image.fromarray(img - prev_img, 'RGB') # im.show() import imageio gif_original = 'results/random.gif' gif_speed_up = 'results/speed_up.gif' gif = imageio.mimread(gif_original) imageio.mimsave(gif_speed_up, gif, fps=30)
###### Loading pictures and video #######
if selection == 2:
#Import Gif
url = "https://media.giphy.com/media/3bb5jcIADH9ewHnpl9/giphy.gif"
fname = "tmp.gif"
## Read the gif from the web, save to the disk
imdata = urllib.request.urlopen(url).read()
imbytes = bytearray(imdata)
open(fname,"wb+").write(imdata)
## Read the gif from disk to `RGB`s using `imageio.miread`
gif = imageio.mimread(fname)
nums = len(gif)
# convert form RGB to BGR
augmentation_gif = [cv.cvtColor(img, cv.COLOR_RGB2BGR) for img in gif]
#Create padded augmentation gif
height_gif, width_gif, cc_gif= augmentation_gif[0].shape
original = cv.VideoCapture('img/video_outside.avi')
else:
augmentation_layer = cv.imread('img/AugmentedLayerCyanLab.png')
original = cv.VideoCapture('img/original_short.avi')
fourcc = cv.VideoWriter_fourcc(*"DIVX")
def test_animated_gif():
# Read newton's cradle
ims = imageio.mimread("imageio:newtonscradle.gif")
assert len(ims) == 36
for im in ims:
assert im.shape == (150, 200, 4)
assert im.min() > 0
assert im.max() <= 255
# Get images
im = get_ref_im(4, 0, 0)
ims = []
for i in range(10):
im = im.copy()
im[:, -5:, 0] = i * 20
ims.append(im)
# Store - animated GIF always poops out RGB
for isfloat in (False, True):
for colors in (3, 4):
ims1 = ims[:]
if isfloat:
ims1 = [x.astype(np.float32) / 256 for x in ims1]
ims1 = [x[:, :, :colors] for x in ims1]
fname = fnamebase + ".animated.%i.gif" % colors
imageio.mimsave(fname, ims1, duration=0.2)
# Retrieve
print("fooo", fname, isfloat, colors)
ims2 = imageio.mimread(fname)
ims1 = [x[:, :, :3] for x in ims] # fresh ref
ims2 = [x[:, :, :3] for x in ims2] # discart alpha
for im1, im2 in zip(ims1, ims2):
assert_close(im1, im2, 1.1)
# We can also store grayscale
fname = fnamebase + ".animated.%i.gif" % 1
imageio.mimsave(fname, [x[:, :, 0] for x in ims], duration=0.2)
imageio.mimsave(fname, [x[:, :, :1] for x in ims], duration=0.2)
# Irragular duration. You probably want to check this manually (I did)
duration = [0.1 for i in ims]
for i in [2, 5, 7]:
duration[i] = 0.5
imageio.mimsave(fnamebase + ".animated_irr.gif", ims, duration=duration)
# Other parameters
imageio.mimsave(fnamebase + ".animated.loop2.gif", ims, loop=2, fps=20)
R = imageio.read(fnamebase + ".animated.loop2.gif")
W = imageio.save(fnamebase + ".animated.palettes100.gif", palettesize=100)
assert W._writer.opt_palette_size == 128
# Fail
assert raises(IndexError, R.get_meta_data, -1)
assert raises(ValueError, imageio.mimsave, fname, ims, palettesize=300)
assert raises(ValueError, imageio.mimsave, fname, ims, quantizer="foo")
assert raises(ValueError, imageio.mimsave, fname, ims, duration="foo")
# Add one duplicate image to ims to touch subractangle with not change
ims.append(ims[-1])
# Test subrectangles
imageio.mimsave(fnamebase + ".subno.gif", ims, subrectangles=False)
imageio.mimsave(fnamebase + ".subyes.gif", ims, subrectangles=True)
s1 = os.stat(fnamebase + ".subno.gif").st_size
s2 = os.stat(fnamebase + ".subyes.gif").st_size
assert s2 < s1
# Meta (dummy, because always {})
imageio.mimsave(fname, [x[:, :, 0] for x in ims], duration=0.2)
assert isinstance(imageio.read(fname).get_meta_data(), dict)
net.to(device)
net_path = os.path.join(model_path,
'{}_epoch-HairMatteNet.ckpt'.format(resume_epochs))
net.load_state_dict(
torch.load(net_path, map_location=lambda storage, loc: storage))
net.eval()
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
############ Main block ############
start = time.time()
video = imageio.mimread(path_input, memtest=False)
video = [frame[..., :3] for frame in video]
old_size = video[0].shape
batch_size = len(video)
print('Number of frames: ', batch_size)
data = Generator(video, transform=transform)
dataloader = DataLoader(data,
shuffle=False,
batch_size=batch_size,
num_workers=1)
result = []
for i, data in enumerate(dataloader, 0):
image = data.to(device)
with torch.no_grad():
def main(args):
if not os.path.isfile(args.input):
print("Error : {} file doesn't exist".format(args.input),
file=sys.stderr)
exit(1)
start = time.time()
gpu_ids = args.gpu
prefs = {
"titsize": args.bsize,
"aursize": args.asize,
"nipsize": args.nsize,
"vagsize": args.vsize,
"hairsize": args.hsize
}
if args.cpu:
gpu_ids = None
elif gpu_ids is None:
gpu_ids = [0]
if not args.gif:
# Read image
file = open(args.input, "rb")
image_bytes = bytearray(file.read())
np_image = np.asarray(image_bytes, dtype=np.uint8)
image = cv2.imdecode(np_image, cv2.IMREAD_COLOR)
# See if image loaded correctly
if image is None:
print("Error : {} file is not valid".format(args.input),
file=sys.stderr)
exit(1)
# Preprocess
if args.overlay:
original_image = image.copy()
image = utils.crop_input(image, args.overlay[0], args.overlay[1],
args.overlay[2], args.overlay[3])
elif args.auto_resize:
image = utils.resize_input(image)
elif args.auto_resize_crop:
image = utils.resize_crop_input(image)
elif args.auto_rescale:
image = utils.rescale_input(image)
# See if image has the correct shape after preprocessing
if image.shape != (512, 512, 3):
print("Error : image is not 512 x 512, got shape: {}".format(
image.shape),
file=sys.stderr)
exit(1)
# Process
if args.n_runs is None or args.n_runs == 1:
result = process(image, gpu_ids, prefs)
if args.overlay:
result = utils.overlay_original_img(original_image, result,
args.overlay[0],
args.overlay[1],
args.overlay[2],
args.overlay[3])
cv2.imwrite(args.output, result)
else:
base_output_filename = utils.strip_file_extension(
args.output, ".png")
def process_one_image(i):
result = process(image, gpu_ids, prefs)
if args.overlay:
result = utils.overlay_original_img(
original_image, result, args.overlay[0],
args.overlay[1], args.overlay[2], args.overlay[3])
cv2.imwrite(base_output_filename + "%03d.png" % i, result)
if args.cpu:
pool = ThreadPool(args.n_cores)
pool.map(process_one_image, range(args.n_runs))
pool.close()
pool.join()
else:
for i in range(args.n_runs):
process_one_image(i)
else:
# Read images
gif_imgs = imageio.mimread(args.input)
print("Total {} frames in the gif!".format(len(gif_imgs)))
# Preprocess
if args.auto_resize:
gif_imgs = [utils.resize_input(img) for img in gif_imgs]
elif args.auto_resize_crop:
gif_imgs = [utils.resize_crop_input(img) for img in gif_imgs]
elif args.auto_rescale:
gif_imgs = [utils.rescale_input(img) for img in gif_imgs]
# Process
if args.n_runs is None or args.n_runs == 1:
process_gif_wrapper(
gif_imgs,
args.output if args.output != "output.png" else "output.gif",
gpu_ids, prefs, args.n_cores)
else:
base_output_filename = utils.strip_file_extension(
args.output,
".gif") if args.output != "output.png" else "output"
for i in range(args.n_runs):
process_gif_wrapper(gif_imgs,
base_output_filename + "%03d.gif" % i,
gpu_ids, prefs, args.n_cores)
end = time.time()
duration = end - start
# Done
print("Done! We have taken", round(duration, 2), "seconds")
# Exit
sys.exit()
import glob
import numpy as np
import imageio
vids = sorted(glob.glob("*.mp4"))
all_vids = []
for v in range(len(vids)):
if v == 0:
all_vids = imageio.mimread(vids[v], memtest=False)[:100]
else:
co = imageio.mimread(vids[v], memtest=False)
try:
composite = []
for f in range(100):
frame = np.concatenate((all_vids[f], co[f]), axis=0)
composite.append(frame)
all_vids = composite
except:
pass
imageio.mimsave("all_comp_xiph.mp4", all_vids, fps=15)
import numpy as np
import imageio as im
ecb = im.mimread('C:\\Users\\Ian\\Desktop\\blockcipher\\animated\\an_ecb.gif')
ctr = im.mimread('C:\\Users\\Ian\\Desktop\\blockcipher\\animated\\an_ctr.gif')
stack = np.hstack((ecb, ctr))
im.mimwrite('C:\\Users\\Ian\\Desktop\\blockcipher\\animated\\stack.gif',
stack,
duration=0.01)
import imageio
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
import imageio
from PIL import ImageSequence
import numpy as np
import sys
gif = imageio.mimread("pp.gif")
nums = len(gif)
im = Image.fromarray(np.uint8(gif[1]))
im = im.convert('RGB')
im.thumbnail((300, 300))
im.show()
'''
# Get user supplied values
imagePath = sys.argv[1]
facePath = sys.argv[2]
if len(sys.argv) < 4:
outputPath = "output"
else:
outputPath = sys.argv[3]
print("arguments given", imagePath, facePath, outputPath)
# if gif
if imagePath.endswith(".gif"):
# Read in images
images = imageio.mimread(imagePath, format="GIF-PIL")
face = imageio.imread(facePath)
output = []
for image in images:
image = find_and_replace(image, face)
output.append(image)
if DEBUG:
cv2.imshow("BGR", output[-1])
cv2.waitKey(0)
cv2.imshow("RGB", output[-1][:, :, ::-1])
cv2.waitKey(0)
if DEBUG:
# The footer for information about how these images were cropped #
footer = '_(%i,%i)_(%ix%i)_(f%i-%i)' % (*Roi_cen, *Roi_size, *Frames)
#%% LOAD AND CROP %%#
print("Starting...")
for f in files:
filename = f.split('\\')[-1]
f_name = FOLD_EXP + filename if (FOLD_EXP not in f) else f
# Skip over already cropped files #
if (filename[:3] == 'roi'): continue
print(filename, end='\t')
# Loading - KEEP IN MIND: THIS IS FOR SINGLE PLANE, MONOCHROMATIC IMAGES #
data = np.array(imageio.mimread(f_name, memtest=False))
while (len(np.shape(data)) > 3):
data = data[0, ...]
print('| Loaded |', end='\t')
# Visualize before cropping #
if (VISUAL):
plt.figure(figsize=(12, 6))
ax = plt.axes(position=[0, 0, 0.5, 1])
ax.imshow(data[Frames[0], :, :], cmap='gray')
ax.plot([rng_x[0], rng_x[0], rng_x[-1], rng_x[-1], rng_x[0]],
[rng_y[0], rng_y[-1], rng_y[-1], rng_y[0], rng_y[0]],
c='r')
ax.text(10, 20, "Frame %i" % (Frames[0]), color='w')
ax.set_xticklabels([])
ax.set_yticklabels([])
# Load an image using OpenCV
cv_img = cv2.cvtColor(cv2.imread("chatbot-image.png"), cv2.COLOR_BGR2RGB)
# Get the image dimensions (OpenCV stores image data as NumPy ndarray)
height, width, no_channels = cv_img.shape
# Create a canvas that can fit the above image
canvas = tkinter.Canvas(window, width=width, height=height)
canvas.pack()
# Use PIL (Pillow) to convert the NumPy ndarray to a PhotoImage
photo = PIL.ImageTk.PhotoImage(image=PIL.Image.fromarray(cv_img))
# Add a PhotoImage to the Canvas
canvas.create_image(0, 0, image=photo, anchor=tkinter.NW)
gif = imageio.mimread("chatgif.gif")
nums = len(gif)
print("Total {} frames in the gif!".format(nums))
imgs = [cv2.cvtColor(img, cv2.COLOR_RGB2BGR) for img in gif]
btn_blur = tkinter.Button(window,
text="Transition",
width=50,
command=transition)
btn_blur.pack(anchor=tkinter.CENTER, expand=True)
# Run the window loop
window.mainloop()
color_list = os.listdir(root)
a = 0
for color in color_list:
# if "拼色" in color or "花色" in color:
# continue
file_names = os.listdir(os.path.join(root, color))
for file_name in file_names:
file_path = os.path.join(root, color, file_name)
file_path = file_path.encode('utf-8').decode('utf-8')
# 加载sklearn中样图
# china = cv2.imread(file_path)
# file_path = "hua2.jpg"
china = cv2.imdecode(np.fromfile(file_path, dtype=np.uint8), -1)
## imdecode读取的是rgb，如果后续需要opencv处理的话，需要转换成bgr，转换后图片颜色会变化
if china is None:
tmp = imageio.mimread(file_path)
if tmp is not None:
imt = np.array(tmp)
imt = imt[0]
china = imt[:, :, 0:3]
china = cv2.cvtColor(china, cv2.COLOR_RGB2BGR)
n_colors = get_n_color(china)
# cv2.imwrite(f"image2/{n_colors}_{a}.jpg", china)
a += 1
# cv2.imshow("image", china)
# cv2.waitKey(800)
# n_colors = 5
china = np.array(china, dtype=np.float64) / 255
def loadFrame(args):
mean = np.asarray([0.485, 0.456, 0.406], np.float32)
std = np.asarray([0.229, 0.224, 0.225], np.float32)
curr_w = 320
curr_h = 240
height = width = 224
(filename, augment) = args
data = np.zeros((3, height, width), dtype=np.float32)
try:
### load file from HDF5
# filename = filename.replace('.avi','.hdf5')
# filename = filename.replace('UCF-101','UCF-101-hdf5')
# h = h5py.File(filename,'r')
h = imageio.mimread(filename)
nFrames = len(h['meta']) - 1
frame_index = np.random.randint(nFrames)
frame = h['meta'][frame_index]
if (augment == True):
## RANDOM CROP - crop 70-100% of original size
## don't maintain aspect ratio
if (np.random.randint(2) == 0):
resize_factor_w = 0.3 * np.random.rand() + 0.7
resize_factor_h = 0.3 * np.random.rand() + 0.7
w1 = int(curr_w * resize_factor_w)
h1 = int(curr_h * resize_factor_h)
w = np.random.randint(curr_w - w1)
h = np.random.randint(curr_h - h1)
frame = frame[h:(h + h1), w:(w + w1)]
## FLIP
if (np.random.randint(2) == 0):
frame = cv2.flip(frame, 1)
frame = cv2.resize(frame, (width, height))
frame = frame.astype(np.float32)
## Brightness +/- 15
brightness = 30
random_add = np.random.randint(brightness + 1) - brightness / 2.0
frame += random_add
frame[frame > 255] = 255.0
frame[frame < 0] = 0.0
else:
# don't augment
frame = cv2.resize(frame, (width, height))
frame = frame.astype(np.float32)
## resnet model was trained on images with mean subtracted
frame = frame / 255.0
frame = (frame - mean) / std
frame = frame.transpose(2, 0, 1)
data[:, :, :] = frame
except:
print("Exception: " + filename)
data = np.array([])
return data
raise argparse.ArgumentTypeError('Boolean value expected.')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Start Image Animation')
parser.add_argument("--image_path", type=str, default='./data/images/02.png')
parser.add_argument("--video_path", type=str, default='./data/videos/04.mp4')
parser.add_argument("--use_relative",type=str2bool, nargs='?',
const=True, default=True)
#parser.add_argument("--exclude_pattern", nargs="+", default=[""])
args = parser.parse_args()
image_path = args.image_path
video_path = args.video_path
source_image = imageio.imread(image_path)
driving_video = imageio.mimread(video_path, memtest=False)
#Resize image and video to 256x256
source_image = resize(source_image, (256, 256))[..., :3]
driving_video = np.array([resize(frame, (256, 256))[..., :3] for frame in driving_video])
imageio.imwrite('./generated/downscaled_image.png',source_image)
generator, kp_detector = load_checkpoints(config_path='config/vox-256.yaml',
checkpoint_path='./models/vox-cpk.pth.tar')
predictions = make_animation(source_image, driving_video, generator, kp_detector, relative=args.use_relative)
#predictions = []
#orig_img = driving_video[0]
#for i in tqdm(range(np.shape(driving_video)[0]-1)):
# Just for lulz.
# REQUIRE: pip install fpdf2 imageio
# USAGE: ./gif2pdf.py $gif_filepath
import sys
from fpdf import FPDF
from imageio import mimread
from PIL import Image
in_filepath = sys.argv[1]
if not in_filepath.endswith(".gif"):
print("Input file must be a GIF", file=sys.stderr)
sys.exit(1)
imgs = mimread(in_filepath)
size = (imgs[0].shape[1], imgs[0].shape[0])
pdf = FPDF(format=size)
pdf.set_margin(0)
duration_in_secs = 0
for img in imgs:
pdf.add_page(duration=duration_in_secs)
# Converting the numpy.ndarray to a PIL.Image:
pdf.image(Image.frombytes("RGBA", size, img.tobytes()), w=pdf.epw)
duration_in_secs = img.meta["duration"] / 1000 or 0.04
out_filepath = in_filepath.replace(".gif", ".pdf")
pdf.output(out_filepath)
print(f'You can now open "{out_filepath}" in Adobe Acrobat Reader,')
print("press CTRL+L to launch presentation mode,")
def vidcreategif(inputvideo, outputgif, fps, colors):
videofile = imageio.mimread(inputvideo)
imageio.mimsave(outputgif, videofile, fps=fps, palettesize=int(colors))
def imread_frames(filename, *args, **kw):
'''reads multiple frames'''
try:
return [Image(i) for i in mimread(filename, *args, **kw)]
except ValueError:
return None
def plot(x, self, n_sample_per_video=5, max_num_video=4):
opt = self.opt
x = [x_ for x_ in x]
if opt.n_past + opt.n_eval > len(x):
for _ in range(opt.n_past + opt.n_eval - len(x)):
x.append(x[-1])
gen_seq = [[] for i in range(n_sample_per_video)]
gt_seq = [x[i] for i in range(len(x))]
recon_seq = [x[i] for i in range(len(x))]
# get reconstruction
frame_predictor_hidden = None
posterior_hidden = None
prior_hidden = None
x_in = x[0]
batch_size = x_in.shape[0]
for i in range(1, opt.n_eval):
h = self.encoder(x_in)
if opt.last_frame_skip or i < opt.n_past + 1:
h, skip = h
else:
h, _ = h
h_target = self.encoder(x[i])
h_target = h_target[0]
_, z_t, _, posterior_hidden = self.posterior(h_target,
posterior_hidden)
h, frame_predictor_hidden = self.frame_predictor(
torch.cat([h, z_t], 1), frame_predictor_hidden)
x_pred = self.decoder([h, skip]).argmax(dim=1, keepdim=True)
recon_seq[i] = x_pred
x_in = x[i]
# get prediction
for s in range(n_sample_per_video):
frame_predictor_hidden = None
posterior_hidden = None
prior_hidden = None
gen_seq[s].append(x[0])
x_in = x[0]
for i in range(1, opt.n_eval):
h = self.encoder(x_in)
if opt.last_frame_skip or i < opt.n_past + 1:
h, skip = h
else:
h, _ = h
if i < opt.n_past:
h_target = self.encoder(x[i])
h_target = h_target[0]
z_t, _, _, posterior_hidden = self.posterior(
h_target, posterior_hidden)
_, _, _, prior_hidden = self.prior(h, prior_hidden)
_, frame_predictor_hidden = self.frame_predictor(
torch.cat([h, z_t], 1), frame_predictor_hidden)
x_in = x[i]
else:
z_t, _, _, prior_hidden = self.prior(h, prior_hidden)
h, frame_predictor_hidden = self.frame_predictor(
torch.cat([h, z_t], 1), frame_predictor_hidden)
x_in = self.decoder([h, skip]).argmax(dim=1, keepdim=True)
gen_seq[s].append(x_in)
gifs = [[] for t in range(opt.n_eval)]
nrow = min(batch_size, max_num_video)
for i in range(nrow):
# ground truth sequence
row = []
for t in range(opt.n_eval):
row.append(gt_seq[t][i])
s_list = list(range(n_sample_per_video))
for ss in range(len(s_list)):
s = s_list[ss]
for t in range(opt.n_eval):
row.append(gen_seq[s][t][i])
for t in range(opt.n_eval):
n_pad = 2
if t < opt.n_past:
canvas_color = green = [0., 1., 0.] # green
elif opt.n_past <= t < opt.n_past + opt.n_future:
canvas_color = yellow = [1., 1., 0.] # yellow
else:
canvas_color = red = [1., 0., 0.] # red
row = []
gen = self.colorize(gt_seq[t][i].unsqueeze(0)).float().div(255)
h, w = gen.shape[-2:]
canvas = torch.tensor(green, device=gen.device,
dtype=gen.dtype).view(3, 1, 1).repeat(
1, h + n_pad * 2, w + n_pad * 2)
canvas[:, n_pad:n_pad + h, n_pad:n_pad + w] = gen
row.append(canvas)
recon = self.colorize(
recon_seq[t][i].unsqueeze(0)).float().div(255)
h, w = recon.shape[-2:]
canvas_recon = torch.tensor(green,
device=recon.device,
dtype=recon.dtype).view(
3, 1,
1).repeat(1, h + n_pad * 2,
w + n_pad * 2)
canvas_recon[:, n_pad:n_pad + h, n_pad:n_pad + w] = recon
row.append(canvas_recon)
for ss in range(len(s_list)):
s = s_list[ss]
gen = self.colorize(
gen_seq[s][t][i].unsqueeze(0)).float().div(255)
h, w = gen.shape[-2:]
canvas = torch.tensor(canvas_color,
device=gen.device,
dtype=gen.dtype).view(3, 1, 1).repeat(
1, h + n_pad * 2, w + n_pad * 2)
canvas[:, n_pad:n_pad + h, n_pad:n_pad + w] = gen
row.append(canvas)
gifs[t].append(row)
fname = '%s/sample_%d.gif' % (self.sample_dir, self.global_iter)
save_gif(fname, gifs)
result = torch.from_numpy(np.array(imageio.mimread(
fname, memtest=False))).transpose(2, 3).transpose(1, 2)
self.writer.add_video('video_pred', result.unsqueeze(0), self.global_iter)
import imageio
import numpy as np
from skimage.transform import resize
import warnings
warnings.filterwarnings("ignore")
source_image = imageio.imread('Adolf-Hitler-1933.jpg')
driving_video = imageio.mimread('jadoo.mp4', memtest=False)
#Resize image and video to 256x256
source_image = resize(source_image, (256, 256))[..., :3]
driving_video = [resize(frame, (256, 256))[..., :3] for frame in driving_video]
from demo import load_checkpoints
generator, kp_detector = load_checkpoints(
config_path='config/vox-256.yaml', checkpoint_path='vox-adv-cpk.pth.tar')
from demo import make_animation
from skimage import img_as_ubyte
predictions = make_animation(source_image,
driving_video,
generator,
kp_detector,
relative=True)
#save resulting video
imageio.mimsave('generated.mp4',
def test_reading_saving():
need_internet()
fname1 = get_remote_file('images/stent.swf', test_dir)
fname2 = fname1[:-4] + '.out.swf'
fname3 = fname1[:-4] + '.compressed.swf'
fname4 = fname1[:-4] + '.out2.swf'
# Read
R = imageio.read(fname1)
assert len(R) == 10
assert R.get_meta_data() == {} # always empty dict
ims1 = []
for im in R:
assert im.shape == (657, 451, 4)
assert mean(im) > 0
ims1.append(im)
# Seek
assert (R.get_data(3) == ims1[3]).all()
# Fails
raises(IndexError, R.get_data, -1) # No negative index
raises(IndexError, R.get_data, 10) # Out of bounds
R.close()
# Test loop
R = imageio.read(fname1, loop=True)
assert (R.get_data(10) == ims1[0]).all()
# setting meta data is ignored
W = imageio.save(fname2)
W.set_meta_data({'foo': 3})
W.close()
# Write and re-read, now without loop, and with html page
imageio.mimsave(fname2, ims1, loop=False, html=True)
ims2 = imageio.mimread(fname2)
# Check images. We can expect exact match, since
# SWF is lossless.
assert len(ims1) == len(ims2)
for im1, im2 in zip(ims1, ims2):
assert (im1 == im2).all()
# Test compressed
imageio.mimsave(fname3, ims2, compress=True)
ims3 = imageio.mimread(fname3)
assert len(ims1) == len(ims3)
for im1, im3 in zip(ims1, ims3):
assert (im1 == im3).all()
# Test conventional, Bonus, we don't officially support this.
_swf = imageio.plugins.swf.load_lib()
_swf.write_swf(fname4, ims1)
ims4 = _swf.read_swf(fname4)
assert len(ims1) == len(ims4)
for im1, im4 in zip(ims1, ims4):
assert (im1 == im4).all()
# We want to manually validate that this file plays in 3d party tools
# So we write a small HTML5 doc that we can load
html = """<!DOCTYPE html>
<html>
<body>
Original:
<embed src="%s">
<br ><br >
Written:
<embed src="%s">
<br ><br >
Compressed:
<embed src="%s">
<br ><br >
Written 2:
<embed src="%s">
</body>
</html>
""" % (fname1, fname2, fname3, fname4)
with open(os.path.join(test_dir, 'test_swf.html'), 'wb') as f:
for line in html.splitlines():
f.write(line.strip().encode('utf-8') + b'\n')
def test_functions():
""" Test the user-facing API functions """
# Test help(), it prints stuff, so we just check whether that goes ok
imageio.help() # should print overview
imageio.help('PNG') # should print about PNG
fname1 = get_remote_file('images/chelsea.png', test_dir)
fname2 = fname1[:-3] + 'jpg'
fname3 = fname1[:-3] + 'notavalidext'
open(fname3, 'wb')
# Test read()
R1 = imageio.read(fname1)
R2 = imageio.read(fname1, 'png')
assert R1.format is R2.format
# Fail
raises(ValueError, imageio.read, fname3) # existing but not readable
raises(FileNotFoundError, imageio.read, 'notexisting.barf')
raises(IndexError, imageio.read, fname1, 'notexistingformat')
# Test save()
W1 = imageio.save(fname2)
W2 = imageio.save(fname2, 'JPG')
W1.close()
W2.close()
assert W1.format is W2.format
# Fail
raises(FileNotFoundError, imageio.save,
'~/dirdoesnotexist/wtf.notexistingfile')
# Test imread()
im1 = imageio.imread(fname1)
im2 = imageio.imread(fname1, 'png')
assert im1.shape[2] == 3
assert np.all(im1 == im2)
# Test imsave()
if os.path.isfile(fname2):
os.remove(fname2)
assert not os.path.isfile(fname2)
imageio.imsave(fname2, im1[:, :, 0])
imageio.imsave(fname2, im1)
assert os.path.isfile(fname2)
# Test mimread()
fname3 = get_remote_file('images/newtonscradle.gif', test_dir)
ims = imageio.mimread(fname3)
assert isinstance(ims, list)
assert len(ims) > 1
assert ims[0].ndim == 3
assert ims[0].shape[2] in (1, 3, 4)
# Test protection
with raises(RuntimeError):
imageio.mimread('imageio:chelsea.png', 'dummy', length=np.inf)
if IS_PYPY:
return # no support for npz format :(
# Test mimsave()
fname5 = fname3[:-4] + '2.npz'
if os.path.isfile(fname5):
os.remove(fname5)
assert not os.path.isfile(fname5)
imageio.mimsave(fname5, [im[:, :, 0] for im in ims])
imageio.mimsave(fname5, ims)
assert os.path.isfile(fname5)
# Test volread()
fname4 = get_remote_file('images/stent.npz', test_dir)
vol = imageio.volread(fname4)
assert vol.ndim == 3
assert vol.shape[0] == 256
assert vol.shape[1] == 128
assert vol.shape[2] == 128
# Test volsave()
volc = np.zeros((10, 10, 10, 3), np.uint8) # color volume
fname6 = os.path.join(test_dir, 'images', 'stent2.npz')
if os.path.isfile(fname6):
os.remove(fname6)
assert not os.path.isfile(fname6)
imageio.volsave(fname6, volc)
imageio.volsave(fname6, vol)
assert os.path.isfile(fname6)
# Test mvolread()
vols = imageio.mvolread(fname4)
assert isinstance(vols, list)
assert len(vols) == 1
assert vols[0].shape == vol.shape
# Test mvolsave()
if os.path.isfile(fname6):
os.remove(fname6)
assert not os.path.isfile(fname6)
imageio.mvolsave(fname6, [volc, volc])
imageio.mvolsave(fname6, vols)
assert os.path.isfile(fname6)
# Fail for save functions
raises(ValueError, imageio.imsave, fname2, np.zeros((100, 100, 5)))
raises(ValueError, imageio.imsave, fname2, 42)
raises(ValueError, imageio.mimsave, fname5, [np.zeros((100, 100, 5))])
raises(ValueError, imageio.mimsave, fname5, [42])
raises(ValueError, imageio.volsave, fname6, np.zeros((100, 100, 100, 40)))
raises(ValueError, imageio.volsave, fname6, 42)
raises(ValueError, imageio.mvolsave, fname6, [np.zeros((90, 90, 90, 40))])
raises(ValueError, imageio.mvolsave, fname6, [42])
def generate_gifs(image, match_info, choice):
if ((int(choice) == 1) and
(match_info["home_score"] > match_info["away_score"])) or (
(int(choice) == 2) and
(match_info["home_score"] <= match_info["away_score"])):
GIF_FOLDERS = ["win"]
else:
GIF_FOLDERS = ["noooo"]
GIF_FOLDERS += ['goal', '1_0', 'woman_red_card'] #, 'noooo', 'win' 'lost']
SCALE_FACTORS = {
'woman_red_card': (2, 2),
'goal': (1, 1),
'1_0': (5, 5),
'noooo': (2, 2),
'win': (2, 2)
} #, 'lost': (2, 2)}
COORDS = {
'woman_red_card': (0.6, 0.8),
'goal': (0, 0.3),
'1_0': (0.1, 0.85),
'noooo': (0, 0.8),
'win': (0.1, 0.75)
} #, 'lost': (0,0.7)}
NUM_FRAMES = {
'woman_red_card': 45,
'goal': 24,
'1_0': 10,
'noooo': 8,
'win': 2
} #, 'lost': 2}
DURATION = {
'woman_red_card': 50,
'goal': 100,
'1_0': 150,
'noooo': 100,
'win': 100
} #, 'lost': 100}
STATIC_IMAGE = cv2.resize(image, dsize=(480, 640))
for i, gif_name in enumerate(GIF_FOLDERS):
static_image = STATIC_IMAGE
if gif_name == 'win' or gif_name == 'noooo':
versus_sticker = create_sticker_with_info(match_info=match_info)
versus_img = versus_sticker[:, :, :3]
versus_img_mask = versus_sticker[:, :, 3]
static_image = overlay_image(bg=static_image,
fg=versus_img,
fgMask=versus_img_mask,
coords=(static_image.shape[1] // 4,
20))
gif = []
for img_name in sorted(glob.glob(f'./Data/{gif_name}/frame_*.gif')):
curr_frame = imageio.mimread(img_name)[0]
gif.append(
cv2.resize(
curr_frame,
dsize=(curr_frame.shape[1] // SCALE_FACTORS[gif_name][0],
curr_frame.shape[0] // SCALE_FACTORS[gif_name][1])))
gif_array = []
for idx, gif_frame in enumerate(gif):
gif_img = gif_frame[:, :, :3]
gif_img_mask = gif_frame[:, :, 3]
overlayed_img = overlay_image(
bg=static_image,
fg=gif_img,
fgMask=gif_img_mask,
coords=(int(static_image.shape[1] * COORDS[gif_name][0]),
int(static_image.shape[1] * COORDS[gif_name][1])))
gif_array.append(Image.fromarray(overlayed_img))
# GIF version
gif_array[0].save(f'./static/gifs/{i}.gif',
save_all=True,
append_images=gif_array[:NUM_FRAMES[gif_name]],
duration=DURATION[gif_name],
loop=0)
optimize(f'./static/gifs/{i}.gif')
# VIDEO version
# dump_video(
# filename=f'./static/gifs/{i}.mp4',
# clip=gif_array
# )
return [
f'95.213.37.132:5000/static/gifs/{i}.gif'
for i in range(len(GIF_FOLDERS))
]
def atlasBrainMatch(
brain_img_dir,
sensory_img_dir,
coords_input,
sensory_match,
mat_save,
threshold,
git_repo_base,
region_labels,
landmark_arr_orig,
use_unet,
use_dlc,
atlas_to_brain_align,
model,
olfactory_check,
plot_landmarks,
align_once,
original_label,
use_voxelmorph,
exist_transform,
voxelmorph_model="motif_model_atlas.h5",
vxm_template_path="templates",
dlc_template_path="dlc_templates",
flow_path="",
):
"""
Align and overlap brain atlas onto brain image based on four landmark locations in the brain image and the atlas.
:param brain_img_dir: The directory containing each brain image to be used.
:param sensory_img_dir: The directory containing each sensory image to be used (if you are aligning each brain
image using a sensory map).
:param coords_input: Predicted locations of the four landmarks on the brain image from the file generated by
DeepLabCut.
:param sensory_match: Whether or not a sensory map is to be used.
:param mat_save: Whether or not to export each brain region to a .mat file in applyMask, which is called at the end
of this function.
:param threshold: The threshold for the cv2.opening operation carried out in applyMask, which is called at the end
of this function.
:param git_repo_base: The path to the base git repository containing necessary resources for MesoNet (reference
atlases, DeepLabCut config files, etc.)
:param region_labels: Choose whether or not to attempt to label each region with its name from the Allen Institute
Mouse Brain Atlas.
:param landmark_arr_orig: The original array of landmarks from DeepLabCut (to be distinguished from any automatic
exclusions to landmark array based on prediction quality).
:param use_unet: Choose whether or not to identify the borders of the cortex using a U-net model.
:param atlas_to_brain_align: If True, registers the atlas to each brain image. If False, registers each brain image
to the atlas.
:param model: The name of the U-net model (for passthrough to mask_functions.py)
:param olfactory_check: If True, draws olfactory bulb contours on the brain image.
:param plot_landmarks: If True, plots DeepLabCut landmarks (large circles) and original alignment landmarks (small
circles) on final brain image.
:param align_once: if True, carries out all alignments based on the alignment of the first atlas and brain. This can
save time if you have many frames of the same brain with a fixed camera position.
:param original_label: if True, uses a brain region labelling approach that attempts to automatically sort brain
regions in a consistent order (left to right by hemisphere, then top to bottom for vertically aligned regions). This
approach may be more flexible if you're using a custom brain atlas (i.e. not one in which region is filled with a
unique number).
:param exist_transform: if True, uses an existing voxelmorph transformation field for all data instead of predicting
a new transformation.
:param voxelmorph_model: the name of a .h5 model located in the models folder of the git repository for MesoNet,
generated using voxelmorph and containing weights for a voxelmorph local deformation model.
:param vxm_template_path: the path to a template atlas (.npy or .mat( to which the brain image will be aligned in
voxelmorph.
:param flow_path: the path to a voxelmorph transformation field that will be used to transform all data instead of
predicting a new transformation if exist_transform is True.
"""
# load brain images folder
brain_img_arr = []
dlc_img_arr = []
peak_arr = []
atlas_label_list = []
dst_list = []
vxm_template_list = []
br_list = []
voxelmorph_model_path = os.path.join(
git_repo_base, "models", "voxelmorph", voxelmorph_model
)
# Prepare template for VoxelMorph
convert_to_png(vxm_template_path)
vxm_template_orig = cv2.imread(
glob.glob(os.path.join(git_repo_base, "atlases", vxm_template_path, "*.png"))[0]
)
# Prepare template for DeepLabCut + VoxelMorph
# convert_to_png(dlc_template_path)
# dlc_template = cv2.imread(
# glob.glob(os.path.join(git_repo_base, "atlases", dlc_template_path, "*.png"))[0]
# )
# dlc_template = np.uint8(dlc_template)
# dlc_template = cv2.resize(dlc_template, (512, 512))
# Prepare output folder
cwd = os.getcwd()
output_mask_path = os.path.join(cwd, "../output_mask")
# Output folder for transparent masks and masks overlaid onto brain image
output_overlay_path = os.path.join(cwd, "../output_overlay")
if not os.path.isdir(output_mask_path):
os.mkdir(output_mask_path)
if not os.path.isdir(output_overlay_path):
os.mkdir(output_overlay_path)
if not atlas_to_brain_align:
im = cv2.imread(
os.path.join(git_repo_base, "atlases/Atlas_workflow2_binary.png")
)
else:
if use_voxelmorph and not use_dlc:
im = cv2.imread(
os.path.join(git_repo_base, "atlases/Atlas_for_Voxelmorph_binary.png")
)
else:
im = cv2.imread(
os.path.join(git_repo_base, "atlases/Atlas_workflow1_binary.png")
)
im_left = cv2.imread(os.path.join(git_repo_base, "atlases/left_hemi.png"))
ret, im_left = cv2.threshold(im_left, 5, 255, cv2.THRESH_BINARY_INV)
im_right = cv2.imread(os.path.join(git_repo_base, "atlases/right_hemi.png"))
ret, im_right = cv2.threshold(im_right, 5, 255, cv2.THRESH_BINARY_INV)
im_left = np.uint8(im_left)
im_right = np.uint8(im_right)
im = np.uint8(im)
# im = atlas_from_mat(os.path.join(git_repo_base, 'atlases/atlas_ROIs.mat'))
atlas = im
# FOR ALIGNING BRAIN TO ATLAS
# im_binary = np.uint8(im)
for num, file in enumerate(os.listdir(cwd)):
if fnmatch.fnmatch(file, "*.png") and "mask" not in file:
dlc_img_arr.append(os.path.join(cwd, file))
for num, file in enumerate(os.listdir(brain_img_dir)):
if fnmatch.fnmatch(file, "*.png"):
brain_img_arr.append(os.path.join(brain_img_dir, file))
brain_img_arr.sort(key=natural_sort_key)
elif fnmatch.fnmatch(file, "*.tif"):
tif_stack = imageio.mimread(os.path.join(brain_img_dir, file))
for tif_im in tif_stack:
brain_img_arr.append(tif_im)
# i_coord, j_coord = np.array([(100, 256, 413, 256), (148, 254, 148, 446)])
# https://www.pyimagesearch.com/2014/07/21/detecting-circles-images-using-opencv-hough-circles/
coord_circles_img = cv2.imread(
os.path.join(
git_repo_base, "atlases", "multi_landmark", "landmarks_new_binary.png"
),
cv2.IMREAD_GRAYSCALE,
)
coord_circles_img = np.uint8(coord_circles_img)
# detect circles in the image
circles, hierarchy = cv2.findContours(
coord_circles_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE
)[-2:]
# ensure at least some circles were found
if circles is not None:
# convert the (x, y) coordinates and radius of the circles to integers
atlas_arr = np.array(
[
(
int(cv2.moments(circle)["m10"] / cv2.moments(circle)["m00"]),
int(cv2.moments(circle)["m01"] / cv2.moments(circle)["m00"]),
)
for circle in circles
]
)
atlas_arr = np.array(
[
(102, 148),
(166, 88),
(214, 454),
(256, 88),
(256, 256),
(256, 428),
(410, 148),
(346, 88),
(298, 454),
]
)
peak_arr_flat = []
peak_arr_total = []
if sensory_match:
for num, file in enumerate(brain_img_arr):
img_name = str(os.path.splitext(os.path.basename(file))[0])
sensory_img_for_brain = os.path.join(sensory_img_dir, img_name)
print(img_name)
print(sensory_img_for_brain)
if glob.glob(sensory_img_for_brain):
sensory_img_for_brain_dir = os.listdir(sensory_img_for_brain)
sensory_img_for_brain_dir.sort(key=natural_sort_key)
for num_im, file_im in enumerate(sensory_img_for_brain_dir):
sensory_im = io.imread(
os.path.join(sensory_img_dir, img_name, file_im)
)
sensory_im = trans.resize(sensory_im, (512, 512))
io.imsave(
os.path.join(sensory_img_dir, img_name, file_im), sensory_im
)
peak = find_peaks(os.path.join(sensory_img_dir, img_name, file_im))
peak_arr.append(peak)
for x in peak_arr:
for y in x:
peak_arr_flat.append(y)
peak_arr_total.append(peak_arr_flat)
peak_arr_flat = []
peak_arr = []
dlc_pts = []
atlas_pts = []
sensory_peak_pts = []
sensory_atlas_pts = []
sub_dlc_pts = []
sub_atlas_pts = []
sub_sensory_peak_pts = []
sub_sensory_atlas_pts = []
bregma_index_list = []
bregma_list = []
if use_dlc:
bregma_present = True
else:
bregma_present = False
coords = pd.read_csv(coords_input)
x_coord = coords.iloc[2:, 1::3]
y_coord = coords.iloc[2:, 2::3]
accuracy = coords.iloc[2:, 3::3]
acc_left_total = accuracy.iloc[:, 0:5]
acc_right_total = accuracy.iloc[:, 3:8]
landmark_indices = [0, 1, 2, 3, 4, 5, 6, 7, 8] # [0, 3, 2, 1]
for arr_index, i in enumerate(range(0, len(x_coord))):
landmark_arr = landmark_arr_orig
x_coord_flat = x_coord.iloc[i].values.astype("float32")
y_coord_flat = y_coord.iloc[i].values.astype("float32")
x_coord_flat = x_coord_flat[landmark_arr]
y_coord_flat = y_coord_flat[landmark_arr]
dlc_list = []
atlas_list = []
for (coord_x, coord_y) in zip(x_coord_flat, y_coord_flat):
dlc_coord = (coord_x, coord_y)
dlc_list.append(dlc_coord)
for coord_atlas in atlas_arr:
atlas_coord = (coord_atlas[0], coord_atlas[1])
atlas_list.append(atlas_coord)
atlas_list = [atlas_list[i] for i in landmark_arr]
# Initialize result as max value
landmark_indices = landmark_indices[0 : len(landmark_arr)]
# atlas_indices = min_landmark_arr
atlas_indices = landmark_arr
# print('atlas indices: {}'.format(atlas_indices))
# print('landmark indices: {}'.format(landmark_indices))
# print('x coords: {}'.format(x_coord_flat))
pts_dist = np.absolute(
np.asarray(atlas_list) - np.asarray((im.shape[0] / 2, im.shape[1] / 2))
)
pts_avg_dist = [np.mean(v) for v in pts_dist]
# print("bregma dist: {}".format(pts_avg_dist))
bregma_index = np.argmin(np.asarray(pts_avg_dist))
# print("bregma index: {}".format(bregma_index))
for j in landmark_indices:
sub_dlc_pts.append([x_coord_flat[j], y_coord_flat[j]])
for j in atlas_indices:
sub_atlas_pts.append([atlas_arr[j][0], atlas_arr[j][1]])
dlc_pts.append(sub_dlc_pts)
atlas_pts.append(sub_atlas_pts)
coords_to_mat(
sub_dlc_pts, i, output_mask_path, bregma_present, bregma_index, landmark_arr
)
bregma_index_list.append(bregma_index)
sub_dlc_pts = []
sub_atlas_pts = []
if sensory_match:
k_coord, m_coord = np.array([(189, 323, 435, 348), (315, 315, 350, 460)])
coords_peak = peak_arr_total
for img_num, img in enumerate(brain_img_arr):
for j in [1, 0, 3, 2]: # Get peak values from heatmaps
sub_sensory_peak_pts.append(
[coords_peak[img_num][j][0], coords_peak[img_num][j][1]]
)
for j in [0, 1, 2, 3]: # Get circle locations
sub_sensory_atlas_pts.append([k_coord[j], m_coord[j]])
sensory_peak_pts.append(sub_sensory_peak_pts)
sensory_atlas_pts.append(sub_sensory_atlas_pts)
sensory_to_mat(
sub_sensory_peak_pts, dlc_pts[img_num][3], img_num, output_mask_path
)
sub_sensory_peak_pts = []
sub_sensory_atlas_pts = []
sensory_peak_pts, sensory_atlas_pts = (
np.asarray(sensory_peak_pts).astype("float32"),
np.asarray(sensory_atlas_pts).astype("float32"),
)
for (n, br) in enumerate(brain_img_arr):
vxm_template = np.uint8(vxm_template_orig)
vxm_template = cv2.resize(vxm_template, (512, 512))
align_val = n
if atlas_to_brain_align:
im = np.uint8(im)
br = cv2.imread(br)
br = np.uint8(br)
br = cv2.resize(br, (512, 512))
else:
# FOR ALIGNING BRAIN TO ATLAS
if ".png" in br:
im = cv2.imread(br)
else:
im = br
im = np.uint8(im)
im = cv2.resize(im, (512, 512))
if atlas_to_brain_align:
# atlas_mask_dir = os.path.join(git_repo_base, "atlases/Atlas_workflow1_smooth_binary.png")
if use_voxelmorph and not use_dlc:
atlas_mask_dir = os.path.join(
git_repo_base, "atlases/Atlas_for_Voxelmorph_border.png"
)
else:
atlas_mask_dir = os.path.join(
git_repo_base, "atlases/atlas_smooth2_binary.png"
)
atlas_mask_dir_left = os.path.join(
git_repo_base, "atlases/left_hemisphere_smooth.png"
)
atlas_mask_dir_right = os.path.join(
git_repo_base, "atlases/right_hemisphere_smooth.png"
)
atlas_label_mask_dir = os.path.join(
git_repo_base, "atlases/diff_colour_regions/Common_atlas.mat"
)
atlas_label_mask_dir_left = os.path.join(
git_repo_base, "atlases/diff_colour_regions/atlas_left_hemisphere.csv"
)
atlas_label_mask_dir_right = os.path.join(
git_repo_base, "atlases/diff_colour_regions/atlas_right_hemisphere.csv"
)
# atlas_label_mask_left = atlas_from_mat(atlas_label_mask_dir_left, [])
# atlas_label_mask_right = atlas_from_mat(atlas_label_mask_dir_right, [])
atlas_label_mask_left = np.genfromtxt(
atlas_label_mask_dir_left, delimiter=","
)
atlas_label_mask_right = np.genfromtxt(
atlas_label_mask_dir_right, delimiter=","
)
atlas_mask_left = cv2.imread(atlas_mask_dir_left, cv2.IMREAD_UNCHANGED)
atlas_mask_left = cv2.resize(atlas_mask_left, (im.shape[0], im.shape[1]))
atlas_mask_left = np.uint8(atlas_mask_left)
atlas_mask_right = cv2.imread(atlas_mask_dir_right, cv2.IMREAD_UNCHANGED)
atlas_mask_right = cv2.resize(atlas_mask_right, (im.shape[0], im.shape[1]))
atlas_mask_right = np.uint8(atlas_mask_right)
else:
atlas_mask_dir = os.path.join(
git_repo_base, "atlases/atlas_smooth2_binary.png"
)
atlas_mask = cv2.imread(atlas_mask_dir, cv2.IMREAD_UNCHANGED)
atlas_mask = cv2.resize(atlas_mask, (im.shape[0], im.shape[1]))
atlas_mask = np.uint8(atlas_mask)
mask_dir = os.path.join(cwd, "../output_mask/{}.png".format(n))
print("Performing first transformation of atlas {}...".format(n))
mask_warped_path = os.path.join(
output_mask_path, "{}_mask_warped.png".format(str(n))
)
if use_dlc:
# First alignment of brain atlas using three cortical landmarks and standard affine transform
atlas_pts_for_input = np.array([atlas_pts[n][0 : len(dlc_pts[n])]]).astype(
"float32"
)
pts_for_input = np.array([dlc_pts[n]]).astype("float32")
if align_once:
align_val = 0
else:
align_val = n
if len(atlas_pts_for_input[0]) == 2:
atlas_pts_for_input = np.append(atlas_pts_for_input[0], [[0, 0]], axis=0)
pts_for_input = np.append(pts_for_input[0], [[0, 0]], axis=0)
if len(atlas_pts_for_input[0]) <= 2:
warp_coords = cv2.estimateAffinePartial2D(
atlas_pts_for_input, pts_for_input
)[0]
if atlas_to_brain_align:
atlas_warped_left = cv2.warpAffine(im_left, warp_coords, (512, 512))
atlas_warped_right = cv2.warpAffine(im_right, warp_coords, (512, 512))
atlas_warped = cv2.bitwise_or(atlas_warped_left, atlas_warped_right)
ret, atlas_warped = cv2.threshold(
atlas_warped, 5, 255, cv2.THRESH_BINARY_INV
)
atlas_left_transform_path = os.path.join(
output_mask_path, "{}_atlas_left_transform.png".format(str(n))
)
atlas_right_transform_path = os.path.join(
output_mask_path, "{}_atlas_right_transform.png".format(str(n))
)
io.imsave(atlas_left_transform_path, atlas_warped_left)
io.imsave(atlas_right_transform_path, atlas_warped_right)
else:
atlas_warped = cv2.warpAffine(im, warp_coords, (512, 512))
elif len(atlas_pts_for_input[0]) == 3:
warp_coords = cv2.getAffineTransform(atlas_pts_for_input, pts_for_input)
atlas_warped = cv2.warpAffine(im, warp_coords, (512, 512))
elif len(atlas_pts_for_input[0]) >= 4:
im_final_size = (512, 512)
left = acc_left_total.iloc[n, :].values.astype("float32").tolist()
right = acc_right_total.iloc[n, :].values.astype("float32").tolist()
left = np.argsort(left).tolist()
right = np.argsort(right).tolist()
right = [x + 1 for x in right]
if set([1, 3, 5, 7]).issubset(landmark_arr):
left = [1, 3, 5]
right = [3, 5, 7]
else:
left = [x for x in landmark_indices if x in range(0, 6)][0:2]
right = [x for x in landmark_indices if x in range(3, 9)][0:2]
try:
atlas_pts_left = np.array(
[
atlas_pts[align_val][left[0]],
atlas_pts[align_val][left[1]],
atlas_pts[align_val][left[2]],
],
dtype=np.float32,
)
atlas_pts_right = np.array(
[
atlas_pts[align_val][right[0]],
atlas_pts[align_val][right[1]],
atlas_pts[align_val][right[2]],
],
dtype=np.float32,
)
dlc_pts_left = np.array(
[
dlc_pts[align_val][left[0]],
dlc_pts[align_val][left[1]],
dlc_pts[align_val][left[2]],
],
dtype=np.float32,
)
dlc_pts_right = np.array(
[
dlc_pts[align_val][right[0]],
dlc_pts[align_val][right[1]],
dlc_pts[align_val][right[2]],
],
dtype=np.float32,
)
except:
atlas_pts_left = np.array(
[
atlas_pts[align_val][0],
atlas_pts[align_val][2],
atlas_pts[align_val][3],
],
dtype=np.float32,
)
atlas_pts_right = np.array(
[
atlas_pts[align_val][1],
atlas_pts[align_val][2],
atlas_pts[align_val][3],
],
dtype=np.float32,
)
dlc_pts_left = np.array(
[
dlc_pts[align_val][0],
dlc_pts[align_val][2],
dlc_pts[align_val][3],
],
dtype=np.float32,
)
dlc_pts_right = np.array(
[
dlc_pts[align_val][1],
dlc_pts[align_val][2],
dlc_pts[align_val][3],
],
dtype=np.float32,
)
warp_coords_left = cv2.getAffineTransform(atlas_pts_left, dlc_pts_left)
warp_coords_right = cv2.getAffineTransform(atlas_pts_right, dlc_pts_right)
if atlas_to_brain_align:
atlas_warped_left = cv2.warpAffine(
im_left, warp_coords_left, im_final_size
)
atlas_warped_right = cv2.warpAffine(
im_right, warp_coords_right, im_final_size
)
atlas_warped = cv2.bitwise_or(atlas_warped_left, atlas_warped_right)
ret, atlas_warped = cv2.threshold(
atlas_warped, 5, 255, cv2.THRESH_BINARY_INV
)
if not original_label:
atlas_label_left = cv2.warpAffine(
atlas_label_mask_left, warp_coords_left, im_final_size
)
atlas_label_right = cv2.warpAffine(
atlas_label_mask_right, warp_coords_right, im_final_size
)
atlas_label = cv2.bitwise_or(atlas_label_left, atlas_label_right)
else:
pts_np = np.array(
[
dlc_pts[align_val][0],
dlc_pts[align_val][1],
dlc_pts[align_val][2],
],
dtype=np.float32,
)
atlas_pts_np = np.array(
[
atlas_pts[align_val][0],
atlas_pts[align_val][1],
atlas_pts[align_val][2],
],
dtype=np.float32,
)
warp_coords = cv2.getAffineTransform(pts_np, atlas_pts_np)
atlas_warped = cv2.warpAffine(im, warp_coords, (512, 512))
print(warp_coords)
# vxm_template = cv2.warpAffine(vxm_template, warp_coords, (512, 512))
# try:
# atlas_warped = niftyreg_align(git_repo_base, atlas_warped, output_mask_path, n)
# except:
# print("ERROR: could not use niftyreg to warp atlas {}! Please check inputs.".format(str(n)))
if atlas_to_brain_align:
if len(atlas_pts_for_input[0]) == 2:
atlas_mask_left_warped = cv2.warpAffine(
atlas_mask_left, warp_coords, (512, 512)
)
atlas_mask_right_warped = cv2.warpAffine(
atlas_mask_right, warp_coords, (512, 512)
)
atlas_mask_warped = cv2.bitwise_or(
atlas_mask_left_warped, atlas_mask_right_warped
)
if len(atlas_pts_for_input[0]) == 3:
atlas_mask_warped = cv2.warpAffine(atlas_mask, warp_coords, (512, 512))
if len(atlas_pts_for_input[0]) >= 4:
atlas_mask_left_warped = cv2.warpAffine(
atlas_mask_left, warp_coords_left, (512, 512)
)
atlas_mask_right_warped = cv2.warpAffine(
atlas_mask_right, warp_coords_right, (512, 512)
)
atlas_mask_warped = cv2.bitwise_or(
atlas_mask_left_warped, atlas_mask_right_warped
)
atlas_mask_warped = np.uint8(atlas_mask_warped)
# Second alignment of brain atlas using cortical landmarks and piecewise affine transform
print("Performing second transformation of atlas {}...".format(n))
atlas_first_transform_path = os.path.join(
output_mask_path, "{}_atlas_first_transform.png".format(str(n))
)
dst = atlas_warped
io.imsave(atlas_first_transform_path, dst)
# If a sensory map of the brain is provided, do a third alignment of the brain atlas using up to
# four peaks of sensory activity
if sensory_match:
original_label = True
# COMMENT OUT FOR ALIGNING BRAIN TO ATLAS
# mask_dir
# atlas_first_transform_path
if atlas_to_brain_align:
dst = getMaskContour(
mask_dir,
atlas_warped,
sensory_peak_pts[align_val],
sensory_atlas_pts[align_val],
cwd,
align_val,
False,
)
atlas_mask_warped = getMaskContour(
atlas_first_transform_path,
atlas_mask_warped,
sensory_peak_pts[align_val],
sensory_atlas_pts[align_val],
cwd,
align_val,
False,
)
atlas_mask_warped = cv2.resize(
atlas_mask_warped, (im.shape[0], im.shape[1])
)
else:
dst = atlas_warped
else:
# If we're not using DeepLabCut...
if atlas_to_brain_align and not use_voxelmorph:
dst = cv2.bitwise_or(im_left, im_right)
ret, dst = cv2.threshold(
dst, 5, 255, cv2.THRESH_BINARY_INV
)
else:
dst = im
dst = np.uint8(dst)
# if use_voxelmorph:
# if atlas_to_brain_align:
# _, flow = voxelmorph_align(
# voxelmorph_model_path, br_vxm, vxm_template, exist_transform, flow_path
# )
# else:
# dst, flow = voxelmorph_align(
# voxelmorph_model_path, dst, vxm_template, exist_transform, flow_path
# )
# flow_path_after = os.path.join(output_mask_path, "{}_flow.npy".format(str(n)))
# np.save(flow_path_after, flow)
# if not exist_transform:
# if atlas_to_brain_align:
# dst_gray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
# dst = vxm_transform(dst_gray, flow_path_after)
# ret, dst = cv2.threshold(
# dst, 5, 255, cv2.THRESH_BINARY
# )
# dst = np.uint8(dst)
if use_dlc:
if atlas_to_brain_align:
io.imsave(mask_warped_path, atlas_mask_warped)
else:
io.imsave(mask_warped_path, atlas_mask)
else:
io.imsave(mask_warped_path, dst)
if atlas_to_brain_align:
if use_voxelmorph:
atlas_mask_warped = atlas_mask
else:
atlas_mask_warped = cv2.bitwise_or(
atlas_mask_left, atlas_mask_right
)
atlas_mask_warped = cv2.cvtColor(atlas_mask_warped, cv2.COLOR_BGR2GRAY)
#
# atlas_mask_warped = vxm_transform(atlas_mask_warped, flow_path_after)
ret, atlas_mask_warped = cv2.threshold(
atlas_mask_warped, 5, 255, cv2.THRESH_BINARY
)
atlas_mask_warped = np.uint8(atlas_mask_warped)
original_label = True
else:
atlas_mask_warped = atlas_mask
#
# atlas_mask_warped = vxm_transform(atlas_mask_warped, flow_path_after)
io.imsave(mask_warped_path, atlas_mask_warped)
# Resize images back to 512x512
dst = cv2.resize(dst, (im.shape[0], im.shape[1]))
atlas_path = os.path.join(output_mask_path, "{}_atlas.png".format(str(n)))
if atlas_to_brain_align:
io.imsave(atlas_path, dst)
br_list.append(br)
else:
brain_warped_path = os.path.join(
output_mask_path, "{}_brain_warp.png".format(str(n))
)
vxm_template_output_path = os.path.join(
output_mask_path, "{}_vxm_template.png".format(str(n))
)
dst_list.append(dst)
if use_voxelmorph:
vxm_template_list.append(vxm_template)
io.imsave(vxm_template_output_path, vxm_template_list[n])
io.imsave(brain_warped_path, dst)
io.imsave(atlas_path, atlas)
if atlas_to_brain_align:
if original_label:
atlas_label = []
atlas_label = atlas_to_mask(
atlas_path,
mask_dir,
mask_warped_path,
output_mask_path,
n,
use_unet,
atlas_to_brain_align,
git_repo_base,
olfactory_check,
atlas_label
)
atlas_label_list.append(atlas_label)
elif not use_dlc:
io.imsave(os.path.join(output_mask_path, "{}.png".format(n)), dst)
if bregma_present:
bregma_val = int(bregma_index_list[n])
bregma_list.append(dlc_pts[n][bregma_val])
# Carries out VoxelMorph on each motif-based functional map (MBFM) that has been aligned to a raw brain image
if use_dlc and use_voxelmorph and align_once:
for (n_post, dst_post), vxm_template_post in zip(enumerate(dst_list), vxm_template_list):
_, flow_post = voxelmorph_align(
voxelmorph_model_path, dst_post, vxm_template_post, exist_transform, flow_path
)
flow_path_after = os.path.join(output_mask_path, "{}_flow.npy".format(str(n_post)))
np.save(flow_path_after, flow_post)
if not exist_transform:
dst_gray = cv2.cvtColor(atlas, cv2.COLOR_BGR2GRAY)
dst_post = vxm_transform(dst_gray, flow_path_after)
ret, dst_post = cv2.threshold(
dst_post, 5, 255, cv2.THRESH_BINARY
)
dst_post = np.uint8(dst_post)
mask_warped_path = os.path.join(
output_mask_path, "{}_mask_warped.png".format(str(n_post))
)
atlas_first_transform_path_post = os.path.join(
output_mask_path, "{}_atlas_first_transform.png".format(str(n_post))
)
io.imsave(atlas_first_transform_path_post, dst_post)
atlas_path = os.path.join(output_mask_path, "{}_atlas.png".format(str(n_post)))
brain_warped_path = os.path.join(
output_mask_path, "{}_brain_warp.png".format(str(n_post))
)
mask_dir = os.path.join(cwd, "../output_mask/{}.png".format(n_post))
dst_post = cv2.resize(dst_post, (im.shape[0], im.shape[1]))
if not atlas_to_brain_align:
atlas_to_brain_align = True
original_label = True
if atlas_to_brain_align:
io.imsave(atlas_path, dst_post)
else:
io.imsave(brain_warped_path, dst_post)
if atlas_to_brain_align:
if original_label:
atlas_label = []
atlas_label = atlas_to_mask(
atlas_path,
mask_dir,
mask_warped_path,
output_mask_path,
n_post,
use_unet,
atlas_to_brain_align,
git_repo_base,
olfactory_check,
atlas_label
)
atlas_label_list.append(atlas_label)
# Converts the transformed brain atlas into a segmentation template for the original brain image
applyMask(
brain_img_dir,
output_mask_path,
output_overlay_path,
output_overlay_path,
mat_save,
threshold,
git_repo_base,
bregma_list,
atlas_to_brain_align,
model,
dlc_pts,
atlas_pts,
olfactory_check,
use_unet,
use_dlc,
plot_landmarks,
align_once,
atlas_label_list,
region_labels,
original_label,
)
