def warp3dLab(lab,
patch_size,
size,
rot=0,
shear=0,
scale=(1, 1, 1),
stretch=(0, 0, 0, 0),
twist=0):
return _warp3dFastLab(lab, patch_size, size, rot, shear, scale, stretch,
twist)
def warp3dJoint(img,
lab,
patch_size,
rot=0,
shear=0,
scale=(1, 1, 1),
stretch=(0, 0, 0, 0),
twist=0):
"""
Warp image and label data jointly. Non-image labels are ignored i.e. lab must be 3d to be warped
Parameters
----------
img: array
Image data
The array must be 4-dimensional (z,ch,x,y) and larger/equal the patch size
lab: array
Label data (with offsets subtracted)
patch_size: 3-tuple
Patch size *excluding* channel for the image: (pz, px, py).
The warping result of the input image is cropped to this size
rot: float
Rotation angle in deg for rotation around z-axis
shear: float
Shear angle in deg for shear w.r.t xy-diagonal
scale: 3-tuple of float
Scale per axis
stretch: 4-tuple of float
Fraction of perspective stretching from the center (where stretching is always 1)
to the outer border of image per axis. The 4 entry correspond to:
- X stretching depending on Y
- Y stretching depending on X
- X stretching depending on Z
- Y stretching depending on Z
twist: float
Dependence of the rotation angle on z in deg from center to outer border
Returns
-------
img, lab: np.ndarrays
Warped image and labels (cropped to patch_size)
"""
if len(lab.shape) == 3:
lab = _warp3dFastLab(lab, patch_size,
np.array(img.shape)[[0, 2, 3]], rot, shear, scale,
stretch, twist)
img = warp3dFast(img, patch_size, rot, shear, scale, stretch, twist)
return img, lab
def warp3dJoint(img, lab, patch_size, rot=0, shear=0, scale=(1, 1, 1), stretch=(0, 0, 0, 0), twist=0):
"""
Warp image and label data jointly. Non-image labels are ignored i.e. lab must be 3d to be warped
Parameters
----------
img: array
Image data
The array must be 4-dimensional (z,ch,x,y) and larger/equal the patch size
lab: array
Label data (with offsets subtracted)
patch_size: 3-tuple
Patch size *excluding* channel for the image: (pz, px, py).
The warping result of the input image is cropped to this size
rot: float
Rotation angle in deg for rotation around z-axis
shear: float
Shear angle in deg for shear w.r.t xy-diagonal
scale: 3-tuple of float
Scale per axis
stretch: 4-tuple of float
Fraction of perspective stretching from the center (where stretching is always 1)
to the outer border of image per axis. The 4 entry correspond to:
- X stretching depending on Y
- Y stretching depending on X
- X stretching depending on Z
- Y stretching depending on Z
twist: float
Dependence of the rotation angle on z in deg from center to outer border
Returns
-------
img, lab: np.ndarrays
Warped image and labels (cropped to patch_size)
"""
if len(lab.shape) == 3:
lab = _warp3dFastLab(lab, patch_size, np.array(img.shape)[[0, 2, 3]], rot, shear, scale, stretch, twist)
img = warp3dFast(img, patch_size, rot, shear, scale, stretch, twist)
return img, lab
for i in xrange(lab1.shape[0]):
plt.imsave('/tmp/%i-lab.png' % (i + off), lab1[i, :, :])
if False: # visual 3d
n = 40
img_s = np.tile(test_img, n)
img_s = img_s.reshape((s1, n, s2))
img_s = np.swapaxes(img_s, 1, 2)
wow1 = warp3dFast(img_s[None], (s1, s2, n), 0, 0, (1, 1, 1),
(0.1, 0.1, 0.1, -0.1), 10)
for i in xrange(n):
plt.imsave('/tmp/%i-ref.png' % i, wow1[:, :, i] / 255)
wow2 = _warp3dFastLab(img_s[20:-20, 20:-20], (s1 - 40, s2 - 40, n),
(s1, s2, n), 0, 0, (1, 1, 1),
(0.1, 0.1, 0.1, -0.1), 10)
for i in xrange(wow2.shape[2]):
plt.imsave('/tmp/%i.png' % i, wow2[:, :, i] / 255)
if False: # 3d timing
s = 400
test = np.random.rand(s, s, s).astype(np.float32)
test2 = np.random.rand(s * 2, s * 2, s * 2).astype(np.float32)
t0 = time.time()
wow1 = warp3dFast(test[None], (s, s, s), 20, 5, (1, 1, 1),
(0.1, 0.1, 0.1, 0.1), 10)
#wow1 = warp3dFast(test[None], (s,s,s))
print time.time() - t0
for i in xrange(n):
plt.imsave('/tmp/%i-img.png' % i, img1[0, i, :, :] / 255)
for i in xrange(lab1.shape[0]):
plt.imsave('/tmp/%i-lab.png' % (i + off), lab1[i, :, :])
if False: # visual 3d
n = 40
img_s = np.tile(test_img, n)
img_s = img_s.reshape((s1, n, s2))
img_s = np.swapaxes(img_s, 1, 2)
wow1 = warp3dFast(img_s[None], (s1, s2, n), 0, 0, (1, 1, 1),
(0.1, 0.1, 0.1, -0.1), 10)
for i in xrange(n):
plt.imsave('/tmp/%i-ref.png' % i, wow1[:, :, i] / 255)
wow2 = _warp3dFastLab(img_s[20:-20, 20:-20], (s1 - 40, s2 - 40, n),
(s1, s2, n), 0, 0, (1, 1, 1), (0.1, 0.1, 0.1, -0.1), 10)
for i in xrange(wow2.shape[2]):
plt.imsave('/tmp/%i.png' % i, wow2[:, :, i] / 255)
if False: # 3d timing
s = 400
test = np.random.rand(s, s, s).astype(np.float32)
test2 = np.random.rand(s * 2, s * 2, s * 2).astype(np.float32)
t0 = time.time()
wow1 = warp3dFast(test[None], (s, s, s), 20, 5, (1, 1, 1), (0.1, 0.1, 0.1, 0.1), 10)
#wow1 = warp3dFast(test[None], (s,s,s))
print time.time() - t0
