def warp3d(img,
patch_size,
rot=0,
shear=0,
scale=(1, 1, 1),
stretch=(0, 0, 0, 0),
twist=0):
return warp3dFast(img, patch_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
plt.figure()
plt.subplot(121)
plt.imshow(test_img, interpolation='none', cmap='gray')
plt.subplot(122)
plt.imshow(out2[0], interpolation='none', cmap='gray')
if False:
img_s = maketestimage((11, 11))
img_s = np.concatenate((img_s[None], np.exp(img_s[None])), axis=0)
out = warp2dFast(img_s, (11, 11), 0, 0, (1, 1), (0.0, 0.0))
if False:
img_s = maketestimage((110, 110))
img_s = np.concatenate((img_s[None], ) * 4, axis=0)
img_s = np.concatenate((img_s[None], np.exp(img_s[None])), axis=0)
out = warp3dFast(img_s, (4, 110, 110), 0, 0, (1, 1, 1),
(0.0, 0.0, 0.0, 0.0), 10)
if False: # visual 3d
n = 100
img_s = np.tile(test_img, n)
img_s = img_s.reshape((s1, n, s2))
img_s = np.swapaxes(img_s, 1, 0)
patch_size = img_s.shape
off = 0
lab = img_s[off:-off, off:-off, off:-off]
img = np.concatenate((test_img[None], np.exp(test_img[None])), axis=0)
img1, lab1 = warpAugment(img_s[None], lab, patch_size=patch_size)
plt.figure()
plt.subplot(121)
plt.imshow(test_img, interpolation='none', cmap='gray')
plt.subplot(122)
plt.imshow(out2[0], interpolation='none', cmap='gray')
if False:
img_s = maketestimage((11, 11))
img_s = np.concatenate((img_s[None], np.exp(img_s[None])), axis=0)
out = warp2dFast(img_s, (11, 11), 0, 0, (1, 1), (0.0, 0.0))
if False:
img_s = maketestimage((110, 110))
img_s = np.concatenate((img_s[None], ) * 4, axis=0)
img_s = np.concatenate((img_s[None], np.exp(img_s[None])), axis=0)
out = warp3dFast(img_s, (4, 110, 110), 0, 0, (1, 1, 1),
(0.0, 0.0, 0.0, 0.0), 10)
if False: # visual 3d
n = 100
img_s = np.tile(test_img, n)
img_s = img_s.reshape((s1, n, s2))
img_s = np.swapaxes(img_s, 1, 0)
patch_size = img_s.shape
off = 0
lab = img_s[off:-off, off:-off, off:-off]
img = np.concatenate((test_img[None], np.exp(test_img[None])), axis=0)
img1, lab1 = warpAugment(img_s[None], lab, patch_size=patch_size)
