def test():
try:
img_s = np.random.rand(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))
except Exception as e:
print """%s
Warping is broken. Most likeley the distributed _warping.so is not binary compatible to your system.""" % (e, )
def warp2dJoint(img, lab, patch_size, rot, shear, scale, stretch):
"""
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 3-dimensional (ch,x,y) and larger/equal the patch size
lab: array
Label data (with offsets subtracted)
patch_size: 2-tuple
Patch size *excluding* channel for the image: (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
Returns
-------
img, lab: np.ndarrays
Warped image and labels (cropped to patch_size)
"""
if len(lab.shape) == 2:
lab = _warp2dFastLab(lab, patch_size, img.shape[1:], rot, shear, scale,
stretch)
img = warp2dFast(img, patch_size, rot, shear, scale, stretch)
return img, lab
def warp2dJoint(img, lab, patch_size, rot, shear, scale, stretch):
"""
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 3-dimensional (ch,x,y) and larger/equal the patch size
lab: array
Label data (with offsets subtracted)
patch_size: 2-tuple
Patch size *excluding* channel for the image: (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
Returns
-------
img, lab: np.ndarrays
Warped image and labels (cropped to patch_size)
"""
if len(lab.shape) == 2:
lab = _warp2dFastLab(lab, patch_size, img.shape[1:], rot, shear, scale, stretch)
img = warp2dFast(img, patch_size, rot, shear, scale, stretch)
return img, lab
# s1 = test_img.shape[0]
# s2 = test_img.shape[1]
ps = (200, 200)
if True:
ext_size, rot, shear, scale, stretch, twist = getWarpParams(ps,
amount=1.0)
t = []
for i in xrange(10000):
ext_size, rot, shear, scale, stretch, twist = getWarpParams(
ps, amount=1.0)
t.append(ext_size)
# img_in = maketestimage(eff_size)
# img_in = paddImage(img_in, ext_size, left_exc)[None]
img_in = maketestimage(ext_size)[None]
out = warp2dFast(img_in, ps, rot, shear, scale, stretch)
plt.figure()
plt.subplot(121)
plt.imshow(img_in[0], interpolation='none', cmap='gray')
plt.hlines(ext_size[0] / 2 - 0.5, 0, ext_size[1] - 1, color='r')
plt.vlines(ext_size[1] / 2 - 0.5, 0, ext_size[0] - 1, color='r')
plt.subplot(122)
plt.imshow(out[0], interpolation='none', cmap='gray')
plt.hlines(ps[0] / 2 - 0.5, 0, ps[1] - 1, color='r')
plt.vlines(ps[1] / 2 - 0.5, 0, ps[0] - 1, color='r')
if False: # visual 2d
#out = _warp2d_c(test_img, 20, 10, (1,1.1), (0.1, 0))
test_img = np.concatenate((test_img[None], np.exp(test_img[None])),
axis=0)
# test_img = io.imread('Lichtenstein.png')
# test_img = test_img.mean(axis=2)
# s1 = test_img.shape[0]
# s2 = test_img.shape[1]
ps = (200, 200)
if True:
ext_size, rot, shear, scale, stretch, twist = getWarpParams(ps,
amount=1.0)
t = []
for i in xrange(10000):
ext_size, rot, shear, scale, stretch, twist = getWarpParams(ps, amount=1.0); t.append(ext_size)
# img_in = maketestimage(eff_size)
# img_in = paddImage(img_in, ext_size, left_exc)[None]
img_in = maketestimage(ext_size)[None]
out = warp2dFast(img_in, ps, rot, shear, scale, stretch)
plt.figure()
plt.subplot(121)
plt.imshow(img_in[0], interpolation='none', cmap='gray')
plt.hlines(ext_size[0] / 2 - 0.5, 0, ext_size[1] - 1, color='r')
plt.vlines(ext_size[1] / 2 - 0.5, 0, ext_size[0] - 1, color='r')
plt.subplot(122)
plt.imshow(out[0], interpolation='none', cmap='gray')
plt.hlines(ps[0] / 2 - 0.5, 0, ps[1] - 1, color='r')
plt.vlines(ps[1] / 2 - 0.5, 0, ps[0] - 1, color='r')
if False: # visual 2d
#out = _warp2d_c(test_img, 20, 10, (1,1.1), (0.1, 0))
test_img = np.concatenate((test_img[None], np.exp(test_img[None])), axis=0)
out2 = warp2dFast(test_img, (512, 512), 20, 10, (1, 1.1), (0.1, 0))
