def warp_piecewise_affine(image, map_args={}, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False):
#image = imageGlobal
rows, cols = image.shape[0], image.shape[1]
src_cols = np.linspace(0, cols, 20)
src_rows = np.linspace(0, rows, 10)
src_rows, src_cols = np.meshgrid(src_rows, src_cols)
src = np.dstack([src_cols.flat, src_rows.flat])[0]
dst_rows = src[:, 1] - np.sin(np.linspace(0, 3 * np.pi, src.shape[0])) * 50
dst_cols = src[:, 0]
dst_rows *= 1.5
dst_rows -= 1.5 * 50
dst = np.vstack([dst_cols, dst_rows]).T
tform = PiecewiseAffineTransform()
tform.estimate(src, dst)
out_rows = image.shape[0] - 1.5 * 50
out_cols = cols
out = warp(image, tform, output_shape=(out_rows, out_cols))
fig, ax = plt.subplots()
ax.imshow(out)
ax.plot(tform.inverse(src)[:, 0], tform.inverse(src)[:, 1], '.b')
ax.axis((0, out_cols, out_rows, 0))
plt.show()
def affine_transform(img):
rows, cols = img.shape[0], img.shape[1]
src_cols = np.linspace(0, cols, 20)
src_rows = np.linspace(0, rows, 20)
src_rows, src_cols = np.meshgrid(src_rows, src_cols)
src = np.dstack([src_cols.flat, src_rows.flat])[0]
# add sinusoidal oscillation to row coordinates
dst_rows = src[:, 1] # - np.sin(np.linspace(0, 3 * np.pi, src.shape[0])) * 50
print(src[:, 1])
print(src[:, 0])
dst_cols = src[:, 0] - np.sin((src[:, 0] / np.max(src[:, 0])) * np.pi) * np.max(src[:, 0])
print(dst_cols)
dst = np.vstack([dst_cols, dst_rows]).T
tform = PiecewiseAffineTransform()
tform.estimate(src, dst)
out_rows = rows
out_cols = cols
out = warp(img, tform, output_shape=(out_rows, out_cols))
fig, ax = plt.subplots()
ax.imshow(out)
ax.plot(tform.inverse(src)[:, 0], tform.inverse(src)[:, 1], '.b')
ax.axis((0, out_cols, out_rows, 0))
plt.savefig('plots/piecewise_affine.png')
plt.show()
def piecewise_affine_transform(): image = data.astronaut() rows, cols = image.shape[0], image.shape[1] src_cols = np.linspace(0, cols, 20) src_rows = np.linspace(0, rows, 10) src_rows, src_cols = np.meshgrid(src_rows, src_cols) src = np.dstack([src_cols.flat, src_rows.flat])[0] # Add sinusoidal oscillation to row coordinates. dst_rows = src[:, 1] - np.sin(np.linspace(0, 3 * np.pi, src.shape[0])) * 50 dst_cols = src[:, 0] dst_rows *= 1.5 dst_rows -= 1.5 * 50 dst = np.vstack([dst_cols, dst_rows]).T tform = PiecewiseAffineTransform() tform.estimate(src, dst) out_rows = image.shape[0] - 1.5 * 50 out_cols = cols out = warp(image, tform, output_shape=(out_rows, out_cols)) fig, ax = plt.subplots() ax.imshow(out) ax.plot(tform.inverse(src)[:, 0], tform.inverse(src)[:, 1], '.b') ax.axis((0, out_cols, out_rows, 0)) plt.show()
def randdistort(img):
image = np.array(plt.imread(img))
rows, cols = image.shape[0], image.shape[1]
src_cols = np.linspace(0, cols, 20)
src_rows = np.linspace(0, rows, 10)
src_rows, src_cols = np.meshgrid(src_rows, src_cols)
src = np.dstack([src_cols.flat, src_rows.flat])[0]
funclist = [
lambda x: np.sin(x), lambda x: np.cos(x), lambda x: np.arctan(x)
]
numberOfFunctions = random.randint(2, 5)
# add sinusoidal oscillation to row coordinates
def func(x):
newfuncs = []
for i in range(numberOfFunctions):
rand1 = random.randint(1, 5)
rand2 = random.randint(0, 30)
rand3 = random.randint(0, 10)
newfuncs.append(lambda x: rand3 * random.choice(funclist)
(1 / rand1 * x + rand2))
for function in newfuncs:
x += function(x)
return x
def func2(x):
newfuncs = []
for i in range(numberOfFunctions):
rand1 = random.randint(1, 5)
rand2 = random.randint(0, 30)
rand3 = random.randint(0, 15)
newfuncs.append(lambda x: rand3 * random.choice(funclist)
(1 / rand1 * x + rand2))
for function in newfuncs:
x += function(x)
return x
dst_rows = src[:, 1] + func(np.linspace(0, 10 * np.pi, src.shape[0]))
dst_cols = src[:, 0] + func2(np.linspace(0, 3 * np.pi, src.shape[0]))
dst_rows *= 1.5
dst_rows -= 1.5 * 50
dst = np.vstack([dst_cols, dst_rows]).T
tform = PiecewiseAffineTransform()
tform.estimate(src, dst)
out_rows = image.shape[0] - 1.5 * 50
out_cols = cols
out = warp(image, tform, output_shape=(out_rows, out_cols))
fig, ax = plt.subplots()
ax.imshow(out)
ax.plot(tform.inverse(src)[:, 0], tform.inverse(src)[:, 1], '.b')
plt.imsave('name.png', out)
ax.axis((0, out_cols, out_rows, 0))
return 'name.png'
from skimage import data image = data.lena() rows, cols = image.shape[0], image.shape[1] src_cols = np.linspace(0, cols, 20) src_rows = np.linspace(0, rows, 10) src_rows, src_cols = np.meshgrid(src_rows, src_cols) src = np.dstack([src_cols.flat, src_rows.flat])[0] # add sinusoidal oscillation to row coordinates dst_rows = src[:, 1] - np.sin(np.linspace(0, 3 * np.pi, src.shape[0])) * 50 dst_cols = src[:, 0] dst_rows *= 1.5 dst_rows -= 1.5 * 50 dst = np.vstack([dst_cols, dst_rows]).T tform = PiecewiseAffineTransform() tform.estimate(src, dst) out_rows = image.shape[0] - 1.5 * 50 out_cols = cols out = warp(image, tform, output_shape=(out_rows, out_cols)) plt.imshow(out) plt.plot(tform.inverse(src)[:, 0], tform.inverse(src)[:, 1], '.b') plt.axis((0, out_cols, out_rows, 0)) plt.show()
def test_piecewise_affine():
tform = PiecewiseAffineTransform()
tform.estimate(SRC, DST)
# make sure each single affine transform is exactly estimated
assert_array_almost_equal(tform(SRC), DST)
assert_array_almost_equal(tform.inverse(DST), SRC)
src_cols = np.linspace(0, cols, 20)
src_rows = np.linspace(0, rows, 10)
src_rows, src_cols = np.meshgrid(src_rows, src_cols)
src = np.dstack([src_cols.flat, src_rows.flat])[0]
# add sinusoidal oscillation to row coordinates
dst_rows = src[:, 1] - np.sin(np.linspace(0, 3 * np.pi, src.shape[0])) * 50
dst_cols = src[:, 0]
dst_rows *= 1.5
dst_rows -= 1.5 * 50
dst_rows *= 1.4
dst = np.vstack([dst_cols, dst_rows]).T
# Define the Affine transform
tform = PiecewiseAffineTransform()
tform.estimate(src, dst)
out_rows = image.shape[0] - (1.5 * 50) * 2.2
out_cols = cols
out = warp(image, tform, output_shape=(out_rows, out_cols))
fig, ax = plt.subplots(figsize=(4.9, 10 / 3.))
ax.imshow(out)
ax.plot(tform.inverse(src)[:, 0], tform.inverse(src)[:, 1], '.b')
ax.axis((0, out_cols, out_rows, 0))
ax.axis('off')
fig.subplots_adjust(0, 0, 1, 1, 0, 0)
fig.savefig('./warp_affine.png', dpi=200)
