def forward(self, data, state):
im = Image.fromarray(data)
width, height = im.size
displacement = int(self.level / 10 * height / 3 * random.choice([1.0, -1.0]))
im = im.transform((width, height),
ImageTransform.AffineTransform((1.0, 0.0, 0.0, 0.0, 1.0, displacement)),
resample=Image.BICUBIC)
return np.copy(np.asarray(im))
def _apply_translatex(data: np.ndarray, factor: float) -> np.ndarray:
im = Image.fromarray(data)
width, height = im.size
displacement = factor * width
im = im.transform((width, height),
ImageTransform.AffineTransform(
(1.0, 0.0, displacement, 0.0, 1.0, 0.0)),
resample=Image.BICUBIC)
return np.array(im)
def test_sanity():
transform = ImageTransform.AffineTransform(seq[:6])
assert_no_exception(lambda: im.transform((100, 100), transform))
transform = ImageTransform.ExtentTransform(seq[:4])
assert_no_exception(lambda: im.transform((100, 100), transform))
transform = ImageTransform.QuadTransform(seq[:8])
assert_no_exception(lambda: im.transform((100, 100), transform))
transform = ImageTransform.MeshTransform([(seq[:4], seq[:8])])
assert_no_exception(lambda: im.transform((100, 100), transform))
def _apply_translatex(self, data: np.ndarray) -> np.ndarray:
im = Image.fromarray(data)
width, height = im.size
displacement = random.uniform(self.shift_limit[0] * width,
self.shift_limit[1] * width)
im = im.transform((width, height),
ImageTransform.AffineTransform(
(1.0, 0.0, displacement, 0.0, 1.0, 0.0)),
resample=Image.BICUBIC)
return np.array(im)
def _apply_sheary(data: np.ndarray, shear_coeff: float) -> np.ndarray:
im = Image.fromarray(data)
width, height = im.size
yshift = round(abs(shear_coeff) * height)
newheight = height + yshift
im = im.transform((width, newheight),
ImageTransform.AffineTransform(
(1.0, 0.0, 0.0, shear_coeff, 1.0,
-yshift if shear_coeff > 0 else 0.0)),
resample=Image.BICUBIC)
im = im.resize((width, height))
return np.array(im)
def test_sanity(self):
im = Image.new("L", (100, 100))
seq = tuple(range(10))
transform = ImageTransform.AffineTransform(seq[:6])
im.transform((100, 100), transform)
transform = ImageTransform.ExtentTransform(seq[:4])
im.transform((100, 100), transform)
transform = ImageTransform.QuadTransform(seq[:8])
im.transform((100, 100), transform)
transform = ImageTransform.MeshTransform([(seq[:4], seq[:8])])
im.transform((100, 100), transform)
def forward(self, data, state):
im = Image.fromarray(data)
shear_coeff = self.shear_coef * random.choice([1.0, -1.0])
width, height = im.size
yshift = int(round(self.shear_coef * height))
newheight = height + yshift
im = im.transform((width, newheight),
ImageTransform.AffineTransform(
(1.0, 0.0, 0.0, shear_coeff, 1.0, -yshift if shear_coeff > 0 else 0.0)),
resample=Image.BICUBIC)
if self.shear_coef > 0:
im = im.resize((width, height))
return np.copy(np.asarray(im))
def _apply_shearx(self, data: np.ndarray) -> np.ndarray:
im = Image.fromarray(data)
shear_coeff = random.uniform(self.shear_coef[0], self.shear_coef[1])
width, height = im.size
xshift = round(abs(shear_coeff) * width)
new_width = width + xshift
im = im.transform(
(new_width, height),
ImageTransform.AffineTransform(
(1.0, shear_coeff, -xshift if shear_coeff > 0 else 0.0, 0.0,
1.0, 0.0)),
resample=Image.BICUBIC)
im = im.resize((width, height))
return np.array(im)