def transform(self, image):
"""
Transform the image.
Args:
image(numpy.ndarray): Original image to be transformed.
Returns:
numpy.ndarray, transformed image.
"""
image = check_numpy_param('image', image)
ori_dtype = image.dtype
rgb, chw, normalized, gray3dim, image = self._check(image)
if rgb:
h, w, _ = np.shape(image)
else:
h, w = np.shape(image)
move_x_centor = w / 2*(1 - self.factor_x)
move_y_centor = h / 2*(1 - self.factor_y)
img = to_pil(image)
trans_image = img.transform(img.size, Image.AFFINE,
(self.factor_x, 0, move_x_centor,
0, self.factor_y, move_y_centor))
trans_image = self._original_format(trans_image, chw, normalized,
gray3dim)
return trans_image.astype(ori_dtype)
def transform(self, image):
"""
Transform the image.
Args:
image(numpy.ndarray): Original image to be transformed.
Returns:
numpy.ndarray, transformed image.
"""
_, chw, normalized, gray3dim, image = self._check(image)
img = to_pil(image)
trans_image = img.rotate(self.angle, expand=False)
trans_image = self._original_format(trans_image, chw, normalized,
gray3dim)
return trans_image
def transform(self, image):
"""
Transform the image.
Args:
image (numpy.ndarray): Original image to be transformed.
Returns:
numpy.ndarray, transformed image.
"""
_, chw, normalized, gray3dim, image = self._check(image)
image = to_pil(image)
trans_image = image.filter(
ImageFilter.GaussianBlur(radius=self.radius))
trans_image = self._original_format(trans_image, chw, normalized,
gray3dim)
return trans_image
def transform(self, image):
"""
Transform the image.
Args:
image (numpy.ndarray): Original image to be transformed.
Returns:
numpy.ndarray, transformed image.
"""
_, chw, normalized, gray3dim, image = self._check(image)
image = to_pil(image)
img_contrast = ImageEnhance.Brightness(image)
trans_image = img_contrast.enhance(self.factor)
trans_image = self._original_format(trans_image, chw, normalized,
gray3dim)
return trans_image
def transform(self, image):
"""
Transform the image.
Args:
image (numpy.ndarray): Original image to be transformed.
Returns:
numpy.ndarray, transformed image.
"""
image = check_numpy_param('image', image)
ori_dtype = image.dtype
_, chw, normalized, gray3dim, image = self._check(image)
image = to_pil(image)
img_contrast = ImageEnhance.Contrast(image)
trans_image = img_contrast.enhance(self.factor)
trans_image = self._original_format(trans_image, chw, normalized,
gray3dim)
return trans_image.astype(ori_dtype)
def transform(self, image):
"""
Transform the image.
Args:
image(numpy.ndarray): Original image to be transformed.
Returns:
numpy.ndarray, transformed image.
"""
_, chw, normalized, gray3dim, image = self._check(image)
img = to_pil(image)
if self.auto_param:
image_shape = np.shape(image)
self.x_bias = image_shape[0] * self.x_bias
self.y_bias = image_shape[1] * self.y_bias
trans_image = img.transform(img.size, Image.AFFINE,
(1, 0, self.x_bias, 0, 1, self.y_bias))
trans_image = self._original_format(trans_image, chw, normalized,
gray3dim)
return trans_image
def transform(self, image):
"""
Transform the image.
Args:
image(numpy.ndarray): Original image to be transformed.
Returns:
numpy.ndarray, transformed image.
"""
image = check_numpy_param('image', image)
ori_dtype = image.dtype
rgb, chw, normalized, gray3dim, image = self._check(image)
img = to_pil(image)
if rgb:
h, w, _ = np.shape(image)
else:
h, w = np.shape(image)
if self.factor_x != 0:
boarder_x = [0, -w, -self.factor_x*h, -w - self.factor_x*h]
min_x = min(boarder_x)
max_x = max(boarder_x)
scale = (max_x - min_x) / w
move_x_cen = (w - scale*w - scale*h*self.factor_x) / 2
move_y_cen = h*(1 - scale) / 2
else:
boarder_y = [0, -h, -self.factor_y*w, -h - self.factor_y*w]
min_y = min(boarder_y)
max_y = max(boarder_y)
scale = (max_y - min_y) / h
move_y_cen = (h - scale*h - scale*w*self.factor_y) / 2
move_x_cen = w*(1 - scale) / 2
trans_image = img.transform(img.size, Image.AFFINE,
(scale, scale*self.factor_x, move_x_cen,
scale*self.factor_y, scale, move_y_cen))
trans_image = self._original_format(trans_image, chw, normalized,
gray3dim)
return trans_image.astype(ori_dtype)
