def rotate(self,
degrees,
clockwise=False,
resample_algorithm=nearest,
resize_canvas=True):
"""
Returns the image obtained by rotating this image by the
given number of degrees.
Anticlockwise unless clockwise=True is given.
"""
# translate to the origin first, then rotate, then translate back
transform = AffineTransform()
transform = transform.translate(self.width * -0.5, self.height * -0.5)
transform = transform.rotate(degrees, clockwise=clockwise)
width, height = self.width, self.height
if resize_canvas:
# determine new width
width, height = get_transformed_dimensions(transform,
(0, 0, width, height))
transform = transform.translate(width * 0.5, height * 0.5)
pixels = resample_algorithm.affine(self,
transform,
resize_canvas=resize_canvas)
return self._copy(pixels)
def affine(self, source, transform, resize_canvas=True):
if resize_canvas:
# get image dimensions
width, height = get_transformed_dimensions(
transform,
(0, 0, source.width, source.height)
)
else:
width = source.width
height = source.height
pixelsize = source.pixelsize
# transparent or black background
background = [0] * pixelsize
# we want to go from dest coords to src coords:
transform = transform.inverse()
# Optimisation:
# Because affine transforms have no perspective component,
# the *gradient* of each source row/column must be constant.
# So, we can calculate the source coordinates for each corner,
# and then interpolate for each pixel, instead of doing a
# matrix multiplication for each pixel.
x_range = range(width)
y_range = range(height)
new_array = source.pixels.copy()
for y in y_range:
line = array.array('B') # initialize a new line
# the 0.5's mean we use the center of each pixel
row_x0, row_y0 = transform * (0.5, y + 0.5)
row_x1, row_y1 = transform * (width + 0.5, y + 0.5)
dx = float(row_x1 - row_x0) / source.width
dy = float(row_y1 - row_y0) / source.width
for x in x_range:
source_x = int(row_x0 + dx * x)
source_y = int(row_y0 + dy * x)
new_array.set(x, y,
self._get_value(source, source_x, source_y, dx, dy)
or background
)
return new_array
def affine(self, source, transform, resize_canvas=True):
if resize_canvas:
# get image dimensions
width, height = get_transformed_dimensions(
transform,
(0, 0, source.width, source.height)
)
else:
width = source.width
height = source.height
pixelsize = source.pixelsize
# transparent or black background
background = [0] * pixelsize
# we want to go from dest coords to src coords:
transform = transform.inverse()
# Optimisation:
# Because affine transforms have no perspective component,
# the *gradient* of each source row/column must be constant.
# So, we can calculate the source coordinates for each corner,
# and then interpolate for each pixel, instead of doing a
# matrix multiplication for each pixel.
x_range = range(width)
y_range = range(height)
new_array = source.pixels.copy()
for y in y_range:
# the 0.5's mean we use the center of each pixel
row_x0, row_y0 = transform * (0.5, y + 0.5)
row_x1, row_y1 = transform * (width + 0.5, y + 0.5)
dx = float(row_x1 - row_x0) / source.width
dy = float(row_y1 - row_y0) / source.width
for x in x_range:
source_x = int(row_x0 + dx * x)
source_y = int(row_y0 + dy * x)
new_array.set(x, y,
self._get_value(source, source_x, source_y, dx, dy)
or background
)
return new_array
def rotate(self, degrees, clockwise=False, resample_algorithm=nearest, resize_canvas=True):
"""
Returns the image obtained by rotating this image by the
given number of degrees.
Anticlockwise unless clockwise=True is given.
"""
# translate to the origin first, then rotate, then translate back
transform = AffineTransform()
transform = transform.translate(self.width * -0.5, self.height * -0.5)
transform = transform.rotate(degrees, clockwise=clockwise)
width, height = self.width, self.height
if resize_canvas:
# determine new width
width, height = get_transformed_dimensions(transform, (0, 0, width, height))
transform = transform.translate(width * 0.5, height * 0.5)
pixels = resample_algorithm.affine(self, transform, resize_canvas=resize_canvas)
return self._copy(pixels)
