def process(self, im):
# if side is right flip so it becomes right
if self.side != 'left':
im = np.fliplr(im)
# slope of the perspective
slope = tan(radians(self.degrees))
(h, w, _) = im.shape
matrix_trans = np.array([[1, 0, 0],
[-slope/2, 1, slope * h / 2],
[-slope/w, 0, 1 + slope]])
trans = ProjectiveTransform(matrix_trans)
img_trans = warp(im, trans)
if self.side != 'left':
img_trans = np.fliplr(img_trans)
return img_trans
python类ProjectiveTransform()的实例源码
def warp(img, corners):
"""
Warpes an image by keeping its size, transforming the pixel data to
be distorted between the four corners.
"""
width = len(img[0])
height = len(img)
src = numpy.array((
corners['upper_left'],
corners['lower_left'],
corners['lower_right'],
corners['upper_right']
))
dst = numpy.array((
(0, 0),
(0, height),
(width, height),
(width, 0)
))
tform = transform.ProjectiveTransform()
tform.estimate(src, dst)
return transform.warp(img, tform, output_shape=(height,width))
def scale_to_fit(img, size):
"""
Scales an image to a given size by warping with no regard to the ratio.
Returns: warped image as ndarray
"""
width = len(img[0])
height = len(img)
src = numpy.array((
(0, 0),
(0, size[1]),
(size[0], size[1]),
(size[0], 0)
))
dst = numpy.array((
(0, 0),
(0, height),
(width, height),
(width, 0)
))
tform = transform.ProjectiveTransform()
tform.estimate(src, dst)
return transform.warp(img, tform, output_shape=(size[1],size[0]))
#########################################################################################################
#########################################################################################################
def warp_image_by_corner_points_projection(corner_points, image):
"""Given corner points of a Sudoku, warps original selection to a square image.
:param corner_points:
:type: corner_points: list
:param image:
:type image:
:return:
:rtype:
"""
# Clarify by storing in named variables.
top_left, top_right, bottom_left, bottom_right = np.array(corner_points)
top_edge = np.linalg.norm(top_right - top_left)
bottom_edge = np.linalg.norm(bottom_right - bottom_left)
left_edge = np.linalg.norm(top_left - bottom_left)
right_edge = np.linalg.norm(top_right - bottom_right)
L = int(np.ceil(max([top_edge, bottom_edge, left_edge, right_edge])))
src = np.array([top_left, top_right, bottom_left, bottom_right])
dst = np.array([[0, 0], [L - 1, 0], [0, L - 1], [L - 1, L - 1]])
tr = ProjectiveTransform()
tr.estimate(dst, src)
warped_image = warp(image, tr, output_shape=(L, L))
out = resize(warped_image, (500, 500))
return out
def projection(self, tile: HipsTile) -> ProjectiveTransform:
"""Estimate projective transformation on a HiPS tile."""
corners = tile.meta.skycoord_corners.to_pixel(self.geometry.wcs)
src = np.array(corners).T.reshape((4, 2))
dst = tile_corner_pixel_coordinates(tile.meta.width)
pt = ProjectiveTransform()
pt.estimate(src, dst)
return pt
def projective_transform_by_points(x, src, dst, map_args={}, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False):
"""Projective transform by given coordinates, usually 4 coordinates. see `scikit-image <http://scikit-image.org/docs/dev/auto_examples/applications/plot_geometric.html>`_.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
src : list or numpy
The original coordinates, usually 4 coordinates of (x, y).
dst : list or numpy
The coordinates after transformation, the number of coordinates is the same with src.
map_args : dict, optional
Keyword arguments passed to inverse_map.
output_shape : tuple (rows, cols), optional
Shape of the output image generated. By default the shape of the input image is preserved. Note that, even for multi-band images, only rows and columns need to be specified.
order : int, optional
The order of interpolation. The order has to be in the range 0-5:
- 0 Nearest-neighbor
- 1 Bi-linear (default)
- 2 Bi-quadratic
- 3 Bi-cubic
- 4 Bi-quartic
- 5 Bi-quintic
mode : {‘constant’, ‘edge’, ‘symmetric’, ‘reflect’, ‘wrap’}, optional
Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad.
cval : float, optional
Used in conjunction with mode ‘constant’, the value outside the image boundaries.
clip : bool, optional
Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range.
preserve_range : bool, optional
Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float.
Examples
--------
>>> Assume X is an image from CIFAR 10, i.e. shape == (32, 32, 3)
>>> src = [[0,0],[0,32],[32,0],[32,32]]
>>> dst = [[10,10],[0,32],[32,0],[32,32]]
>>> x = projective_transform_by_points(X, src, dst)
References
-----------
- `scikit-image : geometric transformations <http://scikit-image.org/docs/dev/auto_examples/applications/plot_geometric.html>`_
- `scikit-image : examples <http://scikit-image.org/docs/dev/auto_examples/index.html>`_
"""
if type(src) is list: # convert to numpy
src = np.array(src)
if type(dst) is list:
dst = np.array(dst)
if np.max(x)>1: # convert to [0, 1]
x = x/255
m = transform.ProjectiveTransform()
m.estimate(dst, src)
warped = transform.warp(x, m, map_args=map_args, output_shape=output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range)
return warped
# Numpy and PIL
def projective_transform_by_points(x, src, dst, map_args={}, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False):
"""Projective transform by given coordinates, usually 4 coordinates. see `scikit-image <http://scikit-image.org/docs/dev/auto_examples/applications/plot_geometric.html>`_.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
src : list or numpy
The original coordinates, usually 4 coordinates of (width, height).
dst : list or numpy
The coordinates after transformation, the number of coordinates is the same with src.
map_args : dict, optional
Keyword arguments passed to inverse_map.
output_shape : tuple (rows, cols), optional
Shape of the output image generated. By default the shape of the input image is preserved. Note that, even for multi-band images, only rows and columns need to be specified.
order : int, optional
The order of interpolation. The order has to be in the range 0-5:
- 0 Nearest-neighbor
- 1 Bi-linear (default)
- 2 Bi-quadratic
- 3 Bi-cubic
- 4 Bi-quartic
- 5 Bi-quintic
mode : {‘constant’, ‘edge’, ‘symmetric’, ‘reflect’, ‘wrap’}, optional
Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad.
cval : float, optional
Used in conjunction with mode ‘constant’, the value outside the image boundaries.
clip : bool, optional
Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range.
preserve_range : bool, optional
Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float.
Examples
--------
>>> Assume X is an image from CIFAR 10, i.e. shape == (32, 32, 3)
>>> src = [[0,0],[0,32],[32,0],[32,32]] # [w, h]
>>> dst = [[10,10],[0,32],[32,0],[32,32]]
>>> x = projective_transform_by_points(X, src, dst)
References
-----------
- `scikit-image : geometric transformations <http://scikit-image.org/docs/dev/auto_examples/applications/plot_geometric.html>`_
- `scikit-image : examples <http://scikit-image.org/docs/dev/auto_examples/index.html>`_
"""
if type(src) is list: # convert to numpy
src = np.array(src)
if type(dst) is list:
dst = np.array(dst)
if np.max(x)>1: # convert to [0, 1]
x = x/255
m = transform.ProjectiveTransform()
m.estimate(dst, src)
warped = transform.warp(x, m, map_args=map_args, output_shape=output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range)
return warped
# Numpy and PIL
def projective_transform_by_points(x, src, dst, map_args={}, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False):
"""Projective transform by given coordinates, usually 4 coordinates. see `scikit-image <http://scikit-image.org/docs/dev/auto_examples/applications/plot_geometric.html>`_.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
src : list or numpy
The original coordinates, usually 4 coordinates of (x, y).
dst : list or numpy
The coordinates after transformation, the number of coordinates is the same with src.
map_args : dict, optional
Keyword arguments passed to inverse_map.
output_shape : tuple (rows, cols), optional
Shape of the output image generated. By default the shape of the input image is preserved. Note that, even for multi-band images, only rows and columns need to be specified.
order : int, optional
The order of interpolation. The order has to be in the range 0-5:
- 0 Nearest-neighbor
- 1 Bi-linear (default)
- 2 Bi-quadratic
- 3 Bi-cubic
- 4 Bi-quartic
- 5 Bi-quintic
mode : {‘constant’, ‘edge’, ‘symmetric’, ‘reflect’, ‘wrap’}, optional
Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad.
cval : float, optional
Used in conjunction with mode ‘constant’, the value outside the image boundaries.
clip : bool, optional
Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range.
preserve_range : bool, optional
Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float.
Examples
--------
>>> Assume X is an image from CIFAR 10, i.e. shape == (32, 32, 3)
>>> src = [[0,0],[0,32],[32,0],[32,32]]
>>> dst = [[10,10],[0,32],[32,0],[32,32]]
>>> x = projective_transform_by_points(X, src, dst)
References
-----------
- `scikit-image : geometric transformations <http://scikit-image.org/docs/dev/auto_examples/applications/plot_geometric.html>`_
- `scikit-image : examples <http://scikit-image.org/docs/dev/auto_examples/index.html>`_
"""
if type(src) is list: # convert to numpy
src = np.array(src)
if type(dst) is list:
dst = np.array(dst)
if np.max(x)>1: # convert to [0, 1]
x = x/255
m = transform.ProjectiveTransform()
m.estimate(dst, src)
warped = transform.warp(x, m, map_args=map_args, output_shape=output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range)
return warped
# Numpy and PIL
def projective_transform_by_points(x, src, dst, map_args={}, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False):
"""Projective transform by given coordinates, usually 4 coordinates. see `scikit-image <http://scikit-image.org/docs/dev/auto_examples/applications/plot_geometric.html>`_.
Parameters
-----------
x : numpy array
An image with dimension of [row, col, channel] (default).
src : list or numpy
The original coordinates, usually 4 coordinates of (x, y).
dst : list or numpy
The coordinates after transformation, the number of coordinates is the same with src.
map_args : dict, optional
Keyword arguments passed to inverse_map.
output_shape : tuple (rows, cols), optional
Shape of the output image generated. By default the shape of the input image is preserved. Note that, even for multi-band images, only rows and columns need to be specified.
order : int, optional
The order of interpolation. The order has to be in the range 0-5:
- 0 Nearest-neighbor
- 1 Bi-linear (default)
- 2 Bi-quadratic
- 3 Bi-cubic
- 4 Bi-quartic
- 5 Bi-quintic
mode : {‘constant’, ‘edge’, ‘symmetric’, ‘reflect’, ‘wrap’}, optional
Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad.
cval : float, optional
Used in conjunction with mode ‘constant’, the value outside the image boundaries.
clip : bool, optional
Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range.
preserve_range : bool, optional
Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float.
Examples
--------
>>> Assume X is an image from CIFAR 10, i.e. shape == (32, 32, 3)
>>> src = [[0,0],[0,32],[32,0],[32,32]]
>>> dst = [[10,10],[0,32],[32,0],[32,32]]
>>> x = projective_transform_by_points(X, src, dst)
References
-----------
- `scikit-image : geometric transformations <http://scikit-image.org/docs/dev/auto_examples/applications/plot_geometric.html>`_
- `scikit-image : examples <http://scikit-image.org/docs/dev/auto_examples/index.html>`_
"""
if type(src) is list: # convert to numpy
src = np.array(src)
if type(dst) is list:
dst = np.array(dst)
if np.max(x)>1: # convert to [0, 1]
x = x/255
m = transform.ProjectiveTransform()
m.estimate(dst, src)
warped = transform.warp(x, m, map_args=map_args, output_shape=output_shape, order=order, mode=mode, cval=cval, clip=clip, preserve_range=preserve_range)
return warped
# Numpy and PIL
