本文整理汇总了Python中skimage.transform.ProjectiveTransform方法的典型用法代码示例。如果您正苦于以下问题:Python transform.ProjectiveTransform方法的具体用法?Python transform.ProjectiveTransform怎么用?Python transform.ProjectiveTransform使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类skimage.transform
的用法示例。
在下文中一共展示了transform.ProjectiveTransform方法的4个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: shear
# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import ProjectiveTransform [as 别名]
def shear(X, skew):
''' given a 2D image, shear and return a 2D image
parameters:
X is the 2D image of shape (nRows, nColumns)
skew is the amount to shear in the range 0 to 1.0
'''
rows = X.shape[0]
cols = X.shape[1]
ratioY = skew*cols/rows
matrix = np.array( [[1, ratioY, 0] ,[0, 1, 0] ,[0, 0, 1 ]])
tp=af.ProjectiveTransform(matrix=matrix)
#tp = tf.AffineTransform(scale=(.3,.3), shear=skew)
f = af.warp(X, tp)
return f
#
# class file_names(object):
# ''' store variants of file a file name with .jpg, .png, .box variations
# '''
# def __init__(selp, base_name, dir_name = ''):
# base = base_name
# jpeg = base_name + '.jpg'
# png = base_name + '.png'
# box = base_name + '.box'
示例2: shear
# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import ProjectiveTransform [as 别名]
def shear(X, skew):
rows = X.shape[0]
cols = X.shape[1]
ratioY = skew*cols/rows
matrix = np.array( [[1, ratioY, 0] ,[0, 1, 0] ,[0, 0, 1 ]])
tp=tf.ProjectiveTransform(matrix=matrix)
f = tf.warp(X, tp)
return f
# make some skewed versions of the shapes
示例3: run
# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import ProjectiveTransform [as 别名]
def run(self):
"""
Run nowcasting calculations.
Returns
-------
nowcasts : 3D numpy array of shape (lead_steps, dim_x, dim_y).
"""
# define available transformations dictionary
transformations = {'euclidean': sktf.EuclideanTransform(),
'similarity': sktf.SimilarityTransform(),
'affine': sktf.AffineTransform(),
'projective': sktf.ProjectiveTransform()}
# scale input data to uint8 [0-255] with self.scaler
data_scaled, c1, c2 = self.scaler(self.input_data)
# set up transformer object
trf = transformations[self.warper]
# obtain source and target points
if self.extrapolation == "linear":
pts_source, pts_target_container = _sparse_linear(data_instance=data_scaled,
of_params=self.of_params,
lead_steps=self.lead_steps)
elif self.extrapolation == "simple_delta":
pts_source, pts_target_container = _sparse_sd(data_instance=data_scaled,
of_params=self.of_params,
lead_steps=self.lead_steps)
# now we can start to find nowcasted image
# for every candidate of projected sets of points
# container for our nowcasts
last_frame = data_scaled[-1]
nowcst_frames = []
for lead_step, pts_target in enumerate(pts_target_container):
# estimate transformation matrix
# based on source and traget points
trf.estimate(pts_source, pts_target)
# make a nowcast
nowcst_frame = sktf.warp(last_frame/255, trf.inverse)
# transformations dealing with strange behaviour
nowcst_frame = (nowcst_frame*255).astype('uint8')
# add to the container
nowcst_frames.append(nowcst_frame)
nowcst_frames = np.stack(nowcst_frames, axis=0)
nowcst_frames = self.inverse_scaler(nowcst_frames, c1, c2)
return nowcst_frames
示例4: projective_transform_by_points
# 需要导入模块: from skimage import transform [as 别名]
# 或者: from skimage.transform import ProjectiveTransform [as 别名]
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