python d用法_Python numpy.dsplit() 使用实例

参考链接： Python中的numpy.less

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 Example 1

 def dsplit(ary, indices_or_sections):

 """Splits an array into multiple sub arrays along the third axis.

 This is equivalent to ``split`` with ``axis=2``.

 .. seealso:: :func:`cupy.split` for more detail, :func:`numpy.dsplit`

 """

 if ary.ndim <= 2:

 raise ValueError('Cannot dsplit an array with less than 3 dimensions')

 return split(ary, indices_or_sections, 2)

 Example 2

 def vsplit(ary, indices_or_sections):

 """Splits an array into multiple sub arrays along the first axis.

 This is equivalent to ``split`` with ``axis=0``.

 .. seealso:: :func:`cupy.split` for more detail, :func:`numpy.dsplit`

 """

 if ary.ndim <= 1:

 raise ValueError('Cannot vsplit an array with less than 2 dimensions')

 return split(ary, indices_or_sections, 0)

 Example 3

 def char2wordBB(self, charBB, text):

 """

 Converts character bounding-boxes to word-level

 bounding-boxes.

 charBB : 2x4xn matrix of BB coordinates

 text : the text string

 output : 2x4xm matrix of BB coordinates,

 where, m == number of words.

 """

 wrds = text.split()

 bb_idx = np.r_[0, np.cumsum([len(w) for w in wrds])]

 wordBB = np.zeros((2,4,len(wrds)), 'float32')

 for i in xrange(len(wrds)):

 cc = charBB[:,:,bb_idx[i]:bb_idx[i+1]]

 # fit a rotated-rectangle:

 # change shape from 2x4xn_i -> (4*n_i)x2

 cc = np.squeeze(np.concatenate(np.dsplit(cc,cc.shape[-1]),axis=1)).T.astype('float32')

 rect = cv2.minAreaRect(cc.copy())

 box = np.array(cv2.cv.BoxPoints(rect))

 # find the permutation of box-coordinates which

 # are "aligned" appropriately with the character-bb.

 # (exhaustive search over all possible assignments):

 cc_tblr = np.c_[cc[0,:],

 cc[-3,:],

 cc[-2,:],

 cc[3,:]].T

 perm4 = np.array(list(itertools.permutations(np.arange(4))))

 dists = []

 for pidx in xrange(perm4.shape[0]):

 d = np.sum(np.linalg.norm(box[perm4[pidx],:]-cc_tblr,axis=1))

 dists.append(d)

 wordBB[:,:,i] = box[perm4[np.argmin(dists)],:].T

 return wordBB

 Example 4

 def dsplit(ary, indices_or_sections):

 """Splits an array into multiple sub arrays along the third axis.

 This is equivalent to ``split`` with ``axis=2``.

 .. seealso:: :func:`cupy.split` for more detail, :func:`numpy.dsplit`

 """

 if ary.ndim <= 2:

 raise ValueError('Cannot dsplit an array with less than 3 dimensions')

 return split(ary, indices_or_sections, 2)

 Example 5

 def vsplit(ary, indices_or_sections):

 """Splits an array into multiple sub arrays along the first axis.

 This is equivalent to ``split`` with ``axis=0``.

 .. seealso:: :func:`cupy.split` for more detail, :func:`numpy.dsplit`

 """

 if ary.ndim <= 1:

 raise ValueError('Cannot vsplit an array with less than 2 dimensions')

 return split(ary, indices_or_sections, 0)

 Example 6

 def _ros_read_images(self, stream_buffer, number, staleness_limit = 10.):

 """ Reads images from a stream buffer

 Parameters

 ----------

 stream_buffer : string

 absolute path to the image buffer service

 number : int

 The number of frames to get. Must be less than the image buffer service's

 current buffer size

 staleness_limit : float, optional

 Max value of how many seconds old the oldest image is. If the oldest image

 grabbed is older than this value, a RuntimeError is thrown.

 If None, staleness is ignored.

 Returns

 -------

 List of nump.ndarray objects, each one an image

 Images are in reverse chronological order (newest first)

 """

 rospy.wait_for_service(stream_buffer, timeout = self.timeout)

 ros_image_buffer = rospy.ServiceProxy(stream_buffer, ImageBuffer)

 ret = ros_image_buffer(number, 1)

 if not staleness_limit == None:

 if ret.timestamps[-1] > staleness_limit:

 raise RuntimeError("Got data {0} seconds old, more than allowed {1} seconds"

 .format(ret.timestamps[-1], staleness_limit))

 data = ret.data.reshape(ret.data_dim1, ret.data_dim2, ret.data_dim3).astype(ret.dtype)

 # Special handling for 1 element, since dstack's behavior is different

 if number == 1:

 return [data]

 return np.dsplit(data, number)

 Example 7

 def _rpc(self, x):

 L, P, H = np.dsplit(x, 3)

 return np.dstack([np.ones((x.shape[0], x.shape[1]), dtype=np.float32), L, P, H, L*P, L*H, P*H, L**2, P**2, H**2,

 L*P*H, L**3, L*(P**2), L*(H**2), (L**2)*P, P**3, P*(H**2),

 (L**2)*H, (P**2)*H, H**3])