本文整理匯總了Python中scipy.signal.medfilt2d方法的典型用法代碼示例。如果您正苦於以下問題：Python signal.medfilt2d方法的具體用法？Python signal.medfilt2d怎麽用？Python signal.medfilt2d使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類scipy.signal的用法示例。
在下文中一共展示了signal.medfilt2d方法的7個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_basic
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import medfilt2d [as 別名]
def test_basic(self):
f = [[50, 50, 50, 50, 50, 92, 18, 27, 65, 46],
[50, 50, 50, 50, 50, 0, 72, 77, 68, 66],
[50, 50, 50, 50, 50, 46, 47, 19, 64, 77],
[50, 50, 50, 50, 50, 42, 15, 29, 95, 35],
[50, 50, 50, 50, 50, 46, 34, 9, 21, 66],
[70, 97, 28, 68, 78, 77, 61, 58, 71, 42],
[64, 53, 44, 29, 68, 32, 19, 68, 24, 84],
[3, 33, 53, 67, 1, 78, 74, 55, 12, 83],
[7, 11, 46, 70, 60, 47, 24, 43, 61, 26],
[32, 61, 88, 7, 39, 4, 92, 64, 45, 61]]
d = signal.medfilt(f, [7, 3])
e = signal.medfilt2d(np.array(f, np.float), [7, 3])
assert_array_equal(d, [[0, 50, 50, 50, 42, 15, 15, 18, 27, 0],
[0, 50, 50, 50, 50, 42, 19, 21, 29, 0],
[50, 50, 50, 50, 50, 47, 34, 34, 46, 35],
[50, 50, 50, 50, 50, 50, 42, 47, 64, 42],
[50, 50, 50, 50, 50, 50, 46, 55, 64, 35],
[33, 50, 50, 50, 50, 47, 46, 43, 55, 26],
[32, 50, 50, 50, 50, 47, 46, 45, 55, 26],
[7, 46, 50, 50, 47, 46, 46, 43, 45, 21],
[0, 32, 33, 39, 32, 32, 43, 43, 43, 0],
[0, 7, 11, 7, 4, 4, 19, 19, 24, 0]])
assert_array_equal(d, e) 示例2: test_basic
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import medfilt2d [as 別名]
def test_basic(self):
f = [[50, 50, 50, 50, 50, 92, 18, 27, 65, 46],
[50, 50, 50, 50, 50, 0, 72, 77, 68, 66],
[50, 50, 50, 50, 50, 46, 47, 19, 64, 77],
[50, 50, 50, 50, 50, 42, 15, 29, 95, 35],
[50, 50, 50, 50, 50, 46, 34, 9, 21, 66],
[70, 97, 28, 68, 78, 77, 61, 58, 71, 42],
[64, 53, 44, 29, 68, 32, 19, 68, 24, 84],
[3, 33, 53, 67, 1, 78, 74, 55, 12, 83],
[7, 11, 46, 70, 60, 47, 24, 43, 61, 26],
[32, 61, 88, 7, 39, 4, 92, 64, 45, 61]]
d = signal.medfilt(f, [7, 3])
e = signal.medfilt2d(np.array(f, float), [7, 3])
assert_array_equal(d, [[0, 50, 50, 50, 42, 15, 15, 18, 27, 0],
[0, 50, 50, 50, 50, 42, 19, 21, 29, 0],
[50, 50, 50, 50, 50, 47, 34, 34, 46, 35],
[50, 50, 50, 50, 50, 50, 42, 47, 64, 42],
[50, 50, 50, 50, 50, 50, 46, 55, 64, 35],
[33, 50, 50, 50, 50, 47, 46, 43, 55, 26],
[32, 50, 50, 50, 50, 47, 46, 45, 55, 26],
[7, 46, 50, 50, 47, 46, 46, 43, 45, 21],
[0, 32, 33, 39, 32, 32, 43, 43, 43, 0],
[0, 7, 11, 7, 4, 4, 19, 19, 24, 0]])
assert_array_equal(d, e) 示例3: overlay_edges
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import medfilt2d [as 別名]
def overlay_edges(slice_one, slice_two, sharper=True):
"""
Makes a composite image with edges from second image overlaid on first.
It will be in colormapped (RGB format) already.
"""
if slice_one.shape != slice_two.shape:
raise ValueError("slices' dimensions do not match: "
" {} and {} ".format(slice_one.shape, slice_two.shape))
# simple filtering to remove noise, while supposedly keeping edges
slice_two = medfilt2d(slice_two, kernel_size=cfg.median_filter_size)
# extracting edges
edges = np.hypot(sobel(slice_two, axis=0, mode='constant'),
sobel(slice_two, axis=1, mode='constant'))
# trying to remove weak edges
if not sharper: # level of removal
edges = med_filter(max_filter(min_filter(edges)))
else:
edges = min_filter(min_filter(max_filter(min_filter(edges))))
edges_color_mapped = hot_cmap(edges, alpha=cfg.alpha_edge_overlay_alignment)
composite = gray_cmap(slice_one, alpha=cfg.alpha_background_slice_alignment)
composite[edges_color_mapped>0] = edges_color_mapped[edges_color_mapped>0]
# mask_rgba = np.dstack([edges>0] * 4)
# composite[mask_rgba] = edges_color_mapped[mask_rgba]
return composite 示例4: dwi_overlay_edges
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import medfilt2d [as 別名]
def dwi_overlay_edges(slice_one, slice_two):
"""
Makes a composite image with edges from second image overlaid on first.
It will be in colormapped (RGB format) already.
"""
if slice_one.shape != slice_two.shape:
raise ValueError("slices' dimensions do not match: "
" {} and {} ".format(slice_one.shape, slice_two.shape))
# simple filtering to remove noise, while supposedly keeping edges
slice_two = medfilt2d(slice_two, kernel_size=cfg.median_filter_size)
# extracting edges
edges = med_filter(np.hypot(sobel(slice_two, axis=0, mode='constant'),
sobel(slice_two, axis=1, mode='constant')))
edges_color_mapped = hot_cmap(edges, alpha=cfg.alpha_edge_overlay_alignment)
composite = gray_cmap(slice_one, alpha=cfg.alpha_background_slice_alignment)
composite[edges_color_mapped>0] = edges_color_mapped[edges_color_mapped>0]
# mask_rgba = np.dstack([edges>0] * 4)
# composite[mask_rgba] = edges_color_mapped[mask_rgba]
return composite 示例5: pit_filter
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import medfilt2d [as 別名]
def pit_filter(self, kernel_size):
"""
Filters pits in the raster. Intended for use with canopy height models (i.e. grid(0.5).interpolate("max", "z").
This function modifies the raster array **in place**.
:param kernel_size: The size of the kernel window to pass over the array. For example 3 -> 3x3 kernel window.
"""
from scipy.signal import medfilt2d
self.array = medfilt2d(self.array, kernel_size=kernel_size) 示例6: generate_depth_image
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import medfilt2d [as 別名]
def generate_depth_image(vertices, kpt, shape, isMedFilter=False):
vertices_map = convert_vertices_2_map_interp2d_directly(vertices, shape)
if isMedFilter:
vertices_map_med = signal.medfilt2d(vertices_map, (5,5))
vertices_map[vertices_map==0] = vertices_map_med[vertices_map==0]
depth_map = np.array(vertices_map, np.uint8)
return depth_map 示例7: enhanced_mean_image
# 需要導入模塊: from scipy import signal [as 別名]
# 或者: from scipy.signal import medfilt2d [as 別名]
def enhanced_mean_image(ops):
""" computes enhanced mean image and adds it to ops
Median filters ops['meanImg'] with 4*diameter in 2D and subtracts and
divides by this median-filtered image to return a high-pass filtered
image ops['meanImgE']
Parameters
----------
ops : dictionary
uses 'meanImg', 'aspect', 'diameter', 'yrange' and 'xrange'
Returns
-------
ops : dictionary
'meanImgE' field added
"""
I = ops['meanImg'].astype(np.float32)
if 'spatscale_pix' not in ops:
if isinstance(ops['diameter'], int):
diameter = np.array([ops['diameter'], ops['diameter']])
else:
diameter = np.array(ops['diameter'])
ops['spatscale_pix'] = diameter[1]
ops['aspect'] = diameter[0]/diameter[1]
diameter = 4*np.ceil(np.array([ops['spatscale_pix'] * ops['aspect'], ops['spatscale_pix']])) + 1
diameter = diameter.flatten().astype(np.int64)
Imed = signal.medfilt2d(I, [diameter[0], diameter[1]])
I = I - Imed
Idiv = signal.medfilt2d(np.absolute(I), [diameter[0], diameter[1]])
I = I / (1e-10 + Idiv)
mimg1 = -6
mimg99 = 6
mimg0 = I
mimg0 = mimg0[ops['yrange'][0]:ops['yrange'][1], ops['xrange'][0]:ops['xrange'][1]]
mimg0 = (mimg0 - mimg1) / (mimg99 - mimg1)
mimg0 = np.maximum(0,np.minimum(1,mimg0))
mimg = mimg0.min() * np.ones((ops['Ly'],ops['Lx']),np.float32)
mimg[ops['yrange'][0]:ops['yrange'][1],
ops['xrange'][0]:ops['xrange'][1]] = mimg0
ops['meanImgE'] = mimg
return ops