10 examples of 'numpy pad 2d array' in Python

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590def pad(array):
591 return numpy.pad(array, ((0, 0), (0, 1)), "constant")
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261def _pad(self, arr, num_step):
262 """Pad sequence to full fit within network"""
263
264 size = len(arr)
265 pad_size = math.ceil(size / num_step) * num_step
266 arr_pad = np.zeros(pad_size)
267 arr_pad[:size] = arr
268
269 return arr_pad
27def _pad(data, shape):
28 """Pad the data to the given shape with zeros.
29
30 Parameters
31 ----------
32 data : 2-d ndarray
33 Input data
34 shape : (2,) tuple
35
36 """
37 out = np.zeros(shape)
38 out[tuple(slice(0, n) for n in data.shape)] = data
39 return out
28def _zero_pad(array, amount, axes=(1, 2)):
29 """
30 .. todo::
31
32 WRITEME
33 """
34 if amount == 0:
35 return array
36 new_shape = []
37 slices = []
38 for i, s in enumerate(array.shape):
39 if i in axes:
40 new_shape.append(s + 2 * amount)
41 slices.append(slice(amount, -amount))
42 else:
43 new_shape.append(s)
44 slices.append(slice(None))
45 new_shape = tuple(new_shape)
46 slices = tuple(slices)
47 new_array = numpy.zeros(new_shape, dtype=array.dtype)
48 new_array[slices] = array
49 return new_array
25def __call__(self, x):
26 if self.pre_pad:
27 return x
28 else:
29 np_pad = ((self.pad_size[0], self.pad_size[1]), (self.pad_size[2], self.pad_size[3]))
30 return np.pad(x, ((0,0), (0,0), np_pad[0], np_pad[1]), mode='reflect')
17def prepad(vec, length, value=0):
18 if length > len(vec):
19 return np.r_[value * np.ones(length - len(vec)), vec]
20 elif length < len(vec):
21 return vec[-length:]
22 else:
23 return vec
288def pad3d( data, nx, ny, nz ):
289 #create undersampling mask
290 datsize = data.shape
291 padsize = np.array(datsize)
292 padsize[0] = nx
293 padsize[1] = ny
294 padsize[2] = nz
295 ndata = np.zeros(tuple(padsize),dtype = data.dtype)
296
297 # center k-space index range
298 datrx = np.int(datsize[0]/2)
299 datry = np.int(datsize[1]/2)
300 datrz = np.int(datsize[2]/2)
301 cx = np.int(nx/2)
302 cy = np.int(ny/2)
303 cz = np.int(nz/2)
304 cxr = np.arange(round(cx-datrx),round(cx-datrx+datsize[0]))
305 cyr = np.arange(round(cy-datry),round(cy-datry+datsize[1]))
306 czr = np.arange(round(cz-datrz),round(cz-datrz+datsize[2]))
307 #print cxr,cyr
308 ndata[np.ix_(map(int,cxr),map(int,cyr),map(int,czr))] = data
309 return ndata
27def pad_image(x, pad_amount, mode='reflect', constant=0.):
28 e = pad_amount
29 shape = list(x.shape)
30 shape[:2] += 2*e
31 if mode == 'constant':
32 x_padded = np.ones(shape, dtype=np.float32)*constant
33 x_padded[e:-e, e:-e] = x.copy()
34 else:
35 x_padded = np.zeros(shape, dtype=np.float32)
36 x_padded[e:-e, e:-e] = x.copy()
37
38 if mode == 'reflect':
39 x_padded[:e, e:-e] = np.flipud(x[:e, :]) # left edge
40 x_padded[-e:, e:-e] = np.flipud(x[-e:, :]) # right edge
41 x_padded[e:-e, :e] = np.fliplr(x[:, :e]) # top edge
42 x_padded[e:-e, -e:] = np.fliplr(x[:, -e:]) # bottom edge
43 x_padded[:e, :e] = np.fliplr(np.flipud(x[:e, :e])) # top-left corner
44 x_padded[-e:, :e] = np.fliplr(np.flipud(x[-e:, :e])) # top-right corner
45 x_padded[:e, -e:] = np.fliplr(np.flipud(x[:e, -e:])) # bottom-left corner
46 x_padded[-e:, -e:] = np.fliplr(np.flipud(x[-e:, -e:])) # bottom-right corner
47 elif mode == 'zero' or mode == 'constant':
48 pass
49 elif mode == 'nearest':
50 x_padded[:e, e:-e] = x[[0], :] # left edge
51 x_padded[-e:, e:-e] = x[[-1], :] # right edge
52 x_padded[e:-e, :e] = x[:, [0]] # top edge
53 x_padded[e:-e, -e:] = x[:, [-1]] # bottom edge
54 x_padded[:e, :e] = x[[0], [0]] # top-left corner
55 x_padded[-e:, :e] = x[[-1], [0]] # top-right corner
56 x_padded[:e, -e:] = x[[0], [-1]] # bottom-left corner
57 x_padded[-e:, -e:] = x[[-1], [-1]] # bottom-right corner
58 else:
59 raise ValueError("Unsupported padding mode \"{}\"".format(mode))
60 return x_padded
60def pad_by(image,r,dtype=None):
61 """Symmetrically pad the image by the given amount"""
62 if dtype is None: dtype = image.dtype
63 w,h = image.shape
64 result = zeros((w+2*r,h+2*r))
65 result[r:(w+r),r:(h+r)] = image
66 return result
162def add_padding(img, image_size=128, verbose=False, pad_value=None):
163 height, width = img.shape
164 if not pad_value:
165 pad_value = img[0][0]
166 if verbose:
167 print('original cropped image size:', img.shape)
168
169 # Adding padding of x axis - left, right
170 pad_x_width = (image_size - width) // 2
171 pad_x = np.full((height, pad_x_width), pad_value, dtype=np.float32)
172 img = np.concatenate((pad_x, img), axis=1)
173 img = np.concatenate((img, pad_x), axis=1)
174
175 width = img.shape[1]
176
177 # Adding padding of y axis - top, bottom
178 pad_y_height = (image_size - height) // 2
179 pad_y = np.full((pad_y_height, width), pad_value, dtype=np.float32)
180 img = np.concatenate((pad_y, img), axis=0)
181 img = np.concatenate((img, pad_y), axis=0)
182
183 # Match to original image size
184 width = img.shape[1]
185 if img.shape[0] % 2:
186 pad = np.full((1, width), pad_value, dtype=np.float32)
187 img = np.concatenate((pad, img), axis=0)
188 height = img.shape[0]
189 if img.shape[1] % 2:
190 pad = np.full((height, 1), pad_value, dtype=np.float32)
191 img = np.concatenate((pad, img), axis=1)
192
193 if verbose:
194 print('final image size:', img.shape)
195
196 return img

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