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imresize.py
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imresize.py
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'''
Credits: https://github.com/fatheral/matlab_imresize/blob/master/imresize.py
'''
from __future__ import print_function
import numpy as np
from math import ceil, floor
def deriveSizeFromScale(img_shape, scale):
output_shape = []
for k in range(2):
output_shape.append(int(ceil(scale[k] * img_shape[k])))
return output_shape
def deriveScaleFromSize(img_shape_in, img_shape_out):
scale = []
for k in range(2):
scale.append(1.0 * img_shape_out[k] / img_shape_in[k])
return scale
def triangle(x):
x = np.array(x).astype(np.float64)
lessthanzero = np.logical_and((x>=-1),x<0)
greaterthanzero = np.logical_and((x<=1),x>=0)
f = np.multiply((x+1),lessthanzero) + np.multiply((1-x),greaterthanzero)
return f
def cubic(x):
x = np.array(x).astype(np.float64)
absx = np.absolute(x)
absx2 = np.multiply(absx, absx)
absx3 = np.multiply(absx2, absx)
f = np.multiply(1.5*absx3 - 2.5*absx2 + 1, absx <= 1) + np.multiply(-0.5*absx3 + 2.5*absx2 - 4*absx + 2, (1 < absx) & (absx <= 2))
return f
def contributions(in_length, out_length, scale, kernel, k_width):
if scale < 1:
h = lambda x: scale * kernel(scale * x)
kernel_width = 1.0 * k_width / scale
else:
h = kernel
kernel_width = k_width
x = np.arange(1, out_length+1).astype(np.float64)
u = x / scale + 0.5 * (1 - 1 / scale)
left = np.floor(u - kernel_width / 2)
P = int(ceil(kernel_width)) + 2
ind = np.expand_dims(left, axis=1) + np.arange(P) - 1 # -1 because indexing from 0
indices = ind.astype(np.int32)
weights = h(np.expand_dims(u, axis=1) - indices - 1) # -1 because indexing from 0
weights = np.divide(weights, np.expand_dims(np.sum(weights, axis=1), axis=1))
aux = np.concatenate((np.arange(in_length), np.arange(in_length - 1, -1, step=-1))).astype(np.int32)
indices = aux[np.mod(indices, aux.size)]
ind2store = np.nonzero(np.any(weights, axis=0))
weights = weights[:, ind2store]
indices = indices[:, ind2store]
return weights, indices
def imresizemex(inimg, weights, indices, dim):
in_shape = inimg.shape
w_shape = weights.shape
out_shape = list(in_shape)
out_shape[dim] = w_shape[0]
outimg = np.zeros(out_shape)
if dim == 0:
for i_img in range(in_shape[1]):
for i_w in range(w_shape[0]):
w = weights[i_w, :]
ind = indices[i_w, :]
im_slice = inimg[ind, i_img].astype(np.float64)
outimg[i_w, i_img] = np.sum(np.multiply(np.squeeze(im_slice, axis=0), w.T), axis=0)
elif dim == 1:
for i_img in range(in_shape[0]):
for i_w in range(w_shape[0]):
w = weights[i_w, :]
ind = indices[i_w, :]
im_slice = inimg[i_img, ind].astype(np.float64)
outimg[i_img, i_w] = np.sum(np.multiply(np.squeeze(im_slice, axis=0), w.T), axis=0)
if inimg.dtype == np.uint8:
outimg = np.clip(outimg, 0, 255)
return np.around(outimg).astype(np.uint8)
else:
return outimg
def imresizevec(inimg, weights, indices, dim):
wshape = weights.shape
if dim == 0:
weights = weights.reshape((wshape[0], wshape[2], 1, 1))
outimg = np.sum(weights*((inimg[indices].squeeze(axis=1)).astype(np.float64)), axis=1)
elif dim == 1:
weights = weights.reshape((1, wshape[0], wshape[2], 1))
outimg = np.sum(weights*((inimg[:, indices].squeeze(axis=2)).astype(np.float64)), axis=2)
if inimg.dtype == np.uint8:
outimg = np.clip(outimg, 0, 255)
return np.around(outimg).astype(np.uint8)
else:
return outimg
def resizeAlongDim(A, dim, weights, indices, mode="vec"):
if mode == "org":
out = imresizemex(A, weights, indices, dim)
else:
out = imresizevec(A, weights, indices, dim)
return out
def imresize(I, scalar_scale=None, method='bicubic', output_shape=None, mode="vec"):
if method is 'bicubic':
kernel = cubic
elif method is 'bilinear':
kernel = triangle
else:
print ('Error: Unidentified method supplied')
kernel_width = 4.0
# Fill scale and output_size
if scalar_scale is not None:
scalar_scale = float(scalar_scale)
scale = [scalar_scale, scalar_scale]
output_size = deriveSizeFromScale(I.shape, scale)
elif output_shape is not None:
scale = deriveScaleFromSize(I.shape, output_shape)
output_size = list(output_shape)
else:
print ('Error: scalar_scale OR output_shape should be defined!')
return
scale_np = np.array(scale)
order = np.argsort(scale_np)
weights = []
indices = []
for k in range(2):
w, ind = contributions(I.shape[k], output_size[k], scale[k], kernel, kernel_width)
weights.append(w)
indices.append(ind)
B = np.copy(I)
flag2D = False
if B.ndim == 2:
B = np.expand_dims(B, axis=2)
flag2D = True
for k in range(2):
dim = order[k]
B = resizeAlongDim(B, dim, weights[dim], indices[dim], mode)
if flag2D:
B = np.squeeze(B, axis=2)
return B
def convertDouble2Byte(I):
B = np.clip(I, 0.0, 1.0)
B = 255*B
return np.around(B).astype(np.uint8)