# Copyright (c) 2017, Combine Control Systems AB
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"""
Some of the docstrings for this module have been
extracted from the `scikit-image <http://scikit-image.org/>`_ library
and are covered by their respective licenses.
"""
from __future__ import (print_function, division, unicode_literals,
absolute_import)
from sympathy.api import node
from sympathy.api.nodeconfig import Port, Ports, Tag, Tags
import numpy as np
import matplotlib.pyplot as plt
from skimage import filters, feature, exposure, color, morphology, transform
from sylib.imageprocessing.image import Image
from sylib.imageprocessing.algorithm_selector import ImageFiltering_abstract
from sylib.imageprocessing.color import grayscale_transform
from sympathy.api.exceptions import SyNodeError, sywarn
import scipy.ndimage.filters
def table_to_image(table):
data = [column.data for column in table.cols()]
return np.column_stack(data)
def alg_prewitt(im, params):
method = params['horizontal/vertical'].value
if method == 'horizontal':
return filters.prewitt_h(im)
elif method == 'vertical':
return filters.prewitt_v(im)
else:
return filters.prewitt(im)
def alg_scharr(im, params):
method = params['horizontal/vertical'].value
if method == 'horizontal':
return filters.scharr_h(im)
elif method == 'vertical':
return filters.scharr_v(im)
else:
return filters.scharr(im)
def alg_sobel(im, params):
method = params['horizontal/vertical'].value
if method == 'horizontal':
return filters.sobel_h(im)
elif method == 'vertical':
return filters.sobel_v(im)
else:
return filters.sobel(im)
def alg_roberts(im, params):
method = params['positive/negative diagonal'].value
if method == 'positive':
return filters.roberts_pos_diag(im)
elif method == 'negative':
return filters.roberts_neg_diag(im)
else:
return filters.roberts(im)
def alg_greyscale(im, params):
if len(im.shape) == 2:
result = im
elif im.shape[2] == 3 and params['luminance preserving'].value:
result = im.dot(grayscale_transform)
elif (im.shape[2] == 4 and
params['luminance preserving'].value and
params['preserve alpha'].value):
result = np.zeros(im.shape[:2]+(2,))
result[:, :, 0] = im[:, :, :3].dot(grayscale_transform)
result[:, :, 1] = im[:, :, 3]
elif im.shape[2] == 4 and not params['preserve alpha'].value:
result = im[:, :, :3].dot(grayscale_transform)
else:
result = im.mean(axis=2).reshape(im.shape[:2]+(1,))
return result
def alg_center_image(im, params):
x_weights = np.ones(im.shape[:2]) * np.arange(im.shape[1])
y_weights = (
np.ones((im.shape[1], im.shape[0])) * np.arange(im.shape[0])
).transpose()
if len(im.shape) < 3:
im = im.reshape(im.shape+(1,))
channels = im.shape[2]
x_w_sum, y_w_sum = 0, 0
x_sum, y_sum = 0, 0
for channel in range(channels):
x_w_sum += np.sum(im[:, :, channel] * x_weights)
y_w_sum += np.sum(im[:, :, channel] * y_weights)
x_sum += np.sum(im[:, :, channel])
y_sum += np.sum(im[:, :, channel])
xpos = x_w_sum / x_sum
ypos = y_w_sum / y_sum
dx = int(xpos - im.shape[1]/2)
dy = int(ypos - im.shape[0]/2)
out = np.zeros(im.shape)
if dx < 0 and dy < 0:
out[-dy:, -dx:, :] = im[:dy, :dx, :]
elif dx < 0 and dy >= 0:
out[:-dy, -dx:, :] = im[dy:, :dx, :]
elif dx >= 0 and dy < 0:
out[-dy:, :-dx, :] = im[:dy, dx:, :]
elif dx >= 0 and dy >= 0:
out[:-dy, :-dx, :] = im[dy:, dx:, :]
return out
def alg_padding(im, params):
if len(im.shape) < 3:
im = im.reshape(im.shape+(1,))
add_alpha = params['add alpha'].value
px, py = params['x'].value, params['y'].value
px, py = int(px), int(py)
k = params['k'].value
max_x = im.shape[1]+abs(px)
max_y = im.shape[0]+abs(py)
result = np.full((max_y, max_x, im.shape[2]+add_alpha), k)
if add_alpha:
result[max(0, py):max(0, py)+im.shape[0],
max(0, px):max(0, px)+im.shape[1], :-1] = im
result[max(0, py):max(0, py)+im.shape[0],
max(0, px):max(0, px)+im.shape[1], -1] = np.ones(im.shape[:2])
else:
result[max(0, py):max(0, py)+im.shape[0],
max(0, px):max(0, px)+im.shape[1]] = im
return result
def alg_crop_image(im, params):
x, y = params['x'].value, params['y'].value
w, h = params['width'].value, params['height'].value
shape = im.shape
x, y = min(x, shape[1]), min(y, shape[0])
w, h = min(w, shape[1]-x), min(h, shape[0]-y)
return im[y:y+h, x:x+w]
def alg_resize(im, params):
req_h = params['height'].value
req_w = params['width'].value
h, w = req_h, req_w
if params['aspect'].value:
aspect = im.shape[1] / float(im.shape[0])
size = min(req_w / aspect, req_h)
w = int(size * aspect)
h = int(size)
shape = (h, w) + im.shape[2:]
result_im = transform.resize(
im, shape, order=params['interpolation degree'].value)
if params['padding'].value:
pad_h = req_h - h
pad_w = req_w - w
padded_im = np.zeros((req_h, req_w) + im.shape[2:])
x0 = int(pad_w/2)
x1 = x0 + result_im.shape[1]
y0 = int(pad_h/2)
y1 = y0 + result_im.shape[0]
padded_im[y0:y1, x0:x1] = result_im
return padded_im
return result_im
def alg_convolution(im, params, tables):
if len(tables) == 0:
raise SyNodeError(
'Convolution filters require second input for kernel '
'(right-click node to create new input)')
kernel = table_to_image(tables[0])
return scipy.ndimage.filters.convolve(
im, kernel, mode=params['border mode'].value,
cval=params['k'].value)
def alg_generic_colourspace(im, params, tables):
if len(tables) == 0:
raise SyNodeError(
'Generic colourspace conversion require second table input '
'(right-click node to create new input)')
conv = table_to_image(tables[0])
print("Conv: ", conv)
rows = conv.shape[0]
out = np.zeros(im.shape[:2]+(rows,))
for out_ch in range(rows):
out[:, :, out_ch] = np.dot(im, conv[out_ch, :])
return out
def alg_colourmap(im, params):
if len(im.shape) >= 3 and im.shape[2] != 1:
sywarn('Colourmap expects a single-channel input')
if len(im.shape) >= 3:
im = im[:, :, 0]
cmap = plt.get_cmap(params['cmap'].value)
try:
cols = np.array(cmap.colors)
except AttributeError:
cols = cmap(np.linspace(0.0, 1.0, 256))
if cols.shape[-1] == 4:
cols = cols[:, :3]
minv = np.nanmin(im)
maxv = np.nanmax(im)
im[np.isnan(im)] = minv
im = np.round((len(cols)-1) * (im-minv) / (maxv-minv)).astype(np.int)
return cols[im.ravel()].reshape(im.shape[:2]+(cols.shape[-1],))
def alg_auto_threshold(im, params):
method = params['auto threshold method'].value
fns = {
'otsu': filters.threshold_otsu,
'yen': filters.threshold_yen,
'isodata': filters.threshold_isodata,
'li': filters.threshold_li,
'minimum': filters.threshold_minimum,
'mean': filters.threshold_mean,
'triangle': filters.threshold_triangle,
'median': lambda x: np.median(x)
}
fn = fns[method]
return im > fn(im)
[docs]class ImageFiltering2(ImageFiltering_abstract, node.Node):
name = 'Filter Image, Dual Output'
author = 'Mathias Broxvall'
version = '0.1'
icon = 'image_filtering_dual.svg'
description = 'Filters one image using algorithms with two images as output'
nodeid = 'syip.imagefiltering2'
tags = Tags(Tag.ImageProcessing.ImageManipulation)
def alg_hessian_eigenval(im, par):
hrr, hrc, hcc = feature.hessian_matrix(im, sigma=par['sigma'].value)
return feature.hessian_matrix_eigvals(hrr, hrc, hcc)
algorithms = {
'corner_foerstner': {
'description': (
'Computes Foerstner corner measure response images. Outputs '
'error eclipse sizes (top) and roundness or error eclipse '
'(bottom).'),
'sigma': 'Standard deviation of gaussian kernel (default 1.0)',
'multi_chromatic': False,
'algorithm': lambda im, par: feature.corner_foerstner(
im, sigma=par['sigma'].value)
},
'hessian eigenvalues': {
'description': (
'Computes the eigenvalues of the hessian matrix for each '
'pixel. Returns larger eigenvalue in first output image and '
'smaller in second'),
'multi_chromatic': False,
'sigma': (
'Standard deviation of gaussian kernel (default 3.0) used for '
'calculating Hessian.\nApproximation is not reliable for '
'sigma < 3.0'),
'algorithm': alg_hessian_eigenval
},
}
options_list = ['n', 'sigma', 'threshold']
options_types = {'n': int, 'sigma': float, 'threshold': float}
options_default = {'n': 12, 'sigma': 1.0, 'threshold': 0.15}
parameters = node.parameters()
parameters.set_string(
'algorithm', value=next(iter(algorithms)), description='', label='Algorithm')
ImageFiltering_abstract.generate_parameters(
parameters, options_types, options_default)
inputs = Ports(
[Image('source image to filter', name='source'), ]
)
outputs = Ports([
Image('result after filtering', name='resultA'),
Image('result after filtering', name='resultB'),
])
__doc__ = ImageFiltering_abstract.generate_docstring(
description, algorithms, options_list, inputs, outputs)
def execute(self, node_context):
source_obj = node_context.input['source']
source = source_obj.get_image()
params = node_context.parameters
alg_name = params['algorithm'].value
if len(source.shape) == 3 and source.shape[2] > 1:
multichannel_image = True
else:
multichannel_image = False
alg = self.algorithms[alg_name]['algorithm']
if (multichannel_image and
not self.algorithms[alg_name]['multi_chromatic']):
# Process each channel separately
imA1, imB1 = alg(source[:, :, 0], params)
imA = np.zeros(imA1.shape[:2]+(source.shape[2],))
imB = np.zeros(imB1.shape[:2]+(source.shape[2],))
imA[:, :, 0] = imA1
imB[:, :, 0] = imA1
for channel in range(1, source.shape[2]):
result = alg(source[:, :, channel], params)
imA[:, :, channel], imB[:, :, channel] = result
else:
# Process all channels at once
if len(source.shape) == 3 and source.shape[2] == 1:
source = source.reshape(source.shape[:2])
imA, imB = alg(source, params)
node_context.output['resultA'].set_image(imA)
node_context.output['resultB'].set_image(imB)
