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Improved drawing in plot_rag_draw.py example (#1872)

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Improved drawing in draw_rag example; changed draw_rag to show_rag, made it return ScalarMappable to draw colorbars; added magma colormap
This commit is contained in:
Vighnesh Birodkar
2016-04-24 09:28:24 +03:00
committed by Egor Panfilov
parent e6fd683d74
commit a406e270b1
6 changed files with 347 additions and 88 deletions
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doc/examples/segmentation/plot_rag_boundary.py
+4 -5
View File
@@ -8,7 +8,7 @@ This example demonstrates construction of region boundary based RAGs with the
"""
from skimage.future import graph
from skimage import data, segmentation, color, filters, io
from skimage.util.colormap import viridis
from matplotlib import pyplot as plt
img = data.coffee()
@@ -19,9 +19,8 @@ edges = filters.sobel(gimg)
edges_rgb = color.gray2rgb(edges)
g = graph.rag_boundary(labels, edges)
lc = graph.show_rag(labels, g, edges_rgb, img_cmap=None, edge_cmap='viridis',
edge_width=1.2)
out = graph.draw_rag(labels, g, edges_rgb, node_color="#999999",
colormap=viridis)
io.imshow(out)
plt.colorbar(lc, fraction=0.03)
io.show()
doc/examples/segmentation/plot_rag_draw.py
+12 -20
View File
@@ -8,33 +8,25 @@ the `rag_draw` method.
"""
from skimage import data, segmentation
from skimage.future import graph
from skimage.util.colormap import viridis
from matplotlib import pyplot as plt, colors
from matplotlib import pyplot as plt
img = data.coffee()
labels = segmentation.slic(img, compactness=30, n_segments=400)
g = graph.rag_mean_color(img, labels)
out = graph.draw_rag(labels, g, img)
plt.figure()
plt.title("RAG with all edges shown in green.")
plt.imshow(out)
# The color palette used was taken from
# http://www.colorcombos.com/color-schemes/2/ColorCombo2.html
cmap = colors.ListedColormap(['#6599FF', '#ff9900'])
out = graph.draw_rag(labels, g, img, node_color="#ffde00", colormap=cmap,
thresh=30, desaturate=True)
plt.figure()
plt.title("RAG with edge weights less than 30, color "
"mapped between blue and orange.")
plt.imshow(out)
fig, ax = plt.subplots()
ax.set_title('RAG drawn with default settings')
lc = graph.show_rag(labels, g, img, ax=ax)
# fraction specifies the fraction of the area of the plot that will be used to
# draw the colorbar
plt.colorbar(lc, fraction=0.03)
plt.figure()
plt.title("All edges drawn with viridis colormap")
out = graph.draw_rag(labels, g, img, colormap=viridis,
desaturate=True)
fig, ax = plt.subplots()
ax.set_title('RAG drawn with grayscale image and viridis colormap')
lc = graph.show_rag(labels, g, img, img_cmap='gray', edge_cmap='viridis',
ax=ax)
plt.colorbar(lc, fraction=0.03)
plt.imshow(out)
plt.show()
doc/source/conf.py
+2
View File
@@ -307,6 +307,8 @@ intersphinx_mapping = {
(None, './_intersphinx/scipy-objects.inv')),
'sklearn': ('http://scikit-learn.org/stable',
(None, './_intersphinx/sklearn-objects.inv')),
'matplotlib': ('http://matplotlib.org/',
(None, 'http://matplotlib.org/objects.inv'))
}
# ----------------------------------------------------------------------------
skimage/future/graph/__init__.py
+2 -2
View File
@@ -1,5 +1,5 @@
from .graph_cut import cut_threshold, cut_normalized
from .rag import rag_mean_color, RAG, draw_rag, rag_boundary
from .rag import rag_mean_color, RAG, show_rag, rag_boundary
from .graph_merge import merge_hierarchical
ncut = cut_normalized
@@ -7,7 +7,7 @@ __all__ = ['rag_mean_color',
'cut_threshold',
'cut_normalized',
'ncut',
'draw_rag',
'show_rag',
'merge_hierarchical',
'rag_boundary',
'RAG']
skimage/future/graph/rag.py
+57 -60
View File
@@ -4,12 +4,10 @@ from numpy.lib.stride_tricks import as_strided
from scipy import ndimage as ndi
from scipy import sparse
import math
from ... import draw, measure, segmentation, util, color
try:
from matplotlib import colors
from matplotlib import cm
except ImportError:
pass
from ... import measure, segmentation, util, color
from matplotlib import colors, cm
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection
def _edge_generator_from_csr(csr_matrix):
@@ -420,14 +418,13 @@ def rag_boundary(labels, edge_map, connectivity=2):
return rag
def draw_rag(labels, rag, img, border_color=None, node_color='#ffff00',
edge_color='#00ff00', colormap=None, thresh=np.inf,
desaturate=False, in_place=True):
def show_rag(labels, rag, img, border_color='black', edge_width=1.5,
edge_cmap='magma', img_cmap='bone', in_place=True, ax=None):
"""Draw a Region Adjacency Graph on an image.
Given a labelled image and its corresponding RAG, draw the nodes and edges
of the RAG on the image with the specified colors. Nodes are marked by
the centroids of the corresponding regions.
of the RAG on the image with the specified colors. Edges are drawn between
the centroid of the 2 adjacent regions in the image.
Parameters
----------
@@ -435,31 +432,30 @@ def draw_rag(labels, rag, img, border_color=None, node_color='#ffff00',
The labelled image.
rag : RAG
The Region Adjacency Graph.
img : ndarray, shape (M, N, 3)
Input image.
border_color : colorspec, optional
Any matplotlib colorspec.
node_color : colorspec, optional
Any matplotlib colorspec. Yellow by default.
edge_color : colorspec, optional
Any matplotlib colorspec. Green by default.
colormap : colormap, optional
Any matplotlib colormap. If specified the edges are colormapped with
the specified color map.
thresh : float, optional
Edges with weight below `thresh` are not drawn, or considered for color
mapping.
desaturate : bool, optional
Convert the image to grayscale before displaying. Particularly helps
visualization when using the `colormap` option.
img : ndarray, shape (M, N[, 3])
Input image. If `colormap` is `None`, the image should be in RGB
format.
border_color : color spec, optional
Color with which the borders between regions are drawn.
edge_width : float, optional
The thickness with which the RAG edges are drawn.
edge_cmap : :py:class:`matplotlib.colors.Colormap`, optional
Any matplotlib colormap with which the edges are drawn.
img_cmap : :py:class:`matplotlib.colors.Colormap`, optional
Any matplotlib colormap with which the image is draw. If set to `None`
the image is drawn as it is.
in_place : bool, optional
If set, the RAG is modified in place. For each node `n` the function
will set a new attribute ``rag.node[n]['centroid']``.
ax : :py:class:`matplotlib.axes.Axes`, optional
The axes to draw on. If not specified, new axes are created and drawn
on.
Returns
-------
out : ndarray, shape (M, N, 3)
The image with the RAG drawn.
lc : :py:class:`matplotlib.collections.LineCollection`
A colection of lines that represent the edges of the graph. It can be
passed to the :meth:`matplotlib.figure.Figure.colorbar` function.
Examples
--------
@@ -468,20 +464,30 @@ def draw_rag(labels, rag, img, border_color=None, node_color='#ffff00',
>>> img = data.coffee()
>>> labels = segmentation.slic(img)
>>> g = graph.rag_mean_color(img, labels)
>>> out = graph.draw_rag(labels, g, img)
>>> lc = graph.show_rag(labels, g, img)
>>> cbar = plt.colorbar(lc)
"""
if not in_place:
rag = rag.copy()
if desaturate:
img = color.rgb2gray(img)
img = color.gray2rgb(img)
if ax is None:
fig, ax = plt.subplots()
out = util.img_as_float(img, force_copy=True)
cc = colors.ColorConverter()
edge_color = cc.to_rgb(edge_color)
node_color = cc.to_rgb(node_color)
if img_cmap is None:
if img.ndim < 3 or img.shape[2] not in [3, 4]:
msg = 'If colormap is `None`, an RGB or RGBA image should be given'
raise ValueError(msg)
# Ignore the alpha channel
out = img[:, :, :3]
else:
img_cmap = cm.get_cmap(img_cmap)
out = color.rgb2gray(img)
# Ignore the alpha channel
out = img_cmap(out)[:, :, :3]
edge_cmap = cm.get_cmap(edge_cmap)
# Handling the case where one node has multiple labels
# offset is 1 so that regionprops does not ignore 0
@@ -496,33 +502,24 @@ def draw_rag(labels, rag, img, border_color=None, node_color='#ffff00',
regions = measure.regionprops(rag_labels)
for (n, data), region in zip(rag.nodes_iter(data=True), regions):
data['centroid'] = region['centroid']
data['centroid'] = tuple(map(int, region['centroid']))
cc = colors.ColorConverter()
if border_color is not None:
border_color = cc.to_rgb(border_color)
out = segmentation.mark_boundaries(out, rag_labels, color=border_color)
if colormap is not None:
edge_weight_list = [d['weight'] for x, y, d in
rag.edges_iter(data=True) if d['weight'] < thresh]
norm = colors.Normalize()
norm.autoscale(edge_weight_list)
smap = cm.ScalarMappable(norm, colormap)
ax.imshow(out)
for n1, n2, data in rag.edges_iter(data=True):
# Defining the end points of the edges
# The tuple[::-1] syntax reverses a tuple as matplotlib uses (x,y)
# convention while skimage uses (row, column)
lines = [[rag.node[n1]['centroid'][::-1], rag.node[n2]['centroid'][::-1]]
for (n1, n2) in rag.edges_iter()]
if data['weight'] >= thresh:
continue
r1, c1 = map(int, rag.node[n1]['centroid'])
r2, c2 = map(int, rag.node[n2]['centroid'])
line = draw.line(r1, c1, r2, c2)
lc = LineCollection(lines, linewidths=edge_width, cmap=edge_cmap)
edge_weights = [d['weight'] for x, y, d in rag.edges_iter(data=True)]
lc.set_array(np.array(edge_weights))
ax.add_collection(lc)
if colormap is not None:
out[line] = smap.to_rgba([data['weight']])[0][:-1]
else:
out[line] = edge_color
circle = draw.circle(r1, c1, 2)
out[circle] = node_color
return out
return lc
skimage/util/colormap.py
+270 -1
View File
@@ -1,4 +1,5 @@
from matplotlib.colors import LinearSegmentedColormap
from matplotlib import cm
viridis_data = [[ 0.26700401, 0.00487433, 0.32941519],
[ 0.26851048, 0.00960483, 0.33542652],
@@ -258,4 +259,272 @@ viridis_data = [[ 0.26700401, 0.00487433, 0.32941519],
[ 0.99324789, 0.90615657, 0.1439362 ]]
viridis = LinearSegmentedColormap.from_list('viridis', viridis_data)
magma_data = [[0.001462, 0.000466, 0.013866],
[0.002258, 0.001295, 0.018331],
[0.003279, 0.002305, 0.023708],
[0.004512, 0.003490, 0.029965],
[0.005950, 0.004843, 0.037130],
[0.007588, 0.006356, 0.044973],
[0.009426, 0.008022, 0.052844],
[0.011465, 0.009828, 0.060750],
[0.013708, 0.011771, 0.068667],
[0.016156, 0.013840, 0.076603],
[0.018815, 0.016026, 0.084584],
[0.021692, 0.018320, 0.092610],
[0.024792, 0.020715, 0.100676],
[0.028123, 0.023201, 0.108787],
[0.031696, 0.025765, 0.116965],
[0.035520, 0.028397, 0.125209],
[0.039608, 0.031090, 0.133515],
[0.043830, 0.033830, 0.141886],
[0.048062, 0.036607, 0.150327],
[0.052320, 0.039407, 0.158841],
[0.056615, 0.042160, 0.167446],
[0.060949, 0.044794, 0.176129],
[0.065330, 0.047318, 0.184892],
[0.069764, 0.049726, 0.193735],
[0.074257, 0.052017, 0.202660],
[0.078815, 0.054184, 0.211667],
[0.083446, 0.056225, 0.220755],
[0.088155, 0.058133, 0.229922],
[0.092949, 0.059904, 0.239164],
[0.097833, 0.061531, 0.248477],
[0.102815, 0.063010, 0.257854],
[0.107899, 0.064335, 0.267289],
[0.113094, 0.065492, 0.276784],
[0.118405, 0.066479, 0.286321],
[0.123833, 0.067295, 0.295879],
[0.129380, 0.067935, 0.305443],
[0.135053, 0.068391, 0.315000],
[0.140858, 0.068654, 0.324538],
[0.146785, 0.068738, 0.334011],
[0.152839, 0.068637, 0.343404],
[0.159018, 0.068354, 0.352688],
[0.165308, 0.067911, 0.361816],
[0.171713, 0.067305, 0.370771],
[0.178212, 0.066576, 0.379497],
[0.184801, 0.065732, 0.387973],
[0.191460, 0.064818, 0.396152],
[0.198177, 0.063862, 0.404009],
[0.204935, 0.062907, 0.411514],
[0.211718, 0.061992, 0.418647],
[0.218512, 0.061158, 0.425392],
[0.225302, 0.060445, 0.431742],
[0.232077, 0.059889, 0.437695],
[0.238826, 0.059517, 0.443256],
[0.245543, 0.059352, 0.448436],
[0.252220, 0.059415, 0.453248],
[0.258857, 0.059706, 0.457710],
[0.265447, 0.060237, 0.461840],
[0.271994, 0.060994, 0.465660],
[0.278493, 0.061978, 0.469190],
[0.284951, 0.063168, 0.472451],
[0.291366, 0.064553, 0.475462],
[0.297740, 0.066117, 0.478243],
[0.304081, 0.067835, 0.480812],
[0.310382, 0.069702, 0.483186],
[0.316654, 0.071690, 0.485380],
[0.322899, 0.073782, 0.487408],
[0.329114, 0.075972, 0.489287],
[0.335308, 0.078236, 0.491024],
[0.341482, 0.080564, 0.492631],
[0.347636, 0.082946, 0.494121],
[0.353773, 0.085373, 0.495501],
[0.359898, 0.087831, 0.496778],
[0.366012, 0.090314, 0.497960],
[0.372116, 0.092816, 0.499053],
[0.378211, 0.095332, 0.500067],
[0.384299, 0.097855, 0.501002],
[0.390384, 0.100379, 0.501864],
[0.396467, 0.102902, 0.502658],
[0.402548, 0.105420, 0.503386],
[0.408629, 0.107930, 0.504052],
[0.414709, 0.110431, 0.504662],
[0.420791, 0.112920, 0.505215],
[0.426877, 0.115395, 0.505714],
[0.432967, 0.117855, 0.506160],
[0.439062, 0.120298, 0.506555],
[0.445163, 0.122724, 0.506901],
[0.451271, 0.125132, 0.507198],
[0.457386, 0.127522, 0.507448],
[0.463508, 0.129893, 0.507652],
[0.469640, 0.132245, 0.507809],
[0.475780, 0.134577, 0.507921],
[0.481929, 0.136891, 0.507989],
[0.488088, 0.139186, 0.508011],
[0.494258, 0.141462, 0.507988],
[0.500438, 0.143719, 0.507920],
[0.506629, 0.145958, 0.507806],
[0.512831, 0.148179, 0.507648],
[0.519045, 0.150383, 0.507443],
[0.525270, 0.152569, 0.507192],
[0.531507, 0.154739, 0.506895],
[0.537755, 0.156894, 0.506551],
[0.544015, 0.159033, 0.506159],
[0.550287, 0.161158, 0.505719],
[0.556571, 0.163269, 0.505230],
[0.562866, 0.165368, 0.504692],
[0.569172, 0.167454, 0.504105],
[0.575490, 0.169530, 0.503466],
[0.581819, 0.171596, 0.502777],
[0.588158, 0.173652, 0.502035],
[0.594508, 0.175701, 0.501241],
[0.600868, 0.177743, 0.500394],
[0.607238, 0.179779, 0.499492],
[0.613617, 0.181811, 0.498536],
[0.620005, 0.183840, 0.497524],
[0.626401, 0.185867, 0.496456],
[0.632805, 0.187893, 0.495332],
[0.639216, 0.189921, 0.494150],
[0.645633, 0.191952, 0.492910],
[0.652056, 0.193986, 0.491611],
[0.658483, 0.196027, 0.490253],
[0.664915, 0.198075, 0.488836],
[0.671349, 0.200133, 0.487358],
[0.677786, 0.202203, 0.485819],
[0.684224, 0.204286, 0.484219],
[0.690661, 0.206384, 0.482558],
[0.697098, 0.208501, 0.480835],
[0.703532, 0.210638, 0.479049],
[0.709962, 0.212797, 0.477201],
[0.716387, 0.214982, 0.475290],
[0.722805, 0.217194, 0.473316],
[0.729216, 0.219437, 0.471279],
[0.735616, 0.221713, 0.469180],
[0.742004, 0.224025, 0.467018],
[0.748378, 0.226377, 0.464794],
[0.754737, 0.228772, 0.462509],
[0.761077, 0.231214, 0.460162],
[0.767398, 0.233705, 0.457755],
[0.773695, 0.236249, 0.455289],
[0.779968, 0.238851, 0.452765],
[0.786212, 0.241514, 0.450184],
[0.792427, 0.244242, 0.447543],
[0.798608, 0.247040, 0.444848],
[0.804752, 0.249911, 0.442102],
[0.810855, 0.252861, 0.439305],
[0.816914, 0.255895, 0.436461],
[0.822926, 0.259016, 0.433573],
[0.828886, 0.262229, 0.430644],
[0.834791, 0.265540, 0.427671],
[0.840636, 0.268953, 0.424666],
[0.846416, 0.272473, 0.421631],
[0.852126, 0.276106, 0.418573],
[0.857763, 0.279857, 0.415496],
[0.863320, 0.283729, 0.412403],
[0.868793, 0.287728, 0.409303],
[0.874176, 0.291859, 0.406205],
[0.879464, 0.296125, 0.403118],
[0.884651, 0.300530, 0.400047],
[0.889731, 0.305079, 0.397002],
[0.894700, 0.309773, 0.393995],
[0.899552, 0.314616, 0.391037],
[0.904281, 0.319610, 0.388137],
[0.908884, 0.324755, 0.385308],
[0.913354, 0.330052, 0.382563],
[0.917689, 0.335500, 0.379915],
[0.921884, 0.341098, 0.377376],
[0.925937, 0.346844, 0.374959],
[0.929845, 0.352734, 0.372677],
[0.933606, 0.358764, 0.370541],
[0.937221, 0.364929, 0.368567],
[0.940687, 0.371224, 0.366762],
[0.944006, 0.377643, 0.365136],
[0.947180, 0.384178, 0.363701],
[0.950210, 0.390820, 0.362468],
[0.953099, 0.397563, 0.361438],
[0.955849, 0.404400, 0.360619],
[0.958464, 0.411324, 0.360014],
[0.960949, 0.418323, 0.359630],
[0.963310, 0.425390, 0.359469],
[0.965549, 0.432519, 0.359529],
[0.967671, 0.439703, 0.359810],
[0.969680, 0.446936, 0.360311],
[0.971582, 0.454210, 0.361030],
[0.973381, 0.461520, 0.361965],
[0.975082, 0.468861, 0.363111],
[0.976690, 0.476226, 0.364466],
[0.978210, 0.483612, 0.366025],
[0.979645, 0.491014, 0.367783],
[0.981000, 0.498428, 0.369734],
[0.982279, 0.505851, 0.371874],
[0.983485, 0.513280, 0.374198],
[0.984622, 0.520713, 0.376698],
[0.985693, 0.528148, 0.379371],
[0.986700, 0.535582, 0.382210],
[0.987646, 0.543015, 0.385210],
[0.988533, 0.550446, 0.388365],
[0.989363, 0.557873, 0.391671],
[0.990138, 0.565296, 0.395122],
[0.990871, 0.572706, 0.398714],
[0.991558, 0.580107, 0.402441],
[0.992196, 0.587502, 0.406299],
[0.992785, 0.594891, 0.410283],
[0.993326, 0.602275, 0.414390],
[0.993834, 0.609644, 0.418613],
[0.994309, 0.616999, 0.422950],
[0.994738, 0.624350, 0.427397],
[0.995122, 0.631696, 0.431951],
[0.995480, 0.639027, 0.436607],
[0.995810, 0.646344, 0.441361],
[0.996096, 0.653659, 0.446213],
[0.996341, 0.660969, 0.451160],
[0.996580, 0.668256, 0.456192],
[0.996775, 0.675541, 0.461314],
[0.996925, 0.682828, 0.466526],
[0.997077, 0.690088, 0.471811],
[0.997186, 0.697349, 0.477182],
[0.997254, 0.704611, 0.482635],
[0.997325, 0.711848, 0.488154],
[0.997351, 0.719089, 0.493755],
[0.997351, 0.726324, 0.499428],
[0.997341, 0.733545, 0.505167],
[0.997285, 0.740772, 0.510983],
[0.997228, 0.747981, 0.516859],
[0.997138, 0.755190, 0.522806],
[0.997019, 0.762398, 0.528821],
[0.996898, 0.769591, 0.534892],
[0.996727, 0.776795, 0.541039],
[0.996571, 0.783977, 0.547233],
[0.996369, 0.791167, 0.553499],
[0.996162, 0.798348, 0.559820],
[0.995932, 0.805527, 0.566202],
[0.995680, 0.812706, 0.572645],
[0.995424, 0.819875, 0.579140],
[0.995131, 0.827052, 0.585701],
[0.994851, 0.834213, 0.592307],
[0.994524, 0.841387, 0.598983],
[0.994222, 0.848540, 0.605696],
[0.993866, 0.855711, 0.612482],
[0.993545, 0.862859, 0.619299],
[0.993170, 0.870024, 0.626189],
[0.992831, 0.877168, 0.633109],
[0.992440, 0.884330, 0.640099],
[0.992089, 0.891470, 0.647116],
[0.991688, 0.898627, 0.654202],
[0.991332, 0.905763, 0.661309],
[0.990930, 0.912915, 0.668481],
[0.990570, 0.920049, 0.675675],
[0.990175, 0.927196, 0.682926],
[0.989815, 0.934329, 0.690198],
[0.989434, 0.941470, 0.697519],
[0.989077, 0.948604, 0.704863],
[0.988717, 0.955742, 0.712242],
[0.988367, 0.962878, 0.719649],
[0.988033, 0.970012, 0.727077],
[0.987691, 0.977154, 0.734536],
[0.987387, 0.984288, 0.742002],
[0.987053, 0.991438, 0.749504]]
def register_cmap_safe(name, data):
"Register a colormap if not already registered."
try:
return cm.get_cmap(name)
except ValueError:
cmap = LinearSegmentedColormap.from_list(name, data)
cm.register_cmap(name, cmap)
return cmap
viridis = register_cmap_safe('viridis', viridis_data)
magma = register_cmap_safe('magma', magma_data)