def hierarchical_merging_of_region_boundary_rags_example():
#img = data.coffee()
edges = sobel(skimage.color.rgb2gray(img))
labels = slic(img, compactness=30, n_segments=400)
g = graph.rag_boundary(labels, edges)
graph.show_rag(labels, g, img)
plt.title('Initial RAG')
labels2 = graph.merge_hierarchical(labels,
g,
thresh=0.08,
rag_copy=False,
in_place_merge=True,
merge_func=merge_boundary,
weight_func=weight_boundary)
graph.show_rag(labels, g, img)
plt.title('RAG after hierarchical merging')
plt.figure()
out = skimage.color.label2rgb(labels2, img, kind='avg')
plt.imshow(out)
plt.title('Final segmentation')
plt.show()
def show_segmen_rag(array,
pathlibpath,
numSegments,
threshold,
cedges=177,
compactness=0.1,
sigma=5,
convert2lab=False):
print('Obtaining superstructures')
segments = slic(array,
compactness=compactness,
n_segments=numSegments,
sigma=sigma,
multichannel=False,
convert2lab=convert2lab)
print('Number of SV: ', len(np.unique(segments)))
segments += 1
edges = filters.sobel(array)
print('Obtaining RAG')
rag = graph.rag_boundary(segments, edges, connectivity=1)
segments2 = graph.merge_hierarchical(segments,
rag,
threshold=threshold,
in_place_merge=True,
rag_copy=False,
merge_func=merge_boundary,
weight_func=weight_boundary)
print('Final Number of SV: ', len(np.unique(segments2)))
graph.show_rag(segments, rag, array, edge_cmap='viridis')
save_yorno(array, segments, pathlibpath, cedges)
def plotRagwithColorMaps(self, img, labels):
g = graph.rag_mean_color(img, labels)
fig, ax = plt.subplots(nrows=2,
sharex=True,
sharey=True,
figsize=(6, 8))
ax[0].set_title('RAG drawn with default settings')
lc = graph.show_rag(labels, g, img, ax=ax[0])
# specify the fraction of the plot area that will be used to draw the colorbar
fig.colorbar(lc, fraction=0.03, ax=ax[0])
ax[1].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[1])
fig.colorbar(lc, fraction=0.03, ax=ax[1])
for a in ax:
a.axis('off')
plt.tight_layout()
plt.show()
def generate_texture(img,
slic_n=200,
slic_compactness=25,
rag_from_binary=False,
edge_threshold=1e-4,
verbose=False):
img = img_as_float(img)
if rag_from_binary:
if verbose:
print('Applying initial thresholding...', flush=True)
grey_img = color.rgb2grey(img)
binary_mask = (grey_img > threshold_li(grey_img)) * 1.0
if verbose:
print('Constructing superpixels...', flush=True)
segments = slic(img, n_segments=slic_n, compactness=slic_compactness)
if verbose:
out_avg = color.label2rgb(segments, img, kind='avg')
io.imshow(out_avg)
io.show()
if verbose:
print('Building RAG...', flush=True)
if rag_from_binary:
RAG = graph.rag_mean_color(binary_mask, segments, mode='similarity')
else:
RAG = graph.rag_mean_color(img, segments, mode='similarity')
if verbose:
graph.show_rag(segments, RAG, img, border_color=(1, 0.7, 0))
io.show()
foreground_mask = find_foreground(img,
segments,
RAG,
eps=edge_threshold,
verbose=verbose)
if foreground_mask.any():
vertex, side = fit_square(foreground_mask, verbose=verbose)
texture_patch = img[vertex[0]:vertex[0] + side + 1,
vertex[1]:vertex[1] + side + 1]
if verbose:
io.imshow(texture_patch)
io.show()
return texture_patch
else:
print('Texture can\'t be generated, try different parameters')
return np.array([])
def make_grid_rag(data, res):
fig, ax = plt.subplots(len(res), 2)
ax = ax.reshape(len(res), 2)
# make preview images
im = data['image_unnormal'].cpu().numpy()
im = [np.rollaxis(im[i, ...], 0, 3) for i in range(im.shape[0])]
truth = data['label/segmentation'].cpu().numpy()
truth = [truth[i, 0, ...] for i in range(truth.shape[0])]
labels = data['labels'].cpu().numpy()
labels = [labels[i, ...][0] for i in range(labels.shape[0])]
for i, (im_, truth_, labels_,
g) in enumerate(zip(im, truth, labels, data['rag'])):
truth_contour = segmentation.find_boundaries(truth_, mode='thick')
g.add_edges_from(
(e[0], e[1], dict(weight=res[i][j].detach().cpu().numpy()))
for j, e in enumerate(g.edges))
lc = show_rag(labels_.astype(int),
g,
im_,
ax=ax[i, 1],
edge_width=0.5,
edge_cmap='viridis')
fig.colorbar(lc, ax=ax[i, 1], fraction=0.03)
im_[truth_contour, ...] = (1, 0, 0)
ax[i, 0].imshow(im_)
return fig
def make_grid_rag(im, labels, rag, probas, truth=None):
fig = plt.figure()
ax = plt.gca()
# make preview images
rag.add_edges_from([(n0, n1, dict(weight=probas[j]))
for j, (n0, n1) in enumerate(rag.edges())])
lc = show_rag(labels.astype(int),
rag,
im,
ax=ax,
edge_width=0.5,
edge_cmap='viridis')
fig.colorbar(lc, ax=ax, fraction=0.03)
if (truth is not None):
truth_contour = segmentation.find_boundaries(truth, mode='thick')
im[truth_contour, ...] = (255, 0, 0)
ax.axis('off')
ax.imshow(im)
fig.tight_layout(pad=0, w_pad=0)
fig.canvas.draw()
im_plot = np.array(fig.canvas.renderer.buffer_rgba())[..., :3]
plt.close(fig)
return im_plot
def merge_hier_boundary(labels, image, thresh=0.03, show_rag=False):
"""
Merges the given labels using a RAG based on boundaries.
Parameters
----------
labels: ndarray
image: ndarray
thresh: float
show_rag: bool
Returns
-------
rag: RAG
labels: ndarray
Merged labels.
"""
edges = filters.sobel(color.rgb2gray(image))
rag = graph.rag_boundary(labels, edges)
rag_copy = False
if show_rag:
rag_copy = True
fig, ax = plt.subplots(1, 2, figsize=(10, 10))
labels = graph.merge_hierarchical(labels,
rag,
thresh=thresh,
rag_copy=rag_copy,
in_place_merge=True,
merge_func=merge_boundary,
weight_func=weight_boundary)
if show_rag:
graph.show_rag(labels, rag, image, ax=ax[0])
ax[0].title('Initial RAG')
graph.show_rag(labels, graph.rag_boundary(labels, edges), ax=ax[1])
ax[1].title('Final RAG')
return rag, labels
def merge_hier_color(labels, image, thresh=0.08, show_rag=False):
"""
Merges the given labels using a RAG based on the mean color.
Parameters
----------
labels: ndarray
image: ndarray
thresh: float
show_rag: bool
Returns
-------
rag: RAG
labels: ndarray
Merged labels.
"""
rag = graph.rag_mean_color(image, labels)
rag_copy = False
if show_rag:
rag_copy = True
fig, ax = plt.subplots(1, 2, figsize=(10, 10))
labels = graph.merge_hierarchical(labels,
rag,
thresh=thresh,
rag_copy=rag_copy,
in_place_merge=True,
merge_func=merge_mean_color,
weight_func=_weight_mean_color)
# labels2 = graph.cut_normalized(img_slic, rag, thresh=30)
# labels2 = graph.cut_threshold(img_slic, rag, 0.2)
if show_rag:
graph.show_rag(labels, rag, image, ax=ax[0])
ax[0].title('Initial RAG')
graph.show_rag(labels, graph.rag_mean_color(image, labels), ax=ax[1])
ax[1].title('Final RAG')
return rag, labels
def region_boundry(image):
#image = imageGlobal
labels = segmentation.slic(image, compactness=30, n_segments=400)
edges = filters.sobel(image)
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)
plt.colorbar(lc, fraction=0.03)
io.show()
def desenho_rag(rag, labels):
'''
Parametro:
labels: array 2d
grafo
Imagem
retorno:
nada
Imprime o grafo em cima da imagem
'''
global image
lc = graph.show_rag(labels, rag, image)
plt.colorbar(lc, fraction=0.03)
io.show()
def make_network(mask, draw=False, save_loc=None):
orig, image_labels, edge_map = get_img_labels_edge_map(mask)
g = graph.rag_boundary(image_labels, edge_map)
g.remove_node(0)
if draw:
fig, ax = plt.subplots(2, 2, figsize=(15, 15), dpi=200)
ax = ax.ravel()
ax[0].imshow(orig, cmap='gray')
ax[2].imshow(label2rgb(image_labels, image=orig))
ax[1].imshow(edge_map)
lc = graph.show_rag(image_labels,
g,
edge_map,
ax=ax[3],
edge_width=5,
edge_cmap='Blues')
fig.colorbar(lc, fraction=0.03, ax=ax[3])
pos = {}
for idx in list(g.nodes):
pos[idx] = (np.array(g.nodes[idx]['centroid'])[::-1])
nx.draw(g, pos, ax=ax[3])
for a in ax:
a.grid('off')
fig.tight_layout()
if save_loc is not None:
fig.savefig(save_loc)
else:
# because if we don't draw then these features aren't added to the graph
props = regionprops(image_labels)
for (n, data), region, idx in zip(g.nodes(data=True), props,
range(len(props))):
data['centroid'] = tuple(map(int, region['centroid']))
data['uid'] = idx
return g
def region_boundry(img):
gimg = color.rgb2gray(img)
fig, ax = plt.subplots(nrows=1)
labels = segmentation.slic(img, compactness=30, n_segments=400)
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)
ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
ax.set_title("Original Image")
plt.colorbar(lc, fraction=0.03)
plt.tight_layout()
plt.show()
g = graph.rag_mean_color(p1, labels)
# In[33]:
labels2 = graph.merge_hierarchical(labels,
g,
thresh=0.025,
rag_copy=True,
in_place_merge=True,
merge_func=merge_mean_color,
weight_func=_weight_mean_color)
# In[34]:
graph.show_rag(labels, g, p1)
# In[35]:
plt.figure()
out = color.label2rgb(labels2, p1)
plt.imshow(out)
# In[38]:
p2 = np.load('0172ML0009240000104879E01_DRLX.npy', allow_pickle=True)
p2 = np.delete(p2, 0, 0)
p2.shape
# In[39]:
flat = np.ma.MaskedArray(flat, mask)
plt.figure(dpi=800)
plt.imshow(flat)
io.show()
edges = filters.sobel(flat)
edges_rgb = color.gray2rgb(edges)
from skimage.future import graph
plt.figure(dpi=800)
g = graph.rag_boundary(flat, edges, connectivity=1)
g.remove_node(0)
lc = graph.show_rag(flat, g, edges_rgb, img_cmap=None, edge_cmap='viridis',
edge_width=1.2)
plt.colorbar(lc, fraction=0.03)
plt.show()
for i in range(1, len(uni)):
print([uni[x] for x in g.neighbors(i)])
# plt.figure(dpi=500)
# plt.scatter(cols[rpos, 0], cols[rpos,1])
# plt.grid(True)
# plt.show()
from skimage import data, segmentation from skimage.future import graph import matplotlib.pyplot as plt img = data.coffee() labels = segmentation.slic(img) g = graph.rag_mean_color(img, labels) lc = graph.show_rag(labels, g, img) cbar = plt.colorbar(lc) plt.show()
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 7 10:13:38 2017
@author: luiz
"""
from skimage.future import graph
from skimage import data, segmentation, color, filters, io
from matplotlib import pyplot as plt
img = data.coffee()
gimg = color.rgb2gray(img)
labels = segmentation.slic(img, compactness=30, n_segments=400)
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)
plt.colorbar(lc, fraction=0.03)
io.show()
g = graph.rag_mean_color(p1, segments)
# In[9]:
labels2 = graph.merge_hierarchical(segments,
g,
thresh=0.05,
rag_copy=True,
in_place_merge=True,
merge_func=merge_mean_color,
weight_func=_weight_mean_color)
# In[10]:
graph.show_rag(segments, g, p1)
# In[11]:
plt.figure()
out = color.label2rgb(labels2, p1)
plt.imshow(out)
# In[12]:
p2 = np.load('0172ML0009240000104879E01_DRLX.npy', allow_pickle=True)
p2 = np.delete(p2, 0, 0)
p2.shape
# In[13]:
if PLOTS_ON:
out_seg = color.label2rgb(segments, img, kind='avg')
out_seg_bound = segmentation.mark_boundaries(out_seg, segments, (0, 0, 0))
out_clust = color.label2rgb(labels, img, kind='avg')
fig, ax = plt.subplots(nrows=1,
ncols=3,
sharex=True,
sharey=True,
figsize=(15, 5))
ax[0].imshow(out_seg)
ax[0].set_title('Oversegmentation', fontsize=15)
ax1 = graph.show_rag(segments,
g,
img,
border_color=None,
img_cmap='gray',
edge_cmap='magma',
ax=ax[1])
# plt.colorbar(ax1, ax=ax[1])
ax[1].set_title('Region Adjacency Graph', fontsize=15)
ax[2].imshow(out_clust)
ax[2].set_title('MinCutPool', fontsize=15)
for a in ax:
a.axis('off')
plt.tight_layout()
plt.show()
# segments = segmentation.felzenszwalb(img, scale=50, sigma=1.5, min_size=50)
# out_seg = color.label2rgb(segments, img, kind='avg')
# plt.imshow(out_seg)
from skimage.future import graph
from skimage import segmentation, color, filters, io
from matplotlib import pyplot as plt
img = io.imread("images/sample.jpg")
gimg = color.rgb2gray(img)
labels = segmentation.slic(img, compactness=25, n_segments=800, start_label=1)
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=1.1)
plt.colorbar(lc, fraction=0.03)
io.show()
image2 = resize(image2, (200, 200)) # resize image to fit axis
image2 = swirl(image2, rotation=0, strength=10, radius=90) # swirling image2
fig, ax1 = plt.subplots() # creates plot
ax1.imshow(image2, cmap=plt.cm.gray,
interpolation='none') # displaying image on plot
ax1.axis('off') # removing axis and labels
plt.show() # displaying whole piece
#-------------------------------------------------------------------------------------------------
# Example 3 - Creating region boundries around a photo of a cat
from skimage.future import graph
from skimage import segmentation, color, filters, io
image3 = mpimg.imread("cat.jpg") # import photo as NumPy array
imagegray = color.rgb2gray(image3) # converts photo to grayscale
outline = segmentation.slic(
image3, compactness=50,
n_segments=1000) # takes grayscaled image and outlines it
imagecolor = color.gray2rgb(
imagegray
) # converts grayscale image to colored (based on intensity of brightness and darkness)
g = graph.rag_boundary(outline, imagegray) # combined graphic broundries
lc = graph.show_rag(
outline, g, imagecolor, img_cmap=None, edge_cmap='viridis', edge_width=1
) # graph itself combining color with outline, creating colored outline
io.show() # display
from skimage import io, segmentation
from skimage.future import graph
from matplotlib import pyplot as plt
img = io.imread("images/sample.jpg")
labels = segmentation.slic(img, compactness=30, n_segments=400, start_label=1)
g = graph.rag_mean_color(img, labels)
fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(6, 8))
ax[0].set_title('RAG drawn with default settings')
lc = graph.show_rag(labels, g, img, ax=ax[0])
# specify the fraction of the plot area that will be used to draw the colorbar
fig.colorbar(lc, fraction=0.03, ax=ax[0])
ax[1].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[1])
fig.colorbar(lc, fraction=0.03, ax=ax[1])
for a in ax:
a.axis('off')
plt.tight_layout()
plt.show()
img = data.coffee()
labels = [
quickshift(img, kernel_size=3, max_dist=6, ratio=0.5),
slic(img, compactness=30, n_segments=400),
felzenszwalb(img, scale=100, sigma=0.5, min_size=50)
]
label_rgbs = [color.label2rgb(label, img, kind='avg') for label in labels]
algos = [["Quickshift", "SLIC (K-Means)", "Felzenszwalb"],
[["Quickshift Before RAG", "Quickshift After RAG"],
["SLIC (K-Means) Before RAG", "SLIC (K-Means) After RAG"],
["Felzenszwalb Before RAG", "Felzenszwalb After RAG"]]]
rags = [graph.rag_mean_color(img, label) for label in labels]
edges_drawn_all = [
plt.colorbar(graph.show_rag(label, rag, img)).set_label(algo)
for label, rag, algo in zip(labels, rags, algos[0])
]
for edge_drawn in edges_drawn_all:
plt.show()
# only display edges with weight > thresh
final_labels = [
graph.cut_threshold(label, rag, 29) for label, rag in zip(labels, rags)
]
final_label_rgbs = [
color.label2rgb(final_label, img, kind='avg')
for final_label in final_labels
]
}
def merge_boundary(graph, src, dst):
"""Call back called before merging 2 nodes.
In this case we don't need to do any computation here.
"""
pass
img = data.coffee()
edges = filters.sobel(color.rgb2gray(img))
labels = segmentation.slic(img, compactness=30, n_segments=400)
g = graph.rag_boundary(labels, edges)
graph.show_rag(labels, g, img)
plt.title('Initial RAG')
labels2 = graph.merge_hierarchical(labels, g, thresh=0.08, rag_copy=False,
in_place_merge=True,
merge_func=merge_boundary,
weight_func=weight_boundary)
graph.show_rag(labels, g, img)
plt.title('RAG after hierarchical merging')
plt.figure()
out = color.label2rgb(labels2, img, kind='avg')
plt.imshow(out)
plt.title('Final segmentation')
def _apply(self, imgmsg, maskmsg):
bridge = cv_bridge.CvBridge()
img = bridge.imgmsg_to_cv2(imgmsg)
if img.ndim == 2:
img = gray2rgb(img)
mask = bridge.imgmsg_to_cv2(maskmsg, desired_encoding='mono8')
mask = mask.reshape(mask.shape[:2])
# compute label
roi = closed_mask_roi(mask)
roi_labels = masked_slic(img=img[roi],
mask=mask[roi],
n_segments=20,
compactness=30)
if roi_labels is None:
return
labels = np.zeros(mask.shape, dtype=np.int32)
# labels.fill(-1) # set bg_label
labels[roi] = roi_labels
if self.is_debugging:
# publish debug slic label
slic_labelmsg = bridge.cv2_to_imgmsg(labels)
slic_labelmsg.header = imgmsg.header
self.pub_slic.publish(slic_labelmsg)
# compute rag
g = rag_solidity(labels, connectivity=2)
if self.is_debugging:
# publish debug rag drawn image
if LooseVersion(skimage.__version__) >= '0.13.0':
fig, ax = plt.subplots(figsize=(img.shape[1] * 0.01,
img.shape[0] * 0.01))
show_rag(labels, g, img, ax=ax)
ax.axis('off')
plt.subplots_adjust(0, 0, 1, 1)
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
rag_img = np.fromstring(fig.canvas.tostring_rgb(),
dtype=np.uint8)
rag_img.shape = (h, w, 3)
plt.close()
else:
rag_img = draw_rag(labels, g, img)
rag_img = img_as_uint(rag_img)
rag_imgmsg = bridge.cv2_to_imgmsg(rag_img.astype(np.uint8),
encoding='rgb8')
rag_imgmsg.header = imgmsg.header
self.pub_rag.publish(rag_imgmsg)
# merge rag with solidity
merged_labels = merge_hierarchical(labels,
g,
thresh=1,
rag_copy=False,
in_place_merge=True,
merge_func=_solidity_merge_func,
weight_func=_solidity_weight_func)
merged_labels += 1
merged_labels[mask == 0] = 0
merged_labelmsg = bridge.cv2_to_imgmsg(merged_labels.astype(np.int32))
merged_labelmsg.header = imgmsg.header
self.pub.publish(merged_labelmsg)
if self.is_debugging:
out = label2rgb(merged_labels, img)
out = (out * 255).astype(np.uint8)
out_msg = bridge.cv2_to_imgmsg(out, encoding='rgb8')
out_msg.header = imgmsg.header
self.pub_label.publish(out_msg)
Drawing Region Adjacency Graphs (RAGs)
======================================
This example constructs a Region Adjacency Graph (RAG) and draws it with
the `rag_draw` method.
"""
from skimage import data, segmentation
from skimage.future import graph
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)
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)
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.show()
