def test_img_to_graph():
x, y = np.mgrid[:4, :4] - 10
grad_x = img_to_graph(x)
grad_y = img_to_graph(y)
assert_equal(grad_x.nnz, grad_y.nnz)
# Negative elements are the diagonal: the elements of the original
# image. Positive elements are the values of the gradient, they
# should all be equal on grad_x and grad_y
np.testing.assert_array_equal(grad_x.data[grad_x.data > 0],
grad_y.data[grad_y.data > 0])
def DBSCAN_cluster(d_array, epsilon=4.0, mini_samples=4):
"""
Uses ready-made clustering algorithm that infers the number of clusters
:return:
"""
adj_mat = img_to_graph(d_array)
db = DBSCAN(eps=epsilon,
min_samples=mini_samples,
metric="precomputed").fit(adj_mat)
labels = db.labels_
print(labels)
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
print(n_clusters_)
n_noise_ = list(labels).count(-1)
print(n_noise_)
a = np.zeros(d_array.shape)
for clust, tup in zip(labels, np.ndindex(a.shape)):
a[tup] = clust
GridEnsemble.plot_cluster(a)
return a
def cluster(raster, pieces):
raster.to_hsv()
graph = image.img_to_graph(raster.get_tiered())
beta = 15
graph.data = exp(-beta * graph.data / raster.get_opaque().std())
labels = []
success = False
while not success:
try:
labels = spectral_clustering(graph,
n_clusters=pieces,
assign_labels='discretize',
random_state=1)
success = True
except (LinAlgError, ValueError):
pieces -= 1
# If clustering is non-convergent, return single cluster
if pieces == 0:
return np.zeros(np.product(raster.shape))
return labels.reshape(raster.with_alpha().shape)[:, 0]
def image_features_labels(img,n_clusters,maxPixel):
# X is the feature vector with one row of features per image
#
imageSize=maxPixel*maxPixel
img = resize(img, (maxPixel, maxPixel))
mask = img.astype(bool)
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = s_im.img_to_graph(img, mask=mask)
# Take a decreasing function of the gradient: we take it weakly
# dependent from the gradient the segmentation is close to a voronoi
graph.data = np.exp(-graph.data / graph.data.std())
# Force the solver to be arpack, since amg is numerically
# unstable on this example
labels = spectral_clustering(graph, n_clusters, eigen_solver='arpack')
label_im = -np.ones(mask.shape)
label_im[mask] = labels
X=np.zeros(imageSize, dtype=float)
# Store the rescaled image pixels
X[0:imageSize] = np.reshape(label_im,(1, imageSize))
return X
def image_features_labels(img,n_clusters,maxPixel):
# X is the feature vector with one row of features per image
#
imageSize=maxPixel*maxPixel
img = resize(img, (maxPixel, maxPixel))
mask = img.astype(bool)
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = s_im.img_to_graph(img, mask=mask)
# Take a decreasing function of the gradient: we take it weakly
# dependent from the gradient the segmentation is close to a voronoi
graph.data = np.exp(-graph.data / graph.data.std())
# Force the solver to be arpack, since amg is numerically
# unstable on this example
labels = spectral_clustering(graph, n_clusters, eigen_solver='arpack')
label_im = -np.ones(mask.shape)
label_im[mask] = labels
X=np.zeros(imageSize, dtype=float)
# Store the rescaled image pixels
X[0:imageSize] = np.reshape(label_im,(1, imageSize))
return X
def spectral_clustering(self, img):
mask = img.astype(bool)
img = img.astype(float)
img += 1 + 0.2 * np.random.randn(*img.shape)
print("start image to graph")
graph = image.img_to_graph(img, mask=mask)
print("finished image to graph")
graph.data = np.exp(-graph.data / graph.data.std())
print("start spectral_cluster")
labels = spectral_clustering(graph,
n_clusters=2,
eigen_solver='arpack')
print("finished spectral_cluster")
label_im = np.full(mask.shape, -1.)
label_im[mask] = labels
cv2.imshow("img", img)
cv2.imshow("label_im", label_im)
cv2.waitKey(0)
return
def test_connect_regions():
face = sp.misc.face(gray=True)
# subsample by 4 to reduce run time
face = face[::4, ::4]
for thr in (50, 150):
mask = face > thr
graph = img_to_graph(face, mask=mask)
assert ndimage.label(mask)[1] == connected_components(graph)[0]
def get_label_v2(data, feature, index, clusters):
all_data = data.sum().sum() + np.sum(feature, axis=-1)
mask = all_data.astype(bool)
from sklearn.feature_extraction import image
graph = image.img_to_graph(all_data, mask=mask)
graph.data = np.exp(-graph.data / graph.data.std())
labels = cluster.spectral_clustering(graph, n_clusters=clusters, eigen_solver='arpack')
return labels
def spectralClusteringTest01(): import numpy as np import matplotlib.pyplot as plt from sklearn.feature_extraction import image from sklearn.cluster import spectral_clustering l = 100 x,y = np.indices((l, l)) #x,y 都是二维矩阵， 表示了某点的x 和 y的坐标 center1 = (28, 24) center2 = (40, 50) center3 = (67, 58) center4 = (24, 70) radius1, radius2, radius3, radius4 = 16, 14, 15, 14 circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1 ** 2 circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2 ** 2 circle3 = (x - center3[0]) ** 2 + (y - center3[1]) ** 2 < radius3 ** 2 circle4 = (x - center4[0]) ** 2 + (y - center4[1]) ** 2 < radius4 ** 2 img = circle1 + circle2 + circle3 + circle4 mask = img.astype(bool) img = img.astype(float) img += 1 + 0.2 * np.random.randn(*img.shape) #Convert the image into a graph with the value of the gradient on the edges #img就是一个100 * 100的图片 #mask是一个bool型的100 * 100模板 #graph是一个稀疏矩阵 -- 不过为什么是2678 * 2678 ? #估计这一步里面计算了梯度 graph = image.img_to_graph(img, mask = mask) print graph.shape graph.data = np.exp(-graph.data / graph.data.std()) #这里还是指定了聚类的中心数目 #这里是只对mask内的点进行聚类 labels = spectral_clustering(graph, n_clusters = 4, eigen_solver = "arpack") print labels label_im = -np.ones(mask.shape) label_im[mask] = labels plt.matshow(img) plt.matshow(label_im) plt.show()
def plot_coin_segmentation():
# these were introduced in skimage-0.14
if parse_version(skimage.__version__) >= parse_version('0.14'):
rescale_params = {'anti_aliasing': False, 'multichannel': False}
else:
rescale_params = {}
# load the coins as a numpy array
orig_coins = coins()
# Resize it to 20% of the original size to speed up the processing
# Applying a Gaussian filter for smoothing prior to down-scaling
# reduces aliasing artifacts.
smoothened_coins = gaussian_filter(orig_coins, sigma=2)
rescaled_coins = rescale(smoothened_coins,
0.2,
mode="reflect",
**rescale_params)
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(rescaled_coins)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 10
eps = 1e-6
graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
# Apply spectral clustering (this step goes much faster if you have pyamg
# installed)
N_REGIONS = 25
""" Visualize the resulting regions """
for assign_labels in ('kmeans', 'discretize'):
t0 = time.time()
labels = spectral_clustering(graph,
n_clusters=N_REGIONS,
assign_labels=assign_labels,
random_state=42)
t1 = time.time()
labels = labels.reshape(rescaled_coins.shape)
plt.figure(figsize=(5, 5))
plt.imshow(rescaled_coins, cmap=plt.cm.gray)
for l in range(N_REGIONS):
plt.contour(labels == l,
colors=[plt.cm.nipy_spectral(l / float(N_REGIONS))])
plt.xticks(())
plt.yticks(())
title = 'Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0))
print(title)
plt.title(title)
plt.show()
def test_connect_regions():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
for thr in (50, 150):
mask = face > thr
graph = img_to_graph(face, mask)
assert_equal(ndimage.label(mask)[1], connected_components(graph)[0])
def test_connect_regions():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
for thr in (50, 150):
mask = face > thr
graph = img_to_graph(face, mask)
assert_equal(ndimage.label(mask)[1], connected_components(graph)[0])
def __init__(self, img=None, dic=None):
# img = misc.imread(image_file, flatten=True)
# print img.shape
if dic != None:
self.graph = copy.deepcopy(dic)
self.volume = 0
for i in self.graph.values():
self.volume += np.sum(map(lambda a: a[1], i))
else:
# We use a mask that limits to the foreground: the problem that we are
# interested in here is not separating the objects from the background,
# but separating them one from the other.
mask = img.astype(bool)
img = img.astype(float)
img += 1 + 0.2 * np.random.randn(*img.shape)
# Convert the image into a graph with the value of the gradient on the
# edges.
self.graph = image.img_to_graph(img, mask=mask)
# Take a decreasing function of the gradient: we take it weakly
# dependent from the gradient the segmentation is close to a voronoi
self.graph.data = np.exp(-self.graph.data / self.graph.data.std())
self.orig = copy.deepcopy(self.graph)
# plt.matshow(self.graph.toarray())
# plt.show()
# clustering = SpectralClustering().fit(img.reshape(img.shape[0] * img.shape[1], 1))
# X = clustering.affinity_matrix_
# print X, X.shape
# # X[X < 0.00001] = 0
# self.graph = coo_matrix(X)
# # plt.matshow(X)
# # plt.show()
# remove self loops
self.graph.setdiag(np.zeros(self.graph.shape[0]))
self.graph = self.graph.todok()
kast = dict()
self.volume = 0
for i in self.graph.items():
if i[0][0] in kast:
kast[i[0][0]].append(i)
else:
kast[i[0][0]] = [i]
self.volume += i[1]
self.graph = kast
def test_img_to_graph_sparse():
# Check that the edges are in the right position
# when using a sparse image with a singleton component
mask = np.zeros((2, 3), dtype=bool)
mask[0, 0] = 1
mask[:, 2] = 1
x = np.zeros((2, 3))
x[0, 0] = 1
x[0, 2] = -1
x[1, 2] = -2
grad_x = img_to_graph(x, mask=mask).todense()
desired = np.array([[1, 0, 0], [0, -1, 1], [0, 1, -2]])
np.testing.assert_array_equal(grad_x, desired)
def sp_clustering(img):
graph = image.img_to_graph(img)
# Take a decreasing function of the gradient: we take it weakly
# dependent from the gradient the segmentation is close to a voronoi
graph.data = np.exp(-graph.data / graph.data.std())
# Force the solver to be arpack, since amg is numerically
# unstable on this example
labels = spectral_clustering(graph, n_clusters=64, eigen_solver='arpack')
plt.matshow(img)
plt.matshow(labels)
def test_connect_regions():
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
# subsample by 4 to reduce run time
face = face[::4, ::4]
for thr in (50, 150):
mask = face > thr
graph = img_to_graph(face, mask)
assert ndimage.label(mask)[1] == connected_components(graph)[0]
def LSTClustering(self):
# 参考“Segmenting the picture of greek coins in regions”方法，Author: Gael Varoquaux <*****@*****.**>, Brian Cheung
# License: BSD 3 clause
orig_coins = self.LST
# these were introduced in skimage-0.14
if LooseVersion(skimage.__version__) >= '0.14':
rescale_params = {'anti_aliasing': False, 'multichannel': False}
else:
rescale_params = {}
smoothened_coins = gaussian_filter(orig_coins, sigma=2)
rescaled_coins = rescale(smoothened_coins,
0.2,
mode="reflect",
**rescale_params)
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(rescaled_coins)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 10
eps = 1e-6
graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
# Apply spectral clustering (this step goes much faster if you have pyamg
# installed)
N_REGIONS = 200
for assign_labels in ('discretize', ):
# for assign_labels in ('kmeans', 'discretize'):
t0 = time.time()
labels = spectral_clustering(graph,
n_clusters=N_REGIONS,
assign_labels=assign_labels,
random_state=42)
t1 = time.time()
labels = labels.reshape(rescaled_coins.shape)
plt.figure(figsize=(5 * 3, 5 * 3))
plt.imshow(rescaled_coins, cmap=plt.cm.gray)
for l in range(N_REGIONS):
plt.contour(
labels == l,
colors=[plt.cm.nipy_spectral(l / float(N_REGIONS))])
plt.xticks(())
plt.yticks(())
title = 'Spectral clustering: %s, %.2fs' % (assign_labels,
(t1 - t0))
print(title)
plt.title(title)
plt.show()
def _spectral_clustering(self,samples):
if sp_version < (0, 12):
raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
"thus does not include the scipy.misc.face() image.")
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(samples)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 5
eps = 1e-6
graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
# Apply spectral clustering (this step goes much faster if you have pyamg
# installed)
N_REGIONS = 4
#############################################################################
# Visualize the resulting regions
for assign_labels in ('kmeans', 'discretize'):
t0 = time.time()
labels = spectral_clustering(graph, n_clusters=N_REGIONS,
assign_labels=assign_labels, random_state=1)
sample=pd.DataFrame(labels)
sample.to_csv(os.path.join(OUTPUT_DIR, "spectral_result.csv"),sep=",")
t1 = time.time()
#classif=labels.fit(samples)
#print classif
print labels
print sample
labels = labels.reshape(samples.shape)
plt.figure(figsize=(5, 5))
plt.imshow(samples, cmap=plt.cm.gray)
for l in range(N_REGIONS):
plt.contour(labels == l, contours=1,
colors=[plt.cm.spectral(l / float(N_REGIONS))])
plt.xticks(())
plt.yticks(())
title = 'Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0))
print(title)
plt.title(title)
plt.show()
def readImg(path):
"""
读取图片，并将其转换为邻接矩阵
"""
# 对于彩色照片，只使用其中一个维度的色彩
im = sp.misc.imread(path)[:, :, 2]
im = im / 255.
# 若运算速度太慢，可使用如下的语句来缩减图片的大小
# im = sp.misc.imresize(im, 0.10) / 255.
# 计算图片的梯度，既相邻像素点之差
graph = image.img_to_graph(im)
beta = 20
# 计算邻接矩阵
graph.data = np.exp(-beta * graph.data / graph.data.std())
return im, graph
def Discretize_Clustering(twoDimg, N_REGIONS):
"""Put clustering code"""
graph = imp.img_to_graph(twoDimg)
beta = 1
eps = 1e-1
graph.data = np.exp(-beta * graph.data / twoDimg.std()) + eps
t0 = time.time()
labels = spectral_clustering(graph,
n_clusters=N_REGIONS,
assign_labels='discretize',
random_state=1)
t1 = time.time()
labels = labels.reshape(twoDimg.shape)
print('time taken', t1 - t0)
return labels, N_REGIONS
def SpectralClusterImage(input_image, beta=5, eps=1e-6, n_regions=11, assign_labels='discretize',downsample_factor=np.NaN, order=3):
""" Spectral Cluster an image
Inputs:
input_image: ndarray of image
beta: Take a decreasing function of the gradient: an exponential
The smaller beta is, the more independent the segmentation is of
the acutal image. For beta=1, the segmentation is close to a
voronoi. Default is 5.
eps: error term. Default is 1E-6
n_regions: number of regions to decompose into. Default is 11.
assign_labels: ways of decomposition. Selecting from 'discretize' and
'kmeans'. Default is 'discretize'.
downsample_factor: downsampling before spectral decomposition. Default
is to keep the original sampling. Enter a single number to apply
the kernel for both dimensions of the image, or enter as a sequence
to apply different kernel for each dimension
order: downsampling method, order of B-spline interpolation
"""
# Downsample the image
if not np.isnan(downsample_factor):
zoom(input_image, zoom=downsample_factor, order=order)
# Convert the image into a graph with the value of the gradient on the edges
graph = image.img_to_graph(input_image)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# acutal image. For beta=1, the segmentation is close to a voronoi
graph.data = np.exp(-beta * graph.data / input_image.std()) + eps
# Apply spectral clustering (this step goes much faster if yuo have pyamg
# installed)
labels = spectral_clustering(graph, n_clusters=n_regions,
assign_labels='discretize')
labels = labels.reshape(input_image.shape)
# Visualizing the resulting regions
pl.figure(figsize=(5,5))
pl.imshow(input_image, cmap=pl.cm.gray)
for lb in range(n_regions):
pl.contour(labels == lb, contour=1,
color=[pl.cm.spectral(lb / float(n_regions)), ])
# Get rid of x, y tick marks
pl.xticks(())
pl.yticks(())
def spectral_segmentation(image_a, bet, N_REGIONS=20):
# Convert the image into a graph with the value of the gradient on the edges
graph = image.img_to_graph(image_a)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = bet
eps = 1e-6
graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
# Apply spectral clustering
reg_range = np.arange(N_REGIONS)
assign_labels = 'discretize'
#labels = spectral_clustering(graph, n_clusters=N_REGIONS,
# assign_labels=assign_labels, random_state=1)
labels = spectral_clustering(graph,
n_clusters=N_REGIONS,
random_state=1,
assign_labels=assign_labels)
new_label_ordering = []
for n in labels:
if n not in new_label_ordering:
new_label_ordering.append(n)
for j in range(len(labels)):
current_label = labels[j]
new_label = reg_range[new_label_ordering.index(current_label)]
labels[j] = new_label
labels = labels.reshape(image_a.shape)
return labels
def defficient_spectral_clustring(name_dict, z_depth, shape_dict):
# requires to re-implement the distance definition on sparse images.
z_stack = next(name_dict.itervalues())
_3D_chan1 = np.zeros((shape_dict[1][0], shape_dict[1][1], z_depth))
_3D_chan2 = np.zeros((shape_dict[2][0], shape_dict[2][1], z_depth))
for depth, bi_image in z_stack.iteritems():
img1 = bi_image[1]
img2 = bi_image[2]
_3D_chan1[:, :, depth-1] = img1
_3D_chan2[:, :, depth-1] = img2
# mlab.pipeline.volume(mlab.pipeline.scalar_field(_3D_chan1), vmin=0.1)
# mlab.show()
_3D_chan2[_3D_chan2<0.04] = 0
mlab.pipeline.volume(mlab.pipeline.scalar_field(_3D_chan2))
mlab.show()
mask = _3D_chan2.astype(bool)
img = _3D_chan2.astype(float)
graph = image.img_to_graph(img, mask=mask)
graph.data = np.exp(-graph.data / graph.data.std())
print graph.shape
print len(graph.nonzero()[0])
clusters = 4
labels = spectral_clustering(graph, n_clusters = clusters, eigen_solver='arpack')
label_im = -np.ones(mask.shape)
label_im[mask] = labels
for i in range(0, clusters):
re_img = copy(_3D_chan2)
re_img[label_im!=i] = 0
mlab.pipeline.volume(mlab.pipeline.scalar_field(re_img))
mlab.show()
def spectral_clusters_scikit(image, clusters=3):
"""Performs spectral clustering for input image. Not very suitable for cartilage segmentation.
Parameters
----------
image : ndarray
Input image to be clustered
clusters : int
Number of clusters.
Returns
-------
Clustered image.
"""
image = zoom(image, (0.125, 0.125))
plt.imshow(image)
plt.show()
# Remove background
mask = image.astype(bool)
# Add random noise
img = image.astype(float)
img += np.random.randn(*img.shape)
# Convert image to graph (gradient on the edges)
graph = im.img_to_graph(img, mask=mask)
# Decreasing function of the gradient
graph.data = np.exp(-graph.data / graph.data.std())
# Solve using arpack
labels = spectral_clustering(graph,
n_clusters=clusters,
eigen_solver='arpack')
label_im = np.full(mask.shape, -1.)
label_im[mask] = labels
plt.imshow(img)
plt.imshow(label_im)
plt.show()
def cluster(face):
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(face)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 5
eps = 1e-6
graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
# Apply spectral clustering (this step goes much faster if you have pyamg
# installed)
N_REGIONS = 25
for assign_labels in ('kmeans', 'discretize'):
t0 = time.time()
labels = spectral_clustering(graph,
n_clusters=N_REGIONS,
assign_labels=assign_labels,
random_state=1)
t1 = time.time()
labels = labels.reshape(face.shape)
plt.figure(figsize=(5, 5))
plt.imshow(face)
for l in range(N_REGIONS):
plt.contour(
labels == l,
contours=1,
)
plt.xticks(())
plt.yticks(())
title = 'Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0))
print(title)
plt.title(title)
plt.show()
def spectral_clustering_func(self, img, K):
mask = img.astype(bool)
img_f = img.astype(float)
img_f += 1 + 0.2 * np.random.randn(*img.shape)
graph = image.img_to_graph(img_f, mask=mask)
labels = spectral_clustering(graph,
n_clusters=K,
eigen_solver='arpack')
label_im = -np.ones(mask.shape)
label_im[mask] = labels
Width = 281
Height = 281
for i in range(0, Height):
for j in range(0, Width):
label_im[i][j] += 1
return label_im.astype(np.uint16)
def run(self):
from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering
# 生成原始图片信息
l = 100
x, y = np.indices((l, l))
center1 = (28, 24)
center2 = (40, 50)
center3 = (77, 58)
radius1, radius2, radius3 = 16, 14, 15
circle1 = (x - center1[0])**2 + (y - center1[1])**2 < radius1**2
circle2 = (x - center2[0])**2 + (y - center2[1])**2 < radius2**2
circle3 = (x - center3[0])**2 + (y - center3[1])**2 < radius3**2
# 生成包括3个圆的图片
img = circle1 + circle2 + circle3
mask = img.astype(bool)
img = img.astype(float)
img += 1 + 0.2 * np.random.randn(*img.shape)
graph = image.img_to_graph(img)
graph.data = np.exp(-graph.data / graph.data.std())
# 聚类输出
labels = spectral_clustering(graph, n_clusters=4)
label_im = -np.ones(mask.shape)
#label_im[mask] = labels
label_im = np.reshape(labels, (100, 100))
import matplotlib.pyplot as plt
plt.matshow(img)
plt.matshow(label_im)
plt.show()
def fit(self, image):
"""
Compute parameters of the model.
Args:
image: a ndarray representing an image (x, y, color_dimension)
Returns:
Nothing
"""
f_dim = image.shape[-1] if len(image.shape) > 2 else 1
X = image.reshape(-1, f_dim)
if self.algorithm == 'aglo':
connectivity = img_to_graph(image)
self.model = AgglomerativeClustering(n_clusters=self.n_clusters,
affinity='euclidean',
connectivity=connectivity,
compute_full_tree=False,
linkage='average')
elif self.algorithm == 'spectral':
return None
self.model.fit(X)
for i in range (0, input_image.shape[0]):
for j in range (0, input_image.shape[1]):
input_image_gray[i, j] = 0
'''
input_image_gray[i, j] += input_image[i, j, 2] * 0.299
input_image_gray[i, j] += input_image[i, j, 1] * 0.587
input_image_gray[i, j] += input_image[i, j, 0] * 0.114
'''
input_image_gray[i, j] += input_image[i, j, 0] * 0.2125
input_image_gray[i, j] += input_image[i, j, 1] * 0.7154
input_image_gray[i, j] += input_image[i, j, 2] * 0.0721
print("GRAYSCALED")
graph = image.img_to_graph(input_image_gray)
print("GRAPHED")
print(graph.shape)
print(type(graph.data))
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 5
eps = 1e-6
graph.data = np.exp(-beta * graph.data / (graph.data.std() + eps)) + eps
print("EXPONENTIALED")
# Apply spectral clustering (this step goes much faster if you have pyamg
if __name__ == "__main__":
matplotlib.rcParams['font.sans-serif'] = ['SimHei']
matplotlib.rcParams['axes.unicode_minus'] = False
pic = Image.open('..\\Chrome.png')
pic = pic.convert('L')
data = np.array(pic).astype(np.float) / 255
plt.figure(figsize=(10, 5), facecolor='w')
plt.subplot(121)
plt.imshow(pic, cmap=plt.cm.gray, interpolation='nearest')
plt.title('原始图片', fontsize=18)
n_clusters = 15
affinity = image.img_to_graph(data)
beta = 3
affinity.data = np.exp(-beta * affinity.data / affinity.data.std()) + 10e-5
# a = affinity.toarray()
# b = np.diag(a.diagonal())
# a -= b
print('开始谱聚类...')
y = spectral_clustering(affinity, n_clusters=n_clusters, assign_labels='kmeans', random_state=1)
print('谱聚类完成...')
y = y.reshape(data.shape)
for n in range(n_clusters):
data[y == n] = n
plt.subplot(122)
clrs = []
for c in np.linspace(16776960, 16711935, n_clusters, dtype=int):
clrs.append('#%06x' % c)
# Downsample the image by a factor of 4
# lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]
# lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]
print lena.shape
lena = lena.astype(float)
# lena = lena[:5, :5]
print lena.shape
# Convert the image into a graph with the value of the gradient on the
# edges.
import random
if True:
graph = image.img_to_graph(lena, return_as=np.ndarray)
print 'ttt'
graph1 = image.img_to_graph(lena)
graph2 = graph
print type(graph1.data)
print type(graph2.data)
# graph2.data = np.exp(-beta * graph2.data / lena.std()) + eps
print graph
print graph.shape
row = []
col = []
data = []
for i in xrange(graph.shape[0]):
# load the raccoon face as a numpy array
try: # SciPy >= 0.16 have face in misc
from scipy.misc import face
orig_face = img_as_float(face(gray=True))
except ImportError:
orig_face = img_as_float(sp.face(gray=True))
# Resize it to 10% of the original size to speed up the processing
# Applying a Gaussian filter for smoothing prior to down-scaling
# reduces aliasing artifacts.
smoothened_face = gaussian_filter(orig_face, sigma=4.5)
rescaled_face = rescale(smoothened_face, 0.1, mode="reflect")
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(rescaled_face)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 5
eps = 1e-6
graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
# Apply spectral clustering (this step goes much faster if you have pyamg
# installed)
N_REGIONS = 25
#############################################################################
# Visualize the resulting regions
prefix = 'Data/Unlabeled/lesion_c4_raw_seg_combine/'
for i in range(len(us_frames)):
try:
img = Image.open(us_frames[i]).convert('L')
img_arr = np.asarray(img)
# Resize it to 10% of the original size to speed up the processing
us = sp.misc.imresize(img_arr, 0.10) / 255.
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(us)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 5
eps = 1e-6
graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
# Apply spectral clustering (this step goes much faster if you have pyamg
# installed)
N_REGIONS = 8
reg_range = np.arange(N_REGIONS)
#for assign_labels in ('discretize'):
#'kmeans'
from sklearn.feature_extraction import image from sklearn.cluster import spectral_clustering # load the coins as a numpy array orig_coins = coins() # Resize it to 20% of the original size to speed up the processing # Applying a Gaussian filter for smoothing prior to down-scaling # reduces aliasing artifacts. smoothened_coins = gaussian_filter(orig_coins, sigma=2) rescaled_coins = rescale(smoothened_coins, 0.2, mode="reflect") # Convert the image into a graph with the value of the gradient on the # edges. graph = image.img_to_graph(rescaled_coins) # Take a decreasing function of the gradient: an exponential # The smaller beta is, the more independent the segmentation is of the # actual image. For beta=1, the segmentation is close to a voronoi beta = 10 eps = 1e-6 graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps # Apply spectral clustering (this step goes much faster if you have pyamg # installed) N_REGIONS = 25 ############################################################################# # Visualize the resulting regions
radius1, radius2, radius3 = 16, 14, 15
circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1 ** 2
circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2 ** 2
circle3 = (x - center3[0]) ** 2 + (y - center3[1]) ** 2 < radius3 ** 2
# 生成包括3个圆的图片
img = circle1 + circle2 + circle3
print ("img_processing.size=",img.size)
mask = img.astype(bool)
img = img.astype(float)
print ("img_processing.size=",img.size)
img += 1 + 0.2 * np.random.randn(*img.shape)
print ("img_processing.size=",img.size)
graph = image.img_to_graph(img, mask=mask)
graph.data = np.exp(-graph.data / graph.data.std())
#########################################################
##########################################################
#imgrobot operation
print ("robotsize=",img_ndarray.size)
mask_robot = img_ndarray.astype(bool)
img_robot = img_ndarray.astype(float)
print ("robotsize=",img_ndarray.size)
graph_robot = image.img_to_graph(img_ndarray, mask=mask_robot)
#graph_robot.data = np.exp(-img_ndarray.data / img_ndarray.data.std())
# ############################################################################# # 4 circles img = circle1 + circle2 + circle3 + circle4 # We use a mask that limits to the foreground: the problem that we are # interested in here is not separating the objects from the background, # but separating them one from the other. mask = img.astype(bool) img = img.astype(float) img += 1 + 0.2 * np.random.randn(*img.shape) # Convert the image into a graph with the value of the gradient on the # edges. graph = image_.img_to_graph(img, mask=mask) # Take a decreasing function of the gradient: we take it weakly # dependent from the gradient the segmentation is close to a voronoi graph.data = np.exp(-graph.data / graph.data.std()) # Force the solver to be arpack, since amg is numerically # unstable on this example labels = spectral_clustering(graph, n_clusters=4, eigen_solver='arpack') label_im = -np.ones(mask.shape) label_im[mask] = labels plt.matshow(img) plt.matshow(label_im) # #############################################################################
import numpy as np import scipy as sp import pylab as pl from sklearn.feature_extraction import image from sklearn.cluster import spectral_clustering lena = sp.misc.lena() # Downsample the image by a factor of 4 lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2] lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2] # Convert the image into a graph with the value of the gradient on the # edges. graph = image.img_to_graph(lena) # Take a decreasing function of the gradient: an exponential # The smaller beta is, the more independent the segmentation is of the # actual image. For beta=1, the segmentation is close to a voronoi beta = 5 eps = 1e-6 graph.data = np.exp(-beta * graph.data / lena.std()) + eps # Apply spectral clustering (this step goes much faster if you have pyamg # installed) N_REGIONS = 11 ############################################################################### # Visualize the resulting regions
# load the coins as a numpy array
orig_coins = coins()
# Resize it to 20% of the original size to speed up the processing
# Applying a Gaussian filter for smoothing prior to down-scaling
# reduces aliasing artifacts.
smoothened_coins = gaussian_filter(orig_coins, sigma=2)
rescaled_coins = rescale(smoothened_coins,
0.2,
mode="reflect",
**rescale_params)
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(rescaled_coins)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 10
eps = 1e-6
graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
# Apply spectral clustering (this step goes much faster if you have pyamg
# installed)
N_REGIONS = 25
#############################################################################
# Visualize the resulting regions
from PIL import Image
matplotlib.rcParams['font.sans-serif'] = [u'SimHei']
matplotlib.rcParams['axes.unicode_minus'] = False
pic = Image.open('../Total_Data/data/Chrome.png')
pic = pic.convert('L')
data = np.array(pic).astype(np.float) / 255
plt.figure(figsize=(10, 5), facecolor='w')
plt.subplot(121)
plt.imshow(pic, cmap=plt.cm.gray, interpolation='nearest')
plt.title(u'原始图片', fontsize=18)
n_clusters = 15
affinity = image.img_to_graph(data)
beta = 3
affinity.data = np.exp(-beta * affinity.data / affinity.data.std()) + 10e-5
# a = affinity.toarray()
# b = np.diag(a.diagonal())
# a -= b
print('开始谱聚类...')
y = spectral_clustering(affinity,
n_clusters=n_clusters,
assign_labels='kmeans',
random_state=1)
print('谱聚类完成...')
y = y.reshape(data.shape)
for n in range(n_clusters):
data[y == n] = n
plt.subplot(122)
#!/usr/bin/env python
__author__ = 'ienek'
from skimage import io, filter, feature
from skimage.color import rgb2gray
from sklearn.feature_extraction import image
# test to load any image
test = io.imread('n02854926_28_0.jpg')
# loading to array
img_arr = image.img_to_graph(test)
patch_g = image.extract_patches(test, (4, 4, 2))
patch_g2 = image.extract_patches_2d(test, (4, 4))
print("Patch 1: %s " % str(patch_g.shape))
print("Patch 2: %s " % str(patch_g2.shape))
# grayscaling
gray_test = rgb2gray(test)
# building hog
hog_ = feature.hog(gray_test)
# building surf
# ORB: An efficient alternative to SIFT and SURF
surf_ = feature.ORB()
surf_.detect_and_extract(gray_test)
def test_connect_regions():
lena = sp.misc.lena()
for thr in (50, 150):
mask = lena > thr
graph = img_to_graph(lena, mask)
assert_equal(ndimage.label(mask)[1], connected_components(graph)[0])
from sklearn.feature_extraction import image
from sklearn.cluster import spectral_clustering
# load the raccoon face as a numpy array
try: # SciPy >= 0.16 have face in misc
from scipy.misc import face
face = face(gray=True)
except ImportError:
face = sp.face(gray=True)
# Resize it to 10% of the original size to speed up the processing
face = sp.misc.imresize(face, 0.10) / 255.
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(face)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 5
eps = 1e-6
graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
# Apply spectral clustering (this step goes much faster if you have pyamg
# installed)
N_REGIONS = 25
for assign_labels in ('kmeans', 'discretize'):
t0 = time.time()
labels = spectral_clustering(graph,
#!/usr/bin/env python
#!encoding=utf8
from SimpleCV import Image
from sklearn.feature_extraction.image import img_to_graph
img = Image('./digits.bmp')
img_np = img.getNumpy()
graph = img_to_graph(img_np)
print graph
# load the raccoon face as a numpy array
try:
face = sp.face(gray=True)
except AttributeError:
# Newer versions of scipy have face in misc
from scipy import misc
face = misc.face(gray=True)
# Resize it to 10% of the original size to speed up the processing
face = sp.misc.imresize(face, 0.10) / 255.
# Convert the image into a graph with the value of the gradient on the
# edges.
graph = image.img_to_graph(face)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 5
eps = 1e-6
graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps
# Apply spectral clustering (this step goes much faster if you have pyamg
# installed)
N_REGIONS = 25
#############################################################################
# Visualize the resulting regions
def build_image(gray):
graph = image.img_to_graph(gray)
print type(graph)
graph.data = np.exp(-graph.data / graph.data.std())
return graph
def compression (lena):
lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]
return lena
# gray_img=compression(gray_img)
# gray_img=compression(gray_img)
# gray_img=compression(gray_img)
print("have compression gray_img.shape",gray_img.shape,type(gray_img))
# Convert the image into a graph with the value of the gradient on the
# edges.
# graph = image.img_to_graph(lena)
graph2 = image.img_to_graph(gray_img)
# graph3 = image.img_to_graph(rgb)
# Take a decreasing function of the gradient: an exponential
# The smaller beta is, the more independent the segmentation is of the
# actual image. For beta=1, the segmentation is close to a voronoi
beta = 5
eps = 1e-6
# graph.data = np.exp(-beta * graph.data / lena.std()) + eps
graph2.data = np.exp(-beta * graph2.data / gray_img.std()) + eps
# graph3.data = np.exp(-beta * graph3.data / rgb.std()) + eps
# Apply spectral clustering (this step goes much faster if you have pyamg
# installed)
s = img.shape[0] * img.shape[1]
img_wide = img.reshape(1, s, 4)
return img_wide[0]
whale = sp.misc.imread('test_whale_square.png', flatten=True)
# plt.imshow(whale)
# plt.show()
print whale.shape
# downsize by a factor of 4
whale = whale[::2, ::2] + whale[1::2, ::2] + whale[::2, 1::2] + whale[1::2, 1::2]
whale = whale[::2, ::2] + whale[1::2, ::2] + whale[::2, 1::2] + whale[1::2, 1::2]
graph = image.img_to_graph(whale)
beta = 5
eps = 1e-6
graph.data = np.exp(-beta * graph.data / whale.std()) # + eps
N_REGIONS = 11
print 'loop go'
# for assign_labels in ('kmeans','discretize'):
t0 = time.time()
print 't0 %.2fs' % t0
labels = spectral_clustering(graph, n_clusters = N_REGIONS,
assign_labels='discretize',
random_state=1,
eigen_solver='arpack')
t1 = time.time()
circle1 = (x - center1[0])**2 + (y - center1[1])**2 < radius1**2 circle2 = (x - center2[0])**2 + (y - center2[1])**2 < radius2**2 circle3 = (x - center3[0])**2 + (y - center3[1])**2 < radius3**2 circle4 = (x - center4[0])**2 + (y - center4[1])**2 < radius4**2 ############################################################################### # 4 circles img = circle1 + circle2 + circle3 + circle4 mask = img.astype(bool) img = img.astype(float) img += 1 + 0.2 * np.random.randn(*img.shape) # Convert the image into a graph with the value of the gradient on the # edges. graph = image.img_to_graph(img, mask=mask) # Take a decreasing function of the gradient: we take it weakly # dependant from the gradient the segmentation is close to a voronoi graph.data = np.exp(-graph.data / graph.data.std()) # Force the solver to be arpack, since amg is numerically # unstable on this example labels = spectral_clustering(graph, n_clusters=4, eigen_solver='arpack') label_im = -np.ones(mask.shape) label_im[mask] = labels pl.matshow(img) pl.matshow(label_im) ###############################################################################
circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1 ** 2 circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2 ** 2 circle3 = (x - center3[0]) ** 2 + (y - center3[1]) ** 2 < radius3 ** 2 circle4 = (x - center4[0]) ** 2 + (y - center4[1]) ** 2 < radius4 ** 2 ############################################################################### # 4 circles img = circle1 + circle2 + circle3 + circle4 mask = img.astype(bool) img = img.astype(float) img += 1 + 0.2 * np.random.randn(*img.shape) # Convert the image into a graph with the value of the gradient on the # edges. graph = image.img_to_graph(img, mask=None) # Take a decreasing function of the gradient: we take it weakly # dependent from the gradient the segmentation is close to a voronoi graph.data = np.exp(-graph.data / graph.data.std()) # Force the solver to be arpack, since amg is numerically # unstable on this example labels = spectral_clustering(graph, n_clusters=4, eigen_solver='arpack') # label_im = -np.ones(mask.shape) label_im = -np.ones(img.shape) # label_im[mask] = labels label_im = labels.reshape((100, 100)) plt.matshow(img) plt.matshow(label_im)
import numpy as np import scipy as sp import pylab as pl from sklearn.feature_extraction import image from sklearn.cluster import spectral_clustering lena = sp.misc.lena() # Downsample the image by a factor of 4 lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2] lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2] # Convert the image into a graph with the value of the gradient on the # edges. graph = image.img_to_graph(lena) # Take a decreasing function of the gradient: an exponential # The smaller beta is, the more independant the segmentation is of the # actual image. For beta=1, the segmentation is close to a voronoi beta = 5 eps = 1e-6 graph.data = np.exp(-beta * graph.data / lena.std()) + eps # Apply spectral clustering (this step goes much faster if you have pyamg # installed) N_REGIONS = 11 labels = spectral_clustering(graph, k=N_REGIONS) labels = labels.reshape(lena.shape) ################################################################################
def scan_received(self, msg):
""" Returns an occupancy grid to publish data to map"""
if len(msg.ranges) != 360:
return
#make a pose stamp that relates to the odom of the robot
p = PoseStamped(header=Header(stamp=msg.header.stamp,frame_id="base_link"), pose=Pose())
self.odom_pose = self.tf_listener.transformPose("odom", p)
# convert the odom pose to the tuple (x,y,theta)
self.odom_pose = TransformHelpers.convert_pose_to_xy_and_theta(self.odom_pose.pose)
time_past = 0
for degree in range(360):
if msg.ranges[degree] > 0.0 and msg.ranges[degree] < 5.0:
#gets the position of the laser data points
data_x = self.odom_pose[0] + msg.ranges[degree]*math.cos(degree*math.pi/180.0 + self.odom_pose[2])
data_y = self.odom_pose[1] + msg.ranges[degree]*math.sin(degree*math.pi/180+self.odom_pose[2])
#maps laser data to a pixel in the map
datax_pixel = int((data_x - self.origin[0])/self.resolution)
datay_pixel = int((data_y - self.origin[1])/self.resolution)
#maps the robot to a position
robot = (data_x - self.odom_pose[0], data_y - self.odom_pose[1])
#finds how far away the point is from the robot
magnitude = math.sqrt(robot[0]**2 + robot[1]**2)
#converts magnitude and robot position to pixels in the map
n_steps = max([1, int(math.ceil(magnitude/self.resolution))])
robot_step = (robot[0]/(n_steps-1), robot[1]/(n_steps-1))
marked = set()
for pixel in range(n_steps):
curr_x = self.odom_pose[0] + pixel*robot_step[0]
curr_y = self.odom_pose[1] + pixel*robot_step[1]
if (self.is_in_map(curr_x, curr_y)):
#make sure its in the map
break
x_ind = int((curr_x - self.origin[0])/self.resolution)
y_ind = int((curr_y - self.origin[1])/self.resolution)
if x_ind == datax_pixel and y_ind==datay_pixel and self.odds_ratios[datax_pixel, datay_pixel] >= 1/60.0:
#set odds ratio equal to past odds ratio
if time_past % 5 == 0:
self.past_odds_ratios[datax_pixel, datay_pixel]=self.odds_ratios[datax_pixel, datay_pixel]
time_past += 1
self.odds_ratios[datax_pixel, datay_pixel] *= self.p_occ[datax_pixel, datay_pixel]/(1-self.p_occ[datax_pixel, datay_pixel]) * self.odds_ratio_hit
if not((x_ind, y_ind) in marked) and self.odds_ratios[x_ind, y_ind] >= 1/60.0:
#If point isn't marked, update the odds of missing and add to the map
if time_past % 5 == 0:
self.past_odds_ratios[x_ind, y_ind]=self.odds_ratios[x_ind, y_ind]
time_past += 1
self.odds_ratios[x_ind, y_ind] *= self.p_occ[x_ind, y_ind] / (1-self.p_occ[x_ind, y_ind]) * self.odds_ratio_miss
#self.p_occ[x_ind, y_ind] *= self.p_occ[x_ind, y_ind] * self.odds_ratio_miss/self.odds_ratio_hit
marked.add((x_ind, y_ind))
#print 'New Point'
if not(self.is_in_map(data_x, data_y)) and self.odds_ratios[data_x, datay_pixel] >= 1/60.0:
#if it is not in the map, update the odds of hitting it
if time_past % 5 == 0:
self.past_odds_ratios[datax_pixel, datay_pixel]=self.odds_ratios[datax_pixel, datay_pixel]
time_past += 1
self.odds_ratios[datax_pixel, datay_pixel] *= self.p_occ[datax_pixel, datay_pixel]/(1-self.p_occ[datax_pixel, datay_pixel]) * self.odds_ratio_hit
self.seq += 1
if self.seq % 10 == 0:
map = OccupancyGrid() #this is a nav msg class
map.header.seq = self.seq
map.header.stamp = msg.header.stamp
map.header.frame_id = "map"
map.header.frame_id = "past_map"
map.info.origin.position.x = self.origin[0]
map.info.origin.position.y = self.origin[1]
map.info.width = self.n
map.info.height = self.n
map.info.resolution = self.resolution
map.data = [0]*self.n**2 #the zero is a formatter, not a multiple of 0
for i in range(self.n):
#this is giving us the i,j grid square, occupancy grid
for j in range(self.n):
idx = i+self.n*j #makes horizontal rows (i is x, j is y)
if self.odds_ratios[i,j] < 1/5.0:
map.data[idx] = 0 #makes the gray
elif self.odds_ratios[i,j] >= 1/5.0 < 0.5:
map.data[idx] = 25
elif self.odds_ratios[i,j] > 0.5:
map.data[idx] = 100 #makes the black walls
else:
map.data[idx] = -1 #makes unknown
self.pub.publish(map)
#TODO: Change display such that we're not just looking at the ratio, but mapping the dynamic archive and current readings
image1 = np.zeros((self.odds_ratios.shape[0], self.odds_ratios.shape[1],3))
image2 = np.zeros((self.odds_ratios.shape[0], self.odds_ratios.shape[1],3))
self.counter+=1
x_odom_index = int((self.odom_pose[0] - self.origin[0])/self.resolution)
y_odom_index = int((self.odom_pose[1] - self.origin[1])/self.resolution)
self.pose.append((x_odom_index, y_odom_index))
#.shape() comes from being related to the np class
for i in range(image1.shape[0]):
for j in range(image1.shape[1]):
#print self.past_odds_ratios[i,j]
#print self.odds_ratios[i,j]
#the thing that just rapidly appeared, disappeared!
delta = (self.odds_ratios[i,j]-self.past_odds_ratios[i,j])
if (delta < 0.0) and (i,j) in self.rapid_appear:
self.dyn_obs.append((i,j,self.counter))
#whoa buddy, a thing just appeared
if delta > 1000.0 and (i,j) not in self.rapid_appear:
self.rapid_appear.add((i,j))
if self.odds_ratios[i,j] < 1/50.0:
image1[i,j,:] = 1.0 #makes open space
elif self.odds_ratios[i,j] >= 1/50.0 and self.odds_ratios[i,j] <4/5.0:
image1[i,j,:] = (0, 255, 0)
elif self.odds_ratios[i,j] > 50.0:
image1[i,j,:] = (0, 0, 255) #makes walls
else:
image1[i,j,:] = 0.5 #not read
if len(self.dyn_obs)>0:
for point in self.dyn_obs:
if (self.counter-point[2])<=100:
image2[point[0],point[1]] = (255,0,255) #makes old/dynamic shapes on other map
graph = image.img_to_graph(image1)
beta = 5
eps = 1e-6
graph.data = np.exp(-beta*graph.data/image1.std())+eps
N_REGIONS = 5
for assign_labels in ('kmeans', 'discretize'):
t0 = time.time()
labels = spectral_clustering(graph, n_clusters=N_REGIONS, assign_labels=assign_labels,random_state=1)
t1 = time.time()
labels = labels.reshape(lena.shape)
plt.figure(figsize=(5, 5))
plt.imshow(image1, cmap=plt.cm.gray)
for l in range(N_REGIONS):
plt.contour(labels == l, contours=1, colors=[plt.cm.spectral(l / float(N_REGIONS)), ])
plt.xticks(())
plt.yticks(())
plt.title('Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0)))
plt.show()
for point in self.pose:
image1[point[0], point[1]] = (255, 0, 0)
#draw it!
cv2.circle(image1,(y_odom_index, x_odom_index), 2,(255,0,0))
cv2.imshow("map", cv2.resize(image1,(500,500)))
cv2.imshow("past_map", cv2.resize(image2,(500,500)))
cv2.waitKey(20) #effectively a delay