def mid_axis(img):
dis = ndimg.distance_transform_edt(img)
dis[[0, -1], :] = 0
dis[:, [0, -1]] = 0
idx = np.argsort(dis.flat).astype(np.int32)
medial_axis(dis, idx, lut)
return dis
def convert_binary_image_to_sdf(self, binary_img, vis = False):
binary_data = np.array(binary_img)
skel, sdf_in = morph.medial_axis(binary_data, return_distance = True)
useless_skel, sdf_out = morph.medial_axis(self.upper_bound_ - binary_data, return_distance = True)
sdf = sdf_out - sdf_in
# display the sdf and skeleton
if vis:
dist_on_skel = sdf * skel
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
ax1.imshow(binary_data, cmap=plt.cm.gray, interpolation='nearest')
ax1.axis('off')
ax2.imshow(dist_on_skel, cmap=plt.cm.spectral, interpolation='nearest')
ax2.contour(binary_data, [0.5], colors='w')
ax2.axis('off')
fig.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
plt.show()
plt.imshow(sdf)
plt.show()
plt.imshow(skel)
plt.show()
return sdf, skel
def medskel(self, return_distance=True, verbose=False):
'''
This function performs the medial axis transform (skeletonization) on the mask.
This is essentially a wrapper function of skimage.morphology.medial_axis. The
returned skeletons are the objects used for the bulk of the analysis.
If the distance transform is returned from the transform, it is used as a pruning
step. Regions where the width of a region are far too small (set to >0.01 pc) are
deleted. This ensures there no unnecessary connections between filaments.
Parameters
----------
return_distance : bool, optional
This sets whether the distance transform is returned from
skimage.morphology.medial_axis.
verbose : bool, optional
If True, the image is overplotted with the skeletons for inspection.
Returns
-------
self.skeleton : numpy.ndarray
The array containing all of the skeletons.
self.medial_axis_distance : numpy.ndarray
The distance transform used to create the skeletons.
Only defined is return_distance=True
'''
if return_distance:
self.skeleton,self.medial_axis_distance = medial_axis(self.mask, return_distance=return_distance)
self.medial_axis_distance = self.medial_axis_distance * self.skeleton
if self.pixel_unit_flag:
print "Setting arbitrary width threshold to 2 pixels"
width_threshold = raw_input("Enter threshold change or pass: "******"":
width_threshold = 2
width_threshold = float(width_threshold)
else:
width_threshold = round((0.1/10.)/self.imgscale) # (in pc) Set to be a tenth of expected filament width
narrow_pts = np.where(self.medial_axis_distance<width_threshold)
self.skeleton[narrow_pts] = 0 ## Eliminate narrow connections
else:
self.skeleton = medial_axis(self.mask)
if verbose: # For examining results of skeleton
masked_image = self.image * self.mask
skel_points = np.where(self.skeleton==1)
for i in range(len(skel_points[0])):
masked_image[skel_points[0][i],skel_points[1][i]] = np.NaN
p.imshow(masked_image,interpolation=None,origin="lower")
p.show()
return self
def medial_axis_Astar(self, data, global_goal, TARGET_ALTITUDE,
SAFETY_DISTANCE):
grid, north_offset, east_offset = create_grid_bfs(
data, TARGET_ALTITUDE, SAFETY_DISTANCE)
skeleton = medial_axis(invert(grid))
print("North offset = {0}, east offset = {1}".format(
north_offset, east_offset))
grid_start = (int(self.local_position[0]) - north_offset,
int(self.local_position[1]) - east_offset)
local_goal = global_to_local(global_goal, self.global_home)
grid_goal = (int(local_goal[0]) - north_offset,
int(local_goal[1]) - east_offset)
skel_start, skel_goal = find_start_goal(skeleton, grid_start,
grid_goal)
mapa = breadth_first(
invert(skeleton).astype(np.int), tuple(skel_goal),
tuple(skel_start))
print('Local Start and Goal: ', grid_start, grid_goal)
path, _ = a_star_bfs(
invert(skeleton).astype(np.int), mapa, tuple(skel_start),
tuple(skel_goal))
path.append(grid_goal)
pruned_path = prune_path_bres(path, grid)
waypoints = [[
int(p[0] + north_offset),
int(p[1] + east_offset), TARGET_ALTITUDE, 0
] for p in pruned_path]
for i in range(1, len(waypoints)):
heading = np.arctan2(
waypoints[i][0] - waypoints[i - 1][0],
waypoints[i][1] - waypoints[i - 1][1]) - pi / 2
waypoints[i][3] = -heading
return waypoints
def _coarse(self):
sigma = self.sigma
method_center = self.method_center
Xcoarse = spim.gaussian_filter(self.X, sigma=sigma)
xmean = np.mean(Xcoarse)
Xcontour = np.zeros(np.shape(Xcoarse))
for i in range(np.shape(Xcoarse)[0]):
for j in range(np.shape(Xcoarse)[1]):
if Xcoarse[i, j] > xmean:
Xcontour[i, j] = 1
else:
Xcontour[i, j] = 0
self.Xcontour = Xcontour # Attribute
# Compute the centerline/skeleton
if method_center == 'medial_axis':
skeleton_coarse = medial_axis(Xcontour) * 1.0
elif method_center == 'skeletonize':
skeleton_coarse = skeletonize(Xcontour) * 1.0
else:
raise ValueError(
'PyCrack: no method to compute the skeleton/centerline is selected.'
)
if self.verbose: print('PyCrack: get nodes and coordinates.')
self.skeleton_coarse = skeleton_coarse # Attribute
nodes_coarse = self._getnodes(skeleton_coarse)
coords_crack = self._getcoords(self.X)
coords_skeleton = self._getcoords(skeleton_coarse)
# Attributes
self.nodes_coarse = nodes_coarse
self.coords_crack = coords_crack
self.coords_skeleton = coords_skeleton
if self.showfig:
if self.verbose: print('PyCrack: plot graphic.')
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
ax.imshow(Xcontour, cmap=plt.cm.gray)
ax.scatter(coords_crack[:, 0], coords_crack[:, 1], s=0.05, c='y')
ax.scatter(coords_skeleton[:, 0],
coords_skeleton[:, 1],
c='b',
marker='o',
s=(72. / fig.dpi / 8)**2)
ax.scatter(nodes_coarse[:, 0], nodes_coarse[:, 1], s=20, c='red')
ax.axis('off')
for i in range(len(nodes_coarse)):
ax.text(nodes_coarse[i, 0], nodes_coarse[i, 1], s=str(i))
fig.tight_layout()
plt.show()
def skeleton_features(mask):
"""calculates the set of cell-skeleton based features
Calculates medial axis of the segmented cell and calculates the length,
maximum and minimum thickness of the skeleton
Parameters
----------
image : 3D array, shape (M, N, C)
The input image with multiple channels.
Returns
-------
features : dict
dictionary including percentiles, moments and sum per channel
"""
# storing the feature values
features = dict()
for ch in range(mask.shape[2]):
# calculating the medial axis and distance on the skeleton
skel, distance = medial_axis(mask[:, :, ch], return_distance=True)
dist_on_skel = distance * skel
# storing the features
features["skeleton_length_Ch" + str(ch + 1)] = skel.sum()
features["skeleton_thickness_max_Ch" +
str(ch + 1)] = dist_on_skel.max()
if dist_on_skel.max() > 0.:
features["skeleton_thickness_min_Ch" +
str(ch + 1)] = dist_on_skel[dist_on_skel > 0.].min()
else:
features["skeleton_thickness_min_Ch" + str(ch + 1)] = 0.
return features
def get_skeleton() -> Tuple[np.ndarray, np.ndarray]:
"""
It skeletonizes an image of the hairpin
"""
# load arena image & threshold
img = Image.open(Hairpin.image_local_path())
new_width = 40
new_height = 60
img = img.resize((new_width, new_height))
img = np.array(img)[:, :, 0]
img[img > 0] = 1
arena = invert(img)
arena[arena == 254] = 0
arena[arena == 255] = 1
# perform skeletonization
skeleton = skeletonize(arena)
_, distance = medial_axis(arena, return_distance=True)
# convolve distance map with gaussian kernel
kernel = kernel_gaussian_2D()
distance = signal.convolve2d(distance,
kernel,
boundary="symm",
mode="same")
return skeleton, distance
def label_watershed(obj: np.array, maxima_threshold):
"""
Separate touching objects based on distance map (similar to imageJ watershed)
:param obj: np.array, 0-and-1
:param maxima_threshold: threshold for identify maxima
:return: seg: np.array, grey scale with different objects labeled with different numbers
"""
_, dis = medial_axis(obj, return_distance=True)
maxima = extrema.h_maxima(dis, maxima_threshold)
# maxima_threshold for Jess data = 1
# maxima_threshold for Jose 60x data = 10
# maxima_threshold for Jose 40x data = 20
maxima_mask = binary_dilation(maxima)
for i in range(6):
maxima_mask = binary_dilation(maxima_mask)
label_maxima = label(maxima_mask, connectivity=2)
markers = label_maxima.copy()
markers[obj == 0] = np.amax(label_maxima) + 1
elevation_map = sobel(obj)
label_obj = segmentation.watershed(elevation_map, markers)
label_obj[label_obj == np.amax(label_maxima) + 1] = 0
return label_obj
def create_skeletons(images):
try:
# Create target directories
os.mkdir(dataset_path)
os.mkdir(train_path)
os.mkdir(test_skeletonize_path)
os.mkdir(test_medial_axis_path)
except FileExistsError:
print("Directory already exists")
for i in range(len(images)):
res = cv2.resize(images[i],
dsize=(256, 256),
interpolation=cv2.INTER_CUBIC)
plt.imsave(fname=train_path + "/train_" + str(i) + ".png",
arr=res,
cmap="gray")
res_medial = medial_axis(res[:, :, 0])
plt.imsave(fname=test_medial_axis_path + "/medial_" + str(i) + ".png",
arr=res_medial,
cmap="gray")
res_skeleton = skeletonize(res[:, :, 0])
plt.imsave(fname=test_skeletonize_path + "/skeletonize_" + str(i) +
".png",
arr=res_skeleton,
cmap="gray")
def segment_cells(frame, mask=None):
"""
Compute the initial segmentation based on ridge detection + watershed.
This works reasonably well, but is not robust enough to use by itself.
"""
blurred = filters.gaussian(frame, 2)
ridges = enhance_ridges(frame)
# threshold ridge image
thresh = filters.threshold_otsu(ridges)
thresh_factor = 0.5
prominent_ridges = ridges > thresh_factor*thresh
prominent_ridges = morphology.remove_small_objects(prominent_ridges, min_size=256)
prominent_ridges = morphology.binary_closing(prominent_ridges)
prominent_ridges = morphology.binary_dilation(prominent_ridges)
# skeletonize
ridge_skeleton = morphology.medial_axis(prominent_ridges)
ridge_skeleton = morphology.binary_dilation(ridge_skeleton)
ridge_skeleton *= mask
ridge_skeleton = np.bitwise_xor(ridge_skeleton, mask)
# label
cell_label_im = measure.label(ridge_skeleton)
# morphological closing to fill in the cracks
for cell_num in range(1, cell_label_im.max()+1):
cell_mask = cell_label_im==cell_num
cell_mask = morphology.binary_closing(cell_mask, disk(3))
cell_label_im[cell_mask] = cell_num
return cell_label_im
def run(self):
print 'Gerando Caracteristicas....'
images = self.__image_list(image_path('circinatum'), image_path('kelloggii'), image_path('negundo'))
arquivo = open(data_path(),'w')
count = 0
for i in images:
count+=1
img, vetor, arclen, classe = self.__features(i)
# Media Curvatura
arquivo.write(str(np.mean(np.abs(vetor))) + ',')
# Comprimento de Arco
arquivo.write(str(arclen) + ',')
# Area
arquivo.write(str(np.sum(img)) + ',')
# Numero Pixels Esqueleto
arquivo.write(str(np.sum(morphology.medial_axis(img))) + ',')
# Classe Folhas
arquivo.write(classe)
arquivo.write("\n")
arquivo.close()
print '100%'
print 'Total Imagens: ' + str(count)
def imageThinning(datasetPath, subFolder, file):
"""Thins and saves binary image
Args:
datasetPath (string): Relative or Absolute path of the dataset.
subFolder (string): Folder name of specific character
file (string): Name of image
Purpose:
Saves the thinned image with tag "-thinned" to the actual file name in the actual images' location
"""
path = os.path.join(datasetPath, subFolder, file)
original = invert(cv2.imread(path, cv2.IMREAD_GRAYSCALE))
binary = original > filters.threshold_otsu(original)
skeleton, distance = medial_axis(binary, return_distance=True)
distanceOnSkeleton = distance * skeleton
invertedImage = invert(distanceOnSkeleton)
index = file.find('.png')
newFile = file[:index] + "-thinned" + file[index:]
newPath = os.path.join(datasetPath, subFolder, newFile)
io.imsave(newPath, invertedImage)
def get_crack_width(input_file, pixel_mm=0.31, draw=False):
img = cv2.imread(input_file, 0)
img_bool = img > 0
# Compute the medial axis (skeleton) and the distance transform
skel, distance = medial_axis(img_bool, return_distance=True)
# Distance to the background for pixels of the skeleton
dist_on_skel = distance * skel
max_width = np.max(dist_on_skel) * pixel_mm * 2
#determine if the defect is critical
critical = False
if max_width >= 0.3:
critical = True
if draw:
i, j = np.unravel_index(dist_on_skel.argmax(), dist_on_skel.shape)
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111)
ax.imshow(dist_on_skel, cmap='gray')
ax.scatter(j, i, color='red')
ax.contour(img, [0.5], colors='w')
img_name = os.path.split(input_file)[-1].split('.')[0] + '_draw.png'
plt.savefig(img_name, bbox_inches='tight')
plt.close(fig)
return max_width, critical
def plan_medial_axis(self, data,local_start, local_goal, TARGET_ALTITUDE, SAFETY_DISTANCE):
print('Medial-Axis planning')
# create grid and skeleton
grid, north_offset, east_offset = create_grid(data, TARGET_ALTITUDE, SAFETY_DISTANCE)
print("North offset = {0}, east offset = {1}".format(north_offset, east_offset))
skeleton = medial_axis(invert(grid))
print('Skeleton generated')
# calculate the closest start/goal points on the "graph"
start_ne = ( local_start[0] - north_offset, local_start[1] - east_offset)
goal_ne = ( local_goal[0] - north_offset, local_goal[1] - east_offset)
skel_start, skel_goal = find_start_goal(skeleton, start_ne, goal_ne)
# run A* to search for the goal along the road network
path, cost = a_star(invert(skeleton).astype(np.int), heuristic, tuple(skel_start), tuple(skel_goal))
# Prune path to minimize number of waypoints
if (args.prune == 'collinearity'):
print('Pruning path with collinearity check')
path = collinearity_prune(path)
elif (args.prune == 'bresenham'):
print('Pruning path with bresenham')
path = bresenham_prune(path,grid)
else:
print('Unrecognized prune option returning full path')
# Convert path to waypoints
waypoints = [[int(p[0]) + north_offset,int(p[1]) + east_offset, TARGET_ALTITUDE, 0] for p in path]
return waypoints
def place_routers_on_skeleton_iterative(d, cmethod):
budget = d['budget']
R = d['radius']
max_num_routers = int(d['budget'] / d['price_router'])
coverage = np.where(d["graph"] == Cell.Wireless, 1, 0).astype(np.bool)
pbar = tqdm(range(max_num_routers), desc="Placing Routers")
while budget > 0:
# perform skeletonization
# skeleton = skeletonize(coverage)
skeleton = medial_axis(coverage)
# get all skeleton positions
pos = np.argwhere(skeleton > 0).tolist()
# escape if no positions left
if not len(pos):
break
# get a random position
shuffle(pos)
a, b = pos[0]
# place router
d["graph"][a][b] = Cell.Router
d, ret, cost = _add_cabel(d, (a, b), budget)
if not ret:
print("No budget available!")
break
budget -= cost
# refresh wireless map by removing new coverage
m = wireless_access(a, b, R, d['graph']).astype(np.bool)
coverage[(a - R):(a + R + 1), (b - R):(b + R + 1)] &= ~m
pbar.update()
pbar.close()
return d
def agreeing_skeletons(float_surface, mito_labels):
topological_skeleton = skeletonize(mito_labels)
medial_skeleton, distance = medial_axis(mito_labels, return_distance=True)
# TODO: test without the active threshold surface
active_threshold = np.mean(float_surface[mito_labels])
transform_filter = np.zeros(mito_labels.shape, dtype=np.uint8)
transform_filter[np.logical_and(medial_skeleton > 0,
float_surface > active_threshold)] = 1
# transform filter is basically medial_skeleton on a field above threshold (mean*5 - wow, that's a lot)
medial_skeleton = transform_filter * distance
median_skeleton_masked = np.ma.masked_array(medial_skeleton,
medial_skeleton > 0)
skeleton_convolve = ndi.convolve(median_skeleton_masked,
np.ones((3, 3)),
mode='constant',
cval=0.0)
divider_convolve = ndi.convolve(transform_filter,
np.ones((3, 3)),
mode='constant',
cval=0.0)
skeleton_convolve[divider_convolve > 0] = skeleton_convolve[divider_convolve > 0] / \
divider_convolve[divider_convolve > 0]
skeletons = np.zeros_like(medial_skeleton)
skeletons[topological_skeleton] = skeleton_convolve[topological_skeleton]
# dbg.skeleton_debug(float_surface, mito_labels, skeletons)
return skeletons
def segment_cells(frame, mask=None):
"""
Compute the initial segmentation based on ridge detection + watershed.
This works reasonably well, but is not robust enough to use by itself.
"""
blurred = filters.gaussian_filter(frame, 2)
ridges = enhance_ridges(frame)
# threshold ridge image
thresh = filters.threshold_otsu(ridges)
thresh_factor = 0.6
prominent_ridges = ridges > thresh_factor*thresh
prominent_ridges = morphology.remove_small_objects(prominent_ridges, min_size=256)
prominent_ridges = morphology.binary_closing(prominent_ridges)
prominent_ridges = morphology.binary_dilation(prominent_ridges)
# skeletonize
ridge_skeleton = morphology.medial_axis(prominent_ridges)
ridge_skeleton = morphology.binary_dilation(ridge_skeleton)
ridge_skeleton *= mask
ridge_skeleton -= mask
# label
cell_label_im = measure.label(ridge_skeleton)
# morphological closing to fill in the cracks
for cell_num in range(1, cell_label_im.max()+1):
cell_mask = cell_label_im==cell_num
cell_mask = morphology.binary_closing(cell_mask, disk(3))
cell_label_im[cell_mask] = cell_num
return cell_label_im
def skeletonize_mitochondria(mch_channel):
mch_collector = np.max(mch_channel, axis=0) # TODO: check max projection v.s. sum
skeleton_labels = np.zeros(mch_collector.shape, dtype=np.uint8)
# thresh = np.max(mch_collector)/2.
thresh = threshold_otsu(mch_collector)
# use adaptative threshold? => otsu seems to be sufficient in this case
skeleton_labels[mch_collector > thresh] = 1
skeleton2 = skeletonize(skeleton_labels)
skeleton, distance = medial_axis(skeleton_labels, return_distance=True)
active_threshold = np.mean(mch_collector[skeleton_labels]) * 5
# print active_threshold
transform_filter = np.zeros(mch_collector.shape, dtype=np.uint8)
transform_filter[np.logical_and(skeleton > 0, mch_collector > active_threshold)] = 1
skeleton = transform_filter * distance
skeleton_ma = np.ma.masked_array(skeleton, skeleton > 0)
skeleton_convolve = ndi.convolve(skeleton_ma, np.ones((3, 3)), mode='constant', cval=0.0)
divider_convolve = ndi.convolve(transform_filter, np.ones((3, 3)), mode='constant', cval=0.0)
skeleton_convolve[divider_convolve > 0] = skeleton_convolve[divider_convolve > 0] / \
divider_convolve[divider_convolve > 0]
new_skeleton = np.zeros_like(skeleton)
new_skeleton[skeleton2] = skeleton_convolve[skeleton2]
skeleton = new_skeleton
return skeleton_labels, mch_collector, skeleton, transform_filter
def remove_narrow_part(thresholded_image, narrow_constant=2):
skel, distance = medial_axis(thresholded_image, return_distance=True)
dist_on_skel = distance * skel
contour_image = get_contour_image(thresholded_image)
distance_no_border = get_complement_image(skel, contour_image)
distance_no_border[np.where(distance_no_border < 0)] = 0
distance_no_border[np.where(distance > narrow_constant)] = 0
distance_labeled_image = measure.label(distance_no_border, connectivity=2)
if len(np.unique(distance_labeled_image)) > 1:
distance_region_props = measure.regionprops_table(
distance_labeled_image, properties=('label', 'area', 'image'))
result_image = np.zeros_like(distance_labeled_image)
for i in range(len(distance_region_props['label'])):
if distance_region_props['area'][i] < 2:
distance_labeled_image[np.where(
distance_labeled_image == distance_region_props['label']
[i])] = 0
result_image[np.where(distance_labeled_image > 0)] = 1
dilated_image = binary_dilation(result_image,
square(narrow_constant + 1))
removed_narrow_part_image = get_complement_image(
thresholded_image, dilated_image)
removed_narrow_part_image[np.where(removed_narrow_part_image < 0)] = 0
return removed_narrow_part_image
else:
return thresholded_image
def vectorize_lines(im: np.ndarray,
threshold: float = 0.2,
min_sp_dist: int = 10):
"""
Vectorizes lines from a binarized array.
Args:
im (np.ndarray): Array of shape (3, H, W) with the first dimension
being probabilities for (start_separators,
end_separators, baseline).
Returns:
[[x0, y0, ... xn, yn], [xm, ym, ..., xk, yk], ... ]
A list of lists containing the points of all baseline polylines.
"""
# split into baseline and separator map
st_map = im[0]
end_map = im[1]
sep_map = st_map + end_map
bl_map = im[2]
# binarize
bin = im > threshold
skel, skel_dist_map = medial_axis(bin[2], return_distance=True)
elongation_offset = np.max(skel_dist_map)
sp_can = _find_superpixels(skel, heatmap=bl_map, min_sp_dist=min_sp_dist)
if not sp_can.size:
logger.warning(
'No superpixel candidates found in network output. Likely empty page.'
)
return []
intensities = _compute_sp_states(sp_can, bl_map, st_map, end_map)
clusters = _cluster_lines(intensities)
lines = _interpolate_lines(clusters, elongation_offset, bl_map.shape,
st_map, end_map)
return lines
def topology_preserving_thinning(bw: np.ndarray,
min_thickness: int = 1,
thin: int = 1) -> np.ndarray:
"""perform thinning on segmentation without breaking topology
Parameters:
--------------
bw: np.ndarray
the 3D binary image to be thinned
min_thinkness: int
Half of the minimum width you want to keep from being thinned.
For example, when the object width is smaller than 4, you don't
want to make this part even thinner (may break the thin object
and alter the topology), you can set this value as 2.
thin: int
the amount to thin (has to be an positive integer). The number of
pixels to be removed from outter boundary towards center.
Return:
-------------
A binary image after thinning
"""
bw = bw > 0
safe_zone = np.zeros_like(bw)
for zz in range(bw.shape[0]):
if np.any(bw[zz, :, :]):
ctl = medial_axis(bw[zz, :, :] > 0)
dist = distance_transform_edt(ctl == 0)
safe_zone[zz, :, :] = dist > min_thickness + 1e-5
rm_candidate = np.logical_xor(bw > 0, erosion(bw > 0, ball(thin)))
bw[np.logical_and(safe_zone, rm_candidate)] = 0
return bw
def skeleton_extraction_medial_axis(self, im):
'''
medial_axis利用中轴变换方法计算前景(1值)目标对象的宽度,
函数原型:
skimage.morphology.medial_axis(image, mask=None, return_distance=False)
mask:掩模. 默认为None, 如果给定一个掩模,
则在掩模内的像素值才执行骨架算法.
return_distance: bool型值，默认为False.
如果为True, 则除了返回骨架, 还将距离变换值也同时返回.
这里的距离指的是中轴线上的所有点与背景点的距离.
'''
#计算中轴和距离变换值
skel, distance = morphology.medial_axis(im, return_distance=True)
#中轴上的点到背景像素点的距离
dist_on_skel = distance * skel
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
ax1.imshow(data, cmap=plt.cm.gray, interpolation='nearest')
#用光谱色显示中轴
ax2.imshow(dist_on_skel, cmap=plt.cm.spectral, interpolation='nearest')
ax2.contour(im, [0.5], colors='w') #显示轮廓线
fig.tight_layout()
plt.show()
return skel, distance, dist_on_skel
def execute_Skeleton(proxy,obj):
from skimage.morphology import medial_axis
threshold=0.1*obj.threshold
try:
img2=obj.sourceObject.Proxy.img
img=img2.copy()
except:
sayexc()
img=cv2.imread(__dir__+'/icons/freek.png')
data = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Compute the medial axis (skeleton) and the distance transform
skel, distance = medial_axis(data, return_distance=True)
# Distance to the background for pixels of the skeleton
dist_on_skel = distance * skel
# entferne ganz duenne linien
dist_on_skelw =(dist_on_skel >= threshold)* distance
say("size of the image ...")
say(dist_on_skelw.shape)
# skel = np.array(dist_on_skelw,np.uint8)
skel = np.array(dist_on_skelw *255/np.max(dist_on_skelw),np.uint8)
obj.Proxy.img=cv2.cvtColor(skel*100, cv2.COLOR_GRAY2BGR)
obj.Proxy.dist_on_skel=dist_on_skelw
def run(img):
if len(img.shape) > 2 and img.shape[2] == 4:
img = color.rgba2rgb(img)
if len(img.shape) == 2:
img = color.gray2rgb(img)
skel = medial_axis(color.rgb2gray(img), return_distance=False)
return to_base64(skel)
def skeletonize_mitochondria(mCh_channel):
mch_collector = np.max(mCh_channel, axis=0) # TODO: check how max affects v.s. sum
labels = np.zeros(mch_collector.shape, dtype=np.uint8)
# thresh = np.max(mch_collector)/2.
thresh = threshold_otsu(mch_collector)
# TODO: use adaptative threshold? => otsu seems to be sufficient in this case
# http://scikit-image.org/docs/dev/auto_examples/xx_applications/plot_thresholding.html#sphx
# -glr-auto-examples-xx-applications-plot-thresholding-py
# log-transform? => Nope, does not work
# TODO: hessian/laplacian of gaussian blob detection?
labels[mch_collector > thresh] = 1
skeleton2 = skeletonize(labels)
skeleton, distance = medial_axis(labels, return_distance=True)
active_threshold = np.mean(mch_collector[labels]) * 5
# print active_threshold
transform_filter = np.zeros(mch_collector.shape, dtype=np.uint8)
transform_filter[np.logical_and(skeleton > 0, mch_collector > active_threshold)] = 1
skeleton = transform_filter * distance
skeleton_ma = np.ma.masked_array(skeleton, skeleton > 0)
skeleton_convolve = ndi.convolve(skeleton_ma, np.ones((3, 3)), mode='constant', cval=0.0)
divider_convolve = ndi.convolve(transform_filter, np.ones((3, 3)), mode='constant', cval=0.0)
skeleton_convolve[divider_convolve > 0] = skeleton_convolve[divider_convolve > 0] \
/ divider_convolve[divider_convolve > 0]
new_skeleton = np.zeros_like(skeleton)
new_skeleton[skeleton2] = skeleton_convolve[skeleton2]
skeleton = new_skeleton
return labels, mch_collector, skeleton, transform_filter
def label_nuclei(binary, min_size):
'''Label, watershed and remove small objects'''
distance = medial_axis(binary, return_distance=True)[1]
distance_blured = gaussian_filter(distance, 5)
local_maxi = peak_local_max(distance_blured, indices=False, labels=binary, min_distance = 30)
markers = measure_label(local_maxi)
# markers[~binary] = -1
# labels_rw = segmentation.random_walker(binary, markers)
# labels_rw[labels_rw == -1] = 0
# labels_rw = segmentation.relabel_sequential(labels_rw)
labels_ws = watershed(-distance, markers, mask=binary)
labels_large = remove_small_objects(labels_ws,min_size)
labels_clean_border = clear_border(labels_large)
labels_from_one = relabel_sequential(labels_clean_border)
# plt.imshow(ndimage.morphology.binary_dilation(markers))
# plt.show()
return labels_from_one[0]
def skeletonization():
from skimage import img_as_bool, io, color, morphology
image = img_as_bool(color.rgb2gray(io.imread('planC2V2black.tiff')))
out = morphology.medial_axis(image)
f, (ax0, ax1) = plt.subplots(1, 2)
ax0.imshow(image, cmap='gray', interpolation='nearest')
ax1.imshow(out, cmap='gray', interpolation='nearest')
DefaultSize = plt.get_size_inches()
plt.set_figsize_inches( (DefaultSize[0]*2, DefaultSize[1]*2) )
plt.savefig("test.png", dpi = (1000))
plt.show()
res = []
for i in range(len(out)):
res.append([])
for j in range(len(out[0])):
if out[i][j]:
res[i].append('1')
else:
if imarray[i][j][0] == 255: ##cette partie a ete modifiee pour garder les murs en memoire
res[i].append('2')
else:
res[i].append('0')
#print(res)
#affCouleurs(suppParasites(res))
return res
def get_length(neurite_mask, show=True):
"""
Arguments:
----------
neurite_mask: string or numpy.bool array
path to binary image indicating the presence of neurites, OR
corresponding boolean numpy.ndarray
show: bool (default True)
if True, plots intermediate steps of image analysis
Returns:
--------
neurite_length: int
total neurite length in pixels
"""
neurite_mask = utils.handle_binary_image_input(neurite_mask)
neurite_skeleton = medial_axis(neurite_mask)
neurite_length = neurite_skeleton.sum()
if show:
images = [neurite_mask, neurite_skeleton]
titles = ['Neurite mask', 'Medial axis']
fig = utils.plot_image_collection(images, titles, nrows=1, ncols=2)
fig.suptitle('Neurite length', fontsize='large')
return neurite_length
def label_nuclei(binary, min_size):
'''Label, watershed and remove small objects'''
distance = medial_axis(binary, return_distance=True)[1]
distance_blured = gaussian_filter(distance, 5)
local_maxi = peak_local_max(distance_blured,
indices=False,
labels=binary,
min_distance=30)
markers = measure_label(local_maxi)
# markers[~binary] = -1
# labels_rw = segmentation.random_walker(binary, markers)
# labels_rw[labels_rw == -1] = 0
# labels_rw = segmentation.relabel_sequential(labels_rw)
labels_ws = watershed(-distance, markers, mask=binary)
labels_large = remove_small_objects(labels_ws, min_size)
labels_clean_border = clear_border(labels_large)
labels_from_one = relabel_sequential(labels_clean_border)
# plt.imshow(ndimage.morphology.binary_dilation(markers))
# plt.show()
return labels_from_one[0]
def skeletonize_mitochondria(mch_channel):
mch_collector = np.max(mch_channel, axis=0) # TODO: check max projection v.s. sum
skeleton_labels = np.zeros(mch_collector.shape, dtype=np.uint8)
# thresh = np.max(mch_collector)/2.
thresh = threshold_otsu(mch_collector)
# use adaptative threshold? => otsu seems to be sufficient in this case
skeleton_labels[mch_collector > thresh] = 1
skeleton2 = skeletonize(skeleton_labels)
skeleton, distance = medial_axis(skeleton_labels, return_distance=True)
active_threshold = np.mean(mch_collector[skeleton_labels]) * 5
# print active_threshold
transform_filter = np.zeros(mch_collector.shape, dtype=np.uint8)
transform_filter[np.logical_and(skeleton > 0, mch_collector > active_threshold)] = 1
skeleton = transform_filter * distance
skeleton_ma = np.ma.masked_array(skeleton, skeleton > 0)
skeleton_convolve = ndi.convolve(skeleton_ma, np.ones((3, 3)), mode='constant', cval=0.0)
divider_convolve = ndi.convolve(transform_filter, np.ones((3, 3)), mode='constant', cval=0.0)
skeleton_convolve[divider_convolve > 0] = skeleton_convolve[divider_convolve > 0] / \
divider_convolve[divider_convolve > 0]
new_skeleton = np.zeros_like(skeleton)
new_skeleton[skeleton2] = skeleton_convolve[skeleton2]
skeleton = new_skeleton
return skeleton_labels, mch_collector, skeleton, transform_filter
def medial_scan_hand(im=None, skeletonize=False, save_path=None):
"""
Read in the hand image and run MAT on it then display next to original.
Code via skimage plot_medial_transform example.
If save_path is given, will write the result to a file.
"""
if im is None:
im = auto_hand_img()
# Compute the medial axis (skeleton) and the distance transform
skel, distance = medial_axis(im, return_distance=True)
# Distance to the background for pixels of the skeleton
dist_on_skel = distance * skel
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
ax1.imshow(im, cmap=plt.get_cmap('gray'), interpolation='nearest')
ax1.axis('off')
ax2.imshow(dist_on_skel, cmap=plt.get_cmap('Spectral'), interpolation='nearest')
ax2.contour(im, [0.5], colors='w')
ax2.axis('off')
fig.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
if save_path is None:
plt.show()
else:
fig.savefig(save_path)
def generate_center_med_axis_from_points(points, mask):
'''Generate medial axis transform for a centerline
from a list of points that the centerline will go through.
This is used to generate all the widths along a particular centerline
Parameters:
------------
points: list of 2-d tuples
List of indices that the centerline will go through.
Indices must be given in the order that the centerline should
encounter each point
mask: array_like (cast to booleans) shape (n,m)
Binary image of the worm mask
Returns:
-----------
traceback: list of 2-d tuples
List of indices associated with the centerline, starting with
one of the endpoints of the centerline and ending with the ending
index of the centerline.
distances: ndarray shape(n,m)
Distance transform from the medial axis transform of the worm mask
'''
skeleton, med_axis = medial_axis(mask, return_distance=True)
traceback = generate_centerline_from_points(points, skeleton)
centerline = generate_centerline(traceback, mask.shape)
distances = centerline * med_axis
return traceback, distances
def update_splines(traceback, mask):
'''Generate center_tck and width_tck from a new traceback
Parameters:
------------
traceback: list of 2-d tuples
List of indices associated with the centerline, starting with
one of the endpoints of the centerline and ending with the ending
index of the centerline.
mask: array_like (cast to booleans) shape (n,m)
Binary image of the worm mask
Returns:
-----------
center_tck: parametric spline tuple
spline tuple for spline corresponding to the centerline
width_tck: nonparametric spline tuple
spline tuple corresponding to the widths along the
centerline of the worm
'''
skeleton, med_axis = medial_axis(mask, return_distance=True)
centerline = generate_centerline(traceback, mask.shape)
distances = centerline * med_axis
center_tck = center_spline(traceback, distances)
width_tck = width_spline(traceback, distances)
return center_tck, width_tck
def __init__(self, glyph, scale_factor):
self.glyph = glyph
self.glyph_img = rasterize(glyph, scale=scale_factor) > 128
h, w = self.glyph_img.shape
self.scale_factor = scale_factor
# Labled by connectivity
self.labeled, n_labels = label(self.glyph_img, return_num=True)
axis, dist = medial_axis(self.glyph_img, return_distance=True)
# Estimated stroke width
self.stroke_width = np.average(dist[axis]) * 2
# Smoothed image
smooth = gaussian(self.glyph_img, 8*scale_factor) > 0.6
# Skeleton image
skel = np.logical_and(
skeletonize(smooth), dist >= self.stroke_width * 0.3)
# Skeleton segments
skel_segments = get_skeleton_segments(skel,
prune_length=self.stroke_width * 0.25)
# Reference skeleton points
self.skel_pts = (np.array(
[ pt[::-1] for segment in skel_segments for pt in segment ])
/scale_factor).astype(int)
# Skeleton points by their belonging region labels
self.skel_pts_by_label = [ [] for _ in range(n_labels) ]
for pt in self.skel_pts:
ptx = int(pt[0] * scale_factor + 0.5)
pty = int(pt[1] * scale_factor + 0.5)
ptx = w-1 if ptx >= w else 0 if ptx < 0 else ptx
pty = h-1 if pty >= h else 0 if pty < 0 else pty
pt_label = self.labeled[pty, ptx]
self.skel_pts_by_label[pt_label-1].append(pt)
def computeSkeleton(data):
skel, dist = medial_axis(data, return_distance=True)
dist_on_skel = dist * skel
print("Medial axis computed...")
return (skel, dist, dist_on_skel)
def skeleton_image(img_array, median=False, dim=28):
"""
Thinners the foreground of an image to one pixel
"""
img = resquare(binarize(img_array)//255, dim)
if median:
return (255*medial_axis(img)).flatten()
return (255*skeletonize(img)).flatten()
def _process_img_morph(img, threshold=.5, scale=1):
if scale > 1:
up_img = transform.pyramid_expand(img, upscale=scale, order=3) # type: np.ndarray
img = (255. * up_img).astype(img.dtype)
img_min, img_max = img.min(), img.max()
bin_img = (img >= img_min + (img_max - img_min) * threshold)
skel, dist_map = morphology.medial_axis(bin_img, return_distance=True)
return img, bin_img, skel, dist_map
def skeleton(seg):
skel, dist = skmorph.medial_axis(seg, return_distance=True)
node, edge, leaf = (spim.label(g, np.ones((3, 3), bool))[0] for g in skel2graph(skel))
trim_edge = (edge != 0) & ~(skmorph.binary_dilation(node != 0, np.ones((3, 3), bool)) != 0)
trim_edge = spim.label(trim_edge, np.ones((3, 3), bool))[0]
leaf_edge_vals = skmorph.binary_dilation(leaf != 0, np.ones((3, 3), bool)) != 0
leaf_edge_vals = np.unique(trim_edge[leaf_edge_vals])
leaf_edge_vals = leaf_edge_vals[leaf_edge_vals > 0]
leaf_edge = leaf != 0
trim_edge = ndshm.fromndarray(trim_edge)
leaf_edge = ndshm.fromndarray(leaf_edge)
Parallel()(delayed(set_msk)(leaf_edge, trim_edge, l) for l in leaf_edge_vals)
trim_edge = np.copy(trim_edge)
leaf_edge = np.copy(leaf_edge)
leaf_edge[(skmorph.binary_dilation(leaf_edge, np.ones((3, 3), bool)) != 0) & (edge != 0)] = True
leaf_edge = spim.label(leaf_edge, np.ones((3, 3), bool))[0]
leaf_edge_node = skmorph.binary_dilation(leaf_edge != 0, np.ones((3, 3), bool)) != 0
leaf_edge_node = ((node != 0) & leaf_edge_node) | leaf_edge
leaf_edge_node = spim.label(leaf_edge_node, np.ones((3, 3), bool))[0]
cand_node = leaf_edge_node * (node != 0)
cand_node = cand_node.nonzero()
cand_node = np.transpose((leaf_edge_node[cand_node],) + cand_node + (2 * dist[cand_node],))
cand_leaf = leaf_edge_node * (leaf != 0)
cand_leaf = cand_leaf.nonzero()
cand_leaf = np.transpose((leaf_edge_node[cand_leaf],) + cand_leaf)
if len(cand_node) > 0 and len(cand_leaf) > 0:
cand_leaf = ndshm.fromndarray(cand_leaf)
cand_node = ndshm.fromndarray(cand_node)
pruned = Parallel()(delayed(prune_leaves)(cand_leaf, cand_node, j) for j in np.unique(cand_node[:, 0]))
cand_leaf = np.copy(cand_leaf)
cand_node = np.copy(cand_node)
pruned_ind = []
for p in pruned:
pruned_ind.extend(p)
pruned_ind = tuple(np.transpose(pruned_ind))
pruned = ~skel
pruned = ndshm.fromndarray(pruned)
leaf_edge = ndshm.fromndarray(leaf_edge)
Parallel()(delayed(set_msk)(pruned, leaf_edge, l) for l in np.unique(leaf_edge[pruned_ind]))
pruned = np.copy(pruned)
leaf_edge = np.copy(leaf_edge)
pruned = ~pruned
else:
pruned = skel
return pruned
def get_text_image(text, font, point_size):
text = get_text(text, font, point_size)
io.imsave(IMAGE_FILENAME, text)
thresh = skfilter.threshold_otsu(text)
binary = text > thresh
skel, distance = morphology.medial_axis(binary, return_distance=True)
distance = distance.astype(np.uint16)
skel = skel.astype(np.uint16)
return skel*distance
def make_skeleton(image, mindist):
"""Return a skeletonization of the image, filtered and normalized."""
skel, distance = morphology.medial_axis(image, return_distance=True)
dist_on_skel = distance * skel
dist_on_skel = filter_skeleton_distances(dist_on_skel)
normalized = normalize_skeleton(dist_on_skel)
return normalized
def test_01_01_rectangle(self):
'''Test skeletonize on a rectangle'''
image = np.zeros((9, 15), bool)
image[1:-1, 1:-1] = True
#
# The result should be four diagonals from the
# corners, meeting in a horizontal line
#
expected = np.array([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,1,0,0,0,0,0,0,0,0,0,0,0,1,0],
[0,0,1,0,0,0,0,0,0,0,0,0,1,0,0],
[0,0,0,1,0,0,0,0,0,0,0,1,0,0,0],
[0,0,0,0,1,1,1,1,1,1,1,0,0,0,0],
[0,0,0,1,0,0,0,0,0,0,0,1,0,0,0],
[0,0,1,0,0,0,0,0,0,0,0,0,1,0,0],
[0,1,0,0,0,0,0,0,0,0,0,0,0,1,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]], bool)
result = medial_axis(image)
assert np.all(result == expected)
result, distance = medial_axis(image, return_distance=True)
assert distance.max() == 4
def loadImage(filename):
global im
global distanceImage
global lapImage
global skeleton
global resultImage
global im1
global skimage
im = np.asarray(Image.open(filename))
distanceImage, lapImage, resultImage = medial(im)
skeleton = lapImage < 0
im1 = ax.imshow(im, cmap = "gray", interpolation="nearest")
skimage = medial_axis(im, return_distance=False)
lapImage = lapImage - np.max(lapImage)
def test_01_02_hole(self):
'''Test skeletonize on a rectangle with a hole in the middle'''
image = np.zeros((9, 15), bool)
image[1:-1, 1:-1] = True
image[4, 4:-4] = False
expected = np.array([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,1,0,0,0,0,0,0,0,0,0,0,0,1,0],
[0,0,1,1,1,1,1,1,1,1,1,1,1,0,0],
[0,0,1,0,0,0,0,0,0,0,0,0,1,0,0],
[0,0,1,0,0,0,0,0,0,0,0,0,1,0,0],
[0,0,1,0,0,0,0,0,0,0,0,0,1,0,0],
[0,0,1,1,1,1,1,1,1,1,1,1,1,0,0],
[0,1,0,0,0,0,0,0,0,0,0,0,0,1,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],bool)
result = medial_axis(image)
assert np.all(result == expected)
def __transform(self):
img_gray = io.imread(self.image_path, True)
#Aplicar otsu para binarizar a imagem
img_otsu = filter.threshold_otsu(img_gray)
self.__img = img_gray < img_otsu
# Procura contornos da imagem binarizada
self.__contours = measure.find_contours(self.__img, 0.5)
arclen=0.0
for n, contour in enumerate(self.__contours):
arclenTemp=0.0
for indice, valor in enumerate(contour):
if indice > 0:
d1 = math.fabs(round(valor[0]) - round(contour[indice-1,0]))
d2 = math.fabs(round(valor[1]) - round(contour[indice-1,1]))
if d1+d2>1.0:
arclenTemp+=math.sqrt(2)
elif d1+d2 == 1:
arclenTemp+=1
if arclenTemp > arclen:
arclen = arclenTemp
bestn = n
#Transforma a lista contours[bestn] em uma matriz[n,2]
aux = np.asarray(self.__contours[bestn])
#--------------------------- Curvatura --------------
vetor = [] #vetor que irá receber as curvaturas k(t)
for i in range(len(aux)-2): #Percorrer ate -2 para não pegar elementos inexistentes
#--------------------------- Curvatura --------------
#Inverter as posições em relação a fórmula pois o x esta no lugar do y
b1 = ( (aux[i-2,1]+aux[i+2,1]) + (2*(aux[i-1,1] + aux[i+1,1])) - (6*aux[i,1]) ) / 12
b2 = ( (aux[i-2,0]+aux[i+2,0]) + (2*(aux[i-1,0] + aux[i+1,0])) - (6*aux[i,0]) ) / 12
c1 = ( (aux[i+2,1]-aux[i-2,1]) + (4*(aux[i+1,1] - aux[i-1,1])) ) / 12
c2 = ( (aux[i+2,0]-aux[i-2,0]) + (4*(aux[i+1,0] - aux[i-1,0])) ) / 12
k = (2*(c1*b1 - c2*b2)) / ((c1**2 + c2**2)**(3/2))
vetor.append(k) #append: insere objeto no final da lista
self.__media_curvatura = np.mean(np.abs(vetor))
self.__comprimento_arco = arclen
self.__area = np.sum(self.__img)
self.__esqueleto_pixel = np.sum(morphology.medial_axis(self.__img))
self.__bestn = bestn
def image_features_morphology(img, maxPixel, num_features,imageSize):
# X is the feature vector with one row of features per image
# consisting of the pixel values a, num_featuresnd our metric
X=np.zeros(num_features, dtype=float)
image = resize(img, (maxPixel, maxPixel))
# Compute the medial axis (skeleton) and the distance transform
skel, distance = medial_axis(image, return_distance=True)
# Distance to the background for pixels of the skeleton
dist_on_skel = distance * skel
# Store the rescaled image pixels
X[0:imageSize] = np.reshape(dist_on_skel,(1, imageSize))
return X
def get_skeleton_of_maze(image_to_analyze, image_to_draw_on=None,
use_medial_axis=True, invert_threshold=False,
locate_critical_points=True, resize_ratio=1):
"""
Computes, returns, and potentially displays the morphological skeleton of the given binary image.
:param image_to_analyze: Image to manipulate in search of results.
:param image_to_draw_on: Image passed in that is to be used for drawing the results of analysis.
:param use_medial_axis: Whether to use an alternative method for finding the skeleton that
allows the computation of the critical points in the image.
:param invert_threshold: Whether the threshold should be inverted before the skeleton is located
:param locate_critical_points: Whether to find and draw critical points on the skeleton.
:param resize_ratio: What ratio to rescale outgoing results to before they're displayed.
:return: The skeleton of the image, and the critical points (If you chose to try to find them)
"""
result = []
# Invert our thresholded image since this will skeletonize white areas
if invert_threshold:
image_to_analyze = cv2.bitwise_not(image_to_analyze)
image_to_analyze = cv2.bitwise_not(image_to_analyze)
if use_medial_axis or locate_critical_points:
# http://scikit-image.org/docs/dev/auto_examples/plot_medial_transform.html
# In short, allows us to find the distance to areas on the skeleton.
# This information can be used to find critical points in the skeleton, theoretically.
path_skeleton, distance = medial_axis(image_to_analyze, return_distance=True)
distance_on_skeleton = path_skeleton * distance
path_skeleton = img_as_ubyte(path_skeleton)
result.append(path_skeleton)
if locate_critical_points:
critical_points = find_critical_points(distance_on_skeleton, number_of_points=10,
minimum_thickness=50, edge_width=20,
image_to_draw_on=image_to_draw_on)
result.append(critical_points)
else:
skeleton = skeletonize(image_to_analyze/255)
path_skeleton = np.array(skeleton*255, np.uint8)
result.append(path_skeleton)
if image_to_draw_on is not None:
path_skeleton_temp = cv2.cvtColor(path_skeleton, cv2.COLOR_GRAY2BGR)
superimposed_skeleton = cv2.add(image_to_draw_on, path_skeleton_temp)
display("Skeleton", superimposed_skeleton, resize_ratio)
return result
def get_skeleton(img):
rows, cols = img.shape
for r in xrange(rows):
img[r][0] = 0
img[r][cols - 1] = 0
for c in xrange(cols):
img[0][c] = 0
img[rows - 1][c] = 0
dst = np.asarray(map(lambda row:
np.asarray(map(lambda x: 1 if x == 255 else 0,row)),
img))
dst, _ = morphology.medial_axis(dst, return_distance=True)
dst = np.asarray(map(lambda row:
np.asarray(map(lambda x: 255 if x else 0,row)),
dst))
return dst
def __transform(self):
img_gray = io.imread(self.__img_path, True)
thresh = filter.threshold_otsu(img_gray)
data = img_gray < thresh
s1, self.__distancia = morphology.medial_axis(data, return_distance=True)
self.__s2 = s1
for i in range(len(s1)):
for j in range(len(s1)):
if (s1[i,j] <> False): #Percorre o esqueleto da imagem
x, y,val = circle_perimeter_aa(i, j, int(self.__distancia[i,j]))
#desenha um circulo ao redor do pixel do esqueleto
#i,j coordenadas do centro -- int(distance[i,j]=raio do circulo)
#x,y = índices dos pixels ---- val = intensidade
#Define quais circulos devem ficar de acordo com o raio
if (int(self.__distancia[i,j]) > 0):
self.__s2[x, y] = True
else:
self.__s2[x, y] = False
else:
self.__s2[i, j] = False
bub = BubbleFinder2D(mom0, sigma=80. * beam.jtok(hi_freq) / 1000.)
# fils = fil_finder_2D(mom0.value, mom0.header, 10, distance=0.84e6)
# fils.mask = ~(bub.mask.copy())
# fils.medskel()
# fils.analyze_skeletons()
# # So at least on of the radial profiles fails. BUT the second fit is to a
# # skeleton that is essentially the entire disk, so plot without interactivity
# # and save the plot and the parameters shown in verbose mode.
# p.ioff()
# fils.find_widths(verbose=True, max_distance=500, auto_cut=False, try_nonparam=False)
# Fit Parameters: [ 541.31726502 129.85351117 180.0710914 304.01262168
# Fit Errors: [ 0.89151974 0.48394493 0.27313627 1.1462345 ]
skeleton = medial_axis(~bub.mask)
# Overplot the skeleton on the moment0
ax = p.subplot(111, projection=mom0.wcs)
ax.imshow(mom0.value, origin='lower')
ax.contour(skeleton, colors='r')
p.draw()
p.savefig(paper1_figures_path("moment0_w_skeletons.pdf"), rasterize=True)
p.savefig(paper1_figures_path("moment0_w_skeletons.png"))
# raw_input("Next plot?")
p.clf()
pixscale = \
mom0.header['CDELT2'] * (np.pi / 180.) * gal.distance.to(u.pc).value
def test_narrow_image(self):
"""Test skeletonize on a 1-pixel thin strip"""
image = np.zeros((1, 5), bool)
image[:, 1:-1] = True
result = medial_axis(image)
assert np.all(result == image)
images = [f for f in listdir(figureDir) if isfile(join(figureDir, f))]
# http://stackoverflow.com/questions/7304117/split-filenames-with-python
imageNames = [fname.rsplit('.', 1)[0].capitalize() for fname in os.listdir(figureDir)]
filenames = dict(zip(imageNames[0::1], images[0::1]))
fig = plt.figure()
ax = fig.add_subplot(111)
fig.subplots_adjust(left=0.25, bottom=0.25)
min0 = 0
max0 = 25000
im = np.asarray(Image.open(os.path.join(figureDir,filenames[imageNames[0]])))
im1 = ax.imshow(im, cmap = "gray")
distanceImage, lapImage, resultImage = medial(im)
skeleton = lapImage < 0
skimage = medial_axis(im, return_distance=False)
lapImage = lapImage - np.max(lapImage)
# distanceImage = distance_transform_edt(im)
# lapImage = laplace(distanceImage, mode="constant")
# resultImage = np.logical_and(np.logical_not(skeleton), im )
def loadImage(filename):
global im
global distanceImage
global lapImage
global skeleton
global resultImage
global im1
global skimage
im = np.asarray(Image.open(filename))
distanceImage, lapImage, resultImage = medial(im)
# objects. As the function ``medial_axis`` returns the distance transform in
# addition to the medial axis (with the keyword argument ``return_distance=True``),
# it is possible to compute the distance to the background for all points of
# the medial axis with this function. This gives an estimate of the local width
# of the objects.
#
# For a skeleton with fewer branches, ``skeletonize`` or ``skeletonize_3d``
# must be preferred.
from skimage.morphology import medial_axis, skeletonize, skeletonize_3d
# Generate the data
data = binary_blobs(200, blob_size_fraction=.2, volume_fraction=.35, seed=1)
# Compute the medial axis (skeleton) and the distance transform
skel, distance = medial_axis(data, return_distance=True)
# Compare with other skeletonization algorithms
skeleton = skeletonize(data)
skeleton3d = skeletonize_3d(data)
# Distance to the background for pixels of the skeleton
dist_on_skel = distance * skel
fig, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True, sharey=True,
subplot_kw={'adjustable': 'box-forced'})
ax = axes.ravel()
ax[0].imshow(data, cmap=plt.cm.gray, interpolation='nearest')
ax[0].set_title('original')
ax[0].axis('off')
import numpy as np
from skimage import io
from skimage import morphology
import matplotlib.pyplot as plt
io.use_plugin('matplotlib')
# test image from Matlab bwmorph('skel') demo
bw = io.imread('circles.png') > 0
sk0 = morphology.skeletonize(bw)
sk1 = morphology.medial_axis(bw).astype(np.uint8)
f, (ax0, ax1) = plt.subplots(1, 2)
ax0.imshow(bw + sk0, cmap=plt.cm.gray, interpolation='nearest')
ax0.set_title('Skeletonize')
ax1.imshow(bw + sk1, cmap=plt.cm.gray, interpolation='nearest')
ax1.set_title('Medial axis')
plt.show()
def process_file(img_id, par, par2, vgg_big_path, vgg_small_path, linknet_small_path, small_res_file_path, inc_file_path,
vgg_smallest_file_path, inc_smallest_file_path, res_smallest_file_path, inc3_520_file_path, inc_v2_520_file_path,
linknet_big_file_path, linknet_520_file_path,
vgg_big_path_1, vgg_smallest_file_path_1,
inc_smallest_file_path_1, res_smallest_file_path_1, inc3_520_file_path_1, inc_v2_520_file_path_1,
linknet_big_file_path_1, linknet_520_file_path_1, save_to=None):
res_rows = []
if vgg_small_path is None:
msk = np.zeros((1300, 1300))
else:
msk = cv2.imread(vgg_small_path, cv2.IMREAD_UNCHANGED)
msk = cv2.resize(msk, (1300, 1300))
if linknet_small_path is None:
msk2 = np.zeros((1300, 1300))
else:
msk2 = cv2.imread(linknet_small_path, cv2.IMREAD_UNCHANGED)
msk2 = cv2.resize(msk2, (1300, 1300))
if vgg_big_path is None:
msk3 = np.zeros((1300, 1300))
msk3_1 = np.zeros((1300, 1300))
else:
msk3 = cv2.imread(vgg_big_path, cv2.IMREAD_UNCHANGED)
msk3_1 = cv2.imread(vgg_big_path_1, cv2.IMREAD_UNCHANGED)
if small_res_file_path is None:
res_msk = np.zeros((1300, 1300))
else:
res_msk = cv2.imread(small_res_file_path, cv2.IMREAD_UNCHANGED)
res_msk = cv2.resize(res_msk, (1300, 1300))
if inc_file_path is None:
inc_msk = np.zeros((1300, 1300))
else:
inc_msk = cv2.imread(inc_file_path, cv2.IMREAD_UNCHANGED)
inc_msk = cv2.resize(inc_msk, (1300, 1300))
if vgg_smallest_file_path is None:
vgg_smlst_msk = np.zeros((1300, 1300))
vgg_smlst_msk_1 = np.zeros((1300, 1300))
else:
vgg_smlst_msk = cv2.imread(vgg_smallest_file_path, cv2.IMREAD_UNCHANGED)
vgg_smlst_msk = cv2.resize(vgg_smlst_msk, (1300, 1300))
vgg_smlst_msk_1 = cv2.imread(vgg_smallest_file_path_1, cv2.IMREAD_UNCHANGED)
vgg_smlst_msk_1 = cv2.resize(vgg_smlst_msk_1, (1300, 1300))
if inc_smallest_file_path is None:
inc_smlst_msk = np.zeros((1300, 1300))
inc_smlst_msk_1 = np.zeros((1300, 1300))
else:
inc_smlst_msk = cv2.imread(inc_smallest_file_path, cv2.IMREAD_UNCHANGED)
inc_smlst_msk = cv2.resize(inc_smlst_msk, (1300, 1300))
inc_smlst_msk_1 = cv2.imread(inc_smallest_file_path_1, cv2.IMREAD_UNCHANGED)
inc_smlst_msk_1 = cv2.resize(inc_smlst_msk_1, (1300, 1300))
if res_smallest_file_path is None:
res_smlst_msk = np.zeros((1300, 1300))
res_smlst_msk_1 = np.zeros((1300, 1300))
else:
res_smlst_msk = cv2.imread(res_smallest_file_path, cv2.IMREAD_UNCHANGED)
res_smlst_msk = cv2.resize(res_smlst_msk, (1300, 1300))
res_smlst_msk_1 = cv2.imread(res_smallest_file_path_1, cv2.IMREAD_UNCHANGED)
res_smlst_msk_1 = cv2.resize(res_smlst_msk_1, (1300, 1300))
if inc3_520_file_path is None:
inc3_520_msk = np.zeros((1300, 1300))
inc3_520_msk_1 = np.zeros((1300, 1300))
else:
inc3_520_msk = cv2.imread(inc3_520_file_path, cv2.IMREAD_UNCHANGED)
inc3_520_msk = cv2.resize(inc3_520_msk, (1300, 1300))
inc3_520_msk_1 = cv2.imread(inc3_520_file_path_1, cv2.IMREAD_UNCHANGED)
inc3_520_msk_1 = cv2.resize(inc3_520_msk_1, (1300, 1300))
if inc_v2_520_file_path is None:
inc_v2_520_msk = np.zeros((1300, 1300))
inc_v2_520_msk_1 = np.zeros((1300, 1300))
else:
inc_v2_520_msk = cv2.imread(inc_v2_520_file_path, cv2.IMREAD_UNCHANGED)
inc_v2_520_msk = cv2.resize(inc_v2_520_msk, (1300, 1300))
inc_v2_520_msk_1 = cv2.imread(inc_v2_520_file_path_1, cv2.IMREAD_UNCHANGED)
inc_v2_520_msk_1 = cv2.resize(inc_v2_520_msk_1, (1300, 1300))
if linknet_big_file_path is None:
link_big_msk = np.zeros((1300, 1300))
link_big_msk_1 = np.zeros((1300, 1300))
else:
link_big_msk = cv2.imread(linknet_big_file_path, cv2.IMREAD_UNCHANGED)
link_big_msk_1 = cv2.imread(linknet_big_file_path_1, cv2.IMREAD_UNCHANGED)
if linknet_520_file_path is None:
link_520_msk = np.zeros((1300, 1300))
link_520_msk_1 = np.zeros((1300, 1300))
else:
link_520_msk = cv2.imread(linknet_520_file_path, cv2.IMREAD_UNCHANGED)
link_520_msk = cv2.resize(link_520_msk, (1300, 1300))
link_520_msk_1 = cv2.imread(linknet_520_file_path_1, cv2.IMREAD_UNCHANGED)
link_520_msk_1 = cv2.resize(link_520_msk_1, (1300, 1300))
msk3 = (msk3 * 0.5 + msk3_1 * 0.5)
inc_smlst_msk = (inc_smlst_msk * 0.5 + inc_smlst_msk_1 * 0.5)
vgg_smlst_msk = (vgg_smlst_msk * 0.5 + vgg_smlst_msk_1 * 0.5)
res_smlst_msk = (res_smlst_msk * 0.5 + res_smlst_msk_1 * 0.5)
inc3_520_msk = (inc3_520_msk * 0.5 + inc3_520_msk_1 * 0.5)
inc_v2_520_msk = (inc_v2_520_msk * 0.5 + inc_v2_520_msk_1 * 0.5)
link_big_msk = (link_big_msk * 0.5 + link_big_msk_1 * 0.5)
link_520_msk = (link_520_msk * 0.5 + link_520_msk_1 * 0.5)
coef = []
tot_sum = par[:12].sum()
for i in range(12):
coef.append(par[i] / tot_sum)
msk = (msk * coef[0] + msk2 * coef[1] + msk3 * coef[2] + res_msk * coef[3] + inc_msk * coef[4]
+ vgg_smlst_msk * coef[5] + inc_smlst_msk * coef[6] + res_smlst_msk * coef[7]
+ inc3_520_msk * coef[8] + inc_v2_520_msk * coef[9] + link_big_msk * coef[10] + link_520_msk * coef[11])
msk = msk.astype('uint8')
if save_to is not None:
cv2.imwrite(save_to, msk, [cv2.IMWRITE_PNG_COMPRESSION, 9])
msk2 = np.lib.pad(msk, ((22, 22), (22, 22)), 'symmetric')
thr = par[12]
msk2 = 1 * (msk2 > thr)
msk2 = msk2.astype(np.uint8)
if par2[0] > 0:
msk2 = dilation(msk2, square(par2[0]))
if par2[1] > 0:
msk2 = erosion(msk2, square(par2[1]))
if 'Shanghai' in img_id:
skeleton = medial_axis(msk2)
else:
skeleton = skeletonize_3d(msk2)
skeleton = skeleton[22:1322, 22:1322]
lbl0 = label(skeleton)
props0 = regionprops(lbl0)
cnt = 0
crosses = []
for x in range(1300):
for y in range(1300):
if skeleton[y, x] == 1:
if skeleton[max(0, y-1):min(1300, y+2), max(0, x-1):min(1300, x+2)].sum() > 3:
cnt += 1
crss = []
crss.append((x, y))
for y0 in range(max(0, y-1), min(1300, y+2)):
for x0 in range(max(0, x-1), min(1300, x+2)):
if x == x0 and y == y0:
continue
if skeleton[max(0, y0-1):min(1300, y0+2), max(0, x0-1):min(1300, x0+2)].sum() > 3:
crss.append((x0, y0))
crosses.append(crss)
cross_hashes = []
for crss in crosses:
crss_hash = set([])
for x0, y0 in crss:
crss_hash.add(point_hash(x0, y0))
skeleton[y0, x0] = 0
cross_hashes.append(crss_hash)
new_crosses = []
i = 0
while i < len(crosses):
new_hashes = set([])
new_hashes.update(cross_hashes[i])
new_crss = crosses[i][:]
fl = True
while fl:
fl = False
j = i + 1
while j < len(crosses):
if len(new_hashes.intersection(cross_hashes[j])) > 0:
new_hashes.update(cross_hashes[j])
new_crss.extend(crosses[j])
cross_hashes.pop(j)
crosses.pop(j)
fl = True
break
j += 1
mean_p = np.asarray(new_crss).mean(axis=0).astype('int')
if len(new_crss) > 1:
t = KDTree(new_crss)
mean_p = new_crss[t.query(mean_p[np.newaxis, :])[1][0][0]]
new_crosses.append([(mean_p[0], mean_p[1])] + new_crss)
i += 1
crosses = new_crosses
lbl = label(skeleton)
props = regionprops(lbl)
connected_roads = []
connected_crosses = [set([]) for p in props]
for i in range(len(crosses)):
rds = set([])
for j in range(len(crosses[i])):
x, y = crosses[i][j]
for y0 in range(max(0, y-1), min(1300, y+2)):
for x0 in range(max(0, x-1), min(1300, x+2)):
if lbl[y0, x0] > 0:
rds.add(lbl[y0, x0])
connected_crosses[lbl[y0, x0]-1].add(i)
connected_roads.append(rds)
res_roads = []
tot_dist_min = par2[2]
coords_min = par2[3]
for i in range(len(props)):
coords = props[i].coords
crss = list(connected_crosses[i])
tot_dist = props0[lbl0[coords[0][0], coords[0][1]]-1].area
if (tot_dist < tot_dist_min) or (coords.shape[0] < coords_min and len(crss) < 2):
continue
if coords.shape[0] == 1:
coords = np.asarray([coords[0], coords[0]])
else:
coords = get_ordered_coords(lbl, i+1, coords)
for j in range(len(crss)):
x, y = crosses[crss[j]][0]
d1 = abs(coords[0][0] - y) + abs(coords[0][1] - x)
d2 = abs(coords[-1][0] - y) + abs(coords[-1][1] - x)
if d1 < d2:
coords[0][0] = y
coords[0][1] = x
else:
coords[-1][0] = y
coords[-1][1] = x
coords_approx = approximate_polygon(coords, 1.5)
res_roads.append(coords_approx)
hashes = set([])
final_res_roads = []
for r in res_roads:
if r.shape[0] > 2:
final_res_roads.append(r)
for i in range(1, r.shape[0]):
p1 = r[i-1]
p2 = r[i]
h1 = pair_hash(p1, p2)
h2 = pair_hash(p2, p1)
hashes.add(h1)
hashes.add(h2)
for r in res_roads:
if r.shape[0] == 2:
p1 = r[0]
p2 = r[1]
h1 = pair_hash(p1, p2)
h2 = pair_hash(p2, p1)
if not (h1 in hashes or h2 in hashes):
final_res_roads.append(r)
hashes.add(h1)
hashes.add(h2)
end_points = {}
for r in res_roads:
h = point_hash(r[0, 0], r[0, 1])
if not (h in end_points.keys()):
end_points[h] = 0
end_points[h] = end_points[h] + 1
h = point_hash(r[-1, 0], r[-1, 1])
if not (h in end_points.keys()):
end_points[h] = 0
end_points[h] = end_points[h] + 1
road_msk = np.zeros((1300, 1300), dtype=np.int32)
road_msk = road_msk.copy()
thickness = 1
for j in range(len(final_res_roads)):
l = final_res_roads[j]
for i in range(len(l) - 1):
cv2.line(road_msk, (int(l[i, 1]), int(l[i, 0])), (int(l[i+1, 1]), int(l[i+1, 0])), j+1, thickness)
connect_dist = par2[4]
min_prob = par2[5]
angles_to_check = [0, radians(5), radians(-5), radians(10), radians(-10), radians(15), radians(-15)]
if 'Paris' in img_id or 'Vegas' in img_id:
angles_to_check += [radians(20), radians(-20), radians(25), radians(-25)]
add_dist = par2[6]
add_dist2 = par2[7]
con_r = par2[8]
for i in range(len(final_res_roads)):
h = point_hash(final_res_roads[i][0, 0], final_res_roads[i][0, 1])
if end_points[h] == 1:
p1 = final_res_roads[i][1]
p2 = final_res_roads[i][0]
p3 = try_connect(p1, p2, 0, connect_dist, road_msk, min_prob, msk, final_res_roads, con_r)
if p3 is not None:
h1 = pair_hash(p2, p3)
h2 = pair_hash(p3, p2)
if not (h1 in hashes or h2 in hashes):
r_id = road_msk[p3[0], p3[1]] - 1
final_res_roads[r_id], new_hashes = inject_point(final_res_roads[r_id], p3)
hashes.update(new_hashes)
tmp_road_msk = np.zeros((1300, 1300), dtype=np.int32)
tmp_road_msk = tmp_road_msk.copy()
cv2.line(tmp_road_msk, (p2[1], p2[0]), (p3[1], p3[0]), i+1, thickness)
road_msk[road_msk == 0] = tmp_road_msk[road_msk == 0]
road_msk = road_msk.copy()
final_res_roads[i] = np.vstack((p3, final_res_roads[i]))
hashes.add(h1)
hashes.add(h2)
end_points[point_hash(p3[0], p3[1])] = 2
h = point_hash(final_res_roads[i][-1, 0], final_res_roads[i][-1, 1])
if end_points[h] == 1:
p1 = final_res_roads[i][-2]
p2 = final_res_roads[i][-1]
p3 = try_connect(p1, p2, 0, connect_dist, road_msk, min_prob, msk, final_res_roads, con_r)
if p3 is not None:
h1 = pair_hash(p2, p3)
h2 = pair_hash(p3, p2)
if not (h1 in hashes or h2 in hashes):
r_id = road_msk[p3[0], p3[1]] - 1
final_res_roads[r_id], new_hashes = inject_point(final_res_roads[r_id], p3)
hashes.update(new_hashes)
tmp_road_msk = np.zeros((1300, 1300), dtype=np.int32)
tmp_road_msk = tmp_road_msk.copy()
cv2.line(tmp_road_msk, (p2[1], p2[0]), (p3[1], p3[0]), i+1, thickness)
road_msk[road_msk == 0] = tmp_road_msk[road_msk == 0]
road_msk = road_msk.copy()
final_res_roads[i] = np.vstack((final_res_roads[i], p3))
hashes.add(h1)
hashes.add(h2)
end_points[point_hash(p3[0], p3[1])] = 2
for i in range(len(final_res_roads)):
h = point_hash(final_res_roads[i][0, 0], final_res_roads[i][0, 1])
if end_points[h] == 1:
p1 = final_res_roads[i][1]
p2 = final_res_roads[i][0]
p3 = None
for a in angles_to_check:
p3 = try_connect(p1, p2, a, connect_dist, road_msk, min_prob, msk, final_res_roads, con_r)
if p3 is not None:
break
if p3 is not None:
h1 = pair_hash(p2, p3)
h2 = pair_hash(p3, p2)
if not (h1 in hashes or h2 in hashes):
r_id = road_msk[p3[0], p3[1]] - 1
final_res_roads[r_id], new_hashes = inject_point(final_res_roads[r_id], p3)
hashes.update(new_hashes)
tmp_road_msk = np.zeros((1300, 1300), dtype=np.int32)
tmp_road_msk = tmp_road_msk.copy()
cv2.line(tmp_road_msk, (p2[1], p2[0]), (p3[1], p3[0]), i+1, thickness)
road_msk[road_msk == 0] = tmp_road_msk[road_msk == 0]
road_msk = road_msk.copy()
final_res_roads[i] = np.vstack((p3, final_res_roads[i]))
hashes.add(h1)
hashes.add(h2)
end_points[point_hash(p3[0], p3[1])] = 2
else:
p3 = get_next_point(p1, p2, add_dist)
if not (p3[0] < 2 or p3[1] < 2 or p3[0] > 1297 or p3[1] > 1297):
p3 = get_next_point(p1, p2, add_dist2)
if (p3[0] != p2[0] or p3[1] != p2[1]) and (road_msk[p3[0], p3[1]] == 0):
h1 = pair_hash(p2, p3)
h2 = pair_hash(p3, p2)
if not (h1 in hashes or h2 in hashes):
final_res_roads[i] = np.vstack((p3, final_res_roads[i]))
hashes.add(h1)
hashes.add(h2)
tmp_road_msk = np.zeros((1300, 1300), dtype=np.int32)
tmp_road_msk = tmp_road_msk.copy()
cv2.line(tmp_road_msk, (p2[1], p2[0]), (p3[1], p3[0]), i+1, thickness)
road_msk[road_msk == 0] = tmp_road_msk[road_msk == 0]
road_msk = road_msk.copy()
end_points[point_hash(p3[0], p3[1])] = 2
h = point_hash(final_res_roads[i][-1, 0], final_res_roads[i][-1, 1])
if end_points[h] == 1:
p1 = final_res_roads[i][-2]
p2 = final_res_roads[i][-1]
p3 = None
for a in angles_to_check:
p3 = try_connect(p1, p2, a, connect_dist, road_msk, min_prob, msk, final_res_roads, con_r)
if p3 is not None:
break
if p3 is not None:
h1 = pair_hash(p2, p3)
h2 = pair_hash(p3, p2)
if not (h1 in hashes or h2 in hashes):
r_id = road_msk[p3[0], p3[1]] - 1
final_res_roads[r_id], new_hashes = inject_point(final_res_roads[r_id], p3)
hashes.update(new_hashes)
tmp_road_msk = np.zeros((1300, 1300), dtype=np.int32)
tmp_road_msk = tmp_road_msk.copy()
cv2.line(tmp_road_msk, (p2[1], p2[0]), (p3[1], p3[0]), i+1, thickness)
road_msk[road_msk == 0] = tmp_road_msk[road_msk == 0]
road_msk = road_msk.copy()
final_res_roads[i] = np.vstack((final_res_roads[i], p3))
hashes.add(h1)
hashes.add(h2)
end_points[point_hash(p3[0], p3[1])] = 2
else:
p3 = get_next_point(p1, p2, add_dist)
if not (p3[0] < 2 or p3[1] < 2 or p3[0] > 1297 or p3[1] > 1297):
p3 = get_next_point(p1, p2, add_dist2)
if (p3[0] != p2[0] or p3[1] != p2[1]) and (road_msk[p3[0], p3[1]] == 0):
h1 = pair_hash(p2, p3)
h2 = pair_hash(p3, p2)
if not (h1 in hashes or h2 in hashes):
final_res_roads[i] = np.vstack((final_res_roads[i], p3))
hashes.add(h1)
hashes.add(h2)
tmp_road_msk = np.zeros((1300, 1300), dtype=np.int32)
tmp_road_msk = tmp_road_msk.copy()
cv2.line(tmp_road_msk, (p2[1], p2[0]), (p3[1], p3[0]), i+1, thickness)
road_msk[road_msk == 0] = tmp_road_msk[road_msk == 0]
road_msk = road_msk.copy()
end_points[point_hash(p3[0], p3[1])] = 2
lines = [LineString(r[:, ::-1]) for r in final_res_roads]
if len(lines) == 0:
res_rows.append({'ImageId': img_id, 'WKT_Pix': 'LINESTRING EMPTY'})
else:
for l in lines:
res_rows.append({'ImageId': img_id, 'WKT_Pix': dumps(l, rounding_precision=0)})
return res_rows
def distance_transform(image):
from skimage.morphology import medial_axis
_, dist = medial_axis(image, return_distance=True)
return dist
def cell_boundaries_detector(data_iterator,
metadata,
show_progress=False,
parameters={}):
"""
Find cell boundary in bright field microscopy image.
Parameters
----------
data_iterator : python iterator
To iterate over data.
metadata : dict
Metadata to scale detected peaks and parameters.
show_progress : bool (default: False)
Print progress bar during detection.
verbose : bool (default: True)
Display informations during detection.
parameters : dict
object_height : float
Typical size of the object in um.
minimal_area : float
Typical area of the object in um^2.
Returns
------()
shapes : :class:`pd.DataFrame`
Contains cell boundary properties for each time_stamp
"""
_parameters = DEFAULT_PARAMETERS.copy()
_parameters.update(parameters)
parameters = _parameters
# Load parameters
sigma = parameters['object_height'] / metadata['PhysicalSizeZ']
minimal_area = parameters['minimal_area'] / metadata['PhysicalSizeX']
sizeX = metadata['SizeX']
sizeY = metadata['SizeY']
# calculate the correlation image from the z-stack
cellprop = []
t_tot = metadata['SizeT']
for t, imt in enumerate(data_iterator):
if show_progress:
p = int(float(t + 1) / t_tot * 100.)
print_progress(p)
if np.any(imt) != 0:
corr = np.zeros((sizeY, sizeX))
for y, x in np.ndindex(sizeY, sizeX):
Iz = imt[:, y, x]
z = np.array(range(len(Iz)))
zf = len(Iz) / 2
corr[y, x] = integrate.simps(Iz[z] * (z - zf) * np.exp(-(zf - z) ** 2 /
(2 * sigma ** 2)), z)
# create binary mask of the correlation image
thresh = threshold_otsu(corr)
mask = corr > thresh
area = 0
n = 2
prevarea = None
prevcellprop = None
# un seuil pas trop petit au cas où il resterait des petits objets
# dans l'image
while area < minimal_area and prevarea != area:
tophat = binary_closing(mask, square(n))
n += 1
skel = medial_axis(tophat)
skel = (skel - 1) * (-1)
cleared = clear_border(skel)
labelized = label(cleared, 8, 0) + 1
# add cell characteristic in the cellprop list
if np.any(labelized):
prevcellprop = regionprops(
labelized, intensity_image=corr)[0]
prevarea = area
if prevcellprop:
area = prevcellprop['area']
if prevcellprop:
cellprop.append(prevcellprop)
else:
if len(cellprop) >= 1:
cellprop.append(cellprop[-1])
else:
cellprop.append(None)
else:
if len(cellprop) >= 1:
cellprop.append(cellprop[-1])
else:
cellprop.append(None)
print_progress(-1)
# class cell morphology in time in the props Dataframe (time, centroid X,
# centroid Y, ...)
listprop = ['centroid_x', 'centroid_y', 'orientation', 'major_axis', 'minor_axis']
cell_Prop = np.zeros((len(listprop) + 1, metadata["SizeT"]))
for i in range(metadata["SizeT"]):
if cellprop[i]:
cell_Prop[0, i] = i
cell_Prop[1, i] = cellprop[i]['centroid'][0] * metadata['PhysicalSizeX']
cell_Prop[2, i] = cellprop[i]['centroid'][1] * metadata['PhysicalSizeX']
cell_Prop[3, i] = cellprop[i]['orientation']
cell_Prop[4, i] = cellprop[i]['major_axis_length'] * metadata['PhysicalSizeX']
cell_Prop[5, i] = cellprop[i]['minor_axis_length'] * metadata['PhysicalSizeX']
cell_Prop = cell_Prop.T
props = pd.DataFrame(cell_Prop, columns=['t_stamp'] + listprop)
props = props.set_index('t_stamp')
props['t'] = props.index.get_level_values('t_stamp') * metadata['TimeIncrement']
props = props.astype(np.float)
if np.all(props == 0):
return pd.DataFrame([])
else:
return props
def get_skeletons(image):
# Compute the medial axis (skeleton) and the distance transform
skel, distance = medial_axis(data, return_distance=True)
dist_on_skel = distance * skel
return dist_on_skel
def test_00_01_zeros_masked(self):
'''Test skeletonize on an array that is completely masked'''
result = medial_axis(np.zeros((10, 10), bool),
np.zeros((10, 10), bool))
assert np.all(result == False)
def test_00_00_zeros(self):
'''Test skeletonize on an array of all zeros'''
result = medial_axis(np.zeros((10, 10), bool))
assert np.all(result == False)
