def binaryFillHoles(binarydata, kernel=np.ones((3, 3, 3))):
result = np.zeros_like(binarydata)
if kernel is None:
ndimage.binary_fill_holes(binarydata, output=result)
else:
ndimage.binary_fill_holes(binarydata, structure=kernel, output=result)
return result
def obj_fill_holes(array, labelled=True):
"""
Fills holes within objects.
"""
if labelled:
fill = ndimage.binary_fill_holes(array > 0)
#holes = fill - array
else:
fill = ndimage.binary_fill_holes(array)
return fill
def pestFeatureExtraction(filename):
selem = disk(8)
image = data.imread(filename,as_grey=True)
thresh = threshold_otsu(image)
elevation_map = sobel(image)
markers = np.zeros_like(image)
if ((image<thresh).sum() > (image>thresh).sum()):
markers[image < thresh] = 1
markers[image > thresh] = 2
else:
markers[image < thresh] = 2
markers[image > thresh] = 1
segmentation = morphology.watershed(elevation_map, markers)
segmentation = dilation(segmentation-1, selem)
segmentation = ndimage.binary_fill_holes(segmentation)
segmentation = np.logical_not(segmentation)
image[segmentation]=0;
hist = np.histogram(image.ravel(),256,[0,1])
hist = list(hist[0])
hist[:] = [float(x) / (sum(hist) - hist[0]) for x in hist]
hist.pop(0)
features = np.empty( (1, len(hist)), 'float' )
a = np.array(list(hist))
f = a.astype('float')
features[0,:]=f[:]
return features
def main():
for file_path in glob.glob("/home/lucas/Downloads/Lucas/GSK 10uM/*.JPG"):
img = data.imread(file_path, as_grey=True)
img = transform.resize(img, [600, 600])
img_color = transform.resize(data.imread(file_path), [600, 600])
img[img >img.mean()-0.1] = 0
# io.imshow(img)
# io.show()
#
edges = canny(img)
bordas_fechadas = closing(img > 0.1, square(15)) # fechando gaps
fill_cells = ndi.binary_fill_holes(bordas_fechadas)
# io.imshow(fill_cells)
# io.show()
img_label = label(fill_cells, background=0)
n= 0
for x in regionprops(img_label):
if x.area < 2000 and x.area > 300:
n +=1
print x.area
minr, minc, maxr, maxc = x.bbox
try:
out_path_name = file_path.split("/")[-1].rstrip(".JPG")
io.imsave("out/cell_{}_pic_{}_area_{}.png".format(n, out_path_name, str(round(x.area))),img_color[minr-3: maxr+3, minc-3: maxc+3])
#io.show()
except:
pass
def clean_by_area(self, binary_image):
image = binary_image.copy()
image = ndi.binary_fill_holes(image)
label_image = label(binary_image)
initial_label = regionprops(label_image[0, :, :])[0].label
for z in range(0, image.shape[0]):
regions = regionprops(label_image[z, :, :])
for region in regions:
if region.label != initial_label:
for coords in region.coords:
image[z, coords[0], coords[1]] = 0
for z in range(0, image.shape[0]):
label_image = label(image[z, :, :], connectivity=1)
regions = regionprops(label_image)
if len(regions) > 1:
max_area = np.max([r.area for r in regions])
for region in regions:
if region.centroid[1] > 120 and region.area < max_area:
for coords in region.coords:
image[z, coords[0], coords[1]] = 0
return image
def segment(self, src):
ndsrc = src.ndarray / 255.
edges = canny(ndsrc,
# low_threshold=0.001,
# high_threshold=0.1,
# low_threshold=self.canny_low_threshold,
# high_threshold=self.canny_high_threshold,
sigma=self.canny_sigma)
filled = ndimage.binary_fill_holes(edges)
filled = invert(filled) * 255
# label_objects, _ = ndimage.label(filled)
# sizes = bincount(label_objects.ravel())
#
# mask_sizes = sizes > 1
# mask_sizes[0] = 0
# cleaned = mask_sizes[label_objects]
# cleaned = asarray(cleaned, 'uint8')
# cleaned = closing(cleaned, square(5))
# self._locate_helper(invert(cleaned), **kw)
nsrc = asarray(filled, 'uint8')
return nsrc
def readBinIm(path): """ desc: Reads an image, converts to binary (0 for background, 1 for shape), and fills any internal holes. arguments: path: desc: The image path. type: [str, unicode] returns: desc: An image array. type: ndarray """ im = ndimage.imread(path, flatten=True) i1 = np.where(im == 255) i2 = np.where(im != 255) im[i1] = 0 im[i2] = 1 im = ndimage.binary_fill_holes(im) return im
def get_segmented_lungs(im):
binary = im < -320
cleared = clear_border(binary)
cleared=morph(cleared,5)
label_image = label(cleared)
areas = [r.area for r in regionprops(label_image)]
areas.sort()
if len(areas) > 2:
for region in regionprops(label_image):
if region.area < areas[-2]:
for coordinates in region.coords:
label_image[coordinates[0], coordinates[1]] = 0
binary = label_image > 0
selem = disk(2)
binary = binary_erosion(binary, selem)
selem = disk(10)
binary = binary_closing(binary, selem)
edges = roberts(binary)
binary = ndi.binary_fill_holes(edges)
get_high_vals = binary == 0
im[get_high_vals] = 0
binary = morphology.dilation(binary,np.ones([5,5]))
return binary
def applyMorphologicalCleaning(self, image):
"""
Applies a variety of morphological operations to improve the detection
of worms in the image.
Takes 0.030 s on MUSSORGSKY for a typical frame region
Takes 0.030 s in MATLAB too
"""
# start with worm == 1
image = image.copy()
segmentation.clear_border(image) # remove objects at edge (worm == 1)
# fix defects in the thresholding by closing with a worm-width disk
# worm == 1
wormSE = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,
(self.wormDiskRadius+1,
self.wormDiskRadius+1))
imcl = cv2.morphologyEx(np.uint8(image), cv2.MORPH_CLOSE, wormSE)
imcl = np.equal(imcl, 1)
# fix defects by filling holes
imholes = ndimage.binary_fill_holes(imcl)
imcl = np.logical_or(imholes, imcl)
# fix barely touching regions
# majority with worm pixels == 1 (median filter same?)
imcl = nf.median_filter(imcl, footprint=[[1, 1, 1],
[1, 0, 1],
[1, 1, 1]])
# diag with worm pixels == 0
imcl = np.logical_not(bwdiagfill(np.logical_not(imcl)))
# open with worm pixels == 1
openSE = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))
imcl = cv2.morphologyEx(np.uint8(imcl), cv2.MORPH_OPEN, openSE)
return np.equal(imcl, 1)
def compute_mask(self, params):
"""Creates the mask for the base image.
Needs the base image, an instance of imageloaderparams
and the clip area, which should be already defined
by the load_base_image method.
Creates the mask by improving the base mask created by the
compute_base_mask method. Applies the mask closing, dilation and
fill holes parameters.
"""
self.compute_base_mask(params)
mask = np.copy(self.base_mask)
closing_matrix = np.ones((params.mask_closing, params.mask_closing))
if params.mask_closing > 0:
# removes small dark spots and then small white spots
mask = img_as_float(morphology.closing(
mask, closing_matrix))
mask = 1 - \
img_as_float(morphology.closing(
1 - mask, closing_matrix))
for f in range(params.mask_dilation):
mask = morphology.erosion(mask, np.ones((3, 3)))
if params.mask_fill_holes:
# mask is inverted
mask = 1 - img_as_float(ndimage.binary_fill_holes(1.0 - mask))
self.mask = mask
self.overlay_mask_base_image()
def clean_classified_image(self):
"""
clean the binary image resulting from pixel classification using morphological operators
"""
if self.class_image is None:
self.classify_image()
bim = self.class_image
feature_mask = self.features_object.mask_image
if feature_mask is not None:
bim = bim & feature_mask
bim = ni.binary_fill_holes(bim)
min_gap = 0
for n in range(min_gap):
bim = ni.binary_dilation(bim)
#bim = ni.binary_closing(bim)
#bim = ni.binary_fill_holes(bim)
min_radius = 8
for n in range(min_radius):
bim = ni.binary_erosion(bim)
#bim = ni.binary_opening(bim)
for n in range(min_radius):
bim = ni.binary_dilation(bim)
#bim = ni.binary_dilation(bim)
#bim = ni.binary_erosion(bim)
self.clean_class_image = bim.astype(np.uint8) * 255
def get_segmented_lungs(im, plot=False):
# Step 1: Convert into a binary image.
binary = im < -400
# Step 2: Remove the blobs connected to the border of the image.
cleared = clear_border(binary)
# Step 3: Label the image.
label_image = label(cleared)
# Step 4: Keep the labels with 2 largest areas.
areas = [r.area for r in regionprops(label_image)]
areas.sort()
if len(areas) > 2:
for region in regionprops(label_image):
if region.area < areas[-2]:
for coordinates in region.coords:
label_image[coordinates[0], coordinates[1]] = 0
binary = label_image > 0
# Step 5: Erosion operation with a disk of radius 2. This operation is seperate the lung nodules attached to the blood vessels.
selem = disk(2)
binary = binary_erosion(binary, selem)
# Step 6: Closure operation with a disk of radius 10. This operation is to keep nodules attached to the lung wall.
selem = disk(10) # CHANGE BACK TO 10
binary = binary_closing(binary, selem)
# Step 7: Fill in the small holes inside the binary mask of lungs.
edges = roberts(binary)
binary = ndi.binary_fill_holes(edges)
# Step 8: Superimpose the binary mask on the input image.
get_high_vals = binary == 0
im[get_high_vals] = -2000
return im, binary
def test_02_02_one_worm(self):
'''Find a single worm'''
image = np.zeros((20, 20), bool)
index, count, i, j = get_line_pts(
np.array([1,6,19,14]),
np.array([5,0,13,18]),
np.array([6,19,14,1]),
np.array([0,13,18,5]))
image[i,j] = True
image = binary_fill_holes(image)
workspace, module = self.make_workspace(image)
module.worm_length.value = 12
module.worm_width.value = 5
module.angle_count.value = 16
module.run(workspace)
m = workspace.measurements
self.assertTrue(isinstance(m, cpmeas.Measurements))
count = m.get_current_image_measurement(
'_'.join((ID.I.C_COUNT, OBJECTS_NAME)))
self.assertEqual(count, 1)
x = m.get_current_measurement(OBJECTS_NAME,
ID.I.M_LOCATION_CENTER_X)
self.assertEqual(len(x), 1)
self.assertAlmostEqual(x[0], 9., 1)
y = m.get_current_measurement(OBJECTS_NAME,
ID.I.M_LOCATION_CENTER_Y)
self.assertEqual(len(y), 1)
self.assertAlmostEqual(y[0], 10., 1)
a = m.get_current_measurement(OBJECTS_NAME,
ID.M_ANGLE)
self.assertEqual(len(a), 1)
self.assertAlmostEqual(a[0], 135, 0)
def alg(ring):
# fill holes in binary objects and then see if anything got filled
xor = scipy.logical_xor(ring, ndimage.binary_fill_holes(ring))
if xor.any():
return True
else:
return False
def load_scenes(filename):
zipped_scenes = []
print 'Working on: ' + filename
img = data.imread('scenes/' + filename, as_grey=True)
tmp = img
tmp = filter.canny(tmp, sigma=2.0)
tmp = ndimage.binary_fill_holes(tmp)
#tmp = morphology.dilation(tmp, morphology.disk(2))
tmp = morphology.remove_small_objects(tmp, 2000)
contours = measure.find_contours(tmp, 0.8)
ymin, xmin = contours[0].min(axis=0)
ymax, xmax = contours[0].max(axis=0)
if xmax - xmin > ymax - ymin:
xdest = 1000
ydest = 670
else:
xdest = 670
ydest = 1000
src = np.array(((0, 0), (0, ydest), (xdest, ydest), (xdest, 0)))
dst = np.array(((xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)))
tform3 = tf.ProjectiveTransform()
tform3.estimate(src, dst)
warped = tf.warp(img, tform3, output_shape=(ydest, xdest))
tmp = filter.canny(warped, sigma=2.0)
tmp = morphology.dilation(tmp, morphology.disk(2))
descriptor_extractor.detect_and_extract(tmp)
obj_key = descriptor_extractor.keypoints
scen_desc = descriptor_extractor.descriptors
zipped_scenes.append([warped, scen_desc, obj_key, filename])
return zipped_scenes
def footprint_fitness_error(self, points): temp_footprint = np.zeros(self.FOOTPRINT_added_boundary.shape, dtype=np.uint8) len_points = len(points) for idx1 in xrange(0, len_points): rr,cc = line(points[idx1][0], points[idx1][1], points[idx1-1][0],points[idx1-1][1]) temp_footprint[rr,cc] = 1 temp_footprint = ndimage.binary_fill_holes(temp_footprint) temp_footprint = temp_footprint * 1 rr,cc = np.nonzero(temp_footprint) #RATIO OF ZEROS AND ONES SA LOOB zero_counter = 0.0 nonzero_counter = 0.0 for point in zip(rr,cc): if self.FOOTPRINT_added_boundary[point[0]][point[1]] == 0: zero_counter += 1.0 else: nonzero_counter += 1.0 footprint_copy = copy.deepcopy(self.FOOTPRINT_added_boundary) footprint_copy[rr,cc] = 0 nonzero = len(footprint_copy[footprint_copy != 0]) total = (len(footprint_copy[footprint_copy == 0]) + nonzero) * 1.0 return (nonzero / total) + (zero_counter / (nonzero_counter + zero_counter))
def __call__(self, image, window_size=10, threshold=0, fill_holes=True,
outline_smoothing=2, remove_borderobjects=True, size_min=1,
*args, **kw):
thresh = threshold_adaptive(image, block_size=window_size,
offset=-1*threshold)
if outline_smoothing >= 1:
thresh = outlineSmoothing(thresh, outline_smoothing)
thresh = remove_small_objects(thresh, size_min)
seeds = ndi.label(clear_border(~thresh))[0]
thresh = ndi.binary_fill_holes(thresh)
smask = seeds.astype(bool)
# object don't touch border after outline smoothing
if remove_borderobjects:
thresh = clear_border(thresh)
img = np.zeros(thresh.shape)
img[~smask] = 1
edt = ndi.morphology.distance_transform_edt(img)
edt -= ndi.morphology.distance_transform_edt(seeds)
labels = watershed(edt, seeds)
labels[smask] = 0
labels[~thresh] = 0
return labels
def compute_mask(aparc, labels=[0, 5000]):
import nibabel as nb
import numpy as np
import os.path as op
import scipy.ndimage as nd
segnii = nb.load(aparc)
seg = segnii.get_data()
mask = np.ones_like(seg, dtype=np.uint8)
for l in labels:
mask[seg == l] = 0
struct = nd.iterate_structure(nd.generate_binary_structure(3, 1), 4)
mask = nd.binary_dilation(mask, structure=struct).astype(np.uint8)
mask = nd.binary_closing(mask, structure=struct)
mask = nd.binary_fill_holes(mask, structure=struct).astype(np.uint8)
mask[mask > 0] = 1
mask[mask <= 0] = 0
hdr = segnii.get_header().copy()
hdr.set_data_dtype(np.uint8)
hdr.set_xyzt_units("mm", "sec")
out_file = op.abspath("nobstem_mask.nii.gz")
nii = nb.Nifti1Image(mask, segnii.get_affine(), hdr).to_filename(out_file)
return out_file
def segmentation(file_name):
data_x, data_y, data_z = get_data(file_name)
shape_x = len(np.unique(data_x))
shape_y = len(np.unique(data_y))
X = data_x.reshape(shape_x, shape_y)
Y = data_y.reshape(shape_x, shape_y)
Z = data_z.reshape(shape_x, shape_y)
markers = np.zeros_like(Z)
markers[Z < 0.15] = 1
markers[Z > 0.3] = 2
elevation_map = roberts(Z)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), sharex=True, sharey=True)
# ax.imshow(Z)
# ax.imshow(elevation_map, cmap=plt.cm.jet, interpolation='nearest')
segmentation = watershed(elevation_map, markers)
ax2.imshow(segmentation, interpolation='nearest')
# ax.axis('off')
# ax.set_title('segmentation')
segmentation = ndi.binary_fill_holes(segmentation - 1)
labeled_coins, _ = ndi.label(segmentation)
ax1.imshow(Z, cmap=plt.cm.gray, interpolation='nearest')
ax1.contour(segmentation, [0.5], linewidths=1.2, colors='y')
ax1.axis('off')
ax1.set_adjustable('box-forced')
plt.show()
def guess_corners(bw):
"""
Infer the corners of an image using a Sobel filter to find the edges and a
Harris filter to find the corners. Takes only as single color chanel.
Parameters
----------
bw : (m x n) ndarray of ints
Returns
-------
corners : pixel coordinates of plot corners, unsorted
outline : (m x n) ndarray of bools True -> plot area
"""
assert len(bw.shape) == 2
bw = img_as_uint(bw)
e_map = ndimage.sobel(bw)
markers = np.zeros(bw.shape, dtype=int)
markers[bw < 30] = 1
markers[bw > 150] = 2
seg = ndimage.watershed_ift(e_map, np.asarray(markers, dtype=int))
outline = ndimage.binary_fill_holes(1 - seg)
corners = harris(np.asarray(outline, dtype=int))
corners = approximate_polygon(corners, 1)
return corners, outline
def form_mask_BR_v2(start_temp, max_temp, temp_rate):
dtype = [('temp', float), ('mask', np.ndarray), ('dist', np.ndarray)]
masks = []
#IMPROVEMENT!!!
#MINUS LAST COORDINATES TO NEXT TEMPERATURE
#LESS POINTS TO CHECK
while (start_temp <= max_temp):
coordinates = []
coor_dist = []
distance = int(round(start_temp))
array_size = distance * 2 + 1
img = np.zeros((array_size, array_size), dtype=np.uint8)
rr, cc = circle_perimeter(distance, distance, distance)
img[rr, cc] = 1
rr,cc = np.nonzero(ndimage.binary_fill_holes(img).astype(int))
img[rr, cc] = 1
for idx in xrange(0, len(rr)):
dist_temp = np.linalg.norm(np.array([rr[idx], cc[idx]]) - np.array([distance, distance]))
if dist_temp <= start_temp:
coordinates.append([rr[idx], cc[idx]])
coor_dist.append(dist_temp)
# coordinates.remove([distance, distance])
coordinates = coordinates - np.array([distance, distance])
masks.append((start_temp, coordinates, np.array(coor_dist)))
start_temp += temp_rate
return np.array(masks, dtype=dtype)
def main():
plt.figure(figsize=(25, 24))
planes = ['samolot00.jpg', 'samolot01.jpg', 'samolot03.jpg', 'samolot04.jpg', 'samolot05.jpg','samolot07.jpg',
'samolot08.jpg', 'samolot09.jpg', 'samolot10.jpg', 'samolot11.jpg', 'samolot12.jpg', 'samolot13.jpg',
'samolot14.jpg', 'samolot15.jpg', 'samolot16.jpg', 'samolot17.jpg', 'samolot18.jpg', 'samolot20.jpg']
i = 1
for file in planes:
img = data.imread(file, as_grey=True)
img2 = data.imread(file)
ax = plt.subplot(6, 3, i)
ax.axis('off')
img **= 0.4
img = filter.canny(img, sigma=3.0)
img = morphology.dilation(img, morphology.disk(4))
img = ndimage.binary_fill_holes(img)
img = morphology.remove_small_objects(img, 1000)
contours = measure.find_contours(img, 0.8)
ax.imshow(img2, aspect='auto')
for n, contour in enumerate(contours):
ax.plot(contour[:, 1], contour[:, 0], linewidth=1.5)
center = (sum(contour[:, 1])/len(contour[:, 1]), sum(contour[:, 0])/len(contour[:, 0]))
ax.scatter(center[0], center[1], color='white')
i += 1
plt.savefig('zad2.pdf')
def findPlantsCanny(stackVar, stackSum, showImages=True):
edges = canny(stackVar)
fill_stack = ndimage.binary_fill_holes(edges)
label_objects, nb_labels = ndimage.label(fill_stack)
sizes = np.bincount(label_objects.ravel())
mask_sizes = sizes > 25
for label in range(len(mask_sizes)):
'''
Get rid of lines in addition to the straight size threshold.
'''
pts = np.where(label_objects == label)
xRange = (max(pts[0]) - min(pts[0]))
yRange = (max(pts[1]) - min(pts[1]))
areaCovered = float(len(pts[0])) / (xRange*yRange)
if (areaCovered < .33) or (xRange < 3) or (yRange < 3):
mask_sizes[label] = False
mask_sizes[0] = 0
plants_cleaned = mask_sizes[label_objects]
labeled_plants, numPlants = ndimage.label(plants_cleaned)
center = findCenters(labeled_plants, stackSum)
if showImages:
fig, axs = plt.subplots(1,3, figsize=(14,4), sharey=True)
axs[0].imshow(stackVar)
axs[1].imshow(stackVar, cmap=plt.cm.jet, interpolation='nearest') #@UndefinedVariable
axs[1].contour(plants_cleaned, [0.5], linewidths=1.2, colors='y')
axs[2].imshow(labeled_plants, cmap=plt.cm.spectral, interpolation='nearest') #@UndefinedVariable
axs[2].scatter(np.array(center.tolist())[:,1], np.array(center.tolist())[:,0],
color='grey')
for ax in axs: ax.axis('off')
fig.subplots_adjust(wspace=.01)
return labeled_plants, center
def im_proc(im):
"""Apply series of morphological procedures on image."""
th = threshold_otsu(im)
im_bin = im > th
return(ndi.binary_fill_holes(
morphology.closing(
im_bin,np.ones((3,3)))))
def _segment_watershed(image): elevation_map = sobel(image) markers = np.zeros(image.shape) # initialize markers as zero array # determine thresholds for markers sorted_pixels = np.sort(image, axis=None) max_int = np.mean(sorted_pixels[-10:]) min_int = np.mean(sorted_pixels[:10]) #max_int = np.max(orig_image) #min_int = np.min(orig_image) alpha_min = 0.01 alpha_max = 0.4 thresh_background = (1-alpha_min)*min_int + alpha_min*max_int thresh_spots = (1-alpha_max)*min_int + alpha_max*max_int markers[image < thresh_background] = 1 # mark background markers[image > thresh_spots] = 2 # mark background segmentation = watershed(elevation_map, markers) segmentation = segmentation-1 segmentation = ndi.binary_fill_holes(segmentation) # fill holes return segmentation
def detect_clouds(self,k=np.array([[-1,-2,-1],[0,0,0],[1,2,1]]), umbral=40): 'Descripcion: Obtiene los bordes de una imagen entregada \n'\ 'puede retornar los bordes y entregar la imagen rellenada \n'\ '\n'\ 'Parametros\n'\ '----------\n'\ 'imageIn : Imagen raster con informacion de Z o de ref.\n'\ 'k : forma del filtro que se utiliza para obtener los bordes.\n'\ 'umbral : Umbral utilizado para determinar si un objeto es o no.\n'\ ' un borde.\n'\ '\n'\ 'Retornos\n'\ '----------\n'\ 'borders : Matriz con los bordes detectados.\n'\ 'binario : Matriz binaria con los bordes rellenos.\n'\ '\n'\ 'Ejemplo\n'\ '----------\n'\ 'borders,binario=detect_clouds(Z).\n'\ #Obtiene el gradiente, y el binario gradiente=radar_f90.detect_clouds(self.Z,k,k.T,radar_f90.ncols,radar_f90.nrows) bordes=np.zeros(gradiente.shape) bordes[gradiente>umbral]=1 #Llena el binario y quita el ruido binario=nd.binary_fill_holes(bordes)*1 #Retorna lo obtenido self.borders=bordes self.binario=binario
def _segment_threshold(image): thresh = threshold_otsu(image) seg = image > thresh seg = ndi.binary_fill_holes(seg) # fill holes return seg
def fill_holes(self, binary_image, selem, iterations):
image = binary_image.copy()
for j in range(0, iterations):
image = dilation(image, selem)
image = ndi.binary_fill_holes(image)
for j in range(0, iterations):
image = erosion(image, selem)
return image
def applyFilter(self, data, chanNum, frNum, im):
import skimage.morphology
if len(data.shape) == 3: #3D
selem = skimage.morphology.ball(self.radius)
else:
selem = skimage.morphology.disk(self.radius)
return ndimage.binary_fill_holes(data, selem)
def analyse(self, **kwargs):
image_object = kwargs['image']
if image_object is None:
raise RuntimeError()
# Read the image
image = cv2.imread(self.image_utils.getOutputFilename(image_object.id))
if image is None:
print('File not found')
return
# Work on green channel
gray = image[:, :, 1]
# Apply otsu thresholding
thresh = filters.threshold_otsu(gray)
gray[gray < thresh] = 0
# Apply histogram equalization
gray = exposure.equalize_adapthist(gray) * 255
# Create elevation map
elevation_map = filters.sobel(gray)
gray = gray.astype(int)
# Create cell markers
markers = numpy.zeros_like(gray)
markers[gray < 100] = 2 # seen as white in plot
markers[gray > 150] = 1 # seen as black in plot
# Segment with watershed using elevation map
segmentation = morphology.watershed(elevation_map, markers)
segmentation = ndi.binary_fill_holes(segmentation - 1)
# labeled_image, n = ndi.label(segmentation)
# Watershed with distance transform
kernel = numpy.ones((5, 5), numpy.uint8)
distance = ndi.distance_transform_edt(segmentation)
distance2 = cv2.erode(distance, kernel)
distance2 = cv2.dilate(distance2, kernel)
local_max = peak_local_max(distance2, num_peaks=1, indices=False, labels=segmentation)
markers2 = ndi.label(local_max)[0]
labels = morphology.watershed(-distance2, markers2, mask=segmentation)
# Extract regions (caching signifies more memory use)
regions = regionprops(labels, cache=True)
# Filter out big wrong regions
regions = [region for region in regions if region.area < 2000]
# Set result
result = str(len(regions))
return result
def run(self, ips, snap, img, para=None):
ndimg.binary_fill_holes(snap, output=img)
img *= 255
# Create method identifier
method_id = [m for m in mlist for s in slist for rdx in range(nrun)]
# Resample images to geometry of template and rescale TSNR value with SQRT(NVol)
imgs = []
for i, e in enumerate(method_id):
if 'spm' in e:
img = resample_to_img(filelist[i], template)
else:
img = load_img(filelist[i])
imgs.append(math_img('img * np.sqrt(%d)' % nvol, img=img))
# Create mask (containing only voxels with values in at least half of the images)
img_concat = concat_imgs(imgs)
mask = np.sum(img_concat.get_data()!=0, axis=-1)>=(img_concat.shape[-1] * 0.8)
mask = binary_fill_holes(
binary_dilation(binary_erosion(mask, iterations=2), iterations=2))
group_mask = new_img_like(img_concat, mask.astype('int'), copy_header=True)
# Create 2nd-level model
design_matrix = design_matrix = pd.get_dummies(method_id)
second_level_model = SecondLevelModel(n_jobs=-1, mask=group_mask)
second_level_model = second_level_model.fit(imgs, design_matrix=design_matrix)
# Compute contrasts, save nifti and plot glass brain
weights = [ [1, 0, 0, 0, 0], [1,-1, 0, 0, 0], [1, 0,-1, 0, 0], [1, 0, 0,-1, 0], [1, 0, 0, 0,-1],
[-1, 1, 0, 0, 0], [0, 1, 0, 0, 0], [0, 1,-1, 0, 0], [0, 1, 0,-1, 0], [0, 1, 0, 0,-1],
[-1, 0, 1, 0, 0], [0,-1, 1, 0, 0], [0, 0, 1, 0, 0], [0, 0, 1,-1, 0], [0, 0, 1, 0,-1],
[-1, 0, 0, 1, 0], [0,-1, 0, 1, 0], [0, 0,-1, 1, 0], [0, 0, 0, 1, 0], [0, 0, 0, 1,-1],
[-1, 0, 0, 0, 1], [0,-1, 0, 0, 1], [0, 0,-1, 0, 1], [0, 0, 0,-1, 1], [0, 0, 0, 0, 1]]
for i, w in enumerate(weights):
from skimage.feature import canny
edges = canny(coins)
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(edges, cmap=plt.cm.gray)
ax.set_title('Canny detector')
ax.axis('off')
plt.savefig('LKP7/canny.png')
######################################################################
# These contours are then filled using mathematical morphology.
from scipy import ndimage as ndi
fill_coins = ndi.binary_fill_holes(edges)
fig, ax = plt.subplots(figsize=(4, 3))
ax.imshow(fill_coins, cmap=plt.cm.gray)
ax.set_title('filling the holes')
ax.axis('off')
plt.savefig('LKP7/filling the holes')
######################################################################
# Small spurious objects are easily removed by setting a minimum size for
# valid objects.
from skimage import morphology
coins_cleaned = morphology.remove_small_objects(fill_coins, 21)
"""
Created on Thu Oct 22 11:30:55 2020
@author: Usuario
"""
import cv2 #OpenCV
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage
img1 = cv2.imread('img/bananas.jpg')
I = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
umbral, _ = cv2.threshold(I, 0, 255, cv2.THRESH_OTSU)
mascara = np.uint8((I < umbral) * 255) #Binary image
#cv2.imshow("Img-COLOR",img1)
#cv2.imshow("Img-GRAY",I)
cv2.imshow("Img-BINARY", mascara)
#Get labels
output = cv2.connectedComponentsWithStats(mascara, 4, cv2.CV_32F)
cantObj = output[0] #objects quantify
labels = output[1] #labels
stats = output[2]
#Get ArgMax
mascara = (np.argmax(stats[:, 4][1:]) + 1 == labels)
mascara = ndimage.binary_fill_holes(mascara).astype(int)
cv2.waitKey(0)
cv2.destroyAllWindows()
def ProcessImage(path):
global count
count += 1
sys.stderr.write('%3d Processing %s...\n' % (count, path))
rects = []
B = LoadAndBinarizeImage(path)
# Exclude borders
B[:10, :] = 0
B[-10:, :] = 0
B[:, :10] = 0
B[:, -10:] = 0
if ShowImage:
ShowBinaryArray(B)
B = ndimage.binary_fill_holes(B, structure=np.ones((2, 2)))
if ShowImage:
ShowBinaryArray(B)
# Following
# http://scipy-lectures.github.com/advanced/image_processing/index.html
s = ndimage.generate_binary_structure(2, 2)
label_im, nb_labels = ndimage.label(B, structure=s)
# remove small components
# TODO(danvk): how does this work?
sizes = ndimage.sum(B, label_im, range(nb_labels + 1))
mask_size = sizes < 1000
remove_pixel = mask_size[label_im]
label_im[remove_pixel] = 0
labels = np.unique(label_im)
label_im = np.searchsorted(labels, label_im)
# Use OpenCV to get info about each component.
# http://stackoverflow.com/questions/9056646/python-opencv-find-black-areas-in-a-binary-image
cs, _ = cv2.findContours(label_im.astype('uint8'),
mode=cv2.RETR_LIST,
method=cv2.CHAIN_APPROX_SIMPLE)
# regions ordered by area
regions = [(cv2.moments(c)['m00'], idx) for idx, c in enumerate(cs)]
regions.sort()
regions.reverse()
for area, idx in regions:
c = cs[idx]
#convexI = np.zeros(label_im.shape[0:2]).astype('uint8')
x, y, w, h = cv2.boundingRect(c)
ConvexHull = cv2.convexHull(c)
ConvexArea = cv2.contourArea(ConvexHull)
Solidity = area / ConvexArea if ConvexArea else 0
#cv2.drawContours( convexI, [ConvexHull], -1,
# color=255, thickness=-1 )
x2 = x + w
y2 = y + h
if w > 20 and h > 20:
sys.stderr.write('%d x %d, solidity %f\n' % (w, h, Solidity))
if w > MIN_PHOTO_SIZE and h > MIN_PHOTO_SIZE and Solidity > MIN_PHOTO_SOLIDITY:
rects.append({
'left': 80 + 5 * x,
'top': 80 + 5 * y,
'right': 80 + 5 * x2,
'bottom': 80 + 5 * y2,
'solidity': Solidity
})
output = {
'file': path,
'shape': {
'w': 5 * B.shape[1] + 160,
'h': 5 * B.shape[0] + 160
}
}
if AcceptPhotoDetection(B, rects):
output['rects'] = rects
print json.dumps(output)
def manualSliceBySlice(img, initLineList=None, lengthScaleRatio=0.2):
if initLineList is not None:
if isinstance(initLineList, list) and len(initLineList[0].shape) == 2:
a = img.show(disable=['click', 'swap'], initLineList=initLineList)
else:
newInitLineList = [[]]
currentslice = initLineList[0][0]
for n in range(len(initLineList)):
if currentslice != initLineList[n][0]:
newInitLineList.append([])
currentslice = initLineList[n][0]
newInitLineList[-1].append(initLineList[n])
for n in range(len(newInitLineList)):
newInitLineList[n] = np.array(newInitLineList[n])
a = img.show(disable=['click', 'swap'],
initLineList=newInitLineList)
else:
a = img.show(disable=['click', 'swap'])
while not (a.enter):
a = img.show(disable=['click', 'swap'], initLineList=a.lines)
for n in range(len(a.lines) - 1, -1, -1):
if len(a.lines[n]) < 3:
a.lines.pop(n)
if a.color:
if len(img.dim) > 4:
raise Exception('Dimension of image more than 3,' + str(img.dim))
else:
if len(img.dim) > 3:
raise Exception('Dimension of image more than 3,' + str(img.dim))
getind = []
for n in range(len(a.lines)):
getind.append(a.lines[n][0][0])
getind = np.array(getind).astype(int)
a.lines = [a.lines[i] for i in np.sort(getind)]
aind = 0
img2 = img.clone()
if a.color:
img2.changeGreyscaleFormat()
img2.data[:] = 0
img2.data = img2.data.astype('uint8')
minArea = float('inf')
for n in range(img2.data.shape[0]):
while a.lines[aind][0][0] == n:
for num in [1000, 10000, 100000, 1000000]:
ar = np.array(a.lines[aind])
tck, temp = interpolate.splprep([ar[:, -2], ar[:, -1]],
s=0,
k=min(4, len(ar)) - 1)
cspline_detectline = np.array(
interpolate.splev(np.linspace(0, 1, num=num), tck)).T
cspline_detectline = np.floor(cspline_detectline).astype(int)
for nn in range(len(cspline_detectline)):
img2.data[n][tuple(cspline_detectline[nn])] = 1
ar2 = np.array([ar[0], 0.5 * (ar[0] + ar[-1]), ar[-1]])
tck, temp = interpolate.splprep([ar2[:, -2], ar2[:, -1]],
s=0,
k=1)
cspline_detectline = np.array(
interpolate.splev(np.linspace(0, 1, num=num), tck)).T
cspline_detectline = np.floor(cspline_detectline).astype(int)
for nn in range(len(cspline_detectline)):
img2.data[n][tuple(cspline_detectline[nn])] = 1
countpixel = img2.data[n].sum()
img2.data[n] = binary_fill_holes(img2.data[n]).astype(
img2.data.dtype)
if img2.data[n].sum() > (countpixel * 1.1):
break
else:
logger.warning(
'Could not form a closed surface with area larger than 1.1 of boundary on Slice '
+ str(n))
temp_area = img2.data[n].sum()
if temp_area < minArea:
minArea = temp_area
aind += 1
if aind >= len(a.lines):
break
if aind >= len(a.lines):
break
img2.data *= 255
lengthScale = max(2, lengthScaleRatio * minArea**0.5)
img2.data = gaussian_filter(img2.data, (0, lengthScale, lengthScale))
img3 = img2.clone()
currentind = 0
for n in range(getind[0] + 1, getind[-1]):
if n == getind[currentind + 1]:
currentind += 1
else:
ratio = float(n - getind[currentind]) / (getind[currentind + 1] -
getind[currentind])
img3.data[n] = (img2.data[getind[currentind + 1]] * ratio +
img2.data[getind[currentind]] *
(1. - ratio)).astype(img2.data.dtype)
sigma = []
if a.color:
nDim = [0, -3, -2]
else:
nDim = [0, -2, -1]
for n in nDim:
sigma.append(img3.dimlen[img3.dim[n]])
sigma = 1. / np.array(sigma)
sigma = sigma / sigma[1:].mean() * lengthScale
img3.data = gaussian_filter(img3.data, sigma)
return (a.lines, img3)
def fillHoles(bImg):
return ndimage.binary_fill_holes(bImg)
def get_segmented_lungs(im, plot=True):
'''
This funtion segments the lungs from the given 2D slice.
'''
# if plot == True:
# f, plots = plt.subplots(8, 1, figsize=(40, 40))
'''
Step 1: Convert into a binary image.
'''
print('step1')
binary = im < 604
plt.imshow(binary, cmap=plt.cm.gray)
plt.show()
'''
Step 2: Remove the blobs connected to the border of the image.
'''
print('step2')
cleared = clear_border(binary)
plt.imshow(cleared, cmap=plt.cm.gray)
plt.show()
'''
Step 3: Label the image.
'''
print('step3')
label_image = label(cleared)
plt.imshow(label_image, cmap=plt.cm.gray)
plt.show()
'''
Step 4: Keep the labels with 2 largest areas.
'''
print('step4')
areas = [r.area for r in regionprops(label_image)]
areas.sort()
if len(areas) > 2:
for region in regionprops(label_image):
if region.area < areas[-2]:
for coordinates in region.coords:
label_image[coordinates[0], coordinates[1]] = 0
binary = label_image > 0
plt.imshow(binary, cmap=plt.cm.gray)
plt.show()
'''
Step 5: Erosion operation with a disk of radius 2. This operation is
seperate the lung nodules attached to the blood vessels.
'''
print('step5')
selem = disk(2)
binary = binary_erosion(binary, selem)
plt.imshow(binary, cmap=plt.cm.gray)
plt.show()
'''
Step 6: Closure operation with a disk of radius 10. This operation is
to keep nodules attached to the lung wall.
'''
print('step6')
selem = disk(10)
binary = binary_closing(binary, selem)
plt.imshow(binary, cmap=plt.cm.gray)
plt.show()
'''
Step 7: Fill in the small holes inside the binary mask of lungs.
'''
print('step7')
edges = roberts(binary)
binary = ndi.binary_fill_holes(edges)
plt.imshow(binary, cmap=plt.cm.gray)
plt.show()
'''
Step 8: Superimpose the binary mask on the input image.
'''
print('step8')
get_high_vals = binary == 0
im[get_high_vals] = 0
plt.imshow(im, cmap=plt.cm.gray)
plt.show()
return im
def load_mask(self, image_id):
"""Generate instance masks for an image.
Returns:
masks: A bool array of shape [height, width, instance count] with
one mask per instance.
class_ids: a 1D array of class IDs of the instance masks.
"""
# If not a Mitochondria dataset image, delegate to parent class.
image_info = self.image_info[image_id]
nuevo = os.path.dirname(os.path.dirname(image_info['path']))
nuevo = os.path.join(nuevo, 'mask')
path, filename = os.path.split(image_info['path'])
final = os.path.join(nuevo, filename)
mask = skimage.io.imread(final)
mask[mask < 150] = 0
mask[mask > 150] = 255
contours = measure.find_contours(mask, 0.6)
lista = []
#TOTALMENTE PRESCINDBLE
for n, contour in enumerate(contours):
if (contour.shape[0] > 0):
centro = np.mean(contour, axis=0)
if (mask[int(centro[0]), int(centro[1])] != 0):
if (False == np.array_equal(contour[0, :],
contour[-1, :])):
w = ((contour[0, :] + contour[-1, :]) / 2).astype(int)
if (mask[w[0], w[1]] != 0):
if ((contour[0, 0] == contour[-1, 0]
or contour[0, 1] == contour[-1, 1])):
lista.append(contour)
else:
if (len(contour) > 30):
if (mask[w[0], w[1] - 1] == 255
and mask[w[0] + 1, w[1]] == 255
and mask[w[0] - 1, w[1]] == 255
and 255 == mask[w[0], w[1] + 1]):
lista.append(contour)
else:
lista.append(contour)
if (len(lista) == 0):
return np.empty([mask.shape[0], mask.shape[1],
1]), np.zeros([1], dtype=np.int32)
lista3 = np.empty([mask.shape[0], mask.shape[1], len(lista)])
for i in range(len(lista)):
if (False == np.array_equal(lista[i][0, :], lista[i][-1, :])):
#centro=lista[i].sum(axis=0)/lista[i].shape[0]
centro = (lista[i][0, :] + lista[i][-1, :]) / 2
#rad=(min(np.sqrt(np.square(lista[i]-centro).sum(axis=1)))+max(np.sqrt(np.square(lista[i]-centro).sum(axis=1))))/2
rad = max(np.sqrt(np.square(lista[i] - centro).sum(axis=1)))
lista3[:, :,
i] = create_circular_mask(mask.shape[0],
mask.shape[1],
center=[centro[1], centro[0]],
radius=rad) * mask
else:
r_mask = np.zeros_like(mask, dtype='bool')
r_mask[np.round(lista[i][:, 0]).astype('int'),
np.round(lista[i][:, 1]).astype('int')] = 1
r_mask = ndimage.binary_fill_holes(r_mask)
lista3[:, :, i] = r_mask * mask
lista3[np.isnan(lista3)] = 0
lista3[lista3 < 150] = 0
lista3[lista3 > 150] = 1
lista4 = np.sum(lista3, axis=2)
for i in range(0, lista3.shape[-1]):
c = measure.find_contours(lista3[:, :, i], 0.5)
if (len(c) > 1):
lista3[:, :i] = lista3[:, :, i][lista4 == 2] = 0
return lista3, np.ones([lista3.shape[-1]], dtype=np.int32)
def generate_datasets(volume_size=(512, 512, 512),
n_fibers=50,
radius_lim=(4, 10),
length_lim=(0.2, 0.8),
gap_lim=(3, 10),
max_fails=100,
median_rad=3,
intersect=False,
output_dir=None,
params=None):
"""Simulates speficied configurations of fibers and stores in a npy file.
Simulates a number of fiber configurations speficied in `params` with `n_fibers` of the
radii and lengths in ranges `radius_lim` and `length_lim`, separated with gaps in a range
of `gap_lim`. The simulation process stops if the number of attempts to generate a fiber
exceeds `max_fails`.
Parameters
----------
volume_size : tuple
Indicates the size of the volume.
n_fibers : integer
Indicates the number of fibers to be generated.
radius_lim : tuple
Indicates the range of radii for fibers to be generated.
length_lim : tuple
Indicates the range of lengths for fibers to be generated.
gap_lim : tuple
Indicates the range of gaps separating the fibers from each other.
max_fails : integer
Indicates the maximum number of failures during the simulation process.
median_rad : integer
Indicates the radius of median filter to fill holes occured due to rounding of
coordinates of the generated fibers.
intersect : boolean
Specifies if generated fibers can intersect.
output_dir : str
Indicates the path to the output folder where the data will be stored.
params : dict
Indicates the configurations of orientation of fibers to be generated.
Returns
-------
out : dict
The dictionary of generated datasets of specified configurations.
"""
if params is None:
params = {
'aligned': {
'lat_rng': (15, 15),
'azth_rng': (27, 27)
},
'medium': {
'lat_rng': (0, 45),
'azth_rng': (-45, 45)
},
'disordered': {
'lat_rng': (0, 90),
'azth_rng': (-89, 90)
}
}
out = {}
for name, config in params.items():
data, lat_data, azth_data, diameter_data, n_gen_fibers, elapsed_time = \
simulate_fibers(volume_size,
lat_lim=tuple([np.deg2rad(v) for v in config['lat_rng']]),
azth_lim=tuple([np.deg2rad(v) for v in config['azth_rng']]),
radius_lim=radius_lim,
n_fibers=n_fibers,
max_fails=max_fails,
gap_lim=gap_lim,
length_lim=length_lim,
intersect=intersect)
data_8bit = data.astype(np.uint8)
data_8bit = ndi.binary_fill_holes(data_8bit)
data_8bit = ndi.median_filter(data_8bit,
footprint=morphology.ball(median_rad))
lat_data = ndi.median_filter(lat_data,
footprint=morphology.ball(median_rad))
azth_data = ndi.median_filter(azth_data,
footprint=morphology.ball(median_rad))
diameter_data = ndi.median_filter(
diameter_data, footprint=morphology.ball(median_rad))
out[name] = {
'data': data_8bit,
'lat': lat_data,
'azth': azth_data,
'diameter': diameter_data,
'skeleton':
morphology.skeletonize_3d(data_8bit).astype(np.float32),
'props': {
'n_gen_fibers': n_gen_fibers,
'time': elapsed_time,
'intersect': intersect
}
}
if output_dir is not None and not os.path.exists(output_dir):
os.makedirs(output_dir)
np.save(
os.path.join(
output_dir, 'dataset_fibers_n{}_r{}_{}_g{}_{}_mr{}_i{}.npy'.format(
n_fibers, radius_lim[0], radius_lim[-1], gap_lim[0],
gap_lim[-1], median_rad, int(intersect))), out)
return out
from skimage.filters import rank
# Loading Images
di1 = 'C:\\Projects\\Images\\diestrus1.tif'
di2 = 'C:\\Projects\\Images\\diestrus2.tif'
es1 = 'C:\\Projects\\Images\\estrus.tif'
es2 = 'C:\\Projects\\Images\\estrus2tif'
met1 = 'C:\\Projects\\Images\\metestrus1.tif'
met2 = 'C:\\Projects\\Images\\metestrus2.tif'
pro1 = 'C:\\Projects\\Images\\proestrus1.tif'
pro2 = 'C:\\Projects\\Images\\proestrus2.tif'
ex1 = 'C:\\Projects\\Images\\example_cells_1.tif'
ex2 = 'C:\\Projects\\Images\\example_cells_2.tif'
img = io.imread(di1)
# Gaussian Smoothing
sigma = 2
smooth = img[:, :, 2]
smooth_filter = ndi.filters.gaussian_filter(smooth, sigma)
plt.imshow(smooth_filter)
plt.show(smooth_filter.any())
# Adaptive background
struct = (
(np.mgrid[:31, :31][0] - 15) ** 2 + (np.mgrid[:31, :31][1] - 15) ** 2
) <= 15 ** 2
bg = rank.mean(smooth_filter, selem=struct)
# Threshold
threshold = smooth_filter >= bg
threshold = ndi.binary_fill_holes(np.logical_not(threshold))
plt.imshow(threshold, interpolation='none', cmap='gray')
plt.show(threshold.any())
def extractFeatures(self, img):
# threshold
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
thresh = threshold_otsu(gray)
binary = gray >= thresh
bw_img1 = morphology.closing(binary, morphology.square(3))
# pad to ensure contour is continuous
bw_img = np.pad(bw_img1, 1, 'constant')
# plt.imshow(bw_img1)
# plt.title('Black and White')
# plt.savefig(self.fnm+"_BW.png",bbox_inches='tight')
# plt.close()
# compute intensity histogram features
gray2 = np.pad(gray, 1, 'constant')
pixVals = gray2[bw_img > 0]
maxPixel = np.max(pixVals)
minPixel = np.min(pixVals)
if (maxPixel == 0):
maxPixel = 1
# normalize histogram
pixVals = (np.float32(pixVals) - minPixel) / np.max(pixVals)
histVals = exposure.histogram(pixVals, nbins=64)
# discrete cosine transform of normalized histogram of pixel values
allHistFeatures = fftpack.dct(np.float32(histVals[0]))
histFeatures = allHistFeatures[1:15]
# Find contours
contours = measure.find_contours(bw_img, 0.5)
# Select largest contour
maxLength = -1
maxContour = []
for cc in contours:
if (len(cc) > maxLength):
maxLength = len(cc)
maxContour = cc
# fig, ax = plt.subplots()
# #ax.imshow(r, interpolation='nearest', cmap=plt.cm.gray)
# for n, contour in enumerate(contours):
# ax.plot(contour[:, 1], contour[:, 0], linewidth=2)
# ax.axis('image')
# plt.title("Contours")
# plt.savefig(self.fnm+"_C.png",bbox_inches='tight')
# plt.close()
# Represent contour in fourier space. Make scale invarient by
# dividing by DC term. Can make rotation invariant by subtracting
# phase of first term
# Interpolate to 4096 point contour
interpX = interpolate.interp1d(range(0, maxLength), maxContour[:, 0])
interpY = interpolate.interp1d(range(0, maxLength), maxContour[:, 1])
newS = np.linspace(0, maxLength - 1, 4096)
cX = interpX(newS)
cY = interpY(newS)
cPath = cX + 1j * cY
FdAll = np.fft.fft(cPath)
FdSave = np.log(np.absolute(FdAll[2:18]) / np.absolute(FdAll[1]))
# Simplify the boundary
cen = np.fft.fftshift(FdAll)
# take first 10% of fourier coefficents
cen2 = np.hstack([np.zeros(1843), cen[1843:2253], np.zeros(1843)])
# Back project to simplified boundary
back = np.fft.ifft(np.fft.ifftshift(cen2))
xx = np.round(back.real)
yy = np.round(back.imag)
m = bw_img.shape[0]
n = bw_img.shape[1]
xx = xx.astype(np.int)
yy = yy.astype(np.int)
simp = np.zeros([m, n])
simp[xx, yy] = 1
# fig, ax = plt.subplots()
# ax.imshow(img)
# ax.plot(maxContour[:, 1], maxContour[:, 0], linewidth=2)
# ax.axis('image')
# plt.title("Max Contour")
# plt.savefig(self.fnm+"_MC.png",bbox_inches='tight')
# plt.close()
# Get the appropriate FFT padded out to 300 x 300
freq_simp = fftpack.fftshift(fftpack.fft2(simp, [300, 300]))
# 48 rings, 50 wedges selected from 0 to pi
ann = AnnulusProcessing(freq_simp, 48,
50) # add number of wedges, etc to init
rings = ann.make_annular_mean()
wedges = ann.make_wedge()
# Fill the simplified boundary
fill = ndimage.binary_fill_holes(simp).astype(int)
masked = fill * np.pad(gray, 1, 'constant')
# plt.imshow(masked)
# plt.title("Masked")
# plt.savefig(self.fnm+"_M.png",bbox_inches='tight')
# plt.close()
# Gray level coocurrence matrix
P = greycomatrix(masked,
distances=self.dist,
angles=self.ang,
normed=True)
grey_mat = np.zeros([24, 2])
flag = 0
for name in self.grey_props:
stat = greycoprops(P, name)
grey_mat[flag:flag + 6, 0] = np.mean(stat, 1)
grey_mat[flag:flag + 6, 1] = np.std(stat, 1)
flag += 6
# Texture descripters
prob = np.histogram(masked, 256) # assume gray scale with 256 levels
prob = np.asarray(prob[0])
prob[0] = 0 # don't count black pixels
prob = prob / prob.sum()
vec = np.arange(0, len(prob)) / (len(prob) - 1)
ind = np.nonzero(prob)[0]
# mean grey value
mu = np.sum(vec[ind] * prob[ind])
# variance
var = np.sum((((vec[ind] - mu)**2) * prob[ind]))
# standard deviation
std = np.sqrt(var)
# contrast
cont = 1 - 1 / (1 + var)
# 3rd moment
thir = np.sum(((vec[ind] - mu)**3) * prob[ind])
# Uniformity
uni = np.sum(prob[0]**2)
# Entropy
ent = -np.sum(prob[ind] * np.log2(prob[ind]))
# Compute morphological descriptors
label_img = measure.label(bw_img, neighbors=8, background=0)
features = measure.regionprops(label_img + 1)
maxArea = 0
maxAreaInd = 0
for f in range(0, len(features)):
if features[f].area > maxArea:
maxArea = features[f].area
maxAreaInd = f
# Compute translation, scal and rotation invariant features
ii = maxAreaInd
aspect = features[ii].minor_axis_length / features[ii].major_axis_length
area1 = features[ii].area / features[ii].convex_area
area2 = features[ii].area / (features[ii].bbox[3] *
features[ii].bbox[2])
area3 = features[ii].area / (features[ii].perimeter *
features[ii].perimeter)
area4 = area2 / area1
area5 = area3 / area1
per = features[ii].perimeter
simp_area = features[ii].area
pa = per / simp_area
fillArea = features[ii].filled_area
ecc = features[ii].eccentricity
esd = features[ii].equivalent_diameter
en = features[ii].euler_number
sol = features[ii].solidity
momC = features[ii].moments_central
ext = features[ii].extent
# copeTestImg = cv2.imread("copeTest.png")
# copegray = cv2.cvtColor(copeTestImg,cv2.COLOR_BGR2GRAY)
# copethresh = threshold_otsu(copegray)
# copebinary = copegray >= copethresh
# cope_img1 = morphology.closing(copebinary,morphology.square(3)) # pad to ensure contour is continuous
# cope_img = np.pad(cope_img1, 1, 'constant')
# size=bw_img.shape
# copeTestImgRes = transform.resize(cope_img,size, mode='nearest')
# intarray=np.around(copeTestImgRes)
# intarray = intarray.astype(dtype="uint8")
# copeTestImgRot = transform.rotate(cope_img,features[ii].orientation).astype(dtype="uint8")
# copeTestImgBoth = transform.rotate(copeTestImgRes, features[ii].orientation).astype(dtype="uint8")
# SS_res = measure.structural_similarity(bw_img,intarray)
# SS_both = measure.structural_similarity(bw_img,copeTestImgBoth)
# MT = feature.match_template(bw_img,copeTestImgBoth)
# maxMT = np.amax(MT)
#likelyT=measure.structural_similarity(bw_img,copeTestImg)
X = np.zeros(212)
X[0:16] = FdSave
X[17] = aspect
X[18] = area1
X[19] = area2
X[20] = area3
X[21] = area4
X[22] = area5
X[23] = fillArea
X[24] = ecc
X[25] = esd
X[26] = en
X[27] = sol
X[28:35] = np.log(features[ii].moments_hu)
X[36:50] = histFeatures
X[50:98] = rings # only use first 10?
X[98:148] = wedges # sort these
X[148:172] = grey_mat[:, 0]
X[172:196] = grey_mat[:, 1]
X[196] = mu
X[197] = std
X[198] = cont
X[199] = thir
X[200] = uni
X[201] = ent
X[202] = per
X[203] = simp_area
X[204] = pa
X[205] = ext
# X[206] = np.log(features[ii].inertia_tensor_eigvals[0])
# X[207] = np.log(features[ii].inertia_tensor_eigvals[1])
# X[208] = features[ii].orientation
# X[209] = maxMT
# X[210] = SS_res
# X[211] = SS_both
return X
def run_loop(bag_path, seg_model, seg_opts, save_images=False):
# Create pipeline
pipeline = rs.pipeline()
# Create a config object
config = rs.config()
# Tell config that we will use a recorded device from filem to be used by the pipeline through playback.
rs.config.enable_device_from_file(config, args.input)
# Start streaming from file
Pipe = pipeline.start(config)
# Getting the depth sensor's depth scale (see rs-align example for explanation)
depth_sensor = Pipe.get_device().first_depth_sensor()
depth_scale = depth_sensor.get_depth_scale()
print("Depth Scale is: ", depth_scale)
# Create opencv window to render image in
cv2.namedWindow("Full Stream", cv2.WINDOW_NORMAL)
# Create colorizer object
colorizer = rs.colorizer()
idx = 0
# initial frame delay
idx_limit = 30
pre_seg_mask_sum = None # previous frame path segmentation area
# Streaming loop
try:
while True:
idx += 1
# Get frameset of depth
frames = pipeline.wait_for_frames()
# ignore first idx frames
if idx < idx_limit:
continue
else:
pass
align = rs.align(rs.stream.color)
frames = align.process(frames)
# Get color frame
color_frame = frames.get_color_frame()
# Get depth frame
depth_frame = frames.get_depth_frame()
# Get intrinsics and extrinsics
if idx == idx_limit:
camera_intrinsics(color_frame, depth_frame, Pipe)
color_image = np.asanyarray(color_frame.get_data())
### Add Segmentation part here ###
pred = test(color_image, seg_model, seg_opts)
# pavement, floor, road, earth/ground, field, path, dirt/track
seg_mask = (pred == 11) | (pred == 3) | (pred == 6) | (
pred == 13) | (pred == 29) | (pred == 52) | (
pred == 91) #.astype(np.uint8)
if idx == idx_limit: # 1st frame detection needs to be robust
pre_seg_mask_sum = np.sum(seg_mask)
# checking for bad detection
new_seg_sum = np.sum(seg_mask)
diff = abs(new_seg_sum - pre_seg_mask_sum)
# if diff > pre_seg_mask_sum/15: # smoothening between segmentation outputs - seems like a bad idea since the model inputs are not connected between timesteps
# seg_mask = np.ones_like(pred).astype(np.uint8) # need to add depth (5mt) criterea for calculation for robustness
# del new_seg_sum
# else:
pre_seg_mask_sum = new_seg_sum
### mask Hole filling
seg_mask = nd.binary_fill_holes(seg_mask).astype(int)
seg_mask = seg_mask.astype(np.uint8)
#####
seg_mask_3d = np.dstack((seg_mask, seg_mask, seg_mask))
pred_color = colorEncode(
pred,
loadmat(os.path.join(model_folder, 'color150.mat'))['colors'])
##################################
depth_frame = depth_filter(depth_frame)
depth_array = np.asarray(depth_frame.get_data())
# Colorize depth frame to jet colormap
depth_color_frame = colorizer.colorize(depth_frame)
# Convert depth_frame to numpy array to render image in opencv
depth_color_image = np.asanyarray(depth_color_frame.get_data())
############ Plane Detection
## need to add smoothening between frames - by plane weights' variance?
try:
### need to add multithreading here (and maybe other methods?)
planes_mask_binary = plane_detection(color_image*seg_mask_3d, depth_array*seg_mask,\
loop=5)
except TypeError as e:
try:
print("plane mask 1st error")
planes_mask, planes_normal, list_plane_params = test_PlaneDetector_send(
img_color=color_image * seg_mask_3d,
img_depth=depth_array * seg_mask)
except TypeError as e:
print("plane mask not detected-skipping frame")
continue
## removed this part
planes_mask = np.ones_like(depth_array).astype(np.uint8)
planes_mask = np.dstack(
(planes_mask, planes_mask, planes_mask))
##############################################
## Hole filling for plane_mask (plane mask isn't binary - fixed that!)
planes_mask_binary = nd.binary_fill_holes(planes_mask_binary)
planes_mask_binary = planes_mask_binary.astype(np.uint8)
# Clean plane mask object detection by seg_mask
planes_mask_binary *= seg_mask
planes_mask_binary_3d = np.dstack(
(planes_mask_binary, planes_mask_binary, planes_mask_binary))
edges = planes_mask_binary - nd.morphology.binary_dilation(
planes_mask_binary) # edges calculation
edges = cv2.cvtColor(edges, cv2.COLOR_GRAY2BGR)
#############################################
# for cv2 output
pred_color = cv2.cvtColor(pred_color, cv2.COLOR_RGB2BGR)
color_image = cv2.cvtColor(color_image, cv2.COLOR_RGB2BGR)
# if save_images == True:
# cv2.imwrite("data_/color/frame%d.png" % idx, color_image) # save frame as JPEG file
# cv2.imwrite("data_/depth/frame%d.png" % idx, depth_array) # save frame as JPEG file
# cv2.imwrite("data_/color_depth/frame%d.png" % idx, depth_color_image) # save frame as JPEG file
# cv2.imwrite("data_/thresholded_color/frame%d.png" % idx, thresholded_color_image) # save frame as JPEG file
# # cv2.imwrite("data_/thresholded_depth/frame%d.png" % idx, thresholded_depth_image) # save frame as JPEG file
# # Blending images
alpha = 0.2
beta = (1.0 - alpha)
dst = cv2.addWeighted(color_image, alpha, pred_color, beta, 0.0)
# kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(11,11))
# res = cv2.morphologyEx(planes_mask,cv2.MORPH_OPEN,kernel)
dst2 = cv2.addWeighted(depth_color_image, alpha, color_image, beta,
0.0)
## for displaying seg_mask
seg_mask = (np.array(seg_mask) * 255).astype(np.uint8)
seg_mask = cv2.cvtColor(seg_mask, cv2.COLOR_GRAY2BGR)
##################################
### delete later
final_output = color_image * planes_mask_binary_3d
mask = (final_output[:, :, 0] == 0) & (
final_output[:, :, 1] == 0) & (final_output[:, :, 2] == 0)
final_output[:, :, :3][mask] = [255, 255, 255]
######
# if np.sum(planes_mask) == depth_array.shape[0]*depth_array.shape[1]:
# image_set1 = np.vstack((dst, color_image))
# else:
image_set1 = np.vstack((color_image, depth_color_image))
# image_set2 = np.vstack((planes_mask_binary_3d*255, seg_mask))
image_set2 = np.vstack((dst, final_output))
# image_set2 = np.vstack((edges, final_output))
combined_images = np.concatenate((image_set1, image_set2), axis=1)
if save_images == True:
cv2.imwrite("./meeting_example/frame%d.png" % idx,
combined_images)
try:
cv2.imshow('Full Stream', combined_images)
except TypeError as e:
print(idx, e)
key = cv2.waitKey(1)
# if pressed escape exit program
if key == 27:
cv2.destroyAllWindows()
break
finally:
pipeline.stop()
cv2.destroyAllWindows()
# if save_images == True:
# pkl.dump( threshold_mask, open( "data_/depth_threshold.pkl", "wb" ) )
# print("Mask pickle saved")
return
fs_area.append(fsarea)
# Area densities inside and outside the focus
in_area_density.append(incount * np.mean(insize) / fsarea)
out_area_density.append(outcount * np.mean(outsize) / (1. - fsarea))
# Point densities inside and outside the focus
in_point_density.append(incount / fsarea)
out_point_density.append(outcount / (1. - fsarea))
print "Densities calculated."
# Variance and CoV of outside the focus
mask = np.zeros_like(clean)
for c in cent:
mask[int(np.round(c[0])), int(np.round(c[1]))] = 1
mask = ndimage.binary_fill_holes(ndimage.maximum_filter(mask, 35))
areadensity = filter.gaussian_filter(clean, 51)
var_area_density.append(np.var(areadensity[mask == 0]))
cov_area_density.append(
np.std(areadensity[mask == 0] / np.mean(areadensity[mask == 0])))
pointdensity = np.zeros_like(clean)
for n in nuclei:
pointdensity[int(np.round(n.centroid[0])),
int(np.round(n.centroid[1]))] = 1
pointdensity = filter.gaussian_filter(pointdensity, 51)
var_point_density.append(np.var(pointdensity[mask == 0]))
cov_point_density.append(
np.std(pointdensity[mask == 0] / np.mean(pointdensity[mask == 0])))
from scipy.ndimage import gaussian_filter gaus = gaussian_filter(test, 4) ut.imshow(gaus) from skimage.feature import canny edges = canny(gaus) type(edges) plt.imshow(edges) from scipy import ndimage as ndi fill = ndi.binary_fill_holes(edges) plt.imshow(fill) from skimage.filters import sobel elevation_map = sobel(test) ut.imshow(elevation_map) markers = np.zeros_like(test) markers[test < 0.1] = 1 markers[test > 0.9] = 2 plt.imshow(markers) from skimage.morphology import watershed segmentation = watershed(elevation_map, markers)
def lungmask_pro(img, morph=True):
# to avoid many tears
img = img.copy()
# set intensity range to standard (corners have problems) -> doesn't really matter, robust method, but looks better
img[img <= -1024] = -1024
img[img >=
1024] = 1024 # also this, because of circular region artifacts (actually also for the one above)
# scale image to be in the range [0,255] -> because method robust enough to handle it -> smarter thresholds
img = np.uint8(maxminscale(img))
# keep scaled original image for masking later
img_orig = img.copy()
# blur img to easier being able to segment the lung using kmeans
img = medianBlur(img, 5)
# get dimensions of image
row_size, col_size = img.shape
# specify window for k-means to work on, such that you get the lung, and not the rest
middle = img[int(col_size / 5):int(col_size / 5 * 4),
int(row_size / 5):int(row_size / 5 * 4)]
# apply otsu's method to threshold image
th = threshold_otsu(middle)
thresh_img = np.where(img < th, 1.0, 0.0)
# label each object from the filtering above and only get the lung including juxta-vascular nodules
labels = label(
thresh_img) # Different labels are displayed in different colors
regions = regionprops(labels)
good_labels = []
for prop in regions:
B = prop.bbox
if B[2] - B[0] < row_size / 10 * 9 and B[3] - B[
1] < col_size / 10 * 9 and B[0] > row_size / 10 and B[
2] < col_size / 10 * 9: # better window!
good_labels.append(prop.label)
mask = np.ndarray([row_size, col_size], dtype=np.int8)
mask[:] = 0
# gets objects of interest from criteria defined and used above
for N in good_labels:
mask = mask + np.where(labels == N, 1, 0)
## fill regions surrounded by lung regions -> final ROI
# to fill holes -> without filling unwanted larger region in the middle
res = binary_fill_holes(mask)
# need to filter out the main airways easily seperated from lung, because else they will affect the resulting algorithm
res2 = remove_small_objects(label(res).astype(bool), min_size=800)
res2[res2 > 0] = 1
# separate each object in image (i.e. each lung part), and do morphology to include juxta-pleural nodules
lungmask = np.zeros(res2.shape)
labels = label(res2)
for i in range(1, len(np.unique(labels))):
tmp = np.zeros(labels.shape)
tmp[labels == i] = 1
# whether or not to apply morphology to fix lung boundary -> to include juxta-pleural nodules (nodules attached to lung boundary)
if (morph == True):
mask = dilate(np.uint8(tmp), disk(17)) # 17 : radius of 2D-disk
mask = remove_small_objects(label(bitwise_not(
maxminscale(mask))).astype(bool),
min_size=500).astype(int)
mask[mask != 0] = -1
mask = np.add(mask, np.ones(mask.shape))
filled_tmp = erode(np.uint8(mask), disk(19))
else:
mask = remove_small_objects(label(bitwise_not(
maxminscale(tmp))).astype(bool),
min_size=500).astype(int)
mask[mask != 0] = -1
filled_tmp = np.add(mask, np.ones(mask.shape))
lungmask += filled_tmp
return lungmask
def fillHoles(image): #0-1 rgb
return ndi.binary_fill_holes(image)
def run(self, ips, imgs, para=None):
imgs[:] = ndimg.binary_fill_holes(imgs)
imgs *= 255
def recognize(out_file, most_common, coord_imgs, imgs_with_staff, imgs_spacing,
imgs_rows):
black_names = [
'4', '8', '8_b_n', '8_b_r', '16', '16_b_n', '16_b_r', '32', '32_b_n',
'32_b_r', 'a_4', 'a_8', 'a_16', 'a_32', 'chord'
]
ring_names = ['2', 'a_2']
whole_names = ['1', 'a_1']
disk_size = most_common / 4
if len(coord_imgs) > 1:
out_file.write("{\n")
for i, img in enumerate(coord_imgs):
res = []
prev = ''
time_name = ''
primitives, prim_with_staff, boundary = get_connected_components(
img, imgs_with_staff[i])
for j, prim in enumerate(primitives):
prim = binary_opening(
prim, square(np.abs(most_common - imgs_spacing[i])))
saved_img = (255 * (1 - prim)).astype(np.uint8)
labels = predict(saved_img)
octave = None
label = labels[0]
if label in black_names:
test_img = np.copy(prim_with_staff[j])
test_img = binary_dilation(test_img, disk(disk_size))
comps, comp_w_staff, bounds = get_connected_components(
test_img, prim_with_staff[j])
comps, comp_w_staff, bounds = filter_beams(
comps, comp_w_staff, bounds)
bounds = [np.array(bound) + disk_size - 2 for bound in bounds]
if len(bounds) > 1 and label not in [
'8_b_n', '8_b_r', '16_b_n', '16_b_r', '32_b_n',
'32_b_r'
]:
l_res = []
bounds = sorted(bounds, key=lambda b: -b[2])
for k in range(len(bounds)):
idx, p = estim(boundary[j][0] + bounds[k][2], i,
imgs_spacing, imgs_rows)
l_res.append(f'{label_map[idx][p]}/4')
if k + 1 < len(bounds) and (
bounds[k][2] -
bounds[k + 1][2]) > 1.5 * imgs_spacing[i]:
idx, p = estim(
boundary[j][0] + bounds[k][2] -
imgs_spacing[i] / 2, i, imgs_spacing,
imgs_rows)
l_res.append(f'{label_map[idx][p]}/4')
res.append(sorted(l_res))
else:
for bbox in bounds:
c = bbox[2] + boundary[j][0]
line_idx, p = estim(int(c), i, imgs_spacing, imgs_rows)
l = label_map[line_idx][p]
res.append(get_note_name(prev, l, label))
elif label in ring_names:
head_img = 1 - binary_fill_holes(1 - prim)
head_img = binary_closing(head_img, disk(disk_size))
comps, comp_w_staff, bounds = get_connected_components(
head_img, prim_with_staff[j])
for bbox in bounds:
c = bbox[2] + boundary[j][0]
line_idx, p = estim(int(c), i, imgs_spacing, imgs_rows)
l = label_map[line_idx][p]
res.append(get_note_name(prev, l, label))
elif label in whole_names:
c = boundary[j][2]
line_idx, p = estim(int(c), i, imgs_spacing, imgs_rows)
l = label_map[line_idx][p]
res.append(get_note_name(prev, l, label))
elif label in [
'bar', 'bar_b', 'clef', 'clef_b', 'natural', 'natural_b',
't24', 't24_b', 't44', 't44_b'
] or label in []:
continue
elif label in ['#', '#_b']:
if prim.shape[0] == prim.shape[1]:
prev = '##'
else:
prev = '#'
elif label in ['cross']:
prev = '##'
elif label in ['flat', 'flat_b']:
if prim.shape[1] >= 0.5 * prim.shape[0]:
prev = '&&'
else:
prev = '&'
elif label in ['dot', 'dot_b', 'p']:
if len(res) == 0 or (len(res) > 0 and res[-1] in [
'flat', 'flat_b', 'cross', '#', '#_b', 't24', 't24_b',
't44', 't44_b'
]):
continue
res[-1] += '.'
elif label in ['t2', 't4']:
time_name += label[1]
elif label == 'chord':
img = thin(1 - prim.copy(), max_iter=20)
head_img = binary_closing(1 - img, disk(disk_size))
if label not in ['flat', 'flat_b', 'cross', '#', '#_b']:
prev = ''
if len(time_name) == 2:
out_file.write("[ " + "\\" + "meter<\"" + str(time_name[0]) + "/" +
str(time_name[1]) + "\">" + ' '.join([
str(elem) if type(elem) != list else
get_chord_notation(elem) for elem in res
]) + "]\n")
elif len(time_name) == 1:
out_file.write("[ " + "\\" + "meter<\"" + '4' + "/" + '2' + "\">" +
' '.join([
str(elem) if type(elem) != list else
get_chord_notation(elem) for elem in res
]) + "]\n")
else:
out_file.write("[ " + ' '.join([
str(elem) if type(elem) != list else get_chord_notation(elem)
for elem in res
]) + "]\n")
if len(coord_imgs) > 1:
out_file.write("}")
print("###########################", res, "##########################")
def filled_image(self):
structure = np.ones((3, ) * self._ndim)
return ndi.binary_fill_holes(self.image, structure)
mark_boundaries) image = data.coins() ############################################################################### # First, we generate the true segmentation. For this simple image, we know # exact functions and parameters that will produce a perfect segmentation. In # a real scenario, typically you would generate ground truth by manual # annotation or "painting" of a segmentation. elevation_map = sobel(image) markers = np.zeros_like(image) markers[image < 30] = 1 markers[image > 150] = 2 im_true = watershed(elevation_map, markers) im_true = ndi.label(ndi.binary_fill_holes(im_true - 1))[0] ############################################################################### # Next, we create three different segmentations with different characteristics. # The first one uses :func:`skimage.segmentation.watershed` with # *compactness*, which is a useful initial segmentation but too fine as a # final result. We will see how this causes the oversegmentation metrics to # shoot up. edges = sobel(image) im_test1 = watershed(edges, markers=468, compactness=0.001) ############################################################################### # The next approach uses the Canny edge filter, :func:`skimage.filters.canny`. # This is a very good edge finder, and gives balanced results.
def seeds(args):
"""
%prog seeds [pngfile|jpgfile]
Extract seed metrics from [pngfile|jpgfile]. Use --rows and --cols to crop image.
"""
p = OptionParser(seeds.__doc__)
p.set_outfile()
opts, args, iopts = add_seeds_options(p, args)
if len(args) != 1:
sys.exit(not p.print_help())
pngfile, = args
pf = opts.prefix or op.basename(pngfile).rsplit(".", 1)[0]
sigma, kernel = opts.sigma, opts.kernel
rows, cols = opts.rows, opts.cols
labelrows, labelcols = opts.labelrows, opts.labelcols
ff = opts.filter
calib = opts.calibrate
outdir = opts.outdir
if outdir != '.':
mkdir(outdir)
if calib:
calib = json.load(must_open(calib))
pixel_cm_ratio, tr = calib["PixelCMratio"], calib["RGBtransform"]
tr = np.array(tr)
pngfile = convert_background(pngfile)
resizefile, mainfile, labelfile, exif = \
convert_image(pngfile, pf, outdir=outdir,
rotate=opts.rotate,
rows=rows, cols=cols,
labelrows=labelrows, labelcols=labelcols)
oimg = load_image(resizefile)
img = load_image(mainfile)
fig, (ax1, ax2, ax3, ax4) = plt.subplots(ncols=4,
nrows=1,
figsize=(iopts.w, iopts.h))
# Edge detection
img_gray = rgb2gray(img)
logging.debug("Running {0} edge detection ...".format(ff))
if ff == "canny":
edges = canny(img_gray, sigma=opts.sigma)
elif ff == "roberts":
edges = roberts(img_gray)
elif ff == "sobel":
edges = sobel(img_gray)
edges = clear_border(edges, buffer_size=opts.border)
selem = disk(kernel)
closed = closing(edges, selem) if kernel else edges
filled = binary_fill_holes(closed)
# Watershed algorithm
if opts.watershed:
distance = distance_transform_edt(filled)
local_maxi = peak_local_max(distance, threshold_rel=.05, indices=False)
coordinates = peak_local_max(distance, threshold_rel=.05)
markers, nmarkers = label(local_maxi, return_num=True)
logging.debug("Identified {0} watershed markers".format(nmarkers))
labels = watershed(closed, markers, mask=filled)
else:
labels = label(filled)
# Object size filtering
w, h = img_gray.shape
canvas_size = w * h
min_size = int(round(canvas_size * opts.minsize / 100))
max_size = int(round(canvas_size * opts.maxsize / 100))
logging.debug("Find objects with pixels between {0} ({1}%) and {2} ({3}%)"\
.format(min_size, opts.minsize, max_size, opts.maxsize))
# Plotting
ax1.set_title('Original picture')
ax1.imshow(oimg)
params = "{0}, $\sigma$={1}, $k$={2}".format(ff, sigma, kernel)
if opts.watershed:
params += ", watershed"
ax2.set_title('Edge detection\n({0})'.format(params))
closed = gray2rgb(closed)
ax2_img = labels
if opts.edges:
ax2_img = closed
elif opts.watershed:
ax2.plot(coordinates[:, 1], coordinates[:, 0], 'g.')
ax2.imshow(ax2_img, cmap=iopts.cmap)
ax3.set_title('Object detection')
ax3.imshow(img)
filename = op.basename(pngfile)
if labelfile:
accession = extract_label(labelfile)
else:
accession = pf
# Calculate region properties
rp = regionprops(labels)
rp = [x for x in rp if min_size <= x.area <= max_size]
nb_labels = len(rp)
logging.debug("A total of {0} objects identified.".format(nb_labels))
objects = []
for i, props in enumerate(rp):
i += 1
if i > opts.count:
break
y0, x0 = props.centroid
orientation = props.orientation
major, minor = props.major_axis_length, props.minor_axis_length
major_dx = cos(orientation) * major / 2
major_dy = sin(orientation) * major / 2
minor_dx = sin(orientation) * minor / 2
minor_dy = cos(orientation) * minor / 2
ax2.plot((x0 - major_dx, x0 + major_dx),
(y0 + major_dy, y0 - major_dy), 'r-')
ax2.plot((x0 - minor_dx, x0 + minor_dx),
(y0 - minor_dy, y0 + minor_dy), 'r-')
npixels = int(props.area)
# Sample the center of the blob for color
d = min(int(round(minor / 2 * .35)) + 1, 50)
x0d, y0d = int(round(x0)), int(round(y0))
square = img[(y0d - d):(y0d + d), (x0d - d):(x0d + d)]
pixels = []
for row in square:
pixels.extend(row)
logging.debug("Seed #{0}: {1} pixels ({2} sampled) - {3:.2f}%".\
format(i, npixels, len(pixels), 100. * npixels / canvas_size))
rgb = pixel_stats(pixels)
objects.append(Seed(filename, accession, i, rgb, props, exif))
minr, minc, maxr, maxc = props.bbox
rect = Rectangle((minc, minr),
maxc - minc,
maxr - minr,
fill=False,
ec='w',
lw=1)
ax3.add_patch(rect)
mc, mr = (minc + maxc) / 2, (minr + maxr) / 2
ax3.text(mc,
mr,
"{0}".format(i),
color='w',
ha="center",
va="center",
size=6)
for ax in (ax2, ax3):
ax.set_xlim(0, h)
ax.set_ylim(w, 0)
# Output identified seed stats
ax4.text(.1, .92, "File: {0}".format(latex(filename)), color='g')
ax4.text(.1, .86, "Label: {0}".format(latex(accession)), color='m')
yy = .8
fw = must_open(opts.outfile, "w")
if not opts.noheader:
print(Seed.header(calibrate=calib), file=fw)
for o in objects:
if calib:
o.calibrate(pixel_cm_ratio, tr)
print(o, file=fw)
i = o.seedno
if i > 7:
continue
ax4.text(.01, yy, str(i), va="center", bbox=dict(fc='none', ec='k'))
ax4.text(.1, yy, o.pixeltag, va="center")
yy -= .04
ax4.add_patch(
Rectangle((.1, yy - .025), .12, .05, lw=0, fc=rgb_to_hex(o.rgb)))
ax4.text(.27, yy, o.hashtag, va="center")
yy -= .06
ax4.text(.1,
yy,
"(A total of {0} objects displayed)".format(nb_labels),
color="darkslategray")
normalize_axes(ax4)
for ax in (ax1, ax2, ax3):
xticklabels = [int(x) for x in ax.get_xticks()]
yticklabels = [int(x) for x in ax.get_yticks()]
ax.set_xticklabels(xticklabels, family='Helvetica', size=8)
ax.set_yticklabels(yticklabels, family='Helvetica', size=8)
image_name = op.join(outdir, pf + "." + iopts.format)
savefig(image_name, dpi=iopts.dpi, iopts=iopts)
return objects
def TestPreprocess(img, plot=False):
if plot == True:
f, plots = plt.subplots(2, 4)
'''
Step 1: 以604(HU=400)为分界点二值化
'''
binary = img < 604
if plot == True:
plt.subplot(2, 4, 1)
plt.axis('off')
plt.title(u"二值化", fontproperties=zhfont)
plt.imshow(binary, cmap=plt.cm.bone)
'''
Step 2: 移除与边界相连的部分
'''
cleared = clear_border(binary)
if plot == True:
plt.subplot(2, 4, 2)
plt.axis('off')
plt.title(u"移除边界", fontproperties=zhfont)
plt.imshow(cleared, cmap=plt.cm.bone)
'''
Step 3: 标记连通区域
'''
label_image = label(cleared)
if plot == True:
plt.subplot(2, 4, 3)
plt.axis('off')
plt.title(u"标记联通区域", fontproperties=zhfont)
plt.imshow(label_image, cmap=plt.cm.bone)
'''
Step 4: 只保留两个最大的连通区域
'''
areas = [r.area for r in regionprops(label_image)]
areas.sort()
if len(areas) > 2:
for region in regionprops(label_image):
if region.area < areas[-2]:
for coordinates in region.coords:
label_image[coordinates[0], coordinates[1]] = 0
binary = label_image > 0
if plot == True:
plt.subplot(2, 4, 4)
plt.axis('off')
plt.title(u"保留最大的两个区域", fontproperties=zhfont)
plt.imshow(binary, cmap=plt.cm.bone)
'''
Step 5: 半径为2的腐蚀操作,分离附着于血管的肺结节
'''
selem = disk(2)
binary = binary_erosion(binary, selem)
if plot == True:
plt.subplot(2, 4, 5)
plt.axis('off')
plt.title(u"腐蚀", fontproperties=zhfont)
plt.imshow(binary, cmap=plt.cm.bone)
'''
Step 6: 半径为10的闭操作,合并粘在肺壁上的结节
'''
selem = disk(10)
binary = binary_closing(binary, selem)
if plot == True:
plt.subplot(2, 4, 6)
plt.axis('off')
plt.title(u"闭", fontproperties=zhfont)
plt.imshow(binary, cmap=plt.cm.bone)
'''
Step 7: 填充小洞
'''
edges = roberts(binary) #边缘检测,Roberts算子,也可以使用sobel算子
binary = ndi.binary_fill_holes(edges) #空洞填充
if plot == True:
plt.subplot(2, 4, 7)
plt.axis('off')
plt.title(u"填充小洞", fontproperties=zhfont)
plt.imshow(binary, cmap=plt.cm.bone)
"此时binnary就是最终的掩码了"
# '''
# 7.1 非肺部区域绿色,肺部区域蓝色
# '''
# # 拷贝一个binnary
# ColorMask = np.zeros((binary.shape[0],binary.shape[1],3), np.uint8)
#
# for i in range(ColorMask.shape[0]):
# for j in range(ColorMask.shape[1]):
# if binary[i,j] == 0:
# ColorMask[i,j]=(0,255,0)
# if binary[i,j] == 1:
# ColorMask[i, j] = (0, 0, 255)
#
# if plot == True:
# plt.subplot(3, 4, 9)
# plt.axis('off')
# plt.title(u"上色", fontproperties=zhfont)
# plt.imshow(ColorMask)
#
# '''
# 7.2 ROI描点,连接成封闭区域,填充
# '''
# if len(rois)>=1:
# for roi in rois:
# cv2.fillPoly(ColorMask, [roi], (255, 0, 0))
#
# # cv2.polylines(ColorMask, [pts], True, (255, 0, 0), 2)
# # cv2.fillPoly(ColorMask, [pts], (255, 0, 0))
# if plot == True:
# plt.subplot(3, 4, 10)
# plt.axis('off')
# plt.title(u"ROI勾画", fontproperties=zhfont)
# plt.imshow(ColorMask)
'''
Step 8: 使用掩码提取原始图像中的肺区域
'''
get_high_vals = binary == 0
img[get_high_vals] = 0
if plot == True:
plt.subplot(2, 4, 8)
plt.axis('off')
plt.title(u"使用掩码提取原始数据", fontproperties=zhfont)
plt.imshow(img, cmap=plt.cm.bone)
return img
#%%
### Watershed segmentation
from skimage import morphology
from scipy import ndimage as ndi
#mask_combo = normalize(mask_combo)
markers = np.zeros_like(mask_combo)
markers[mask_combo < .12] = 1
#markers[mask_combo > .12] = 0
fill_markers = ndi.binary_fill_holes(markers)
new_edges = 0.5*fill_markers + mask_combo
fig, ax = plt.subplots(figsize=(8, 8))
ax.imshow(new_edges[0:1000, 0:1000], cmap = "viridis", interpolation='nearest')
ax.set_title('markers')
ax.axis('off')
#segmentation = morphology.watershed(mask_combo, markers)
#fig, ax = plt.subplots(figsize=(8, 8))
#ax.imshow(segmentation[3000:4000, 2000:3000], cmap=plt.cm.nipy_spectral, interpolation='nearest')
#ax.set_title('segmentation')
#ax.axis('off')
#from rasterio.plot import show_hist
#import skimage
def edge_segmentation(img, low_th=25):
""" Detect edges to create a mask that indicates where the paintings are located """
sx, sy = np.shape(img)[:2]
datatype = np.uint8
kernel = np.ones((15,15), dtype=np.uint8)
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
done = False
while not done:
edges = cv2.Canny(img, low_th, 80, None, 3)
# Closing to ensure edges are continuous
edges = cv2.dilate(edges, kernel, iterations=1)
edges = cv2.erode(edges, kernel, iterations=1)
# Filling
kernel = np.ones((15,15), dtype=np.uint8)
mask = (ndimage.binary_fill_holes(edges)).astype(np.float64)
mask = cv2.erode(mask, kernel, iterations=1)
mask = cv2.erode(mask, kernel, iterations=1)
#mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, np.ones((1,int(mask.shape[1]*0.05))))
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(mask.astype(np.uint8), connectivity=8)
sizes = stats[:, -1]
top_two_conn_comp_idx = sizes.argsort()
top_two_conn_comp_idx = top_two_conn_comp_idx[top_two_conn_comp_idx!=0]
idxs_tt = ((np.arange(0, min(3, len(top_two_conn_comp_idx)))+1)*(-1))[::-1]
top_two_conn_comp_idx = top_two_conn_comp_idx[idxs_tt][::-1]
idxs = [idx for idx in top_two_conn_comp_idx]
bc = np.zeros(output.shape)
bc[output == idxs[0]] = 255
bc = create_convex_painting(mask, bc)
#cv2.waitKey(0)
#bc = refine_mask(img, bc, get_bbox(bc))
if len(idxs) > 1:
sbc = np.zeros(output.shape)
sbc[output == idxs[1]] = 255
sbc = create_convex_painting(mask, sbc)
#if sbc.astype(np.uint8).sum() > 0:
# sbc = refine_mask(img, sbc, get_bbox(sbc))
if len(idxs) > 2:
tbc = np.zeros(output.shape)
tbc[output == idxs[2]] = 255
tbc = create_convex_painting(mask, tbc)
#if tbc.astype(np.uint8).sum() > 0:
# tbc = refine_mask(img, tbc, get_bbox(tbc))
bboxes = [get_bbox(bc)]
resulting_masks = bc
splitted_resulting_masks = [bc]
# Second painting if first one does not take most part + more or less a rectangular shape + no IoU
if len(idxs) > 1:
if not takes_most_part_image(bc) and regular_shape(sbc) and check_no_iou(bc, sbc):
bbox_sbc = get_bbox(sbc)
if ((bbox_sbc[2]-bbox_sbc[0])*(bbox_sbc[3]-bbox_sbc[1]))/(img.shape[0]*img.shape[1]) > 0.05:
bboxes.append(bbox_sbc)
resulting_masks = np.logical_or(resulting_masks==255, sbc==255).astype(np.uint8)*255
splitted_resulting_masks.append(sbc)
# Third painting
if len(idxs) > 2:
if regular_shape(tbc) and check_no_iou(bc, tbc) and check_no_iou(sbc, tbc):
bbox_tbc = get_bbox(tbc)
if ((bbox_tbc[2]-bbox_tbc[0])*(bbox_tbc[3]-bbox_tbc[1]))/(img.shape[0]*img.shape[1]) > 0.05:
bboxes.append(bbox_tbc)
resulting_masks = np.logical_or(resulting_masks==255, tbc==255).astype(np.uint8)*255
splitted_resulting_masks.append(tbc)
#cv2.imshow("bc", cv2.resize(bc,(512,512)))
#cv2.waitKey(0)
done = True
if not rectangular_shape2(bc):
done = False
low_th = low_th - 4
if low_th < 0:
done = True
return resulting_masks, bboxes, splitted_resulting_masks
sigma_spatial=15,
multichannel=False)
buf = np.zeros((2 * width, 2 * width))
buf[:sub.shape[0], :sub.shape[1]] = sub
thresh = threshold_otsu(sub) * 1.15
bin = sub > thresh
# area[i] = np.sum(bin)
labeled, n = ndimage.label(bin)
thresh = threshold_otsu(sub) * 1.3
bin = sub > thresh
# bin = convex_hull_image(bin)
bin = ndimage.binary_fill_holes(bin)
sub = buf
sub = np.flipud(sub)
p2, p98 = np.percentile(sub, (2, 100))
sub = exposure.rescale_intensity(sub, in_range=(p2, p98))
# labeled, n = ndimage.label(bin)
distance = ndimage.distance_transform_edt(bin)
distance = filters.gaussian_filter(distance, sigma=5)
local_maxi = peak_local_max(distance,
indices=False,
footprint=np.ones((3, 3)),
labels=bin)
markers = measure.label(local_maxi)
labeled = watershed(-distance, markers, mask=bin)
# markers[~bin] = -1
def get_segmented_lungs_mask(im, i, plot=False):
size = im.shape[1]
if plot == True:
f, plots = plt.subplots(8, 1, figsize=(5, 40))
'''
Step 1: Convert into a binary image.
'''
binary = im < -320
if plot == True:
plots[0].axis('off')
plots[0].imshow(binary, cmap=plt.cm.bone)
'''
Step 2: Remove the blobs connected to the border of the image.
'''
cleared = clear_border(binary)
temp_label = label(cleared)
for region in regionprops(temp_label):
if region.area < 50:
# print region.label
for coordinates in region.coords:
temp_label[coordinates[0], coordinates[1]] = 0
cleared = temp_label > 0
cleared = ndi.binary_dilation(cleared, iterations=5)
###################################################
if plot == True:
plots[1].axis('off')
plots[1].imshow(cleared, cmap=plt.cm.bone)
'''
Step 3: Label the image.
'''
label_image = label(cleared)
if plot == True:
plots[2].axis('off')
plots[2].imshow(label_image, cmap=plt.cm.bone)
'''
Step 4: Keep the labels with 2 largest areas.
'''
for region in regionprops(label_image):
if region.eccentricity > 0.99 \
or region.centroid[0] > 0.90 * size \
or region.centroid[0] < 0.12 * size \
or region.centroid[1] > 0.88 * size \
or region.centroid[1] < 0.10 * size \
or (region.centroid[1] > 0.46 * size and region.centroid[1] < 0.54 * size and region.centroid[
0] > 0.75 * size) \
or (region.centroid[0] < 0.2 * size and region.centroid[1] < 0.2 * size) \
or (region.centroid[0] < 0.2 * size and region.centroid[1] > 0.8 * size) \
or (region.centroid[0] > 0.8 * size and region.centroid[1] < 0.2 * size) \
or (region.centroid[0] > 0.8 * size and region.centroid[1] > 0.8 * size):
for coordinates in region.coords:
label_image[coordinates[0], coordinates[1]] = 0
binary = label_image > 0
if plot == True:
plots[3].axis('off')
plots[3].imshow(binary, cmap=plt.cm.bone)
'''
Step 5: Erosion operation with a disk of radius 2. This operation is
seperate the lung nodules attached to the blood vessels.
'''
selem = disk(2)
binary = binary_erosion(binary, selem)
if plot == True:
plots[4].axis('off')
plots[4].imshow(binary, cmap=plt.cm.bone)
'''
Step 6: Closure operation with a disk of radius 10. This operation is
to keep nodules attached to the lung wall.
'''
selem = disk(10)
binary = binary_closing(binary, selem)
if plot == True:
plots[5].axis('off')
plots[5].imshow(binary, cmap=plt.cm.bone)
'''
Step 7: Fill in the small holes inside the binary mask of lungs.
'''
edges = roberts(binary)
binary = ndi.binary_fill_holes(edges)
if plot == True:
plots[6].axis('off')
plots[6].imshow(binary, cmap=plt.cm.bone)
# print "step 7", time()-t1
return binary, i
from scipy import ndimage as ndi
from skimage.feature import canny
from skimage import morphology
import numpy as np
from skimage.segmentation import watershed
from skimage.feature import peak_local_max
data_array = cv2.imread("./CIRA/dehaze_example.tif", 1)
b, g, r = cv2.split(data_array)
# Sauvola
binary_global = canny(r)
fill_masks = ndi.binary_fill_holes(binary_global)
cells_cleaned = morphology.remove_small_objects(fill_masks, 70)
labeled_cells, num = ndi.label(cells_cleaned)
print("Number of Cells detected: " + str(num))
# Now we want to separate the two objects in image
# Generate the markers as local maxima of the distance to the background
distance = ndi.distance_transform_edt(cells_cleaned)
local_maxi = peak_local_max(distance,
indices=False,
footprint=np.ones((100, 100)),
labels=labeled_cells)
markers = ndi.label(local_maxi)[0]
labels = watershed(-distance, markers, mask=cells_cleaned)
fig, axes = plt.subplots(ncols=4, figsize=(9, 3), sharex=True, sharey=True)
import numpy as np
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
from skimage import io
from skimage import feature
filename = "r1240_39_satellite_image_spot5_2.5m_gambia_river_gambia_2006.jpg"
image = io.imread(filename, as_grey=True)
print(image.size)
edges = feature.canny(image, sigma=3)
fill_segment = ndi.binary_fill_holes(edges)
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1,
ncols=3,
sharex=True,
sharey=True,
figsize=(12, 8))
ax1.imshow(io.imread(filename))
ax1.axis('off')
ax1.set_title('Original image', fontsize=20)
ax2.imshow(edges, cmap=plt.cm.gray)
ax2.axis('off')
ax2.set_title('Canny filter, $\sigma=1$', fontsize=20)
(ellipse[0], (ellipse[1][0], ellipse[1][1]), ellipse[2]),
1,
thickness=-1)
ellipse_mask[z][tmp_img > 0] = 1
ellipse_mask = ellipse_mask.resample2D(img.header['pixelSize'][0],
interpolation='nearest')
#irtk.imwrite(output_dir + "/ellipse_mask.nii", ellipse_mask )
mask[ellipse_mask == 1] = 1
# fill holes, close and dilate
disk_close = irtk.disk(5)
disk_dilate = irtk.disk(2)
for z in xrange(mask.shape[0]):
mask[z] = nd.binary_fill_holes(mask[z])
mask[z] = nd.binary_closing(mask[z], disk_close)
mask[z] = nd.binary_dilation(mask[z], disk_dilate)
neg_mask = np.ones(mask.shape, dtype='uint8') * 2
# irtk.imwrite(output_dir + "/mask.nii", mask )
# irtk.imwrite(output_dir + "/mask.vtk", mask )
#x,y,z = img.WorldToImage(center)
x, y, z = center
x = int(round(x / img.header['pixelSize'][0]))
y = int(round(y / img.header['pixelSize'][1]))
z = int(round(z / img.header['pixelSize'][2]))
w = h = int(round(ofd / img.header['pixelSize'][0]))
