def foreground(img,save_folder,v,inumber):
try:
h = ex.equalize_hist(img[:,:])*255
oi = np.zeros_like(img, dtype=np.uint16)
oi[(img > threshold_otsu(img)) == True] = 1
oh = np.zeros_like(img, dtype=np.uint16)
oh[(h > threshold_otsu(h)) == True] = 1
nm = img.shape[0] * img.shape[1]
w1 = np.sum(oi)/(nm)
w2 = np.sum(oh)/(nm)
ots = np.zeros_like(img, dtype=np.uint16)
new =( w1 * img) + (w2 * h)
ots[(new > threshold_otsu(new)) == True] = 1
conv_hull = convex_hull_image(ots)
conv_hull = convex_hull_image(ots)
ch = np.multiply(conv_hull, 1)
fore_image = ch * img
back_image = (1 - ch) * img
except Exception:
fore_image = img.copy()
back_image = np.zeros_like(img, dtype=np.uint16)
conv_hull = np.zeros_like(img, dtype=np.uint16)
ch = np.multiply(conv_hull, 1)
# if not os.path.isdir(save_folder + os.sep + v[1]['ID']):
return fore_image, back_image, conv_hull, img[conv_hull], img[conv_hull==False]
def select_label(img, file_name):
labels = measure.label(img, connectivity=2)
if labels.max() == 1:
img = img.astype('bool')
img = morphology.convex_hull_image(img)
img = morphology.binary_dilation(img, morphology.disk(3))
return img
props = measure.regionprops(labels)
areas = list(map(lambda x: x.area, props))
max_area = np.max(areas)
min_area = int(max_area / 1.3)
img = morphology.remove_small_objects(labels,
min_size=min_area,
connectivity=1)
img = img.astype('bool')
labels = measure.label(img, connectivity=2)
if labels.max() == 1:
img = img.astype('bool')
img = morphology.convex_hull_image(img)
props = measure.regionprops(labels)
areas = list(map(lambda x: x.area, props))
r = math.sqrt(areas[0] / pi)
img = morphology.binary_dilation(img, morphology.disk(int(r / 3)))
else:
img = img.astype('bool')
with open(
'/home/zhangqianru/data/seg_of_rectum/div_of_rectum/model_results_with_seg/special_1',
'a') as f:
f.write(file_name + '\n')
return img
def test_non_c_contiguous():
# 2D Fortran-contiguous
image = np.ones((2, 2), order='F', dtype=bool)
assert_array_equal(convex_hull_image(image), image)
# 3D Fortran-contiguous
image = np.ones((2, 2, 2), order='F', dtype=bool)
assert_array_equal(convex_hull_image(image), image)
# 3D non-contiguous
image = np.transpose(np.ones((2, 2, 2), dtype=bool), [0, 2, 1])
assert_array_equal(convex_hull_image(image), image)
def get_vat_mask(f_mask, abdominal_water_mask):
"""FIXME! briefly describe function
:param f_mask:
:param abdominal_water_mask:
:returns:
:rtype:
"""
vat_mask = convex_hull_image(abdominal_water_mask) & f_mask
vat_mask = convex_hull_image(binary_opening(vat_mask)) & f_mask
return vat_mask
def find_center(mask, key):
"""
Find the centroid of the binary mask.
Args:
mask (array): Binary array mask.
key (string): Key to access the sample elements. Also used
in training for prediction.
"""
mask = np.uint8(mask > 0)
if key == 'image' or key == 'target':
if not is_predictable(mask):
return 0, 0
else:
mask = convex_hull_image(mask).astype(np.uint8)
output = cv2.connectedComponentsWithStats(mask, 8, cv2.CV_32S)
elif key == 'pred':
try:
if not is_predictable(mask):
return 0, 0
output = cv2.connectedComponentsWithStats(mask, 8, cv2.CV_32S)
if len(output[3]) != 2:
# only two centroids: one for background and one for grape
raise IndexError('Could not find centroids of one grape.')
except IndexError:
mask = convex_hull_image(mask).astype(np.uint8)
if not is_predictable(mask):
return 0, 0
output = cv2.connectedComponentsWithStats(mask, 8, cv2.CV_32S)
if len(output[3]) != 2:
return 0, 0
# centroids of grape class is always in index 1 (last)
coords = output[3][-1]
if np.isnan(coords[0]) or np.isnan(coords[1]):
return 0, 0
x_f, y_f = int(np.floor(coords[0])), int(np.floor(coords[1]))
x_c, y_c = int(np.ceil(coords[0])), int(np.ceil(coords[1]))
if mask[y_f, x_f] == 1:
x, y = x_f, y_f
elif mask[y_c, x_c] == 1:
x, y = x_c, y_c
elif mask[y_c, x_f] == 1:
x, y = x_f, y_c
elif mask[y_f, x_c] == 1:
x, y = x_c, y_c
else:
x, y = 0, 0
return x, y
def segmentation(cropped):
#cropped = equalization(cropped)
# ------------- k-means algorithm --------------------------
seg = Segment()
# Here, we extract a label map, an image of the resulting clusters, and the table of the cluster centers
# (here, 4 clusters and 4 centers)
label, result, center = seg.kmeans(cropped)
# We extract the pixels belonging to the cluster with highest gray-level
# (belonging to the OC)
extracted = seg.extractComponent(cropped, label, np.argmax(center))
# Binarisation
ret, thresh = cv2.threshold(extracted, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
# Morphological closing
kernel = np.ones((3, 3), np.uint8)
#closing_oc = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
opening_oc = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
center[np.argmax(center)] = 0
# We extract the pixels belonging to the cluster with the second highest gray-level
# (belonging to the OD)
extracted2 = seg.extractComponent(cropped, label, np.argmax(center))
# Binarisation de l'image résultat d'extraction du DO
ret2, thresh2 = cv2.threshold(extracted2, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
complete_od = disque_optique_entier(thresh2, thresh)
# Morphological operation
#closing_od = cv2.morphologyEx(complete_od, cv2.MORPH_CLOSE, kernel)
opening_od = cv2.morphologyEx(complete_od, cv2.MORPH_OPEN, kernel)
""" ------------- Convex hull transform -------------------------- """
OC_chull = convex_hull_image(opening_oc)
#hullPoints = ConvexHull(OC_chull)
#print(hullPoints)
OC_chull = OC_chull * 255
OD_chull = convex_hull_image(opening_od)
OD_chull = OD_chull * 255
return result, opening_oc, opening_od, OC_chull, OD_chull
def find_refine_boundaries(img: "NDArray[np.uint8]",
segments_slic: "NDArray[int]",
tissue_img: "NDArray[np.uint8]") -> "NDArray[bool]":
"""
Find and refine boundaries of the tissue image using convex hull.
Inputs:
img : 2D ndarray. Input image, must be grayscale.
segments_slic : 2D ndarray. Integer mask indicating SLIC segment labels.
tissue_img : 2D ndarray. Binary integer mask indicating the tissue.
Output:
tissue_img : 2D bool ndarray. Binary integer refined mask indicating the tissue.
"""
assert img.dtype == np.uint8, 'Wrong numpy dtype, expected "np.uint8" but got "{}"'.format(
img.dtype)
assert img.ndim == 2, 'Wrong numpy dimension, expected "2" but got "{}" with shape {}'.format(
img.ndim, img.shape)
# Find boundaries
boundaries = find_boundaries(tissue_img, connectivity=2, mode='thick')
labels = np.unique(segments_slic[boundaries])
# Convex hull on boundaries
for region in regionprops(segments_slic, img):
if region.label in labels:
min_r, min_c, max_r, max_c = region.bbox
img_tile = tissue_img[min_r:max_r, min_c:max_c]
outlier_hull_img = convex_hull_image(img_tile)
#tissue_img[min_r:max_r, min_c:max_c] = (outlier_hull_img * 255).astype('uint8')
tissue_img[min_r:max_r, min_c:max_c] = (outlier_hull_img * 1)
return tissue_img.astype('bool')
def ImageEffect_ConvexHull(I, obj=False):
if obj:
I_rgb = cv2.cvtColor(I, cv2.COLOR_RGBA2GRAY)
I_filtered = morphology.convex_hull_object(I_rgb)
else:
I_filtered = morphology.convex_hull_image(I[:, :, :3])
return FilterPostProcess(I, I_filtered)
def nucleiBinarySplit(mask, thr):
mask00 = ndi.binary_fill_holes(mask)
maskConv = convex_hull_image(mask00)
maskB = tinyDottsRemove(maskConv - mask)
notConv = np.sum(maskB) / np.sum(mask00)
if (notConv < thr):
return mask00
nn = np.max(label(maskB))
if nn > 1:
while (np.max(label(maskB)) == nn):
maskB = binary_closing(binary_dilation(maskB, selem=disk(1)),
selem=disk(2))
pass
maskC = binary_dilation(skeletonize(maskB), selem=disk(1))
maskD = tinyDottsRemove(binary_erosion(
imgToMask(binary_dilation(maskConv, selem=disk(1)) + (-1) * maskC),
selem=disk(1)),
minimum=10)
maskE = binary_opening(maskD, selem=disk(1))
#maskE = imgToMask(maskD - maskConv)
#maskF = ndi.binary_fill_holes(maskE)
markers = ndi.label(maskE)[0]
markers[np.where(mask00 == 0)] = 0
labels = watershed(-mask, markers, mask=mask)
return labels
def hull(self):
thresh_ivt = self.green_channel_thresh()
# non-hull part: False, hull part: True
chull = convex_hull_image(thresh_ivt)
# view the hull image
#plant part: 2, non-hull part: 0 (False) (black), hull-part excluding plant: 1 (True) gray
chull_diff = np.where(thresh_ivt == 255, 2, chull)
def cluster_process(labels, original, activations):
rbase = np.zeros(labels.shape)
rubase = np.zeros(labels.shape)
rubase[range(0,20),:] = 1
rubase[:,range(0,20)] = 1
rubase[range(-20,-1),:] = 1
rubase[:,range(-20,-1)] = 1
for i in range(1, int(np.max(labels))+1):
base = np.zeros(labels.shape)
base[labels==i] = 1
li = len(base.nonzero()[0])
if li>0:
hull = convex_hull_image(base)
lh =len(hull.nonzero()[0])
sel_org = base*original
sel_act = base*activations
cond = (li > 4000 and float(lh) / float(li) < 1.07 and perimeter(base)**2.0 / li < 30) or np.max(base * rubase) > 0.5
# print li>4000 and float(lh)/float(li)<1.07, perimeter(base)**2.0/li<30, np.max(base*rubase)>0.5, np.min(original[base>0])
hard_array =[li > 4000, float(lh) / float(li) < 1.07]
optional_array = [perimeter(base)**2.0/li < 25,
np.percentile(sel_org[sel_org>0], 5) > 0.2,
np.percentile(sel_act, 90) - np.percentile(sel_act, 90)]
print hard_array, optional_array
if debug and li>1000:
rs(base,'subspread cluster')
if cond:
rbase = rbase + base
rbase[rubase.astype(np.bool)] = 1
return dilation(rbase, selem)
def find_eye(label_img, img):
# image_label_overlay = label2rgb(label_image, image=img)
# fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(6, 6))
# ax.imshow(image_label_overlay)
for region in regionprops(label_image):
# skip small images
if region.area < 100:
continue
# draw rectangle around segmented coins
minr, minc, maxr, maxc = region.bbox
rect = mpatches.Rectangle((minc, minr),
maxc - minc,
maxr - minr,
fill=False,
edgecolor='red',
linewidth=2)
# ax.add_patch(rect)
img[minr:maxr, minc:maxc] = convex_hull_image(img[minr:maxr, minc:maxc])
binary_mask = imresize(img, base_shape) / 255
### binarization ###
binary_mask = (binary_mask > 0.01) * 1
### find right rectangles ###
koeff = base_shape / new_shape
return binary_mask, koeff[0] * minr, koeff[0] * maxr, koeff[
1] * minc, koeff[1] * maxc
def process_mask(mask):
"""
:param mask: 输入的mask
:return:
"""
"""
1. 对每一个slices的2d图像进行处理
"1. 获得当前层的凸包，如果当潜藏的凸包面积过大，或者没有没有mask，则放弃凸包的提取，直接赋值原始的图像
2. 对当前凸包mask进行膨胀处理，获得膨胀凸包
"""
convex_mask = np.copy(mask)
for i_layer in range(convex_mask.shape[0]):
mask1 = np.ascontiguousarray(mask[i_layer])
if np.sum(mask1) > 0:
mask2 = convex_hull_image(mask1)
if np.sum(mask2) > 1.5 * np.sum(
mask1): # 凸包生成的凸多边形太过分，掩盖了原始mask1的大致形状信息，则放弃凸包处理
mask2 = mask1
else: # 没有mask目标
mask2 = mask1
convex_mask[i_layer] = mask2
struct = generate_binary_structure(3, 1)
dilatedMask = binary_dilation(convex_mask, structure=struct, iterations=10)
return dilatedMask
def convex_hull(image, selem=None, out=None):
orig_phantom = img_as_ubyte(image)
fig, ax = plt.subplots()
ax.imshow(orig_phantom, cmap=plt.cm.gray)
def plot_comparison(original, filtered, filter_name):
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True,
sharey=True)
ax1.imshow(original, cmap=plt.cm.gray)
ax1.set_title('original')
ax1.axis('off')
ax2.imshow(filtered, cmap=plt.cm.gray)
ax2.set_title(filter_name)
ax2.axis('off')
phantom = orig_phantom.copy()
phantom[340:350, 200:210] = 255
phantom[100:110, 200:210] = 0
phantom = orig_phantom.copy()
phantom[10:30, 200:210] = 0
#img = imageGlobal
hull1 = convex_hull_image(image == 0)
plot_comparison(image, hull1, 'convex hull')
plt.show()
def deep_struct_filter(self, data3dr_tmp, body):
""" Najde vsechny hluboke (v ose z) objekty a jejich okoli 5 pixelu """
min_bone_thr = 220
bone = (data3dr_tmp>min_bone_thr) & body
bone_sum = np.sum(bone, axis = 0) #> 150
bone_mask = bone_sum>=1
bone_hull = convex_hull_image(bone_mask)#convex_hull_image(bone_mask)
def weak_dist_bone(self, bone_hull, body):
""" Metoda pro zjisteni, jak blizko jsme stredu (nejvzdalenejsimu mistu od povrchu) """
blank_body = np.ones(body.shape)
bone_edge_dist = scipy.ndimage.morphology.distance_transform_edt(bone_hull)
bone_edge_dist_maximum = np.max(bone_edge_dist)
bone_edge_dist_focus = bone_edge_dist > 0*bone_edge_dist_maximum
blank_body[:] = bone_edge_dist_focus
ld = scipy.ndimage.morphology.distance_transform_edt(blank_body)
return resize_to_shape(ld, self.ss.orig_shape)
dh = weak_dist_bone(bone_hull, body)
blur = scipy.ndimage.filters.gaussian_filter(copy.copy(data3dr_tmp), sigma=[17, 3, 3]) > 100
db = scipy.ndimage.morphology.distance_transform_edt(1-blur)
dbf = db >= 6
struct_filter = ((dbf==False) & (dh>15))==False
return struct_filter
def deep_struct_filter(self, data3dr_tmp, body):
""" Najde vsechny hluboke (v ose z) objekty a jejich okoli 5 pixelu """
min_bone_thr = 220
bone = (data3dr_tmp > min_bone_thr) & body
bone_sum = np.sum(bone, axis=0) #> 150
bone_mask = bone_sum >= 1
bone_hull = convex_hull_image(bone_mask) #convex_hull_image(bone_mask)
def weak_dist_bone(self, bone_hull, body):
""" Metoda pro zjisteni, jak blizko jsme stredu (nejvzdalenejsimu mistu od povrchu) """
blank_body = np.ones(body.shape)
bone_edge_dist = scipy.ndimage.morphology.distance_transform_edt(
bone_hull)
bone_edge_dist_maximum = np.max(bone_edge_dist)
bone_edge_dist_focus = bone_edge_dist > 0 * bone_edge_dist_maximum
blank_body[:] = bone_edge_dist_focus
ld = scipy.ndimage.morphology.distance_transform_edt(blank_body)
return resize_to_shape(ld, self.ss.orig_shape)
dh = weak_dist_bone(bone_hull, body)
blur = scipy.ndimage.filters.gaussian_filter(copy.copy(data3dr_tmp),
sigma=[17, 3, 3]) > 100
db = scipy.ndimage.morphology.distance_transform_edt(1 - blur)
dbf = db >= 6
struct_filter = ((dbf == False) & (dh > 15)) == False
return struct_filter
def post_process_v2(mask):
"""Mainly remove the convex areas"""
bw = label(mask == 1)
# 1) detach mislabeled pixels
bw = binary_opening(bw, rectangle(2, 20))
# 2) remove small objects
bw = remove_small_objects(bw, min_size=4096, connectivity=2)
# 3) solve the defeat, typically the convex outline
coords = corner_peaks(corner_harris(bw, k=0.2), min_distance=5)
valid = [c for c in coords
if 100 < c[1] < 476] # only cares about this valid range
if valid:
y, x = zip(*valid)
# corners appear in pair
if len(y) % 2 == 0:
# select the lowest pair
left_x, right_x = [func(x[0], x[1]) for func in (min, max)]
sep_x = np.arange(left_x, right_x + 1).astype(int)
sep_y = np.floor(np.linspace(y[0], y[1] + 1,
len(sep_x))).astype(int)
# make the gap manually
bw[sep_y, sep_x] = 0
bw = binary_opening(bw, disk(6))
else:
mask = np.zeros_like(bw)
mask[y, x] = 1
chull = convex_hull_image(mask)
bw = np.logical_xor(chull, bw)
bw = binary_opening(bw, disk(6))
return bw
def internal_filter(self, lungs, coronal, body):
#lungs = ss.get_lungs()
lungs_sum = np.sum(lungs, axis = 0)>=5
lungs_hull = convex_hull_image(lungs_sum)
def dist_lungs(lungs_hull, body):
""" Metoda pro zjisteni, jak blizko jsme stredu (nejvzdalenejsimu mistu od povrchu) """
blank_body = np.ones(body.shape)
lungs_edge_dist = scipy.ndimage.morphology.distance_transform_edt(lungs_hull)
lungs_edge_dist_maximum = np.max(lungs_edge_dist)
lungs_edge_dist_focus = lungs_edge_dist > 0.2 * lungs_edge_dist_maximum # 0.1 puvodne
blank_body[:] = lungs_edge_dist_focus
ld = scipy.ndimage.morphology.distance_transform_edt(blank_body)
# resized_to_orig = resize_to_shape(ld, self.ss.orig_shape)
# return resized_to_orig
return ld
dist_lungs_with_body = dist_lungs(lungs_hull, body)
lungs_mask = (dist_lungs_with_body > 0 ) & (coronal > 0)
#print_2D_gray(lungs_mask[100])
#print_it_all(ss, data3dr_tmp, lungs_mask*3, "001")
def improve_lungs(dist_lungs_hulls, final_area_filter):
""" Upravi masku lungs pro specialni ucely """
lungs_hulls = dist_lungs_hulls > 0
new_filter = np.zeros(final_area_filter.shape)
for i, area_slice in enumerate(final_area_filter):
convex = convex_hull_image(area_slice)
new_filter[i] = lungs_hulls[i] & convex
return new_filter
return lungs_mask
def shift(img):
"""Shift a binary image randomly within the frame
Uses a convex hull calculation to make sure it doesn't translate
the image out of the frame.
"""
hull = morphology.convex_hull_image(1-img)
horizontal = np.where(np.sum(hull, axis=0) > 0)[0]
vertical = np.where(np.sum(hull, axis=1) > 0)[0]
max_left = -np.min(horizontal)
max_right = img.shape[1] - np.max(horizontal)
max_down = -np.min(vertical)
max_up = img.shape[0] - np.max(vertical)
shift_x = np.random.randint(max_left, max_right)
shift_y = np.random.randint(max_down, max_up)
#print "SHIFT", shift_x, shift_y
def shift(xy):
xy[:, 0] -= shift_x
xy[:, 1] -= shift_y
return xy
return np.logical_not(transform.warp(np.logical_not(img), shift))
def get_segmented_lungs(im):
# 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
if np.sum(binary) > 4:
binary = dilation(binary, square(5))
binary = convex_hull_image(binary)
return binary
def bbox(row):
mask_list = []
max_len = 40
x_min = -20
x_max = 180
x_start = np.argmin(np.abs(row['xcm_um'] - x_min))
x_stop = np.argmin(np.abs(row['xcm_um'] - x_max))
image = row['mask'][x_start:x_stop]
for i, mask in enumerate(image):
x_min = np.min(np.where(mask)[0])
x_max = np.max(np.where(mask)[0])
y_min = np.min(np.where(mask)[1])
y_max = np.max(np.where(mask)[1])
width = x_max - x_min
height = y_max - y_min
# Apply convex hull to avoid introducing bias and improve fit
chull = convex_hull_image(mask)
screened = chull[x_min:x_max, y_min:y_max]
screened = tf.expand_dims(tf.convert_to_tensor(screened), axis=-1)
screened = tf.image.pad_to_bounding_box(screened, int(
(90 - width) / 2), int((90 - height) / 2), 90, 90).numpy()
rr, cc = skimage.draw.ellipse(r=45,
c=45,
r_radius=row['b'][i] * 2.0,
c_radius=row['a'][i] * 2.0,
shape=(90, 90))
ellipse = np.zeros((90, 90, 1))
ellipse[rr, cc] = 1
mask_list.append(np.stack((screened, ellipse), axis=2)[:, :, :, 0])
if len(mask_list) < max_len:
for _ in range(max_len - len(mask_list)):
mask_list.append(np.zeros((90, 90, 2), dtype=bool))
return np.stack(mask_list)
def onclick(self,event):
if event.inaxes==ax:
if event.button==1:
if np.sum(MASKs[self.ind])==0:
cx, cy = circle_perimeter(int(event.ydata), int(event.xdata), 3)
MASKs[self.ind][cx,cy] = (220, 80, 20, 1)
original_image = np.copy(MASKs[self.ind][:,:,3])
chull = convex_hull_image(original_image)
MASKs[self.ind][chull,:] = (255, 0, 0, .4)
ax.cla()
data = areaFile.attrs['ROI_patches'][:,:,self.ind]
ax.imshow(data,cmap='binary_r')
ax.imshow(MASKs[self.ind])
elif event.button==3:
MASKs[self.ind] = np.zeros(MASKs[self.ind].shape)
data = areaFile.attrs['ROI_patches'][:,:,self.ind]
ax.cla()
ax.imshow(data,cmap='binary_r')
ax.imshow(MASKs[self.ind])
def get_convex(ori_seg_path, save_path):
for file_name in os.listdir(ori_seg_path):
img = imageio.imread(os.path.join(ori_seg_path, file_name))
img = np.where(img > 0, True, False)
convex = morphology.convex_hull_image(img).astype('uint8')
convex = np.where(convex > 0, 255, 0).astype('uint8')
imageio.imwrite(os.path.join(save_path, file_name), convex)
def convex_hull_dilate(binary_mask, dilate_factor=1.5, iterations=10):
"""
Replace each slice with convex hull of it then dilate. Convex hulls used
only if it does not increase area by dilate_factor. This applies mainly to
the inferior slices because inferior surface of lungs is concave.
binary_mask: 3D binary numpy array with the same shape of the image,
that only region of interest is True. One side of the lung in this
specifical case.
dilate_factor: float, factor of increased area after dilation
iterations: int, number of iterations for dilation
return: 3D binary numpy array with the same shape of the image,
that only region of interest is True. Each binary mask is ROI of one
side of the lung.
"""
binary_mask_dilated = np.array(binary_mask)
for i in range(binary_mask.shape[0]):
slice_binary = binary_mask[i]
if np.sum(slice_binary) > 0:
slice_convex = morphology.convex_hull_image(slice_binary)
if np.sum(slice_convex) <= dilate_factor * np.sum(slice_binary):
binary_mask_dilated[i] = slice_convex
struct = scipy.ndimage.morphology.generate_binary_structure(3, 1)
binary_mask_dilated = scipy.ndimage.morphology.binary_dilation(
binary_mask_dilated, structure=struct, iterations=10)
return binary_mask_dilated
def change_perspective(image):
orig = image
image = color.rgb2gray(image)
image = skimage.morphology.dilation(image, skimage.morphology.disk(5))
image = feature.canny(image, sigma=3)
image = morphology.convex_hull_image(image)
(im_height, im_width) = image.shape
loc = np.array(np.where(image)).T
target_points = np.float32([[0, 0], [im_height, 0], [0, im_width],
[im_height, im_width]])
top_left = loc[np.argmin(
np.linalg.norm(target_points[0] - loc, axis=1, ord=1))]
top_right = loc[np.argmin(
np.linalg.norm(target_points[1] - loc, axis=1, ord=1))]
bot_left = loc[np.argmin(
np.linalg.norm(target_points[2] - loc, axis=1, ord=1))]
bot_right = loc[np.argmin(
np.linalg.norm(target_points[3] - loc, axis=1, ord=1))]
current_points = np.float32([top_left, top_right, bot_left, bot_right])
matrix = cv2.getPerspectiveTransform(target_points, current_points)
result = skimage.transform.warp(orig, matrix)
# skimage.io.imshow(result)
return result
def warp_coeffs_to_template(I, T, plot=True, initial_warp_matrix = None):
Ib = I
Tb = (T != [255,255,255]).all(axis=2)
if initial_warp_matrix is None:
convex = morphology.convex_hull_image(morphology.binary_erosion(Ib))
# convex = morphology.convex_hull_image(Ib)
bbox = measure.regionprops(convex.astype(int))[0].bbox
w = bbox[2] - bbox[0]
h = bbox[3] - bbox[1]
initial_warp_matrix = np.array([
[h / T.shape[1], 0, bbox[1]],
[0, w / T.shape[0], bbox[0]]], dtype=np.float32)
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 400, 0.0001)
ecc, warp_matrix = cv2.findTransformECC(
Tb.astype(np.float32), Ib.astype(np.float32),
initial_warp_matrix.astype(np.float32), cv2.MOTION_AFFINE, criteria, None, 1)
Tbr = cv2.warpAffine(Tb.astype(np.float32), warp_matrix, dsize=tuple(np.array(Ib.shape)[::-1]))
if plot:
fig, ax = plt.subplots()
ax.cla()
ax.imshow(Ib , alpha=0.5)
ax.imshow(Tbr, alpha=0.5)
plt.title('input-to-template warp')
plt.show()
return warp_matrix, Tbr
def chull_area(image):
#print(image)
#im = np.copy(image)
#print(im)
#im[im > 0] = 1
hull = convex_hull_image(image > 0)
return np.count_nonzero(hull)
def patch_up_roi(roi, sigma=0.5, truncate=2):
"""
After being non-linearly transformed, ROIs tend to have holes in them.
We perform a couple of computational geometry operations on the ROI to
fix that up.
Parameters
----------
roi : 3D binary array
The ROI after it has been transformed.
sigma : float
The sigma for initial Gaussian smoothing.
truncate : float
The truncation for the Gaussian
Returns
-------
ROI after dilation and hole-filling
"""
return convex_hull_image(
gaussian(ndim.binary_fill_holes(roi), sigma=sigma, truncate=truncate) >
0)
def calculate_oct_roi_mask(img, tresh=1e-10):
"""
Calculate the interesting region MASK of the image using entropy
:param img:
:param tresh: entropy cutoff threshold
:return: mask of the interesting region (MASK = 1 interesting)
"""
if img.ndim == 2:
im_slice = skimage.img_as_float(img.astype(np.float32) / 128. - 1.)
elif img.ndim == 3:
im_slice = skimage.img_as_float(img[:, :, 1].astype(np.float32) /
128. - 1.)
assert img.ndim in {2, 3}
im_slice_ = entropy(im_slice, disk(11))
im_slice_ = im_slice_ / (np.max(im_slice_) + 1e-16)
im_slice_ = np.asarray(im_slice_ > tresh, dtype=np.int8)
selem = disk(35)
im_slice_ = binary_closing(im_slice_, selem=selem)
im_slice_ = convex_hull_image(im_slice_)
plt.imshow(im_slice, cmap='gray')
plt.imshow(im_slice_, cmap='jet', alpha=0.5)
plt.pause(.1)
return im_slice_
def comp_external_contour(orig,thresh):
img_height, img_width, img_channels = orig.shape
#Convert the mean shift image to grayscale, then apply Otsu's thresholding
gray = cv2.cvtColor(orig, cv2.COLOR_BGR2GRAY)
convexhull = convex_hull_image(thresh)
img_convexhull = np.uint8(convexhull)*255
#Obtain the threshold image using OTSU adaptive filter
thresh_hull = cv2.threshold(img_convexhull, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
#find contours and get the external one
image_result, contours, hier = cv2.findContours(img_convexhull, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
print("len(contours)")
print(len(contours))
# Measure properties
regions = regionprops(img_convexhull)
#center location of region
y_cvh, x_cvh = regions[0].centroid
print("Convexhull center of root system: {0}, {1} \n".format(int(x_cvh),int(y_cvh)))
convexhull_diameter = regions[0].equivalent_diameter
return img_convexhull,convexhull_diameter, y_cvh, x_cvh
def get_bg_mask(img):
#if img.ndim == 3:
# bg_mask = img.any(axis=-1)
# bg_mask = np.invert(bg_mask) # consistent with np.ma, True if masked
# # make multichannel (is it really this hard?)
# bg_mask = np.repeat(bg_mask[:,:,np.newaxis], 3, axis=2)
#
#else:
# bg_mask = (img != 0)
# bg_mask = np.invert(bg_mask) # see above
#bound = segmentation.find_boundaries(bg_mask, mode='inner', background=1)
#bg_mask[bound] = 1
#min_size = img.shape[0] * img.shape[1] // 4
#holes = morphology.remove_small_holes(bg_mask, min_size=min_size)
#bg_mask[holes] = 1
#bg_mask = segmentation.find_boundaries(img)
#bg_mask = morphology.remove_small_objects(bg_mask)
#bg_mask = morphology.remove_small_holes(bg_mask)
bg_mask = morphology.convex_hull_image(img)
bg_mask = np.zeros_like(img)
return bg_mask
def boundary_polygon(self, time):
"""
Get coordinates of object boundary in counter-clockwise order
"""
ti = np.where(time == self.times)[0][0]
com_x, com_y = self.center_of_mass(time)
# If at least one point along perimeter of the mask rectangle is unmasked, find_boundaries() works.
# But if all perimeter points are masked, find_boundaries() does not find the object.
# Therefore, pad the mask with zeroes first and run find_boundaries on the padded array.
padded_mask = np.pad(self.masks[ti], 1, 'constant', constant_values=0)
chull = convex_hull_image(padded_mask)
boundary_image = find_boundaries(chull, mode='inner', background=0)
# Now remove the padding.
boundary_image = boundary_image[1:-1, 1:-1]
boundary_x = self.x[ti].ravel()[boundary_image.ravel()]
boundary_y = self.y[ti].ravel()[boundary_image.ravel()]
r = np.sqrt((boundary_x - com_x)**2 + (boundary_y - com_y)**2)
theta = np.arctan2((boundary_y - com_y),
(boundary_x - com_x)) * 180.0 / np.pi + 360
polar_coords = np.array([(r[x], theta[x]) for x in range(r.size)],
dtype=[('r', 'f4'), ('theta', 'f4')])
coord_order = np.argsort(polar_coords, order=['theta', 'r'])
ordered_coords = np.vstack(
[boundary_x[coord_order], boundary_y[coord_order]])
return ordered_coords
def active_snake(image_file, num_iters):
#Parse image path and create result image path
path, filename = os.path.split(image_file)
print("processing image : {0} \n".format(str(filename)))
#load the image and perform pyramid mean shift filtering to aid the thresholding step
imgcolor = cv2.imread(image_file)
img_gray = cv2.cvtColor(imgcolor, cv2.COLOR_BGR2GRAY)
# Image binarization by applying otsu threshold
thresh = threshold_otsu(img_gray)
binary = img_gray > thresh
# Extract convex hull of the binary image
convexhull = convex_hull_image(invert(binary))
# label image regions
label_image_convexhull = label(convexhull)
# Measure properties of labeled image regions.
regions = regionprops(label_image_convexhull)
# center location of region
y0, x0 = regions[0].centroid
#print(y0,x0)
print("Coordinates of centroid: {0} , {0} \n".format(y0, x0))
# axis length of region
d_major = regions[0].major_axis_length
d_minor = regions[0].minor_axis_length
diameter = regions[0].equivalent_diameter
minr, minc, maxr, maxc = regions[0].bbox
d_bbox = max(maxr - minr, maxc - minc)
radius = int(max(d_major, d_minor, d_bbox) / 2) + 20
print("Radius of convex hull region is: {0} \n".format(radius))
gI = morphsnakes.gborders(img_gray, alpha=5, sigma=1)
# Morphological GAC. Initialization of the level-set.
mgac = morphsnakes.MorphGAC(gI, smoothing=1, threshold=0.24, balloon=-1)
mgac.levelset = circle_levelset(img_gray.shape, (y0, x0),
radius,
scalerow=0.75)
# Visual evolution.
morphsnakes.evolve_visual(mgac, num_iters=num_iters, background=imgcolor)
#define result path for simplified segmentation result
result_img_path = save_path_ac + str(filename[0:-4]) + '.png'
# suppose that img's dtype is 'float64'
img_uint8 = img_as_ubyte(mgac.levelset)
cv2.imwrite(result_img_path, img_uint8)
def run(img, **args):
if len(img.shape) > 2 and img.shape[2] == 4:
img = color.rgba2rgb(img)
if len(img.shape) == 2:
img = color.gray2rgb(img)
img = color.rgb2grey(img)
return to_base64(convex_hull_image(img, **args))
def patch_up_roi(roi, bundle_name="ROI"):
"""
After being non-linearly transformed, ROIs tend to have holes in them.
We perform a couple of computational geometry operations on the ROI to
fix that up.
Parameters
----------
roi : 3D binary array
The ROI after it has been transformed.
sigma : float
The sigma for initial Gaussian smoothing.
truncate : float
The truncation for the Gaussian
bundle_name : str, optional
Name of bundle, which may be useful for error messages.
Default: None
Returns
-------
ROI after dilation and hole-filling
"""
hole_filled = ndim.binary_fill_holes(roi > 0)
if not np.any(hole_filled):
raise ValueError((
f"{bundle_name} found to be empty after "
"applying the mapping."))
try:
return convex_hull_image(hole_filled)
except QhullError:
return hole_filled
def internal_filter(self, lungs, coronal, body):
#lungs = ss.get_lungs()
lungs_sum = np.sum(lungs, axis=0) >= 5
lungs_hull = convex_hull_image(lungs_sum)
def dist_lungs(lungs_hull, body):
""" Metoda pro zjisteni, jak blizko jsme stredu (nejvzdalenejsimu mistu od povrchu) """
blank_body = np.ones(body.shape)
lungs_edge_dist = scipy.ndimage.morphology.distance_transform_edt(
lungs_hull)
lungs_edge_dist_maximum = np.max(lungs_edge_dist)
lungs_edge_dist_focus = lungs_edge_dist > 0.2 * lungs_edge_dist_maximum # 0.1 puvodne
blank_body[:] = lungs_edge_dist_focus
ld = scipy.ndimage.morphology.distance_transform_edt(blank_body)
# resized_to_orig = resize_to_shape(ld, self.ss.orig_shape)
# return resized_to_orig
return ld
dist_lungs_with_body = dist_lungs(lungs_hull, body)
lungs_mask = (dist_lungs_with_body > 0) & (coronal > 0)
#print_2D_gray(lungs_mask[100])
#print_it_all(ss, data3dr_tmp, lungs_mask*3, "001")
def improve_lungs(dist_lungs_hulls, final_area_filter):
""" Upravi masku lungs pro specialni ucely """
lungs_hulls = dist_lungs_hulls > 0
new_filter = np.zeros(final_area_filter.shape)
for i, area_slice in enumerate(final_area_filter):
convex = convex_hull_image(area_slice)
new_filter[i] = lungs_hulls[i] & convex
return new_filter
return lungs_mask
def add_auto_masks_area(areaFile,addMasks=False):
from skimage.morphology import binary_dilation, binary_erosion, disk
from skimage import exposure
from skimage.transform import hough_circle
from skimage.morphology import convex_hull_image
from skimage.feature import canny
from skimage.draw import circle_perimeter
import h5py
MASKs = np.zeros(areaFile.attrs['ROI_patches'].shape)
if 'ROI_masks' in (areaFile.attrs.iterkeys()):
print 'Masks have already been created'
awns = raw_input('Would you like to redo them from scratch: (answer y/n): ')
else:
awns = 'y'
if awns=='y':
MASKs = np.zeros(areaFile.attrs['ROI_patches'].shape)
for i in range(areaFile.attrs['ROI_patches'].shape[2]):
patch = areaFile.attrs['ROI_patches'][:,:,i]
tt0 = exposure.equalize_hist(patch)
tt = 255*tt0/np.max(tt0)
thresh = 1*tt>0.3*255
thresh2 = 1*tt<0.1*255
tt[thresh] = 255
tt[thresh2] = 0
edges = canny(tt, sigma=2, low_threshold=20, high_threshold=30)
try_radii = np.arange(3,5)
res = hough_circle(edges, try_radii)
ridx, r, c = np.unravel_index(np.argmax(res), res.shape)
r, c, try_radii[ridx]
image = np.zeros([20,20,4])
cx, cy = circle_perimeter(c, r, try_radii[ridx]+2)
try:
image[cy, cx] = (220, 80, 20, 1)
original_image = np.copy(image[:,:,3])
chull = convex_hull_image(original_image)
image[chull,:] = (255, 0, 0, .4)
except:
pass
MASKs[:,:,i] = (1*image[:,:,-1]>0)
if addMasks:
areaFile.attrs['ROI_masks'] = MASKs
else:
pass
return np.array(MASKs)
def improve_lungs(dist_lungs_hulls, final_area_filter):
""" Upravi masku lungs pro specialni ucely """
lungs_hulls = dist_lungs_hulls > 0
new_filter = np.zeros(final_area_filter.shape)
for i, area_slice in enumerate(final_area_filter):
convex = convex_hull_image(area_slice)
new_filter[i] = lungs_hulls[i] & convex
return new_filter
def find_convex_hull_rectangle(grey_image, mask=None, canny_threshold=50):
edge_map = cv2.Canny(grey_image, canny_threshold, 3 * canny_threshold, apertureSize=3)
if mask is not None:
edge_map = edge_map*mask
hull = convex_hull_image(edge_map)
(_, contours, _) = cv2.findContours(hull.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
assert len(contours) == 1
rect = cv2.minAreaRect(contours[0])
max_box = cv2.boxPoints(rect)
return max_box.astype(np.int)
def test_pathological_qhull_example():
image = np.array(
[[0, 0, 0, 0, 1, 0, 0],
[0, 0, 1, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 0]], dtype=bool)
expected = np.array(
[[0, 0, 0, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 0, 0, 0]], dtype=bool)
assert_array_equal(convex_hull_image(image), expected)
def fill_poly(poly_y, poly_x, shape):
bbox = np.zeros((4), dtype=np.int32)
bbox[0] = np.min(poly_y)
bbox[1] = np.min(poly_x)
bbox[2] = np.max(poly_y)
bbox[3] = np.max(poly_x)
mask = np.zeros(shape, dtype = np.bool_)
mask[poly_y.astype(np.int), poly_x.astype(np.int)] = True
mask = morph.convex_hull_image(mask).astype(np.int8)
return mask, bbox
def wrapper_regions(bestregions, opening_param = 3, mshape = ((0,1,0),(1,1,1),(0,1,0)) ):
zdim, xdim, ydim = bestregions.shape
wregions = np.zeros_like(bestregions)
for sidx in range(zdim):
if np.sum(bestregions[sidx]) > 0:
wregions[sidx] = convex_hull_image(bestregions[sidx])
return wregions
def improve_bone_hull(bone_area_filter):
basic_loc_filter = body & (coronal > 0)
mask = np.zeros(bone_area_filter.shape)
for i in range(len(bone_area_filter)):
try:
down_mask = bone_area_filter[i] & basic_loc_filter[i]
mask[i] = convex_hull_image(down_mask) | bone_area_filter[i]
mask[i] = scipy.ndimage.morphology.binary_fill_holes(mask[i])
except:
pass
mask = mask > 0
return mask
def test_basic():
image = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
expected = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
assert_array_equal(convex_hull_image(image), expected)
# Test that an error is raised on passing a 3D image:
image3d = np.empty((5, 5, 5))
with pytest.raises(ValueError):
convex_hull_image(image3d)
def process_mask(mask):
convex_mask = np.copy(mask)
for i_layer in range(convex_mask.shape[0]):
mask1 = np.ascontiguousarray(mask[i_layer])
if np.sum(mask1)>0:
mask2 = convex_hull_image(mask1)
if np.sum(mask2)>2*np.sum(mask1):
mask2 = mask1
else:
mask2 = mask1
convex_mask[i_layer] = mask2
struct = generate_binary_structure(3,1)
dilatedMask = binary_dilation(convex_mask,structure=struct,iterations=10)
return dilatedMask
def create_mask(frame):
""""Create a big mask that encompasses all the cells"""
# detect ridges
ridges = enhance_ridges(frame)
# threshold ridge image
thresh = filters.threshold_otsu(ridges)
thresh_factor = 1.1
prominent_ridges = ridges > thresh_factor*thresh
prominent_ridges = morphology.remove_small_objects(prominent_ridges, min_size=128)
# the mask contains the prominent ridges
mask = morphology.convex_hull_image(prominent_ridges)
mask = morphology.binary_erosion(mask, disk(10))
return mask
def test_qhull_offset_example():
nonzeros = (([1367, 1368, 1368, 1368, 1369, 1369, 1369, 1369, 1369, 1370,
1370, 1370, 1370, 1370, 1370, 1370, 1371, 1371, 1371, 1371,
1371, 1371, 1371, 1371, 1371, 1372, 1372, 1372, 1372, 1372,
1372, 1372, 1372, 1372, 1373, 1373, 1373, 1373, 1373, 1373,
1373, 1373, 1373, 1374, 1374, 1374, 1374, 1374, 1374, 1374,
1375, 1375, 1375, 1375, 1375, 1376, 1376, 1376, 1377]),
([151, 150, 151, 152, 149, 150, 151, 152, 153, 148, 149, 150,
151, 152, 153, 154, 147, 148, 149, 150, 151, 152, 153, 154,
155, 146, 147, 148, 149, 150, 151, 152, 153, 154, 146, 147,
148, 149, 150, 151, 152, 153, 154, 147, 148, 149, 150, 151,
152, 153, 148, 149, 150, 151, 152, 149, 150, 151, 150]))
image = np.zeros((1392, 1040), dtype=bool)
image[nonzeros] = True
expected = image.copy()
assert_array_equal(convex_hull_image(image), expected)
def test_basic():
image = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
expected = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
assert_array_equal(convex_hull_image(image), expected)
def get_bone_hull(data3dr_tmp, body):
# jeste by bylo dobre u patere vynechat konvexni obal
bone = (data3dr_tmp>min_bone_thr) & body
mask = np.zeros(data3dr_tmp.shape)
step = 32
for i in range(len(data3dr_tmp)):
mask[i] = np.sum(
bone[
int(max(0, i - step / 2)):
int(min(len(data3dr_tmp), i + step / 2))
], axis = 0
) >= 1
try:
mask[i] = convex_hull_image(mask[i])
except:
pass
mask = mask>0
return mask
def mask_readoutstreaks(image):
'''logarithmic image to edge detect faint features
This requires the `scikit-image <http://scikit-image.org/>`_ package.
'''
from skimage import filters as skfilter
from skimage.morphology import convex_hull_image
logimage = np.log10(np.clip(image, 1, 1e5)) / 5
# Mask overexposed area + sobel edge detect
mask = (skfilter.sobel(logimage) > 0.1) | (image > 0.6 * np.max(image))
# pick out the feature that contain the center
# I hope that this always is bit enough
mask, lnum = ndimage.label(mask, structure=np.ones((3, 3), dtype=bool))
i = mask[ndimage.center_of_mass(image)]
mask = (mask == i)
# fill any holes in that region
return convex_hull_image(mask)
def find_pupil(darkest_cluster):
"""Find the pupil area by cleaning the darkest cluster."""
x, y = cluster_mean(darkest_cluster)
# Find pupil region
cleaned_pupil = np.copy(darkest_cluster)
labels = label(darkest_cluster)
c = Counter(labels.flatten())
label_pupil = c.most_common(2)[1][0]
clean_pupil = labels == label_pupil
# Naive radius as the half distance between
# the most right element and most left element
# of the pupil region
sums = np.sum(clean_pupil, axis=1)
radius = np.amax(sums) / 2
# Delete areas that are not in the naive radius of the iris
for i in range(cleaned_pupil.shape[0]):
for j in range(cleaned_pupil.shape[1]):
if np.linalg.norm([x - i, y - j]) > 1.1 * radius:
cleaned_pupil[i,j] = 0
# Add again the region labelled as the pupil before
cleaned_pupil[clean_pupil] += 1
# Find new naive center
sums = np.sum(clean_pupil, axis=1)
radius = np.amax(sums) / 2
# Fill using a convex hull the pupil
chull = convex_hull_image(cleaned_pupil)
cleaned_pupil[chull] += 1
return cleaned_pupil, radius
import matplotlib.pyplot as plt
from skimage.morphology import convex_hull_image
image = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 1, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=float)
original_image = np.copy(image)
chull = convex_hull_image(image)
image[chull] += 1
# image is now:
# [[ 0. 0. 0. 0. 0. 0. 0. 0. 0.]
# [ 0. 0. 0. 0. 2. 0. 0. 0. 0.]
# [ 0. 0. 0. 2. 1. 2. 0. 0. 0.]
# [ 0. 0. 2. 1. 1. 1. 2. 0. 0.]
# [ 0. 2. 1. 1. 1. 1. 1. 2. 0.]
# [ 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
fig, axes = plt.subplots(1, 2, figsize=(9, 3))
ax = axes.ravel()
ax[0].set_title('Original picture')
ax[0].imshow(original_image, cmap=plt.cm.gray, interpolation='nearest')
sk = skeletonize(horse == 0) plot_comparison(horse, sk, 'skeletonize') ###################################################################### # # As the name suggests, this technique is used to thin the image to 1-pixel # wide skeleton by applying thinning successively. # # Convex hull # =========== # # The ``convex_hull_image`` is the *set of pixels included in the smallest # convex polygon that surround all white pixels in the input image*. Again # note that this is also performed on binary images. hull1 = convex_hull_image(horse == 0) plot_comparison(horse, hull1, 'convex hull') ###################################################################### # As the figure illustrates, ``convex_hull_image`` gives the smallest polygon # which covers the white or True completely in the image. # # If we add a small grain to the image, we can see how the convex hull adapts # to enclose that grain: import numpy as np horse_mask = horse == 0 horse_mask[45:50, 75:80] = 1 hull2 = convex_hull_image(horse_mask)
def main():
print("Usage: python3 img2poly.py <image> <min_dist_canny> <min_dist_extra>\n")
img_path = sys.argv[1]
min_dist = int(sys.argv[2])
min_distX = int(sys.argv[3])
# validate inputs (check if we have enough points in critical places!)
np.random.seed(8)
seed(8)
img = io.imread(img_path)
img_gray = color.rgb2gray(img)
canny = feature.canny(img_gray, sigma=3)
print("canny done")
edges = []
for i in range(len(canny)):
for j in range(len(canny[0])):
if canny[i][j]:
edges += [(i, j)]
print("canny size: ", len(edges))
#plt.imshow(canny, cmap = 'gray')
#plt.show()
#edges = np.transpose(np.nonzero(canny))
uni_points = []
'''
block_size = 100
for i in range(0, len(canny)-block_size, block_size):
for j in range(0, len(canny[0])-block_size, block_size):
uni_points += [(i+randint(0, block_size), j+randint(0, block_size))]
np.random.shuffle(uni_points)
'''
'''for i in range(n_upoints):
uni_points += [(randint(0, len(canny)-1), randint(0, len(canny[0])-1))]
'''
'''
uni_points = poisson(min_dist, len(canny), len(canny[0]), n_upoints)
np.random.shuffle(edges)
'''
points = poisson_filter(edges, min_dist, len(canny[0]), len(canny))
print("filtered canny size: ", len(points))
uni_points = poisson(min_distX, len(canny), len(canny[0]), 16)
print("generated random poisson points: ", len(uni_points))
points += uni_points
#img_points = np.zeros((len(canny), len(canny[0])))
#img_points[canny] = 255
#for x,y in points:
# img_points[x,y] = 255
#plt.imshow(img_points, cmap = 'gray')
#plt.show()
points=np.vstack((points,np.array([
[0,0],
[0,np.shape(canny)[1] - 1],
[np.shape(canny)[0] - 1,0],
[np.shape(canny)[0] - 1,np.shape(canny)[1] - 1]
])))
#points=points[:,[1,0]]
#points=np.vstack(points)
tri=Delaunay(points)
#plt.triplot(points[:,0], (-1)*points[:,1], tri.simplices.copy())
#plt.plot(points[:,0], points[:,1], 'o')
#plt.show()
#img_points = np.zeros((len(canny), len(canny[0])))
img_points = img
in_tri = np.zeros((len(canny), len(canny[0])))
total = len(tri.simplices)
print("#tri: ", total)
count = 0
for t in tri.simplices:
count += 1
if (count % 10 == 0):
print("%3d%%" % (100*count/total))
in_tri[points[t,0], points[t,1]] = 255
chull = convex_hull_image(in_tri)
img_points[chull, 0] = np.mean(img_points[chull, 0])
img_points[chull, 1] = np.mean(img_points[chull, 1])
img_points[chull, 2] = np.mean(img_points[chull, 2])
'''
img_points[chull, 0] = int(np.mean(np.sqrt(img_points[chull, 0]))**2)
img_points[chull, 1] = int(np.mean(np.sqrt(img_points[chull, 1]))**2)
img_points[chull, 2] = int(np.mean(np.sqrt(img_points[chull, 2]))**2)
'''
in_tri[points[t,0], points[t,1]] = 0
plt.imshow(img_points)
plt.show()
def distance_transform(network, geometry, offset, **kwargs):
r"""
Use the Voronoi vertices and perform image analysis to obtain throat properties
"""
import math
import numpy as np
from skimage.morphology import convex_hull_image
from skimage.measure import regionprops
from scipy import ndimage
Nt = geometry.num_throats()
area = sp.zeros(Nt)
perimeter = sp.zeros(Nt)
centroid = sp.zeros([Nt, 3])
incentre = sp.zeros([Nt, 3])
inradius = sp.zeros(Nt)
equiv_diameter = sp.zeros(Nt)
eroded_verts = sp.ndarray(Nt, dtype=object)
res = 200
vertices = geometry['throat.vertices']
normals = geometry['throat.normal']
z_axis = [0, 0, 1]
for i in range(Nt):
logger.info("Processing throat " + str(i+1)+" of "+str(Nt))
# For boundaries some facets will already be aligned with the axis - if this
# is the case a rotation is unnecessary and could also cause problems
angle = tr.angle_between_vectors(normals[i], z_axis)
if angle == 0.0 or angle == np.pi:
# We are already aligned
rotate_facet = False
facet = vertices[i]
else:
rotate_facet = True
M = tr.rotation_matrix(tr.angle_between_vectors(normals[i], z_axis),
tr.vector_product(normals[i], z_axis))
facet = np.dot(vertices[i], M[:3, :3].T)
x = facet[:, 0]
y = facet[:, 1]
z = facet[:, 2]
# Get points in 2d for image analysis
pts = np.column_stack((x, y))
# Translate points so min sits at the origin
translation = [pts[:, 0].min(), pts[:, 1].min()]
pts -= translation
order = np.int(math.ceil(-np.log10(np.max(pts))))
# Normalise and scale the points so that largest span equals the resolution
# to save on memory and create clear image"
max_factor = np.max([pts[:, 0].max(), pts[:, 1].max()])
f = res/max_factor
# Scale the offset and define a circular structuring element with radius
r = f*offset
# Only proceed if r is less than half the span of the image"
if r <= res/2:
pts *= f
minp1 = pts[:, 0].min()
minp2 = pts[:, 1].min()
maxp1 = pts[:, 0].max()
maxp2 = pts[:, 1].max()
img = np.zeros([np.int(math.ceil(maxp1-minp1)+1),
np.int(math.ceil(maxp2-minp2)+1)])
int_pts = np.around(pts, 0).astype(int)
for pt in int_pts:
img[pt[0]][pt[1]] = 1
# Pad with zeros all the way around the edges
img_pad = np.zeros([np.shape(img)[0] + 2, np.shape(img)[1] + 2])
img_pad[1:np.shape(img)[0]+1, 1:np.shape(img)[1]+1] = img
# All points should lie on this plane but could be some rounding errors
# so use the order parameter
z_plane = sp.unique(np.around(z, order+2))
if len(z_plane) > 1:
print('Rotation for image analysis failed')
temp_arr = np.ones(1)
temp_arr.fill(np.mean(z_plane))
z_plane = temp_arr
"Fill in the convex hull polygon"
convhullimg = convex_hull_image(img_pad)
# Perform a Distance Transform and black out points less than r to create
# binary erosion. This is faster than performing an erosion and dt can
# also be used later to find incircle"
eroded = ndimage.distance_transform_edt(convhullimg)
eroded[eroded <= r] = 0
eroded[eroded > r] = 1
# If we are left with less than 3 non-zero points then the throat is
# fully occluded
if np.sum(eroded) >= 3:
# Do some image analysis to extract the key properties
regions = regionprops(eroded[1:np.shape(img)[0]+1,
1:np.shape(img)[1]+1].astype(int))
# Change this to cope with genuine multi-region throats
if len(regions) == 1:
for props in regions:
x0, y0 = props.centroid
equiv_diameter[i] = props.equivalent_diameter
area[i] = props.area
perimeter[i] = props.perimeter
coords = props.coords
# Undo the translation, scaling and truncation on the centroid
centroid2d = [x0, y0]/f
centroid2d += (translation)
centroid3d = np.concatenate((centroid2d, z_plane))
# Distance transform the eroded facet to find the incentre and
# inradius
dt = ndimage.distance_transform_edt(eroded)
inx0, iny0 = \
np.asarray(np.unravel_index(dt.argmax(), dt.shape)) \
.astype(float)
incentre2d = [inx0, iny0]
# Undo the translation, scaling and truncation on the incentre
incentre2d /= f
incentre2d += (translation)
incentre3d = np.concatenate((incentre2d, z_plane))
# The offset vertices will be those in the coords that are
# closest to the originals"
offset_verts = []
for pt in int_pts:
vert = np.argmin(np.sum(np.square(coords-pt), axis=1))
if vert not in offset_verts:
offset_verts.append(vert)
# If we are left with less than 3 different vertices then the
# throat is fully occluded as we can't make a shape with
# non-zero area
if len(offset_verts) >= 3:
offset_coords = coords[offset_verts].astype(float)
# Undo the translation, scaling and truncation on the
# offset_verts
offset_coords /= f
offset_coords_3d = \
np.vstack((offset_coords[:, 0]+translation[0],
offset_coords[:, 1]+translation[1],
np.ones(len(offset_verts))*z_plane)).T
# Get matrix to un-rotate the co-ordinates back to the
# original orientation if we rotated in the first place
if rotate_facet:
MI = tr.inverse_matrix(M)
# Unrotate the offset coordinates
incentre[i] = np.dot(incentre3d, MI[:3, :3].T)
centroid[i] = np.dot(centroid3d, MI[:3, :3].T)
eroded_verts[i] = np.dot(offset_coords_3d, MI[:3, :3].T)
else:
incentre[i] = incentre3d
centroid[i] = centroid3d
eroded_verts[i] = offset_coords_3d
inradius[i] = dt.max()
# Undo scaling on other parameters
area[i] /= f*f
perimeter[i] /= f
equiv_diameter[i] /= f
inradius[i] /= f
else:
area[i] = 0
perimeter[i] = 0
equiv_diameter[i] = 0
if kwargs['set_dependent'] is True:
geometry['throat.area'] = area
geometry['throat.perimeter'] = perimeter
geometry['throat.centroid'] = centroid
geometry['throat.diameter'] = equiv_diameter
geometry['throat.indiameter'] = inradius*2
geometry['throat.incentre'] = incentre
return eroded_verts
def convex_image(self):
return convex_hull_image(self.image)
.. image:: PLOT2RST.current_figure As the name suggests, this technique is used to thin the image to 1-pixel wide skeleton by applying thinning successively. Convex hull =========== The ``convex_hull_image`` is the *set of pixels included in the smallest convex polygon that surround all white pixels in the input image*. Again note that this is also performed on binary images. """ hull1 = convex_hull_image(horse) plot_comparison(horse, hull1, 'convex hull') """ .. image:: PLOT2RST.current_figure As the figure illustrates, ``convex_hull_image`` gives the smallest polygon which covers the white or True completely in the image. If we add a small grain to the image, we can see how the convex hull adapts to enclose that grain: """ import numpy as np horse2 = np.copy(horse)
def hull_percent_filled(filled_blob):
edge = sobel(filled_blob)
hull_img = convex_hull_image(edge)
return float(len(np.where(filled_blob.astype("bool"))[0])) / len(np.where(hull_img.astype("bool"))[0])
def find_map(url, min_int=0.03, max_int=0.97, disk_sz=2, opt=None):
"""Find the map in an image (using morphological operations) and return it.
Heuristic assumption the map is the largest object in the map.
Parameters
----------
img: (M, N, 3) or (M, N, 4)
An RGB or RGBA image.
min_int : threshold value to eliminate ~black background.
If min_int is not given, a default value of 0.03 is uded.
max_int : threshold value to eliminate ~white background.
If max_int is not given, a default value of 0.97 is uded.
disk_sz : size of disk-shaped structuring element for opening.
If disk_sz is not given, a default value of 2 is uded.
opt : optional flag. Default is None; if set to not None,
the convex hull of the largest detected object is returned.
Returns
-------
out : (M, N, 3) array
An image with only the main map.
"""
# rgb from url
rspns = requests.get(url)
img = np.asarray(Image.open(StringIO(rspns.content)))[:, :, :3]
# image must be RGB or RGB(A)
if not len(img.shape) > 2:
raise ValueError("Sorry, image has to be RGB (M, N, 3) or RGBA (M, N, 4)")
# remove alpha channel
img = img[:, :, :3]
# stretch contrast
p2, p98 = np.percentile(img, (2, 98))
rescale = exposure.rescale_intensity(img, in_range=(p2, p98))
# binary from rgb
binary = np.logical_and(color.rgb2gray(rescale) > min_int, color.rgb2gray(rescale) < max_int)
# apply very mild opening
binary = opening(binary, disk(disk_sz))
# keep only largest white object
label_objects, nb_labels = ndi.label(binary)
sizes = np.bincount(label_objects.ravel())
sizes[0] = 0
if nb_labels < 2: # background not included in the count
binary_objects = binary # in case the image already contained only the map
else:
binary_objects = remove_small_objects(binary, max(sizes))
# remove holes from it
binary_holes = ndi.morphology.binary_fill_holes(binary_objects)
# optional: get convex hull image (smallest convex polygon that surround all white pixels)
if opt is not None:
binary_holes = convex_hull_image(binary_holes)
# use it to make 3D mask
mask3 = np.zeros(img.shape)
mask3[:, :, 0] = binary_holes
mask3[:, :, 1] = binary_holes
mask3[:, :, 2] = binary_holes
# use mask to get only map in original image
final = np.ma.masked_where(mask3 == 0, img)
final = final.filled(0)
# crop zero columns and zero rows
# see http://stackoverflow.com/a/31402351/1034648
# plus a few columns and rows to counter the initial opening
non_empty = np.where(final != 0)
out = final[np.min(non_empty[0]) : np.max(non_empty[0]), np.min(non_empty[1]) : np.max(non_empty[1])][
disk_sz:-disk_sz, disk_sz:-disk_sz
]
# output
return out
tempcounter = 0
for ii in range(endpt.shape[0] - 1):
for jj in np.arange(ii + 1, endpt.shape[0]):
print(ii, jj)
dicescore[tempcounter], imgregion[:, :, tempcounter], coverage[tempcounter], tempval = gaussianRectangle.compare(endpt[ii], endpt[jj], data)
imgcoordinate.append(tempval)
tempcounter += 1
# filtering the object parts
for ii in range(tempcounter):
labelimg, nums = label(imgregion[:,:,ii] > 0)
imgarea[ii] = np.argwhere(imgregion[:,:,ii]>0).shape[0]
if(nums > 1 or imgarea[ii] < 500):
dicescore[ii] = 0
convarea = np.argwhere(skmorph.convex_hull_image(imgregion[:,:,ii]>0)).shape[0]
areabyconvexarea[ii] = np.float64(imgarea[ii])/convarea
#imgdatasegment = np.zeros((img.shape),np.uint8)
#imgdatasegment = np.copy(data)
#for ii in range(accsize):
# imgdatasegment[imgcoordinate[ii][:,0], imgcoordinate[ii][:,1]] = imgdatasegment[imgcoordinate[ii][:,0], imgcoordinate[ii][:,1]] +1
#eigval, eigvec = principalComponents(np.argwhere(imgpart1))
#print(eigval[0:5])
#dt = cv2.distanceTransform(data, 2, 3)
#
#min0 = coord[:,0].min()
