def evaluate(img_out, img_GT, do_map: bool = False):
"""
Evaluate is a function which measures how good the image has been segmented.
The criterium is imposed by the exercise.
Parameters
----------
img_out: np.ndarray<float||int> (square)
The input image to evaluate.
img_GT: np.ndarray<float||int> (square - same size as img_out)
The ground-truth image to compare to img_out.
do_map: bool, default is False
if do_map is true, return the false positive and false negative images.
Returns
-------
float
The accuracy: the part of real positives in the found positives.
float
The recall: the part of found positives in the total number of positives.
float
The F1 score. Defined by 2/(1/Acc+1/Rec).
np.ndarray<int>
The skeleton of the proposed image.
np.ndarray<int>
The skeleton of the ground truth image.
np.ndarray<int>
The map of false positives. Only if do_map is True.
np.ndarray<int>
The map of false negatives. Only if do_map is True.
"""
GT_skel = thin(img_GT, max_iter=15)
img_out_skel = thin(img_out, max_iter=15)
TP = np.sum(img_GT & img_out) # Vrais positifs
if not do_map:
FP = np.sum(img_out_skel & ~img_GT) # Faux positifs (relaxes)
FN = np.sum(GT_skel & ~img_out) # Faux negatifs (relaxes)
else:
FP_map = img_out_skel & ~img_GT
FN_map = GT_skel & ~img_out
FP = np.sum(FP_map)
FN = np.sum(FN_map)
ACCU = TP / (TP + FP) # Precision
RECALL = TP / (TP + FN) # Rappel
if TP != 0:
# F1 score - same weight for both measures
F1 = 2 / (1/RECALL + 1/ACCU)
else:
F1 = 0
print("Erreur lors du script. Résultat incohérent.")
if not do_map:
return ACCU, RECALL, F1, img_out_skel, GT_skel
else:
return ACCU, RECALL, F1, img_out_skel, GT_skel, FP_map, FN_map
def thin_fringes(image, inverse=False):
if inverse:
# If we want to trace the inverse, we invert the image with some
# simple maths
print("Thinning dark fringes...")
return thin(-1*(image-1), max_iter=100)
else:
print("Thinning bright fringes...")
return thin(image, max_iter=100)
def step_last(self):
# Assign attributes to local variables for convenience.
u = self._u
mask = thin(
find_boundaries(gvoronoi(label(u, connectivity=1)), mode='inner'))
if u is None:
raise ValueError(
"the levelset function is not set (use set_levelset)")
data = self.data
# Determine c0 and c1.
inside = u > 0
outside = u <= 0
c0 = data[outside].sum() / float(outside.sum())
c1 = data[inside].sum() / float(inside.sum())
# Image attachment.
dres = np.array(np.gradient(u))
abs_dres = np.abs(dres).sum(0)
aux = abs_dres * (self.lambda1 * (data - c1)**2 - self.lambda2 *
(data - c0)**2)
res = np.copy(u)
res[aux < 0] = 1
res[aux > 0] = 0
# Smoothing.
for i in range(self.smoothing):
res = curvop(res)
res[mask] = 0
self._u = res
def processBinarization(self, algorithm=ImageAlgorithm.SAUVOLA):
if algorithm == ImageAlgorithm.SAUVOLA:
image = skimage.io.imread(fname=self.pathImg, as_gray=True)
thresh_sauvola = threshold_sauvola(image, window_size=81)
self.binary_sauvola = image > thresh_sauvola
"""
self.binary_sauvola = invert(self.binary_sauvola)
chull = convex_hull_image(self.binary_sauvola)
[rows, columns] = np.where(chull)
EPS = 50
row1 = min(rows) - EPS
row2 = max(rows) + EPS
col1 = min(columns) - EPS
col2 = max(columns) + EPS
self.binary_sauvola = self.binary_sauvola[row1:row2, col1:col2]
self.binary_sauvola = invert(self.binary_sauvola)
"""
self.binary_sauvola = invert(self.binary_sauvola)
#selem = disk(6)
self.binary_sauvola = thin(self.binary_sauvola, np.int(15))
self.binary_sauvola = np.invert(self.binary_sauvola)
#thresh_sauvola = threshold_sauvola(self.binary_sauvola, window_size=21)
#self.binary_sauvola = self.binary_sauvola > thresh_sauvola
self.binary_sauvola = erosion(self.binary_sauvola)
#self.binary_sauvola = gaussian(self.binary_sauvola)
elif algorithm == ImageAlgorithm.OTSU:
self.otsuBinarization()
def extract_graph(self, img):
img = self._to_numpy(img)
vpp = self._vertical_pp(img)
segments = self._profile_segment(img, vpp, True)
v_sgmts = []
for sgmt in segments:
v_sgmts += self._segment_equidistant(sgmt[2], self.d_v, True,
sgmt[0], sgmt[1])
final_sgmts = []
for sgmt in v_sgmts:
hpp = self._horizontal_pp(sgmt[2])
segments = self._profile_segment(sgmt[2], hpp, False, sgmt[0],
sgmt[1])
for s in segments:
final_sgmts += self._segment_equidistant(
s[2], self.d_h, False, s[0], s[1])
nodes = []
locations = []
for sgmt in final_sgmts:
x, y = self._center_of_mass(sgmt[2])
node = np.array([x + sgmt[0], y + sgmt[1]])
nodes.append(node)
locations.append((node, sgmt))
thin = morph.thin(img)
edges = []
for loc in locations:
neighbors = self._find_neighbors(loc, locations, thin)
for n in neighbors:
if loc[0][0] <= n[0][0]:
edges.append((loc[0], n[0]))
assert len(nodes) > 1, 'only one node found'
return self._build_graph(nodes, edges)
def wallExtraction(self, img):
if len(img.shape) > 2: # if image is not grayscale
# Converting to grayscale if it is not already
img = sl.rgb2gray(img)
# Thresholding it to binary just in case it is not already
bwImage = np.array((img > (np.max(np.max(img)) / 2)), dtype=bool)
# Make sure structures are in black (flase) and background is white (true)
if (np.sum(bwImage) > np.sum(~bwImage)):
bwImage = ~bwImage
print('Image complemented')
# Pre processing the image to make it nice and clean for the job :)
bwImage = thin(bwImage) # converting to int after thining
bwImage = sl.bwmorph().diag(bwImage)
bwImage = np.array(bwImage * 255, dtype=np.uint8)
############################################################################
(labledImage, wallsCenter, _, numWalls) = sl.preciseHough(bwImage)
labledImage = np.asarray(labledImage, dtype=np.int64)
wallsCenter = np.asarray(wallsCenter, dtype=np.int64)
wallLables = np.unique(labledImage) # each lable is an individual wall
wallLables = np.delete(
wallLables,
0) # removing the first element as it's for the background
###########################################################################
return labledImage, wallLables, wallsCenter
def Preprocessing(image):
blur = cv2.GaussianBlur(image, (1, 1), 0)
binImage = cv2.threshold(blur, 0, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
# binImage = remove_noise_and_smooth(image)
processedImage = thin(binImage)
return binImage
def energy_baseline(msk = None,
energy = None,
threshold = 0.5,
thin_labels = False):
msk_ths = (np.copy(msk)>255*threshold)*1
energy_ths = (np.copy(energy)>255*0.4)*1
distance = ndi.distance_transform_edt(msk_ths)
# Marker labelling
markers = label(energy_ths)
labels = watershed(-distance,
markers,
mask=msk_ths)
if thin_labels == True:
for i,lbl in enumerate(np.unique(labels)):
if i == 0:
# pass the background
pass
else:
current_label = (labels==lbl) * 1
thinned_label = thin(current_label,max_iter=1)
labels[labels==lbl] = 0
labels[thinned_label] = lbl
return labels
def erode_segmentation(labels_3d):
kernel = np.array(
[[[0, 0, 0], [0, 1, 0], [0, 0, 0]], [[0, 1, 0], [1, 1, 1], [0, 1, 0]],
[[0, 0, 0], [0, 1, 0], [0, 0, 0]]],
dtype=bool)
labels_3d_binary = np.array(labels_3d, dtype=bool)
closed_seg = morph.binary_closing(labels_3d_binary, structure=kernel)
eroded_seg = np.zeros(labels_3d.shape)
for i in range(labels_3d.shape[2]):
eroded_seg[:, :, i] = mp.thin(closed_seg[:, :, i], max_iter=3)
dilated_seg = morph.binary_dilation(closed_seg,
structure=kernel,
iterations=3)
markers = np.zeros(labels_3d.shape)
fg_markers = (np.logical_and(eroded_seg, dilated_seg)) * 1
bg_markers = (np.logical_and(np.logical_not(markers),
np.logical_not(dilated_seg))) * 2
markers = fg_markers + bg_markers
return markers
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.rgb2gray(img)
return to_base64(thin(img, **args))
def erode_seg_markers(rw_data):
closing_kernel = np.array([[[0, 0, 0], [0, 1, 0], [0, 0, 0]],
[[0, 1, 0], [1, 1, 1], [0, 1, 0]],
[[0, 0, 0], [0, 1, 0], [0, 0, 0]]], dtype=bool)
erosion_kernel = closing_kernel
dilation_kernel = erosion_kernel
rw_bool = np.array(rw_data, dtype=bool)
closed_seg = morph.binary_closing(rw_bool, structure=closing_kernel)
# skeletonized_seg = mp.skeletonize_3d(closed_seg)
# medial_axis_seg = mp.medial_axis(closed_seg)
thinned_seg = np.zeros(rw_data.shape)
for i in range(rw_data.shape[2]):
thinned_seg[:,:,i] = mp.thin(closed_seg[:,:,i], max_iter = 5)
# eroded_seg = morph.binary_erosion(closed_seg,structure=erosion_kernel,iterations=3)
dilated_seg = morph.binary_dilation(closed_seg, structure=dilation_kernel, iterations=6)
fg_markers = np.zeros(rw_data.shape)
bg_markers = np.zeros(rw_data.shape)
markers = np.zeros(rw_data.shape)
# fg_markers = (np.logical_and(eroded_seg,dilated_seg))*1
fg_markers = (np.logical_and(thinned_seg, dilated_seg))*1
bg_markers = (np.logical_and(np.logical_not(markers), np.logical_not(dilated_seg)))*2
markers = fg_markers + bg_markers
return markers, fg_markers, bg_markers
def filterset(signal, pics = 1, sizesToRemove = 700, dilationSquare = 30, closingSquare = 30,boundary=1):
grayscale2 =signal
gaussed = skif.gaussian(grayscale2,3)
if pics: plt.imshow(gaussed, cmap='gray')
if pics: skif.thresholding.try_all_threshold(gaussed,figsize=(25, 25))
thresh = skif.thresholding.threshold_li(gaussed)
binary = gaussed > thresh
if pics: plt.imshow(binary, cmap='gray')
label_objects, nb_labels = ndi.label(binary)
sizes = np.bincount(label_objects.ravel())
mask_sizes = sizes > sizesToRemove
mask_sizes[0] = 0
binary_cleaned = mask_sizes[label_objects]
if pics: plt.imshow(binary_cleaned, cmap='gray')
diamclo = skimo.dilation(binary_cleaned, skimo.square(dilationSquare))
if pics: plt.imshow(diamclo, cmap='gray')
diamclo2 = skimo.binary_closing(diamclo, skimo.square(closingSquare))
diamclo2 = addBoundaries(diamclo2, boundary)
if pics: plt.imshow(diamclo2, cmap='gray', interpolation='none')
eroded = skimo.thin(diamclo2).astype(int)
if pics: plt.imshow(eroded, cmap='gray', interpolation='none')
erodedWide = skimo.dilation(eroded, skimo.square(5))
if pics: plt.imshow(erodedWide, cmap='gray', interpolation='none')
diamclo4 = np.where(erodedWide==0, 1, erodedWide)
diamclo4 = np.where(erodedWide==1, 0, diamclo4)
all_labels = skim.label(diamclo4)
blobs_labels = skim.label(diamclo4, background=0)
return blobs_labels, erodedWide
def convert_to_imgs(traces_data, box_size=int(100)):
patterns_enc = []
classes_rejected = []
for pattern in traces_data:
trace_group = pattern['trace_group']
'mid coords needed to shift the pattern'
min_x, min_y, max_x, max_y = get_min_coords(trace_group)
'traceGroup dimensions'
trace_grp_height, trace_grp_width = max_y - min_y, max_x - min_x
'shift pattern to its relative position'
shifted_trace_grp = shift_trace_grp(trace_group,
min_x=min_x,
min_y=min_y)
'Interpolates a pattern so that it fits into a box with specified size'
'method: LINEAR INTERPOLATION'
try:
interpolated_trace_grp = interpolate(
shifted_trace_grp,
trace_grp_height=trace_grp_height,
trace_grp_width=trace_grp_width,
box_size=box_size - 1)
except Exception as e:
print(e)
print('This data is corrupted - skipping.')
classes_rejected.append(pattern.get('label'))
continue
'Get min, max coords once again in order to center scaled patter inside the box'
min_x, min_y, max_x, max_y = get_min_coords(interpolated_trace_grp)
centered_trace_grp = center_pattern(interpolated_trace_grp,
max_x=max_x,
max_y=max_y,
box_size=box_size)
'Center scaled pattern so it fits a box with specified size'
pattern_drawn = draw_pattern(centered_trace_grp, box_size=box_size)
# Make sure that patterns are thinned (1 pixel thick)
pat_thinned = 1.0 - thin(1.0 - np.asarray(pattern_drawn))
plt.imshow(pat_thinned, cmap='gray')
plt.show()
pattern_enc = dict({
'features': pat_thinned,
'label': pattern.get('label')
})
# Filter classes that belong to categories selected by the user
# if pattern_enc.get('label') in self.classes:
patterns_enc.append(pattern_enc)
return patterns_enc, classes_rejected
def wallExtraction(img):
if len(img.shape) > 2: # if image is not grayscale
# Converting to grayscale if it is not already
img = skc.rgb2gray(img)
# Thresholding it to binary just in case it is not already
bwImage = np.array((img > (img.max()/2)), dtype=bool)
#bwImage = np.array(bwImage*255,dtype=np.uint8) # converting to a uint8 image
# bwImage = cv2.cvtColor(bwImage,cv2.COLOR_BRG2GRAY) # figure is loded with mpl so it's RGB nor BRG
# Make sure structures are in black (flase) and background is white (true)
if (np.sum(bwImage) > np.sum(~bwImage)):
bwImage = ~bwImage
print('Image complemented')
############################################################################
### END OF INITIALIZATION
############################################################################
# Pre processing the image to make it nice and clean for the jon :)
bwImage = thin(bwImage) # converting to int after thining
bwImage = sl.bwmorph().diag(bwImage)
bwImage = np.array(bwImage*255,dtype=np.uint8)
############################################################################
(labledImage,wallsCenter,_,numWalls) = sl.preciseHough(bwImage)
wallLables = np.unique(labledImage) # each lable is an individual wall
wallLables = np.delete(wallLables,0) # removing the first element as it's for the background
###########################################################################
return labledImage,wallLables,wallsCenter
def thinning_img_new(img, median=False, resize=False):
"""
No use opencv-contrib(for kernel competition)
But little slow and results changed from Opencv
TODO: optimizer when resize
"""
height = img.shape[0]
width = img.shape[1]
img2 = img.copy()
if resize:
# For speed
r = 3
img2 = cv2.resize(img2, (width // r, height // r))
img2 = img2.mean(axis=2)
img3 = ((img2 < 220) * 255).astype(np.uint8)
img4 = img3.copy()
if median:
img4 = cv2.medianBlur(img4, 5)
kernel = np.ones((5, 5), np.uint8)
img5 = cv2.erode(img4, kernel, iterations=1)
img5 = cv2.dilate(img5, kernel, iterations=4)
img5 = morphology.thin(img5 // 255)
img5 = (img5 * 255).astype(np.uint8)
if resize:
img5 = cv2.resize(img5, (width, height))
_, img5 = cv2.threshold(img5, 127, 255, cv2.THRESH_BINARY)
return img5
def extract_contours(image_abs_path):
max_intensity = 1
# Here we define the size of the square box that will contain a single pattern
box_size = 32
binary_img = binarize(image_abs_path)
# Apply erosion step - make patterns thicker
eroded_img = erosion(binary_img, selem=square(3))
# Inverse colors: black --> white | white --> black
binary_inv_img = max_intensity - eroded_img
# Apply thinning algorithm
thinned_img = thin(binary_inv_img)
# Before we apply opencv method, we need to convert scikit image to opencv image
thinned_img_cv = img_as_ubyte(thinned_img)
# Find contours
_, contours, _ = cv2.findContours(thinned_img_cv,
mode=cv2.RETR_EXTERNAL,
method=cv2.CHAIN_APPROX_SIMPLE)
# Sort contours from left to right (sort by bounding rectangle's X coordinate)
contours = sorted(contours, key=lambda cont: cv2.boundingRect(cont)[0])
# Initialize patterns array
patterns = []
return contours
def weighted_masked_embeddings(fmap_shape, label, fconv_norm, n_classes):
label = label.unsqueeze(0).unsqueeze(0).cpu()
thinned = thin(label[0][0])
dt = ndimage.distance_transform_edt(np.logical_not(thinned))
dt = torch.tensor(dt)
dt[label[0][0] == 0] = 0
dt = dt.max() - dt
dt[label[0][0] == 0] = 0
dt = dt / dt.sum()
fconv_norm = nn.functional.interpolate(fconv_norm,
size=(int(label.shape[2]),
int(label.shape[3])),
mode='nearest')
dt = dt.unsqueeze(0).float().cuda()
fconv_pooled = torch.zeros(fmap_shape[0], n_classes + 1, fmap_shape[1], 1,
1)
for i in range(int(fconv_norm.shape[1])):
temp = fconv_norm[:, i, ...]
for c in range(n_classes + 1):
if len(temp[label[0] == c]) == 0:
tempv = 0
else:
tempv = torch.sum(temp[label[0] == c] * dt[label[0] == c])
fconv_pooled[:, c, i, 0, 0] = tempv
return fconv_pooled
def walk_neighbourhood(image):
"""Walk the neighbourhood of an edge detected image"""
thinned_image = morphology.thin(np.where(image < 0.1, 0, 1))
thinned_image = np.where(thinned_image < 0.1, 0, 1)
centre = get_centre(thinned_image)
try:
totalPixels = exposure.histogram(thinned_image, nbins=2)[0][1]
#return an empty list if no pixels were found to draw
except IndexError:
return []
currentPixel = np.array([-1, -1])
walkingOrder = []
index = -1
while totalPixels > 0:
if np.array_equal(currentPixel, np.array([-1, -1])):
currentPixel = get_unwalked_coords(thinned_image)[0]
walkingOrder.append([])
index += 1
else:
thinned_image[currentPixel[0], currentPixel[1]] = 0
walkingOrder[index].append(currentPixel)
currentPixel = next_neighbour(currentPixel, thinned_image, centre)
totalPixels -= 1
return walkingOrder
def _apply_morph_thinning(self,
img: np.ndarray,
niter: int = 3) -> np.ndarray:
"""Apply morphological thinning."""
thin_img = thin(img, niter)
return thin_img
def _preprocess_lung_mask(self, msk, voxel_dimensions):
print('Eroding')
start_time = time()
selem = self._make_structure_element(voxel_dimensions)
inner = binary_erosion(msk, selem=selem)
outer = binary_erosion(np.logical_not(msk), selem=selem)
print('Eroding took %.3f sec' % (time() - start_time))
print('Thinning')
start_time = time()
for k in range(msk.shape[2]):
inner[..., k] = thin(inner[..., k], max_iter=self.thinnings)
outer[..., k] = thin(outer[..., k], max_iter=self.thinnings)
print('Thinning took %.3f sec' % (time() - start_time))
return inner, outer
def test_noiter(self):
result = thin(self.input_image).astype(np.uint8)
expected = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]],
dtype=np.uint8)
assert_array_equal(result, expected)
def extract_skeleton(image):
# get thinned image (skeleton)
image = morphology.remove_small_holes(image, 2)
image = morphology.thin(image)
# convert skeleton into network of junction nodes (undirected graph)
graph = build_sknw(image.astype(np.uint16))
return graph
def test_iter_1(self):
result = thin(self.input_image, 1).astype(np.uint8)
expected = np.array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]],
dtype=np.uint8)
numpy.testing.assert_array_equal(result, expected)
def get_best_skeleton(img):
s1 = morphology.skeletonize(img)
s2 = morphology.medial_axis(img)
s3 = morphology.thin(img)
values = np.asarray([s1, s2])
sums = np.asarray([s1.sum(), s2.sum()])
return values[np.argmin(sums)]
def convert_to_img(self, trace_grps, req_classes=None, box_size=100):
classes = set()
patterns_enc = []
classes_rejected = []
for pattern in trace_grps:
if req_classes and pattern["label"] not in req_classes:
continue
if "label" in pattern and pattern["label"] not in classes:
classes.add(pattern["label"])
trace_group = pattern['traces']
# mid coords needed to shift the pattern
min_x, min_y, max_x, max_y = self.get_min_coords(trace_group)
# traceGroup dimensions
trace_grp_height, trace_grp_width = max_y - min_y, max_x - min_x
# shift pattern to its relative position
shifted_trace_grp = self.shift_trace_grp(trace_group,
min_x=min_x,
min_y=min_y)
# Interpolates a pattern so that it fits into a box with specified size
# method: LINEAR INTERPOLATION
try:
interpolated_trace_grp = self.interpolate(
shifted_trace_grp,
trace_grp_height=trace_grp_height,
trace_grp_width=trace_grp_width,
box_size=box_size - 1)
except Exception as e:
print(e)
print('This data is corrupted - skipping.')
classes_rejected.append(pattern.get('label'))
continue
# Get min, max coords once again in order to center scaled patter inside the box
min_x, min_y, max_x, max_y = self.get_min_coords(
interpolated_trace_grp)
centered_trace_grp = self.center_pattern(interpolated_trace_grp,
max_x=max_x,
max_y=max_y,
box_size=box_size)
# Center scaled pattern so it fits a box with specified size
pattern_drawn = self.draw_pattern(centered_trace_grp,
box_size=box_size)
# Make sure that patterns are thinned (1 pixel thick)
pat_thinned = 1.0 - thin(1.0 - np.asarray(pattern_drawn))
# plt.imshow(pat_thinned, cmap='gray')
# plt.show()
pattern_enc = {
'features': pat_thinned,
'label': pattern.get('label')
}
patterns_enc.append(pattern_enc)
return patterns_enc, classes_rejected, classes
def skeletonize_edge(src):
# Process sketch to fit input. Only used for test input
src = np.asarray(src * 255, np.uint8)
# Crop the sketch and minimize white padding.
cropped = crop_and_resize(src, return_gray=True)
# Skeletonize the lines
skeleton = thin(cv2.bitwise_not(cropped))
final = np.asarray(1 - np.float32(skeleton))
return cv2.cvtColor(final, cv2.COLOR_GRAY2BGR)
def preprocessing(data): # Turn the Data to Tensor type .
a = []
for i in range(len(data)):
a.append(np.asarray(thin(stretch(
data[i])))) # do the centralize and strectch and thinning
a1 = np.asarray(a)
return torch.Tensor(a1).view(
len(data), 1, 32,
32) # add one dimension to make it suitable to put in dataloader
def QuitarLineaRef(Im_BW, grosor_linea):
suma_y = profile_y(thin(Im_BW))
linea = np.argmax(suma_y)
Im_BW_sin_linea = 1 * Im_BW
Im_BW_sin_linea[linea - grosor_linea:linea + grosor_linea + 1,
0:Im_BW.shape[1]] = 0 * Im_BW[linea - grosor_linea:linea +
grosor_linea + 1,
0:Im_BW.shape[1]]
return Im_BW_sin_linea
def ridge_thinning(image: np.ndarray) -> np.ndarray:
"""
Ridge thinning or image skeletonization
:param image: the original image
:return: the skeleton of the image
"""
thinned = thin(image)
thinned = thinned.astype(np.float)
return thinned
def thinning2(name):
image = img_as_float(color.rgb2gray(io.imread(name)))
image_binary = image < 0.5
out_skeletonize = morphology.skeletonize(image_binary)
out_thin = morphology.thin(image_binary)
plt.imsave('gaps.jpg', out_skeletonize, cmap='gray')
img = cv2.imread("gaps.jpg")
cv2.imshow("Thinning2", img)
cv2.waitKey(0)
def test_iter_1(self):
result = thin(self.input_image, 1).astype(np.uint8)
expected = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
numpy.testing.assert_array_equal(result, expected)
def test_noiter(self):
result = thin(self.input_image).astype(np.uint8)
expected = np.array([[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0]], dtype=np.uint8)
assert_array_equal(result, expected)
def preprocess(path, info_fn):
with open(info_fn) as f:
content = f.readlines()
content = [x.strip() for x in content]
for person, signum in zip(content[::2], content[1::2]):
signum = signum.split(' ')
ngenuines, nforgeries = signum
ngenuines = int(ngenuines.replace("g:", ""))
nforgeries = int(nforgeries.replace("f:", ""))
genuines = []
forgeries = []
for gn in range(1, ngenuines+1):
f = "g-%03d.png" % gn
fn = P.join(P.join(path, person), f)
im = cv2.imread(fn, 0)
im = np.abs(im - 255)
skel = thin(im)
plt.imshow(skel, 'gray')
plt.show()
plt.figure()
def test_baddim(self):
for ii in [np.zeros((3)), np.zeros((3, 3, 3))]:
with testing.raises(ValueError):
thin(ii)
def test_zeros(self):
assert np.all(thin(np.zeros((10, 10))) == False)
# **Morphological thinning**
#
# Morphological thinning, implemented in the `thin` function, works on the
# same principle as `skeletonize`: remove pixels from the borders at each
# iteration until none can be removed without altering the connectivity. The
# different rules of removal can speed up skeletonization and result in
# different final skeletons.
#
# The `thin` function also takes an optional `max_iter` keyword argument to
# limit the number of thinning iterations, and thus produce a relatively
# thicker skeleton.
from skimage.morphology import skeletonize, thin
skeleton = skeletonize(image)
thinned = thin(image)
thinned_partial = thin(image, max_iter=25)
fig, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True, sharey=True,
subplot_kw={'adjustable': 'box-forced'})
ax = axes.ravel()
ax[0].imshow(image, cmap=plt.cm.gray, interpolation='nearest')
ax[0].set_title('original')
ax[0].axis('off')
ax[1].imshow(skeleton, cmap=plt.cm.gray, interpolation='nearest')
ax[1].set_title('skeleton')
ax[1].axis('off')
ax[2].imshow(thinned, cmap=plt.cm.gray, interpolation='nearest')
