def test_object():
image = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 1, 0],
[1, 0, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=bool)
expected_conn_1 = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 0, 1], [1, 1, 1, 0, 0, 0, 0, 1, 0],
[1, 1, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=bool)
assert_array_equal(convex_hull_object(image, connectivity=1),
expected_conn_1)
expected_conn_2 = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 1, 1, 1],
[1, 1, 0, 0, 0, 0, 1, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=bool)
assert_array_equal(convex_hull_object(image, connectivity=2),
expected_conn_2)
with testing.raises(ValueError):
convex_hull_object(image, connectivity=3)
out = convex_hull_object(image, connectivity=1)
assert_array_equal(out, expected_conn_1)
def test_object():
image = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 1, 0],
[1, 0, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=bool)
expected4 = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 0, 1], [1, 1, 1, 0, 0, 0, 0, 1, 0],
[1, 1, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=bool)
assert_array_equal(convex_hull_object(image, 4), expected4)
expected8 = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 1, 1, 1],
[1, 1, 0, 0, 0, 0, 1, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=bool)
assert_array_equal(convex_hull_object(image, 8), expected8)
assert_raises(ValueError, convex_hull_object, image, 7)
def test_object():
image = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 1, 0],
[1, 0, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=bool)
expected4 = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 0, 1], [1, 1, 1, 0, 0, 0, 0, 1, 0],
[1, 1, 0, 0, 0, 0, 1, 0, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=bool)
assert_array_equal(convex_hull_object(image, 4), expected4)
expected8 = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 1, 1], [1, 1, 1, 0, 0, 0, 1, 1, 1],
[1, 1, 0, 0, 0, 0, 1, 1, 1], [1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=bool)
assert_array_equal(convex_hull_object(image, 8), expected8)
assert_raises(ValueError, convex_hull_object, image, 7)
# Test that an error is raised on passing a 3D image:
image3d = np.empty((5, 5, 5))
assert_raises(ValueError, convex_hull_object, image3d)
def process(self):
# self.open()
# self.show()
self.mark_white()
# self.show()
self.open()
# self.show()
self.altered_image = morphology.convex_hull_object(self.altered_image)
self.image = self.image_X_mask(mask=self.altered_image,
source=self.image)
# self.show()
self.mark_colors()
self.altered_image = morphology.closing(
self.altered_image, morphology.disk(len(self.altered_image) / 35))
self.image = self.image_X_mask(mask=self.altered_image,
source=self.image)
# self.find_contours() # najpierw znajdź całą kartkę paragonu
# potem konkretne plamki (albo od razu plamik)
# przejdź po znalezionych plamkach (konturach) i znajdź tą, która może mieć ceny na sobie.
# np. (zobaczyć która ma dużo podobnych kolorów + jest otoczona przez biel albo jakkolwiek inaczej.)
# lub wziąć tą najbardziej na prawo
# spośród pixeli wewnątrz konturu(plamki) stworzyć nowy obrazek // kontur to tylko ramka
# wysłać ten obrazek do jakiejś funkcji foo, którą ja będę pisał :)
# foo powinna zwracać true jeśli znalazła ceny w plamce, false jeśli nie
return self.image
def pictureFindEdge(pic,multiple=0,threshold=0.5):
img_gray = pic
img_gray_bin=(img_gray<threshold)*1#二值化
chull = morphology.convex_hull_object(img_gray_bin)#框定连通区域
labels=measure.label(chull,connectivity=2) # 8连通区域标记
point_l=np.zeros(labels.max()+1,dtype=np.int16);point_r=np.zeros(labels.max()+1,dtype=np.int16)
point_t=np.zeros(labels.max()+1,dtype=np.int16);point_b=np.zeros(labels.max()+1,dtype=np.int16)
for i in range(labels.max()+1):
point_l[i] = labels.shape[1];point_t[i] = labels.shape[0]
for i in range(labels.shape[0]):#宽
for j in range(labels.shape[1]):#长
if labels[i][j] != 0:
if point_l[labels[i][j]] > j :
point_l[labels[i][j]] = j
if point_r[labels[i][j]] < j :
point_r[labels[i][j]] = j
if point_t[labels[i][j]] > i :
point_t[labels[i][j]] = i
if point_b[labels[i][j]] < i :
point_b[labels[i][j]] = i
point_l=point_l.tolist();point_l.pop(0);point_r=point_r.tolist();point_r.pop(0)
point_t=point_t.tolist();point_t.pop(0);point_b=point_b.tolist();point_b.pop(0)
if(multiple==0):
point_l=[min(point_l)];point_r=[max(point_r)];point_t=[min(point_t)];point_b=[max(point_b)];
return point_l,point_r,point_t,point_b,labels
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 process(self):
'''Main function that lead's threw the image altering process'''
seed = (min(len(self.altered_image), len(self.altered_image[0]))**
2) / 10000
if seed > 70:
print("size too big, returning! {}".format(seed))
return
self.mark_colors() # puts white wherever it thinks there is a color
self.open(
1) # normal opening on those colors to blurr out any letters etc.
self.find_contours() # find all contours
self.contour = self.get_biggest_contour(
) # if there was a colorfull background
# you choose the biggest white contour
self.altered_image = morphology.convex_hull_object(
self.altered_image, 8)
self.image, self.altered_image = self.trim_to_mask(
source=self.image, mask=self.convex(self.altered_image))
self.altered_image = self.erase_colors(0.0)
''' go threw some of the possibilities to find all numbers! (10,40) (5,90) etc'''
p5, p95 = np.percentile(self.altered_image,
(PERCENTILE_0, PERCENTILE_1))
self.altered_image = exposure.rescale_intensity(self.altered_image,
in_range=(p5, p95))
'''OTSU ITADAKIMASU!'''
threshold = filters.threshold_otsu(self.altered_image)
self.altered_image = self.altered_image > threshold
self.altered_image = morphology.opening(self.altered_image,
morphology.disk(2))
## label everything and check for numbers
self.read_numbers(source=self.altered_image)
pass
def binarize(imag, debug=False):
"""
This function returns a binarized version of the image.
"""
imag = imag.copy()
# We compute an optimal threshold and form a binary image
##################
# YOUR CODE HERE #
##################
thresh = threshold_otsu(imag)
black_white = closing(imag > thresh, square(3))
cleared = clear_border(black_white)
binar = convex_hull_object(cleared)
if debug:
fig, ax = plt.subplots()
ax.imshow(imag)
ax.imshow(binar, alpha=0.4)
fig.suptitle("Binary image")
plt.show()
return binar
def test_object():
image = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 1, 0],
[1, 0, 0, 0, 0, 0, 1, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
expected_conn_1 = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 0, 1],
[1, 1, 1, 0, 0, 0, 0, 1, 0],
[1, 1, 0, 0, 0, 0, 1, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
assert_array_equal(convex_hull_object(image, connectivity=1),
expected_conn_1)
expected_conn_2 = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 1, 1, 1],
[1, 1, 0, 0, 0, 0, 1, 1, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
assert_array_equal(convex_hull_object(image, connectivity=2),
expected_conn_2)
with testing.raises(ValueError):
convex_hull_object(image, connectivity=3)
with expected_warnings(['`neighbors` is deprecated']):
out = convex_hull_object(image, neighbors=4)
assert_array_equal(out, expected_conn_1)
def test_object():
image = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 1, 0],
[1, 0, 0, 0, 0, 0, 1, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
expected4 = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 0, 1],
[1, 1, 1, 0, 0, 0, 0, 1, 0],
[1, 1, 0, 0, 0, 0, 1, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
assert_array_equal(convex_hull_object(image, 4), expected4)
expected8 = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 1, 1, 1],
[1, 1, 0, 0, 0, 0, 1, 1, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
assert_array_equal(convex_hull_object(image, 8), expected8)
assert_raises(ValueError, convex_hull_object, image, 7)
# Test that an error is raised on passing a 3D image:
image3d = np.empty((5, 5, 5))
assert_raises(ValueError, convex_hull_object, image3d)
def test_object():
image = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 1, 0],
[1, 0, 0, 0, 0, 0, 1, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
expected4 = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 0, 1],
[1, 1, 1, 0, 0, 0, 0, 1, 0],
[1, 1, 0, 0, 0, 0, 1, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
assert_array_equal(convex_hull_object(image, 4), expected4)
expected8 = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 0, 0, 1, 1, 1],
[1, 1, 1, 0, 0, 0, 1, 1, 1],
[1, 1, 0, 0, 0, 0, 1, 1, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=bool)
assert_array_equal(convex_hull_object(image, 8), expected8)
with testing.raises(ValueError):
convex_hull_object(image, 7)
def polish_mask(self, binary_mask):
"Elije la mejor region y completa huecos"
filled = morphology.convex_hull_object(binary_mask)
labels = measure.label(filled)
rprops = measure.regionprops(labels)
if len(rprops) == 0:
return binary_mask, np.inf, -1
disparity = self.calculate_disparity(rprops,
np.prod(binary_mask.shape))
I = np.argmin(disparity)
polished_mask = (labels == (I + 1))
polished_mask = morphology.convex_hull_image(polished_mask)
return polished_mask, disparity[I], rprops[I].orientation
def _fill_objects(vol, axis=0):
filled_vol = np.copy(vol)
del vol
if axis != 0:
filled_vol = np.moveaxis(filled_vol, axis, 0)
for idx, slc in enumerate(filled_vol):
slc = convex_hull_object(slc, neighbors=4)
filled_vol[idx, ...] = slc
if axis != 0:
filled_vol = np.moveaxis(filled_vol, 0, axis)
return filled_vol
def fill(lung):
struct = disk(5)
test = lung.copy()
for i in range(lung.shape[2]):
test[..., i] = binary_fill_holes(lung[..., i])
label_test, num = label(test[..., i], return_num=True)
if num != 2:
continue
chull = convex_hull_object(test[..., i])
coords = []
for contour in find_contours(chull, 0):
coords.append(approximate_polygon(contour, tolerance=0))
if len(coords) < 2:
continue
inverted = np.invert(test[..., i])
distance = ndi.distance_transform_edt(inverted)
peaks = peak_local_max(distance, labels=inverted)
left_peaks = points_in_poly(peaks, coords[0])
right_peaks = points_in_poly(peaks, coords[1])
peaks_mask = left_peaks | right_peaks
peaks = peaks[peaks_mask]
peak_image = np.zeros(lung[..., i].shape)
peak_image[peaks[:, 0], peaks[:, 1]] = 1
if len(peaks == 1):
peak_image[0, 0] = 1
markers = ndi.label(peak_image)[0]
labels = watershed(-distance, markers, mask=inverted)
labels[labels == 1] = 0
for region in regionprops(labels):
centroid = np.asarray([int(x) for x in region.centroid]).reshape(1, 2)
if ((np.sum(points_in_poly(region.coords, coords[0])) < region.area and
np.sum(points_in_poly(region.coords, coords[1])) < region.area) or
(not points_in_poly(centroid, coords[1]) and not points_in_poly(centroid, coords[1]))):
centroid = tuple(int(x) for x in region.centroid)
labels = flood_fill(labels, centroid, 0)
labels[labels > 0] = 255
test[..., i][labels > 0] = 255
return test
def get_glass_mask(thumbnail_gray: np.ndarray):
"""
获取玻璃盖板的mask, 用于判断相似组织是否被标记
注:比赛中相似组织可能只标记一份
:param thumbnail_gray:
:return:
"""
# print(thumbnail_gray.shape)
# assert thumbnail_gray.shape[-1] == 1
mask = thumbnail_gray != 1 # background is 1, samples was splited by background glass
mask = morphology.binary_dilation(mask)
mask = morphology.binary_erosion(mask)
convex = morphology.convex_hull_object(mask)
# convex = morphology.remove_small_holes(convex)
convex = morphology.remove_small_objects(convex)
return convex
def threshold(self):
"""
Thresholds by first finding the Otsu threshold. Holes are removed from Otsu thresholded image and
convex hulls are created for foreground objects. Foreground objects are expanded to fill the convex
hulls.
"""
thresh_val = self.thresh_otsu(self.im_smooth)
# convex hull threshold result
thresh_mask = self.im_smooth > thresh_val
thresh_mask = ndi.binary_fill_holes(thresh_mask)
thresh_mask = morphology.convex_hull_object(thresh_mask)
self.im_thresh = self.im_smooth
self.im_thresh[~thresh_mask] = 0
return thresh_val
def binarize(image, debug=False):
"""
This function returns a binarized version of the image.
"""
# We make sure that we work on a local copy of the image
img = image.copy()
thresh = threshold_otsu(img)
img = img > thresh
img = closing(img, square(3))
# remove artifacts connected to image border
img = clear_border(img)
# take only the frame of the cubes, not the inside
img = convex_hull_object(img)
if debug: show_image(img, "Binary image")
return img
def threshold(self):
"""
Thresholds by first finding the Otsu threshold. Holes are removed from Otsu thresholded image and
convex hulls are created for foreground objects. Foreground objects are expanded to fill the convex
hulls.
"""
otsu_thresh = self.thresh_otsu(self.im_smooth)
thresh_val = self.bounded_thresh(otsu_thresh)
# convex hull threshold result
thresh_mask = self.im_smooth > thresh_val
thresh_mask = ndi.binary_fill_holes(thresh_mask)
thresh_mask = morphology.convex_hull_object(thresh_mask)
self.im_thresh = morphology.closing(self.im_smooth,
selem=morphology.disk(
self.C['CLOSING_FILTER_SZ']))
self.im_thresh[~thresh_mask] = 0
return otsu_thresh, thresh_val
def esqueletoFechoConvexoVermelho(img):
axs[1].set_title('Esqueleto do fecho convexo da imagem de cor vermelha')
axs[1].set_axis_off() #tira o eixo x e y das imagem que fica na coluna 1
fechamento = morphology.area_closing(img, 64, 1) #retira todos os pontos
linha, coluna = fechamento.shape
for i in range(linha):
for j in range(coluna):
if (
fechamento[i][j] != 72
): #seta os pixels da fig de cor não vermelha(na escala de cinza) para branco
fechamento[i][j] = 255
thresh = threshold_otsu(
fechamento
) #auxilia na transformação da img na escola de cinza para binária(0-ausencia de cor 1-presença de cor)
binary = invert(
fechamento > thresh
) #tranforma a imagem em binaria em seguida faz a troca das cor dos pixels
fechoConvexo = morphology.convex_hull_object(
binary) #retorna o fecho convexo de cada objeto
esqueleto = morphology.skeletonize(fechoConvexo)
axs[1].imshow(esqueleto, cmap='gray')
def split_space_content():
for root, dirs, files in os.walk('data'):
for fname in files:
bg_name = "data/" + fname
img = io.imread(bg_name, as_grey=True)
# 检测canny边缘,得到二值图片
edgs = feature.canny(img, sigma=3)
chull = morphology.convex_hull_object(edgs)
#
# fig, axes = plt.subplots(1, 2, figsize=(8, 8))
# ax0, ax1 = axes.ravel()
# ax0.imshow(edgs, plt.cm.gray)
# ax0.set_title('many objects')
# ax1.imshow(chull, plt.cm.gray)
# ax1.set_title('convex_hull image')
# # plt.show()
plt.imsave("data2/" + fname, chull)
def make_mask(image,
t=1,
s=1,
hardImageThreshold=None,
hardSizeThreshold=None,
local=False,
convexhull=False):
'''
Identifies suitable morphological components from image by thresholding.
'''
if local:
mask = image > t * threshold_local(image, 151)
else:
if hardImageThreshold:
thresh = hardImageThreshold
else:
thresh = t * threshold_otsu(image)
mask = image > thresh
#Fill holes. Warning: takes a long time
if convexhull:
mask = convex_hull_object(mask)
#Filter small components. The default threshold is 0.00005 of the image area
if hardSizeThreshold:
size_thresh = hardSizeThreshold
else:
size_thresh = s * np.prod(image.shape) * 0.00005
mask = remove_small_objects(mask, min_size=size_thresh)
#Clear border
mask = clear_border(mask)
#Label connected components
mask = label(mask)
return mask
def binarize(imag, debug=False):
"""
This function returns a binarized version of the image.
"""
imag = imag.copy()
# We compute an optimal threshold and form a binary image
th_value = threshold_otsu(imag)
binar = imag > th_value
binar = closing(binar, square(20))
clear_border(binar)
binar = convex_hull_object(binar)
if debug:
fig, ax = plt.subplots()
#ax.imshow(imag, cmap='gray')
ax.imshow(binar, alpha=0.4, cmap='gray')
fig.suptitle("Binary image")
plt.show()
return binar
def fechoConvexo(img):
axs[1].set_title('Imagem com fecho convexo dos objetos')
axs[1].set_axis_off() #tira o eixo x e y das imagem que fica na coluna 1
fechamento = morphology.area_closing(
img, 64,
1) #retira os pontos para fazer o fecho apenas das figuras restantes
linha, coluna = fechamento.shape
for i in range(linha):
for j in range(coluna):
if (
fechamento[i][j] == 88
): #seta os pixels da fig de cor magenta(na escala de cinza) para branco
fechamento[i][j] = 255
thresh = threshold_otsu(
fechamento
) #auxilia na transformação da img na escola de cinza para binária(0-ausencia de cor 1-presença de cor)
binary = invert(
fechamento > thresh
) #tranforma a imagem em binaria em seguida faz a troca das cor dos pixels
fechoConvexo = morphology.convex_hull_object(
binary) #retorna o fecho convexo de cada objeto
axs[1].imshow(fechoConvexo, cmap='gray')
import matplotlib.pyplot as plt
from skimage import data, color, morphology, feature
# 生成二值测试图像
img = color.rgb2gray(data.coins())
# 检测canny边缘,得到二值图片
edgs = feature.canny(img, sigma=3, low_threshold=10, high_threshold=50)
chull = morphology.convex_hull_object(edgs)
# 绘制轮廓
fig, axes = plt.subplots(1, 2, figsize=(8, 8))
ax0, ax1 = axes.ravel()
ax0.imshow(edgs, plt.cm.gray)
ax0.set_title('many objects')
ax1.imshow(chull, plt.cm.gray)
ax1.set_title('convex_hull image')
plt.show()
def transform_rois(
roi_file,
source_image_filename,
destination_image_filename,
control_point_file,
output_filename,
temp_output_filename,
log_file_path,
error_file_path,
roi_reference_image=None,
selem_size=15,
nii_scale=None,
transformation_matrix=None,
debug=False,
print_value_round_decimals=2,
z_filter_padding=2,
):
"""
Using a source image (e.g. downsampled stack), transform an ImageJ
zipped collection of ROIs into the coordinate space of a
destination image (e.g. an atlas), using the inverse control point file
from an existing niftyreg registration
:param roi_file: .zip collection of ImageJ ROIs
:param source_image_filename: Image that the ROIs are defined in
:param destination_image_filename: Image in the destination coordinate space
:param control_point_file: Transformation from source to destination
:param output_filename: output filename for the resulting nifti file
:param temp_output_filename: Temporary file for registration
:param log_file_path: Path to save niftyreg logs
:param error_file_path: Path to save niftyreg errors
:param roi_reference_image: Image on which the ROIs are defined (if not the
downsampled image in the registration directory)
:param selem_size: Structure element size for closing
:param nii_scale: Scaling to correctly save the temporary nifti image
:param transformation_matrix: Affine transform for the temporary nifti
image
:param print_value_round_decimals: How many decimal places to round
values printed to console.
:param z_filter_padding: Size of the filter in z when correcting for
unlabled slices.
:param debug: If True, don't delete temporary files
"""
print("Loading ROIs")
rois = read_roi_zip(roi_file)
number_rois = len(rois)
print(f"{number_rois} rois found")
x = []
y = []
z = []
for key in rois:
for position in range(0, len(rois[key]["x"])):
x.append(rois[key]["x"][position])
y.append(rois[key]["y"][position])
z.append(rois[key]["position"])
print("Loading downsampled image image")
downsampled_source_image = brainio.load_any(str(source_image_filename))
print(f"Source image size: "
f"x:{downsampled_source_image.shape[0]}, "
f"y:{downsampled_source_image.shape[1]}, "
f"y:{downsampled_source_image.shape[2]}")
downsampled_source_image[:] = 0
if roi_reference_image is not None:
print("Reference image flag used. Loading reference image")
reference_image_shape = brainio.get_size_image_from_file_paths(
roi_reference_image)
print(f"Reference image shape is "
f"x:{reference_image_shape['x']}, "
f"y:{reference_image_shape['y']}, "
f"z:{reference_image_shape['z']}")
x_downsample_factor = (reference_image_shape["x"] /
downsampled_source_image.shape[0])
y_downsample_factor = (reference_image_shape["y"] /
downsampled_source_image.shape[1])
z_downsample_factor = (reference_image_shape["z"] /
downsampled_source_image.shape[2])
print(f"ROIs will be downsampled by a factor of "
f"x:{round(x_downsample_factor, print_value_round_decimals)}, "
f"y:{round(y_downsample_factor, print_value_round_decimals)}, "
f"z:{round(z_downsample_factor, print_value_round_decimals)}")
# TODO: optimise this
print("Creating temporary ROI image")
for position in range(0, len(x)):
if roi_reference_image is None:
downsampled_source_image[x[position], y[position], z[position]] = 1
else:
x_scale = int(round(x[position] / x_downsample_factor))
y_scale = int(round(y[position] / y_downsample_factor))
z_scale = int(round(z[position] / z_downsample_factor))
downsampled_source_image[x_scale, y_scale, z_scale] = 1
print("Cleaning up ROI image")
# TODO speed this up - parallelise?
selem = morphology.selem.square(selem_size)
for plane in tqdm(range(0, downsampled_source_image.shape[2])):
tmp = morphology.binary.binary_closing(downsampled_source_image[:, :,
plane],
selem=selem)
tmp = morphology.convex_hull_object(tmp)
downsampled_source_image[:, :,
plane] = morphology.binary.binary_closing(
tmp, selem=selem)
if roi_reference_image is not None:
if z_downsample_factor < 1:
print("ROI was defined at a lower z-resolution than the atlas. "
"Correcting with a maximum filter")
z_filter_size = int(round(1 / z_scale)) + z_filter_padding
downsampled_source_image = maximum_filter1d(
downsampled_source_image, z_filter_size, axis=2)
print(f"Saving temporary ROI image at: {temp_output_filename}")
brainio.to_nii(
downsampled_source_image,
str(temp_output_filename),
scale=nii_scale,
affine_transform=transformation_matrix,
)
print("Preparing ROI registration")
nifty_reg_binaries_folder = get_niftyreg_binaries()
program_path = get_binary(nifty_reg_binaries_folder, PROGRAM_NAME)
reg_cmd = prepare_segmentation_cmd(
program_path,
temp_output_filename,
output_filename,
destination_image_filename,
control_point_file,
)
print("Running ROI registration")
try:
safe_execute_command(reg_cmd, log_file_path, error_file_path)
except SafeExecuteCommandError as err:
raise RegistrationError("ROI registration failed; {}".format(err))
print(f"Registered ROI image can be found at {output_filename}")
if not debug:
print("Deleting temporary files")
remove(temp_output_filename)
remove(log_file_path)
remove(error_file_path)
def run(self, ips, snap, img, para=None):
img[convex_hull_object(snap)] = 255
def immune_cell_identi(path,
filename,
plot=False,
convex=False,
Low_thre=100,
High_thre=170):
img = cv.imread(path + '\\' + filename)
# RGB to Gray
GRAY = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
mask = cv.inRange(GRAY, Low_thre, High_thre)
# through convex hull
if convex:
mask = morphology.convex_hull_object(mask, neighbors=4)
_, label_c = cv.connectedComponents(mask, connectivity=4)
label_c = np.array(label_c, dtype='bool')
area1 = 17
chull3 = morphology.remove_small_objects(label_c,
area1,
connectivity=1,
in_place=False)
mask2 = np.array(chull3, dtype='uint8')
mask3 = np.where(mask2 == 0, mask2, 255)
area2 = 4
chull5 = morphology.remove_small_objects(label_c,
area2,
connectivity=1,
in_place=False)
mask4 = np.array(chull5, dtype='uint8')
mask5 = np.where(mask4 == 0, mask4, 255)
# mask6 is the region of interest, where the immune cells are located.
mask6 = mask5 - mask3
_, label_connect = cv.connectedComponents(mask6, connectivity=4)
# the label_num is the number of connectivity domains
label_num = label_connect.max()
if plot:
cv.imshow('original image', img)
cv.imshow('grayscale image', mask)
cv.imshow('final image', mask6)
cv.waitKey(0)
if convex:
number = 0
contours, hireachy = cv.findContours(mask, cv.RETR_EXTERNAL,
cv.CHAIN_APPROX_SIMPLE)
for i, contour in enumerate(contours):
hull = cv.convexHull(contour)
ishull = cv.isContourConvex(hull)
cv.drawContours(img, list(hull), -1, (0, 0, 255), 2)
if ishull:
hull = cv.convexHull(contour, returnPoints=False)
defects = cv.convexityDefects(contour, hull)
if defects is not None:
for j in range(defects.shape[0]):
s, e, f, d = defects[j, 0]
start = tuple(contour[s][0])
end = tuple(contour[e][0])
far = tuple(contour[f][0])
cv.line(img, start, end, (0, 255, 0), 2)
cv.circle(img, far, 5, (0, 0, 255), -1)
print(j)
number = number + len(defects) - 1
label_num = label_num + number
return label_num
def convex(in_img):
in_img[convex_hull_object(in_img)] = 255
return in_img
def getGapDistances(chunk, edgeThresh=0.1, sizeThresh=10, plotIt=False):
chunkBi, _ = binarizeImg(chunk,
threshFn=skimfilt.threshold_otsu,
greater=False,
plotIt=plotIt)
# get convex hulls of connected components
chunk_hull = convex_hull_object(chunkBi, neighbors=4)
nrow, ncol = chunk.shape
# Label convex hulls
chunk_hull_labels, nrObj = ndimage.label(chunk_hull)
# Remove all objects touching the top and bottom that have a
# center of mass also close to the top or bottom.
centers = center_of_mass(chunk_hull, chunk_hull_labels,
range(1, nrObj + 1))
thresh_top = nrow * edgeThresh
thresh_bot = nrow - nrow * edgeThresh
# top edge
for j in range(ncol):
if chunk_hull_labels[0, j] != 0:
lab = chunk_hull_labels[0, j]
if centers[lab - 1][0] < thresh_top:
chunk_hull_labels[chunk_hull_labels == lab] = 0
# bottom edge
for j in range(ncol):
if chunk_hull_labels[nrow - 1, j] != 0:
lab = chunk_hull_labels[nrow - 1, j]
if centers[lab - 1][0] > thresh_bot:
chunk_hull_labels[chunk_hull_labels == lab] = 0
# Remove all objects that are just absolutely tiny
labs = np.unique(chunk_hull_labels)
for lab in labs:
size = np.sum(chunk_hull_labels == lab)
if size < sizeThresh:
chunk_hull_labels[chunk_hull_labels == lab] = 0
# Relabel convex hulls after previous removal
chunk_hull = chunk_hull_labels > 0
chunk_hull_labels, nrObj = ndimage.label(chunk_hull)
# Sort labels by means x values
osli = ndimage.find_objects(chunk_hull_labels)
mean_x = [np.mean(x[1].indices(10**10)[:2]) for x in osli]
order = np.argsort(mean_x)
for j in range(len(order)):
chunk_hull_labels[chunk_hull_labels == order[j] + 1] = -(j + 1)
chunk_hull_labels *= -1
# Get centers of mass for convex hulls
centers = center_of_mass(chunk_hull, chunk_hull_labels,
range(1, nrObj + 1))
# Find contours of objects and use these with lines between
# centers of mass to get distance between objects
contours = find_contours(chunk_hull, 0.5)
contours = [contours[o] for o in order]
distances = []
break_centers = []
for j in range(len(contours) - 1):
obj1 = LineString(contours[j])
obj2 = LineString(contours[j + 1])
center_dist = LineString([centers[j], centers[j + 1]])
edge1 = obj1.intersection(center_dist)
edge2 = obj2.intersection(center_dist)
distances.append(edge1.distance(edge2))
try:
break_centers.append(
LineString([edge1, edge2]).centroid.coords[0][1])
except:
break_centers.append(0)
distances = np.array(distances, ndmin=2).T
if plotIt:
plt.imshow(chunk_hull, cmap="gray")
plt.show()
plt.imshow(chunk_hull_labels, cmap="nipy_spectral")
plt.plot([x[1] for x in centers], [y[0] for y in centers], "ro")
plt.show()
return distances, chunk_hull_labels, break_centers
from skimage.filters import gaussian
from skimage.segmentation import active_contour
from skimage.segmentation import (morphological_chan_vese,
morphological_geodesic_active_contour,
inverse_gaussian_gradient,
checkerboard_level_set)
from skimage import measure
from skimage.morphology import convex_hull_image, convex_hull_object
from skimage.morphology import square, dilation
import cv2 as cv
img = io.imread('lesao.png')
img_gray = rgb2gray(img)
hull = convex_hull_object(img)
#hull_dilated = dilation(hull, square(28 ))
contours = measure.find_contours(hull, 0.5)
#order the contours in decrescent order and get the biggest one
biggest_contour = sorted(contours, key=lambda x: len(x), reverse=True)[0]
#s = np.linspace(0, 2*np.pi, 400)
#r = 100 + 100*np.sin(s)
#c = 220 + 100*np.cos(s)
#init = np.array([r, c]).T
smoothed_img = gaussian(img_gray, 4)
snake = active_contour(smoothed_img,
biggest_contour,
alpha=0.001,
def FindBars(self, bands, close=True, remove_small=True):
'''
BarFinder.FindBars(bands) - Uses Otsu's global thresholding
with both MNDWI and NDVI indexes
in order to find channel bars.
A convex hull is applied to the
unwrapper bar objects.
Args
====
bands: dictionary - must contain R, G, B, NIR, MIR bands
Kwargs
======
close: boolean - perform binary closing on bar mask
Returns
=======
FindBars: Labeled Channel Bars
'''
Wbands = {} # Water bands
Vbands = {} # Vegetation bands
for nband in ['R', 'G', 'B', 'NIR', 'MIR', 'SWIR']:
Wbands[nband] = ndimage.interpolation.map_coordinates(
bands[nband][::-1, :], [self.unwrapper.Yc, self.unwrapper.Xc])
Idx, Bars, otsu_glob = SegmentationIndex(
R=Wbands['R'],
G=Wbands['G'],
B=Wbands['B'],
NIR=Wbands['NIR'],
MIR=Wbands['MIR'],
SWIR=Wbands['SWIR'],
index='BAR',
method='global'
) ## This must be made locally, otherwise we dont see bars eventually
# Apply a Convex Hull to Channel Bars
#Bars = mm.convex_hull_object( Bars )
if close:
# 1/8 of the total average channel width is used ( 1/4*mean(b) )
rad = 1 #max( 0, self.unwrapper.b.mean()/self.unwrapper.GeoTransf['PixelSize'] )
Bars = mm.binary_closing(Bars, mm.disk(rad))
if remove_small:
Amin = 0.1 * self.unwrapper.N.size / 2 * (
self.unwrapper.b.mean() /
(self.unwrapper.s[1] - self.unwrapper.s[0]))
mm.remove_small_objects(Bars, 1,
in_place=True) # Remove small Bars
mm.remove_small_holes(Bars, Amin,
in_place=True) # Remove Internal Spots
# Apply a Convex Hull to Channel Bars
Bars = mm.convex_hull_object(Bars)
# Identify the Largest Bar on the Bend
labeled_array, num_features = ndimage.measurements.label(Bars)
self.Bars = labeled_array
self.BarIdx = np.arange(num_features, dtype=int) + 1
return self.Bars
def AnalyzeMergedZones(diretorio,
ImgRefRoot='/Binarized',
XYField=[319.45, 319.45],
RawImageDefinition=[1024, 1024],
ZStep=1,
ImportstackRootName='/Binarized',
importFormat='.png',
MergedRegion=1,
FirstStack=1,
LastStack=2,
FirstSlice=1,
LastSlice=2,
initialTimePoint=30,
timeinterval=30,
BACVol_ylim=800,
BACVel_ylim=800,
EPSVol_ylim=800,
EPSVel_ylim=800):
StackList = list(range(FirstStack, LastStack + 1))
SliceRange = list(range(FirstSlice, LastSlice + 1))
print('\n Encontrando as regioes para calcular \n')
LengthPixelRatio = XYField[0] / RawImageDefinition[0]
VoxelVal = ZStep * (XYField[0] / RawImageDefinition[0]) * (
XYField[1] / RawImageDefinition[1])
ImgZProj1 = io.imread(diretorio + ImgRefRoot + '/bac' +
'/ZProjection_t18' + importFormat,
dtype='i4')
ImgZProj2 = io.imread(diretorio + ImgRefRoot + '/bac' +
'/ZProjection_t15' + importFormat,
dtype='i4')
ImgZProj3 = io.imread(diretorio + ImgRefRoot + '/bac' +
'/ZProjection_t10' + importFormat,
dtype='i4')
ImgZProj4 = io.imread(diretorio + ImgRefRoot + '/bac' + '/ZProjection_t5' +
importFormat,
dtype='i4')
ImgZProj5 = io.imread(diretorio + ImgRefRoot + '/bac' + '/ZProjection_t2' +
importFormat,
dtype='i4')
ImgZProjF1 = ImgZProj1 + ImgZProj2 + ImgZProj3 + ImgZProj4 + ImgZProj5
ImgZProjF2 = (ImgZProjF1 / ImgZProjF1.max()) * 255
# Plot Imagens
#Figura 1 - Grid
fig1 = plt.figure(figsize=(12, 7), facecolor='w', edgecolor='k')
plt.subplots_adjust(wspace=1.5, hspace=0.6)
#Mapa 1
map1 = plt.subplot2grid((2, 3), (0, 0), rowspan=1, colspan=1)
map1axi = map1.imshow(ImgZProjF2, alpha=0.8, cmap='jet')
map1.set_title('Colony merging', fontsize=11)
map1.tick_params(which='both',
bottom='off',
labelbottom='on',
top='off',
labeltop='off',
left='off',
labelleft='on',
right='off',
labelright='off',
labelsize=11)
map1.set_xlabel('$\\mu$m', fontsize=11)
map1.set_xticks([0, int(ImgZProjF2.shape[1] / 2), ImgZProjF2.shape[1]])
map1.set_xticklabels([
str(0),
str(int((ImgZProjF2.shape[1] / 2) * LengthPixelRatio)),
str(int(ImgZProjF2.shape[1] * LengthPixelRatio))
])
map1.set_yticks([0, int(ImgZProjF2.shape[0] / 2), ImgZProjF2.shape[0]])
map1.set_yticklabels([
str(int(ImgZProjF2.shape[0] * LengthPixelRatio)),
str(int((ImgZProjF2.shape[0] / 2) * LengthPixelRatio)),
str(0)
])
Colbar1 = fig1.colorbar(map1axi,
fraction=0.04,
pad=0.08,
orientation='vertical',
ticks=list(
np.arange(0,
ImgZProjF2.max() + 1,
int(ImgZProjF2.max() / 5))))
Colbar1.set_label('time (min)', fontsize=11)
Colbar1.set_ticklabels(['B'] + [str(i) for i in [510, 420, 270, 120, 30]])
Colbar1.ax.tick_params(labelsize=11)
#Mapa 2
ImgRef1a = io.imread(diretorio + ImgRefRoot + '/bac' + '/ConvexHull1' +
importFormat)
ImgRef1b = morphology.label(ImgRef1a, connectivity=1)
ImgRef1Props = measure.regionprops(ImgRef1b)
CentroidList = []
for elementN in list(range(ImgRef1b.max())):
CentroidList.append([
ImgRef1Props[elementN].centroid[0],
ImgRef1Props[elementN].centroid[1]
])
ImgConvex1a = io.imread(diretorio + ImgRefRoot + '/bac' + '/ConvexHull2' +
importFormat)
ImgConvex1b = morphology.convex_hull_object(ImgConvex1a)
ImgConvex2 = np.where(ImgConvex1b == True, 1, 0)
ImgConvex3 = morphology.label(ImgConvex2, connectivity=1)
HexColors = []
openHexColors = open(diretorio + '/HexColors.txt', 'r')
for line in openHexColors:
HexColors.append(str(line)[:-1])
colorlist = []
for labels in list(range(0, ImgConvex3.max() + 1)):
colorlist.append(HexColors[labels + 47])
ColorBounds = list(range(0, ImgConvex3.max() + 1))
Colormap2 = colors.ListedColormap(colorlist)
ColorNormalization = colors.BoundaryNorm(ColorBounds, Colormap2.N)
map2 = plt.subplot2grid((2, 3), (1, 0), rowspan=1, colspan=1)
map2final = map2.imshow(ImgConvex3, alpha=0.9, cmap=Colormap2)
map2.set_title('Convex hull regions', fontsize=11)
map2.tick_params(which='both',
bottom='off',
labelbottom='on',
top='off',
labeltop='off',
left='off',
labelleft='on',
right='off',
labelright='off',
labelsize=11)
map2.set_xlabel('$\\mu$m', fontsize=11)
map2.set_xticks([0, int(ImgConvex3.shape[1] / 2), ImgConvex3.shape[1] - 1])
map2.set_xticklabels([
str(0),
str(int((ImgConvex3.shape[1] / 2) * LengthPixelRatio)),
str(int(ImgConvex3.shape[1] * LengthPixelRatio))
])
map2.set_yticks([0, int(ImgConvex3.shape[0] / 2), ImgConvex3.shape[0] - 1])
map2.set_yticklabels([
str(int(ImgConvex3.shape[0] * LengthPixelRatio)),
str(int((ImgConvex3.shape[0] / 2) * LengthPixelRatio)),
str(0)
])
Colbar2 = fig1.colorbar(map2final,
cmap=Colormap2,
norm=ColorNormalization,
ticks=list(
np.arange(0, ColorBounds[-1],
ColorBounds[-1] / len(ColorBounds))),
fraction=0.04,
pad=0.08,
orientation='vertical')
Colbar2.set_label('Regions', fontsize=11)
Colbar2.set_ticklabels(['B'] + [str(i) for i in ColorBounds[1:]])
Colbar2.ax.tick_params(labelsize=11)
#Adicionando os centroides e img referencia
map2.imshow(ImgRef1a, alpha=0.5, cmap='Greens')
map2.scatter(np.array(CentroidList)[:, 1],
np.array(CentroidList)[:, 0],
color='k',
s=5)
# Grafico 1. Growth BAC
g1 = plt.subplot2grid((2, 3), (0, 1), rowspan=1, colspan=1)
g1.set_title('Bacteria growth', fontsize=11)
g1.set_ylabel('Growth ($\\mu$$m^3$)', labelpad=5, fontsize=11)
g1.set_xlabel('Time (min)', labelpad=5, fontsize=11)
g1.tick_params(axis="y",
labelleft='on',
left='on',
labelright='off',
right='off',
colors='k',
width=1.5,
length=3.5,
labelsize=11)
g1.tick_params(axis="x",
labelbottom='on',
bottom='on',
labeltop='off',
top='off',
colors='k',
width=1.5,
length=3.5,
labelsize=11)
g1.grid(True, color='k', linestyle='-', linewidth=0.4, alpha=0.1)
# Grafico 2. Growth EPS
g2 = plt.subplot2grid((2, 3), (1, 1), rowspan=1, colspan=1)
g2.set_title('EPS growth', fontsize=11)
g2.set_ylabel('Growth ($\\mu$$m^3$)', labelpad=5, fontsize=11)
g2.set_xlabel('Time (min)', labelpad=5, fontsize=11)
g2.tick_params(axis="y",
labelleft='on',
left='on',
labelright='off',
right='off',
colors='k',
width=1.5,
length=3.5,
labelsize=11)
g2.tick_params(axis="x",
labelbottom='on',
bottom='on',
labeltop='off',
top='off',
colors='k',
width=1.5,
length=3.5,
labelsize=11)
g2.grid(True, color='k', linestyle='-', linewidth=0.4, alpha=0.1)
# Grafico 3. Growth rate BAC
g3 = plt.subplot2grid((2, 3), (0, 2), rowspan=1, colspan=1)
g3.set_title('Bacteria growth rate', fontsize=11)
g3.set_ylabel('Growth rate ($\\mu$$m^3$/min)', labelpad=5, fontsize=11)
g3.set_xlabel('Time (min)', labelpad=5, fontsize=11)
g3.tick_params(axis="y",
labelleft='on',
left='on',
labelright='off',
right='off',
colors='k',
width=1.5,
length=3.5,
labelsize=11)
g3.tick_params(axis="x",
labelbottom='on',
bottom='on',
labeltop='off',
top='off',
colors='k',
width=1.5,
length=3.5,
labelsize=11)
g3.grid(True, color='k', linestyle='-', linewidth=0.4, alpha=0.1)
# Grafico 4. Growth rate EPS
g4 = plt.subplot2grid((2, 3), (1, 2), rowspan=1, colspan=1)
g4.set_title('EPS growth rate', fontsize=11)
g4.set_ylabel('Growth rate ($\\mu$$m^3$/min)', labelpad=5, fontsize=11)
g4.set_xlabel('Time (min)', labelpad=5, fontsize=11)
g4.tick_params(axis="y",
labelleft='on',
left='on',
labelright='off',
right='off',
colors='k',
width=1.5,
length=3.5,
labelsize=11)
g4.tick_params(axis="x",
labelbottom='on',
bottom='on',
labeltop='off',
top='off',
colors='k',
width=1.5,
length=3.5,
labelsize=11)
g4.grid(True, color='k', linestyle='-', linewidth=0.4, alpha=0.1)
#Figura 2 - Campo escalar
#fig2=plt.figure(figsize=(6, 6), facecolor='w', edgecolor='k')
#plotPositions=plt.subplot2grid((1,1),(0,0),rowspan=1,colspan=1)
g1_YboundMAX = 0
g2_YboundMAX = 0
g3_YboundMAX = 0
g4_YboundMAX = 0
for elementN in list(range(1, ImgConvex3.max() + 1)):
YXPosList = []
for Ypos in list(range(ImgConvex3.shape[0])):
for Xpos in list(range(ImgConvex3.shape[1])):
if ImgConvex3[Ypos, Xpos] == elementN:
YXPosList.append([Ypos, Xpos])
ElementPlotListBAC = []
ElementPlotListEPS = []
time = initialTimePoint
for stackNumber in StackList:
print('\n Encontrando os volume para o stack ', str(stackNumber),
' \n')
ImgListBAC = []
ImgListEPS = []
for x in SliceRange:
aa = io.imread(diretorio + ImportstackRootName + '/bac' +
'/t' + str(stackNumber) + '/Slice' + str(x) +
importFormat)
bb = io.imread(diretorio + ImportstackRootName + '/EPS' +
'/t' + str(stackNumber) + '/Slice' + str(x) +
importFormat)
ImgListBAC.append(aa)
ImgListEPS.append(bb)
Img3DarrayBAC = np.array(ImgListBAC)
Img3DarrayBACF = np.where(Img3DarrayBAC > 0, 1, 0)
Img3DarrayEPS = np.array(ImgListEPS)
Img3DarrayEPSF = np.where(Img3DarrayEPS > 0, 1, 0)
VolElemCountListBAC = []
VolElemCountListEPS = []
for sliceN in list(range(Img3DarrayBACF.shape[0])):
for Position in YXPosList:
VolElemCountListBAC.append(Img3DarrayBACF[sliceN,
Position[0],
Position[1]])
VolElemCountListEPS.append(Img3DarrayEPSF[sliceN,
Position[0],
Position[1]])
ElementPlotListBAC.append(
[elementN, time,
sum(VolElemCountListBAC) * VoxelVal])
ElementPlotListEPS.append(
[elementN, time,
sum(VolElemCountListEPS) * VoxelVal])
time += timeinterval
with open(diretorio + '/Images/MergedRegion.txt', 'a') as textfile:
for val in ElementPlotListBAC:
textfile.write(str(val) + '\n')
textfile.close()
# Calculando as velocidades
VelListBAC = AvgVelocity(
np.array(ElementPlotListBAC)[:, 2], initialTimePoint, timeinterval,
elementN)
VelListEPS = AvgVelocity(
np.array(ElementPlotListEPS)[:, 2], initialTimePoint, timeinterval,
elementN)
#Acertando os limites do gráfico
if g1_YboundMAX < np.array(ElementPlotListBAC)[:, 2].max():
g1_YboundMAX = np.array(ElementPlotListBAC)[:, 2].max()
if g2_YboundMAX < np.array(ElementPlotListEPS)[:, 2].max():
g2_YboundMAX = np.array(ElementPlotListEPS)[:, 2].max()
if g3_YboundMAX < np.array(VelListBAC)[:, 2].max():
g3_YboundMAX = np.array(VelListBAC)[:, 2].max()
if g4_YboundMAX < np.array(VelListEPS)[:, 2].max():
g4_YboundMAX = np.array(VelListEPS)[:, 2].max()
#Plot graficos Growth
g1.plot(np.array(ElementPlotListBAC)[:, 1],
np.array(ElementPlotListBAC)[:, 2],
color=colorlist[elementN],
alpha=0.5)
g1.scatter(np.array(ElementPlotListBAC)[:, 1],
np.array(ElementPlotListBAC)[:, 2],
color=colorlist[elementN],
label='Element' + str(elementN),
alpha=0.5,
s=20)
g1.axvline(x=(10 * 30) - 30, color="r", linewidth=1.5, alpha=0.1)
g1.axvline(x=(11 * 30) - 30, color="b", linewidth=1.5, alpha=0.1)
g1.axvline(x=(15 * 30) - 30, color="y", linewidth=1.5, alpha=0.1)
g2.plot(np.array(ElementPlotListEPS)[:, 1],
np.array(ElementPlotListEPS)[:, 2],
color=colorlist[elementN],
alpha=0.5)
g2.scatter(np.array(ElementPlotListEPS)[:, 1],
np.array(ElementPlotListEPS)[:, 2],
color=colorlist[elementN],
label='Element' + str(elementN),
alpha=0.5,
s=20)
g2.axvline(x=(10 * 30) - 30, color="r", linewidth=1.5, alpha=0.1)
g2.axvline(x=(11 * 30) - 30, color="b", linewidth=1.5, alpha=0.1)
g2.axvline(x=(15 * 30) - 30, color="y", linewidth=1.5, alpha=0.1)
#Plot graficos Growth rate
g3.plot(np.array(VelListBAC)[:, 1],
np.array(VelListBAC)[:, 2],
color=colorlist[elementN],
alpha=0.5)
g3.scatter(np.array(VelListBAC)[:, 1],
np.array(VelListBAC)[:, 2],
color=colorlist[elementN],
label='Element' + str(elementN),
alpha=0.5,
s=20)
g3.axvline(x=(10 * 30) - 30, color="r", linewidth=1.5, alpha=0.1)
g3.axvline(x=(11 * 30) - 30, color="b", linewidth=1.5, alpha=0.1)
g3.axvline(x=(15 * 30) - 30, color="y", linewidth=1.5, alpha=0.1)
g4.plot(np.array(VelListEPS)[:, 1],
np.array(VelListEPS)[:, 2],
color=colorlist[elementN],
alpha=0.5)
g4.scatter(np.array(VelListEPS)[:, 1],
np.array(VelListEPS)[:, 2],
color=colorlist[elementN],
label='Element' + str(elementN),
alpha=0.5,
s=20)
g4.axvline(x=(10 * 30) - 30, color="r", linewidth=1.5, alpha=0.1)
g4.axvline(x=(11 * 30) - 30, color="b", linewidth=1.5, alpha=0.1)
g4.axvline(x=(15 * 30) - 30, color="y", linewidth=1.5, alpha=0.1)
#Plotar as posicoes usadas no campo escalar
#plotPositions.scatter(np.array(YXPosList)[:,1], np.array(YXPosList)[:,0], color=colorlist[elementN])
# Definindo os eixos do gráfico
g1.set_ylim(-40, BACVol_ylim)
g1.spines["left"].set_visible(True)
g1.spines["left"].set_linewidth(1.0)
g1.spines["right"].set_visible(False)
g1.spines["top"].set_visible(False)
g1.spines["bottom"].set_visible(True)
g1.spines["bottom"].set_linewidth(1.0)
g1.set_aspect('auto')
g2.set_ylim(-40, EPSVol_ylim)
g2.spines["left"].set_visible(True)
g2.spines["left"].set_linewidth(1.0)
g2.spines["right"].set_visible(False)
g2.spines["top"].set_visible(False)
g2.spines["bottom"].set_visible(True)
g2.spines["bottom"].set_linewidth(1.0)
g2.set_aspect('auto')
g3.set_ylim(-5, BACVel_ylim)
g3.spines["left"].set_visible(True)
g3.spines["left"].set_linewidth(1.0)
g3.spines["right"].set_visible(False)
g3.spines["top"].set_visible(False)
g3.spines["bottom"].set_visible(True)
g3.spines["bottom"].set_linewidth(1.0)
g3.set_aspect('auto')
g4.set_ylim(-5, EPSVel_ylim)
g4.spines["left"].set_visible(True)
g4.spines["left"].set_linewidth(1.0)
g4.spines["right"].set_visible(False)
g4.spines["top"].set_visible(False)
g4.spines["bottom"].set_visible(True)
g4.spines["bottom"].set_linewidth(1.0)
g4.set_aspect('auto')
plt.tight_layout()
fig1.savefig(diretorio + '/Images/MergedRegion' + str(MergedRegion) +
'.png',
dpi=300)
#fig2.savefig(diretorio + '/Images/MergedRegion' + str(MergedRegion) + '_PLOT.png')
plt.show()
