def threshold_test(data, input_params):
img = data.nuc_img
z_size = img.shape[0]
med_img = np.zeros(shape=img.shape)
for z in range(z_size):
temp_img = img[z, :, :]
med_img[z, :, :] = cv2.medianBlur(temp_img, ksize=5)
med_img = img_as_float(med_img)
## THRESHOLD TEST
z = int(med_img.shape[0] / 2)
fig, ax = filters.try_all_threshold(med_img[z, :, :],
figsize=(10, 8),
verbose=False)
# plt.show()
plt.savefig(os.path.join(input_params.output_path,
data.rep_name + 'thresh_test.png'),
dpi=300)
plt.close()
# threshold = filters.threshold_otsu(med_img)
threshold = filters.threshold_triangle(med_img)
nuc_mask = med_img >= threshold
for z in range(z_size):
nuc_mask[z, :, :] = nd.morphology.binary_fill_holes(nuc_mask[z, :, :])
nuc_mask = nd.morphology.binary_opening(nuc_mask)
### WATERSHED TEST
labels, _ = nd.label(nuc_mask)
distance = nd.distance_transform_edt(nuc_mask)
##custom faster solution for getting markers
sure_fg = distance
sure_fg[sure_fg <= 0.4 * distance.max()] = 0
sure_fg = sure_fg > 0
sure_fg = nd.morphology.binary_erosion(sure_fg)
markers, num_regions = nd.label(sure_fg)
row, col = optimum_subplots(nuc_mask.shape[0])
fig, ax = plt.subplots(row, col)
ax = ax.flatten()
for idx, a in enumerate(ax):
if idx < nuc_mask.shape[0]:
labeled_image = label2rgb(markers[idx, :, :],
image=exposure.equalize_adapthist(
data.nuc_img[idx, :, :]),
alpha=0.3,
bg_label=0,
bg_color=[0, 0, 0])
a.imshow(labeled_image)
clear_axis_ticks(a)
plt.savefig(os.path.join(input_params.output_path,
data.rep_name + 'watershed_test.png'),
dpi=300)
plt.close()
def find_watershed_3D(data, vol):
# vol is the binary nuclear mask
# sure_bg = nd.morphology.binary_dilation(vol, iterations=3)
'''
## EROSION TEST
fig, ax = plt.subplots(3, 3)
ax = ax.flatten()
size = 21
# z = 10
# c = make_struct_element(size, shape='circle').astype(vol.dtype)
# print(c)
# print(c.dtype)
# e_t = make_struct_element(size, shape='ellipse_tall').astype(vol.dtype)
e_w = make_struct_element(size, shape='ellipse_wide').astype(vol.dtype)
c_temp1 = nd.morphology.binary_erosion(vol, structure=c, iterations=1)
c_temp2 = nd.morphology.binary_erosion(vol, structure=c, iterations=3)
c_temp3 = nd.morphology.binary_erosion(vol, structure=c, iterations=9)
e_t_temp1 = nd.morphology.binary_erosion(vol, structure=e_t, iterations=1)
e_t_temp2 = nd.morphology.binary_erosion(vol, structure=e_t, iterations=3)
e_t_temp3 = nd.morphology.binary_erosion(vol, structure=e_t, iterations=9)
e_w_temp1 = nd.morphology.binary_erosion(vol, structure=e_w, iterations=1)
e_w_temp2 = nd.morphology.binary_erosion(vol, structure=e_w, iterations=3)
e_w_temp3 = nd.morphology.binary_erosion(vol, structure=e_w, iterations=9) # money beet
print(f'Image size is {c_temp1.shape}')
ax[0].imshow(c_temp1[z, :, :], cmap='gray')
ax[1].imshow(c_temp2[z, :, :], cmap='gray')
ax[2].imshow(c_temp3[z, :, :], cmap='gray')
ax[3].imshow(e_t_temp1[z, :, :], cmap='gray')
ax[4].imshow(e_t_temp2[z, :, :], cmap='gray')
ax[5].imshow(e_t_temp3[z, :, :], cmap='gray')
ax[6].imshow(e_w_temp1[z, :, :], cmap='gray')
ax[7].imshow(e_w_temp2[z, :, :], cmap='gray')
ax[8].imshow(e_w_temp3[z, :, :], cmap='gray')
for a in ax:
clear_axis_ticks(a)
plt.tight_layout()
plt.show()
input('Press enter to continue')
sys.exit(0)
'''
labels, _ = nd.label(vol)
distance = nd.distance_transform_edt(vol)
##custom faster solution for getting markers
sure_fg = distance
sure_fg[sure_fg <= 0.2 * distance.max()] = 0
sure_fg = sure_fg > 0
struct_size = 19
e_w = make_struct_element(struct_size,
shape='ellipse_wide').astype(vol.dtype)
sure_fg = nd.morphology.binary_erosion(sure_fg,
structure=e_w,
iterations=5)
# sure_fg = nd.morphology.binary_opening(sure_fg, structure=e_w, iterations=15) # iterations < 1 keeps it going until nothing changes
# sure_fg = nd.morphology.binary_erosion(sure_fg)
markers, num_regions = nd.label(sure_fg)
'''
### WATERSHED TEST
row, col = optimum_subplots(vol.shape[0])
fig, ax = plt.subplots(row, col)
ax = ax.flatten()
for idx, a in enumerate(ax):
if idx < vol.shape[0]:
labeled_image = label2rgb(markers[idx, :, :], image=exposure.equalize_adapthist(data.nuc_img[idx, :, :]),
alpha=0.3, bg_label=0, bg_color=[0, 0, 0])
a.imshow(labeled_image)
# ax[1].imshow(sure_bg[15, :, :], cmap='Blues')
# ax[2].imshow(data.nuc_img[15, :, :], cmap='gray')
# ax[3].imshow(vol[15, :, :], cmap='gray')
clear_axis_ticks(a)
plt.show()
input('Press enter to continue')
plt.close()
sys.exit(0)
'''
# local_maxi = peak_local_max(distance, min_distance=20, indices=False, labels=labels, exclude_border=True)
# markers = nd.label(local_maxi)[0]
output = watershed(-distance, markers, mask=vol, watershed_line=True)
return output
def threshold_puncta(img, data, input_params, channel):
z_size = img.shape[0]
float_img = img_as_float(img)
# THRESHOLD TEST
z = int(float_img.shape[0] / 2)
fig, ax = filters.try_all_threshold(float_img[z, :, :],
figsize=(10, 8),
verbose=False)
plt.savefig(os.path.join(input_params.output_path, data.rep_name + " " +
channel + 'thresh_test.png'),
dpi=300)
plt.close()
if '6h Cis HP1 HCT 002' in data.rep_name:
threshold = 2125
elif '12h Cis HP1 HCT 003' in data.rep_name:
threshold = 2978
elif '6h Cis HP1 HCT 001' in data.rep_name:
threshold = 1734
elif '6h Cis Med1 HCT 001' in data.rep_name:
threshold = 743
elif '6h Cis Med1 HCT 002' in data.rep_name:
threshold = 569
elif '6h Cis Med1 HCT 003' in data.rep_name:
threshold = 562
elif 'IF 6h fib1 Cis HCT116 002' in data.rep_name:
threshold = 2819
else:
threshold = filters.threshold_triangle(float_img)
mask = float_img >= threshold
puncta_mask = float_img >= threshold
fig, ax = plt.subplots(1, 1)
ax.imshow(exposure.equalize_adapthist(mask[z, :, :]), cmap='gray')
plt.tight_layout()
plt.savefig(os.path.join(
input_params.output_path,
data.rep_name + " " + channel + 'chosen_thresh_test.png'),
dpi=300)
plt.close()
## quantify number of regions at chosen threshold
labels, num_regions_threshold = nd.label(mask)
distance = nd.distance_transform_edt(mask)
##custom faster solution for getting markers
sure_fg = distance
sure_fg[sure_fg <= 0.4 * distance.max()] = 0
sure_fg = sure_fg > 0
sure_fg = nd.morphology.binary_erosion(sure_fg)
## number of regions for fast solution
markers, num_regions = nd.label(sure_fg)
## print number of regions and find fraction of total nuclear area
print(num_regions_threshold)
markers_no_nuc = np.where(data.nuc_mask != False, labels, 0)
puncta = regionprops(markers_no_nuc)
total_area = []
for pt in puncta:
total_area.append(pt.area)
row, col = optimum_subplots(mask.shape[0])
fig, ax = plt.subplots(row, col)
img = img.astype(np.uint16) # not sure if this is allowed
ax = ax.flatten()
for idx, a in enumerate(ax):
if idx < mask.shape[0]:
labeled_image = label2rgb(markers[idx, :, :],
image=exposure.equalize_adapthist(
img[idx, :, :]),
alpha=0.3,
bg_label=0,
bg_color=[0, 0, 0])
a.imshow(labeled_image)
clear_axis_ticks(a)
plt.savefig(os.path.join(
input_params.output_path,
data.rep_name + " " + channel + 'watershed_test.png'),
dpi=300)
plt.close()
return labels, num_regions, puncta, puncta_mask