def active_nuclei(self):
'''Return binary image with active nuclei'''
x_max, y_max = self.shape
nuclei_peaces = []
for cur_cell in self.active_cells():
nuclei_peaces.append(peace(cur_cell.nucleus, cur_cell.coords))
return join_peaces(nuclei_peaces, x_max, y_max)
def get_all_pics(self, nuclei_color = 0.66, foci_color = 0.33, seed_circles = False):
'''Return all calculated pics'''
if self.number_of_cells() == 0:
print "No cells found in " + self.dir_path
# return (None, None, None, None, None)
rescaled_nuclei_peaces = []
rescaled_foci_peaces = []
seed_peaces = []
foci_bin_peaces = []
pic_nuclei = self.get_source_pic_nuclei()
x_max, y_max = self.nuclei.shape
for cur_cell in self.active_cells():
coords = cur_cell.coords
rescaled_nuclei_peaces.append(peace(cur_cell.rescaled_nucleus_pic, coords))
rescaled_foci_peaces.append(peace(cur_cell.rescaled_foci_pic, coords))
seed_peaces.append(peace(cur_cell.foci_seeds, coords))
foci_bin_peaces.append(peace(cur_cell.foci_binary, coords))
rescaled_nuclei_pic = join_peaces(rescaled_nuclei_peaces, x_max, y_max, dtype = np.uint8)
rescaled_foci_pic = join_peaces(rescaled_foci_peaces, x_max, y_max, dtype = np.uint8)
foci_binary = join_peaces(foci_bin_peaces, x_max, y_max)
seeds = self.get_merged_pic(nuclei_color, foci_color, seeds = True)
nuclei_colored = self.get_pic_with_nuclei_colored()
merged = self.get_merged_pic(nuclei_color, foci_color)
return (rescaled_nuclei_pic, nuclei_colored, rescaled_foci_pic, seeds, merged)
def get_merged_pic(self, nuclei_color = 0.66, foci_color = 0.33, seeds = False):
'''Return merged pic with foci and nuclei'''
x_max, y_max = self.nuclei.shape
active_cells = self.active_cells()
cell_number = len(active_cells)
if cell_number == 0:
return np.zeros((x_max, y_max, 3), dtype = np.uint8)
merged_pic_peaces = []
nuclei_rgb_koef = hsv2rgb(np.array([nuclei_color, 1., 1.]).reshape((1,1,3))).reshape(3)
foci_rgb_koef = hsv2rgb(np.array([foci_color, 1., 1.]).reshape((1,1,3))).reshape(3)
for cur_cell in active_cells:
if seeds:
foci = cur_cell.foci_seeds
else:
foci = cur_cell.foci_binary
nucleus_only = cur_cell.nucleus - foci
pic_foci_enhanced = 255 - np.floor((255 - cur_cell.pic_foci)*0.6)
pic_foci_enhanced = foci*pic_foci_enhanced
pic_nucleus_only = cur_cell.pic_nucleus*nucleus_only
pic_foci_enhanced_3d = np.dstack((pic_foci_enhanced, pic_foci_enhanced, pic_foci_enhanced))
pic_nucleus_only_3d = np.dstack((pic_nucleus_only, pic_nucleus_only, pic_nucleus_only))
pic_foci_rgb = pic_foci_enhanced_3d*foci_rgb_koef
pic_nucleus_only_rgb = pic_nucleus_only_3d*nuclei_rgb_koef
pic_merged_rgb = np.floor(pic_foci_rgb + pic_nucleus_only_rgb).astype(np.uint8)
merged_pic_peaces.append(peace(pic_merged_rgb, cur_cell.coords))
merged_pic = join_peaces_3d(merged_pic_peaces, x_max, y_max, dtype = np.uint8)
return merged_pic
def get_pic_with_nuclei_colored(self):
'''Return pic with colored nuclei'''
pic_nuclei = self.get_source_pic_nuclei()
x_max, y_max = self.shape
cell_number = len(self.cells)
if cell_number == 0:
return np.dstack((pic_nuclei, pic_nuclei, pic_nuclei))
hue_step = 0.29
colored_nuclei_peaces = []
for cur_cell, cur_num in zip(self.cells, range(cell_number)):
if not cur_cell.is_active: continue
pic_nucleus = cur_cell.pic_nucleus
pic_nucleus_3d = np.dstack((pic_nucleus, pic_nucleus, pic_nucleus))
rgb_koef = hsv2rgb(np.array([hue_step*cur_num, 0.5, 1.]).reshape((1,1,3))).reshape(3)
colored_nuclei_peaces.append(peace(np.floor(pic_nucleus_3d*rgb_koef).astype(np.uint8),cur_cell.coords))
pic_bg = pic_nuclei*(self.active_nuclei() == 0)
pic_bg_3d = np.dstack((pic_bg, pic_bg, pic_bg))
colored_nuclei_only = join_peaces_3d(colored_nuclei_peaces, x_max, y_max, dtype = np.uint8)
colored_nuclei = pic_bg_3d + colored_nuclei_only
return colored_nuclei