def make_mask(self):
mask = copy.copy(self.img)
mask = spacetransformer.im2c(mask, 8)
nrows, ncols = mask.shape
row, col = np.ogrid[:nrows, :ncols]
cnt_row, cnt_col = nrows / 2, ncols / 2
outer_disk_mask = ((row - cnt_row)**2 + (col - cnt_col)**2 < (nrows / 2)**2)
mask[mask<0.3] = -1
mask[outer_disk_mask==0] = -1
self.mask = mask
def cell_watershed(img):
"""
Performs watershed on an RGB-image. Returns the image with the new boarders and the markers
"""
img = img[:,:,0:3]
kernel = np.ones((3,3),np.uint8)
# Mask the background to get the whole cells
color = 10
bg_transform = spacetransformer.im2c(img, color)
bg_transform = np.array(bg_transform * 255, dtype=np.uint8)
bg_img = bg_transform.copy()
unknown_mask = bg_transform.copy()
unknown_mask[bg_img<=0.45*np.amax(bg_img)] = 0
unknown_mask[bg_img>0.45*np.amax(bg_img)] = 1
# Erode and close to remove trash
#unknown_mask = cv2.erode(unknown_mask, kernel, iterations =1)
unknown_mask = cv2.morphologyEx(unknown_mask,cv2.MORPH_CLOSE,kernel, iterations = 2)
# Mask to get the nuclei
color = 4
nuclei = spacetransformer.im2c(img, color)
nuclei = np.array(nuclei * 255, dtype=np.uint8)
nuclei_mask = nuclei.copy()
nuclei_mask[nuclei< 0.07*np.amax(nuclei)] = 1 #0.05
nuclei_mask[nuclei>=0.07*np.amax(nuclei)] = 0
# Dilate and close to fill the nuclei
nuclei_mask = cv2.dilate(nuclei_mask, kernel, iterations = 1)
nuclei_mask = cv2.erode(nuclei_mask, kernel, iterations = 2)
#nuclei_mask = cv2.morphologyEx(nuclei_mask,cv2.MORPH_CLOSE,kernel, iterations = 2)
nuclei_mask[nuclei_mask > 0] = 1
unknown_mask[unknown_mask > 0] = 1
# Create image with unknown region (between membrane and nucleus)
unknown_region = nuclei_mask - unknown_mask
unknown_region[unknown_region > 0] = 1
#plt.figure("unknown_region")
#plt.imshow(unknown_region)
#plt.figure("unknown_mask")
#plt.imshow(unknown_mask)
plt.figure("nuclei_mask")
plt.imshow(nuclei_mask)
#print(np.amax(unknown_region))
#print(np.amax(unknown_mask))
#print(np.amax(nuclei_mask))
# Create the markers for the nuclei
ret, markers_nuc = cv2.connectedComponents(nuclei_mask)
# Add the markers for the nuclei with the mask for the whole cells
markers = markers_nuc.copy()
markers += 1
ret += 1
markers[unknown_region == 0] = 0
# Create a background image to use in the watershed
background = img.copy()
background[:,:,0] = unknown_mask*255
background[:,:,1] = unknown_mask*255
background[:,:,2] = unknown_mask*255
# Perform watershed and mark the boarders on the image
markers = cv2.watershed(background, markers)
img[markers == -1] = [255,0,0]
return img, ret, markers, nuclei_mask
import numpy as np
import cv2
import pylab
import copy
from ColourNM import spacetransformer
WBCarray = []
WBCarray_hsv = []
WBCarray_bw = []
for i in range(1, 21):
# WBCarray.append(np.load("white_" + str(i) + ".npy"));
WBCarray_hsv.append(cv2.cvtColor(np.load("white_" + str(i) + ".npy"), cv2.COLOR_BGR2HSV))
# WBCarray_bw.append(cv2.cvtColor(np.load("white_" + str(i) + ".npy"), cv2.COLOR_BGR2GRAY))
WBCarray_bw.append(spacetransformer.im2c(np.load("white_" + str(i) + ".npy"), 8))
WBC_masked = []
for i in range(0, 20):
# plt.figure(1)
test_img = copy.copy(WBCarray_bw[i])
nrows, ncols = test_img.shape
row, col = np.ogrid[:nrows, :ncols]
cnt_row, cnt_col = nrows / 2, ncols / 2
outer_disk_mask = (row - cnt_row) ** 2 + (col - cnt_col) ** 2 < (nrows / 2) ** 2
# test_img[outer_disk_mask==0] = -1
test_img[test_img < 0.3] = -1
test_img[outer_disk_mask == 0] = -1
WBC_masked.append(test_img)
# plt.subplot(5,4,i+1)
# plt.imshow(test_img)
def hist_features(self, color, bins, interval = [0,1]):
col = spacetransformer.im2c(self.img, color)
col[self.mask == -1] = -1
hist,_ = np.histogram(col,bins,interval)
hist = hist/float(np.size(col[col>=0]))
return hist
def get_mean_var_energy(self, color):
col = spacetransformer.im2c(self.img, color)
energy = np.sum(np.power(col, 2))/self.size
return [np.mean(col), np.var(col), energy]
def cell_watershed(img):
"""
Performs watershed on an RGB-image. Returns the image with the new boarders and the markers
"""
img = img[:,:,0:3]
kernel = np.ones((3,3),np.uint8)
# Mask the background to get the whole cells
color = 10
bg_transform = spacetransformer.im2c(img, color)
bg_transform = np.array(bg_transform * 255, dtype=np.uint8)
blur = cv2.GaussianBlur(bg_transform,(5,5),0)
thres,unknown_mask = cv2.threshold(bg_transform,0,1,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# Erode and close to remove trash
unknown_mask = cv2.morphologyEx(unknown_mask,cv2.MORPH_OPEN,kernel, iterations = 2)
# Get colorspace 4 and set the background of whole cells to -1
color = 4
nuclei = spacetransformer.im2c(img, color)
nuclei = np.array(nuclei * 255, dtype=np.uint8)
nuclei[unknown_mask == 1] = -1
# Equalize histogram and blur image
nuclei = cv2.equalizeHist(nuclei)
blur = cv2.GaussianBlur(nuclei,(5,5),0)
blur = cv2.medianBlur(blur,11)
# Get threshold and mask the image to get nuclei set background of the whole cells to 0
hist,_ = np.histogram(blur,255,[0,254])
thres = get_threshold(hist)
_,nuclei_mask = cv2.threshold(blur,thres,1,cv2.THRESH_BINARY_INV)
nuclei_mask[unknown_mask == 1] = 0
# Get contours and find joined nuclei, look at hull if possible
_, all_nuclei_cont, _ = cv2.findContours(nuclei_mask.copy(), 1, 2)
joined_nuclei_cont = []
max_radius = 0
for c in all_nuclei_cont:
(x,y), radius = cv2.minEnclosingCircle(c)
if radius > 0:
center = (int(x),int(y))
radius = int(radius)
if radius > max_radius:
max_radius = radius
circle_area = 3.14*radius*radius
nuclei_area = cv2.contourArea(c)
if nuclei_area/circle_area < .6 and nuclei_area > 150:
joined_nuclei_cont.append(c)
# Draw contours of joined nuclei
empty = np.zeros(np.shape(nuclei))
empty = np.array(empty*255, dtype=np.uint8)
joined_nuclei = empty.copy()
cv2.drawContours(joined_nuclei, joined_nuclei_cont, -1, (255,255,255), -1)
# Erode joined nuclei and get new contours
kernelll = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (20,20))
eroded_joined_nuclei = cv2.erode(joined_nuclei,kernelll,iterations=1)
_, eroded_nuclei_cont,_ = cv2.findContours(eroded_joined_nuclei.copy(), 1, 2)
# Find contours to be removed, assumed to be joined red blood cells
cells_to_remove = []
for i,c in enumerate(joined_nuclei_cont):
x,y,w,h = cv2.boundingRect(c)
erode = eroded_joined_nuclei[y:y+h,x:x+w]
temp = np.zeros(np.shape(erode))
cv2.drawContours(temp, [c], -1, (1,1,1), -1, offset=(-x,-y))
if np.sum(temp*erode) == 0:
cells_to_remove.append(c)
cells_to_remove_im = empty.copy()
cv2.drawContours(cells_to_remove_im, cells_to_remove, -1, (255,255,255), -1)
# Get new mask for large nuclei
joined_mask, large_nuclei_cont = mask_joined_WBC(eroded_nuclei_cont, nuclei_mask, max_radius)
# Create the markers for the nuclei
ret, markers_nuc = cv2.connectedComponents(nuclei_mask)
markers_nuc[markers_nuc == 0] = -1
markers_nuc = markers_nuc + 1
# Add the markers for the nuclei and the mask for the whole cells
markers = markers_nuc | unknown_mask
# Create a background image to use in watershed
background =img.copy()
background[:,:,0] = unknown_mask*255
background[:,:,1] = unknown_mask*255
background[:,:,2] = unknown_mask*255
###save images!!###
im = Image.fromarray(bg_transform)
im.save('../results/bg_transform.png')
im = Image.fromarray(nuclei)
im.save('../results/nuclei.png')
im = Image.fromarray(markers)
im.save('../results/watershed_result.png')
# Perform watershed and mark the borders on the image
markers = cv2.watershed(background, markers)
# Get an image of the markers and find contours
cytoplasm_markers_im = empty.copy()
cytoplasm_markers_im[markers > 1] = 1
cytoplasm_markers_im[markers == -1] = 0
cytoplasm_markers_im = cv2.erode(cytoplasm_markers_im, np.ones(2)) #ska det inte vara en (2,2)
_,cytoplasm_cont,_ = cv2.findContours(cytoplasm_markers_im, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
to_be_removed = []
to_be_inserted = []
removed_cells = []
exchanged_cells = []
for i,c in enumerate(cytoplasm_cont):
x,y,w,h = cv2.boundingRect(c)
cell_img = cells_to_remove_im[y:y+h,x:x+w]
join = joined_mask[y:y+h,x:x+w]
emp = np.zeros(np.shape(cell_img))
temp = emp.copy()
cv2.drawContours(temp, [c], -1, (1,1,1), -1, offset=(-x,-y))
if np.sum(temp*cell_img) > 0:
to_be_removed.append(i)
removed_cells.append(c)
if np.sum(temp*join)> 0:
to_be_removed.append(i)
exchanged_cells.append(c)
for con in large_nuclei_cont:
joined = emp.copy()
cv2.drawContours(joined, [con], -1, (1,1,1), -1,offset=(-x,-y))
if np.sum(temp*joined) > 0:
to_be_inserted.append(con)
for i in sorted(to_be_removed, reverse=True):
del cytoplasm_cont[i]
for con in to_be_inserted:
cytoplasm_cont.append(con)
return cytoplasm_cont, nuclei_mask, removed_cells, exchanged_cells
