def watershed_3d(sphere):
"""
Markers should be int8
Image should be uint8
"""
sphere = median_filter(sphere, 3)
thresh = threshold_otsu(sphere)
sphere = (sphere >= thresh) * 1
sphere = sobel(sphere)
size = (sphere.shape[0], sphere.shape[1], sphere.shape[2])
marker = np.zeros(size, dtype=np.int16)
pl.imshow(sphere[:,:,50])
pl.show()
# mark everything outside as background
marker[5, :, :] = -1
marker[size[0] - 5, :, :] = -1
marker[:, :, 5] = -1
marker[:, :, size[2] - 5] = -1
marker[:, 5, :] = -1
marker[:, size[1] - 5, :] = -1
marker[:,0,0] = -1
# mark everything inside as a sphere
marker[size[0] / 2., size[1] / 2., size[2] / 2.] = 5
result = measurements.watershed_ift(sphere.astype(dtype=np.uint16), marker)
pl.imshow(result[:,:,50])
pl.show()
return result
def watershed_3d(sphere):
"""
Markers should be int8
Image should be uint8
"""
sphere = median_filter(sphere, 3)
thresh = threshold_otsu(sphere)
sphere = (sphere >= thresh) * 1
sphere = sobel(sphere)
size = (sphere.shape[0], sphere.shape[1], sphere.shape[2])
marker = np.zeros(size, dtype=np.int16)
pl.imshow(sphere[:, :, 50])
pl.show()
# mark everything outside as background
marker[5, :, :] = -1
marker[size[0] - 5, :, :] = -1
marker[:, :, 5] = -1
marker[:, :, size[2] - 5] = -1
marker[:, 5, :] = -1
marker[:, size[1] - 5, :] = -1
marker[:, 0, 0] = -1
# mark everything inside as a sphere
marker[size[0] / 2., size[1] / 2., size[2] / 2.] = 5
result = measurements.watershed_ift(sphere.astype(dtype=np.uint16), marker)
pl.imshow(result[:, :, 50])
pl.show()
return result
def watershed2(img, markers):
markers = np.int8(imtransform.setMarkersValues(markers, black=1, gray=-1, white=0))
# markers2 = np.zeros(markers.shape, np.uint8)
# markers2[markers == 1] = 0
# markers2[markers ==-1] = 127
# markers2[markers == 0] = 255
# imsave(settings.UPLOAD_FOLDER + '/markers222222.png', markers2)
greyImg = imtransform.rgb_2_greyscale(img)
mask = watershed_ift(greyImg, markers)
mask[mask < 0] = 0
mask = 255 * mask
return np.array(mask, dtype=np.uint8)
def watershed2(img, markers):
markers = np.int8(
imtransform.setMarkersValues(markers, black=1, gray=-1, white=0))
# markers2 = np.zeros(markers.shape, np.uint8)
# markers2[markers == 1] = 0
# markers2[markers ==-1] = 127
# markers2[markers == 0] = 255
# imsave(settings.UPLOAD_FOLDER + '/markers222222.png', markers2)
greyImg = imtransform.rgb_2_greyscale(img)
mask = watershed_ift(greyImg, markers)
mask[mask < 0] = 0
mask = 255 * mask
return np.array(mask, dtype=np.uint8)
def processImage(self):
log.debug("IMAGE: " + self.image.z + ',' + self.image.t + ',' + self.image.ch)
print 'In process image'
if int(self.image.z) == 1 and int(self.image.t) == 1 and (int(self.image.ch) ==1 or int(self.image.ch) == 3):
# read image from Bisque system
resp, content = request(self.image.src + '?format=tiff', "GET", userpass = self.userpass )
# convert stream into Image
im = cStringIO.StringIO(content)
img = Image.open(im)
log.debug("IMAGE: " + str(img.format) + ',' + str(img.size) + ',' + str(img.mode))
''' IMAGE PROCESSING '''
# convert color image into grayscale image
grayimg = ImageOps.grayscale(img)
# convert Image into numpy array
in_im = asarray(grayimg)
# normalize image
norm_im = self.normalizeImage(in_im)
# set the threshold value
thNorm = double((double(self.thValue)/100.0))
# threshold image with thNorm value
th_im = norm_im < thNorm;
# label image with 8conn
structure = [[1,1,1], [1,1,1], [1,1,1]]
th_im = binary_erosion(~th_im,structure)
label_tuple = label(th_im,structure) #(data, dtype, number of labels)
label_im = label_tuple[0]-1
# wathershed
wh_im = watershed_ift(in_im, label_im)
# convert numpy array into Image
img_out = Image.fromarray(wh_im.astype('uint8'))
''' IMAGE PROCESSING '''
# convert Image into stream
buffer = StringIO.StringIO()
img_out.save(buffer, 'TIFF')
# upload image into Bisque system
buffer.seek(0)
buffer.name = 'file.tif'
fields = { 'file' : buffer }
resp, content = post_files (self.client_server + '/bisquik/upload_images', fields=fields, userpass = self.userpass,)
log.debug("RESP: " + str(content))
self.image_out_url = str(content)
from scipy.cluster.vq import vq, kmeans2, whiten
from scipy.ndimage.measurements import watershed_ift
from scipy.misc.pilutil import imread, imsave
from matplotlib.pyplot import imshow
import color
img = imread("blue.jpg")
markers = imread("bluem2.jpg")
markers = int32(markers[:, :, 1])
markers = threshold(markers, 100, None, 1) # black to 1
print max(markers)
print min(markers)
markers = threshold(markers, None, 200, -1) # white to -1
print max(markers)
print min(markers)
markers = threshold(markers, None, 2, 0) # gray to 0
markers = int8(markers)
mask = watershed_ift(img[:, :, 1], markers)
print mask
mask = mask + 1
print mask
print max(mask)
mask = threshold(mask, None, 1, 1)
print max(mask)
mask = 255 * mask
imsave("mask_watershed.jpg", mask)
a = 1
from scipy.cluster.vq import vq, kmeans2, whiten
from scipy.ndimage.measurements import watershed_ift
from scipy.misc.pilutil import imread, imsave
from matplotlib.pyplot import imshow
import color
img = imread("blue.jpg")
markers = imread("bluem2.jpg")
markers = int32(markers[:, :, 1])
markers = threshold(markers, 100, None, 1) # black to 1
print max(markers)
print min(markers)
markers = threshold(markers, None, 200, -1) # white to -1
print max(markers)
print min(markers)
markers = threshold(markers, None, 2, 0) # gray to 0
markers = int8(markers)
mask = watershed_ift(img[:, :, 1], markers)
print mask
mask = mask + 1
print mask
print max(mask)
mask = threshold(mask, None, 1, 1)
print max(mask)
mask = 255 * mask
imsave("mask_watershed.jpg", mask)
a = 1
