def chip_images():
print("\nStarting to chip the xView dataset.\n")
thechips = []
theclasses = []
thecoords = []
thecoords, thechips, theclasses = get_labels()
per = 1
X_data = []
files2 = glob.glob ("/home/ubuntu/8x8_chips/*.tif")# Change this to ur own directory
files = glob.glob ("/home/ubuntu/downsampling_testing/*.tif")# Change this to ur own directory
for myFile in files:
t = 0
print('\nChipping image at this location: ', myFile)
image = cv2.imread (myFile)
#X_data.append (image) # https://stackoverflow.com/questions/37747021/create-numpy-array-of-images
chipped_img, chipped_box, chipped_classes = wv.chip_image(img = image, coords = thecoords, classes=theclasses, shape=(8, 8))
numberOfChips = chipped_img.shape[0]
print("This image created %d chips." % chipped_img.shape[0])
while t < numberOfChips:
#print(t + 1)
os.chdir(r"/home/ubuntu/8x8_chips") # Change this to ur own directory
mh.imsave('%d.tif' % per, chipped_img[t])
os.chdir(r"/home/ubuntu/") # Change this to ur own directory
t += 1
per += 1
def resample(self, sample_distance_m = 1.5):
'''Resample the image at every 1.5 meters using a nearest-neighbor approach.
This filters out "standing still" portions of each traverse, and makes the
standard deviation convolutions span consistent distances. Save the file
when finished.'''
infilename = self.img_filename
outfilename = self.img_filename_resampled
corobject = GPR_Coords(self.corfilename)
# First, concentrate on the points themselves, from the corfile.
trace_resamples_i = corobject.resample(verbose=False)
# Read data
traces = self.img_data(fignum=1) # Use the de-striped (but not stddev) image
print os.path.split(infilename)[-1], traces.shape, "-->", len(trace_resamples_i)
resampled_traces = numpy.empty([traces.shape[0], len(trace_resamples_i)], dtype=traces.dtype)
# i refers to the new (resampled) trace index
# j refers to the old (original) trace index
for i,old_tracenum in enumerate(trace_resamples_i):
j = old_tracenum - 1
resampled_traces[:,i] = traces[:,j]
print "Writing", os.path.split(outfilename)[-1]
mahotas.imsave(outfilename, resampled_traces)
return
def create_fullContour_labels():
for purpose in ['train','validate','test']:
img_search_string = '/media/vkaynig/Data1/Cmor_paper_data/labels/' + purpose + '/*.tif'
img_files = sorted( glob.glob( img_search_string ) )
for img_index in xrange(np.shape(img_files)[0]):
print 'reading image ' + img_files[img_index] + '.'
label = mahotas.imread(img_files[img_index])
#membranes = np.logical_and(label[:,:,0]==0, label[:,:,1]==255)
boundaries = label == -1
boundaries[0:-1,:] = np.logical_or(boundaries[0:-1,:], np.diff(label, axis=0)!=0)
boundaries[:,0:-1] = np.logical_or(boundaries[:,0:-1], np.diff(label, axis=1)!=0)
membranes = np.logical_or(boundaries, label[:,:]==0)
shrink_radius=5
y,x = np.ogrid[-shrink_radius:shrink_radius+1, -shrink_radius:shrink_radius+1]
disc = x*x + y*y <= (shrink_radius ** 2)
non_membrane = 1-mahotas.dilate(membranes, disc)
img_file_name = os.path.basename(img_files[img_index])[:-4]+ '.tif'
outputPath = '/media/vkaynig/Data1/Cmor_paper_data/labels/'
print 'writing image: ' + img_file_name
mahotas.imsave(outputPath + 'background_fullContour/' + purpose + '/' + img_file_name, np.uint8(non_membrane*255))
mahotas.imsave(outputPath + 'membranes_fullContour/' + purpose + '/' + img_file_name, np.uint8(membranes*255))
def normalize_all_input(img_search_string = '/media/vkaynig/NewVolume/IAE_ISBI2012/images/images/*.tif'):
img_files = sorted( glob.glob( img_search_string ) )
for fileName in img_files:
img = mahotas.imread(fileName)
clahe.clahe(img, img, 2.0)
# img = normalizeImage(img, saturation_level=0.05)
mahotas.imsave(fileName, np.uint8(img))
def create_fullContour_labels():
#img_search_string = '/media/vkaynig/NewVolume/IAE_ISBI2012/fullContours/I*_train.tif'
img_search_string = '/media/vkaynig/NewVolume/Cmor_paper_data/labels/*.tif'
img_files = sorted(glob.glob(img_search_string))
for img_index in xrange(np.shape(img_files)[0]):
print 'reading image ' + img_files[img_index] + '.'
label = mahotas.imread(img_files[img_index])
#membranes = np.logical_and(label[:,:,0]==0, label[:,:,1]==255)
membranes = label[:, :] == 0
shrink_radius = 5
y, x = np.ogrid[-shrink_radius:shrink_radius + 1,
-shrink_radius:shrink_radius + 1]
disc = x * x + y * y <= (shrink_radius**2)
non_membrane = 1 - mahotas.dilate(membranes, disc)
#fileName = "/media/vkaynig/NewVolume/IAE_ISBI2012/fullContours/"
fileName = "/media/vkaynig/NewVolume/Cmor_paper_data/labels/"
fileName_label = fileName + (
img_files[img_index])[52:58] + "_fullContour.tif"
fileName_background = fileName + (
img_files[img_index])[52:58] + "_background.tif"
print 'writing image' + fileName_label
mahotas.imsave(fileName_label, np.uint8(membranes * 255))
mahotas.imsave(fileName_background, np.uint8(non_membrane * 255))
def __init__(self, name):
super(ImageThreshold, self).__init__(name)
self._gray_image = mh.colors.rgb2gray(self._image, dtype=np.uint8)
self._otsu_thresh = 0
self._rc_thresh = 0
self._gaussian_image = None
mh.imsave("threshold-base.jpeg", self._image)
def write_image (output_path, data, image_num=0, downsample=1):
if downsample != 1:
data = np.float32(mahotas.imresize(data, downsample))
maxdata = np.max(data)
mindata = np.min(data)
normdata = (np.float32(data) - mindata) / (maxdata - mindata)
mahotas.imsave(output_path, np.uint16(normdata * 65535))
def view(array,
color=True,
large=False,
crop=False,
text=None,
no_axis=True,
file=''):
if large:
figsize = (10, 10)
else:
figsize = (3, 3)
fig = plt.figure(figsize=figsize)
if crop:
array = mh.croptobbox(array)
if text:
text = '\n\n\n' + str(text)
fig.text(0, 1, text)
if no_axis:
plt.axis('off')
if color:
plt.imshow(Util.colorize(array), picker=True)
iii = Util.colorize(array)
else:
plt.imshow(array, cmap='gray', picker=True)
iii = array
if file != '':
mh.imsave(file, iii.astype(np.uint8))
def write_image(output_path, data, image_num=0, downsample=1):
if downsample != 1:
data = np.float32(mahotas.imresize(data, downsample))
maxdata = np.max(data)
mindata = np.min(data)
normdata = (np.float32(data) - mindata) / (maxdata - mindata)
mahotas.imsave(output_path, np.uint16(normdata * 65535))
def output_image (data, layer, index, unpad_by, image_num=0, downsample=1):
data = data[unpad_by:data.shape[0]-unpad_by,unpad_by:data.shape[1]-unpad_by]
if downsample != 1:
data = np.float32(mahotas.imresize(data, downsample))
maxdata = np.max(data)
mindata = np.min(data)
normdata = (np.float32(data) - mindata) / (maxdata - mindata)
mahotas.imsave(output_path + '/{0:04d}_classify_output_layer{1}_{2}.tif'.format(image_num, layer, index), np.uint16(normdata * 65535))
def processImage(im):
imb = m.overlay(im)
mahotas.imsave("before.png",imb)
print imb.max()
f = FingerProcess()
b3 = f.process(im)
imgout = m.overlay(im,b3)
mahotas.imsave("lol.png", imgout)
def save_image(pathpart1, pathpart2, imagefile):
"""
Method for GUI display, save an image somewhere
:param path: where to save the image
:param imagefile: the actual image
"""
joined_path = join_path(pathpart1, pathpart2)
mahotas.imsave(joined_path, imagefile)
def save_greyscale(path, image):
out = []
for i in range(len(image)):
row = []
for j in range(len(image[i])):
row.append((np.uint8(image[i][j]), np.uint8(image[i][j]), np.uint8(image[i][j])))
out.append(row)
out = np.array(out)
mahotas.imsave(path, out)
def volume_to_folder(data, outp, ext='.tif', typ='float', min_digit=5):
""" Outputs the given volume of data into the path. Extension is 'tif' by default
and output type 'float' """
create_dir(outp)
digits = max(len(str(data.shape[0])), min_digit)
for i in range(0, data.shape[0]):
print('Saving image {}'.format(str(i)))
to_save = data[i, ...].astype(typ)
mh.imsave(os.path.join(outp, str(i).zfill(digits) + ext), to_save)
def normalize_all_input(
img_search_string='/media/vkaynig/NewVolume/IAE_ISBI2012/images/images/*.tif'
):
img_files = sorted(glob.glob(img_search_string))
for fileName in img_files:
img = mahotas.imread(fileName)
clahe.clahe(img, img, 2.0)
# img = normalizeImage(img, saturation_level=0.05)
mahotas.imsave(fileName, np.uint8(img))
def processImage(im):
imb = m.overlay(im)
mahotas.imsave("before.png", imb)
print imb.max()
f = FingerProcess()
b3 = f.process(im)
imgout = m.overlay(im, b3)
mahotas.imsave("lol.png", imgout)
def save_greyscale(path, image):
out = []
for i in range(len(image)):
row = []
for j in range(len(image[i])):
row.append((np.uint8(image[i][j]), np.uint8(image[i][j]),
np.uint8(image[i][j])))
out.append(row)
out = np.array(out)
mahotas.imsave(path, out)
def output_image(data, layer, index, unpad_by, image_num=0, downsample=1):
data = data[unpad_by:data.shape[0] - unpad_by,
unpad_by:data.shape[1] - unpad_by]
if downsample != 1:
data = np.float32(mahotas.imresize(data, downsample))
maxdata = np.max(data)
mindata = np.min(data)
normdata = (np.float32(data) - mindata) / (maxdata - mindata)
mahotas.imsave(
output_path + '/{0:04d}_classify_output_layer{1}_{2}.tif'.format(
image_num, layer, index), np.uint16(normdata * 65535))
def readfile_process_on_click(self):
# Get the path choosed by the user""
self.path = self.pathchooserinput_3.cget('path')
# show the path
if self.path:
tkMessageBox.showinfo('You choosed', str(self.path))
# get the file name and exclude the rest of the path
matchedpattern = [x.start()
for x in re.finditer(str(os.sep), self.path)]
matchedpattern1 = [x.start()
for x in re.finditer(r'[.]', self.path)]
# construct the file name
self.getFIleName = str(
self.path[matchedpattern[-1] + 1: matchedpattern1[-1]])
self.getFIleName = path.join(path.expanduser(
'~'), 'Documents', self.getFIleName)
self.segmentPreview_dir = path.join(
self.getFIleName, 'segment preview')
self.rawimg_dir = path.join(self.getFIleName, 'raw files')
self.getFIleName = self.getFIleName
if os.path.exists(self.getFIleName) is False:
os.makedirs(self.getFIleName)
os.makedirs(self.rawimg_dir)
os.makedirs(self.segmentPreview_dir)
# Read input data files
self.frames, self.timestamp = Filesprocessor.readFile(
self.path, str(self.rawimg_dir), self.progressdialog)
# now display a sample of the input data to the panel
tmp_img = self.frames[0]
resized = cv2.resize(tmp_img, (600, 350))
if os.path.exists(path.join(self.getFIleName, 'displayImg.gif')):
os.remove(path.join(self.getFIleName, 'displayImg.gif'))
mahotas.imsave(path.join(self.getFIleName,
'displayImg.gif'), resized)
# image1 = img2.open(path.join(self.getFIleName, 'displayImg.gif'))
image1 = tk.PhotoImage(
file=str(path.join(self.getFIleName, 'displayImg.gif')))
root.image1 = image1
_ = self.convax1.create_image(300, 185, image=image1)
def save_as_random_color_img(_dataarr, filepath):
rows, cols = _dataarr.shape
re = np.zeros((rows, cols, 3),dtype=np.uint8)
colormap = dict()
colormap[0.0] = [0, 0, 0]
for row in range(rows):
for col in range(cols):
if _dataarr[row][col] in colormap:
re[row, col, :] = colormap[_dataarr[row][col]]
else:
color = [random.randint(0, 255),random.randint(0, 255),random.randint(0, 255)]
re[row, col, :] = color
colormap[_dataarr[row][col]] = color
mahotas.imsave(filepath, re)
def merge_dataset(folder, histories, thresh, outp):
""" Thresholds images by merging all supervoxels up to a given threshold
:param folder: Folder containing superpixel images
:param histories: Folder containing the corresponding merge tree histories
:param thresh: Merge threshold up to which merge regions
:param outp: Folder where to store resulting images
"""
dataio.create_dir(outp)
# Read superpixels and histories
sps = dataio.FileReader(folder).extract_files()
hists = dataio.FileReader(histories, exts=['.txt', '.dat', '.data']).extract_files()
for (s, h) in zip(sps, hists):
img = merge_superpixels(s, h, thresh)
name, ext = os.path.splitext(s)
mh.imsave(os.path.join(outp, os.path.basename(name) + ext), img.astype(float))
def test_upload(request):
file_object = request.FILES['foto']
file_name = file_object.name
nama_gambar = "001_01.jpg"
with open("ladun/data_pengujian/" + nama_gambar, "wb+") as f:
for chunk in file_object.chunks():
f.write(chunk)
img_uji = mahotas.imread("ladun/data_pengujian/" + nama_gambar)
img_uji = img_uji[:, :, 0]
img_uji = mahotas.gaussian_filter(img_uji, 1)
img_uji = (img_uji > img_uji.mean())
radius = 10
mahotas.imsave('ladun/data_zernike/' + nama_gambar, img_uji)
value_zernike = mahotas.features.zernike_moments(img_uji, radius)
value_to_list = value_zernike.tolist()
context = {'nama_file': file_name, 'nilai_zernike': value_to_list}
return JsonResponse(context, safe=False)
def get_chips():
print("\nStarting to chip the xView dataset.\n")
thechips = []
theclasses = []
thecoords = []
thecoords, thechips, theclasses = get_labels()
per = 1
X_data = []
files2 = glob.glob("/media/root/New Volume/chipped_images1/*.tif"
) # Change this to ur own directory
files = glob.glob("/media/root/New Volume/*.tif"
) # Change this to ur own directory
for myFile in files:
t = 0
print('\nChipping image at this location: ', myFile)
image = cv2.imread(myFile)
#X_data.append (image) # https://stackoverflow.com/questions/37747021/create-numpy-array-of-images
chipped_img, chipped_box, chipped_classes = wv.chip_image(
img=image, coords=thecoords, classes=theclasses, shape=(256, 256))
numberOfChips = chipped_img.shape[0]
print("This image created %d chips." % chipped_img.shape[0])
while t < numberOfChips:
#print(t + 1)
os.chdir(r"/media/root/New Volume/chipped_images1"
) # Change this to ur own directory
mh.imsave('%d.tif' % per, chipped_img[t])
os.chdir(r"/media/root/New Volume"
) # Change this to ur own directory
t += 1
per += 1
os.chdir(r"/media/root/New Volume/chipped_images1"
) # Change this to ur own directory
for myFile in files2:
chipimage = mh.imread(myFile)
X_data.append(chipimage)
npchipped = np.array(
[np.array(Image.open(myFile)) for myFile in files2]
) ### This puts all of the images in to one nparray, ( i think the block of code above does the same thing)
# https://stackoverflow.com/questions/39195113/how-to-load-multiple-images-in-a-numpy-array
npchipped2 = np.array(
X_data) # nchipped2 is the numpy array, I use it down below
#npchipped and npchipped are the same
return npchipped2, numberOfChips
def subtract_horizontal_mean_transform(self):
'''This helps "destripe" the image from horizontal stripes that still exist
after the REFLEXW de-wow filter is applied. It is important to get rid of these
stripes in order not to have them affect our measurements. Problem is, this
"smooths out" sections of the data that would otherwise be noisy enough to NOT
be ice lenses, such as the surface snow. What combination of filters needs to be
applied to detect both? However, for now, keep it a simple mean subtraction.
This function takes the base image, subtracts the mean, and saves it as a secondary processed image.'''
self.open_image()
trace_means = numpy.mean(self.traces,axis=1)
traces_mean_corrected = numpy.copy(self.traces)
for i in range(self.traces.shape[0]):
traces_mean_corrected[i,:] = self.traces[i,:] - trace_means[i]
print "Saving", os.path.split(self.img_filename_2)[1]
mahotas.imsave(self.img_filename_2, traces_mean_corrected)
def create_membrane_and_background_images():
for purpose in ['train', 'validate', 'test']:
#img_search_string = '/media/vkaynig/NewVolume/IAE_ISBI2012/ground_truth/' + purpose + '/*.tif'
img_search_string = '/media/vkaynig/NewVolume/Cmor_paper_data/labels/' + purpose + '/*.tif'
# img_gray_search_string = '/media/vkaynig/NewVolume/IAE_ISBI2012/images/' + purpose + '/*.tif'
# img_gray_search_string = '/media/vkaynig/NewVolume/Cmor_paper_data/images/' + purpose + '/*.tif'
img_files = sorted(glob.glob(img_search_string))
# img_gray_files = sorted( glob.glob( img_gray_search_string ) )
for img_index in xrange(np.shape(img_files)[0]):
print 'reading image ' + img_files[img_index] + '.'
label_img = mahotas.imread(img_files[img_index])
# gray_img = mahotas.imread(img_gray_files[img_index])
boundaries = label_img == 0
# boundaries = label_img == -1
boundaries[0:-1, :] = np.logical_or(
boundaries[0:-1, :],
np.diff(label_img, axis=0) != 0)
boundaries[:,
0:-1] = np.logical_or(boundaries[:, 0:-1],
np.diff(label_img, axis=1) != 0)
boundaries = 1 - boundaries
shrink_radius = 1
y, x = np.ogrid[-shrink_radius:shrink_radius + 1,
-shrink_radius:shrink_radius + 1]
disc = x * x + y * y <= (shrink_radius**2)
background = mahotas.erode(boundaries, disc) + 1
membranes = 1 - background
img_file_name = os.path.basename(img_files[img_index])
#outputPath = '/media/vkaynig/NewVolume/IAE_ISBI2012/labels/'
outputPath = '/media/vkaynig/NewVolume/Cmor_paper_data/labels/'
print 'writing image' + img_file_name
mahotas.imsave(
outputPath + 'background/' + purpose + '/' + img_file_name,
np.uint8(background * 255))
mahotas.imsave(
outputPath + 'membranes/' + purpose + '/' + img_file_name,
np.uint8(membranes * 255))
def save_as_image(patch):
figsize = (2, 2)
for s in patch.keys():
if type(patch[s]) == type(np.zeros((1, 1))) and patch[s].ndim == 2:
# fig = plt.figure(figsize=figsize)
# plt.imshow(patch[s], cmap='gray')
# plt.savefig('/tmp/'+s+'.png')
if patch[s].dtype == np.bool or s == 'dyn_obj':
mh.imsave('/tmp/' + s + '.tif',
(patch[s] * 255).astype(np.uint8))
else:
mh.imsave('/tmp/' + s + '.tif',
(patch[s]).astype(np.uint8))
def file_writer(filename,
signal,
_rescale=True,
file_format='tif',
only_view=False,
**kwds):
'''Writes data to any format supported by PIL or freeimage if mahotas is
installed
Note that only when mahotas and freeimage are installed it is possible
to write 16-bit tiff files.
Parameters
----------
filename: str
signal: a Signal instance
rescale: bool
Rescales the data to use the full dynamic range available in the
chosen encoding. Note that this operation (obviously) affects the
scale of the data what might not always be a good idea
file_format : str
The fileformat defined by its extension that is any one supported by
PIL of mahotas if installed.
'''
if only_view is True and signal.axes_manager.signal_dimension == 2:
dc = signal()
elif only_view is False and len(signal.data.squeeze().shape) == 2:
dc = signal.data.squeeze()
else:
raise IOError("This format also support writing of 2D data")
if file_format in ('tif', 'tiff') and mahotas_installed is True:
bits = 16
else:
# Only tiff supports 16-bits
bits = 8
if _rescale is True:
dc = rescale(dc, bits)
imsave(filename, dc.astype('uint%s' % bits))
print "Image saved"
def process(self, im):
#single pixel restructure element
elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]])
print "starting img process.. binarizing"
b1 = im > 205 #binarize
print "removing single pixels"
#remove single pixels
singpix = mahotas.morph.hitmiss(b1, elem)
b1 = (b1 - singpix) > 0
print "closing holes"
b1 = m.close_holes(b1)
print "thinning"
#b2 = m.thin(b1) #thin
b2 = self.shitthin(b1) #thin
print "pruning"
b3 = m.thin(b2, m.endpoints('homotopic'), 8)
#remove single pixels
singpix = mahotas.morph.hitmiss(b3, elem)
b3 = b3 - singpix
b3 = b3 > 0
struct = np.ndarray((4, 3, 3), dtype=np.uint8)
struct[0, :, :] = [[1, 2, 1], [0, 1, 0], [0, 1, 0]]
for i in range(1, 4):
print "gen %i structure.." % (i)
struct[i, :, :] = np.rot90(struct[0], i)
#struct = struct == 1
print "using struct for branch summing:"
print struct
b4 = np.zeros((301, 398), dtype=bool)
for i in range(0, 4):
b4 = b4 | mahotas.morph.hitmiss(b3, struct[i])
b4 = b4 > 0
imgout = m.overlay(b1, b2, b4)
mahotas.imsave("thresh.png", imgout)
return b4
def process(self, im):
# single pixel restructure element
elem = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]])
print "starting img process.. binarizing"
b1 = im > 205 # binarize
print "removing single pixels"
# remove single pixels
singpix = mahotas.morph.hitmiss(b1, elem)
b1 = (b1 - singpix) > 0
print "closing holes"
b1 = m.close_holes(b1)
print "thinning"
# b2 = m.thin(b1) #thin
b2 = self.shitthin(b1) # thin
print "pruning"
b3 = m.thin(b2, m.endpoints("homotopic"), 8)
# remove single pixels
singpix = mahotas.morph.hitmiss(b3, elem)
b3 = b3 - singpix
b3 = b3 > 0
struct = np.ndarray((4, 3, 3), dtype=np.uint8)
struct[0, :, :] = [[1, 2, 1], [0, 1, 0], [0, 1, 0]]
for i in range(1, 4):
print "gen %i structure.." % (i)
struct[i, :, :] = np.rot90(struct[0], i)
# struct = struct == 1
print "using struct for branch summing:"
print struct
b4 = np.zeros((301, 398), dtype=bool)
for i in range(0, 4):
b4 = b4 | mahotas.morph.hitmiss(b3, struct[i])
b4 = b4 > 0
imgout = m.overlay(b1, b2, b4)
mahotas.imsave("thresh.png", imgout)
return b4
def crnnRec(model, converter, im, text_recs):
index = 0
for rec in text_recs:
pt1 = (rec[0], rec[1])
pt2 = (rec[2], rec[3])
pt3 = (rec[6], rec[7])
pt4 = (rec[4], rec[5])
partImg = dumpRotateImage(
im, degrees(atan2(pt2[1] - pt1[1], pt2[0] - pt1[0])), pt1, pt2,
pt3, pt4)
mahotas.imsave('%s.jpg' % index, partImg)
image = Image.fromarray(partImg).convert('L')
#height,width,channel=partImg.shape[:3]
#print(height,width,channel)
#print(image.size)
#image = Image.open('./img/t4.jpg').convert('L')
scale = image.size[1] * 1.0 / 32
w = image.size[0] / scale
w = int(w)
#print(w)
transformer = dataset.resizeNormalize((w, 32))
image = transformer(image).cuda()
image = image.view(1, *image.size())
image = Variable(image)
model.eval()
preds = model(image)
_, preds = preds.max(2)
preds = preds.squeeze(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
preds_size = Variable(torch.IntTensor([preds.size(0)]))
raw_pred = converter.decode(preds.data, preds_size.data, raw=True)
sim_pred = converter.decode(preds.data, preds_size.data, raw=False)
#print('%-20s => %-20s' % (raw_pred, sim_pred))
print index
print sim_pred.encode('utf-8')
index = index + 1
def compute_spot_stats(image, target, directory):
if image.dtype != np.uint8:
channel_mins = image.min(axis=0).min(axis=0)[np.newaxis, np.newaxis, :]
channel_maxs = image.max(axis=0).max(axis=0)[np.newaxis, np.newaxis, :]
image = ((image.astype(float) - channel_mins) * 255 /
(channel_maxs - channel_mins)).astype(np.uint8)
v = viewer.ImageViewer(image)
v += CentroPlugin()
overlay = v.show()[0][0]
overlay = seg.relabel_sequential(overlay)[0]
mask = (overlay == 1)
objects = nd.label(mask)[0]
property_names = (['size', 'mean'] +
['quantile-%i' % i for i in [5, 25, 50, 75, 95]])
props = [np.concatenate(([prop.area, prop.mean_intensity], prop.quantiles))
for prop in measure.regionprops(objects, intensity_image=target)]
props = np.array(props)
fout_txt = os.path.join(directory, 'measure.txt')
np.savetxt(fout_txt, props, fmt='%.2f', delimiter='\t',
header='\t'.join(property_names))
fout_im = os.path.join(directory, 'mask.png')
mh.imsave(fout_im, 64 * overlay.astype(np.uint8))
def seg_display(self, prev_image):
tmp_pre, segmentationPanel = [], []
if os.path.exists(path.join(self.segmentPreview_dir, 'SegImage.gif')):
os.remove(path.join(self.segmentPreview_dir, 'SegImage.gif'))
mahotas.imsave(path.join(self.segmentPreview_dir,
'frame0.png'), prev_image)
r = 550.0 / prev_image.shape[1]
dim = (550, int(prev_image.shape[0] * r))
# perform the actual resizing of the image and show it
prev_image = cv2.resize(prev_image, dim, interpolation=cv2.INTER_AREA)
resized = cv2.resize(prev_image, (600, 350))
mahotas.imsave(
path.join(self.segmentPreview_dir, 'SegImage.gif'), resized)
tmp_pre = tk.PhotoImage(
file=str(path.join(self.segmentPreview_dir, 'SegImage.gif')))
root.tmp_pre = tmp_pre
_ = self.preconvax.create_image(283, 182, image=tmp_pre)
def file_writer(filename, signal, _rescale = True, file_format='tif',
only_view = False, **kwds):
'''Writes data to any format supported by PIL or freeimage if mahotas is
installed
Note that only when mahotas and freeimage are installed it is possible
to write 16-bit tiff files.
Parameters
----------
filename: str
signal: a Signal instance
rescale: bool
Rescales the data to use the full dynamic range available in the
chosen encoding. Note that this operation (obviously) affects the
scale of the data what might not always be a good idea
file_format : str
The fileformat defined by its extension that is any one supported by
PIL of mahotas if installed.
'''
if only_view is True and signal.axes_manager.signal_dimension == 2:
dc = signal()
elif only_view is False and len(signal.data.squeeze().shape) == 2:
dc = signal.data.squeeze()
else:
raise IOError("This format also support writing of 2D data")
if file_format in ('tif', 'tiff') and mahotas_installed is True:
bits = 16
else:
# Only tiff supports 16-bits
bits = 8
if _rescale is True:
dc = rescale(dc, bits)
imsave(filename, dc.astype('uint%s' % bits))
print "Image saved"
def create_membrane_and_background_images():
for purpose in ['train','validate','test']:
#img_search_string = '/media/vkaynig/NewVolume/IAE_ISBI2012/ground_truth/' + purpose + '/*.tif'
img_search_string = '/media/vkaynig/Data1/Cmor_paper_data/labels/' + purpose + '/*.tif'
# img_gray_search_string = '/media/vkaynig/NewVolume/IAE_ISBI2012/images/' + purpose + '/*.tif'
# img_gray_search_string = '/media/vkaynig/NewVolume/Cmor_paper_data/images/' + purpose + '/*.tif'
img_files = sorted( glob.glob( img_search_string ) )
# img_gray_files = sorted( glob.glob( img_gray_search_string ) )
for img_index in xrange(np.shape(img_files)[0]):
print 'reading image ' + img_files[img_index] + '.'
label_img = mahotas.imread(img_files[img_index])
# gray_img = mahotas.imread(img_gray_files[img_index])
#boundaries = label_img==0
boundaries = label_img == -1
boundaries[0:-1,:] = np.logical_or(boundaries[0:-1,:], np.diff(label_img, axis=0)!=0)
boundaries[:,0:-1] = np.logical_or(boundaries[:,0:-1], np.diff(label_img, axis=1)!=0)
boundaries = 1-boundaries
shrink_radius=10
y,x = np.ogrid[-shrink_radius:shrink_radius+1, -shrink_radius:shrink_radius+1]
disc = x*x + y*y <= (shrink_radius ** 2)
background = boundaries
membranes = 1-background
#membranes = boundaries
background = 1-(mahotas.erode(boundaries, disc) + 1)
img_file_name = os.path.basename(img_files[img_index])
#outputPath = '/media/vkaynig/NewVolume/IAE_ISBI2012/labels/'
outputPath = '/media/vkaynig/Data1/Cmor_paper_data/labels/'
print 'writing image' + img_file_name
mahotas.imsave(outputPath + 'background_nonDilate/' + purpose + '/' + img_file_name, np.uint8(background*255))
mahotas.imsave(outputPath + 'membranes_nonDilate/' + purpose + '/' + img_file_name, np.uint8(membranes*255))
def proses_uji(request):
dataImg = request.POST.get("citraData")
format, imgstr = dataImg.split(";base64,")
dataDecode = ContentFile(base64.b64decode(imgstr))
# imgdata = base64.b64decode(dataImg)
imgRandom = get_random_string(10)
nama_gambar = imgRandom + ".png"
now = datetime.datetime.now()
with open("ladun/data_pengujian/" + nama_gambar, "wb+") as f:
for chunk in dataDecode.chunks():
f.write(chunk)
# start perhitungan zernike
data_secret = lsb.reveal("ladun/data_pengujian/" + nama_gambar)
img_uji = mahotas.imread("ladun/data_pengujian/" + nama_gambar)
img_uji = img_uji[:, :, 0]
img_uji = mahotas.gaussian_filter(img_uji, 1)
img_uji = (img_uji > img_uji.mean())
radius = 10
mahotas.imsave('ladun/data_zernike/' + nama_gambar, img_uji)
value_zernike = mahotas.features.zernike_moments(img_uji, radius)
value_to_list = value_zernike.tolist()
citra_save = Pengujian_Citra.objects.create(
kd_uji=imgRandom,
nama_pengujian='Pengujian Citra',
waktu_pengujian=now,
base_svm_final='0',
hasil_final=data_secret)
citra_save.save()
context = {
'status': 'sukses',
'dataCitra': nama_gambar,
'zernikeValue': value_to_list,
'secret': data_secret
}
return JsonResponse(context, safe=False)
def test_zernike(request):
file_gambar = 'naskhi.png'
img = mahotas.imread('ladun/data_latih/' + file_gambar)
img = img[:, :, 0]
img = mahotas.gaussian_filter(img, 1)
img = (img > img.mean())
kd = "004"
# radius
radius = 10
mahotas.imsave('ladun/data_zernike/' + file_gambar, img)
# computing zernike moments
value_zernike = mahotas.features.zernike_moments(img, radius)
value_to_list = value_zernike.tolist()
panjang = len(value_to_list)
awal = 0
no = 1
for x in value_to_list:
awal = awal + x
d = Nilai_Data_Latih.objects.create(kd_class=kd, node=no, value=awal)
d.save()
no += 1
context = {'status': value_to_list, 'panjang': panjang, 'total': awal}
return JsonResponse(context, safe=False)
def thresholding():
img = cv2.imread(path + '.jpg', 0)
ret, th1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
cv2.THRESH_BINARY,55,4)
th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
cv2.THRESH_BINARY,55,4)
mahotas.imsave(path + '_Binary.jpg', th1)
enhance_black(path + '_Binary')
mahotas.imsave(path + '_Mean.jpg', th2)
enhance_black(path + '_Mean')
mahotas.imsave(path + '_Guassian.jpg', th3)
enhance_black(path + '_Guassian')
def watershed(img):
# Diminui ruidos
imgf = ndimage.gaussian_filter(img, 16)
mahotas.imsave("dnaf.jpeg", imgf)
rmax = pymorph.regmax(imgf)
T = mahotas.thresholding.otsu(imgf)
dist = ndimage.distance_transform_edt(imgf > T)
dist = dist.max() - dist
dist -= dist.min()
dist = dist / float(dist.ptp()) * 255
dist = dist.astype(np.uint8)
mahotas.imsave("dist.jpeg", dist)
seeds, nr_nuclei = ndimage.label(rmax)
nuclei = pymorph.cwatershed(dist, seeds)
mahotas.imsave("watershed.jpeg", nuclei)
img = cv2.cvtColor(img, cv2.cv.CV_BGR2GRAY)
img /= 255
mask = cv2.imread("mask.png")
mask = cv2.cvtColor(mask, cv2.cv.CV_BGR2GRAY)
mask /= 255
print_img(img)
print_img(mask)
new_img = geodesic_dilation(img, mask) * 255
cv2.imwrite("./geodesic_dilation.png", new_img)
# Reconstrucao geodesica
f = np.asarray([0, 0, 1, 3, 3, 7, 7, 7, 7, 5, 2, 1, 1])
g = np.asarray([0, 0, 1, 2, 2, 2, 5, 2, 2, 2, 2, 1, 1])
geodesic_reconstruction_1d(f, g)
# watershed
img = mahotas.imread("dna.jpeg", as_grey=True)
img = img.astype(np.uint8)
# watershed(img)
# skeleton by influence zone (skiz)
img = mahotas.imread("bla.jpeg", as_grey=True)
img = img.astype(np.bool)
skiz = pymorph.skiz(img)
skiz *= 255 / skiz.max()
mahotas.imsave("skiz.png", skiz)
pylab.imshow(skiz)
pylab.show()
print '('+str(k)+', '+str(score)+')'
plt.show()
"""
heatmap=[]
x=0
for k in range(15,25):
for i in range(100):
heatmap.append([])
for pad in range(0, 10):
train_data=knn_trainer_helper.sortClasses(pad,trainPoints, trainimages, 1)
test_data=knn_trainer_helper.sortClasses(pad,testPoints, testimages, 1)
knn = sklearn.neighbors.KNeighborsClassifier(n_neighbors=k, weights='uniform', algorithm='ball_tree')
knn.fit(train_data[0], train_data[1])
test_predict = knn.predict(test_data[0])
score = sklearn.metrics.accuracy_score(test_data[1], test_predict, normalize=True)
for i in range(100):
for j in range(100):
heatmap[x*100+j].append(score*255)
print '('+str(k)+', '+str(pad)+', '+str(score)+")"
x+=1
plt.gray()
plt.imshow(heatmap)
plt.show()
map = np.matrix(heatmap, dtype=np.uint8, copy=True)
mahotas.imsave('results/heatmap.tif', map)
def test_as_grey():
colour = np.arange(16*16*3).reshape((16,16,3))
imsave(_testimgname, colour.astype(np.uint8))
c2 = imread(_testimgname, as_grey=True)
assert len(c2.shape) == 2
assert c2.shape == colour.shape[:-1]
from matplotlib import pyplot as plt
import numpy as np
import mahotas as mh
image = mh.imread('../1400OS_10_01.jpeg')
image = mh.colors.rgb2gray(image)
im8 = mh.gaussian_filter(image,8)
im16 = mh.gaussian_filter(image,16)
im32 = mh.gaussian_filter(image,32)
h,w = im8.shape
canvas = np.ones((h,3*w+256), np.uint8)
canvas *= 255
canvas[:,:w] = im8
canvas[:,w+128:2*w+128] = im16
canvas[:,-w:] = im32
mh.imsave('../1400OS_10_05+.jpg', canvas[:,::2])
im32 = mh.stretch(im32)
ot32 = mh.otsu(im32)
mh.imsave('../1400OS_10_06+.jpg', (im32 > ot32).astype(np.uint8)*255)
import mahotas as mh
from jugfile import method1
from matplotlib import cm
import numpy as np
im = mh.imread('images/dna-21.jpg')
mh.imsave('image_stretched.jpeg', mh.stretch(im.astype(float)**.01))
m1 = method1.f('images/dna-21.jpg', 2)
m1 = m1.astype(np.uint8)
color = ((cm.rainbow(m1.astype(float)/m1.max())[:,:,:3]).reshape(m1.shape+(3,)))
color[m1 == 0] = (0,0,0)
mh.imsave('image_method1.jpeg', mh.stretch(color))
ref = mh.imread('references/dna-21.png')
color = ((cm.rainbow(ref.astype(float)/ref.max())[:,:,:3]).reshape(m1.shape+(3,)))
color[ref == 0] = (0,0,0)
mh.imsave('image_reference.jpeg', mh.stretch(color))
for ele in range(0, 4):
b = b | mahotas.morph.hitmiss(b, structleft[ele])
b = b | mahotas.morph.hitmiss(b, structright[ele])
print (np.all(lastFrame == b))
if np.all(lastFrame == b) == True:
break
lastFrame = np.copy(b).astype(bool)
ct += 1
return lastFrame > 0
def kmeans(self, img, maxiter):
X = img
thresh = X.mean()
for iter in range(maxiter):
thresh = (X[X < thresh].mean() + X[X >= thresh].mean()) / 2.0
X[X < thresh] = 0
X[X >= thresh] = 255
return X == 255
if __name__ == "__main__":
im = mahotas.imread("before.png")
im = im[:, :, 0]
f = FingerProcess()
b4 = f.process(im)
imgout = m.overlay(im, b4)
mahotas.imsave("lol.png", imgout)
import numpy as np
import mahotas as mh
image = mh.imread('../SimpleImageDataset/building05.jpg')
image = mh.colors.rgb2gray(image)
# Compute Gaussian filtered versions with increasing kernel widths
im8 = mh.gaussian_filter(image, 8)
im16 = mh.gaussian_filter(image, 16)
im32 = mh.gaussian_filter(image, 32)
# We now build a composite image with three panels:
#
# [ IM8 | | IM16 | | IM32 ]
h, w = im8.shape
canvas = np.ones((h, 3 * w + 256), np.uint8)
canvas *= 255
canvas[:, :w] = im8
canvas[:, w + 128:2 * w + 128] = im16
canvas[:, -w:] = im32
mh.imsave('../1400OS_10_05+.jpg', canvas[:, ::2])
# Threshold the image
# We need to first stretch it to convert to an integer image
im32 = mh.stretch(im32)
ot32 = mh.otsu(im32)
# Convert to 255 np.uint8 to match the other images
im255 = 255 * (im32 > ot32).astype(np.uint8)
mh.imsave('../1400OS_10_06+.jpg', im255)
# This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
import numpy as np
import mahotas as mh
# This little script just builds an image with two examples, side-by-side:
text = mh.imread("simple-dataset/text21.jpg")
scene = mh.imread("simple-dataset/scene00.jpg")
h, w, _ = text.shape
canvas = np.zeros((h, 2 * w + 128, 3), np.uint8)
canvas[:, -w:] = scene
canvas[:, :w] = text
canvas = canvas[::4, ::4]
mh.imsave('../1400OS_10_10+.jpg', canvas)
import mahotas as mh
import numpy as np
im = mh.imread('lenna.jpg')
r,g,b = im.transpose(2,0,1)
h,w = r.shape
r12 = mh.gaussian_filter(r, 12.)
g12 = mh.gaussian_filter(g, 12.)
b12 = mh.gaussian_filter(b, 12.)
im12 = mh.as_rgb(r12,g12,b12)
X,Y = np.mgrid[:h,:w]
X = X-h/2.
Y = Y-w/2.
X /= X.max()
Y /= Y.max()
C = np.exp(-2.*(X**2+ Y**2))
C -= C.min()
C /= C.ptp()
C = C[:,:,None]
ring = mh.stretch(im*C + (1-C)*im12)
mh.imsave('lenna-ring.jpg', ring)
if Debug:
ncolors = 10000
np.random.seed(7)
color_map = np.uint8(np.random.randint(0,256,(ncolors+1)*3)).reshape((ncolors + 1, 3))
import mahotas
# output full block images
# for image_i in range(block1.shape[2]):
# mahotas.imsave('block1_z{0:04}.tif'.format(image_i), color_map[block1[:, :, image_i] % ncolors])
# mahotas.imsave('block2_z{0:04}.tif'.format(image_i), color_map[block2[:, :, image_i] % ncolors])
#output overlap images
if single_image_matching:
mahotas.imsave('packed_overlap1.tif', color_map[np.squeeze(inverse[packed_overlap1]) % ncolors])
mahotas.imsave('packed_overlap2.tif', color_map[np.squeeze(inverse[packed_overlap2]) % ncolors])
else:
debug_out1 = b = np.rollaxis(packed_overlap1, direction, 3)
debug_out2 = b = np.rollaxis(packed_overlap2, direction, 3)
for image_i in range(debug_out1.shape[2]):
mahotas.imsave('packed_overlap1_z{0:04}.tif'.format(image_i), color_map[inverse[debug_out1[:, :, image_i]] % ncolors])
mahotas.imsave('packed_overlap2_z{0:04}.tif'.format(image_i), color_map[inverse[debug_out2[:, :, image_i]] % ncolors])
# import pylab
# pylab.figure()
# pylab.imshow(block1[0, :, :] % 13)
# pylab.title('block1')
# pylab.figure()
# pylab.imshow(block2[0, :, :] % 13)
# pylab.title('block2')
test_i = test_i + 1
sample_type = 'test'
#print "Sampled {5} at {0}, {1}, {2}, r{3:.2f} ({4})".format(samp_i, samp_j, imgi, rotation, sample_type, membrane_type)
sample_count = sample_count + 1
if sample_count % 5000 == 0:
print "{0} samples ({1}, {2}, {3}).".format(sample_count, train_i, valid_i, test_i)
print "Made a total of {0} samples ({1}, {2}, {3}).".format(sample_count, train_i, valid_i, test_i)
if PNG_OUTPUT:
outdir = "LGN1_MembraneSamples_{0}x{0}x{1}_mp{2:0.2f}\\train\\".format(imgd, zd, membrane_proportion)
if not os.path.exists(outdir): os.makedirs(outdir)
for imgi in range(ntrain):
mahotas.imsave(outdir + "{0:08d}_{1}.png".format(imgi, train_set[1][imgi]), train_set[0][imgi,:].reshape((imgd,imgd)))
outdir = "LGN1_MembraneSamples_{0}x{0}x{1}_mp{2:0.2f}\\valid\\".format(imgd, zd, membrane_proportion)
if not os.path.exists(outdir): os.makedirs(outdir)
for imgi in range(nvalid):
mahotas.imsave(outdir + "{0:08d}_{1}.png".format(imgi, valid_set[1][imgi]), valid_set[0][imgi,:].reshape((imgd,imgd)))
outdir = "LGN1_MembraneSamples_{0}x{0}x{1}_mp{2:0.2f}\\test\\".format(imgd, zd, membrane_proportion)
if not os.path.exists(outdir): os.makedirs(outdir)
for imgi in range(ntest):
mahotas.imsave(outdir + "{0:08d}_{1}.png".format(imgi, test_set[1][imgi]), test_set[0][imgi,:].reshape((imgd,imgd)))
if DOWNSAMPLE_BY != 1:
ds_string = '_ds{0}b'.format(DOWNSAMPLE_BY)
else:
ds_string = ''
os.unlink(output_file)
os.rename(temp_path, output_file)
if Debug:
output_image_basename = output_file + '_debug_output'
# for classi in range(model.nclass):
# output_image_file = output_image_basename + '_class{0}.png'.format(classi + 1)
# mahotas.imsave(output_image_file, np.uint8(prob_image[:,:,classi] * 255))
if prob_image.shape[2] == 3:
output_image_file = output_image_basename + '_allclass.png'
mahotas.imsave(output_image_file, np.uint8(prob_image * 255))
win_0 = np.logical_and(prob_image[:,:,0] > prob_image[:,:,1], prob_image[:,:,0] > prob_image[:,:,2])
win_2 = np.logical_and(prob_image[:,:,2] > prob_image[:,:,0], prob_image[:,:,2] > prob_image[:,:,1])
win_1 = np.logical_not(np.logical_or(win_0, win_2))
win_image = prob_image
win_image[:,:,0] = win_0 * 255
win_image[:,:,1] = win_1 * 255
win_image[:,:,2] = win_2 * 255
output_image_file = output_image_basename + '_winclass.png'
mahotas.imsave(output_image_file, np.uint8(win_image))
elif prob_image.shape[2] == 2:
#
# It is made available under the MIT License
import numpy as np
import mahotas as mh
# Load our example image:
image = mh.imread('../SimpleImageDataset/building05.jpg')
# Convert to greyscale
image = mh.colors.rgb2gray(image, dtype=np.uint8)
# Compute a threshold value:
thresh = mh.thresholding.otsu(image)
print('Otsu threshold is {0}'.format(thresh))
# Compute the thresholded image
otsubin = (image > thresh)
print('Saving thresholded image (with Otsu threshold) to otsu-threshold.jpeg')
mh.imsave('otsu-threshold.jpeg', otsubin.astype(np.uint8) * 255)
# Execute morphological opening to smooth out the edges
otsubin = mh.open(otsubin, np.ones((15, 15)))
mh.imsave('otsu-closed.jpeg', otsubin.astype(np.uint8) * 255)
# An alternative thresholding method:
thresh = mh.thresholding.rc(image)
print('Ridley-Calvard threshold is {0}'.format(thresh))
print('Saving thresholded image (with Ridley-Calvard threshold) to rc-threshold.jpeg')
mh.imsave('rc-threshold.jpeg', (image > thresh).astype(np.uint8) * 255)
boundaries = output_ids==1
for ci in range(3):
overlay_colors[:,:,ci][boundaries] = 128
current_image_f = np.float32(mahotas.imread(input_image_path)[:output_size, :output_size])
if len(current_image_f.shape) == 3:
current_image_f = current_image_f[:,:,0]
overlay_colors[:,:,0] = (1-alpha) * overlay_colors[:,:,0] + alpha * current_image_f
overlay_colors[:,:,1] = (1-alpha) * overlay_colors[:,:,1] + alpha * current_image_f
overlay_colors[:,:,2] = (1-alpha) * overlay_colors[:,:,2] + alpha * current_image_f
mahotas.imsave(output_path.replace('.', '_partial.', 1), overlay_colors)
shutil.move(output_path.replace('.', '_partial.', 1), output_path)
# tif = TIFF.open(output_path, mode='w')
# tif.write_image(overlay_colors, compression='lzw')
except IOError as e:
print "I/O error({0}): {1}".format(e.errno, e.strerror)
except KeyboardInterrupt:
pass
# except:
# print "Unexpected error:", sys.exc_info()[0]
# if repeat_attempt_i == job_repeat_attempts:
# raise
# Save / display results
votes = d_votes.get()
prob_image = np.float32(votes) / ntree
output_image_basename = file.replace(input_image_folder, output_folder)
# for classi in range(nclass):
# output_image_file = output_image_basename.replace(input_image_suffix, '_class{0}.png'.format(classi + 1))
# mahotas.imsave(output_image_file, np.uint8(prob_image[:,:,classi] * 255))
output_image_file = output_image_basename.replace(input_image_suffix,
'_allclass.png')
mahotas.imsave(output_image_file, np.uint8(prob_image * 255))
win_0 = np.logical_and(prob_image[:, :, 0] > prob_image[:, :, 1],
prob_image[:, :, 0] > prob_image[:, :, 2])
win_2 = np.logical_and(prob_image[:, :, 2] > prob_image[:, :, 0],
prob_image[:, :, 2] > prob_image[:, :, 1])
win_1 = np.logical_not(np.logical_or(win_0, win_2))
win_image = prob_image
win_image[:, :, 0] = win_0 * 255
win_image[:, :, 1] = win_1 * 255
win_image[:, :, 2] = win_2 * 255
output_image_file = output_image_basename.replace(input_image_suffix,
'_winclass.png')
mahotas.imsave(output_image_file, np.uint8(win_image))
extra_cellular_id = np.max(seeds)+1
seeds[extra_cellular_indices] = extra_cellular_id
with timer.Timer("second watershed"):
ws = mahotas.cwatershed(blur_prob, seeds)
dx, dy = np.gradient(ws)
ws_boundary = np.logical_or(dx!=0, dy!=0)
## Optional - mark the extra-cellular space as boundary
#ws_boundary[np.nonzero(ws == extra_cellular_id)] = 1
segmentations[:,:,segmentation_count] = ws_boundary > 0
if Debug:
mahotas.imsave(output_path + '.seg_{0}.png'.format(segmentation_count), np.uint8(segmentations[:,:,segmentation_count] * 255))
segmentation_count = segmentation_count + 1
print "Segmentation {0} produced aftert {1} seconds".format(segmentation_count, int(time.time() - main_st))
sys.stdout.flush()
# move to final destination
out_hdf5.close()
# move to final location
if os.path.exists(output_path):
os.unlink(output_path)
os.rename(temp_path, output_path)
print "Success"
except Exception as e:
print 'Running time: ', total_time / 60.
print 'finished sampling data'
return data_set
if __name__=="__main__":
import uuid
test = generate_experiment_data_patch_prediction(purpose='train', nsamples=30, patchSize=572, outPatchSize=388)
dir_path = './training_patches/'
for i in xrange(30):
unique_filename = str(uuid.uuid4())
img = np.reshape(test[1][i],(388,388))
img_gray = np.reshape(test[0][i],(572,572))
mahotas.imsave(dir_path+unique_filename+'.tif', np.uint8(img*255))
mahotas.imsave(dir_path+unique_filename+'_gray.tif', np.uint8((img_gray+0.5)*255))
#data_val = generate_experiment_data_supervised(purpose='validate', nsamples=10000, patchSize=65, balanceRate=0.5)
#data = generate_experiment_data_patch_prediction(purpose='validate', nsamples=2, patchSize=315, outPatchSize=215)
# plt.imshow(np.reshape(data[0][0],(315,315))); plt.figure()
# plt.imshow(np.reshape(data[1][0],(215,215))); plt.figure()
# plt.imshow(np.reshape(data[2][0],(215,215))); plt.show()
# image = mahotas.imread('ac3_input_0141.tif')
# image = normalizeImage(image)
# label = mahotas.imread('ac3_labels_0141.tif') / 255.
# test = adjust_imprecise_boundaries(image, label, 10)
# plt.imshow(label+image); plt.show()
#plt.title('normal')
#plt.show()
pca = PCA().fit(train_data)
variance=pca.explained_variance_ratio_
sum = 0
i=0
accuracy=.995
while sum<=accuracy:
sum+=variance[i]
i+=1
print i
#plt.ylim(ymax=.05)
#plt.bar(np.arange(0,49**2),variance)
#plt.ylabel('variance ratio')
#plt.xlabel('components')
#plt.show()
pca = PCA(n_components=i)
fitted = pca.fit_transform(train_data)
patch=PCA.inverse_transform(pca,fitted)[0]
mahotas.imsave('results/pca images/variance_sum_'+str(accuracy)+'.tif',np.uint16np.array(patch).reshape(401,401))
plt.title(str(i))
plt.imshow(np.array(patch).reshape(401,401))
plt.show()
if __name__ == '__main__':
import mahotas
import matplotlib.pyplot as plt
# image = mahotas.imread('train-input0099.tif')
image = mahotas.imread('ac3_input_0141.tif')
x = T.matrix('x')
test2 = MLP(rng=numpy.random.RandomState(1), input=x, n_out=2, fileName = 'tmp.pkl')
prob = test2.classify_image(img=image, normMean=0.5, norm_std=1.0)
plt.imshow(1-prob)
plt.show()
mahotas.imsave('tmp_output_05.png', np.uint8((1-prob)*255))
hl = test2.hiddenLayers[0]
plt.imshow(hl.visualize_filters())
plt.show()
mahotas.imsave('filter_output_05.png', np.uint8(hl.visualize_filters()))
plt.plot(np.array(test2.trainingCost), label='training')
plt.plot(np.array(test2.validationError), label='validation')
plt.legend()
plt.show()
if len(test2.validationError) > 5000:
plt.plot(np.array(test2.trainingCost)[-5000:], label='training')
plt.plot(np.array(test2.validationError)[-5000:], label='validation')
plt.legend()
import numpy as np
from matplotlib import pyplot as plt
# 이미지 로드 & B&W로 변환
image = mh.imread('../SimpleImageDataset/scene00.jpg')
image = mh.colors.rgb2grey(image, dtype=np.uint8)
plt.imshow(image)
plt.gray()
plt.title('original image')
thresh = mh.thresholding.otsu(image)
print('Otsu threshold is {}.'.format(thresh))
threshed = (image > thresh)
plt.figure()
plt.imshow(threshed)
plt.title('threholded image')
mh.imsave('thresholded.png', threshed.astype(np.uint8)*255)
im16 = mh.gaussian_filter(image, 16)
# 블러링 이미지로 경계 연산 반복
thresh = mh.thresholding.otsu(im16.astype(np.uint8))
threshed = (im16 > thresh)
plt.figure()
plt.imshow(threshed)
plt.title('threholded image (after blurring)')
print('Otsu threshold after blurring is {}.'.format(thresh))
mh.imsave('thresholded16.png', threshed.astype(np.uint8)*255)
plt.show()