def loadims(afolder):
folderi = f"Ground truth/{afolder}/image"
folderm = f"Ground truth/{afolder}/mask"
images = []
masks = []
names = []
for mask in os.listdir((folderm)):
names.append(mask)
maskfile = np.asarray(Image.open(os.path.join(folderm, mask)))
if afolder == 'AuGe TEM':
image = mask[:-7] + ".tiff"
thing = np.zeros_like(maskfile)
thing[maskfile == 255] = 1
thing = thing + 1
else:
image = mask[:-7] + ".dm4"
thing = ps.toggle_channels(maskfile[:, :, :3],
colors=['#0000ff', '#ff0000'])
#warnings.filterwarnings("ignore")
imagefile = hs.load(os.path.join(folderi, image))
if afolder == 'PdC TEM' or afolder == 'AuGe TEM':
im = imagefile.data
normed = (im - im.min()) * (1000 / (im.max() - im.min()))
images.append(normed)
else:
images.append(imagefile.data)
masks.append(thing)
return (images[0:11], masks[0:11])
def test_man_seg(afolder):
folderm = f"Ground truth/{afolder}/mask"
folderm2 = f"Ground truth/{afolder}/mask2 EB"
masks = []
masks2 = []
names = []
for mask in os.listdir((folderm2)):
maskfile2 = np.asarray(Image.open(os.path.join(folderm2, mask)))
if afolder == 'AuGe TEM':
maskfile1 = np.asarray(
Image.open(os.path.join(folderm, mask)[:-3] + 'tif'))
thing = np.zeros_like(maskfile1)
thing[maskfile1 == 255] = 1
maskfile1 = thing
else:
maskfile1 = np.asarray(Image.open(os.path.join(folderm, mask)))
maskfile1 = ps.toggle_channels(maskfile1[:, :, :3],
colors=['#0000ff', '#ff0000'])
maskfile1 = (maskfile1 - 1).astype(np.bool_).astype(np.int)
maskfile2 = ps.toggle_channels(maskfile2[:, :, :3],
colors=['#0000ff', '#ff0000'])
maskfile2 = maskfile2 - 1
masks.append(maskfile1)
masks2.append(maskfile2)
data = open(f"Results/manual seg{afolder}.csv", 'w')
data.write("pixel accuracy,dice01,dice11,dice00,dice10\n")
for i in range(len(masks)):
pixacc = ac.check_ground(masks[i], masks2[i])
dice = ac.dice(masks[i], masks2[i])
data.write("{},{},{},{},{}\n".format(pixacc, dice[0, 1], dice[1, 1],
dice[0, 0], dice[1, 0]))
data.close()
return
def ClusterPercent(image, labels, classifier, number, params=None):
#just does one image trained on a random percentage of the image pixels
if params == None:
params = ps.trainableParameters()
if len(labels.shape) != 2:
labels = ps.toggle_channels(labels)
#makes sure labels aren't empty
if (labels != 0).any() == True:
thin_mask = labels.astype(np.float64)
image = image.data
features = ps.CreateFeatures(image, parameters=params)
area = image.shape[0] * image.shape[1]
indexes = np.random.randint(0, high=area, size=number)
frac_array = np.zeros(area)
frac_array[indexes] = 1
frac_array = np.reshape(
frac_array, (image.shape[0], image.shape[1])).astype(np.bool)
features = np.rot90(np.rot90(features, axes=(2, 0)), axes=(1, 2))
#features are num/x/ymini
training_data = features[:, frac_array].T
#training data is number of labeled pixels by number of features
training_labels = thin_mask[frac_array].ravel()
training_labels = training_labels.astype('int')
#training labels is labelled pixels in 1D array
tic = time.perf_counter()
classifier.fit(training_data, training_labels)
#will crash for one image with no labels
toc = time.perf_counter() - tic
thin_mask = labels.astype(np.float64)
output = np.copy(thin_mask)
if (labels == 0).any() == True:
#train classifier on labelled data
data = features[:, thin_mask == 0].T
#unlabelled data
pred_labels = classifier.predict(data)
#predict labels for rest of image
output[thin_mask == 0] = pred_labels
#adds predicted labels to unlabelled data
return output, classifier, toc
def create_smol_features(afolder, num, params):
#creates set of featues using CS and opens the right folders
folderi = "Ground truth/{}/image".format(afolder)
folderm = "Ground truth/{}/mask".format(afolder)
images = []
masks = []
names = []
for mask in os.listdir((folderm)):
names.append(mask)
maskfile = np.asarray(Image.open(os.path.join(folderm, mask)))
if afolder == 'AuGe TEM':
image = mask[:-7] + ".tiff"
thing = np.zeros_like(maskfile)
thing[maskfile == 255] = 1
thing = thing + 1
else:
image = mask[:-7] + ".dm4"
thing = ps.toggle_channels(maskfile[:, :, :3],
colors=['#0000ff', '#ff0000'])
imagefile = hs.load(os.path.join(folderi, image))
images.append(imagefile.data)
masks.append(thing)
mask = masks[0]
image = images[0]
features = ps.CreateFeatures(image, params)
area = image.shape[0] * image.shape[1]
indexes = np.random.randint(0, high=area, size=num)
frac_array = np.zeros(area)
frac_array[indexes] = 1
frac_array = np.reshape(frac_array,
(image.shape[0], image.shape[1])).astype(np.bool)
features = np.rot90(np.rot90(features, axes=(2, 0)), axes=(1, 2))
training_data = features[:, frac_array].T
mask = mask[frac_array]
mask = mask.ravel()
labelled_features = np.concatenate(
(np.reshape(mask, (mask.shape[0], 1)), training_data), axis=1)
return labelled_features
def test_classifier_mult(afolder, clf, n=1, parameters=None):
folderi = f"Ground truth/{afolder}/image"
folderm = f"Ground truth/{afolder}/mask"
if parameters == None:
params = ps.trainableParameters()
images = []
masks = []
names = []
for mask in os.listdir((folderm)):
names.append(mask)
maskfile = np.asarray(Image.open(os.path.join(folderm, mask)))
if afolder == 'AuGe TEM':
image = mask[:-7] + ".tiff"
thing = np.zeros_like(maskfile)
thing[maskfile == 255] = 1
thing = thing + 1
else:
image = mask[:-7] + ".dm4"
thing = ps.toggle_channels(maskfile[:, :, :3],
colors=['#0000ff', '#ff0000'])
imagefile = hs.load(os.path.join(folderi, image))
import matplotlib.pyplot as plt
im = imagefile.data
normed = (im - im.min()) * (1000 / (im.max() - im.min()))
images.append(normed)
masks.append(thing)
data = open(f"results/dicedata{afolder}{str(clf)}.csv", 'w')
data.write("pixel accuracy,dice01,dice11,dice00,dice10\n")
tic = time.perf_counter()
output, clf = ps.ClusterTrained(images[:n],
masks[:n],
clf,
parameters=parameters)
#output, clf = ps.ClusterTrained(images[:n], masks[:n], clf, membrane = [1,1,1,1,1,1], texture= True, minimum = True,sigma = p[0], high_sigma = p[1], disk_size = p[2])
toc = time.perf_counter() - tic
print('trained classifier in {} seconds'.format(toc))
data.write('{}\n'.format(toc))
tic = time.perf_counter()
if n == 1:
output = [output]
for i in range(n, len(masks)):
output.append(
ps.ClassifierSegment(clf, images[i], parameters=parameters))
#output.append(ps.ClassifierSegment(clf, images[i], membrane = [1,1,1,1,1,1], texture= True, minimum = True, sigma = p[0], high_sigma = p[1], disk_size = p[2]))
toc = time.perf_counter() - tic
data.write('{}\n'.format(toc))
print('classified images in {} seconds'.format(toc))
for i in range(len(masks)):
output[i] = 2 - output[i]
masks[i] = 2 - masks[i]
tempp = ps.toggle_channels(2 - output[i])
maskim = Image.fromarray(tempp)
maskim.save(f'results/{names[i]}')
pixacc = ac.check_ground(output[i], masks[i])
print("accuracy: {}".format(pixacc))
dice = ac.dice(output[i], masks[i])
print("dice {} {}\n {} {}".format(dice[0, 1], dice[1, 1],
dice[0, 0], dice[1, 0]))
data.write("{},{},{},{},{}\n".format(pixacc, dice[0, 1], dice[1, 1],
dice[0, 0], dice[1, 0]))
data.close()
return
def test_classifier(afolder, clf):
folderi = f"Ground truth/{afolder}/image"
folderm = f"Ground truth/{afolder}/mask"
images = []
masks = []
names = []
for mask in os.listdir((folderm)):
names.append(mask)
maskfile = np.asarray(Image.open(os.path.join(folderm, mask)))
if afolder == 'AuGe TEM':
image = mask[:-7] + ".tiff"
imagefile = hs.load(os.path.join(folderi, image))
images.append(imagefile.data)
thing = np.zeros_like(maskfile)
thing[maskfile == 255] = 1
thing = thing + 1
else:
image = mask[:-5] + ".dm4"
imagefile = hs.load(os.path.join(folderi, image))
images.append(imagefile.data)
thing = ps.toggle_channels(maskfile[:, :, :3],
colors=['#0000ff', '#ff0000'])
masks.append(thing)
data = open(f"results/dicedata{afolder}{str(clf)}.csv", 'w')
data.write("pixel accuracy,dice01,dice11,dice00,dice10\n")
for i in range(len(masks)):
mk = np.copy(masks[i])
im = np.copy(images[i])
if i == 0:
tic = time.perf_counter()
_, clf = ps.ClusterTrained(
im,
mk,
clf,
sigma=1,
high_sigma=16,
disk_size=20,
)
toc = time.perf_counter() - tic
print('trained classifier in {} seconds'.format(toc))
data.write('{}\n'.format(toc))
tic = time.perf_counter()
output = ps.ClassifierSegment(
clf,
im,
sigma=10,
high_sigma=16,
disk_size=20,
)
im = ps.toggle_channels(output)
mk = 2 - mk
output = 2 - output
maskim = Image.fromarray(im)
maskim.save("ims/{}".format(names[i]))
print("accuracy: {}".format(ac.check_ground(output, mk)))
dice = ac.dice(output, mk)
print("dice {} {}\n {} {}".format(dice[0, 1], dice[1, 1],
dice[0, 0], dice[1, 0]))
data.write("{},{},{},{},{}\n".format(ac.check_ground(output, mk),
dice[0, 1], dice[1, 1],
dice[0, 0], dice[1, 0]))
toc = time.perf_counter() - tic
data.write('{}\n'.format(toc))
print('classified images in {} seconds'.format(toc))
data.close()
return