def wrapped():
dataset_path = 'image_data'
imgs_list = glob.glob(dataset_path + '/img/*.jpg')
random.shuffle(imgs_list)
# gather all corresponding masks for each image
all_masks_files = glob.glob(dataset_path + '/bool_mask_sep_inst/*.npy')
image_to_masks = defaultdict(list)
for x in all_masks_files:
x = os.path.basename(x)
# MaskId := ImageId_MaskNum.npy
image_id = x[:x.rindex('_')]
image_to_masks[image_id].append(x)
for fname in imgs_list:
image_id = os.path.basename(fname).rstrip('.jpg')
mask_base = random.choice(image_to_masks[image_id])
ref_mask_path = random.choice(all_masks_files)
image = skimage.img_as_float(
imread(dataset_path + '/img/' + image_id + '.jpg'))
mask = np.load(dataset_path + '/bool_mask_sep_inst/' + mask_base)
ref_mask = np.load(ref_mask_path)
if patch_size is not None:
image = sk_resize(image, (patch_size, patch_size))
mask = skimage.img_as_bool(
sk_resize(mask * 255., (patch_size, patch_size)))
ref_mask = skimage.img_as_bool(
sk_resize(ref_mask * 255., (patch_size, patch_size)))
if align:
ref_mask = align_mask(mask, ref_mask)
yield (image, mask, ref_mask)
def read_binary_img(self, flipped, x_img):
if self.augment:
x_arr, flipped = image_binary_augment_array(
x_img, self.prop_image, self.prop_array)
x_arr = img_as_bool(x_arr)
else:
x_arr = np.array(x_img)
x_arr = img_as_bool(x_arr)
x_arr = np.array(x_arr, dtype=int)
return flipped, x_arr
def __data_generation(self, list_ids_temp):
# Generates data containing batch_size samples X : (n_samples, *dim, n_channels)
x_image_raw = np.zeros((4, *self.dim, self.n_channels))
x_images = {'raw': x_image_raw}
if self.finger_feature:
finger = []
# Generate data
sample = list_ids_temp[0]
with Image.open(os.path.join(self.path,
sample.split('/')[-1] + '.png')) as x_img:
x_img = x_img.convert(mode=self.mode)
x_arr_org = np.array(x_img)
x_arr_org_zoom = zoom_image(x_arr_org, False, True)
x_arr_lr, _ = flip_image(x_arr_org, False)
x_arr_lr_zoom = zoom_image(x_arr_lr, False, True)
if self.binary_mode:
x_images['raw'][0] = np.array(np.expand_dims(
img_as_bool(x_arr_org), 2),
dtype=int)
x_images['raw'][1] = np.array(np.expand_dims(
img_as_bool(x_arr_org_zoom), 2),
dtype=int)
x_images['raw'][2] = np.array(np.expand_dims(
img_as_bool(x_arr_lr), 2),
dtype=int)
x_images['raw'][3] = np.array(np.expand_dims(
img_as_bool(x_arr_lr_zoom), 2),
dtype=int)
else:
x_images['raw'][0] = np.expand_dims(x_arr_org, 2)
x_images['raw'][1] = np.expand_dims(x_arr_org_zoom, 2)
x_images['raw'][2] = np.expand_dims(x_arr_lr, 2)
x_images['raw'][3] = np.expand_dims(x_arr_lr_zoom, 2)
if self.finger_feature:
index = int(sample[-2:])
for _ in range(4):
finger.append(index - 1)
if self.finger_feature:
x_images['finger'] = keras.utils.to_categorical(finger,
num_classes=10)
return x_images
def unet_pred(img, mean, mod):
prev_shape = img.shape[0:2]
img = resize(img, (512, 512),
order=0,
preserve_range=True,
mode='reflect',
anti_aliasing=True).astype(img.dtype)
unet_img = expend(img, 92, 92)
#mask = model.predict(rgb, mean=mean_array)['predictions'].astype('uint8')
dic_mask = slidding_window_predict(unet_img, mean, mod)
dic_mask['predictions'][dic_mask['predictions'] > 0] = 255
dic_mask['predictions'] = img_as_bool(
resize(dic_mask['predictions'],
prev_shape,
order=0,
preserve_range=True,
mode='reflect',
anti_aliasing=True))
dic_mask['probability'] = resize(dic_mask['probability'],
prev_shape,
order=0,
preserve_range=True,
mode='reflect',
anti_aliasing=True).astype(
dic_mask['probability'].dtype)
return dic_mask
def budget_binary_dilation(img, radius, fac=2):
if radius < 0:
return np.ones(img.shape, dtype=np.bool)
ori_shape = img.shape
# plt.imshow (img)
# plt.show ()
if (len(img.shape) == 3):
img = img[::fac, ::fac, ::fac]
img = binary_dilation(img, ball(radius // fac))
else:
img = img[::fac, ::fac]
img = binary_dilation(img, disk(radius // fac))
# plt.imshow (img)
# plt.show ()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
img = img_as_bool(
resize(img,
ori_shape,
order=cv2.INTER_NEAREST,
mode='reflect',
anti_aliasing=False))
# plt.imshow (img)
# plt.show ()
return img
def graph_build():
name = "Cables"
thresh = 127
threshold(name + ".jpg", thresh)
img = img_as_bool(
color.rgb2gray(io.imread("Cables_threshold_{}.jpg".format(thresh))))
ske = skeletonize(~img).astype(np.uint16)
# build graph from skeleton
graph = sknw.build_sknw(ske)
# draw image
plt.imshow(img, cmap='gray')
manager = plt.get_current_fig_manager()
manager.resize(*manager.window.maxsize())
# draw edges by pts
for (s, e) in graph.edges():
ps = graph[s][e]['pts']
plt.plot(ps[:, 1], ps[:, 0], 'green')
# draw node by o
nodes = graph.nodes()
ps = np.array([nodes[i]['o'] for i in nodes])
plt.plot(ps[:, 1], ps[:, 0], 'r.')
# title and show
plt.title('')
plt.axis('off')
plt.show()
def read_train_data(train_path, d=3):
train_ids = next(os.walk(train_path))[1]
train_images = []
train_masks = []
train_labels = []
for id_ in tqdm(train_ids, desc='Reading train data..'):
path = train_path + id_
image_file = next(os.walk(path + '/images/'))[2][0]
image = img_as_ubyte(
cv2.imread(path + '/images/' + image_file)[:, :, :d])
train_images.append(image)
mask = None
label = None
for i, mask_file in enumerate(next(os.walk(path + '/masks/'))[2]):
mask_ = img_as_bool(cv2.imread(path + '/masks/' + mask_file))
if len(mask_.shape) > 2:
mask_ = mask_[..., 0]
if mask is None:
mask = np.zeros_like(mask_)
if label is None:
label = np.zeros_like(mask_, dtype=int)
mask = np.maximum(mask, mask_)
label += (i + 1) * mask_
train_masks.append(img_as_float(mask))
train_labels.append(label)
return train_ids, train_images, train_masks, train_labels
def img2line(img):
"""Convert an image to a sequence of indexes
Parameters
----------
img : 2d array_like
image to extract line from
Returns
-------
iseq : array
1d sequnce of i coordinates
jseq : array
1d sequnce of j coordinates
"""
img = img_as_bool(img)
Ns = sum(img, axis=1)
N = sum(Ns)
ni, nj = img.shape
jrange = np.arange(nj)
iseq = np.zeros(N, dtype=int)
jseq = np.zeros(N, dtype=int)
ii = iseq
jj = jseq
for i, n in enumerate(Ns):
ii, ii[:n] = ii[:n], i
jj, jj[:n] = jj[:n], jrange[img[i, :] == 1]
assert not ii
assert not jj
return iseq, jseq
def image_with_sections_contounered_in_cicle(img):
circle_image_mask = create_internal_circle_mask(img)
inverted_circle_image_mask = np.logical_not(circle_image_mask)
img = cv2.multiply(img, circle_image_mask)
edges = cv2.Canny(img,CANNY_THRESHOLD,CANNY_THRESHOLD)
final_sobely = np.uint8(edges)
final_image = np.zeros([img.shape[0],img.shape[1],1], dtype=np.uint8)
thresh_gaussian = cv2.adaptiveThreshold(final_sobely,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,ADAPTATIVE_THRESHOLD_BLOCK_SIZE,ADAPTATIVE_THRESHOLD_C)
(contours,hierarchy) = cv2.findContours(thresh_gaussian,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
contours = [c for c in contours if cv2.contourArea(c) > MIN_CONTOUR_AREA ] #remove smalls
final_contours = [contour_validation(img,idx,c,inverted_circle_image_mask) for idx,c in enumerate(contours)]
inner_holes = [c[0] for c in final_contours if c[1]]
cv2.drawContours(final_image, inner_holes, -1, (255), 1)
cv2.fillPoly(final_image, inner_holes, color=(255))
vessels = [c[0] for c in final_contours if not c[1]]
cv2.drawContours(final_image, vessels, -1, (255), 1)
cv2.fillPoly(final_image, vessels, color=(255))
final_image_bit = skimage.img_as_bool(cv2.bitwise_not(final_image))
final_image_bit = np.bitwise_and(final_image_bit,circle_image_mask)
return final_image_bit,inner_holes,final_image
def resize_reshape(self, resize, width, height):
"""
Resizes each scan and target segmentation image of a patient to a
given width and height. It also turns the target into a one-hot
encoded tensor of shape: [depth, width, height, num_classes].
Args:
resize (bool): Whether to resize or not the scans.
width (int): Width to resize all scans and targets in dataset.
height (int): Height to resize all scans and targets in dataset.
"""
# resize scans
if resize:
depth = self.scans.shape[0]
scans = skimage.transform.resize(image=self.scans,
output_shape=(depth, width,
height))
self.scans = scans
# turns target into one-hot tensor, adopted from:
# https://stackoverflow.com/a/36960495
n_classes = self.seg.max() + 1
seg = (np.arange(n_classes) == self.seg[..., None]).astype(bool)
# resize targets while preserving their boolean nature
if resize:
seg = skimage.img_as_bool(
skimage.transform.resize(image=seg,
output_shape=(depth, width, height,
n_classes)))
self.seg = seg.astype(int)
def segment(self, image):
w, h = image.size
image = cv2.resize(np.array(image), self.CCNET_INPUT_SIZE,
cv2.INTER_CUBIC)
outputs = self.model(Variable(
self.input_transform(image).unsqueeze(0)))
logprob = self.softmax(outputs).data.cpu().numpy()
pred = np.argmax(logprob, axis=1) * 255
pred = Image.fromarray(pred[0].astype(np.uint8))
pred = np.array(pred)
# Optional: uncomment the following lines to take only the biggest blob returned by CCNet
'''
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(pred, connectivity=4)
if nb_components > 1:
sizes = stats[:, -1]
max_label = 1
max_size = sizes[1]
for i in range(2, nb_components):
if sizes[i] > max_size:
max_label = i
max_size = sizes[i]
pred = np.zeros(output.shape)
pred[output == max_label] = 255
pred = np.asarray(pred, dtype=np.uint8)
'''
# Resize the mask to the original image size
pred = img_as_bool(
cv2.resize(np.array(pred), (w, h), cv2.INTER_NEAREST))
return pred
def prepare_model(dataset_path: Path = DATASET_PATH,
clf_dump_path: Path = CLASSIFIER_DUMP_PATH,
dimred_dump_path: Path = None,
scaler_dump_path: Path = None):
'''
Запускаем один раз, для тренировки модели с последующим сохранением,
для дальнейшего использования.
:param dataset_path: путь до датасета
:param clf_dump_path: путь до дампа классификатора
:param dimred_dump_path: путь до дампа декомпозера
:param scaler_dump_path: путь до дампа шкалировщика
:return:
'''
letters = []
flat_images = []
for folder in range(1, 34):
path_gen = Path(Path.cwd().parent / dataset_path / str(folder)).glob(
'*.jpg') # Создаем генератор путей картинок
# Записываем пути картинок
paths = [path for path in path_gen if path.is_file()]
for i in range(len(paths)):
flat_images.append(
img_as_ubyte(img_as_bool(img_as_ubyte(
imread(paths[i])).ravel())))
letters.append(folder)
# Картинка представляется IMG_SIZE * IMG_SIZE признаками (пикселями),
# в каждом из которых берем интенсивность белого
print(
f'Модель учится на {len(paths)} изображениях с разрешением '
f'{IMG_SIZE}x{IMG_SIZE} в 33 категориях.')
if dimred_dump_path:
try:
pca = PCA(n_components=0.9) # Оставляем признаки, объясняющие 90% зависимостей
flat_images = pca.fit_transform(flat_images, letters)
print(f'Оставлено {pca.n_components_} признаков, объясняющих 90% зависимостей.')
dump(pca, Path.cwd().parent / dimred_dump_path)
except MemoryError: # fixme Решить проблему с памятью
print('Недостаточно памяти для понижения размерности. '
'Этап понижения размерности пропущен.')
pass
if scaler_dump_path:
scaler = StandardScaler()
flat_images = scaler.fit_transform(flat_images)
dump(scaler, Path.cwd().parent / scaler_dump_path)
# svm_clf = SVC(kernel='poly', degree=2, C=1, cache_size=1000)
# svm_clf.fit(flat_images, letters)
rf_clf = RandomForestClassifier(n_estimators=750, random_state=1, n_jobs=-1,
verbose=True)
rf_clf.fit(flat_images, letters)
dump(rf_clf, Path.cwd().parent / clf_dump_path)
print(f'Модель обучена. Дамп модели сохранен в {clf_dump_path}')
def disk(mesh, center, pat, profile, gap):
npat = np.copy(pat)
rad1 = [center[0]]
rad2 = [center[1]]
r1 = np.asarray(rad1)
r2 = np.asarray(rad2)
# print(type(r1), type(r2), np.shape(r1), np.shape(r2))
for j in range(len(profile)):
r2j = float(rad1[int(j)]) + float(profile[int(j)])
rad2.append(r2j)
r1jj = float(rad2[int(j)]) + float(gap) * 0.3
rad1.append(r1jj)
value = 0
for i in rad1[::-1]:
value = (value + 1) % 2
Rad = i * 2
print(i, j)
# circ = OsiteDif(mesh, r1, r2, pat, value, Rad);
circ = Osite(mesh, i, j, pat)
bpatBW = Im2bin(pat)
#It seems DMD doesn't with this format only
dummie = sk.img_as_bool(bpatBW)
def img_as_MNIST(self, img, mask):
"""
Transform the digit image so that it looks like a MNIST image in term of
range ang shape.
"""
# put image in grayscale
img = skimage.color.rgb2gray(img)
# resize max_axis to 28
img = self._resize_max(img, 23)
mask = self._resize_max(mask, 23)
# pad to 28,28
h, w = img.shape
pad_h = (int(np.ceil((28 - h) / 2)), int(np.floor((28 - h) / 2)))
pad_w = (int(np.ceil((28 - w) / 2)), int(np.floor((28 - w) / 2)))
img = skimage.util.pad(img, (pad_h, pad_w), constant_values=0)
mask = skimage.util.pad(mask, (pad_h, pad_w), constant_values=0)
# inverse colorspace and mask image
img_masked = (255 -
skimage.img_as_ubyte(img)) * skimage.img_as_bool(mask)
# contrast stretch of images --> saturate upper 1% of pixel
img_masked = skimage.exposure.rescale_intensity(
img_masked,
in_range=(0, np.percentile(img_masked, 100)),
out_range=(0, 255))
return img_masked
def get_iou(img, mask):
img_mask = img[:, :, 3] > 0
mask_resized = img_as_bool(resize(mask, img_mask.shape))
overlap = img_mask * mask_resized # Logical AND
union = img_mask + mask_resized # Logical OR
IOU = overlap.sum() / float(union.sum())
return IOU
def cv2_resize(array, size=500, is_bool=False):
if is_bool:
return np.array([img_as_bool(resize(x, (size, size))) for x in array])
return np.array([
resize(x, (size, size), preserve_range=True).astype("uint8")
for x in array
])
def skeletonization():
from skimage import img_as_bool, io, color, morphology
image = img_as_bool(color.rgb2gray(io.imread('planC2V2black.tiff')))
out = morphology.medial_axis(image)
f, (ax0, ax1) = plt.subplots(1, 2)
ax0.imshow(image, cmap='gray', interpolation='nearest')
ax1.imshow(out, cmap='gray', interpolation='nearest')
DefaultSize = plt.get_size_inches()
plt.set_figsize_inches( (DefaultSize[0]*2, DefaultSize[1]*2) )
plt.savefig("test.png", dpi = (1000))
plt.show()
res = []
for i in range(len(out)):
res.append([])
for j in range(len(out[0])):
if out[i][j]:
res[i].append('1')
else:
if imarray[i][j][0] == 255: ##cette partie a ete modifiee pour garder les murs en memoire
res[i].append('2')
else:
res[i].append('0')
#print(res)
#affCouleurs(suppParasites(res))
return res
def thinning(image, foreground_bias=10):
"""given a RGB image, get its foreground skeleton
:param image: 3-channel image
:param foreground_bias: bias ostu theshold value to make sure all fishes are foreground
:return: thinned image which is a binary image
"""
start = time()
# binarization
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# gray = cv2.GaussianBlur(gray, (3, 3), 0)
res, _ = cv2.threshold(gray, 180, 255,
cv2.THRESH_BINARY) # + cv2.THRESH_OTSU
res, binary = cv2.threshold(gray, res + foreground_bias, 255,
cv2.THRESH_BINARY)
binary = cv2.bitwise_not(binary)
# remove fish fins
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
erosion = cv2.erode(binary, kernel, iterations=1)
dilation = cv2.dilate(erosion, kernel, iterations=1)
# perform skeletonization or thinning
bn = img_as_bool(dilation, force_copy=True)
skeleton = skeletonize(bn)
thin_img = img_as_ubyte(skeleton)
cv2.imshow('thin_img', dilation)
print('--- thinning taking time: {:.2f}'.format(time() - start))
return thin_img
def disk2(mesh, center, pat, profile, gap):
#copy of the original pattern to be modify
npat = np.copy(pat);
x0 = [center[0]];
y0 = [center[1]];
#for loop for the creation of the different rings
for j in range(len(profile)):
r2j = float(x0[int(j)]) + float(profile[int(j)]);
y0.append(r2j);
r1jj = float(y0[int(j)]) + float(gap)*0.3;
x0.append(r1jj);
value = 0;
for i in x0[::-1]:
value = (value+1)%2;
Rad = i*2;
circ = OsiteDif(mesh, center[0], center[1], pat, value, Rad);
#getting bin image of pattern
bpatBW = Im2bin(pat); #It seems DMD doesn't with this format only
# bpat = sk.color.grey2rgb(rpat); #In case RGB format is need
dummie = sk.img_as_bool(bpatBW);
def get_branch_meas(path):
read_lab_skel = img_as_bool(color.rgb2gray(io.imread(path)))
lab_skel = skeletonize(read_lab_skel).astype("uint8")
branch_data = summarise(lab_skel)
curve_ind = []
for bd, ed in zip(branch_data["branch-distance"],
branch_data["euclidean-distance"]):
if ed != 0.0:
curve_ind.append((bd - ed) / ed)
else:
curve_ind.append(bd - ed)
branch_data["curvature-index"] = curve_ind
grouped_branch_data_mean = branch_data.groupby(["skeleton-id"],
as_index=False).mean()
grouped_branch_data_sum = branch_data.groupby(["skeleton-id"],
as_index=False).sum()
counter = collections.Counter(branch_data["skeleton-id"])
n_branches = []
for i in grouped_branch_data_mean["skeleton-id"]:
n_branches.append(counter[i])
branch_len = grouped_branch_data_mean["branch-distance"].tolist()
tot_branch_len = grouped_branch_data_sum["branch-distance"].tolist()
curv_ind = grouped_branch_data_mean["curvature-index"].tolist()
return n_branches, branch_len, tot_branch_len, curv_ind
def __init__(self, input_path, output_path):
""" Конструктор """
# @var input_path string - Путь к входному изображению
# @var output_path string - Путь к выходному изображению
self.image = img_as_bool(io.imread(input_path))
self.output_path = output_path
def preprocess_train(self):
max_s = self.params['max_scans']
w, h = self.params['train_img_size']
n_classes = self.params['num_classes']
datas = []
labels = []
self.train_volume = []
for i, nii in enumerate(self.train_niis):
data = nii[0].get_data()
label = nii[1].get_data()
depth = np.shape(data)[-1]
data = np.moveaxis(data, -1, 0)
min, max, data = normalize(data)
data = skimage.transform.resize(image=data,
output_shape=(depth, w, h))
data = np.expand_dims(data, -1)
label = np.moveaxis(label, -1, 0)
label = (np.arange(n_classes) == label[..., None]).astype(bool)
label = skimage.img_as_bool(
skimage.transform.resize(image=label,
output_shape=(depth, w, h,
n_classes)))
label = label.astype(int)
self.train_volume.append([])
de = 0
while de < depth:
begin = de - self.overlap
begin = begin if begin >= 0 else 0
end = begin + max_s
end = end if end < depth else depth
self.train_volume[i].append([begin, end])
data_volume = data[begin:end, :, :, :]
label_volume = label[begin:end, :, :, :]
d = end - begin
if d < max_s:
zerod = np.zeros((max_s - d, w, h, 1))
zerol = np.zeros((max_s - d, w, h, n_classes))
data_volume = np.concatenate((data_volume, zerod))
label_volume = np.concatenate((label_volume, zerol))
datas.append(data_volume)
labels.append(label_volume)
de = end
print("preprocess train data %s" % i)
self._datas = np.asarray(datas, dtype=np.float32)
self._labels = np.asarray(labels, dtype=np.int32)
def check_chord_or_beam(img_input, staff_space):
'''
**img is assumed to be binarized
returns:
0 --> chord
1 --> beam /16
2 --> beam /32
-1 --> neither
'''
se = sk.morphology.disk(staff_space//2-1)
# img = sk.morphology.binary_opening(img_input, se)
# img = sk.morphology.binary_erosion(img, se)
# img = sk.morphology.binary_erosion(img)
# se = sk.morphology.disk(staff_space//4)
# img = sk.morphology.binary_dilation(img, se)
img = sk.morphology.binary_opening(img_input, se)
# show_images([img])
# se = sk.morphology.disk((staff_space//3))
se = sk.morphology.disk(staff_space//4)
img = sk.morphology.binary_erosion(img)
img = sk.morphology.binary_dilation(img)
se = sk.morphology.disk(staff_space//2-1)
img = sk.morphology.binary_erosion(img, se)
# img = sk.morphology.binary_erosion(img)
se = sk.morphology.disk(staff_space//8+1)
img = sk.morphology.binary_dilation(img, se)
img = sk.morphology.binary_erosion(img)
# show_images([img])
bounding_boxes = sk.measure.find_contours(img, 0.8)
if len(bounding_boxes) < 2:
return -1
newImg = img.copy()
centers, count_disks_spacing = [], 0
for box in bounding_boxes:
[Xmin, Xmax, Ymin, Ymax] = [np.min(box[:, 1]), np.max(
box[:, 1]), np.min(box[:, 0]), np.max(box[:, 0])]
centers.append([Ymin+Ymin//2, Xmin+Xmin//2])
for i in range(1, len(centers)):
if abs(centers[i][1] - centers[i-1][1]) > 2*staff_space:
count_disks_spacing += 1
if count_disks_spacing != len(centers)-1:
return 0
img = sk.morphology.thin(sk.img_as_bool(img_input))
h, theta, d = sk.transform.hough_line(img)
h, theta, d = sk.transform.hough_line_peaks(h, theta, d)
angels = np.rad2deg(theta)
number_of_lines = np.sum(np.abs(angels) > 10)
if number_of_lines < 1 or number_of_lines > 2:
return -1
else:
return number_of_lines
def complete(img):
image = img_as_bool(io.imread(img))
out = ndi.distance_transform_edt(~image)
out = out < 0.02 * out.max()
out = morphology.skeletonize(out)
out = segmentation.clear_border(out)
out = out | image
cv2.imwrite('gaps_filled.jpg', out)
def train():
np.random.seed(0)
patch_size = 512
batch_size = 8
images, masks = load_parsed_sod()
images = np.array([resize(im, (patch_size, patch_size)) for im in images])
masks = np.array([
np.expand_dims(
skimage.img_as_bool(
resize(skimage.img_as_float(ms), (patch_size, patch_size))), 2)
for ms in masks
])
sess = tf.Session()
net = ParameterizedUNet(patch_size=patch_size)
net.build()
sess.run(tf.global_variables_initializer())
print("start training")
for epoch in range(500):
out_im = None
truth_img = None
tweaked = None
truth_mask = None
index = np.arange(len(images))
np.random.shuffle(index)
for i, batch_index in enumerate(
np.array_split(index,
len(index) // batch_size)):
truth_img = images[batch_index]
truth_mask = masks[batch_index]
tweaked = [
tweak_foreground(im, ms)
for im, ms in zip(truth_img, truth_mask)
]
# TODO: tweaked is in (0, 1) which is good but why
_, loss, out_im = sess.run(
[net.train_op, net.loss, net.output_img],
feed_dict={
net.input_img: tweaked,
net.input_mask: truth_mask,
net.truth_img: truth_img
})
print('epoch', epoch, 'batch', i, loss, flush=True)
else:
plt.subplot(2, 3, 1)
plt.imshow(tweaked[0])
plt.title('Input')
plt.subplot(2, 3, 2)
plt.imshow(truth_img[0])
plt.title('Truth')
plt.subplot(2, 3, 3)
plt.imshow(out_im[0])
plt.title('out')
plt.subplot(2, 3, 5)
plt.imshow(truth_mask[0])
plt.savefig(f'tmp/{epoch}.png')
plt.close()
def binary_image(dst1, t):
for i in range(dst1.shape[0]):
for j in range(dst1.shape[1]):
if (dst1[i, j] <= t):
dst1[i, j] = 0
else:
dst1[i, j] = 1
dst1 = img_as_bool(dst1)
return dst1
def fillInContours(img): img = img_as_bool(img) img_out = ndi.distance_transform_edt(~img) img_out = img_out < 0.05 * img_out.max() img_out = morphology.skeletonize(img_out) img_out = morphology.binary_dilation(img_out, morphology.selem.disk(1)) img_out = segmentation.clear_border(img_out) img_out = img_out | img return img_out
def main(input_data_path, output_data_path, window):
"""
Convert the Volumetric CT data and mask (in NIfTI format) to a dataset of 2D images in tif and masks in bitmap for the brain extraction.
"""
# open data info dataframe
info_df = pd.read_csv(os.path.join(input_data_path, 'info.csv'), index_col=0)
# make patient directory
if not os.path.exists(output_data_path): os.mkdir(output_data_path)
# iterate over volume to extract data
output_info = []
for n, id in enumerate(info_df.id.values):
# read nii volume
ct_nii = nib.load(os.path.join(input_data_path, f'ct_scans/{id}.nii'))
mask_nii = nib.load(os.path.join(input_data_path, f'masks/{id}.nii.gz'))
# get np.array
ct_vol = ct_nii.get_fdata()
mask_vol = skimage.img_as_bool(mask_nii.get_fdata())
# rotate 90° counter clockwise for head pointing upward
ct_vol = np.rot90(ct_vol, axes=(0,1))
mask_vol = np.rot90(mask_vol, axes=(0,1))
# window the ct volume to get better contrast of soft tissues
if window is not None:
ct_vol = window_ct(ct_vol, win_center=window[0], win_width=window[1], out_range=(0,1))
if mask_vol.shape != ct_vol.shape:
print(f'>>> Warning! The ct volume of patient {id} does not have '
f'the same dimension as the ground truth. CT ({ct_vol.shape}) vs Mask ({mask_vol.shape})')
# make patient directory
if not os.path.exists(os.path.join(output_data_path, f'{id:03}/ct/')): os.makedirs(os.path.join(output_data_path, f'{id:03}/ct/'))
if not os.path.exists(os.path.join(output_data_path, f'{id:03}/mask/')): os.makedirs(os.path.join(output_data_path, f'{id:03}/mask/'))
# iterate over slices to save slices
for i, slice in enumerate(range(ct_vol.shape[2])):
ct_slice_fn =f'{id:03}/ct/{slice+1}.tif'
# save CT slice
skimage.io.imsave(os.path.join(output_data_path, ct_slice_fn), ct_vol[:,:,slice], check_contrast=False)
is_low = True if skimage.exposure.is_low_contrast(ct_vol[:,:,slice]) else False
# save mask if some brain on slice
if np.any(mask_vol[:,:,slice]):
mask_slice_fn = f'{id:03}/mask/{slice+1}_Seg.bmp'
skimage.io.imsave(os.path.join(output_data_path, mask_slice_fn), skimage.img_as_ubyte(mask_vol[:,:,slice]), check_contrast=False)
else:
mask_slice_fn = 'None'
# add info to output list
output_info.append({'volume':id, 'slice':slice+1, 'ct_fn':ct_slice_fn, 'mask_fn':mask_slice_fn, 'low_contrast_ct':is_low})
print_progessbar(i, ct_vol.shape[2], Name=f'Volume {id:03} {n+1:03}/{len(info_df.id):03}',
Size=20, erase=False)
# Make dataframe of outputs
output_info_df = pd.DataFrame(output_info)
# save df
output_info_df.to_csv(os.path.join(output_data_path, 'slice_info.csv'))
print('>>> Slice informations saved at ' + os.path.join(output_data_path, 'slice_info.csv'))
# save patient df
info_df.to_csv(os.path.join(output_data_path, 'volume_info.csv'))
print('>>> Volume informations saved at ' + os.path.join(output_data_path, 'volume_info.csv'))
def medial_axis_image(thresh):
#convert an image from OpenCV to skimage
thresh_sk = img_as_float(thresh)
image_bw = img_as_bool((thresh_sk))
image_medial_axis = medial_axis(image_bw)
return image_medial_axis
def fillgaps(impath,am = 0.05):
image = img_as_bool(io.imread(impath))
out = ndi.distance_transform_edt(~image)
out = out < am * out.max()
out = morphology.skeletonize(out)
out = morphology.binary_dilation(out, morphology.selem.disk(1))
out = segmentation.clear_border(out)
out = out | image
return out
def img2bool(grayimage, **kwargs):
""" Allow for *args, **kwargs to be passed """
return img_as_bool(grayimage)
1. Erosion <-> Dilation 2. Opening <-> Closing 3. White tophat <-> Black tophat Skeletonize =========== Thinning is used to reduce each connected component in a binary image to a *single-pixel wide skeleton*. It is important to note that this is performed on binary images only. """ from skimage import img_as_bool horse = ~img_as_bool(io.imread(data_dir+'/horse.png', as_grey=True)) sk = skeletonize(horse) plot_comparison(horse, sk, 'skeletonize') """ .. image:: PLOT2RST.current_figure As the name suggests, this technique is used to thin the image to 1-pixel wide skeleton by applying thinning successively. Convex hull =========== The ``convex_hull_image`` is the *set of pixels included in the smallest
def Flatten2DMphMath(image, **kwargs):
# GOAL: flatten the 2d image using some morpho mat and warping
# Parameters - check kawrgs.pop
### Original pipeline proposed by:
### Liu et al. "Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and
### local binary patterns in texture and shape encoding", Medical Image Anlysis 15 (2011)
### 1. Thresholding
### 2. Median filtering
### 3. Closing + opening
### 4. Fitting via 2nd order polynomial
### 5. Warping the entire image to get a straight line
# ----- 1. THRESHOLDING ----- #
# PARAMETERS
### Get the type of threshold needed to segment the image
thres_type = kwargs.pop('thres_type', 'static')
if (thres_type == 'static'):
# Get the threshold if specified
thres_val = kwargs.pop('thres_val', .2) # By default the static threshold will be .2
elif (thres_type == 'otsu'):
# Import the threshold otsu from skimage
from skimage.filters import threshold_otsu
thres_val = threshold_otsu(image)
else:
raise ValueError('protoclass.preprocessing.flattening.Flatten2DMphMath: Unrecognise type of thresholding.')
# PROCESSING
### Apply the thresholding
bin_img = (image > thres_val)
# ----- 2. MEDIAN FILTERING ----- #
# PARAMETERS
### Get the morphological operator for the median filtering
median_kernel_type = kwargs.pop('median_kernel_type', 'square') # Default type of kernel square
median_kernel_size = kwargs.pop('median_kernel_size', 5) # Default kernel size 5
if (median_kernel_type == 'square'):
from skimage.morphology import square
median_kernel = square(median_kernel_size)
elif (median_kernel_type == 'disk'):
from skimage.morphology import disk
median_kernel = disk(median_kernel_size)
else:
raise ValueError('protoclass.preprocessing.flattening.Flatten2DMphMath: Median kernel type unrecognized')
# CONVERSION INTO UBYTE
from skimage import img_as_ubyte
bin_img_uint8 = img_as_ubyte(bin_img)
# PROCESSING
from skimage.filters import median
bin_filt_img_uint8 = median(bin_img_uint8, median_kernel)
# CONVERSION INTO BOOL
from skimage import img_as_bool
bin_filt_img = img_as_bool(bin_filt_img_uint8)
# ----- 3. MORPHO MATH ----- #
# PARAMETERS
### Get the morphological operator for the opening operation
opening_kernel_type = kwargs.pop('opening_kernel_type', 'disk') # Default type of kernel disk
opening_kernel_size = kwargs.pop('opening_kernel_size', 5) # Default kernel size 5
if (opening_kernel_type == 'square'):
from skimage.morphology import square
opening_kernel = square(opening_kernel_size)
elif (opening_kernel_type == 'disk'):
from skimage.morphology import disk
opening_kernel = disk(opening_kernel_size)
else:
raise ValueError('protoclass.preprocessing.flattening.Flatten2DMphMath: Opening kernel type unrecognized')
### Get the morphological operator for the closing operation
closing_kernel_type = kwargs.pop('closing_kernel_type', 'disk') # Default type of kernel disk
closing_kernel_size = kwargs.pop('closing_kernel_size', 35) # Default kernel size 35
if (closing_kernel_type == 'square'):
from skimage.morphology import square
closing_kernel = square(closing_kernel_size)
elif (closing_kernel_type == 'disk'):
from skimage.morphology import disk
closing_kernel = disk(closing_kernel_size)
else:
raise ValueError('protoclass.preprocessing.flattening.Flatten2DMphMath: Closing kernel type unrecognized')
# PROCESSING
### Apply a closing operation
from skimage.morphology import binary_closing
bin_closing = binary_closing(bin_filt_img, closing_kernel)
### Apply an opening operation
from skimage.morphology import binary_opening
bin_opening = binary_opening(bin_closing, opening_kernel)
# ----- 4. POLYNOMIAL FITTING ----- #
# PROCESSING
### Get the x,y position of True value in the binary image
y_img, x_img = np.nonzero(bin_opening)
### Fit a second order polynomial
x_interp = np.arange(bin_opening.shape[1])
p_fitted = np.poly1d(np.polyfit(x_img, y_img, 2))
y_interp = p_fitted(x_interp)
# ----- 5. WARPING OF THE IMAGE ----- #
### Allocate the memory for the image
warped_img = np.zeros(image.shape)
### Get each column of the original image
### Get the minimum y of the interpolated value in order to align the value to this baseline
baseline_y = np.min(y_interp)
for col_ind, col_img in enumerate(image.T):
### Compute the distance to apply the rolling
dist_roll = int(np.round(baseline_y - y_interp[col_ind]))
### Assign the new column to the warped image
warped_img[:, col_ind] = np.roll(col_img, dist_roll)
# Finally return the unflatten image
return (np.round(baseline_y), warped_img)
def run(self, workspace):
self.function = lambda(x_data): skimage.measure.label(skimage.img_as_bool(x_data))
super(ConvertImageToObjects, self).run(workspace)
#Don't forget to change the file range in the following line - (for f in files[0:1000]:)
###############################################################
###############################################################
#Writes features to CSV
#Path to Training pics library
#trainImgs = "/home/kwyn/GalaxyQuest/images_training_rev1/"
#Looping through the trainImgs directory
with open(outputFilename, 'wb') as csvfile:
writer = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)
a = [0] * numFeatures
writer.writerow(a)
for root, dirs, files in os.walk(inputImgs):
#sort file names into numeric order
files = sorted(files)
for f in files[0:10000]:
galName = np.array(f[:-4])
path = inputImgs + f
img = io.imread(path, as_grey=True)
cropped = img[137:287,137:287]
resized = resize(cropped, (20,20))
im = ~img_as_bool(resized)
# selem = disk(6)
# d = dilation(resized, selem)
sk = skeletonize(im)
i = np.vstack(sk)
flat = i.flatten()
total = np.append(galName, flat)
writer.writerow(total)
def doProcessing(self):
print("========== AccivImageProcessing: Process Frames =========")
# load experiment settings
self.allExpSet = AttrMap(self.cF.jsonLoad(self.cS.experimentSettingsFile))
ex = "%i" % self.cS.experimentNumber
if ex not in self.allExpSet["experiments"]:
raise ValueError("Experiment number %i not found in %s" % (self.cS.experimentNumber, self.cS.experimentSettingsFile))
# make experiment settings
self.expSet = AttrMap(self.allExpSet["experiments"][ex])
print("Experiment Settings: ", self.expSet)
# load intensity shift values if we have a numpy .npz file
try:
l=self.cF.jsonLoad(self.cS.intensityShift)
self.intensityShift = dict(l)
print("Loaded %i intensity shift values" % len(self.intensityShift) )
except:
self.intensityShift = None
print("No intensity shift values found! -> using none")
# load background image
# and try to shift intensity
self.cS.background = self.loadImage( self.cS.backgroundFile )
if self.intensityShift is not None and self.intensityShift:
try:
frameIdx = int(re.match(self.cS.frameExtractRegex, self.cS.backgroundFile).group(1))
shiftI = self.intensityShift[frameIdx]
print("Shift background intensity by -%f" % shiftI)
self.cS.background -= shiftI;
except:
print("Did not shift background intensity")
# load additional mask
if "additionalMask" in self.cS and os.path.exists(self.cS.additionalMask):
self.cS.additionalMask = img_as_bool(imread(self.cS.additionalMask))
else:
self.cS.additionalMask = None
# set height/width
self.cS.height = self.cS.background.shape[0];
self.cS.width = self.cS.background.shape[1];
self.cS.mask = np.ones(self.cS.background.shape,np.uint8)
# bounds (with x-Axis to the right, y-Axis up, like ACCIV)
if "offsetZero" not in self.expSet:
self.expSet.offsetZero = [0,0]
min=self.expSet.aabbMeterCoordinates["minPoint"]
max=self.expSet.aabbMeterCoordinates["maxPoint"]
self.cS.bounds = np.array([ min[0], max[0], min[1],max[1] ] )
# process all frames
for frame in self.fr:
self._processFrame(frame)
print("============== AccivImageProcessing: finished ===========")
mindist.append(pairwise.min())
# Tissue to sinusoid ratio
tissue = np.sum(ski.img_as_bool(Es) * (1 - ski.img_as_bool(total)))
sinusoids = np.sum(Es.shape[0] * Es.shape[1] - np.sum(ski.img_as_bool(total)))
ratio.append(tissue.astype(float) / sinusoids)
# Pickle results
results = {}
results["ratio"] = ratio
results["inflcount"] = inflcount