def data_target(dir, label):
"""Process data and attribuited label"""
count = 0
data = []
target = []
for file in cv.list_files(dir):
file_name = dir + file
#contour = process_file(file_name) # method snake
image = lib.read_image(file_name) # feature hematoma, utilize hu
hematoma = lib.substance_interval(image, 40, 90)
#data.append(contour.flatten())
data.append(hematoma.flatten())
#data.append(np.append(contour.flatten(),hematoma.flatten()))
target.append(label)
count += 1
if count == amount_files:
break
return [data, target]
def processing_thread(dir, files, label, id_core):
features = []
labels = []
for file_name in files:
# scaler = MinMaxScaler() # example minmax, scaler
# hematoma = scaler.fit_transform(hematoma)
norm = 'max'
image = lib.read_image(dir + file_name) # feature hematoma, utilize hu
# print("image matrix size : {:.2f}MB".format(image.nbytes / (1024 * 1000.0)))
# lib.plot('original', image)
# selem = disk(1)
# image = skimage.morphology.dilation(image, selem)
# lib.plot('dilation', image)
# features
snake = process_file(dir + file_name) # method snake
hematoma = pre.normalize(lib.substance_interval(image, 30, 90),
norm=norm).astype('float16')
white_matter = pre.normalize(lib.substance_interval(image, 20, 30),
norm=norm).astype('float16')
ventriculo = pre.normalize(lib.substance_interval(image, 0, 15),
norm=norm).astype('float16')
white_tophat = pre.normalize(skimage.morphology.white_tophat(image),
norm=norm).astype('float16')
blood = pre.normalize(lib.substance_interval(image, 45, 65),
norm=norm).astype('float16')
# x = np.concatenate((snake,hematoma,white_matter,ventriculo,white_tophat,blood))
# print("XXData matrix size : {:.2f}MB".format(x.nbytes / (1024 * 1000.0)))
# con_hem = hematoma + ventriculo + white_tophat + blood
# print("Data matrix size : {:.2f}MB".format(con_hem.nbytes / (1024 * 1000.0)))
# con_hem = hematoma + white_matter + ventriculo
# print("Data matrix size : {:.2f}MB".format(con_hem.nbytes / (1024 * 1000.0)))
np.savez_compressed(label + 'features/snake/' + file_name, snake=snake)
np.savez_compressed(label + 'features/hematoma/' + file_name,
hematoma=hematoma)
np.savez_compressed(label + 'features/white_matter/' + file_name,
white_matter=white_matter)
np.savez_compressed(label + 'features/ventriculo/' + file_name,
ventriculo=ventriculo)
np.savez_compressed(label + 'features/white_tophat/' + file_name,
white_tophat=white_tophat)
np.savez_compressed(label + 'features/blood/' + file_name, blood=blood)
cv.calculate_process((amount_files) / n_cores)
# lib.plot('eroted', eroded)
'''edges = canny(image, sigma=3, low_threshold=10, high_threshold=50)
hough_radii = np.arange(1, 10, 1)
hough_res = hough_circle(edges, hough_radii)
accums, cx, cy, radii = hough_circle_peaks(hough_res, hough_radii,total_num_peaks=3)
# Draw them
fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10, 4))
image = color.gray2rgb(image)
color.rgb2gray(image)
for center_y, center_x, radius in zip(cy, cx, radii):
circy, circx = circle_perimeter(center_y, center_x, radius, shape=image.shape)
image[circy, circx] = (220, 20, 20)
ax.imshow(image, cmap=plt.cm.gray)
plt.show()
lib.plot('sem rgb', color.rgb2gray(image))
'''
# plt.imshow(white_tophat, cmap=plt.cm.bone)
# plt.title('white_tophat')
# plt.show()
# hematoma =lib.substance_interval(image, 30, 90)
# white_matter = lib.substance_interval(image, 20, 30)
# resultado ruim se adicionar. Teste em 80 imagens
# snake_method2 = snake_GAC(dir + file_name)
# bone = pre.normalize(lib.substance_interval(image, 700, 3000), norm=norm)
# print("aqui1")
# multiotsu = threshold_multiotsu(image, classes=5) # melhora com o aumento de classe, mas piorou ao adicionar outras features
# print("aqui")
# regions_multi = pre.normalize(np.digitize(image, bins=multiotsu), norm=norm)
''' colorized, otsu, thresholds = lib.multiotsu(image, 3)
mask_ossos = np.zeros((512, 512))
mask_ossos[otsu == 2] = 1
ossos = image * mask_ossos
ossos = pre.normalize(ossos, norm=norm)'''
# con_hem = np.append(snake,hematoma)
# con_hem = np.append(con_hem,white_matter)
'''con_hem = snake + hematoma + white_matter + ventriculo + white_tophat + blood
import lib
import os
input_path = "dataset/epidural"
input_folder = os.fsencode(input_path)
files = os.listdir(input_folder)
files.sort()
slices = []
for file in files:
# if os.fsdecode(file) == "ID_3580adc72.dcm": # "ID_635f084fc.dcm": # "ID_559b1d8f7.dcm": #"ID_894a589ad.dcm":
if os.fsdecode(file) == "ID_0ed10ec08.dcm":
file = os.fsdecode(file)
filename = "{}/{}".format(input_path, file)
image = lib.read_image(filename)
lib.histogram(image, True)
lib.plot("original: {} ".format(file), image)
# features
hematoma = lib.substance_interval(image, 40, 90)
white_matter = lib.substance_interval(image, 20, 30)
blood = lib.substance_interval(image, 30, 45)
bone = lib.substance_interval(image, 600, 4000)
#lib.plot("blood: {}".format(file), blood)
#lib.plot("hematoma: {}".format(file), hematoma)
lib.histogram(blood, True)
print("Done")