def calculate_dgms_of_shape(bin_img, name, save_path, orientation_num=30):
leaf_dgms = calculate_discrete_NPHT_2d(bin_img, orientation_num)
dgms = []
for index, dgm in enumerate(leaf_dgms):
dgm_arr = np.array(dgm[0])
dgms.append(dgm_arr)
# print("dgm shape is: {}".format(dgm_arr.shape))
file_name = "{}_{}.txt".format(name, index)
np.savetxt(os.path.join(save_path, file_name), dgm_arr)
def calculate_npht_2d(monochrome_image, directions):
label_map, count = skimage.morphology.label(monochrome_image,
neighbors=4,
background=0,
return_num=True)
volumes = [np.count_nonzero(label_map == (i + 1)) for i in range(count)]
arg_max = np.argmax(volumes)
label_image = (label_map == (arg_max + 1))
label_image = np.ndarray.astype(label_image, bool)
return calculate_discrete_NPHT_2d(label_image, directions)
def get_npht(img, number_of_directions):
img = np.ndarray.astype(img, bool)
npht = calculate_discrete_NPHT_2d(img, number_of_directions)
return npht
# img = X_300[i].reshape(8,8).copy()
# pdgms_300_8angl.append(calculate_discrete_NPHT_2d(img,8))
#
## With threshold
#pdgms_300_8angl_thr = []
#for i in range(X_300.shape[0]):
# img = X_300[i].reshape(8,8).copy()
# val = filters.threshold_otsu(img)
# img[img<val] = 0
# img[img>=val] = 1
# pdgms_300_8angl_thr.append(calculate_discrete_NPHT_2d(img,8))
pdgms_all_4angl = []
for i in range(X_all.shape[0]):
img = X_all[i].reshape(8,8).copy()
pdgms_all_4angl.append(calculate_discrete_NPHT_2d(img,4))
#pdgms_all_8angl = []
#for i in range(X_all.shape[0]):
# img = X_all[i].reshape(8,8).copy()
# pdgms_all_8angl.append(calculate_discrete_NPHT_2d(img,8))
#pdgms_300_4angl = np.asarray(pdgms_300_4angl)
#pdgms_300_4angl_thr = np.asarray(pdgms_300_4angl_thr)
#pdgms_300_8angl = np.asarray(pdgms_300_8angl)
#pdgms_300_8angl_thr = np.asarray(pdgms_300_8angl_thr)
pdgms_all_4angl = np.asarray(pdgms_all_4angl)
#pdgms_all_8angl = np.asarray(pdgms_all_4angl)
#np.save("../data/pdgms/pdgms_300_4angl", pdgms_300_4angl)
#np.save("../data/pdgms/pdgms_300_4angl_thr", pdgms_300_4angl_thr)