def random_snips(image, r, n_snips):
#Arguments -->
#image : 3D array for snipping. (Note: It must be MNI registered.)
#r : radius of the snipped image(in pixels)
#n_snips : no of snips to be generated
from random import randint
import numpy as np
import os
cur_path = os.getcwd()
indexes = []
snips = []
#import sys, os
#sys.path.append(os.path.)
from pfca.core.preprocessing import mni_template_registration
harvard_mni = mni_template_registration(cur_path, harvard_atlas, 'harvard')
harvard_mask = (harvard_mni.numpy() > 0) * 1
indices = np.asarray(np.where(harvard_mask == 1)).T
n_indices = len(indices)
for i in range(n_snips):
t = randint(0, n_indices)
tmp_pt = indices[t]
indexes.append(tmp_pt)
tmp_snip = image[tmp_pt[0] - r:tmp_pt[0] + r,
tmp_pt[1] - r:tmp_pt[1] + r,
tmp_pt[2] - r:tmp_pt[2] + r]
snips.append(tmp_snip)
return snips, indexes
def disp_roi(self, patient):
import pandas as pd
import numpy as np
from pfca.exp.results import draw_roi
from pfca.core.preprocessing import nifti_ANTS, mni_template_registration
from pfca import init_path
import os
raw_dir, nifti_dir = init_path()
cur_path = os.getcwd()
k = self.dataset
im_nifti = nifti_ANTS(nifti_dir,
patient,
category='eswan',
unskulled=True)
im_patient = mni_template_registration(cur_path, im_nifti, patient)
patient_data = k.loc[k['patient_name'] == patient]
peaks = patient_data['RST_peak'].tolist()
draw_roi(im_patient, peaks, 5)
def store_results(dataset, r, folder_name):
d = dataset #Writing so long names is not cool enough
###First make a folder for storing all the images
from pfca import init_path
import os
from skimage.draw import rectangle_perimeter
import matplotlib.patches as mpathches
from pfca.core.preprocessing import nifti_ANTS, mni_template_registration
cur_path = os.getcwd()
raw_dir, nifti_dir = init_path()
snips_dir = str(cur_path) + "/visuals/RST_snips/" + folder_name + "/"
try:
os.makedirs(snips_dir)
except FileExistsError:
print("Directory Exists!")
############
#Moving to the important stuff...Get Data...
#peaks = d['RST_peak'].tolist()
###########
from matplotlib import pyplot as plt
for i in range(len(d)):
plt.figure(figsize=(12, 14))
p = (d.iloc[i]['RST_peak']).tolist()
patient = d.iloc[i]['patient_name']
im_nifti = nifti_ANTS(nifti_dir,
patient,
category='eswan',
unskulled=True)
image = mni_template_registration(cur_path, im_nifti, patient)
temp = image[:, :, p[2]]
plt.imshow(temp, cmap=plt.get_cmap('gray'))
plt.title("Patient : " + patient + "; " + "Coordinate : " + str(p))
ax = plt.gca()
rect = mpathches.Rectangle((p[1] - r, p[0] - r),
2 * r,
2 * r,
fill=False,
edgecolor='red',
linewidth=1)
ax.add_patch(rect)
#ax.set_axis_off()
plt.savefig(snips_dir + patient + "_" + str(i) + ".png")
plt.close()