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Samuel Callahan
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Radiology-and-AI
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[44bf8c]: / Radiology_and_AI / visuals / gif_functions.py

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from mpl_toolkits.mplot3d import Axes3D

import numpy

import os

import imageio

import matplotlib.pyplot as plt



#Creates images at specified angle intervals using voxels to display in 3D

#Downscales original resolution of input

def create_images(out_dir,seg_1= numpy.array([None]),seg_2 = numpy.array([None]), seg_3 = numpy.array([None]), mri_chan=numpy.array([None]),downsize_factor=5,angle_num = 20,angle_view= 30,fig_size=(50,25)): 

    for angle in range(0, 360, angle_num):

        ax = make_ax(True,fig_size=fig_size)

        if (seg_1.any() == None) == False: #core        

            seg_1_small = make_smaller(seg_1,downsize_factor)

            ax.voxels(seg_1_small, facecolors='#FF000080', edgecolors='gray', shade=False)

        if (seg_2.any() == None) == False: #enhancing

            seg_2_small = make_smaller(seg_2,downsize_factor)

            ax.voxels(seg_2_small, facecolors='#0000FF40', shade=False) 

        if (seg_3.any() == None) == False: # edema

            seg_3_small = make_smaller(seg_3,downsize_factor)

            ax.voxels(seg_3_small, facecolors='#00FF0020', shade=False) 



        if (mri_chan.any() == None) == False:

            mri_chan_small = make_smaller(mri_chan,downsize_factor) 

            ax.voxels(mri_chan_small, facecolors='#3eb19c20', shade=False) 

        ax.view_init(angle_view, angle)

        fig1 = plt.gcf()

        fig1.savefig(f'{out_dir}/{angle}.png')

#USes images created using create_images method to create a gif of brain spinning on vertical axis

def make_gif(in_dir,out_file,angle_num):

    images = []

    filenames = [f'{in_dir}/{x}.png' for x in range(0,360,angle_num)]

    for filename in filenames:

        images.append(imageio.imread(filename))

    imageio.mimsave(out_file, images)



    

def make_ax(grid=False,fig_size = (50,25)):

    fig = plt.figure(figsize=fig_size)

    ax = fig.gca(projection='3d')

    ax.set_xlabel("x")

    ax.set_ylabel("y")

    ax.set_zlabel("z")

    ax.grid(grid)

    return ax



#

def make_smaller(seg, factor = 4):

    smal = seg[0:(numpy.shape(seg)[0]):factor]

    smal =  numpy.stack([smal[x][0:(numpy.shape(seg)[1]):factor] for x in range((numpy.shape(smal)[0]))])

    return  numpy.stack([[smal[x][y][0:(numpy.shape(seg)[2]):factor] for x in range((numpy.shape(smal)[0]))] for y in range((numpy.shape(smal)[0]))])