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arpitmaheshwari213 · Feb 14, 2021 · 2d78432 · 2d78432
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diff --git a/tumour_detection/brain_cancer/__init__.py b/tumour_detection/brain_cancer/__init__.py
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diff --git a/tumour_detection/brain_cancer/__pycache__/__init__.cpython-36.pyc b/tumour_detection/brain_cancer/__pycache__/__init__.cpython-36.pyc
diff --git a/tumour_detection/brain_cancer/__pycache__/admin.cpython-36.pyc b/tumour_detection/brain_cancer/__pycache__/admin.cpython-36.pyc
diff --git a/tumour_detection/brain_cancer/__pycache__/apps.cpython-36.pyc b/tumour_detection/brain_cancer/__pycache__/apps.cpython-36.pyc
diff --git a/tumour_detection/brain_cancer/__pycache__/models.cpython-36.pyc b/tumour_detection/brain_cancer/__pycache__/models.cpython-36.pyc
diff --git a/tumour_detection/brain_cancer/__pycache__/urls.cpython-36.pyc b/tumour_detection/brain_cancer/__pycache__/urls.cpython-36.pyc
diff --git a/tumour_detection/brain_cancer/__pycache__/views.cpython-36.pyc b/tumour_detection/brain_cancer/__pycache__/views.cpython-36.pyc
diff --git a/tumour_detection/brain_cancer/admin.py b/tumour_detection/brain_cancer/admin.py
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+from django.contrib import admin
+
+# Register your models here.
diff --git a/tumour_detection/brain_cancer/apps.py b/tumour_detection/brain_cancer/apps.py
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+from django.apps import AppConfig
+
+
+class BrainCancerConfig(AppConfig):
+    name = 'brain_cancer'
diff --git a/tumour_detection/brain_cancer/migrations/__init__.py b/tumour_detection/brain_cancer/migrations/__init__.py
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diff --git a/tumour_detection/brain_cancer/migrations/__pycache__/__init__.cpython-36.pyc b/tumour_detection/brain_cancer/migrations/__pycache__/__init__.cpython-36.pyc
diff --git a/tumour_detection/brain_cancer/models.py b/tumour_detection/brain_cancer/models.py
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+from django.db import models
+
+# Create your models here.
diff --git a/tumour_detection/brain_cancer/templates/brain_cancer/index.html b/tumour_detection/brain_cancer/templates/brain_cancer/index.html
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+{% if error_message %}
+    <p><strong>{{error_message}}</strong></p>
+{%endif%}
+
+  <h1>Brain Tumour Classification Model</h1>
+  <h3>Project Developers:-</h3>
+  <ul>
+    <li><h4>Vansh Jain</h4></li>
+    <li><h4>Chayan Dhaddha</h4></li>
+    <li><h4>Anuraag Kansara</h4></li>
+    <li><h4>Arpit Maheshwari</h4></li>
+
+      <a href="/media/report/finalreport.pdf" target="_blank"> <button id="know_more"><label>Know More</label></button></a>
+
+  </ul>
+
+ <br>
+  <h2>Please select the MRI of Brain as input (in Nifti(3-D) format) :-</h2>
+  <form method="post" enctype="multipart/form-data" name="input_file" action="{%url 'brain_cancer:process'%}">
+      {% csrf_token %}
+      <h3>Selected File :-</h3>
+      <input type="file" id="input_image" name="input_image" required>
+      <br><br><button type="reset" id="reset"><label>Reset</label></button>
+      <button type="submit" id="submit"><label>Upload</label></button>
+  </form>
+
+
diff --git a/tumour_detection/brain_cancer/templates/brain_cancer/result.html b/tumour_detection/brain_cancer/templates/brain_cancer/result.html
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+
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Brain Cancer</title>
+</head>
+<body>
+{% if error_message %}
+    <p><strong>{{error_message}}</strong></p>
+{%endif%}
+{% if str %}
+    <h3>Output : {{ str }}</h3>
+{% endif %}
+{% if uploaded_file_url %}
+<h3>Image shown below:- </h3>
+     <img src="{{uploaded_file_url}}.jpg">
+{% endif %}
+</body>
+</html>
diff --git a/tumour_detection/brain_cancer/tests.py b/tumour_detection/brain_cancer/tests.py
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+from django.test import TestCase
+
+# Create your tests here.
diff --git a/tumour_detection/brain_cancer/urls.py b/tumour_detection/brain_cancer/urls.py
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+from django.conf.urls import url
+from . import views
+app_name='brain_cancer'
+
+urlpatterns = [
+    #/brain_tumour/
+    url(r'^$',views.index,name='index'),
+    url(r'^result/$',views.result,name='result'),
+    url(r'^process/$',views.process,name='process')
+]
diff --git a/tumour_detection/brain_cancer/views.py b/tumour_detection/brain_cancer/views.py
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+from django.shortcuts import render
+from django.http import HttpResponse
+from django.template import loader
+from django.shortcuts import render
+from django.conf import settings
+from django.core.files.storage import FileSystemStorage
+from lib.test import *
+import os
+from lib.show_images import save_image_as_jpeg
+# Create your views here.
+def index(request):
+    template = loader.get_template('brain_cancer/index.html')
+    context = {}
+    return HttpResponse(template.render(context, request))
+
+
+def result(request):
+    template = loader.get_template('brain_cancer/result.html')
+    context = {}
+    return HttpResponse(template.render(context, request))
+
+
+def process(request):
+    if request.method == 'POST' and request.FILES.get('input_image'):
+        myfile = request.FILES.get('input_image')
+        fs = FileSystemStorage()
+        filename = fs.save(myfile.name, myfile)
+        uploaded_file_url = fs.url(filename)
+        path_img=os.getcwd()+uploaded_file_url
+        save_image_as_jpeg(path_img)
+        if output(path_img) == 0:
+            str1 = 'Benign'
+        else:
+            str1 = 'Malignant'
+        return render(request, 'brain_cancer/result.html/', {'str': str1, 'uploaded_file_url': uploaded_file_url})
+    return render(request, 'brain_cancer/result.html')
diff --git a/tumour_detection/db.sqlite3 b/tumour_detection/db.sqlite3
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+
diff --git a/tumour_detection/lib/Image_Preprocessing.py b/tumour_detection/lib/Image_Preprocessing.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jun 13 19:28:41 2018
+
+@author: arpit
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jun 12 20:18:29 2018
+
+@author: Chayan-Dhaddha
+"""
+
+import os # for doing directory operations 
+#import pandas as pd
+
+import numpy as np
+from nipype.interfaces import fsl
+#import matplotlib.pyplot as plt
+
+file_path='/home/arpit/Desktop/Project'
+data_dir = file_path+'/NiFTiSegmentationsEdited/'
+patients = os.listdir(data_dir)
+#len(patients)
+
+
+for patient in patients[:]:
+    #print(patient)
+    path = data_dir + patient
+    input_file = path + '/' + patient+'_T2.nii.gz'
+    print(input_file)
+    #Preprocessing 
+    #Skull Stripping  /home/arpit/Desktop/Project/Skull _Stripped
+    output_strip_file = "skull_stripping_"+patient+"_T2.nii.gz"
+    skullstrip = fsl.BET(in_file=input_file, out_file=file_path+'/Skull_Stripped/'+output_strip_file,mask=True)
+    skullstrip.run()
+
+    #Inhomogeneity Correction(Smoothing)
+    output_smooth_file = "smooth_"+patient+"_T2.nii.gz"
+    smooth = fsl.IsotropicSmooth(in_file=file_path+'/Skull_Stripped/'+output_strip_file, out_file=file_path+'/Smooth/'+output_smooth_file,fwhm=4)
+    smooth.run()
diff --git a/tumour_detection/lib/__init__.py b/tumour_detection/lib/__init__.py
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+
+
+
diff --git a/tumour_detection/lib/__pycache__/__init__.cpython-36.pyc b/tumour_detection/lib/__pycache__/__init__.cpython-36.pyc
diff --git a/tumour_detection/lib/__pycache__/functions.cpython-36.pyc b/tumour_detection/lib/__pycache__/functions.cpython-36.pyc
diff --git a/tumour_detection/lib/__pycache__/show_images.cpython-36.pyc b/tumour_detection/lib/__pycache__/show_images.cpython-36.pyc
diff --git a/tumour_detection/lib/__pycache__/test.cpython-36.pyc b/tumour_detection/lib/__pycache__/test.cpython-36.pyc
diff --git a/tumour_detection/lib/final.py b/tumour_detection/lib/final.py
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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Jun 12 20:18:29 2018
+
+@author: Chayan-Dhaddha
+"""
+
+import os # for doing directory operations 
+import pandas as pd
+
+import numpy as np
+
+from functions import *
+#from nipype.interfaces import fsl
+#import matplotlib.pyplot as plt
+
+#used to reshape and resize
+IMG_PX_SIZE = 64
+NUM_SLICES = 20
+
+
+
+#path of folders
+#file_path='/home/arpit/Desktop/Project'
+file_path = os.getcwd()
+
+#folder name
+data_dir = file_path+'/Smooth/'
+#listing of all folder in data_dir
+patients = os.listdir(data_dir)
+
+#resize & reshape all nifti file to  64*64*20
+
+
+
+#get labels from xlsx file
+labels_df=pd.ExcelFile(file_path+"/TCIA_LGG_cases_159.xlsx")
+labels_df=labels_df.parse("Sheet1",index_col=0)
+labels_df=labels_df.drop(['1p/19q','Type'],axis=1)
+#print(labels_df)        
+
+final_img=[]
+final_labels=[]
+for patient in patients[:]:
+    print("hiii ",patient)
+
+    img_data,label=preprocess_data(patient,labels_df,img_px_size=IMG_PX_SIZE, hm_slices=NUM_SLICES)
+    #print(img_data,label)
+    """if label==2:
+        file_out = file_path + "/final_data/benign/"
+    elif label==3:
+        file_out = file_path + "/final_data/malignant/" """
+    final_img.append([img_data])
+
+    final_labels.append(label-2)
+    print(np.array(img_data).shape)
+    #convert smooth_LGG-343_T2.nii.gz to LGG-343
+    #s_patient=str(patient)
+    #split_patient=s_patient.split('_')
+    #np.save(file_out+'final_img-{}-{}-{}-{}.npy'.format(split_patient[1],IMG_PX_SIZE,IMG_PX_SIZE,NUM_SLICES),final_img)
+np.save(file_path+'/final_img-{}-{}-{}.npy'.format(IMG_PX_SIZE,IMG_PX_SIZE,NUM_SLICES),final_img)
+np.save(file_path+'/final_labels.npy',final_labels)   
+print(np.array(final_img).shape)
+
diff --git a/tumour_detection/lib/final_labels.npy b/tumour_detection/lib/final_labels.npy
diff --git a/tumour_detection/lib/functions.py b/tumour_detection/lib/functions.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jun 17 04:26:41 2018
+
+@author: arpit
+"""
+import os # for doing directory operations 
+import pandas as pd
+import nibabel as nib
+import numpy as np
+import cv2
+import math
+
+
+#path of folders
+#file_path='/home/arpit/Desktop/Project'
+file_path = os.getcwd()
+
+#folder name
+data_dir = file_path+'/Smooth/'
+#listing of all folder in data_dir
+patients = os.listdir(data_dir)
+#used in reshape to decrease fix size of slices eg:60->20 by chunk_size of 3
+def chunks(l, n):
+    """Yield successive n-sized chunks from l."""
+    for i in range(0, len(l), n):
+        yield l[i:i + n]
+
+#used to merge slices by taking mean of them  
+def mean(l):
+    return sum(l) / len(l)
+
+def preprocess_data(patient,labels_df,img_px_size=50, hm_slices=20, visualize=False):
+    #patient of type smooth_LGG-343_T2.nii.gz
+    path = data_dir + patient
+    #print(path)
+
+    #convert smooth_LGG-343_T2.nii.gz to LGG-343
+    s_patient=str(patient)
+    split_patient=s_patient.split('_')
+    #read label of LGG-343
+    label = labels_df.get_value(split_patient[1], 'Grade')
+
+    #load nifti file as numpy array
+    img = nib.load(path) 
+    img_data =img.get_data()
+    shape=img_data.shape
+
+    #resize slices from 256*256 to 64*64
+    slices = [cv2.resize(img_data[:,:,i],(img_px_size,img_px_size)) for i in range(0,shape[2])]
+
+    #make slice no. = 20
+    new_slices = []
+    chunk_sizes = math.ceil(len(slices) / hm_slices)
+    for slice_chunk in chunks(slices, chunk_sizes):
+        slice_chunk = list(map(mean, zip(*slice_chunk)))
+        new_slices.append(slice_chunk)
+    #print(len(slices),len(new_slices))
+    empty_slice=list(np.zeros([img_px_size,img_px_size]))
+
+    for i in range(0,hm_slices-len(new_slices)):
+        new_slices.append(empty_slice)
+
+    print(len(slices),len(new_slices))
+
+    return new_slices,label
diff --git a/tumour_detection/lib/show_images.py b/tumour_detection/lib/show_images.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jun 17 19:50:51 2018
+
+@author: arpit
+"""
+
+import nibabel as nib
+import matplotlib.pyplot as plt
+import os,math
+import numpy as np
+import matplotlib.gridspec as gridspec
+
+
+'''def show_slices(slices,x):
+    """ Function to display row of image slices """
+    fig, axes = plt.subplots(x/4,4)       # changes as per the length of slice
+    for i, slice in enumerate(slices):
+        axes[i].imshow(slice.T, cmap="gray", origin="lower")
+'''
+
+
+def save_image_as_jpeg(input_file):
+    #input_file = os.getcwd()+input_img
+    img = nib.load(input_file)
+    img_data = img.get_data()
+    shape = img_data.shape 
+    slice_list=[]
+    for i in range(0,shape[2]):               #for different parts scanned during MRI 
+        slice_list.append(img_data[:, :, i])         # making a 3D matrix
+
+
+    plt.suptitle("Original NIfTI Images")
+    fig=plt.figure(figsize=(5,10))
+    plt.axis('off')
+    columns = 5
+    rows = math.ceil((shape[2])/5)
+    gs1 = gridspec.GridSpec(rows,columns)
+    gs1.update(wspace=0.05, hspace=0.05)
+    for i in range(0,shape[2]):
+    #img = np.random.randint(10, size=(h,w))
+        fig.add_subplot(gs1[i],xticks=[],yticks=[])
+        plt.imshow(slice_list[i],cmap="gray",origin="upper",aspect="auto")
+    fig.subplots_adjust(wspace=0,hspace=0)
+    plt.savefig(input_file+'.jpg',bbox_inches='tight')
+    #plt.show()
+
+
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		from django.contrib import admin

		# Register your models here.
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		from django.db import models

		# Create your models here.
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		from django.test import TestCase

		# Create your tests here.