Functions

import numpy as np
import csv 
from PIL import Image
import os,sys
!{sys.executable} -m pip install idx2numpy
import idx2numpy

def dimensionImages(train_imgs):
  dimensionImgs = []
  for i in train_imgs:
    dimensionImgs.append(i.reshape(100,100))

  return dimensionImgs


def flattenImages(imgArray):
  flattenImgsTrain = []
  for i in imgArray:
    flattenImgsTrain.append(i.reshape(-1))

  return flattenImgsTrain

def imageToArray(path):
  label=[]
  feature=[]

  # Transform normal file : resize + array + gray scale
  for i in os.listdir(path+'/NORMAL'):
    label.append(0)
    feature.append(np.array(Image.open(path+'/NORMAL/'+i).resize((100,100)).convert('L')))

  # Transform pneumonia file : resize + array + gray scale
  for j in os.listdir(path+'/PNEUMONIA'):
    if 'bacteria' in j:
      label.append(1)
      feature.append(np.array(Image.open(path+'/PNEUMONIA/'+j).resize((100,100)).convert('L')))
    else:
      label.append(2)
      feature.append(np.array(Image.open(path+'/PNEUMONIA/'+j).resize((100,100)).convert('L')))
  print(label)
  return label, feature
Requirement already satisfied: idx2numpy in /usr/local/lib/python3.7/dist-packages (1.2.3)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from idx2numpy) (1.19.5)
Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from idx2numpy) (1.15.0)

Tranformation

Val set

val_label, val_feature=imageToArray('/content/drive/MyDrive/chest_xray/val')
val_feature=flattenImages(val_feature)

np.savetxt('/content/drive/MyDrive/val_label.csv', val_label, delimiter=',')
np.savetxt('/content/drive/MyDrive/val_feature.csv', val_feature, delimiter=',')
[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]

Test set

test_label, test_feature=imageToArray('/content/drive/MyDrive/chest_xray/test')
test_feature=flattenImages(test_feature)

np.savetxt('/content/drive/MyDrive/test_label.csv', test_label, delimiter=',')
np.savetxt('/content/drive/MyDrive/test_feature.csv', test_feature, delimiter=',')
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

Train set

train_label, train_feature=imageToArray('/content/drive/MyDrive/chest_xray/train')
train_feature=flattenImages(train_feature)

np.savetxt('/content/drive/MyDrive/train_label.csv', train_label, delimiter=',')
np.savetxt('/content/drive/MyDrive/train_feature.csv', train_feature, delimiter=',')
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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Graphiques

import matplotlib.pyplot as plt


def countSet():
  dr = {}
  path='/content/drive/MyDrive/chest_xray/'
  for i in os.listdir(path):
    for j in os.listdir(path+i):
      dr[i+'_'+j]=0
      if j=='PNEUMONIA':
        dr[i+'_'+j+'_virus']=0
        dr[i+'_'+j+'_bacterie']=0
      for k in os.listdir(path+i+'/'+j):
        if 'PNEUMONIA' in j:
          if 'virus' in k:
            dr[i+'_'+j+'_virus']=dr[i+'_'+j+'_virus']+1
            dr[i+'_'+j]=dr[i+'_'+j]+1
          elif 'bacteria' in k:
            dr[i+'_'+j+'_bacterie']=dr[i+'_'+j+'_bacterie']+1
            dr[i+'_'+j]=dr[i+'_'+j]+1
          else:
            dr[i+'_'+j]=dr[i+'_'+j]+1
        else:
          dr[i+'_'+j]=dr[i+'_'+j]+1
  return dr

dr=countSet()
print(dr)
plt.bar(range(len(dr.values())),dr.values())
plt.xticks(range(len(dr.keys())),dr.keys(),rotation='vertical')

{'test_NORMAL': 234, 'test_PNEUMONIA': 390, 'test_PNEUMONIA_virus': 148, 'test_PNEUMONIA_bacterie': 242, 'val_NORMAL': 8, 'val_PNEUMONIA': 8, 'val_PNEUMONIA_virus': 0, 'val_PNEUMONIA_bacterie': 8, 'train_NORMAL': 1333, 'train_PNEUMONIA': 3873, 'train_PNEUMONIA_virus': 1344, 'train_PNEUMONIA_bacterie': 2529}





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  <matplotlib.axis.XTick at 0x7fcaa3781c10>,
  <matplotlib.axis.XTick at 0x7fcaa3781910>,
  <matplotlib.axis.XTick at 0x7fcaa3642f10>,
  <matplotlib.axis.XTick at 0x7fcaa36504d0>,
  <matplotlib.axis.XTick at 0x7fcaa3650a10>,
  <matplotlib.axis.XTick at 0x7fcaa36508d0>,
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  <matplotlib.axis.XTick at 0x7fcaa3654e90>,
  <matplotlib.axis.XTick at 0x7fcaa365f410>],
 [Text(0, 0, 'test_NORMAL'),
  Text(0, 0, 'test_PNEUMONIA'),
  Text(0, 0, 'test_PNEUMONIA_virus'),
  Text(0, 0, 'test_PNEUMONIA_bacterie'),
  Text(0, 0, 'val_NORMAL'),
  Text(0, 0, 'val_PNEUMONIA'),
  Text(0, 0, 'val_PNEUMONIA_virus'),
  Text(0, 0, 'val_PNEUMONIA_bacterie'),
  Text(0, 0, 'train_NORMAL'),
  Text(0, 0, 'train_PNEUMONIA'),
  Text(0, 0, 'train_PNEUMONIA_virus'),
  Text(0, 0, 'train_PNEUMONIA_bacterie')])

png