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classifyImages.py
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# importing os module
import os
# Parent Directory path
import pandas as pd
from glob import glob
from shutil import copyfile, copy
from sklearn.model_selection import train_test_split
import re
"""
Train will suppose the 60% of the data
Validation will supppose 30% of the data
Test will only be 10% of all the data
"""
#Csv with values for the dataset
xray_data = pd.read_csv('D:\DescargasChrome\Data_Entry_2017.csv')
regexList =['.*Atelectasis.*', '.*Consolidation.*', '.*Infiltration.*', '.*Pneumothorax.*', '.*Edema.*', '.*Emphysema.*', '.*Fibrosis.*',
'.*Effusion.*', '.*Pneumonia.*', '.*Pleural_Thickening.*',
'.*Cardiomegaly.*', '.*Nodule.*', '.*Mass.*', '.*Hernia.*', '.*No Finding.*']
labels = ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis',
'Effusion', 'Pneumonia', 'Pleural_Thickening',
'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', 'NoFinding']
#Arrays for images paths
Atelectasis=[]
Consolidation=[]
Infiltration=[]
Pneumothorax=[]
Edema=[]
Emphysema=[]
Fibrosis=[]
Effusion=[]
Pneumonia=[]
Pleural_Thickening=[]
Cardiomegaly=[]
Nodule=[]
Mass=[]
Hernia=[]
NoFinding=[]
"""
Adds the path to the file to the data frame as a column called FullPath
"""
def add_full_path():
my_glob = glob('D:\DescargasChrome\input\images*\images\*.png')
full_img_paths = {os.path.basename(x): x for x in my_glob}
#print(full_img_paths)
xray_data['FullPath'] = xray_data['Image Index'].map(full_img_paths.get)
add_full_path()
"""
Adds a vector of 0 or 1 values corresponding to the label if it's present or not
"""
def add_target_vector():
dummy_labels = ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis',
'Effusion', 'Pneumonia', 'Pleural_Thickening',
'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', 'NoFinding']
for label in dummy_labels:
xray_data[label] = xray_data['Finding Labels'].map(lambda result: 1.0 if label in result else 0)
xray_data['TargetVector'] = xray_data.apply(lambda target: [target[dummy_labels].values], 1).map(
lambda target: target[0])
add_target_vector()
train, testValidation = train_test_split(xray_data, test_size=0.40, random_state=100)
validation, test = train_test_split(testValidation, test_size=0.20, random_state=100)
# train, validate, test = pd.np.split(xray_data, [int(.6 * (xray_data)), int(.8 * len(xray_data))])
print(len(train), len(validation), len(test))
"""
Save Xray train test and validation dataframes as csv
"""
xray_data.to_csv(r'C:\Users\Alejandro\Documents\GitHub\MachineLearningModels\xrayCsv.csv', sep=",", index=False)
train.to_csv(r'C:\Users\Alejandro\Documents\GitHub\MachineLearningModels\trainCsv.csv', sep=",", index=False)
test.to_csv(r'C:\Users\Alejandro\Documents\GitHub\MachineLearningModels\testCsv.csv', sep=",", index=False)
validation.to_csv(r'C:\Users\Alejandro\Documents\GitHub\MachineLearningModels\validationCsv.csv', sep=",", index=False)
def create_labelled_dir():
parent_dir_test = r"D:\DescargasChrome\data\test"
dummy_labels = ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion', 'Pneumonia', 'Pleural_Thickening',
'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', 'NoFinding']
for i in dummy_labels:
if not os.path.exists(os.path.join(parent_dir_test,i)):
os.makedirs(os.path.join(parent_dir_test,i))
parent_dir_train = r"D:\DescargasChrome\data\train"
dummy_labels = ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion', 'Pneumonia', 'Pleural_Thickening',
'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', 'NoFinding']
for i in dummy_labels:
if not os.path.exists(os.path.join(parent_dir_train,i)):
os.makedirs(os.path.join(parent_dir_train,i))
parent_dir_train = r"D:\DescargasChrome\data\validation"
dummy_labels = ['Atelectasis', 'Consolidation', 'Infiltration', 'Pneumothorax', 'Edema', 'Emphysema', 'Fibrosis', 'Effusion', 'Pneumonia', 'Pleural_Thickening',
'Cardiomegaly', 'Nodule', 'Mass', 'Hernia', 'NoFinding']
for i in dummy_labels:
if not os.path.exists(os.path.join(parent_dir_train,i)):
os.makedirs(os.path.join(parent_dir_train,i))
create_labelled_dir()
# def fillAtelectasisLabel():
# """
# Si la finding label es Atelectasis entonces mover ese archivo desde su full path al nuevo path
# """
# labelsTest= test['Finding Labels'].tolist()
# labelsValidation= validation['Finding Labels'].tolist()
# labelsTrain= train['Finding Labels'].tolist()
#
# #print(labels)
# imagePathTest= test['FullPath'].tolist()
# imagePathValidation = validation['FullPath'].tolist()
# imagePathTrain = train['FullPath'].tolist()
# listToAddTest = []
# listToAddValidation = []
# listToAddTrain = []
#
# for i in range(len(labelsTest)):
# print(labelsTest[i])
# if labelsTest[i] == "Atelectasis": #regex
# listToAddTest.append(imagePathTest[i])
#
# for i in range(len(labelsValidation)):
# print(labelsValidation[i])
# if labelsValidation[i] == "Atelectasis": #regex
# listToAddValidation.append(imagePathValidation[i])
#
# for i in range(len(labelsTrain)):
# print(labelsTrain[i])
# if labelsTrain[i] == "Atelectasis": #regex
# listToAddTrain.append(imagePathTrain[i])
#
# print(len(listToAddTest), len(listToAddValidation), len(listToAddTrain))
#
#
# for t in range(len(listToAddTest)):
# print(listToAddTest[t])
# copy(listToAddTest[t],r'D:\DescargasChrome\data\test\Atelectasis')#Directory+label
#
# for v in range(len(listToAddValidation)):
# print(listToAddValidation[v])
# copy(listToAddValidation[v],r'D:\DescargasChrome\data\validation\Atelectasis')#Directory+label
#
# for tr in range(len(listToAddTrain)):
# print(listToAddTrain[tr])
# copy(listToAddTrain[tr],r'D:\DescargasChrome\data\train\Atelectasis')#Directory+label
#
#
#
#
# fillAtelectasisLabel()
def fillDirectoryLabel(regex, label):
"""
Si la finding label es Atelectasis entonces mover ese archivo desde su full path al nuevo path
"""
labelsTest = test['Finding Labels'].tolist()
labelsValidation = validation['Finding Labels'].tolist()
labelsTrain = train['Finding Labels'].tolist()
# print(labels)
imagePathTest = test['FullPath'].tolist()
imagePathValidation = validation['FullPath'].tolist()
imagePathTrain = train['FullPath'].tolist()
listToAddTest = []
listToAddValidation = []
listToAddTrain = []
for ltest in range(len(labelsTest)):
print(labelsTest[ltest])
if re.search(regex,labelsTest[ltest]): # regex
listToAddTest.append(imagePathTest[ltest])
for lval in range(len(labelsValidation)):
print(labelsValidation[lval])
if re.search(regex,labelsValidation[lval]): # regex
listToAddValidation.append(imagePathValidation[lval])
for ltrain in range(len(labelsTrain)):
print(labelsTrain[ltrain])
if re.search(regex,labelsTrain[ltrain]): # regex
listToAddTrain.append(imagePathTrain[ltrain])
print(len(listToAddTest), len(listToAddValidation), len(listToAddTrain))
for t in range(len(listToAddTest)):
print(listToAddTest[t])
copy(listToAddTest[t], os.path.join(r'D:\DescargasChrome\data\test',label)) # Directory+label
for v in range(len(listToAddValidation)):
print(listToAddValidation[v])
copy(listToAddValidation[v], os.path.join(r'D:\DescargasChrome\data\validation',label)) # Directory+label
for tr in range(len(listToAddTrain)):
print(listToAddTrain[tr])
copy(listToAddTrain[tr], os.path.join(r'D:\DescargasChrome\data\train',label)) # Directory+label
for regex in range(len(regexList)):
print(regex)
fillDirectoryLabel(regexList[regex],labels[regex])
print(xray_data)