Pneumothorax_extract_metadata

def extract_metadata_from_images(file_path):
    '''
        Extracts metadata present in DICOM file
        Args: 
            file :  DICOM file path
        Returns:
                a dictionary containing important metadata 
    '''

    dataset = pydicom.dcmread(file_path)
    d = {}
    d['ImageID'] = os.path.splitext(file_path.split('/')[-1])[0] # Exculding the format (.dcm)
    d['patientID'] = dataset.PatientID
    d['age'] = dataset.PatientAge
    d['sex'] = dataset.PatientSex
    d['view_position'] = dataset.ViewPosition
    d['pixel_spacing']  = dataset.PixelSpacing
    d["modality"] = dataset.Modality
    d["body_part_examined"] =  dataset.BodyPartExamined
    d['pixel_mean'] =  np.mean(dataset.pixel_array)
    d['pixel_min'] =  np.min(dataset.pixel_array)
    d['pixel_max'] =  np.max(dataset.pixel_array)
    return d
    
start = datetime.now()
num_cores = mp.cpu_count()
pool = Pool(num_cores)
results = pool.map(extract_metadata_from_images,train_files)
pool.close()
print("Total time taken {0}".format(datetime.now() - start))
df_meta = pd.DataFrame(results)
# Saving it for later use
df_meta.to_pickle('df_meta.pkl')

df_meta = pd.read_pickle('df_meta.pkl')
# Merging the class label file with extracted metadata
df_meta = df_meta.merge(class_df , left_on = 'ImageID',right_on='ImageId' ,how='left')
nan_rows = df_meta[df_meta.ImageId.isna()]
print('There are {0} rows that does not have Class label'.format(nan_rows.shape[0]))
# Dropping nan rows
df_meta.dropna(subset = ['ImageId'],inplace= True)
# Drop duplicates
df_meta.drop_duplicates(subset = ['ImageId'],inplace=True)
# Adding a column to differentiate rows with and without Pneumothorax
df_meta['class_'] = 'Pneumothorax'
df_meta.loc[df_meta['EncodedPixels'] == "-1",'class_'] = 'NotPneumothorax'
# Casting ages as int
df_meta['age'] = df_meta['age'].astype(int)