dicom_to_jpg.py

import pandas as pd
import pydicom
import numpy as np
import os
import cv2
from pathlib import Path
from datetime import datetime as dt
print(pd.__version__)


def win_scale(data, wl, ww, dtype, out_range):
    """
    Scale pixel intensity data using specified window level, width, and intensity range.
    """
    data_new = np.empty(data.shape, dtype=np.double)
    data_new.fill(out_range[1]-1)
    data_new[data <= (wl-ww/2.0)] = out_range[0]
    data_new[(data>(wl-ww/2.0))&(data<=(wl+ww/2.0))] = \
         ((data[(data>(wl-ww/2.0))&(data<=(wl+ww/2.0))]-(wl-0.5))/(ww-1.0)+0.5)*(out_range[1]-out_range[0])+out_range[0]
    data_new[data > (wl+ww/2.0)] = out_range[1]-1
    return data_new.astype(dtype)


labels = pd.read_csv('cxr_news2_pseudonymised.csv')
#labels = labels.sort_values('AccessionID')
print('Labels', labels.shape)

#labels = labels.drop_duplicates(subset=['Accession'], ignore_index=True)
#print('Unique Accession', labels.shape)
#exit(0)

PATH = '/nfs/project/covid/CXR/KCH_CXR'
save_path = '/nfs/project/covid/CXR/tmp'
#save_path = '/nfs/project/covid/CXR/KCH_CXR_JPG_CONTRAST_FIX'

csv = [f for f in Path(PATH).rglob('*.dcm')]
print('Dicoms', len(csv))

Filename = []
Accession = []
CXR_datetime = []
Pixel_data = []
acc_count = 0
pix_count = 0
combined_count = 0
for f in csv:
    a = False
    p = False
    print (f)
    ds = pydicom.dcmread(f)
    acc = ds.AccessionNumber
    if labels.Accession.str.contains(acc).any():
        acc_count += 1
        a = True
    Accession.append(acc)
    print(acc)
    if "AcquisitionDateTime" in ds:
        datetime = ds.AcquisitionDateTime.split('.')[0]
    elif "ContributionDateTime" in ds:
        datetime = ds.ContributionDateTime.split('.')[0]
    elif ("StudyDate" in ds) and ("StudyTime" in ds):
        datetime = ds.StudyDate + ds.StudyTime
    else:
        print('No date time for', f)
        exit(0)
    name = acc
    fname = str(name) + '_' + str(datetime)
    Filename.append(fname)
    datetime = pd.to_datetime(datetime, format='%Y%m%d%H%M%S')
    CXR_datetime.append(datetime)
    try:
        wl, ww = ds.WindowCenter, ds.WindowWidth
        if isinstance(wl, pydicom.multival.MultiValue):
            img = win_scale(data=ds.pixel_array, wl= int(wl[0]), ww=int(ww[0]), dtype = np.uint16, out_range=[0, 16383])
        else:
            img = win_scale(data=ds.pixel_array, wl= int(wl), ww=int(ww), dtype = np.uint16, out_range=[0, 16383])

        #img = ds.pixel_array.astype(np.float32)
        img = img.astype(np.float32)
        img -= img.min()
        img /= img.max()
        img = np.uint8(255.0*img)
        print(img.shape)
        save_name = os.path.join(save_path, (fname + '.jpg'))
        print(save_name)
        cv2.imwrite(save_name, img)
        Pixel_data.append('Y')
        pix_count += 1
        p = True
    except:
        Pixel_data.append('N')
    if a and p:
        combined_count += 1
print('Accession', acc_count)
print('Pixel data', pix_count)
print('Combined', combined_count)
df = pd.DataFrame({'Filename':Filename, 'Accession':Accession, 'CXR_datetime':CXR_datetime, 'Pixel_data':Pixel_data})
print(df)
#df.to_csv('all_data.csv', index=False)
exit(0)

files = 0
count =0
for i, name in enumerate(labels.Accession):
    #print(i, name)
    img_dir = os.path.join(PATH, name)
    tmp = os.path.exists(img_dir)
    if not tmp:
        print('Cant find', img_dir)
    else:
        print('Found', img_dir)
        count += 1
        csv = [f for f in Path(img_dir).rglob('*.dcm')]
        files += len(csv)
print('Matching files', count)
print('Matching dicoms', files)
exit(0)

PatientID = []
Filename = []
Accession = []
#Examination_Title = []
#Age = []
#Gender = []
#SymptomOnset_DTM = []
#Death_DTM = []
#Died = []
count=0

for i, name in enumerate(labels.Accession):
    pid = labels.patient_pseudo_id[i]
    print(i, name, pid)
    img_dir = os.path.join(PATH, name)
    files = [f for f in Path(img_dir).rglob('*.dcm')]
    #print(files)
    #if i > 100:
    #    break
    y = -1
    month = -1
    day = -1
    for f in files:
        #print(os.fspath(f.absolute()))
        ds = pydicom.dcmread(f)
        #print(ds)
        #exit(0)

        if "AcquisitionDateTime" in ds:
            datetime = ds.AcquisitionDateTime.split('.')[0]
        elif "ContributionDateTime" in ds:
            datetime = ds.ContributionDateTime.split('.')[0]
        year = datetime[:4]
        month = datetime[4:6]
        day = datetime[6:8]
        time = datetime[8::]
        #print(year, month, day, time)
        fname = name + '_' + datetime

        acc = labels.Accession[i]
        #ext = labels.Examination_Title[i]
        #age = labels.Age[i]
        #gen = labels.Gender[i]
        #sym = labels.SymptomOnset_DTM[i]
        #dtm = labels.Death_DTM[i]
        #ddd = labels.Died[i]

        try:
            img = ds.pixel_array.astype(np.float32)
            img -= img.min()
            img /= img.max()
            img = np.uint8(255.0*img)
            print(img.shape)
            count += 1

            save_name = os.path.join(save_path, (fname + '.jpg'))
            print(save_name)
            cv2.imwrite(save_name, img)

            PatientID.append(pid)
            Filename.append(fname)
            Accession.append(acc)
            #Examination_Title.append(ext)
            #Age.append(age)
            #Gender.append(gen)
            #SymptomOnset_DTM.append(sym)
            #Death_DTM.append(dtm)
            #Died.append(ddd)
            #print(len(Filename), len(AccessionID), len(Died))
        except:
            print('Cannot load image')

print('Total', count)

#print(len(Filename), len(AccessionID), len(Died))

df = pd.DataFrame({'Filename':Filename,
                   'PatientID':PatientID,
                   'Accession':Accession,
                   #'Examination_Title':Examination_Title,
                   #'Age':Age,
                   #'Gender':Gender,
                   #'SymptomOnset_DTM':SymptomOnset_DTM,
                   #'Death_DTM':Death_DTM,
                   #'Died':Died
                    })
df.to_csv('KCH_CXR_JPG.csv')

exit(0)







#csv = [f for f in Path(r'./').rglob('*.png')]
#csv = [f for f in Path(r'./').rglob('*.jpg')]
#csv = [f for f in Path(r'./').rglob('*.dcm')]
#files = []
#for f in csv:
#    files.append(os.fspath(f.absolute()))
#df = pd.DataFrame({'filepath':files})
#df.to_csv('out.csv')

filepaths = 'KCH_CXR.csv'
#filepaths = 'PUBLIC.csv'
df = pd.read_csv(filepaths)
print(df.shape)
print(df.head())

save_path = '/nfs/project/richard/COVID/KCH_CXR_JPG'
filelist = []
for f in df.filepath:
    #print(f)
    head, tail = os.path.split(f)
    tail = os.path.splitext(tail)[0]
    #print(tail)
    ds = pydicom.dcmread(f)
    try:
        img = ds.pixel_array.astype(np.float32)
        img -= img.min()
        img /= img.max()
        img = np.uint8(255.0*img)
        save_name = os.path.join(save_path, tail+'.jpg')
        cv2.imwrite(save_name, img)
        filelist.append(save_name)
    except:
        print('Cannot load image')

df = pd.DataFrame({'filepath':filelist})
df.to_csv('KCH_CXR_JPG.csv')