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src/Dataset/dataset/dicom_to_png_for_VinDR_sampled_using_mammo.py

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+import os
+import cv2
+import csv
+import json
+import imageio
+
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+import matplotlib.pyplot as plt
+from pydicom import dcmread
+
+def dcm_to_png(dcm_path,save_png_path):
+    ds = dcmread(dcm_path)
+    arr = ds.pixel_array
+    img_array = arr.copy()
+    cv2.normalize(arr, img_array, 0, 255, cv2.NORM_MINMAX)
+    img_array = np.array(img_array,dtype='uint8')
+    # img_array = cv2.resize(img_array, (512,512), interpolation = cv2.INTER_LINEAR)
+    imageio.imwrite(save_png_path,img_array) 
+
+def preprocess_csv(csv_path,data_dir,save_data_dir):
+    data_info = pd.read_csv(csv_path)
+    patient_file_list = data_info.iloc[:,0]
+    img_file_list = data_info.iloc[:,2]
+    for idx in tqdm(range(len(img_file_list))):
+        patient_file = patient_file_list[idx]
+        img_file = img_file_list[idx]
+        img_path = os.path.join(data_dir,str(patient_file),str(img_file)+'.dicom')
+        os.makedirs(os.path.join(save_data_dir,str(patient_file)), exist_ok=True)
+        save_img_path = os.path.join(save_data_dir,str(patient_file),str(img_file)+'.png')
+        dcm_to_png(img_path,save_img_path)
+
+
+csv_path = './DATA/VinDr/VinDr-Mammo/1.0.0/breast-level_annotations.csv'
+data_dir = './DATA/VinDr/VinDr-Mammo/1.0.0/images'
+save_data_dir = './DATA/VinDr/VinDr-Mammo/process/images'
+os.makedirs(save_data_dir, exist_ok=True)
+preprocess_csv(csv_path,data_dir,save_data_dir)