Method 1:
In your case,
The answer is ...
import pydicom
import skimage, os
import numpy as np
FolderPathName = r'FolderPathName'
slices = [pydicom.read_file(FolderPathName + imagename) for imagename in os.listdir(FolderPathName)]
# Sort the dicom slices in their respective order
slices.sort(key=lambda x: int(x.InstanceNumber))
jpg_folder = '' # Set your jpg folder
for idx in range(len(slices)):
#write the images in a new folder
jpg_filepath = os.path.join( jpg_folder, "pic-{}.jpg".format(idx) )
np_pixel_array = slices[idx].pixel_array
cv2.imwrite(jpg_filepath, np_pixel_array)
Method 2:
But, there is better way to process dicom files ...
import pydicom
import os
import numpy as np
import cv2
dicom_folder = '' # Set the folder of your dicom files that inclued images
jpg_folder = '' # Set the folder of your output folder for jpg files
# Step 1. prepare your input(.dcm) and output(.jpg) filepath
dcm_jpg_map = {}
for dicom_f in os.listdir(dicom_folder):
dicom_filepath = os.path.join(dicom_folder, dicom_f)
jpg_f = dicom_f.replace('.dcm', '.jpg')
jpg_filepath = os.path.join(jpg_folder,jpg_f)
dcm_jpg_map[dicom_filepath] = jpg_filepath
# Now, dcm_jpg_map is key,value pair of input dcm filepath and output jpg filepath
# Step 2. process your image by input/output information
for dicom_filepath, jpg_filepath in dcm_jpg_map.items():
# convert dicom file into jpg file
dicom = pydicom.read_file(dicom_filepath)
np_pixel_array = dicom.pixel_array
cv2.imwrite(jpg_filepath, np_pixel_array)
In above code,
the Step 1 is focus on file path processing. It's good for your to porting your code into different environment easily.
The Step 2 is major code which focus on any kind of image processing.