DicomToRGB

# import the necessary packages
from imutils import contours
import scipy
from skimage import measure
import numpy as np # numeric library needed
import pandas as pd #for datafrome
import argparse # simple argparser
import imutils
import cv2  # for opencv image recognising tool
import dicom
filename = askopenfilename()
dicom_file = dicom.read_file(filename) ## original dicom File
#### a dicom monochrome-2 file has pixel value between approx -2000 and +2000, opencv doesn't work with it#####
#### in a first step we transform those pixel values in (R,G,B)
### to have gray in RGB, simply give the same values for R,G, and B, 
####(0,0,0) will be black, (255,255,255) will be white,

## the threeshold to be automized with a proper quartile function of the pixel distribution
black_threeshold=0###pixel value below 0 will be black,
white_threeshold=1400###pixel value above 1400 will be white
wt=white_threeshold
bt=black_threeshold

def DicomtoRGB(dicomfile,bt,wt):
    #load the dicom_file as a numpy pixel array
    img=np.array(dicom_file.pixel_array, np.int16)
    # enforce boundary conditions
    clipped_img = np.clip(img,bt,wt)

    # linear transformation
    # additive
    rgb_add = clipped_img - bt
    
    #multiplicative
    rgb_mult = np.multiply(rgb_add,255/(wt-bt))
    
    # round to nearest int
    rgb_int = np.around(rgb_mult,0).astype(np.uint8)
    
    # stack thrice on the last axis for R G B
    rgb_img = np.stack([rgb_int]*3,axis=-1)
    return rgb_img

image=DicomtoRGB(dicom_file,bt=0,wt=1400)
## loading the RGB in a proper opencv format
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
## look at the gray file
cv2.imshow("gray", gray)
cv2.waitKey(0)
cv2.destroyWindow("gray")