和dicom文件

Question

在尝试将opencv与dicom单色文件一起使用时，我只看到了一种解决方案:首先，使用0<=R=G=B<=255将具有像素值在- 2000 (黑色)到2000年(白色)之间的monochrome文件进行转换。(为了确保灰度，我必须设置R=G=B)，所以我做了一个线性插值，从第一-2000年；2000年到0,255。我的照片的结果不好，所以我决定把一个黑色的三层，所有的像素都是黑色的，白色的三倍于此，所有的像素都是白色的。这样做，我可以使用opencv，但我想要自动化的黑色阈值和白色三人持有2)，因为我有512*512像素，双for循环需要时间来执行。

你知道我怎么能自动化和加速这个过程吗？或者仅仅是个好主意？守则是：

    # import the necessary packages
from imutils import contours
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import scipy
from skimage import measure
import numpy as np # numeric library needed
import pandas as pd #for dataframe
import argparse # simple argparser
import imutils
import cv2  # for opencv image recognising tool
import dicom
from tkinter import Tk
from tkinter.filedialog import askopenfilename
import pdb

#filename = askopenfilename() # show an "Open" dialog box and return the path to the selected file
#filename ="../inputs/12e0e2036f61c8a52ee4471bf813c36a/7e74cdbac4c6db70bade75225258119d.dcm"
dicom_file = dicom.read_file(filename) ## original dicom File
#### a dicom monochrome 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

###### function to transform a dicom to RGB for the use of opencv, 
##to be strongly improved, as it takes to much time to run,
## and the linear process should be replaced with an adapted weighted arctan function.
def DicomtoRGB(dicomfile,bt,wt):
    """Create new image(numpy array) filled with certain color in RGB"""
    # Create black blank image
    image = np.zeros((dicomfile.Rows, dicomfile.Columns, 3), np.uint8)
    #loops on image height and width
    i=0
    j=0
    while i<dicomfile.Rows:
        j=0
        while j<dicomfile.Columns:
            color = yaxpb(dicom_file.pixel_array[i][j],bt,wt) #linear transformation to be adapted
            image[i][j] = (color,color,color)## same R,G, B value to obtain greyscale
            j=j+1
        i=i+1
    return image
##linear transformation : from [bt < pxvalue < wt] linear to [0<pyvalue<255]: loss of information... 
def yaxpb(pxvalue,bt,wt):
    if pxvalue < bt:
        y=0
    elif pxvalue > wt:
        y=255
    else:
        y=pxvalue*255/(wt-bt)-255*bt/(wt-bt)
    return y



image=DicomtoRGB(dicom_file,bt=0,wt=1400)
>>image
array([[[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       ..., 
       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0],
        ..., 
        [0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]]], dtype=uint8)

## 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")

Answer 1

EDIT2 -现在正在执行适当的转换

我们可以使用numpy将整个代码向量化。下面是一个例子：

import numpy as np

def dicom_to_rgb(img,bt,wt):

    # enforce boundary conditions
    img = np.clip(img,bt,wt)

    # linear transformation
    # multiplicative
    img = np.multiply(img,-255/(wt-bt)).astype(np.int)
    # additive
    img += 255

    # stack thrice on the last axis for R G B
    rgb_img = np.stack([img]*3,axis=-1)

    return rgb_img


    pixels = 512
    img = np.random.randint(-2000,2000,pixels**2).reshape(pixels,pixels)
    bt = 0
    wt = 1400

    rgb = dicom_to_rgb(img,bt,wt)

或者你的意见：

dicom_file = dicom.read_file(filename)
img = np.array(dicom_file.pixel_array)

rgb = dicom_to_rgb(img,wt,bt)

Answer 2

我想你的问题就在于此：

..。双for循环需要时间执行。

您可以使用从opencv重新映射函数:参见这个例子