Cfrtjryk

This script replaces the red hair in image to black color (replace one color to another)

Three major parts in this script

• Not just replace color red to black, I hope the output image looks natural, so I will replace the color close to red to black. This "close" is measured by Euclidean distance.




• And not just replace by black color, user can modify the red color's r, g, b value separately, like add more green.




• Choose the area to be changed, rather than change the whole picture

Suggestions I am looking for:

• I did some NumPy practice before, so any suggestions about NumPy are welcome.


• 
  

  I think the code to replace target area is not so elegant, I am not quite sure about this part:

  
  
  

  (c1, r1), (c2, r2) = area
  
  for i in range(r1, r2+1):
  
      l, r = i*h+c1, i*h+c2+1
  
      data[l:r,:k][D[l:r]] = modification(data[l:r,:k][D[l:r]])
  
  

  
  

Full code:

import numpy as np

from PIL import Image

from scipy.spatial.distance import cdist



def replace_corlor(image, original_corlor, modification, area=None, distance=1000, output="new_test.jpg"):

    print("[*] START Replace Color")

    img = Image.open(image)

    data = np.asarray(img, dtype="int32")

    w,h,k = data.shape

    data = np.reshape(data, (w*h,k))

    distMatrix = cdist(data, np.array([original_corlor]))

    D = distMatrix<=distance

    D = np.reshape(D,w*h)

    if area is None:

        data[:,:k][D] = modification(data[:,:k][D])

    else:

        (c1, r1), (c2, r2) = area

        for i in range(r1, r2+1):

            l, r = i*h+c1, i*h+c2+1

            data[l:r,:k][D[l:r]] = modification(data[l:r,:k][D[l:r]])

    data = np.reshape(data, (w,h,k))

    img = Image.fromarray(np.asarray(np.clip(data, 0, 255), dtype="uint8"), "RGB")

    img.save(output)

    print("[*] DONE Replace Color")



if __name__ == "__main__":

    def modification(color):

        return color * [2,0,0]

        # return [255,0,0]

    replace_corlor("test.jpg",(36,35,30),modification, ((0,0),(400,100)))