Preprocessing RGB image Masks to Segmentation Masks

Hi, with this kernel, I will transfer your RGB color image masks to pre-process and make it ready for the semantic segmentation model.

The first step in training our segmentation model is to prepare the dataset. We would need the input RGB images and the corresponding segmentation images. If you want to make your own dataset, a tool like labelme or GIMP can be used to manually generate the ground truth segmentation masks. Assign each class a unique ID. In the segmentation images, the pixel value should denote the class ID of the corresponding pixel. You can examine for Deep Learning based Semantic Segmentation example

If you have RGB color image masks (like Aerial Semantic Segmentation Drone Dataset RGB_color_masks Folder ) , you can follow the steps below. First of all, our goal is to obtain a pixel-based class image from RGB color image masks as follows.RGB color image maskspixel-based class image masks

The dataset has RGB colored segmentation masks, and there are 24 classes in the dataset. The dataset has a file named class_dict.csv and it contains the name of the classes and their respective RGB values. The contents of the file are as follows:

name	r	g	b
unlabeled	0	0	0
paved-area	128	64	128
dirt	130	76	0
grass	0	102	0
gravel	112	103	87
water	28	42	168
rocks	48	41	30
pool	0	50	89
vegetation	107	142	35
roof	70	70	70
wall	102	102	156
window	254	228	12
door	254	148	12
fence	190	153	153
fence-pole	153	153	153
person	255	22	96
dog	102	51	0
car	9	143	150
bicycle	119	11	32
tree	51	51	0
bald-tree	190	250	190
ar-marker	112	150	146
obstacle	2	135	115
conflicting	255	0	0

import os,re
from PIL import Image
import numpy as np
import pandas as pd
import cv2
import matplotlib.pyplot as plt
%matplotlib inline

def atoi(text) : 
    return int(text) if text.isdigit() else text

def natural_keys(text) :
    return [atoi(c) for c in re.split('(\d+)', text)]


read_csv = pd.read_csv('/kaggle/input/semantic-drone-dataset/class_dict_seg.csv',index_col=False,skipinitialspace=True)
read_csv.head()


def segmantation_images(path,new_path, debug_test_num):
    filenames = []

    for root, dirnames, filenames in os.walk(path):
        filenames.sort(key = natural_keys)
        rootpath = root

    #print(filenames)
    count = 0
    for item in filenames:

        if debug_test_num !=0:
            if debug_test_num <= count:
                break

        count = count + 1

        if os.path.isfile(path+item):
            f, e = os.path.splitext(item)
            image_rgb = Image.open(path+item)
            image_rgb = np.asarray(image_rgb)
            new_image = np.zeros((image_rgb.shape[0],image_rgb.shape[1],3)).astype('int')

            for index, row  in read_csv.iterrows():
                new_image[(image_rgb[:,:,0]==row.r)&
                          (image_rgb[:,:,1]==row.g)&
                          (image_rgb[:,:,2]==row.b)]=np.array([index+1,index+1,index+1]).reshape(1,3)

            new_image = new_image[:,:,0]
            output_filename = new_path+f+'.png'
            cv2.imwrite(output_filename,new_image)
            print('writing file: ',output_filename)

        else:
            print('no file')

    print("number of files written: ",count)

debug_test_num = 5 # 5 samples are enough just to see it working. If you set 0, you can do all the datasets.
segmantation_images("/kaggle/input/semantic-drone-dataset/RGB_color_image_masks/RGB_color_image_masks/", "", debug_test_num=debug_test_num)

from PIL import Image
input_image = "/kaggle/input/semantic-drone-dataset/RGB_color_image_masks/RGB_color_image_masks/"+"002.png"
output_image = "002.png"

fig, axs = plt.subplots(1, 2, figsize=(16, 8), constrained_layout=True)

img_orig = Image.open(input_image)
axs[0].imshow(img_orig)
axs[0].grid(False)

output_image = Image.open(output_image)
out_array = np.asarray(output_image)
axs[1].imshow(out_array)
axs[1].grid(False)

Preprocessing RGB image Masks to Segmentation Masks

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