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Dataset for semantic Segmentation of Underwater IMagery.

Python 100.00%
computer-vision semantic-segmentation underwater-images underwater-image-processing underwater-image-segmentation underwater-datasets

suim's Introduction

SUIM Dataset

SUIM is a dataset proposed by Minnesota Interactive Robotics and Vision Laboratory. The details about this dataset are demonstrated below:

  • For semantic segmentation of natural underwater images
  • 1525 annotated images for training/validation and 110 samples for testing
  • Categories:
    • BW: Background/waterbody, 000 (black)
    • HD: human divers, 001 (blue)
    • PF: Aquatic plants and sea-grass, 010 (green)
    • WR: Wrecks/ruins, 011 (sky)
    • RO: Robots/instruments, 100 (red)
    • RI: Reefs/invertebrates, 101 (pink)
    • FV: Fish and vertebrates, 110 (yellow)
    • SR: Sea-floor/rocks, 111 (white)
  • Samples: samples

Thanks to this awesome dataset, we can do research on underwater image segmentation more easily! 😊

Why I create this repo?

However, I found there exits two problems in this dataset:

  1. Some masks in train_val folder are annotated with inaccurate RGB colors, thereby causing an obstacle for extracting masks information from masks files.

    For example, magnifying these three masks files (i.e., d_r_5_.bmp, f_r_1007_.bmp, and w_r_24_.bmp in train_val/masks folder), and you can find values of colors are improper in the border between two segments and other areas. You can use color picking tools to check them.

    d_r_5_ f_r_1007_ w_r_24_
    The list of masks files with improper colors: 

      d_r_112_.jpg d_r_112_.bmp
  d_r_135_.jpg d_r_135_.bmp
  d_r_174_.jpg d_r_174_.bmp
  d_r_179_.jpg d_r_179_.bmp
  d_r_189_.jpg d_r_189_.bmp
  d_r_20_.jpg d_r_20_.bmp
  d_r_270_.jpg d_r_270_.bmp
  d_r_273_.jpg d_r_273_.bmp
  d_r_293_.jpg d_r_293_.bmp
  d_r_301_.jpg d_r_301_.bmp
  d_r_310_.jpg d_r_310_.bmp
  d_r_333_.jpg d_r_333_.bmp
  d_r_470_.jpg d_r_470_.bmp
  d_r_473_.jpg d_r_473_.bmp
  d_r_564_.jpg d_r_564_.bmp
  d_r_59_.jpg d_r_59_.bmp
  d_r_5_.jpg d_r_5_.bmp
  d_r_633_.jpg d_r_633_.bmp
  d_r_65_.jpg d_r_65_.bmp
  d_r_741_.jpg d_r_741_.bmp
  d_r_759_.jpg d_r_759_.bmp
  f_r_1006_.jpg f_r_1006_.bmp
  f_r_1007_.jpg f_r_1007_.bmp
  f_r_1013_.jpg f_r_1013_.bmp
  f_r_1058_.jpg f_r_1058_.bmp
  f_r_1068_.jpg f_r_1068_.bmp
  f_r_1069_.jpg f_r_1069_.bmp
  f_r_1070_.jpg f_r_1070_.bmp
  f_r_1133_.jpg f_r_1133_.bmp
  f_r_1142_.jpg f_r_1142_.bmp
  f_r_1151_.jpg f_r_1151_.bmp
  f_r_1154_.jpg f_r_1154_.bmp
  f_r_1183_.jpg f_r_1183_.bmp
  f_r_1214_.jpg f_r_1214_.bmp
  f_r_1233_.jpg f_r_1233_.bmp
  f_r_1246_.jpg f_r_1246_.bmp
  f_r_1259_.jpg f_r_1259_.bmp
  f_r_1267_.jpg f_r_1267_.bmp
  f_r_1274_.jpg f_r_1274_.bmp
  f_r_1289_.jpg f_r_1289_.bmp
  f_r_1290_.jpg f_r_1290_.bmp
  f_r_1300_.jpg f_r_1300_.bmp
  f_r_1302_.jpg f_r_1302_.bmp
  f_r_1318_.jpg f_r_1318_.bmp
  f_r_1324_.jpg f_r_1324_.bmp
  f_r_1332_.jpg f_r_1332_.bmp
  f_r_1382_.jpg f_r_1382_.bmp
  f_r_1394_.jpg f_r_1394_.bmp
  f_r_1424_.jpg f_r_1424_.bmp
  f_r_1491_.jpg f_r_1491_.bmp
  f_r_1515_.jpg f_r_1515_.bmp
  f_r_1570_.jpg f_r_1570_.bmp
  f_r_1664_.jpg f_r_1664_.bmp
  f_r_1779_.jpg f_r_1779_.bmp
  f_r_1812_.jpg f_r_1812_.bmp
  f_r_1816_.jpg f_r_1816_.bmp
  f_r_1866_.jpg f_r_1866_.bmp
  f_r_1879_.jpg f_r_1879_.bmp
  f_r_401_.jpg f_r_401_.bmp
  f_r_407_.jpg f_r_407_.bmp
  f_r_43_.jpg f_r_43_.bmp
  f_r_499_.jpg f_r_499_.bmp
  f_r_500_.jpg f_r_500_.bmp
  f_r_546_.jpg f_r_546_.bmp
  f_r_647_.jpg f_r_647_.bmp
  f_r_797_.jpg f_r_797_.bmp
  f_r_829_.jpg f_r_829_.bmp
  f_r_903_.jpg f_r_903_.bmp
  f_r_907_.jpg f_r_907_.bmp
  f_r_921_.jpg f_r_921_.bmp
  f_r_934_.jpg f_r_934_.bmp
  f_r_936_.jpg f_r_936_.bmp
  f_r_940_.jpg f_r_940_.bmp
  f_r_963_.jpg f_r_963_.bmp
  f_r_968_.jpg f_r_968_.bmp
  f_r_991_.jpg f_r_991_.bmp
  w_r_136_.jpg w_r_136_.bmp
  w_r_158_.jpg w_r_158_.bmp
  w_r_198_.jpg w_r_198_.bmp
  w_r_1_.jpg w_r_1_.bmp
  w_r_24_.jpg w_r_24_.bmp
  w_r_25_.jpg w_r_25_.bmp
  w_r_27_.jpg w_r_27_.bmp
  w_r_47_.jpg w_r_47_.bmp
  w_r_7_.jpg w_r_7_.bmp

  2. The sizes of some masks do not match those of their corresponding images.

    For example:

    f_r_1058_.jpg: (590, 375)
f_r_1058_.bmp: (590, 430)
    The list of masks files with improper size: 

      f_r_1058_.jpg f_r_1058_.bmp
  f_r_1068_.jpg f_r_1068_.bmp
  f_r_1069_.jpg f_r_1069_.bmp
  f_r_1070_.jpg f_r_1070_.bmp
  f_r_1133_.jpg f_r_1133_.bmp
  f_r_1142_.jpg f_r_1142_.bmp
  f_r_1151_.jpg f_r_1151_.bmp
  f_r_1154_.jpg f_r_1154_.bmp
  f_r_1214_.jpg f_r_1214_.bmp
  f_r_1233_.jpg f_r_1233_.bmp
  f_r_1259_.jpg f_r_1259_.bmp
  f_r_1289_.jpg f_r_1289_.bmp
  f_r_1290_.jpg f_r_1290_.bmp
  f_r_1302_.jpg f_r_1302_.bmp
  f_r_1318_.jpg f_r_1318_.bmp
  f_r_1394_.jpg f_r_1394_.bmp
  f_r_1424_.jpg f_r_1424_.bmp
  f_r_1491_.jpg f_r_1491_.bmp
  f_r_1515_.jpg f_r_1515_.bmp
  f_r_1570_.jpg f_r_1570_.bmp
  f_r_1779_.jpg f_r_1779_.bmp
  f_r_1812_.jpg f_r_1812_.bmp
  f_r_1816_.jpg f_r_1816_.bmp
  f_r_1866_.jpg f_r_1866_.bmp
  f_r_1879_.jpg f_r_1879_.bmp
  f_r_401_.jpg f_r_401_.bmp
  f_r_829_.jpg f_r_829_.bmp
  f_r_921_.jpg f_r_921_.bmp
  f_r_934_.jpg f_r_934_.bmp
  f_r_968_.jpg f_r_968_.bmp
  f_r_991_.jpg f_r_991_.bmp
  w_r_1_.jpg w_r_1_.bmp
  w_r_24_.jpg w_r_24_.bmp
  w_r_25_.jpg w_r_25_.bmp
  w_r_27_.jpg w_r_27_.bmp
  w_r_47_.jpg w_r_47_.bmp
  w_r_7_.jpg w_r_7_.bmp

You can use this script to check the original train_val set of SUIM. Please note that you should change image_dir and masks_dir to your own in this python script.

New SUIM Dataset

Based on problems mentioned above, I re-annotated these images with improper masks. You can download the new SUIM dataset from 👉this link.

How these improper masks impact our task

To present how these improper masks impact our task, please see the instance below.

If we want to extract segments of human divers and Robots/instruments from images, we can obtain two boolean masks based on colors used to label these two classes. In SUIM, the two colors is blue and red. I write a script here:

from PIL import Image
import torch
from torchvision.utils import draw_segmentation_masks
import numpy as np
import matplotlib.pyplot as plt

# the re-annotated SUIM
img_fp = '/DataA/pwz/workshop/Datasets/SUIM_fix/train_val/images/d_r_59_.jpg'
mask_fp = '/DataA/pwz/workshop/Datasets/SUIM_fix/train_val/masks/d_r_59_.bmp'

# the original SUIM
# img_fp = '/DataA/pwz/workshop/Datasets/SUIM/train_val/images/d_r_59_.jpg'
# mask_fp = '/DataA/pwz/workshop/Datasets/SUIM/train_val/masks/d_r_59_.bmp'

img_size = (256, 256)
img = Image.open(img_fp).resize(img_size)
img = np.asarray(img, dtype=np.uint8)
mask = Image.open(mask_fp).resize(img_size)
mask = np.asarray(mask, dtype=np.uint8)

mask_shape = mask.shape[0:2]
# Human Diver
hd_mask = np.stack((np.zeros(mask_shape), np.zeros(mask_shape), np.ones(mask_shape)), axis=2)
hd_mask = (hd_mask * 255).astype(np.uint8)
# Robots
ro_mask = np.stack((np.ones(mask_shape), np.zeros(mask_shape), np.zeros(mask_shape)), axis=2)
ro_mask = (ro_mask * 255).astype(np.uint8)
hd_boolean_mask = np.all(mask == hd_mask, axis=2)
ro_boolean_mask = np.all(mask == ro_mask, axis=2)
img = torch.tensor(img.transpose(2,0,1), dtype=torch.uint8)
boolean_masks = torch.tensor(np.stack((hd_boolean_mask, ro_boolean_mask), axis=0), dtype=torch.bool)
imgs_with_masks = [draw_segmentation_masks(img, boolean_masks, alpha=0.5, colors=['blue', 'red'])]
show(imgs_with_masks)

If you use the original SUIM dataset, you will got this result:

visual_seg_1

If you use the new SUIM dataset, you will got this result:

visual_seg_2

Acknowledgements

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