How to prepare datasets/CIHP/labels and datasets/CIHP/edges

Hello!

Can you explain please how to prepare files, which should be located in this directories: datasets/CIHP/labels and datasets/CIHP/edges

I’ve prepared the image file datasets/CIHP/images/image.jpg,

file datasets/CIHP/list/val_id.txt with content:

image

and file datasets/CIHP/list/val.txt with content:

images/image.jpg /labels/image.png

But I don’t understand how I can generate datasets/CIHP/labels/image.png and datasets/CIHP/edges/image.png ?

Because I need them for test_pgn.py. You answered here that in this dir can be placed any png image. But if I just convert image.jpg to image.png and put converted file into labels and edges dir the execution of the test_pgn.py failed with error

InvalidArgumentError (see above for traceback): assertion failed: [`labels` out of bound] [Condition x < y did not hold element-wise:] [x (mean_iou/confusion_matrix/control_dependency:0) = ] [255 255 255...] [y (mean_iou/ToInt64_1:0) = ] [20]

Please explain how to prepare image files for starting the segmentation process? Because your README.md file doesn’t explain how to do it. Just “Prepare the images and store in $HOME/datasets”. But what steps need to be done to prepare images in datasets dir?

Inference

• Download the pre-trained model and store in $HOME/checkpoint.
• Prepare the images and store in $HOME/datasets.
• Run test_pgn.py.
• The results are saved in $HOME/output
• Evaluation scripts are in $HOME/evaluation. Copy the groundtruth files (in Instance_ids folder) - into $HOME/evaluation/Instance_part_val before you run the script.

[neonb88]

I think a lot of people got confused about this. I was confused too, initially.

You don’t actually have to install MATLAB or Octave, create good edges and labels. That won’t make the result better. The only reason it takes in edges and labels at all is to measure accuracy and precision.

I just wrote a method that doesn’t require MATLAB or Octave, only the python modules PGN already requires:

import numpy as np
import imageio as ii
from PIL import Image
nxb_img   = Image.open('datasets/CIHP/images/front.jpg')      # This is your image.
 
# Reshape their label image to our size 
label_img = Image.open('datasets/CIHP/labels/0005008.png')  # This is the label image from CIHP.
nxb_label_img = label_img.resize(nxb_img.size, Image.NEAREST)
nxb_label_img.save('datasets/CIHP/labels/front.png')

# Reshape their edge image to our size 
edge_img  = Image.open('/home/nathanbendich/CIHP_PGN/datasets/CIHP/edges/0005008.png')
nxb_edge_img  = edge_img.resize(nxb_img.size, Image.NEAREST)
nxb_edge_img.save('datasets/CIHP/edges/front.png')

You can use Logwell’s MATLAB code, but it won’t improve performance.

[LogWell]

感觉作者的回复有些敷衍, 希望作者能把README写得更清楚点~