Question about image ids

[uehara-mech]

I thought the key in the tsv file ( visualgenome/label_danfeiX_overlap.new.tsv ) represented the image ID, but I found out that it does not.

from maskrcnn_benchmark.structures.tsv_file_ops import tsv_reader
import glob

# get image ids from Visual Genome image files
img_files = glob.glob('/VisualGenome/VG_100K/*.jpg') + glob.glob('/VisualGenome/VG_100K_2/*.jpg')
image_ids_from_files = [img_file.split('/')[-1].split('.')[0] for img_file in img_files]

# get image ids from the scene graph annotation files
tsv = tsv_reader('datasets/visualgenome/label_danfeiX_overlap.new.tsv')
image_ids_from_annos = [row[0] for row in tsv]

# extract the overlap between the two data
overlap = set(image_ids_from_annos) & set(image_ids_from_files)

print(f'size of iid from files: {len(image_ids_from_files)}')
print(f'size of iid from annotations: {len(image_ids_from_annos)}')
print(f'size of overlapped iid: {len(overlapped_iid)}')

The output of this code looks like this:

size of iid from files: 108249
size of iid from annotations: 108073
size of overlapped iid: 5196

I think this shows that about 95% of the image ids are mismatched. How can I get the correct image id mapping?

[Pter61]

I thought the key in the tsv file ( visualgenome/label_danfeiX_overlap.new.tsv ) represented the image ID, but I found out that it does not.

from maskrcnn_benchmark.structures.tsv_file_ops import tsv_reader
import glob

# get image ids from Visual Genome image files
img_files = glob.glob('/VisualGenome/VG_100K/*.jpg') + glob.glob('/VisualGenome/VG_100K_2/*.jpg')
image_ids_from_files = [img_file.split('/')[-1].split('.')[0] for img_file in img_files]

# get image ids from the scene graph annotation files
tsv = tsv_reader('datasets/visualgenome/label_danfeiX_overlap.new.tsv')
image_ids_from_annos = [row[0] for row in tsv]

# extract the overlap between the two data
overlap = set(image_ids_from_annos) & set(image_ids_from_files)

print(f'size of iid from files: {len(image_ids_from_files)}')
print(f'size of iid from annotations: {len(image_ids_from_annos)}')
print(f'size of overlapped iid: {len(overlapped_iid)}')

The output of this code looks like this:

size of iid from files: 108249
size of iid from annotations: 108073
size of overlapped iid: 5196

I think this shows that about 95% of the image ids are mismatched. How can I get the correct image id mapping?

Same problem, do you find the image id mapping now?

[uehara-mech]

No, I didn't find the image id mapping. Finally, I ended up comparing all the image arrays in Visual Genome dataset and img.tsv one by one, looking for the same image (this is incredibly cumbersome, but I couldn't think of any other way...)

[Pter61]

No, I didn't find the image id mapping. Finally, I ended up comparing all the image arrays in Visual Genome dataset and img.tsv one by one, looking for the same image (this is incredibly cumbersome, but I couldn't think of any other way...)

Thank you. I usedimg.tsvto build a id mapping and verified it. It is indeed one-to-one correspondence.

[zhuang-li]

No, I didn't find the image id mapping. Finally, I ended up comparing all the image arrays in Visual Genome dataset and img.tsv one by one, looking for the same image (this is incredibly cumbersome, but I couldn't think of any other way...)

Thank you. I usedimg.tsvto build a id mapping and verified it. It is indeed one-to-one correspondence.

I tried to encode the image and find the correspondence mapping. But I can not map the hashcode in img.tsv with the hashcodes I built with Visualgenome images. May you share the code you build the mapping? Thank you for the help.

[uehara-mech]

You can use a script like this: https://gist.github.com/uehara-mech/f4c850999f1c90f61851951e1183ef0a (you may need to modify the code to make it work in your environment)