Closed hectormdom closed 3 years ago
Also if you will please, let me know if this warning is normal
/workspace/tlt-ds-face_mask_detect/data_utils/mafa2kitti.py:51: RuntimeWarning: overflow encountered in ubyte_scalars
bbox = [_bbox_label[0], _bbox_label[1], _bbox_label[0]+_bbox_label[2], _bbox_label[1]+_bbox_label[3]]
And if not, if you have any leads as to how should I go solving it.
Cheers!
data2kitti.py
Thank you for working on this. In arguments; you have to have a absolute path to the directories. Thanks for pointing this out, I will update the readme according to this.
Also if you will please, let me know if this warning is normal
/workspace/tlt-ds-face_mask_detect/data_utils/mafa2kitti.py:51: RuntimeWarning: overflow encountered in ubyte_scalars bbox = [_bbox_label[0], _bbox_label[1], _bbox_label[0]+_bbox_label[2], _bbox_label[1]+_bbox_label[3]]
And if not, if you have any leads as to how should I go solving it.
Cheers!
This warning is okay, it mainly because [one of the functions] (https://github.com/NVIDIA-AI-IOT/face-mask-detection/blob/master/data_utils/mafa2kitti.py#L122) currently does not support bounding box scaling for some of the image sizes. You can proceed to next steps, without worrying too much about this warning,
Thanks to you, also is my output correct (besides the warning)? it seems to me like I'm not getting images from anywhere except MAFA and WideFace.
I just found out that, even though the output says this:
Final: Total Mask Labelled:6000
Total No-Mask Labelled:6265
The folder actually contains only 3182 images
Thanks to you, also is my output correct (besides the warning)? it seems to me like I'm not getting images from anywhere except MAFA and WideFace.
Thank you for diligently going through the repo. Let me check on my end, if any issue with generating FDDB dataset in Kitti format. Once you get FDDB dataset completely, you should have good accuracy.
Also, I am assuming, for accuracy evaluations, what batch size are you using?
I just found out that, even though the output says this:
Final: Total Mask Labelled:6000 Total No-Mask Labelled:6265
The folder actually contains only 3182 images
This is okay, as most of the images have multiple faces. Especially WiderFace
dataset, has more number of people in a single image.
Hi, thanks for the reply, I intend to go through the whole thing and report back, I find it very interesting thanks for authoring this.
Thanks to you, also is my output correct (besides the warning)? it seems to me like I'm not getting images from anywhere except MAFA and WideFace.
Thank you for diligently going through the repo. Let me check on my end, if any issue with generating FDDB dataset in Kitti format. Once you get FDDB dataset completely, you should have good accuracy.
Also, I am assuming, for accuracy evaluations, what batch size are you using?
I tried running all the data_utils scripts individually and: kaggle2kitti.py no data, throws an error widerface2kitti.py no data, no error (it instantaneously says that it generated 1000 items but there's nothing in the folder and no processing time) mafa2kitti.py generates 13,882 images and labels fddb2kitti.py generates 780 images and labels (All the paths in the files are correctly set and absolute)
Originally I tried the default batch size 24 that was written on the file for my first run, the I upped it to 32 because I read it in an article, and now I'm going to try 16 as it says in the blog entry corresponding to this project.
In widerface2kitti.py
I changed line 33 from file_image = os.path.splitext(image_name)[0].split('\\')[1]
to file_image = os.path.splitext(os.path.split(image_name)[1])[0]
ran it alone and it actually generated 413 images and labels
Ah. Nice. One more quick question, are you running this on Linux or Windows?
Linux, Ubuntu 18.04.5 LTS
Ok. Let me test on that as well. i am currently testing on Windows.
Thanks a lot, I've tried a ton of things through comparison of the scripts that do work against the kaggle one, but to no avail
Closing as no activity for more than 20days. Please re-open if needed.
Hi I'm trying to convert the data set to KITTI format, and I figured this command would be correct:
I modified the paths in the data2kitti.py file like so:
However when I try to run the command I get this output:
I supposed it had something to do with that double backlash, I changed the lines 47
mafa_base_dir = os.path.join(mafa_base_dir, 'train-images\images')
tomafa_base_dir = os.path.join(mafa_base_dir, 'train-images/images')
and 50mafa_base_dir = os.path.join(mafa_base_dir, 'test-images\images')
tomafa_base_dir = os.path.join(mafa_base_dir, 'test-images/images')
indata2kitti.py
I managed to run it, this is my output