ObjTest: Object-Level Mutation for Testing Object Detection Systems

ObjTest has conducted experiments on 3 datasets (COCO, VOC, BDD100K) and the testing subject is YOLOv5 with 3 different model sizes (YOLOv5s, YOLOv5m, YOLOv5l).

Environment

• python=3.9
• Rembg

We recommend using conda to configure the environment:

conda create -n python=3.9
conda activate objtest
pip install rembg

Object-level Transformation

We have implemented three object-level transformations: object insertion, removing, replacing.

Object Insertion

• Step1: Extract objects and remove their background:

python remove_bg.py --dataset coco

Manually selecte some clear and clean images and save to ObjTest/coco_classes folder. In our demo repository, the object images have already saved in this folder.

• Step2: Run img_add.py to overlay the mask image onto the original image and save the new image and the corresponding label:

python img_add.py --task insertion --dataset coco --type val

The generated datasets after executing the insertion operator are in the E:/code/datasets/obj_aug_coco1000/insertion/images and E:/code/datasets/obj_aug_coco1000/insertion/ labels folders, they can be input to YOLOv5 directly. You can change the file path in the code yourself.

Object Removing

• Step1: Run ObjTest/gen_mask_img.py to generate the mask image corresponding to COCO1000 and the label after removing:

python gen_mask_img.py --task remove --dataset coco --type val

• Step2: Download and configure the [lama](Download and configure the lama project) project. Then, go to the lama-main project and perform the fix for mask. You can change the floaer path by yourself.

cd lama-main
conda activate lama
# coco val:
python ./bin/predict.py model.path=E:/code/lama-main/big-lama indir=E:/code/datasets/obj_aug_coco1000/remove/images_tmp outdir=E:/code/datasets/obj_aug_coco1000/remove/images

The generated datasets after executing the remove operator are in the E:/code/datasets/obj_aug_coco1000/remove/images and E:/code/datasets/obj_aug_coco1000/remove/labels folders, they can be input to YOLOv5 directly.

Object Replacing

• Step1: prepare the mask image:

python gen_mask_img.py --task replace --dataset coco --type val

After running, datasets/obj_aug_coco1000/replace/images_tmp1 contains the original map and the corresponding mask location map; datasets/obj_aug_coco1000/replace/labels_tmp contains the new label after removing object from the original image and the deleted object’s label file.

• Step2: Use lama to repair the removed object image. You need to change the path location yourself.

cd lama-main
conda activate lama
# coco val:
python ./bin/predict.py model.path=E:/code/lama-main/big-lama indir=E:/code/datasets/obj_aug_coco1000/replace/images_tmp1 outdir=E:/code/datasets/obj_aug_coco1000/replace/images_tmp2

Finally, adding Obj with img_add.py requires adding Obj to datasets/obj_aug_coco1000/replace/labels_tmp2 in conjunction with datasets/obj_aug_coco1000/replace/labels_tmp /xxx_replace.txt with the category and location information provided:

• Step3: Add object with img_add.py. It requires adding Object to datasets/obj_aug_coco1000/replace/labels_tmp2, combined with datasets/obj_aug_coco1000/replace/labels_tmp /xxx_replace.txt to provide the category and location information.

python img_add.py --task replace --dataset coco --type val

The generated datasets after executing the replace operator are in the E:/code/datasets/obj_aug_coco1000/replace/images and E:/code/datasets/obj_aug_coco1000/replace/labels folders, they can be input to YOLOv5 directly.

Verification in YOLOv5 OD System

In the YOLOv5 project, verify each of the generated dataset with three transformations:

conda activate yolov5
python val.py --weights ./torch_models/yolov5s.pt --data coco_insertion.yaml --img 640 --batch-size 4 --verbose
python val.py --weights ./torch_models/yolov5s.pt --data coco_remove.yaml --img 640 --batch-size 4 --verbose
python val.py --weights ./torch_models/yolov5s.pt --data coco_replace.yaml --img 640 --batch-size 4 --verbose

Baselines

• Baseline1: The first type of baseline, which directly performs random transformations of brightness, contrast, and Gaussian blur on the image as a whole, without changing the label information.

python baseline1_aug.py --img_dir ../datasets/coco1000/images/val2017 --label_dir ../datasets/coco1000/labels/val2017 --save_dir ../datasets/obj_aug_coco1000

• Baseline2: The second type of baseline, cutting and stitching of images, requires changing the label information.

python baseline2_aug.py --img_dir ../datasets/coco1000/images/val2017 --label_dir ../datasets/coco1000/labels/val2017 --save_dir ../datasets/obj_aug_coco1000

Naturalness verification of generated images

In the pytorch-fid folder, the FID score is calculated by inputing in the path of two image folders; the smaller the FID score, the more realistic and natural the image is.

cd pytorch_fid
conda activate fid
run_coco.sh