conradry / copy-paste-aug Goto Github PK

Running the example jupyter , the "bboxes" is [] for both img_data and paste_img_data in code
img_data = self.copy_paste(**img_data, **paste_img_data) . The empty bboxe causes the augmentation failed. I'm sure that "bboxes" extracted from annotations. My data is standard coco format. And the program works when drop other aug before CopyPaste.

img_data = self.load_example(idx) File "/home/sanjayghanagiri/copy-paste-aug/coco.py", line 54, in load_example img_id = self.ids[index] IndexError: list index out of range

I am facing issues with finding right dataset to test.
I tried with http://images.cocodataset.org/zips/test2017.zip test dataset and get index out of range issue.
Can you anyone please provide the right dataset to test?

I have successfully run your program. Now I want to know how to use the data， to train the model directly, or to save the data and train the model with the original data?

TypeError: init() takes from 3 to 5 positional arguments but 6 were given.

Hi, may I ask you how to use this method to augment the dataset of semantic segmentation?
Thank you.

Hi, Thanks for your work. Could you tell me how do I use copy-paste-aug in instance segmentation. Thanks a lot :)

Hey @conradry,Thank you for your work!
Could you tell me how can i use the copy-paste augmentation in YOLOX. The dataset for YOLOX should be in COCO format, and I already have my custom dataset, so I tried do some copy-paste-aug on my data, but it didn't work...any idea how can I use it in yoloX?
And if theres is a way to save augmented data (img and annotations.json) to different folder, I'd be glad to know.

@conradry Very interesting work done. Thanks. The 'example.ipynb' does not display augmented images as presented in figure 2 of the original paper. Can you advise on how to reproduce and visualize the augmentation effect as observed in the newly created images as presented in Fig 2 of the paper?

The Mosaic transformation can not be used for Instance Segmentation?

I constructed the instance segmentation config according to the format of objecti detection (add ‘gt_masks’), but get an error.

As a user design data augmentation pipline that could be used in torchvision.transform.Compose or Extend and use custom pipelines, for excample. Thanks!

Thank you for your work. But may more situations should be considered. With no changes to your example script , the aug failed for my aerial image. There is no data format problem in the process.

Have you been able to reproduce the results achieved in the paper with this unofficial implementation?

how to integrate with detectron2?

I developed my own dataset class. However, I keep getting a strange error.
This is my code

from copy_paste import copy_paste_class
from torch.utils.data import Dataset
import torchvision.transforms as transforms

@copy_paste_class
class DatasetClass(Dataset):
    def __init__(self, df, transforms = None):
        self.df = df
        self.transforms = transforms
        
    def __len__(self):
        return len(self.df)
    
    def load_example(self,index):
        #locally
#         img_path = self.df['imagePath'][index]
        # on DGX    
        img_path = self.df['imagePath'][index]
        
        img_label = self.df['class'][index]
        img_PIL = Image.open(img_path)
        print('done')
        if img_PIL.mode != "RGB":
            img_PIL = img_PIL.convert('RGB')          
        border = (int(self.df['xmin'][index]),\
                  int(self.df['ymin'][index]),\
                  int(self.df['xmax'][index]),\
                  int(self.df['ymax'][index])) # left, top, right, bottom
        img_PIL = img_PIL.crop(border)
        image = img_PIL
        image = self.transforms(image)
        
        return image, img_label_idx




import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2
from copy_paste import CopyPaste


transforms = A.Compose([
      A.RandomScale(scale_limit=(-0.9, 1), p=1), #LargeScaleJitter from scale of 0.1 to 2
      A.PadIfNeeded(256, 256, border_mode=0), #constant 0 border
      A.HorizontalFlip(p=0.5),
      CopyPaste(blend=True, sigma=1, pct_objects_paste=0.5, p=1)
    ], bbox_params=A.BboxParams(format="coco")
)

Finally, this is the error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/tmp/ipykernel_16726/2411785438.py in <module>
----> 1 dataset[0]

~/Desktop/nas/vision/Orjuwan/LDR/Experiments/LogoDetection/YOLOv5/copy_paste.py in __getitem__(self, idx)
    298             self._split_transforms()
    299 
--> 300         img_data = self.load_example(idx)
    301         if self.copy_paste is not None:
    302             paste_idx = random.randint(0, self.__len__() - 1)

/tmp/ipykernel_16726/579992425.py in load_example(self, index)
     29         img_PIL = img_PIL.crop(border)
     30         image = img_PIL
---> 31         image = self.transforms(image)
     32 
     33         return image, img_label_idx

~/anaconda3/lib/python3.8/site-packages/albumentations/core/composition.py in __call__(self, force_apply, *args, **data)
    191     def __call__(self, *args, force_apply=False, **data):
    192         if args:
--> 193             raise KeyError("You have to pass data to augmentations as named arguments, for example: aug(image=image)")
    194         if self.is_check_args:
    195             self._check_args(**data)

KeyError: 'You have to pass data to augmentations as named arguments, for example: aug(image=image)'

ValueError: Expected x_max for bbox (0.65, 0.51, 1.12, 0.64, 3) to be in the range [0.0, 1.0], got 1.1234809015877545
Augmentation I use:
aug_list = [A.Resize(800,800),
CopyPaste(blend=True, sigma=1, pct_objects_paste=0.6, p=0.5) #pct_objects_paste is a guess
]
transform = A.Compose(
aug_list, bbox_params=A.BboxParams(format="coco")
)
Can anyone tell me where is the problem?

Hi @conradry , first of all thanks for your great work,
I want to use copy paste to copy annotation from original image to a background image without annotations. How can I do it ?

Hey @conradry,
I'm curios to know how can i use the copy-paste augmentation in YOLO. Im using albumentations to do some augmentation on my custom dataset and I have added the copy-paste aug as you said in the readme file but im not able to make it work! im getting an error that needs the mask params... which I dont have it! any idea how can I use it in yolov5 detection??

transform = A.Compose([
       A.NoOp(p=0.4),
       A.RandomScale(p=0.2)
       CopyPaste(blend=True, sigma=1, pct_objects_paste=0.5, p=1)
], bbox_params=A.BboxParams(format="pascal_voc"))

Thanks!