Image Augmentations 🚀

This is a augemntation tool for Object Detection , Image classification and Instance Segmentation , it can perform 14 annotations. The important ones are rotation, affine, zooming in and out, noise, and blur. The augmentations were applied to a fraction of the data (40 - 50 percent of the images out of 100 can be augmented).When rotating or zooming in and out, the bounding box coordinates will also change as the image is rotated or zoomed.

Code Starts from here

👩🏼‍💻Create a folder first, inside that folder keep your image annotation folder 👩🏼‍💻

👩🏼‍💻 Create a virtual environment 👩🏼‍💻

pip install virtualenv
#name your environment
python3 -m venv <your env name>

#activate the environment --> for linux user
source <your env name>/bin/activate

#activate the environment  --> for windows user
<your env name>/Source/activate.ps1

Installation (for pip installation) 🚀

pip install image_augs

After installation 🎯

Create a .py script inside your created folder

This Script is for OBJECT DETECTION

#import these modules in your created <scriptname>.py file


from object_detection_new.txt_reader_rect import RectAugmentation

################# image height and width combination  ##################

# first combination --> for custom image size
#  image_height = < custom image size > ; 640
#  image_width = < custom image size > ; 320


# second combination --> keep aspect ratio of the image
#  image_height = 640
#  image_width  = 'keep_aspect_ratio_False'

# Third combination --> keeping original image height and width
#  image_height = 'keep_original_image_height'
#  image_width = 'keep_original_image_width'



annotation_folder = 'your folder'
new_aug_saved_folder = 'new saved folder'
train_split = 0.90
image_H = 640  #check above for height and width setting
image_W = 'keep_aspect_ratio'


rect_aug = RectAugmentation(new_aug_saved_folder)

rect_aug.Image_augmentation(annotation_folder,
                                 
                                train_split=train_split,
                                 image_height= image_H,
                                 image_width= image_W,


                                 blur=False,  blur_f=0.8,

                                 motionBlur= False , motionBlur_f= 0.8 ,

                                 rotate=False, rotate_f = 0.8, 

                                 noise=False, noise_f=0.8,

                                 perspective=False, perspective_f = 0.8,

                                 affine=False, affine_f=0.8,

                                 brightness=False, brightness_f=0.8,
                                    
                                 hue=False, hue_f=0.8,

                                 removesaturation=False, removesaturation_f=0.8,

                                 contrast=False, contrast_f=0.8,

                                 upflip=False, upflip_f=0.8,

                                 shear=False, shear_f=0.8, 

                                 rotate90=False, rotate90_f =0.8,

                                 blur_and_noise=False, blur_and_noise_f=0.8,

                                 image_cutout = False, image_cutout_f=0.8,
                                    
                                 mix_aug= False, mix_aug_f=0.8, 
                                    
                                 temperature_change= False, temperature_change_f=0.8,  # change color temperature from cool to warm color

                                 weather_change=True,weather_change_f=0.8), # add rain , fog , snow in your images
                               
                                

#results will be saved in < your given folder >

This Script is for INSTANCE SEGMENTATION

#import these modules in your created <scriptname>.py file
from instance_seg.json_reader_poly import PolygonAugmentation


################# image height and width combination  ##################

# first combination --> for custom image size
#  image_height = < custom image size > ; 640
#  image_width = < custom image size > ; 320


# second combination --> keep aspect ratio of the image
#  image_height = 640
#  image_width  = 'keep_aspect_ratio_False'

# Third combination --> keeping original image height and width
#  image_height = 'keep_original_image_height'
#  image_width = 'keep_original_image_width'


#### yolo ####
# if yolo False then it will normalize all images and save it as txt , if false augmentations will be saved as json.

annotation_folder = 'your data'
new_aug_saved_folder = 'new saved dataset name'
train_split = 0.70
image_H = 640  #check above for height and width setting
image_W = 'keep_aspect_ratio'
yolo = True


im_aug_helper = PolygonAugmentation(aug_save_folder_name=new_aug_saved_folder,
                                    yolo=yolo)

im_aug_helper.Image_augmentation(annotation_folder,
                                 
                                 train_split=train_split,
                                 image_height= image_H,
                                 image_width= image_W,


                                 blur=True,  blur_f=0.8,

                                 motionBlur= False , motionBlur_f= 0.5,

                                 rotate=True, rotate_f = 0.8, 

                                 noise=True, noise_f=0.6,

                                 perspective=True, perspective_f = 0.6,

                                 affine=True, affine_f=0.6,

                                 brightness=True, brightness_f=0.6,
                                    
                                 hue=True, hue_f=0.6,

                                 removesaturation=True, removesaturation_f=0.6,

                                 contrast=True, contrast_f=0.6,

                                 upflip=True, upflip_f=0.8,

                                 shear=True , shear_f=0.7, 

                                 rotate90=True, rotate90_f =1.0,

                                 blur_and_noise=True, blur_and_noise_f=0.6,

                                 image_cutout = True, image_cutout_f=0.6,
                                    
                                 mix_aug=True, mix_aug_f=0.7,
                                    
                                 temperature_change=True, temperature_change_f=0.5,
                                 
                                 weather_change=True,weather_change_f=0.3)
#results will be saved in < your given folder >
***

This Script is for IMAGE CLASSIFICATION

#import these modules in your created <scriptname>.py file
from classification.classification_combined import ImageAugmentHelper


### PARAMS ###
source_folder = '<source folder>'
aug_saved_folder = '<augmentation saved folder>'
train_split = 0.5
image_height = 512
image_width = 512

classification_aug = ImageAugmentHelper(source_folder=source_folder,
                                        aug_save_folder_name=aug_saved_folder,
                                        train_split=train_split,
                                        height=image_height,
                                        width=image_width)


classification_aug.augmentations(

    save_raw_images=True,

    blur=True, blur_f=1.0,

    noise=True,noise_f=1.0,

    horizontalFlip=True, horizontalFlip_f=1.0,

    brightness=True, brightness_f=1.0,

    contrast=True, contrast_f=1.0,

    hue=True, hue_f=1.0,

    saturation=True, saturation_f=1.0,

    zoom=True, zoom_f=1.0,

    perspective=True, perspective_f=1.0,

    translation=True, translation_f=1.0,

    sharpen=True, sharpen_f=1.0,
    
    randomShadow=True, randomShadow_f=1.0
)

Use github to clone image_augmentations repo 🖥️ Use instanceSeg_aug_script.py / classification_aug_script.py / objectDetection_augScript.py according to your needs.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

License

MIT