waste-classifier / efficientdet /effdet /data /transforms_albumentation.py
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Create the streamlit app that classifies the trash in an image into classes
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import albumentations as A
from albumentations.augmentations.transforms import (
RandomBrightness, Downscale, RandomFog, RandomRain, RandomSnow)
from albumentations.augmentations.blur.transforms import Blur
def get_transform():
transforms = A.Compose([
#HorizontalFlip(p=0.5),
#VerticalFlip(p=0.5),
#RandomSizedBBoxSafeCrop(700, 700, erosion_rate=0.0, interpolation=1, always_apply=False, p=0.5),
Blur(blur_limit=7, always_apply=False, p=0.5),
RandomBrightness(limit=0.2, always_apply=False, p=0.5),
#Downscale(scale_min=0.5, scale_max=0.9, interpolation=0, always_apply=False, p=0.5),
#PadIfNeeded(min_height=1024, min_width=1024, pad_height_divisor=None, pad_width_divisor=None, border_mode=4, value=None, mask_value=None, always_apply=False, p=1.0),
#RandomFog(fog_coef_lower=0.3, fog_coef_upper=1, alpha_coef=0.08, always_apply=False, p=0.2),
#RandomRain(slant_lower=-10, slant_upper=10, drop_length=20, drop_width=1, drop_color=(200, 200, 200), p=0.2),
#RandomSnow(snow_point_lower=0.1, snow_point_upper=0.3, brightness_coeff=2.5, always_apply=False, p=0.2)
], bbox_params=A.BboxParams(format='pascal_voc', label_fields=['bbox_classes'])
)
return transforms