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retriever.py
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retriever.py
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import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import pandas as pd
import numpy as np
import pickle
import cv2
import albumentations as A
from albumentations.core.composition import Compose
from typing import Callable, List
from pathlib import Path
import os
from torch.utils.data import Dataset
import torch
import sys
def pad_to_multiple(x, k=32):
return int(k*(np.ceil(x/k)))
def get_train_transforms(height: int = 437,
width: int = 582,
level: str = 'hard'):
if level == 'light':
return A.Compose([
A.HorizontalFlip(p=0.5),
A.IAAAdditiveGaussianNoise(p=0.2),
A.OneOf(
[A.CLAHE(p=1.0),
A.RandomBrightness(p=1.0),
A.RandomGamma(p=1.0),
],p=0.5),
A.OneOf(
[A.IAASharpen(p=1.0),
A.Blur(blur_limit=3, p=1.0),
A.MotionBlur(blur_limit=3, p=1.0),
],p=0.5),
A.OneOf(
[A.RandomContrast(p=1.0),
A.HueSaturationValue(p=1.0),
],p=0.5),
A.Resize(height=height, width=width, p=1.0),
A.PadIfNeeded(pad_to_multiple(height),
pad_to_multiple(width),
border_mode=cv2.BORDER_CONSTANT,
value=0,
mask_value=0)
], p=1.0)
elif level == 'hard':
return A.Compose([
A.HorizontalFlip(p=0.5),
A.IAAAdditiveGaussianNoise(p=0.2),
A.OneOf(
[A.GridDistortion(border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=1.0),
A.ElasticTransform(alpha_affine=10, border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=1.0),
A.ShiftScaleRotate(
shift_limit=0,
scale_limit=0,
rotate_limit=10,
border_mode=cv2.BORDER_CONSTANT,
value=0,
mask_value=0,
p=1.0
),
A.OpticalDistortion(border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=1.0),
],p=0.5),
A.OneOf(
[A.CLAHE(p=1.0),
A.RandomBrightness(p=1.0),
A.RandomGamma(p=1.0),
A.ISONoise(p=1.0)
],p=0.5),
A.OneOf(
[A.IAASharpen(p=1.0),
A.Blur(blur_limit=3, p=1.0),
A.MotionBlur(blur_limit=3, p=1.0),
],p=0.5),
A.OneOf(
[A.RandomContrast(p=1.0),
A.HueSaturationValue(p=1.0),
],p=0.5),
A.Resize(height=height, width=width, p=1.0),
A.Cutout(p=0.3),
A.PadIfNeeded(pad_to_multiple(height),
pad_to_multiple(width),
border_mode=cv2.BORDER_CONSTANT,
value=0,
mask_value=0)
], p=1.0)
elif level == 'hard_weather':
return A.Compose([
A.HorizontalFlip(p=0.5),
A.IAAAdditiveGaussianNoise(p=0.2),
A.OneOf(
[A.GridDistortion(border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=1.0),
A.ElasticTransform(alpha_affine=10, border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=1.0),
A.ShiftScaleRotate(
shift_limit=0,
scale_limit=0,
rotate_limit=10,
border_mode=cv2.BORDER_CONSTANT,
value=0,
mask_value=0,
p=1.0
),
A.OpticalDistortion(border_mode=cv2.BORDER_CONSTANT, value=0, mask_value=0, p=1.0),
],p=0.5),
A.OneOf(
[A.CLAHE(p=1.0),
A.RandomBrightness(p=1.0),
A.RandomGamma(p=1.0),
A.ISONoise(p=1.0)
],p=0.5),
A.OneOf(
[A.IAASharpen(p=1.0),
A.Blur(blur_limit=3, p=1.0),
A.MotionBlur(blur_limit=3, p=1.0),
],p=0.5),
A.OneOf(
[A.RandomContrast(p=1.0),
A.HueSaturationValue(p=1.0),
],p=0.5),
A.OneOf(
[A.RandomFog(fog_coef_upper=0.8, p=1.0),
A.RandomRain(p=1.0),
A.RandomSnow(p=1.0),
A.RandomSunFlare(src_radius=100, p=1.0)
],p=0.4),
A.Resize(height=height, width=width, p=1.0),
A.Cutout(p=0.3),
A.PadIfNeeded(pad_to_multiple(height),
pad_to_multiple(width),
border_mode=cv2.BORDER_CONSTANT,
value=0,
mask_value=0)
], p=1.0)
def get_valid_transforms(height: int = 437,
width: int = 582):
return A.Compose([
A.Resize(height=height, width=width, p=1.0),
A.PadIfNeeded(pad_to_multiple(height),
pad_to_multiple(width),
border_mode=cv2.BORDER_CONSTANT,
value=0,
mask_value=0)
], p=1.0)
def to_tensor(x, **kwargs):
return x.transpose(2, 0, 1).astype('float32')
def get_preprocessing(preprocessing_fn: Callable):
_transform = [
A.Lambda(image=preprocessing_fn),
A.Lambda(image=to_tensor, mask=to_tensor),
]
return A.Compose(_transform)
class TrainRetriever(Dataset):
def __init__(self,
data_path: Path,
image_names: List[str],
preprocess_fn: Callable,
transforms: Compose,
class_values: List[int]):
super().__init__()
self.data_path = data_path
self.image_names = image_names
self.transforms = transforms
self.preprocess = get_preprocessing(preprocess_fn)
self.class_values = class_values
self.images_folder = 'imgs'
self.masks_folder = 'masks'
def __getitem__(self, index: int):
image_name = self.image_names[index]
image = cv2.imread(str(self.data_path/self.images_folder/image_name))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
mask = cv2.imread(str(self.data_path/self.masks_folder/image_name), 0).astype('uint8')
if self.transforms:
sample = self.transforms(image=image, mask=mask)
image = sample['image']
mask = sample['mask']
mask = np.stack([(mask == v) for v in self.class_values], axis=-1).astype('uint8')
if self.preprocess:
sample = self.preprocess(image=image, mask=mask)
image = sample['image']
mask = sample['mask']
return image, mask
def __len__(self) -> int:
return len(self.image_names)