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dataset.py
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import cv2
import pandas as pd
import numpy as np
from tqdm import tqdm
import json
import torch
from torch.utils.data import Dataset
import albumentations as albu
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from albumentations.pytorch import ToTensorV2
class PlantDACON(Dataset):
def __init__(self,
image_size,
files,
csv_files=None,
avail_features=None,
label_description=None,
aug_ver=0,
tta_transform=None,
mode='train'):
self.mode = mode
self.image_size = image_size
self.files = files
self.aug_ver = aug_ver
self.avail_features = avail_features
self.csv_files = csv_files
self.csv_feature_check = [0] * len(self.files)
self.csv_features = [None] * len(self.files)
self.max_len = 320
self.label_description = label_description
self.label_encoder = {key: idx for idx, key in enumerate(self.label_description)}
self.base_transform_list = [
albu.Resize(self.image_size, self.image_size),
albu.ShiftScaleRotate(shift_limit=0.05, scale_limit=0.1,
rotate_limit=30, interpolation=1, border_mode=0,
value=0, p=0.5),
albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5)
]
self.tta_transform = tta_transform
self.transform_normalize = [
albu.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
ToTensorV2()
]
if self.mode == 'train':
# Only-flip
if self.aug_ver == 1:
self.transform = albu.Compose([albu.Resize(self.image_size, self.image_size),
albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
albu.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
ToTensorV2(),
])
# Base Augmentation
elif self.aug_ver == 2:
self.transform = albu.Compose(
self.base_transform_list
+ self.transform_normalize)
# Base + Transpose
elif self.aug_ver == 3:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
]
+ self.transform_normalize)
# Base + HueSaturationValue
elif self.aug_ver == 4:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.5),
]
+ self.transform_normalize)
# Base + RandomBrightnessContrast
elif self.aug_ver == 5:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.5),
]
+ self.transform_normalize)
# Base + CLAHE
elif self.aug_ver == 6:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.CLAHE(p=0.5),
]
+ self.transform_normalize)
# Base + Sharpen
elif self.aug_ver == 7:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.CLAHE(p=0.5),
albu.Sharpen(p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + HueSaturationValue
elif self.aug_ver == 8:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + RandomBrightnessContrast
elif self.aug_ver == 9:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + CLAHE
elif self.aug_ver == 10:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.CLAHE(p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + Sharpen
elif self.aug_ver == 11:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.Sharpen(p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + HueSaturationValue + RandomBrightnessContrast
elif self.aug_ver == 12:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.5),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + HueSaturationValue + CLAHE
elif self.aug_ver == 13:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.5),
albu.CLAHE(p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + HueSaturationValue + Sharpen
elif self.aug_ver == 14:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.5),
albu.Sharpen(p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + HueSaturationValue + Sharpen
elif self.aug_ver == 15:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.5),
albu.CLAHE(p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + HueSaturationValue + Sharpen
elif self.aug_ver == 16:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.5),
albu.Sharpen(p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + HueSaturationValue + Sharpen
elif self.aug_ver == 17:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.CLAHE(p=0.5),
albu.Sharpen(p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + HueSaturationValue + Sharpen
elif self.aug_ver == 18:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.5),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.5),
albu.CLAHE(p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + HueSaturationValue + Sharpen
elif self.aug_ver == 19:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.5),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.5),
albu.Sharpen(p=0.5),
]
+ self.transform_normalize)
# Base + Transpose + HueSaturationValue + Sharpen
elif self.aug_ver == 20:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.5),
albu.HueSaturationValue(hue_shift_limit=20, sat_shift_limit=30, val_shift_limit=20, p=0.5),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.5),
albu.CLAHE(p=0.5),
albu.Sharpen(p=0.5),
]
+ self.transform_normalize)
elif self.aug_ver == 21:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.5),
albu.CLAHE(p=0.5),
]
+ self.transform_normalize)
elif self.aug_ver == 22:
self.transform = albu.Compose(
[
albu.RandomResizedCrop(height=self.image_size, width=self.image_size,
scale=(0.5, 1.0), ratio=(0.75, 1.3333333333333333),
interpolation=1, p=1.0),
albu.ShiftScaleRotate(shift_limit=0.05, scale_limit=0.1,
rotate_limit=30, interpolation=1, border_mode=0,
value=0, p=0.5),
albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
albu.CLAHE(clip_limit=2,p=0.25),
albu.Sharpen(p=0.25),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.25),
]
+ self.transform_normalize)
elif self.aug_ver == 23:
self.transform = albu.Compose(
[
albu.RandomResizedCrop(height=self.image_size, width=self.image_size,
scale=(0.5, 1.0), ratio=(0.75, 1.3333333333333333),
interpolation=1, p=1.0),
albu.RandomRotate90(p=1.0),
albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
albu.CLAHE(clip_limit=2,p=0.25),
albu.Sharpen(p=0.25),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.25),
]
+ self.transform_normalize)
elif self.aug_ver == 24:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.CLAHE(clip_limit=2, p=0.25),
albu.Sharpen(p=0.25),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.25),
albu.RandomResizedCrop(height=self.image_size, width=self.image_size,
scale=(0.5, 1.0), ratio=(0.75, 1.3333333333333333),
interpolation=1, p=1.0),
]
+ self.transform_normalize)
elif self.aug_ver == 25:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.Transpose(p=0.25),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.25),
albu.Sharpen(p=0.25),
albu.RandomResizedCrop(height=self.image_size, width=self.image_size,
scale=(0.5, 1.0), ratio=(0.75, 1.3333333333333333),
interpolation=1, p=1.0),
]
+ self.transform_normalize)
elif self.aug_ver == 26:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.25),
albu.Sharpen(p=0.25),
albu.RandomResizedCrop(height=self.image_size, width=self.image_size,
scale=(0.5, 1.0), ratio=(0.75, 1.3333333333333333),
interpolation=1, p=1.0),
]
+ self.transform_normalize)
elif self.aug_ver == 27:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.RandomRotate90(p=0.5),
albu.OneOf(
[
albu.CLAHE(clip_limit=2, p=0.5),
albu.Sharpen(p=0.5),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.5)],
p=1.0),
albu.GridDistortion(interpolation=1,
border_mode=0,
value=0,
p=0.25),
albu.RandomResizedCrop(height=self.image_size, width=self.image_size,
scale=(0.5, 1.0), ratio=(0.75, 1.3333333333333333),
interpolation=1, p=1.0),
]
+ self.transform_normalize)
elif self.aug_ver == 28:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.CLAHE(clip_limit=2, p=0.25),
albu.Sharpen(p=0.25),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.25),
albu.GridDistortion(interpolation=1,
border_mode=0,
value=0,
p=0.25),
albu.RandomResizedCrop(height=self.image_size, width=self.image_size,
scale=(0.5, 1.0), ratio=(0.75, 1.3333333333333333),
interpolation=1, p=1.0),
]
+ self.transform_normalize)
elif self.aug_ver == 29:
self.transform = albu.Compose(
self.base_transform_list +
[
albu.RandomRotate90(p=1.0),
albu.CLAHE(clip_limit=2, p=0.25),
albu.Sharpen(p=0.25),
albu.RandomBrightnessContrast(brightness_limit=(-0.1, 0.1),
contrast_limit=(-0.1, 0.1), p=0.25),
albu.RandomResizedCrop(height=self.image_size, width=self.image_size,
scale=(0.5, 1.0), ratio=(0.75, 1.3333333333333333),
interpolation=1, p=1.0),
]
+ self.transform_normalize)
else :
self.transform = albu.Compose([albu.Resize(self.image_size, self.image_size),
albu.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
ToTensorV2(),
])
elif self.mode == 'valid':
# self.transform = albu.Compose([
# albu.CenterCrop(self.image_size, self.image_size),
# albu.HorizontalFlip(p=0.5),
# albu.VerticalFlip(p=0.5),
# albu.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
# ToTensorV2(),
# ])
self.transform = albu.Compose([
albu.Resize(self.image_size, self.image_size),
albu.HorizontalFlip(p=0.5),
albu.VerticalFlip(p=0.5),
albu.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
ToTensorV2(),
])
elif self.mode == 'test':
if self.tta_transform:
self.transform = albu.Compose(
[albu.Resize(self.image_size, self.image_size)]+
[
self.tta_transform
]
+ self.transform_normalize)
else:
# self.transform = albu.Compose([
# albu.CenterCrop(self.image_size, self.image_size),
# albu.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
# ToTensorV2(),
# ])
self.transform = albu.Compose([
albu.Resize(self.image_size, self.image_size),
albu.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
ToTensorV2(),
])
else:
self.transform = albu.Compose([
albu.Resize(self.image_size, self.image_size),
albu.Normalize(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
ToTensorV2(),
])
if avail_features:
# self.csv_feature_dict = self.make_csv_feature_dict()
self.csv_feature_dict = {'내부 온도 1 평균': [3.4, 47.3],
'내부 온도 1 최고': [3.4, 47.6],
'내부 온도 1 최저': [3.3, 47.0],
'내부 습도 1 평균': [23.7, 100.0],
'내부 습도 1 최고': [25.9, 100.0],
'내부 습도 1 최저': [0.0, 100.0],
'내부 이슬점 평균': [0.1, 34.5],
'내부 이슬점 최고': [0.2, 34.7],
'내부 이슬점 최저': [0.0, 34.4]}
def make_csv_feature_dict(self):
# 분석에 사용할 feature 선택
temp_csv = pd.read_csv(self.csv_files[0])[self.avail_features]
max_arr, min_arr = temp_csv.max().to_numpy(), temp_csv.min().to_numpy()
# feature 별 최대값, 최솟값 계산
for csv in tqdm(self.csv_files[1:]):
temp_csv = pd.read_csv(csv)[self.avail_features]
temp_csv = temp_csv.replace('-', np.nan).dropna()
if len(temp_csv) == 0:
continue
temp_csv = temp_csv.astype(float)
temp_max, temp_min = temp_csv.max().to_numpy(), temp_csv.min().to_numpy()
max_arr = np.max([max_arr, temp_max], axis=0)
min_arr = np.min([min_arr, temp_min], axis=0)
# feature 별 최대값, 최솟값 dictionary 생성
csv_feature_dict = {self.avail_features[i]: [min_arr[i], max_arr[i]] for i in range(len(self.avail_features))}
return csv_feature_dict
def get_csv_feature_dict(self):
return self.csv_feature_dict
def __len__(self):
return len(self.files)
def __getitem__(self, i):
file = self.files[i]
file_name = file.split('/')[-1]
json_path = f'{file}/{file_name}.json'
image_path = f'{file}/{file_name}.jpg'
if self.avail_features:
if self.csv_feature_check[i] == 0:
csv_path = f'{file}/{file_name}.csv'
df = pd.read_csv(csv_path)[self.csv_feature_dict.keys()]
df = df.replace('-', 0)
# MinMax scaling
for col in df.columns:
df[col] = df[col].astype(float) - self.csv_feature_dict[col][0]
df[col] = df[col] / (self.csv_feature_dict[col][1] - self.csv_feature_dict[col][0])
# zero padding
pad = np.zeros((self.max_len, len(df.columns)))
length = min(self.max_len, len(df))
pad[-length:] = df.to_numpy()[-length:]
# transpose to sequential data
csv_feature = pad.T
self.csv_features[i] = csv_feature
self.csv_feature_check[i] = 1
else:
csv_feature = self.csv_features[i]
else:
pass
img = cv2.imread(image_path)
# if self.mode in ['valid','test']:
# h, w, _ = img.shape
# if w < 384:
# ratio = 384 / w
# new_h = int(h * ratio)
# img = cv2.resize(img, (384, new_h), interpolation=cv2.INTER_LINEAR)
# # print(h, w, '->', new_h, 384)
# elif w == 384:
# pass
# elif w > 384 and w <= 512:
# ratio = 384 / w
# new_h = int(h * ratio)
# img = cv2.resize(img, (384, new_h), interpolation=cv2.INTER_NEAREST_EXACT)
# # print(h, w, '->', new_h, 384)
# else:
# ratio = 384 / h
# new_w = int(w * ratio)
# img = cv2.resize(img, (new_w, 384), interpolation=cv2.INTER_NEAREST_EXACT)
# # print(h, w, '->', 384, new_w)
img = self.transform(image=img)
if self.avail_features:
if self.mode in ['train', 'valid']:
with open(json_path, 'r') as f:
json_file = json.load(f)
crop = json_file['annotations']['crop']
disease = json_file['annotations']['disease']
risk = json_file['annotations']['risk']
label = f'{crop}_{disease}_{risk}'
return {
'img': img,
'csv_feature': torch.tensor(csv_feature, dtype=torch.float32),
'label': torch.tensor(self.label_encoder[label], dtype=torch.long)
}
else:
return {
'img': img,
'csv_feature': torch.tensor(csv_feature, dtype=torch.float32)
}
else:
if self.mode in ['train', 'valid']:
with open(json_path, 'r') as f:
json_file = json.load(f)
crop = json_file['annotations']['crop']
disease = json_file['annotations']['disease']
risk = json_file['annotations']['risk']
label = f'{crop}_{disease}_{risk}'
return {
'img': img,
'label': torch.tensor(self.label_encoder[label], dtype=torch.long)
}
else:
return {
'img': img,
}