forked from VitjanZ/DRAEM
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data_loader.py
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data_loader.py
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import os
import numpy as np
from torch.utils.data import Dataset
import torch
import cv2
import glob
import imgaug.augmenters as iaa
from perlin import rand_perlin_2d_np
class MVTecDRAEMTestDataset(Dataset):
def __init__(self, root_dir, resize_shape=None):
self.root_dir = root_dir
self.images = sorted(glob.glob(root_dir+"/*/*.png"))
self.resize_shape=resize_shape
def __len__(self):
return len(self.images)
def transform_image(self, image_path, mask_path):
image = cv2.imread(image_path, cv2.IMREAD_COLOR)
if mask_path is not None:
mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
else:
mask = np.zeros((image.shape[0],image.shape[1]))
if self.resize_shape != None:
image = cv2.resize(image, dsize=(self.resize_shape[1], self.resize_shape[0]))
mask = cv2.resize(mask, dsize=(self.resize_shape[1], self.resize_shape[0]))
image = image / 255.0
mask = mask / 255.0
image = np.array(image).reshape((image.shape[0], image.shape[1], 3)).astype(np.float32)
mask = np.array(mask).reshape((mask.shape[0], mask.shape[1], 1)).astype(np.float32)
image = np.transpose(image, (2, 0, 1))
mask = np.transpose(mask, (2, 0, 1))
return image, mask
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
img_path = self.images[idx]
dir_path, file_name = os.path.split(img_path)
base_dir = os.path.basename(dir_path)
if base_dir == 'good':
image, mask = self.transform_image(img_path, None)
has_anomaly = np.array([0], dtype=np.float32)
else:
mask_path = os.path.join(dir_path, '../../ground_truth/')
mask_path = os.path.join(mask_path, base_dir)
mask_file_name = file_name.split(".")[0]+"_mask.png"
mask_path = os.path.join(mask_path, mask_file_name)
image, mask = self.transform_image(img_path, mask_path)
has_anomaly = np.array([1], dtype=np.float32)
sample = {'image': image, 'has_anomaly': has_anomaly,'mask': mask, 'idx': idx}
return sample
class MVTecDRAEMTrainDataset(Dataset):
def __init__(self, root_dir, anomaly_source_path, resize_shape=None):
"""
Args:
root_dir (string): Directory with all the images.
transform (callable, optional): Optional transform to be applied
on a sample.
"""
self.root_dir = root_dir
self.resize_shape=resize_shape
self.image_paths = sorted(glob.glob(root_dir+"/*.png"))
self.anomaly_source_paths = sorted(glob.glob(anomaly_source_path+"/*/*.jpg"))
self.augmenters = [iaa.GammaContrast((0.5,2.0),per_channel=True),
iaa.MultiplyAndAddToBrightness(mul=(0.8,1.2),add=(-30,30)),
iaa.pillike.EnhanceSharpness(),
iaa.AddToHueAndSaturation((-50,50),per_channel=True),
iaa.Solarize(0.5, threshold=(32,128)),
iaa.Posterize(),
iaa.Invert(),
iaa.pillike.Autocontrast(),
iaa.pillike.Equalize(),
iaa.Affine(rotate=(-45, 45))
]
self.rot = iaa.Sequential([iaa.Affine(rotate=(-90, 90))])
def __len__(self):
return len(self.image_paths)
def randAugmenter(self):
aug_ind = np.random.choice(np.arange(len(self.augmenters)), 3, replace=False)
aug = iaa.Sequential([self.augmenters[aug_ind[0]],
self.augmenters[aug_ind[1]],
self.augmenters[aug_ind[2]]]
)
return aug
def augment_image(self, image, anomaly_source_path):
aug = self.randAugmenter()
perlin_scale = 6
min_perlin_scale = 0
anomaly_source_img = cv2.imread(anomaly_source_path)
anomaly_source_img = cv2.resize(anomaly_source_img, dsize=(self.resize_shape[1], self.resize_shape[0]))
anomaly_img_augmented = aug(image=anomaly_source_img)
perlin_scalex = 2 ** (torch.randint(min_perlin_scale, perlin_scale, (1,)).numpy()[0])
perlin_scaley = 2 ** (torch.randint(min_perlin_scale, perlin_scale, (1,)).numpy()[0])
perlin_noise = rand_perlin_2d_np((self.resize_shape[0], self.resize_shape[1]), (perlin_scalex, perlin_scaley))
perlin_noise = self.rot(image=perlin_noise)
threshold = 0.5
perlin_thr = np.where(perlin_noise > threshold, np.ones_like(perlin_noise), np.zeros_like(perlin_noise))
perlin_thr = np.expand_dims(perlin_thr, axis=2)
img_thr = anomaly_img_augmented.astype(np.float32) * perlin_thr / 255.0
beta = torch.rand(1).numpy()[0] * 0.8
augmented_image = image * (1 - perlin_thr) + (1 - beta) * img_thr + beta * image * (
perlin_thr)
no_anomaly = torch.rand(1).numpy()[0]
if no_anomaly > 0.5:
image = image.astype(np.float32)
return image, np.zeros_like(perlin_thr, dtype=np.float32), np.array([0.0],dtype=np.float32)
else:
augmented_image = augmented_image.astype(np.float32)
msk = (perlin_thr).astype(np.float32)
augmented_image = msk * augmented_image + (1-msk)*image
has_anomaly = 1.0
if np.sum(msk) == 0:
has_anomaly=0.0
return augmented_image, msk, np.array([has_anomaly],dtype=np.float32)
def transform_image(self, image_path, anomaly_source_path):
image = cv2.imread(image_path)
image = cv2.resize(image, dsize=(self.resize_shape[1], self.resize_shape[0]))
do_aug_orig = torch.rand(1).numpy()[0] > 0.7
if do_aug_orig:
image = self.rot(image=image)
image = np.array(image).reshape((image.shape[0], image.shape[1], image.shape[2])).astype(np.float32) / 255.0
augmented_image, anomaly_mask, has_anomaly = self.augment_image(image, anomaly_source_path)
augmented_image = np.transpose(augmented_image, (2, 0, 1))
image = np.transpose(image, (2, 0, 1))
anomaly_mask = np.transpose(anomaly_mask, (2, 0, 1))
return image, augmented_image, anomaly_mask, has_anomaly
def __getitem__(self, idx):
idx = torch.randint(0, len(self.image_paths), (1,)).item()
anomaly_source_idx = torch.randint(0, len(self.anomaly_source_paths), (1,)).item()
image, augmented_image, anomaly_mask, has_anomaly = self.transform_image(self.image_paths[idx],
self.anomaly_source_paths[anomaly_source_idx])
sample = {'image': image, "anomaly_mask": anomaly_mask,
'augmented_image': augmented_image, 'has_anomaly': has_anomaly, 'idx': idx}
return sample