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import os
import random
import time
import numpy as np
import torch
from PIL import Image
import cv2
from torchvision import transforms, datasets
from torchvision.transforms import functional as F
def _getvocpallete(num_colors):
return [0, 0, 0] * num_colors
# ----------------------
# Augmentation Classes
# ----------------------
class Rotate:
def __init__(self, angle):
self.angle = random.randint(-angle, angle)
def __call__(self, img):
return F.rotate(img, angle=self.angle)
class Shear:
def __init__(self, shear=10, scale=(1.0, 1.0)):
self.shear = random.uniform(-shear, shear)
self.scale = random.uniform(scale[0], scale[1])
def __call__(self, img):
return F.affine(img, angle=0, translate=(0, 0), scale=self.scale, shear=[self.shear, self.shear])
class Skew:
def __init__(self, magnitude=0.2):
self.xshift = random.uniform(-magnitude, magnitude)
self.yshift = random.uniform(-magnitude, magnitude)
def __call__(self, img):
width, height = img.size
x_shift = int(self.xshift * width)
y_shift = int(self.yshift * height)
return img.transform(img.size, Image.AFFINE, (1, 0, x_shift, 0, 1, y_shift))
class Crop:
def __init__(self, min_crop=0.8, max_crop=0.9):
self.crop_scale = random.uniform(min_crop, max_crop)
self.seed = time.time()
def __call__(self, img):
width, height = img.size
crop_width = int(self.crop_scale * width)
crop_height = int(self.crop_scale * height)
random.seed(self.seed)
left = random.randint(0, width - crop_width)
top = random.randint(0, height - crop_height)
return F.crop(img, top, left, crop_height, crop_width).resize((width, height))
class GaussianNoise:
def __init__(self, mean=0, std=(10,20)):
self.mean = mean
self.std = random.uniform(std[0], std[1])
def __call__(self, img):
img = np.array(img)
noise = np.random.normal(self.mean, self.std, img.shape)
img = img + noise
img = np.clip(img, 0, 255).astype(np.uint8)
return Image.fromarray(img)
class SaltAndPepperNoise:
def __init__(self, min_prob=0.01, max_prob=0.05):
self.salt_prob = random.uniform(min_prob, max_prob)
self.pepper_prob = random.uniform(min_prob, max_prob)
def __call__(self, img):
img_array = np.array(img)
salt_mask = np.random.rand(*img_array.shape[:2]) < self.salt_prob
pepper_mask = np.random.rand(*img_array.shape[:2]) < self.pepper_prob
img_array[salt_mask] = 255
img_array[pepper_mask] = 0
return Image.fromarray(img_array.astype(np.uint8))
class MotionBlur:
def __init__(self, min_size=3, max_size=21):
self.kernel_size = random.randint(min_size, max_size)
def __call__(self, img):
img_array = np.array(img)
kernel = np.zeros((self.kernel_size, self.kernel_size))
kernel[int((self.kernel_size - 1) / 2), :] = np.ones(self.kernel_size)
kernel = kernel / self.kernel_size
blurred = cv2.filter2D(img_array, -1, kernel)
return Image.fromarray(blurred.astype(np.uint8))
class HideAndSeekNoise:
def __init__(self, min_size=90, max_size=190):
self.patch_size = random.randint(min_size, max_size)
self.seed = time.time()
def __call__(self, img):
img_array = np.array(img)
height, width, _ = img_array.shape
random.seed(self.seed)
top = random.randint(0, height - self.patch_size)
left = random.randint(0, width - self.patch_size)
img_array[top:top + self.patch_size, left:left + self.patch_size] = [0, 0, 0]
return Image.fromarray(img_array)
class BaseDataset(torch.utils.data.Dataset):
def __init__(self, path_list, transform = None, data_set = 'val', seed=None,
img_size=768, interpolation=Image.BILINEAR, color_pallete = 'city'):
"""
:param path_list: Path to file listing image paths.
:param transform: Additional torchvision transforms.
:param data_set: 'train' or other mode.
:param seed: Seed for shuffling.
:param img_size: Resize dimensions.
:param interpolation: Interpolation method for resizing.
"""
self.transform = transform
self.data_set = data_set
self.color_pallete = color_pallete
with open(path_list, "r") as file:
self.imgs = file.readlines()
if seed:
random.seed(seed)
random.shuffle(self.imgs)
self.masks = [img_path for img_path in self.imgs]
self.learning_map = None
self.aug_weights = [0.4, 0.3, 0.3, 0.2, 0.2, 0.05, 0.05, 0.02, 0.02]
if img_size:
self.transform_resize = transforms.Resize((img_size, img_size), interpolation=Image.BILINEAR)
def convert_label(self, label, inverse=False):
temp = label.copy()
converted_label = np.zeros_like(label)
for k, v in self.learning_map.items():
converted_label[temp == k] = v
return converted_label
def get_color_pallete(self, npimg, dataset='city'):
out_img = Image.fromarray(npimg.astype('uint8')).convert('P')
if dataset == 'city':
cityspallete = [
0, 0, 0,
128, 64, 128,
244, 35, 232,
70, 70, 70,
102, 102, 156,
190, 153, 153,
153, 153, 153,
250, 170, 30,
220, 220, 0,
107, 142, 35,
152, 251, 152,
0, 130, 180,
220, 20, 60,
255, 0, 0,
0, 0, 142,
0, 0, 70,
0, 60, 100,
0, 80, 100,
0, 0, 230,
119, 11, 32,
]
out_img.putpalette(cityspallete)
else:
vocpallete = _getvocpallete(256)
out_img.putpalette(vocpallete)
return out_img.convert("RGB")
def __getitem__(self, index):
img_path, mask_path = self.imgs[index].rstrip(), self.masks[index].rstrip()
# Load and resize the image
img = Image.open(img_path).convert('RGB')
img = self.transform_resize(img)
# Load, convert, and resize the mask
mask = Image.open(mask_path)
mask = np.array(mask)
mask = self.convert_label(mask)
mask = mask.astype(np.uint8)
mask = self.get_color_pallete(mask, self.color_pallete)
mask = self.transform_resize(mask)
# Augmentation stage
augmentation_num = random.choices(range(9), weights=self.aug_weights, k=1)[0] if self.data_set == 'train' else 0
if augmentation_num > 0:
augmentation_set = [
transforms.RandomHorizontalFlip(p=1), # Flip horizontally
transforms.RandomVerticalFlip(p=1), # Flip vertically
Crop(min_crop=0.6, max_crop=0.9), # Random crop
Rotate(angle=90), # Rotate
Shear(shear=10, scale=(0.8, 1.2)), # Shear
Skew(magnitude=0.2), # Skew
HideAndSeekNoise(min_size=90, max_size=210), #Hide and seek noise
GaussianNoise(mean=0, std=(5,20)), # Gaussian noise (only for image) / std 10-20
SaltAndPepperNoise(min_prob=0.01, max_prob=0.03), #Salt and pepper noise (only for image)
transforms.GaussianBlur(kernel_size=3, sigma=(0.2, 1)), # Gaussian blur (only for image)
MotionBlur(min_size=3, max_size=15), # Motion blur (only for image)
transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2), # Color jitter (only for image)
]
random.shuffle(augmentation_set)
augmentation_set = augmentation_set[:augmentation_num]
for aug in augmentation_set:
if isinstance(aug, (transforms.GaussianBlur, transforms.ColorJitter, GaussianNoise, SaltAndPepperNoise, MotionBlur)):
img = aug(img)
else:
img = aug(img)
mask = aug(mask)
if self.transform:
img = self.transform(img)
mask = self.transform(mask)
return img, mask, img_path
def __len__(self):
return len(self.imgs) |