/
dataloader.py
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/
dataloader.py
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import utility
import sqlalchemy as sa
import numpy as np
from matplotlib import pyplot as plt
from torch.utils.data import Dataset
from pathlib import Path
from scipy.ndimage import imread
from torchvision.transforms import functional as func
class Transformer:
def __init__(self, tf):
if tf:
self.transformations = [self._to_pil,
self._resize,
self._adjust_brightness,
self._adjust_contrast,
self._adjust_gamma,
self._adjust_saturation,
self._rotate,
self._to_label,
self._to_tensor]
else:
self.transformations = [self._to_pil,
self._resize,
self._to_label,
self._to_tensor]
def __call__(self, imgset):
for f in self.transformations:
imgset = f(imgset)
return imgset
@staticmethod
def _to_label(imgset):
return imgset[0], utility.image_to_labels(imgset[1])
@staticmethod
def _to_pil(imgset):
return func.to_pil_image(imgset[0]), func.to_pil_image(imgset[1])
@staticmethod
def _resize(imgset):
size = (512, 1024)
return func.resize(imgset[0], size), func.resize(imgset[1], size)
@staticmethod
def _to_tensor(imgset):
return func.to_tensor(imgset[0]), imgset[1]
@staticmethod
def _adjust_brightness(imgset):
chance = 0.2
if np.random.random() > chance:
return imgset
v = np.random.uniform(low=0.5, high=1.5)
return func.adjust_brightness(imgset[0], v), imgset[1]
@staticmethod
def _adjust_contrast(imgset):
chance = 0.25
if np.random.random() > chance:
return imgset
v = np.random.uniform(low=0.2, high=1.8)
return func.adjust_contrast(imgset[0], v), imgset[1]
@staticmethod
def _adjust_gamma(imgset):
chance = 0.25
if np.random.random() > chance:
return imgset
v = np.random.uniform(low=0.2, high=1.8)
return func.adjust_gamma(imgset[0], v), imgset[1]
@staticmethod
def _adjust_saturation(imgset):
chance = 0.25
if np.random.random() > chance:
return imgset
v = np.random.uniform(low=0.5, high=1.5)
return func.adjust_saturation(imgset[0], v), imgset[1]
@staticmethod
def _rotate(imgset):
chance = 0.4
if np.random.random() > chance:
return imgset
v = np.random.randint(-30, 30)
return func.rotate(imgset[0], v), func.rotate(imgset[1], v)
class ImageSet(Dataset):
def __init__(self, itype, tf=True, t=False):
super(ImageSet, self).__init__()
self.transformer = Transformer(tf)
self.itype = itype
self.t = t
self._build_dataset()
def _build_dataset(self):
if not self.t:
engine = sa.create_engine('sqlite:///cardata_1.db') #
self.data = utility.get_imageset(engine, self.itype)
else:
self.data = [d for d in Path('E:/testimages').iterdir()]
def __len__(self):
return len(self.data)
@staticmethod
def _get_image(path):
p = Path(path)
img = imread(p)
return img
def _process_image(self, image):
if not self.t:
(p1, p2) = image
image = self._get_image(p1)
target = self._get_image(p2)
else:
image = self._get_image(image)
target = np.zeros(image.shape, dtype=np.uint8)
return self.transformer((image, target))
def __getitem__(self, item):
image = self._process_image(self.data[item])
if self.t:
image = (image[0], image[1], self.data[item].stem)
return image
if __name__ == '__main__':
ts = ImageSet(1)
# loader = DataLoader(ts, batch_size=1)
img = ts.__getitem__(5)
plt.imshow(img[0].numpy().transpose((1, 2, 0)))
plt.show()
data = img[1]
for r in data:
for c in r:
if c not in [0, 1, 2]:
print(c, end="", flush=True)
print("")
result = utility.labels_to_image(data)
print(result)
plt.imshow(result)
plt.show()