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# SPDX-License-Identifier: MIT
import random
import torch
from torch.utils.data import Dataset
from torch.utils.data import sampler
# import lmdb
import torchvision.transforms as transforms
import six
import sys
from PIL import Image
import numpy as np
import os
import sys
import pickle
import numpy as np
from params import *
import glob, cv2
import torchvision.transforms as transforms
def get_transform(grayscale=False, convert=True):
transform_list = []
if grayscale:
transform_list.append(transforms.Grayscale(1))
if convert:
transform_list += [transforms.ToTensor()]
if grayscale:
transform_list += [transforms.Normalize((0.5,), (0.5,))]
else:
transform_list += [transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
return transforms.Compose(transform_list)
class TextDataset:
def __init__(self, base_path=DATASET_PATHS, num_examples=20, target_transform=None):
self.NUM_EXAMPLES = num_examples
# base_path = DATASET_PATHS
file_to_store = open(base_path, "rb")
self.IMG_DATA = pickle.load(file_to_store)["train"]
self.IMG_DATA = dict(list(self.IMG_DATA.items())) # [:NUM_WRITERS])
if "None" in self.IMG_DATA.keys():
del self.IMG_DATA["None"]
self.author_id = list(self.IMG_DATA.keys())
self.data = []
for idx, (author_id, images) in enumerate(self.IMG_DATA.items()):
for img_data in images:
self.data.append(
{
"author_idx": idx,
"author_id": author_id,
"img": img_data["img"],
"label": img_data["label"],
}
)
self.transform = get_transform(grayscale=True)
self.target_transform = target_transform
self.collate_fn = TextCollator()
def __len__(self):
return len(self.data)
def __getitem__(self, index):
NUM_SAMPLES = self.NUM_EXAMPLES
item_data = self.data[index]
author_id = item_data["author_id"]
img = item_data["img"]
label = item_data["label"]
author_idx = item_data["author_idx"]
self.IMG_DATA_AUTHOR = self.IMG_DATA[author_id]
random_idxs = np.random.choice(
len(self.IMG_DATA_AUTHOR), NUM_SAMPLES, replace=True
)
rand_id_real = np.random.choice(len(self.IMG_DATA_AUTHOR))
real_img = self.transform(Image.fromarray(np.array(img.convert("L"))))
real_labels = label.encode()
imgs = [
np.array(self.IMG_DATA_AUTHOR[idx]["img"].convert("L"))
for idx in random_idxs
]
labels = [self.IMG_DATA_AUTHOR[idx]["label"].encode() for idx in random_idxs]
max_width = 192 # [img.shape[1] for img in imgs]
imgs_pad = []
imgs_wids = []
for img in imgs:
img = 255 - img
img_height, img_width = img.shape[0], img.shape[1]
outImg = np.zeros((img_height, max_width), dtype="float32")
outImg[:, :img_width] = img[:, :max_width]
img = 255 - outImg
imgs_pad.append(self.transform((Image.fromarray(img))))
imgs_wids.append(img_width)
imgs_pad = torch.cat(imgs_pad, 0)
item = {
"simg": imgs_pad,
"swids": imgs_wids,
"img": real_img,
"label": real_labels,
"img_path": "img_path",
"idx": "indexes",
"wcl": author_idx,
}
return item
class TextDatasetval:
def __init__(self, base_path=DATASET_PATHS, num_examples=20, target_transform=None):
self.NUM_EXAMPLES = num_examples
# base_path = DATASET_PATHS
file_to_store = open(base_path, "rb")
self.IMG_DATA = pickle.load(file_to_store)["test"]
self.IMG_DATA = dict(list(self.IMG_DATA.items())) # [NUM_WRITERS:])
if "None" in self.IMG_DATA.keys():
del self.IMG_DATA["None"]
self.author_id = list(self.IMG_DATA.keys())
self.data = []
for idx, (author_id, images) in enumerate(self.IMG_DATA.items()):
for img_data in images:
self.data.append(
{
"author_idx": idx,
"author_id": author_id,
"img": img_data["img"],
"label": img_data["label"],
}
)
self.transform = get_transform(grayscale=True)
self.target_transform = target_transform
self.collate_fn = TextCollator()
def __len__(self):
return len(self.data)
def __getitem__(self, index):
NUM_SAMPLES = self.NUM_EXAMPLES
item_data = self.data[index]
author_id = item_data["author_id"]
img = item_data["img"]
label = item_data["label"]
author_idx = item_data["author_idx"]
self.IMG_DATA_AUTHOR = self.IMG_DATA[author_id]
random_idxs = np.random.choice(
len(self.IMG_DATA_AUTHOR), NUM_SAMPLES, replace=True
)
rand_id_real = np.random.choice(len(self.IMG_DATA_AUTHOR))
real_img = self.transform(Image.fromarray(np.array(img.convert("L"))))
real_labels = label.encode()
imgs = [
np.array(self.IMG_DATA_AUTHOR[idx]["img"].convert("L"))
for idx in random_idxs
]
labels = [self.IMG_DATA_AUTHOR[idx]["label"].encode() for idx in random_idxs]
max_width = 192 # [img.shape[1] for img in imgs]
imgs_pad = []
imgs_wids = []
for img in imgs:
img = 255 - img
img_height, img_width = img.shape[0], img.shape[1]
outImg = np.zeros((img_height, max_width), dtype="float32")
outImg[:, :img_width] = img[:, :max_width]
img = 255 - outImg
imgs_pad.append(self.transform((Image.fromarray(img))))
imgs_wids.append(img_width)
imgs_pad = torch.cat(imgs_pad, 0)
item = {
"simg": imgs_pad,
"swids": imgs_wids,
"img": real_img,
"label": real_labels,
"img_path": "img_path",
"idx": "indexes",
"wcl": author_idx,
}
return item
class TextCollator(object):
def __init__(self):
self.resolution = resolution
def __call__(self, batch):
img_path = [item["img_path"] for item in batch]
width = [item["img"].shape[2] for item in batch]
indexes = [item["idx"] for item in batch]
simgs = torch.stack([item["simg"] for item in batch], 0)
wcls = torch.Tensor([item["wcl"] for item in batch])
swids = torch.Tensor([item["swids"] for item in batch])
imgs = torch.ones(
[
len(batch),
batch[0]["img"].shape[0],
batch[0]["img"].shape[1],
max(width),
],
dtype=torch.float32,
)
for idx, item in enumerate(batch):
try:
imgs[idx, :, :, 0 : item["img"].shape[2]] = item["img"]
except:
print(imgs.shape)
item = {
"img": imgs,
"img_path": img_path,
"idx": indexes,
"simg": simgs,
"swids": swids,
"wcl": wcls,
}
if "label" in batch[0].keys():
labels = [item["label"] for item in batch]
item["label"] = labels
if "z" in batch[0].keys():
z = torch.stack([item["z"] for item in batch])
item["z"] = z
return item
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